{"input": "composite of the effect of skills at Grade 6, as captured by ENLACE, on future outcomes\n\nFurthermore, we find evidence that strongly supports the case that ENLACE captures\n\nmain specification and run a regression of ENLACE test scores in a particular subject\n\n<!-- PAGE 8 -->\nscribes the ENLACE national examination and the steps followed to construct the longi\n\ntudinal data. Section 3 of the paper describes the empirical approach followed to study\n\nWe construct two longitudinal datasets for this paper: (1) the ENLACE panel and (2)", "output": {"entities": {"named_data": ["ENLACE national examination", "ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, there is no consensus or agreed best practices on the use of these methods in different contexts or stages of displacement (Brookings 2013). 92 The absence of data on new displacement may simply mean that no displacement has taken place (IDMC 2016). 93 IDMC reports that data, disaggregated by age and sex, were available for 15 of the 60 countries it monitored in 2014, however these data were not comprehensive and are not published. Additionally, in some countries there are data provided by IOM on IDP populations by location from which the urban or rural character of the population may be inferred (e. g. if the camp is located in the capital), but data are not comprehensive and not published. While the majority of humanitarian profile data does not typically cover IDPs living outside of camp or camp-like settings (the large majority of IDPs), IOM ’ s DTM in countries such as Nigeria, Iraq, Yemen and Libya do include information about those residing in host communities. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Location or domain Eritrean 16% 73% Somali 38% 76% South Sudanese 36% 89% Addis Ababa 18% 7% Sex of head Female 34% 87% 16% 10% Male 30% 74% 19% 3% Head education No education 38% 87% 35% 16% Primary incomplete 30% 81% 26% 13% Primary complete 29% 79% 14% 6% Secondary incomplete 24% 65% 14% 5% Secondary complete 16% 63% 14% 3% Post-secondary 19% 68% 12% 0% Sector of head’s employment Agriculture 44% 87% 76% Industry 24% 75% 17% 0% Service 23% 84% 17% 8% Unemployed 35% 84% 18% 8% Main livelihood source Salary 21% 62% 20% 6% Casual labor 45% 66% 37% 20% Crop/livestock farming 40% 87% Manufacturing 13% 65% 0% Trade and services 19% 67% 15% 0% Safety nets or aid 33% 87% 27% 0% Remittances 40% 53% 0% 7% Others 66% 85% 8% 0% Market accessibility Low accessibility 34% 78% Medium accessibility 45% 92% High accessibility 16% 77% Proximity to resource hubs Nearest to zone 34% 93% Nearest to woreda 24% 72% Nearest to border 37% 81% Remote 33% 78% Source: World Bank Staff based on SESRE 2023. Annexes 113 Table D.12: Determinants of welfare (total expenditure per capita) (1) (2) (3) (4) In-camp refugees In-camp hosts", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "An additional 33 blocks were assigned to the cash group, where participants earned 450 taka (USD $ 5. 30) per week as compensation for survey participation. Finally, 83 blocks were assigned to a work group, where we oﬀered participants gainful employment. We compensated participants in this treatment arm with 150 taka (USD $ 1. 77) per day of work. Households were assigned an average of three days of work per week, resulting in 450 taka per week on average over the course of the eight weeks and thereby equivalent to that received by the cash group. All participants were aware of the randomization process: enumerators described the three arms and displayed the random number to the participant as it appeared on their tablet, assigning the participant to his or her treatment group. Employment intervention details We now turn to the nature of the employment we oﬀer. Employees were asked to engage in a data collection exercise in which they completed time-use sheets describing the activities of ﬁfteen unnamed, same-sex neighbors of their choosing four times per day. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Yes Yes Yes Control: Occupation/Sector Yes Yes Sample: Working outside camp Yes Sample Size 743 742 742 572 Source: World Bank Staff based on SESRE 2023. Note: Monthly earnings are collected for employees only (including work for government, NGOs, and private households). Log earnings are winsorized at the 1st and 99th percentile within the domain. Regression coefficients are transformed to percent change interpretation using. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 106 Table D.7: Determinants of employment outcomes (1) (2) (3) (4) (5) (6) (7) (8) Hosts Refugees Working High-Skill Ln Income Working High-Skill Work Outside Ln Income Ln Income Male 0.235*** 0.030* 0.270*** 0.033 0.031* 0.172*** 0.506*** 0.876*** (0.021) (0.018) (0.061) (0.033) (0.017) (0.049) (0.101) (0.226) Age 0.067*** 0.008** 0.064*** 0.045*** 0.004 -0.005 0.007 -0.043 (0.004) (0.004) (0.017) (0.005) (0.003) (0.013) (0.028) (0.046) Age Sq. -0.001*** -0.000* -0.001*** -0.001*** -0.000 0.000 -0.000 0.001 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) Educ: < Primary - - - - - - - - Educ: Primary -0.028 0.058*** 0.127 -0.042* 0.077** -0.104 -0.172 0.121 (0.020) (0.021) (0.107) (0.022) (0.032) (0.069) (0.122) (0.361) Educ: Secondary 0.115*** 0.506*** 0.700*** -0.045", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 five methods, it was not possible to consider non-sampling error. This paper goes a step further by using simulations to describe the sampling error and a field experiment in an IDP camp in South Sudan to measure the total survey error of each design compared to a census, allowing for the disaggregation of the total error into sampling and non-sampling components. In addition, we attempt to separate the components of non-sampling error linked to the sample method from those common across all methods, such as interviewers selecting larger households and other issues in properly implementing the household survey protocols. The next section briefly describes each method and highlights the literature as it relates to the relevant selection methods. Section 3 describes the data set and protocols for each method included in the experiment, followed by Section 4, which discusses implementation issues. Section 5 reports the results of the analysis, and section 6 concludes with further discussion of the overall performance and areas for future research. 2. Description of Methods This paper compares five alternatives of second stage selection (satellite mapping, segmentation, grid squares, “ Qibla ” (or “ walk north ”) method, and random walk) to a human canvassing operation. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 Pregnancy Care A woman who gave birth in the last 2 years did not visit a clinic while pregnant or have a trained assistant during delivery 1 / 16 Physical Safety Any member feels unsafe at home or walking alone14 1 / 16 Early Marriage A member was married before age 19 1 / 16 Living Standards Garbage Disposal Main method of solid waste disposal is dumping, burying in own compound, burning, or other 1 / 24 Drinking Water Main source of drinking water is unsafe, or it takes more than 20 minutes (round-trip) to get water15 1 / 24 Electricity It does not have electricity 1 / 24 Cooking Fuel Main energy source for cooking is solid fuels 1 / 24 Housing It is an unimproved housing type 1 / 24 Sanitation Main toilet facility is unimproved, or shared with other households16 1 / 24 Financial Security Unemployment Any member 15 or older is unemployed and looking for work17 1 / 12 Legal Identification No member has a form of legal identification 1 / 12 Bank Account No member has a bank or mobile money account 1 / 12 The MPI presented here uses equal nested weights with all four dimensions considered to be equally important, and all indicators within a dimension receiving an equal share of the total weight.", "output": {"entities": {"named_data": ["mpi"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 measured by the number of days missed during the academic year, can function as an indicator of school attachment or integration for refugee students. To address potential biases from teacher subjectivity, the analysis of score disparities relies on INVALSI test results. The standardized and anonymized nature of these tests helps mitigate subjectivity in assessment. First, the results section presents some summary statistics of the main outcomes across the different categories of students. Second, we use the administrative data to analyze empirically how Ukrainian refugees and newly arrived foreigners compared to other students as regards their education performance. This estimation is based on an OLS model with the following econometric speciϐication: 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 + 𝛽𝛽1 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 + 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 + 𝑓𝑓𝑔𝑔 + 𝑓𝑓𝑠𝑠 + 𝑓𝑓𝑙𝑙 + ϵigs (1) where 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 represents the outcome of interest (such as test scores, absenteeism, or high-track recommendation) for student i in school s, in grade g, and with language l. The variable 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 is a dummy indicating whether the student is a Ukrainian refugee, and 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 indicates if the student is a newly arrived foreigner. 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 represents the Economic, Social, and Cultural Status of the student, and 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 indicates the student ’ s gender.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "When general living conditions in one ’ s residence or home area are worse compared to a camp environment, e. g. because health services are available in the latter, mortality may also be lower in the camp. The strong presence of children in Africa ’ s refugee population implies that we should also look at the potential long ‐ term effects of forced displacement on survivors. Given the composition of the refugee population, such long ‐ term effects will be more important in Africa compared to elsewhere. Few studies have followed children exposed to forced displacement over a long time to directly infer the long ‐ term effects of forced displacement, in particular on health, education and labor market participation. Most studies of the long term effects of conflict use an indicator of exposure to violent conflict, but few of them have forced displacement as one of the indicators. There is however a very well established literature (see Currie and Vogl, 2013 for an overview) on the long ‐ term consequences of deprivation in early childhood which can be applied to the situation of refugees. If young children between the ages of 0 to 3 years old are exposed to malnutrition, disease, stress and violence during episodes of forced displacement, then, this literature shows that this deprivation will have negative long ‐ term effects. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "First, the cross-country literature has not found that negative, contemporaneous shocks to growth systematically lead to violence. 8 Second, we have run a large number of robustness checks by adding time trends or lagged growth to our speciﬁcation and controlling for rainfall shocks directly. 9 The upshot from this is not only that results remain signiﬁcant but also that the estimated coeﬃcients barely change. This does not mean that a causal link from falling growth to conﬂict can be ruled out. But it is unlikely to drive the macro relationship we see in the data. In order to further explore the relationship between violence and country-level out- put we run two speciﬁcations of the model described above. In the ﬁrst model conﬂict in country i at time t is deﬁned by any violence, i. e. if at least one battle related deaths occurs. In the second speciﬁcation, conﬂict is deﬁned by a higher threshold, by 0. 008 deaths per 1000 population. 10 We expect to get diﬀerent results from the two speciﬁcations. From the analysis of Figure 1 we know that economic damage of civil war increases with the severity of conﬂict. The estimated impact from the second model should therefore be more acute. Table 1, panel A and B, reports the results. Each column contains one of our measures for economic growth.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the standard ethnic diversity indices to include the annual variation in refugee ethnicities. 8 We then construct a measure of proximity between the clusters in the host country and refugees in surrounding camps by defining an 80-km buffer around each cluster. 9 To control for unobserved heterogeneity and changes within a given cluster, we introduce cluster and year fixed effects, αj and δt. To minimize the risk of confounding the refugee-induced changes in diversity with the annual changes in refugee numbers, we also control for the presence of refugees based on the same buffer as the one used to construct the refugee-induced change in diversity. More specifically, the variable Refugeesjt − 1 counts the number of refugees present in cluster j at year t − 1 within the predefined buffer. The variable is also transformed into an inverse hyperbolic sine to ease interpretation. Finally, Qjt controls for yearly shocks at the cluster level, such as weather shocks. In particular, we control for rain and temperature anomalies. Standard errors are clustered at the Afrobarometer cluster level. 4. 2 Data and descriptive statistics Our analysis combines various sources of data: Afrobarometer, UNHCR refugee camp data, Armed Conflict Location and Event Data (ACLED), Uppsala Conflict Data (UCDP), and the Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset.", "output": {"entities": {"named_data": ["Armed Conflict Location and Event Data", "Uppsala Conflict Data"], "descriptive_data": ["UNHCR refugee camp data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Maps produced by the national cartographic institute at 1:50,000 scale were used as base layers for the project's infrastructure planning. Where the maps predated significant settlement expansion, they were supplemented with recent satellite imagery to ensure accuracy of community boundary delineations. Updated maps were shared with the district land offices responsible for issuing construction permits, confirming that proposed subproject sites fell within the designated community development zones.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "REFodet = αod + γe + τt + β1Conflictot − 1 + β2Conflictet − 1 + β3Distanceed + ϵodet, (5) where REFodet is the stock of refugees of ethnic group e from country o in country d at year t. As we have data on yearly refugee stocks and would like to estimate the changes in these stocks over time using a gravity model, we include origin – destination fixed effects αod so that identification is based only on changes in stock over time (Zylkin, 2019). 20 We also include time τt and ethnic group fixed effects γe. Here we obtain data on the ethnicity of refugees from Murdock ’ s Atlas, which provides a map of ethnographic regions for Africa and the historical homelands of refugees (Murdock, 1967). To match ethnic groups across datasets, we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset and, later, with data from Afrobarometer. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Murdock ’ s Atlas", "LEDA21", "EPR-ER dataset", "Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Religious School Enrollment in Pakistan A Look at the Data Tahir Andrabi1 Pomona College Jishnu Das The World Bank Asim Ijaz Khwaja Harvard University Tristan Zajonc Harvard University Abstract Bold assertions have been made in policy reports and popular articles on the high and increasing enrollment in Pakistani religious schools, commonly known as madrassas. Given the importance placed on the subject by policy makers in Pakistan and those internationally, it is troubling that none of the reports and articles reviewed based their analysis on publicly available data or established statistical methodologies. This paper uses published data sources and a census of schooling choice to show that existing estimates are inflated by an order of magnitude. Madrassas account for less than 1 percent of all enrollment in the country and there is no evidence of a dramatic increase in recent years. The educational landscape in Pakistan has changed substantially in the last decade, but this is due to an explosion of private schools, an important fact that has been left out of the debate on Pakistani education. Moreover, when we look at school choice, we find that no one explanation fits the data.", "output": {"entities": {"named_data": [], "descriptive_data": ["census of schooling choice"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "which increases signiﬁcantly under both treatment arms. In other words, the psychosocial value to employment appears to be driven largely by the non-pecuniary dimensions of the employment experience. 5. 4 Impacts of employment on reported physical health, cognitive function, and economic decision-making The positive eﬀects of employment extend to other measures beyond psychosocial health. Table 3 presents results on reported physical health, cognitive function, and incentivized measures of risk and time preference. We observe a signiﬁcant increase in the days reported healthy. This eﬀect may be due to ‘ real ’ health improvements from increased exercise (which has also been documented to translate to improved mental health (Herbert et al. (2020))) from the employment task or ‘ perceived ’ health improvements in which improved psychoso- cial well-being translates into feeling less physically ill. Should the channel be exercise, we may expect health improvements to grow over time. Our weekly data on days healthy sug- gests this is not the case: we observe the treatment eﬀect on health from the ﬁrst week of working, and the gap remains steady throughout the following two months (Appendix Figure A3). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["weekly data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3) Female teacher who is HIV�/ but not sick\nshould be allowed to continue teaching in\nschool.\n\nPEPFAR\n\n4) Would not want to keep the HIV�/ status PEPFAR\nof a family member a secret.\n\nPractices\n\nCondom use 1) Percent of men and women (aged 15 �24)\nwho used a condom at last sex with a\nnon-marital, non-cohabiting partner, of\nthose who have had sex with a non-marital,\nnon-cohabiting partner in the last 12\nmonths. [bc]\n\nUNGASS, MDG, PEPFAR\n\na prior to the UNGASS indicators in 2002, the UNAIDS stated indicators did not specify youth\n(15 �24 years).\nb the MDG indicator replaces ‘have’ with ‘transmit’.\nc the MDG indicators do not specify ‘non-marital, non-cohabiting’ but add ‘high risk’. PEFPAR\nuses 15 �49 years.\n\nwith the surrounding host population response and a sub-regional approach\nundertaken in order to take into account the displacement cycle (UNHCR 2005).\nTimely and accurate data are needed to provide targeted and effective\ninterventions in conflict and post-conflict settings. Unfortunately, due to unstable", "output": {"entities": {"named_data": ["UNGASS indicators"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 11055 The paper examines the early integration of Ukrainian ref­ugee students into Italy ’ s education system following the Russia ’ s invasion of Ukraine in 2022. Using administrative and survey data, the study presents enrollment trends, aca­demic performance, and barriers to educational integration. Findings from the analysis indicate that Ukrainian refugees face lower enrollment rates, higher absenteeism, and lower test scores than other students, particularly in subjects requiring language proficiency. Despite these challenges, teachers often recommend Ukrainian refugee students for advanced educational tracks, thus revealing their optimism about the potential of these students. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["administrative and survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 population density across a state, the ecological inference issue is alleviated as we directly test the propensity of any population group to experience a conflict. Through disaggregation, we may succeed in supporting explanations based on variables such as the distance from the capital and the overall size of the country's population if we know at which locations conflicts occur. If conflicts are located mainly at some distance from countries'capitals, we might infer that large countries have more conflicts because of the difficulties of projecting governmental power. If they are located in population concentrations irrespective of location relative to the capita, other explanations should be sought. The paper makes use a new dataset called ACLED (Armed Conflict Location and Events Dataset) to allow for this type of disaggregated analysis. The dataset currently codes the location of all reported conflict events in 14 countries in Central Africa in the 1960 – 2004 period. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper suggests some adaptions to a statistical method to allow for analyzing data at this level of analysis. Related to the size of populations is their distributions. The Democratic Republic of Congo, for instance, is not only characterized by being enormously large, but also shows tremendous variation in population densities.", "output": {"entities": {"named_data": ["Armed Conflict Location and Events Dataset", "ACLED"], "descriptive_data": [], "vague_data": ["conflict event data", "geographically disaggregated data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "By 2002, ReproSalud had reached over 123, 000 women and 66, 000 men. Qualitative and quantitative evaluation data suggest that the community-based PLA approach had a positive impact on attitudes and behaviors related to gender based violence (Rogow and Bruce 2000; Ferrando, Serrano, and Pure, 2002, cited in Boender et al., 2004). The quantitative evaluation (using community based surveys) was complicated by the fact that the project coincided with a period of strong investment by the Ministry of Health, which made it difficult to isolate the project ’ s impact. Gender-equitable attitudes and practices increased significantly in both intervention and control communities, though improvements in intervention sites were slightly higher. The qualitative data suggested a much greater difference in intervention and control sites and gathered evidence of dramatic changes in social relations and men's behavior. Respondents spoke at length about decreased alcohol consumption, domestic violence, and forced sex in all intervention villages studied. In the words of one 35 year-old woman,\"Before, they brutally forced sex. They hit, especially when they were drunk. Now, no more\"(Rogow and Bruce, 2000, page 20). Individual behavior change strategies Many other programs have attempted to produce individual (rather than community-level) behavior change by working with individual men and boys. White, Greene and Murphy (2003) reviewed the literature on such programs aimed at men. That review suggests that less information is available on the effectiveness of individual behavior change strategies compared to community-level approaches. Some", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative evaluation data", "community based surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "##### **September/ October 2023 report**\n\n###### Venezuela\n\nand Gender-Based Violence for officials\n\nof the Women's Secretariat of the State\n\nof Miranda. Conducted from September\n\n28th to October 11, 2023, the PA facil\nitated the first virtual session on key\n\nconcepts, receiving positive feedback\n\nfrom the 35 participating officials.\n\n**XVII.** **September’s** **Monthly**\n\n**Meeting with Partners**\n\n**On Thursday, September 28th, the PC**\n\n**convened its monthly meeting with**\n\n**53 members representing 41 partner**\n\n**organizations.** The agenda covered an\n\ninformation exchange between partners\n\nwhich expressed concerns on the way\n\nnational IDs are processed in light of the\n\nsoon elections; saw a presentation of\n\nthe New Accountability Framework for\n\nAffected Populations (AAP), its objectives\n\nand work plan; updates from the national\n\nsubcluster of Ciudad Guayana on their\n\n**11**\n\n**October.** The focus was on ensuring\n\nthe accuracy of registered services and\n\ncollaborating with partner organizations\n\nto edit or confirm the removal of inactive\n\nservices. The revised database is under\ngoing verification for coherence by the\n\nIM team before being uploaded onto the\n\nplatform. Considerations regarding the\n\nplatform itself and the inclusion of other\n\nsectors are also under review, and once\n\nfinalized, the PC will implement its com\nmunication strategy on Service Mapping.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Caregiver-Reported Early Development Instrument (CREDI) long- form was used for children under 36 months of age. The CREDI is a globally-validated developmental assessment tool (Waldman et al., 2021), and the short-form version of the CREDI has previously been validated and used in Pakistan (Hentschel et al., 2024; McCoy et al., 2018). The tool consists of up to 100 caregiver-reported items developed according to typical abilities by age within the birth to age 3 range in 6-month increments (e. g., for children 0-6 months, 6-12 months, and so forth) and administration ends based on 5 consecutive incorrect answers so as not to distress the child. Items measured children ’ s developmental status across four primary domains: motor, language, cognition and social-emotional development. An age-standardized Z-score, created by uploading deidentified data to the CREDI application, allows comparison of a given sample to a global, advantaged, sample based on children from 15 low- and middle-income countries. 5 A child scoring more than 2 standard deviations below the mean is typically considered to be developmentally “ off-track.\"5 The reference sample contains 19, 165 children who all have a mother who completed secondary school or higher education and live in a household where least one adult had engaged in 4 or more of the 6 “ Play activities ” from the Family Care Indicators with the child. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The observations of positive events contain more information than the non-event observations We therefore sample asymmetrically: We sample all of the transition events and 1. 0 % of the non-transition events. 3. 4 Disaggregated Independent Variables Local level data on land, population, and elevation is available in the geospatial format of raster files with a resolution of 1km. Using Geographic Information systems (GIS), attributes from raster and point data are associated with the grid square in which they lie. In this way, spatial data is georeferenced to a location that is defined by the grid cell. This process results in a data structure in which each row has within it combined information on a square defined by the grid, the national level information in which is it located, and () () () ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ ∑ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ = = ∉ = t X t X t d d j j p j R i d j j p j w w t β β 1 1 exp exp at out breaks war a | square a in war Pr Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The monthly precipitation data comes from the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric Association's Climate Prediction Center, is based on ground station measurements of precipitation over the period 1948-2001.\n\nSoil data was obtained from FAO (2003). The FAO data provides information about the\nmajor and minor soils in each location. Data concerning the hydrology was predicted from a\nhydrological model for Africa (Strzepek & McCluskey 2006). The model calculated the\nwater flow through each district in the surveyed countries. Data on elevation at the centroid\nof each district was obtained through GIS manipulation using data from the United States\nGeological Survey (USGS, 2004). The USGS data are derived from a global digital elevation\nmodel with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).\n\nWeng F & Grody N, 1998. Physical retrieval of land surface temperature using the Special Sensor Microwave Imager. _Journal of Geophysical Research_ 103: 8839-8848. World Bank, 2003. Africa rainfall and temperature evaluation system (ARTES). World Bank, Washington DC.", "output": {"entities": {"named_data": ["Africa Rainfall and Temperature Evaluation System (ARTES)", "ARTES"], "descriptive_data": [], "vague_data": ["Soil data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Regional refugee response activities in the Eastern sub-region are coordinated through the Regional Refugee Response Plan, which aligns funding from bilateral donors and UN agencies against the Refugee Coordination Model's joint response priorities. The plan is revised annually following a joint needs assessment and endorsed by the Regional Refugee Coordinator. Implementation progress is tracked against the plan's indicators through a shared activity monitoring dashboard accessible to all participating agencies. The dashboard is updated monthly by designated focal points from each implementing partner.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In all speciﬁcations, conﬂict incidence correlates nega- tively with country-level economic performance. The estimated coeﬃcients of conﬂict incidence are statistically signiﬁcant and negative. We also ﬁnd that the coeﬃcients 7For a discussion see Henderson et al. (2012). In order to calculate light per capita we use popu- lation data that is provided by a World Bank dataset. 8The standard reference here is Miguel et al. (2004). Ciccone (2011) shows that high rainfall levels three years earlier seem to be best predictors of conﬂict in the reduced form. Miguel and Satyanath (2011) argue that lagged negative growth shocks are a predictor of conﬂict onset. In any case, there is no evidence from this literature that contemporaneous growth declines cause conﬂict. Bazzi and Blattman (2014) corroborate the view that the relationship between income shocks and conﬂict is not straightforward. They do not ﬁnd evidence of an eﬀect of price shocks on conﬂict onset and only weak evidence on incidence. 9Results from this are presented in the Appendix. 10We take the threshold from Mueller (2016) who shows that a threshold like this leads to a similar number of coded civil wars as the threshold of 1000 battle-related deaths often used in the conﬂict literature. In the context here, this is a conservative approach as it is not the threshold which yields the biggest diﬀerence between conﬂict and non-conﬂict countries. 10", "output": {"entities": {"named_data": ["World Bank dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "would like to thank colleagues from RRS who provided invaluable comments on the various draft stages of this report: Bruhtesfa Mulugeta, Zewdu Bedada, Daniel Adefires, Anteneh Mekasha, Anteneh Gorfu, Yewulsew Nigussie, Fantaw Kabtamu, Biruk Kebede, Dr. Goitom Ademnur, Dr. Tagay Kelil, Daniel Darcha. Additionally, we acknowledge the Household Expenditure and Welfare Statistics core team at ESS for their exceptional work on the data collection, from the survey's inception to the report's culmination: Amare Legesse, Alemayehu Teferi, Efrem Belachew, Salah Yusuf, Seid Jemal, Hagos Haile, Zenaselase Siyum, Tsigab Halefom, Yirga Nigussie, Kassu Gebeyehu, Zemecha Abdella, Mengistu Abebe, Aklilu Fikre, and Sisay Guta. Our gratitude goes to Leslie Velez (Assistant Representative (Protection, UNHCR) for her unwavering support and encouragement throughout the whole SESRE process. We are particularly thankful to UNHCR colleagues Yonas Lemma, Yonatan Assefa, Michel Uwamahoro, Millicent Lusigi, and Mekdes Aschalew for enabling access to administrative refugee data and their support during the sampling and data collection stages. Moreover, our thanks go to the following UNHCR colleagues for their invaluable comments on the various draft stages of this report: Emily Lugano, Annick-Laure Tchuendem, Jed Fix, Theresa Beltramo, Alessio Baldaccini, Anna Gaunt, Asaad Kadhum, Benoit d’Ansembourg, Berhanu Geneti, Campbell Macknight, Daniel Gebrekidan,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["administrative refugee data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Afrobarometer finds that the vast majority of Malians want their country to remain a single and unified nation and that the attempt by armed groups to create a breakaway state in Mali ’ s northern territories is decisively rejected. See Afrobarometer Policy Paper 10 (Dec 2013). This difference with the Afrobarometer survey can be explained by the fact that the latter survey only focused on Malians inside the country and did not take the views of refugees into account. 5 93 2 6 86 75 20 2 3 94 Independence of the North Autonomy of the North Establish full government control over the North Figure 19: How do you envision the future of Mali? IDPs Refugees Niger Refguees Mauritania Returnees Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Specifically, a 1 percent increase of the refugees ’ presence5 leads to a 2. 7 and 15. 9 percent increase in the diversification of livelihood activities as a secondary occupation and value of livestock product sale, respectively. It should be noted that this analysis is taking place during a period where refugees in Ethiopia were prohibited by law from seeking work outside designated camps. This has changed after 2019 because of the revised Ethiopian Refugee Law. These effects tend to be heterogeneous across regions and to a limited extent, vary depending on the gender of the household head. The negative effects tend to be concentrated in Gambella, a region that hosts most of the refugee population in Ethiopia and where the refugee population is as large as the population of the region. Overall, compared to women-headed households, households with a male head seem to benefit through increased diversification of activities as a secondary 4 Region refers to the administration level 1 from the Database of Global Administrative Areas (GADM). The nearest region to the refugee camp is identified as the one that has the shortest straight distance to the refugee camp among all neighboring regions in the major refugee source countries. 5 As explained above, refugee presence is the number of refugees (population) in the nearest refugee camp to the household location weighted by the household ’ s inverted distance to the camp. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Database of Global Administrative Areas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The conflict in Sudan continues to steadily trigger waves of population outflows to\nneighbouring countries. At the beginning of July, based on registration and government\nstatistics, almost 490,000 newly arrived refugees and asylum seekers were recorded in\nneighbouring Chad, Egypt, Ethiopia and South Sudan, and almost 142,000 South\nSudanese were recorded as returnees in their country of origin.\n\n_[Source: UNHCR Sudan Situation, Operational Data Portal https://data.unhcr.org/en/situations/sudansituation](https://data.unhcr.org/en/situations/sudansituation)_\n\nWithin Sudan, the internal movement of refugees fleeing insecurity and active conflict\ncontinued unabated, particularly from Khartoum, which traditionally hosted the highest\nnumbers of refugees, mainly from Eritrea and Ethiopia [4] . UNHCR estimates that more than\n187,000 refugees may have left their areas of residence to seek safety in other regions of\nSudan unaffected by the conflict. UNHCR continues to work with concerned authorities,\nincluding the Commissioner for Refugees (COR) to identify their locations to provide the\nneeded support. The tracking and registration so far conducted highlighted how White Nile\nState has been one of the main areas of initial destination, followed by other States of East\nSudan, notably Gedaref and Kassala, where refugees continue to seek shelter in the\nexisting sites, as well as the Red Sea State.", "output": {"entities": {"named_data": ["UNHCR Sudan Situation, Operational Data Portal"], "descriptive_data": [], "vague_data": ["registration and government\nstatistics"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Bank and Israel and occupied territories strongly suggest that commuters would be differently affected during the pandemic when border closures were enacted. Another unique feature is the presence of a large refugee population in the West Bank and Gaza. However, it is important to note that refugees in this context are de- fined quite differently from other contexts. Here, not only the individuals immediately displaced are considered refugees, but also their patrilineal descendants, even if born many decades later. In particular, the LFS dataset follows the United Nations Relief and Works Agency (UNRWA) definition of refugees, which is “ persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948, and who lost both home and means of livelihood as a result of the 1948 conflict, ” as well as “ the descendants of Palestine refugee males, including adopted children ” (UNRWA, 2023). Consequently, most refugees are indistinguishable in socio-economic outcomes and labor market behavior from non-refugees. However, residence in refugee camps does make a significant difference. As of 2019Q4, 5 % of the West Bank ’ s residents live in refugee camps, as do 14 % in Gaza.", "output": {"entities": {"named_data": ["LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The cross-dimensional poverty cut-off is defined as k = 50 %, with those deprived in half or more of the weighted indicators identified as multidimensionally poor. 4. Data Data on forcibly displaced populations are scarce, with many household surveys excluding refugees and IDPs from the sample framework. To ensure that MPI results are representative of these communities and that they can be disaggregated for comparative analysis, an initial review of possible data sets was conducted. Feasibility was determined based on the availability of sufficient sample sizes for forcibly displaced persons for quantitative analyses, as well as inclusion of many of the indicators (on health, education, living standards, etc.) 14 A household is deprived if the respondent reports feeling moderately or very unsafe when alone at home, walking alone after dark, or walking around during the day. In Sudan, the indicator on the ‘ feeling safe from crime and violence when at home ’ was not available, and the indicator only considers answers to the questions on safety when walking alone. 15 Unprotected dug well, unprotected spring, carts with tank, tanker-truck, surface water, or other are considered as unsafe water sources according to international guidelines. See https: / / washdata. org / monitoring / drinking-water. 16 Pit latrine without slab, bucket, hanging toilet, and no facility (open defecation) are considered as unimproved sanitation facilities according to international guidelines. See https: / / washdata. org / monitoring / sanitation. 17 According to the ILO definition, those who did not participate in employment in the last four weeks (and have no work to return to) are actively looking for work and are available to start, or those currently waiting to start work are classed as unemployed. See https: / / www. ilo. org / ilostat-files / Documents / description_UR_EN. pdf.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5. Time Use and Child Labor: The fifth module examined how children spend their leisure time, their involvement in child labor, and interactions with peers. 6. Pro-social Preferences and Migration Outlook: The sixth module concentrated on adolescents ’ pro-social behaviors, such as altruism and trust, and explored their expectations and intentions regarding migration. 7. Socio-emotional and Mental Health: The final module involved the administration of various scales to assess socio-emotional well-being and mental health, includ- ing trauma, behavioral problems, anxiety, and depression. The scales include the Trauma Symptom Checklist for Young Children (TSCYC), Strengths and Difficulties Questionnaire (SDQ), General Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). All these scales and the corresponding outcomes that we evaluated are described in the next subsection. The survey also employed the Peabody vocabulary test to evaluate the cognitive devel- opment of all participating children and adolescents. A summary of the survey modules is depicted in Table A. 1. III. C Sample comparability While Medell ´ ın ranks as the third city with the highest migration in Colombia, it is crucial to recognize the degree to which migrants arriving in the city differ from those migrating to other regions in Colombia. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "25 contributions to the local communities. More than other studies, this analysis points to the transfer of physical and human capital by refugees as an important source of benefits for the local economies. Interestingly, Kreibaum (2016) provides a more quantitative approach to the issue by assessing the impact of an increase in the presence of Congolese refugees on the hosting population in the Southern and Western parts of Uganda. The results indicate a positive ‐ although small in magnitude ‐ impact on the hosts ’ welfare (consumption per adult equivalent) but with distributional effects. Those depending on wage income and transfers experienced a deterioration in welfare, suggesting labor substitutability with rural landless workers. That seems to constitute a commonality with the Tanzanian case study. In addition, increase in the provision of private education services are also found, which is consistent with the move to the so ‐ called self ‐ reliance strategy in Uganda (see below). A major contribution of this paper is to contrast these results to the Ugandan households ’ perceptions in local communities. Conditional on assuming a common trend (that could not be tested with the available data), people are found to perceive their living conditions as having worsened off in areas with a higher number of refugees. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Yit is the outcome of interest for individual i and time t. Treati is an indicator which is equal to 1 for treated individuals and 0 otherwise. Postt is an indicator equal to 1 for midline observations and 0 for baseline. Xit is a vector of controls at baseline (t = 0), including individual characteristics (education, age, current pregnancy, marital, parental, and orphan status) and household characteristics (sex of household head and household size). β1 is the coefficient of interest that defines the “ impact ” of the program on individuals in the treatment group. The model also includes dummy variables for the communities where the program was implemented (and where the trainees resided) as well as the program track (business skills or job skills) to which the respondent was assigned. Finally, in order to control for household wealth, we compute an index based on household asset ownership at baseline using multiple component analysis, similar to the method described in Filmer and Pritchett (2001). After constructing the index, which includes thirteen household assets and six indicators of housing conditions, we control for the quintile of household ’ s overall asset position in all regressions. We augment this basic specification with an individual fixed effects model and find that the results are almost identical. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 household displacement and the destruction of household dwellings during the violent events. We are also able to measure conflict intensity across time – including peaks of violence at various stages of the conflict – at the district level from event data on violence intensity during the conflict in Timor Leste. We focus on primary school effects because only a small percentage of the Timorese population attended secondary school. Our results show mixed evidence for the impact of violent conflict on educational outcomes. Mirroring some of the findings of Bellows and Miguel (2006) and others, we find evidence for a rapid recovery of the education sector in Timor Leste, and of educational outcomes, particularly for girls. However, in line with emerging results in the micro-level literature, we find that the 1999 wave of violence in Timor Leste – as well as peaks of violence in the 1970s and 1980s – resulted in negative effects on primary school attendance and attainment. This effect is particularly strong for boys. We attribute the first result to a process of educational catch-up among girls in Timor Leste that started before the conflict and continued despite the conflict.", "output": {"entities": {"named_data": [], "descriptive_data": ["event data on violence intensity during the conflict"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "13 Where 𝑌𝑌𝑖𝑖 represents one of the indicators of social cohesion explained above, 𝛿𝛿𝑗𝑗 is the province indicator, 𝑅𝑅𝑐𝑐 is the share of returnees in the community, 𝐻𝐻𝑖𝑖 indicates a series of household level controls and 𝐶𝐶𝑐𝑐 are a series of community level of controls. In the main regressions we estimate the share of returnees in the community, using the information from the survey (i. e. share who are returnees), but in the robustness section we show that results are robust to the use of an alternative indicator in which the information is provided by a community leader. The Appendix (Table A2) includes the descriptive statistics for the control variables. We present results for the full sample and divided by communities with lower / higher ethnic diversity, less / more pre-1993 war land availability and better / worse attitudes towards return. In the robustness checks we also present the results if we limit the analysis to stayees only. Limiting the sample in this way does not affect the main results of the paper. 4. 4 Identification As mentioned above, Tanzania mandated the return of all Burundian refugees from the 1993 conflict. Returnees also had a very strong incentive to return to their communities of origin as this was the place in which they were entitled to land, a very scarce resource in the country. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 16 of 51 Figure 11: Education profile of Temporary employees. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys However, it should be noted that the improvement of the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in Annex II of the paper. Figure 12 presents the Kernel distribution of the labor income per working hour by country and type of employment. When we analyze what has happened at the salary level and distribution in the period under analysis, two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by these economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases.. 6 The second trend identified as generalized in the countries of study is the increase in the variance of the wage distribution. In fact, in most of the countries considered, non-standard employment wages currently show a significantly greater dispersion than that registered a decade ago.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "mean CEI over our sample period. Since return decisions are likely impacted by the persistence of conﬂict, we classify districts into high and low conﬂict districts, using the top 10th percentile of the mean CEI as a cut-oﬀpoint. 11 We will use this classiﬁcation to explore the extent to which return decisions of key social and demographic groups are impacted by the persistence of conﬂict. Survey of refugees in Lebanon and Jordan: In a survey of 900 Syrian refugees in Jordan and Lebanon, we randomly varied the details of the scenario or vignette presented to a given individual respondent. Some refugee families are certainly more predisposed to wanting to return than others. By describing hypothetical scenarios, but ones which hit fairly close to home, and varying key factors within those scenarios should help us identify what factors are important to many refugee families when deciding whether to return. For all respondents in all vignettes, we asked “ How likely is this family to return to Syria in the next 2 months? ” where the respondent could answer using a Likert scale, ranging from “ Very likely ” to “ Very unlikely ”. For the analysis below, we use an indicator which is equal to one if a respondent says the family is either very likely or likely to return, and 0 if the respondent says neutral, unlikely or very unlikely. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Self-reported data from this survey suggest that firms in Dar es Salaam, Dodoma, and Tanga incurred at least $7.6 million in direct losses and damages due to flooding on business premises in 2018.\n\nThe data collected through this survey suggest that the primary reasons for general supply and delivery delays in Tanzanian firms are upstream supply chain issues.\n\nRentschler et al. (2019a) build a microdata set of about 143,000 firms to estimate the monetary costs of infrastructure disruptions in 137 low- and middle-income countries.\n\nThe lack of reliable and resilient infrastructure systems causes economic efficiency losses. A global study\nby Rentschler et al (2019) highlights the substantial drag that unreliable infrastructure imposes on firms\nin developing countries. In Tanzania, firms are incurring estimated utilization losses of nearly $670 million\na year (1.8 percent of national GDP) from power and water outages and transport disruptions. The firm\nsurvey collected for this study allows us to estimate the share of these utilization losses that are caused\nby natural shocks.\n\nWe use consumption and price data from the National Risk and Vulnerability Assessment (NRVA) 2007/08, conducted by the Government of Afghanistan Central Statistics Organization and the Ministry of Rural Rehabilitation and Development. The survey was administered between August 2007 and September 2008 and covered over 20,500 households (over 150,000 individuals) in 2,572 communities in all 34 provinces of Afghanistan.", "output": {"entities": {"named_data": ["National Risk and Vulnerability Assessment"], "descriptive_data": ["Self-reported data from this survey", "microdata set of about 143,000 firms"], "vague_data": ["data collected through this survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This assistance is crucial and has been reported to support a meal a day, a better roof, and dignity for Syrians who have fled to Jordan. 8 The JD ‐ HV database has detailed information on expenditure, sources of income, and indicators of household ‐ level welfare, for example, as reflected by recourse to coping mechanisms, standard of accommodation, or access to water, sanitation, and hygiene (WASH). JD ‐ HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need. Verme et al. (2016) draw attention to the precarious circumstances of Syrian refugees in Jordan and Lebanon. Around 55 percent of refugees in Jordan are vulnerable to monetary poverty and more than half are vulnerable to food shocks. Family size increases the probability of being poor, with the poverty rate almost doubling if the size of the family goes from one to two members and increasing by 17 percent when the number of children increases from one to two. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["JD ‐ HV database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Road data from the transport sector review were used to construct a road condition index for each of the 89 communes in the project area. The index combines pavement condition scores, bridge load ratings, and seasonal accessibility ratings into a composite infrastructure access score. Communes ranked in the bottom tertile of the index were given priority consideration for infrastructure investments under Component 2, subject to confirmation through community consultation that road rehabilitation was among the top three priorities identified by community members.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The remote sensing inputs comprise  weather data, such as rain and temperature, as well as soil and terrain data. These sources  are then used by GeoSFM to model basins across Indonesia and the stream flow in each of  these.\n\nfrom ENLACE, a census-based standardized test that primary and secondary school\n\nof the Mexican labor force survey (ENOE) applied to individuals aged 18 to 20 years old", "output": {"entities": {"named_data": ["Mexican labor force survey (ENOE)", "ENOE"], "descriptive_data": ["ENLACE, a census-based standardized test"], "vague_data": ["weather data", "soil and terrain data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 13 of 51 Figure 8: Temporary employment by gender. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 4. 1. 2 Profile of Non-Standard Employment The next objective of this work is to investigate possible changes in the profile of non-standard employment, usually associated with lower productivity and greater vulnerability. The analysis of the employment profile was made based on access to social security benefits, education level, labor income and the task content performed by the workers. From the point of view of social security, we find that NSE shows a higher prevalence of informality compared with SE. For instance, the average prevalence of informality for our set of countries among NSE in the ending point of the study is 40 % while the prevalence among SE is 20 %. In this sense, a rise in the prevalence of NSE could be associated to a big set of workers without access to social security benefits. From a dynamic perspective, there is no a common trend across countries regarding the prevalence of informality among NSE workers (Figure 9). Indeed, several countries (Uruguay, Brazil, Chile, and Peru) registered a small decrease in the prevalence of informality among NSE but there is another set of countries for which the opposite is observed (Mexico, El Salvador, Bolivia and Argentina). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 13 of 51 Figure 8: Temporary employment by gender. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 4. 1. 2 Profile of Non-Standard Employment The next objective of this work is to investigate possible changes in the profile of non-standard employment, usually associated with lower productivity and greater vulnerability. The analysis of the employment profile was made based on access to social security benefits, education level, labor income and the task content performed by the workers. From the point of view of social security, we find that NSE shows a higher prevalence of informality compared with SE. For instance, the average prevalence of informality for our set of countries among NSE in the ending point of the study is 40 % while the prevalence among SE is 20 %. In this sense, a rise in the prevalence of NSE could be associated to a big set of workers without access to social security benefits. From a dynamic perspective, there is no a common trend across countries regarding the prevalence of informality among NSE workers (Figure 9). Indeed, several countries (Uruguay, Brazil, Chile, and Peru) registered a small decrease in the prevalence of informality among NSE but there is another set of countries for which the opposite is observed (Mexico, El Salvador, Bolivia and Argentina).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "7 educational institutions, suspensions on in-person meetings and a dusk-to-dawn curfew. The lockdown was subsequently lifted in May 2021. Refugees living in Kenya were subject to the same containment measures as nationals, although refugee camps were in addition closed off from the outside in the very beginning of the pandemic. When the nation-wide lockdown was stepwise lifted in the fall of 2020, permissions to enter the camp for outsiders continued to be strongly limited to protect the refugees living in the densely populated camp settings. Figure 1: COVID-19 cases and RRPS timeline in Kenya Source: Ritchie, et al (2020). Data downloaded on June 14, 2021, here. 3. Data and methodology 3. 1 Survey design The Kenya COVID-19 Rapid-Response Phone Survey (RRPS) is structured as a five-waves bi- monthly panel survey that targets nationals, refugees, and the Shona community. 9 The first wave of interviews was administered in May-June 2020 and the last available wave in April- June 2021. The questionnaires capture extensive demographic and socioeconomic data with modules on employment, income, coping strategies, food security, access to education and health services, child labor, subjective well-being, knowledge of COVID-19, changes in behavior in response to the pandemic, and perceptions of the government ’ s response. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 Last, specific adjustments are made in the case of Germany and the Republic of Korea. For Germany, bilateral data are available only by nationality. However, these data fail to take adequate account of the large number of ethnic Germans who arrived from other countries between 1944 and 1950 (mainly expellees) and those who arrived after1950 (mainly resettlers). Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3). In the case of Korea, data by nationality are readily available for each census round. However, these data fail to account for the large numbers of migrants from the People ‘ s Democratic Republic of Korea living in the Republic of Korea. Since the United Nations Trends in International Migrant Stock details the total migrant stock in the Republic of Korea by the country of birth definition and because citizenship is rarely granted to people from outside, it is simply assumed that the nationality data were comparable to the foreign-born definition. The nationality total was then subtracted from the UN total and the remaining migrants were assigned to the People ‘ s Democratic Republic of Korea. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "over 15,000 refugees relocated from the Tigray camps to Alemwach, and an additional 7,000 refugees were resettled in November 2022 following the cessation of hostilities (UNHCR, 2022). Before the conflict, 64 percent of all Eritrean refugees were hosted in camps in Tigray and 36 percent in camps in Afar (UNHCR, 2020b). Refugees going to either Tigray or Afar are distinct culturally and linguistically. Eritrean refugees in Afar are Muslim and speak Afar, as do the Ethiopian hosts in Afar. Many Eritrean refugees in Tigray—and thus the ones who moved to Amhara during the conflict—are Orthodox Christians and speak Tigrinya. The SESRE sample, therefore, includes in-camp Eritrean refugees in two regions: Afar (216 households) and Amhara (216 households). This means that Eritrean refugees in Amhara, representing half of the Eritrean refugee sample, were displaced from Tigray only a few months before the SESRE was implemented, thus have had less time to integrate into the surrounding community and labor market. Among Eritreans in Afar and Amhara, the share working is 43 percent and 9 percent, respectively. Among workers, Eritreans in Amhara are three times as likely to work for NGOs or RRS, and very few work outside the camp. Box 3.1: Eritrean", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In terms of statistically insignificant effects, Block et al. (2004) examine the effect of Indonesia's drought and financial crisis of 1997-98 using time-age-cohort decomposition analyses employing high-frequency nutrition data.\n\nWith regards to social shocks, in Rwanda, Akresh et al. (2011) find that an additional month of exposure to civil war lowers child HAZ by 0.11 SD. Using qualitative data, the authors argue that this effect is due to increased theft of livestock or crops and exposure to water- and vector-borne diseases, with internal displacement driving this exposure to disease.\n\n7 Measurement error in consumption data could be behind this.\n\nWe use weather data taken from the _Terrestrial Air Temperature and Precipitation Version 4.01_ compiled by\n\nWe use recent global sub-national aggregated output data (Kummu, Taka and Guillaume 2018a) derived from", "output": {"entities": {"named_data": ["Terrestrial Air Temperature and Precipitation Version 4.01"], "descriptive_data": ["global sub-national aggregated output data"], "vague_data": ["high-frequency nutrition data", "consumption data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 1 ‐ Number of Forcibly Displaced Persons (1951 ‐ 2015) Source: Constructed from UNHCR population data (http: / / popstats. unhcr. org / en / time_series). Note: 2015 data are mid ‐ year and lower than end of year data. 5 http: / / www. unhcr. org / en ‐ us / figures ‐ at ‐ a ‐ glance. html. 0 10000000 20000000 30000000 40000000 50000000 60000000 70000000 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Our evaluation explicitly investigated these spillovers by including household-level indicators such as food security and attitudes of the household head toward gender norms. Our results show strong impacts on economic outcomes, including large and statistically significant increases in employment and earnings of EPAG participants. We show mixed results on empowerment- related outcomes, and very little evidence of spillovers on non-participants. Self-assessed measures of self-confidence show huge gains, as does ownership and control over monetary resources such as savings. The remainder of the paper is organized as follows: Section 2 describes the EPAG project including some of its innovative design features and implementation details. Section 3 reviews the methodology of the evaluation and Section 4 presents results on the three groups of outcomes discussed above: economic, empowerment, and spillovers. Section 5 includes a short discussion of cost-effectiveness. Section 6 presents a series of robustness checks and Section 7 concludes with a discussion of next steps and policy implications. 2. The EPAG Project The EPAG project is part of a larger Adolescent Girls Initiative (AGI) administered by the World Bank with support from the Nike Foundation and the Governments of Australia, the United Kingdom, Norway, Denmark, and Sweden. Launched in Washington DC in October 2008, the AGI was spearheaded by President Ellen Johnson Sirleaf, who signed on to undertake the initiative ’ s first pilot project in Liberia. The Liberian pilot was launched in March 2010 and has served as a role model to seven subsequent pilot projects in Rwanda, South Sudan, Nepal, Afghanistan, Haiti, Jordan, and Lao PDR. Under the global AGI, young women and adolescent girls are given a package of skills training and complementary services in order to facilitate their successful transition to employment. In the case of EPAG, the intervention consisted of a six month phase of classroom-based training, followed by a six 4 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This paper ’ s scope of analysis includes the country as a whole using a nationally representative survey. While regional case studies may reveal salient stresses on public services and job displacement, nationally, there is no significant impact. Over the period of 2009 to 2013, the poverty rates of host community households have stayed relatively stable near the Syrian border; despite the high poverty rates experienced among the recent migrants. By country of origin, the displacement of Syrians is one of the largest in recent history. As a result of the civil war that began in 2011, Syrians started to leave their homes and look for safety in neighboring countries across the region. By November 2015, about 4. 3 million Syrians were seeking refuge in primarily Turkey, Lebanon, Jordan, Iraq, and the Arab Republic of Egypt. 3 The only other time in the last half century that the world experienced a larger group of refugees from a single country is the case of Afghan refugees during the 1980s to 1990s. Refugee displacements of this size are rare. Consequently, they are not well studied and their impacts are not well understood. Moreover, the case of Afghan refugees in Pakistan is different, since they were stigmatized to a larger extent, which limited their movement in Pakistan. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["nationally representative survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "are located. Seven Zones host refugees; this study calls them hosting Zones. North Gondar Zone hosts Eritreans in Alemwach camp. Awsi (Zone 1) hosts Eritreans in Asayita camp. Liben Zone hosts Somali refugees in Bokolmanyo, Buramino, Hilaweyn, Kobe, and Melkadida camp. Fafan Zone hosts Somali refugees in Aw-barre, Kebribeyah, and Sheder camp. Agnuak Zone hosts refugees from South Sudan in Pinyudo 1 and 2, Jewi and Okugo camp. Itang Special Zone hosts refugees from South Sudan in Tierkidi, Kule, and Nguenyyiel. Assesa hosts refugees from South Sudan in Bambasi, Sherkole, and Tsore. Probability of being employed 1 .8 .6 .4 Female 50 60 70 80 90 100 Male Labor force participation rate .2 Figure 6.6: Local labor supply effect of refugee’s odds of employment Source: World Bank Staff based on SESRE 2023. Note: Predicted marginal probabilities of being employed based on the labor force participation rate of the local market, tabulated by employment experience. Probability of being employed .8 .6 .4 Female 2 6 10 14 18 4 8 12 16 20 Male Unemployment rate (5%) .2 Figure 6.7: Local unemployment level matters to obtain jobs Note: Predicted marginal probabilities of being employed based on the unemployment rate of the", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "LEDA, which computes the minimum linguistic distance between two ethnic groups and, therefore, provides the closest linguistic neighbor for each given ethnic group (see Figure A. 1). This function computes a variable called distance, which measures the linguistic distance between two ethnic groups. Mathematically, these distances are calculated as DL1L2 = 1 − \u0012 2d (ω (L1,..., O) ∩ ω (L2,..., O)) d (ω (L1,..., O)) + d (ω (L2,..., O)) \u0013 δ, (A. 1) where d (ω (L1,..., O) is the length of the path from the first language to the tree ’ s origin and d (ω (L1,..., O) ∩ ω (L2,..., O) is the length of the intersection of the paths from the first and second language to the origin. δ is an exponent to discount distances further away from the root of the tree; it is typically set to 0. 5. Figure A. 1: Linking Ethnic Data from Africa Source: M ¨ uller-Crepon et al., 2020. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Impacts on the labor market are unclear; initial research suggests that there has been a supply shock to informal labor markets. This has had a large-scale impact on the employment of natives in the informal sector. At the same time, research suggests there has been a boost to formal employment for the Turks, but this has been uneven: the low educated and women experience net displacement from the labor market and, together with those in the informal sector, declining earning opportunities.  The “ Socio-economic Assessment of the Impact of Syrians under Temporary Protection (SuTPs) on Turkish Hosting Communities ” [ongoing], to be undertaken in partnership with the Government of Turkey, will include a nationally representative household survey with SuTP and local Turkish households including camp and non-camp environments. The questionnaire will cover welfare (assets, income, expenditure), municipal services, labor and employment, education, social networks and quality of life. F. Options to improve forced displacement statistics Significant efforts are needed to enhance the reliability, comparability, quality and scope of the global data on forced displacement. In particular, more robust estimates are needed of the scale (stocks, flows and locations) and typology (demographics, location and accommodation) of forced displacement crises. This requires substantial improvements in the rigor of data collection and compilation methodologies including: (a) Harmonization of definitions and methodologies used in the collection and analysis of statistical data on forced displacement — covering stocks and flows of refugees, asylum- seekers and IDPs — to ensure comparability across regions and countries; Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 The SETAM platform has only one electronic trading platform that is run by the system administrator, creating a conflict of interest through the possibility of accessing auction data and using these to interfere in the auction process.\n\nAuction data for our analysis come from two sources. Information on 31,483 auctions of public land for\n\nSpecifically, we use data from longitudinal and cross-country comparable national phone surveys implemented between March 2021 and January 2023. These multi-topic phone surveys were conceived in order to track the effects of the COVID-19 pandemic in the absence of in-person data collection (Himelein et al. 2020). Our experimentation with survey design choices draws on five of these surveys in Sub-Saharan Africa that were supported by the Living Standards Measurement Study (LSMS) team at the World Bank and implemented by the respective National Statistical Offices.\n\nThese surveys are re-contact surveys, drawing their samples from the latest nationally representative, in-person LSMS-ISA household survey conducted in each country before the pandemic. As part of the LSMS-ISA surveys, phone contact numbers of all household members (where available) as well as from a reference contact such as a neighbor were collected (Gourlay et al. 2021). The list of households with a phone contact, or a random subset of it, constituted the sample to be contacted for the phone surveys and covered between 73% (Malawi) and 99% (Nigeria) of households included in the in-person LSMS-ISA survey.", "output": {"entities": {"named_data": ["LSMS-ISA surveys"], "descriptive_data": ["longitudinal and cross-country comparable national phone surveys"], "vague_data": ["auction data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "team of the Poverty and Equity Global Practice at the World Bank led by Christina Wieser (Senior Economist, World Bank), including Wondimagegn Mesfin Tesfaye (Economist, World Bank), Fikirte Girmachew (Consultant, World Bank), Jeremey Aaron Lebow (Young Professional, World Bank), and Manex Bule Yonis (Economist, World Bank), under the adept guidance of Pierella Paci (Practice Manager, World Bank). The report is an output of a collaboration effort between the World Bank, the Ethiopian Statistical Service (ESS), the Ethiopia Refugees and Returnees Service (RRS), and UNHCR with generous financial support from the World Bank and UNHCR Joint Data Center on Forced Displacement (JDC). Our appreciation extends to the UNHCR and RRS colleagues for their unwavering support. We offer our special thanks to Mulualem Desta (Deputy Director General, RRS) and Ashenafi Demeke (Education Team leader, RRS) for their critical guidance and support throughout the survey design, implementation, and report development phases. Moreover, we would like to thank colleagues from RRS who provided invaluable comments on the various draft stages of this report: Bruhtesfa Mulugeta, Zewdu Bedada, Daniel Adefires, Anteneh Mekasha, Anteneh Gorfu, Yewulsew Nigussie, Fantaw Kabtamu, Biruk Kebede, Dr. Goitom Ademnur, Dr. Tagay Kelil, Daniel Darcha. Additionally, we acknowledge the Household Expenditure and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "_Thailand_ . As for Thailand, it is the third largest country, fourth most populous and boasts the\nsecond largest GDP of our countries. In terms of disasters, Thailand is mostly at risk to floods\nand typhoons, but earthquakes happen occasionally, with the largest recorded being a 6.1\nmagnitude earthquake that occurred in May 2014. The lower risk compared to our other\ncountries is also mentioned by INFORM in their country profile of Thailand (INFORM 2019).\n\n\n_Vietnam_ . The final country is Vietnam, which is the smallest, but has the third highest\npopulation just below 100 million people. In terms of GDP, Vietnam rank fourth among our\nfive countries. When it comes to disaster risk, they are on par with Thailand in that INFORM\nranks them low and exposed primarily to floods and typhoons (INFORM 2019).\n\nNational Disaster Management Agency, B. N. P. B. 2016. \"DiBi database (Data and Information\non Disaster in Indonesia).\" http://dibi.bnpb.go.id/.\n\n\nRahman, Md. Mizanur, Dhyan Singh Arya, Narendra Kumar Goel, and Ashis Kumar Mitra. 2011.\n\"Rainfall statistics evaluation of ECMWF model and TRMM data over Bangladesh for\nflood related studies.\" _Meteorological_ _Applications_ (Wiley) 19: 501-512.\ndoi:10.1002/met.293.\n\nThe underlying data are based on 2015 and combines census data with satellite images from DigitalGlobe. The population is allocated according to subdivision censuses once settlements have been identified from the satellite images.\n\n(2018) used the underlying NPP-VIIRS DNB Daily Data to analyze selected natural disasters. They found that the images were useful for detecting damages and power outages, but that cloud coverage was a major limitation in the assessment.\n\nThe first dataset combines information on flood extent and flood depth from pluvial and fluvial flooding into one flood map. It was developed by the company Fathom, formerly known as SSBN. The map covers all of Vietnam at a resolution of about 90 by 90 meters.", "output": {"entities": {"named_data": ["NPP-VIIRS DNB Daily Data"], "descriptive_data": ["satellite images from DigitalGlobe", "flood map"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Disaster and Emergency Management Presidency of Turkey (AFAD) provides information on the number of Syrian refugees. The numbers used in this paper are taken from Erdogan (2014), who draws on information from AFAD and the Ministry of Interior and reports the number of refugees by NUTS 2 subregion. To construct our instrument we use the Syrian Labor Force Survey for 2010 (the year before the beginning of the war). Finally, Google Maps was used to derive the travel distance between each governorate in Syria and the most populous city in each NUTS 2 subregion in Turkey. 14 Most recently, in January 2016, labor market access for Syrian refugees in Turkey was eased considerably. Importantly, they now can benefit from vocational training under the Turkish Employment Agency, employers will be able have to Syrians comprise up to 10 percent of their staff, and seasonal workers are exempted from the work permit, see http: / / www. resmigazete. gov. tr / eskiler / 2016 / 01 / 20160115-23. pdf. It is of course too early to evaluate the impact of these legislative changes. 15 Hurriyet Daily News (February 2015) http: / / www. hurriyetdailynews. com / turkey-urges-worlds-help-on- syrian-refugees-as-spending-reaches-6-billion. aspx? pageID = 238 & nID = 78951 & NewsCatID = 359. 16 Starting with 2014 there was a change in the design of the Household Labour Force Survey to ensure full compliance with European Union standards. This has caused some difficulty in making comparisons across years. However, our identification strategy does not use aggregate variation across years for identification and should hence be unaffected by the changes to the design of the survey.", "output": {"entities": {"named_data": ["Household Labour Force Survey", "Syrian Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "[8] F. N. P. Nimoh, T. P. Beltramo, J. R. Fix, F. K. Appler, U. J. Pape, and L. A. Rios Rivera, ‘Understanding the Socioeconomic Conditions of\nthe Stateless Shona Community in Kenya: Results from the 2019 Socioeconomic Survey’, Dec. 2020. Accessed: Apr. 02, 2024. [Online].\nAvailable: https://documents.worldbank.org/en/publication/documentsreports/documentdetail/356511608745182603/Understanding-the-Socioeconomic-Conditions-of-the-Stateless-Shona-Community-inKenya-Results-from-the-2019-Socioeconomic-Survey\n\n[9] U. J. Pape _et al._, ‘How COVID-19 Continues to Affect Lives of Refugees in Kenya : Rapid Response Phone Survey - Rounds 1 to 5’, World\nBank Group, Washington, D.C., Policy Note 166098, Oct. 2021. Accessed: Oct. 10, 2024. [Online]. Available:\nhttps://documents1.worldbank.org/curated/en/202201637042522937/pdf/How-COVID-19-Continues-to-Affect-Lives-of-Refugeesin-Kenya-Rapid-Response-Phone-Survey-Rounds-1-to-5.pdf\n\n[10] Kenya National Bureau of Statistics and ICF, ‘Kenya Demographic and Health Survey 2022. Key Indicators Report’, KNBS and ICF,\n\nNairobi, Kenya, and Rockville, Maryland, USA, 2023. Accessed: Oct. 10, 2024. [Online]. Available: https://www.knbs.or.ke/wpcontent/uploads/2023/08/Kenya-Demographic-and-Health-Survey-2022-Key-Indicators-Report.pdf", "output": {"entities": {"named_data": ["2019 Socioeconomic Survey", "Kenya Demographic and Health Survey 2022"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Geographic Information Systems (GIS) and geospatial analysis can be used to map, monitor and analyze data on forced displacement. Triangulation of this information with socio-economic and other indicators can provide a rich source of data and enable insights into underlying patterns and trends over time. (e) Use of big data (mobile phone data, news scraping, social media). IDMC is pursuing big data approaches to capturing displacement data in real time in order to report on displacement situations as they are happening and to provide updates on how they are evolving (IDMC 2015). These data are not necessarily representative but can be used in conjunction with other methods to triangulate trends. For example, the Swedish NGO, Flowminder, has pioneered the use of de-identified data from mobile operators to track population displacement caused by natural disasters such as earthquakes in Haiti in 2010 and Nepal in 2015, and these techniques may also have applications in conflict-induced displacement crises. 100 (f) High-resolution satellite imagery and unmanned drones. High resolutions satellite imagery can be used to map physical structures in refugee and IDP camps including changes to the number and type of these over time, support the remote detection of displaced populations in hard to reach or insecure settings; and conduct rapid assessments during or immediately after a mass displacement (Harvard Humanitarian Initiative 2014). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["de-identified data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "some but not all of the high trust in Somali camps relative to other domains. 7.2 Social interactions Social and community integration is fundamental to refugees’ ability to improve their livelihoods, support systems, and economic integration. Having friends in the host country is a valuable resource for refugees; Ethiopian friends can provide valuable information, employer connections, assistance with language, and countless other types of social and economic support. This can also promote more positive attitudes between groups. Social integration has improved well-being, health, and educational achievement for refugee adolescents across various settings (Boda et al., 2023). 0 5 10 15 20 25 30 35 Eritrean Somali South Sudanese Addis Ababa All Refugees Family Friends Figure 7.12: Share with family or friends in Ethiopia Source: World Bank Staff based on SESRE 2023. 0 5 10 15 20 25 30 35 40 Age Under 30 Age 30-44 Age 45-64 Age Over 64 Female Male Figure 7.13: Share with friends in Ethiopia by demographic group Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 69 Despite the generally positive attitudes described, social integration—measured by the friends and family refugees have in Ethiopia—is low. Only 7 percent of refugees report having family", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 neighboring community, (iii) residing elsewhere in the Kagera Region and (iv) residing outside the Kagera Region. Table 3 shows that the basic needs poverty rate declined 8 percentage points in the full sample. This figure masks significant differences in changes between subgroups based on migration. For those found residing in the baseline community, poverty rates dropped by 4 percentage points, but rates dropped by 11, 13 and 23 percentage points for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. A similar pattern is found for consumption per capita. While consumption per capita grew by $ 65 overall, it grew by only $ 30 for those found in the same community and by $ 65, $ 100 and $ 287 for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. Dividing consumption into food and non-food components gives the same result. The most basic assessment of welfare changes would have been wrong if we had focused only on individuals still residing in the community, a practice found in many panel data surveys. We would have underestimated the growth in consumption by half of its true increase. The differences in consumption changes of groups in Table 3 are statistically significant, as shown in Table 4. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "<!-- PAGE 10 --> ##### **3 District Expenditure Data** The financial data used are the District budget data for the years from 2001 to 2012 (Fiscal  Year of January-December every year).\n\nThey were derived from the Regional Financial In formation System (Sistem Informasi Keuangan Daerah, SIKD) of the Ministry of Finance.  The district expenditures are available for 12 different sectors/functions (such as agriculture,  health, education, etc.) and for four economic classifications (personnel, goods and services,  capital, and other).\n\n- floods, earthquakes, volcanic eruptions and the 2004 tsunami - that are combined with  nightlight data - used as a proxy for economic activity - to construct an index that estimates  the impact on districts and provinces.  More specifically, the nightlight data used provide a normalized annual light value ranging  from 0 (no light) to 63 (maximum light) and are from the Defense Meteorological Satellite  Program (DMSP) satellites.\n\nRaschky (2014) and Michalopoulos & Papaioannou (2014). In our case, the nightlight data  have been employed as a weight for the economic impact of disasters.  Floods are modeled through a combination of remote sensing images and GIS-modeling  using the Geospatial Stream Flow Model (GeoSFM).", "output": {"entities": {"named_data": ["Sistem Informasi Keuangan Daerah"], "descriptive_data": ["District budget data"], "vague_data": ["nightlight data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 2: Project Cost by Component and Expenditure Category**\n\n| Component | IDA (USD M) | Govt (USD M) | Total (USD M) |\n|---|---|---|---|\n| 1. Livelihoods and Social Protection | 18.5 | 2.0 | 20.5 |\n| 2. Infrastructure and Service Delivery | 24.0 | 3.5 | 27.5 |\n| 3. Institutional Strengthening | 4.0 | 0.5 | 4.5 |\n| 4. Project Management and M&E | 3.5 | 0.0 | 3.5 |\n| **Total** | **50.0** | **6.0** | **56.0** |\n\nProject management costs include operation of the PIU, SIGAF licensing and maintenance fees, and the annual external audit. M&E costs cover baseline and endline surveys, the MIS and activity monitoring dashboard platform, and supervision of protection monitoring activities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 4. 1 Variation across Districts Pakistan is divided administratively into four provinces with 102 districts — Punjab, Balochistan, North-West Frontier Provinces (NWFP), and Sindh — plus the federal capital Islamabad, the Federally Administered Tribal Areas (FATA), the federally administered Northern Areas and Azad Jammu and Kashmir (AJK). The four provinces or Punjab, Balochistan, Sindh and NWFP, together with Islamabad, account for more than 97 percent of the population. Geographically, parts of Balochistan, the NWFP and FATA border Afghanistan. Sindh and Balochistan are sparsely populated provinces, with the exception of Karachi in Sindh, which is the single biggest metropolis in the country with a population approaching 10 million. We use data from the population census, 1998, as well as the census of private schooling, 2000, to provide estimates of madrassa, private, and government school enrollment in each district except for those in the province of FATA. The geographical dispersion of madrassa enrollment depends on how we define madrassa prevalence. There are three alternatives. We could present a geographical breakdown of the total number of children enrolled in madrassas. This number is related to the total population of the district, and may thus reflect only the size of the district relative to others.", "output": {"entities": {"named_data": [], "descriptive_data": ["population census, 1998", "census of private schooling, 2000"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "be imputed into the LFS, it may be unclear, ex-ante, if SUTPs are adequately included in the survey. Despite a small sample of foreigners and other concerns, there is evidence that the LFS sample does include some “ recent foreign ” migrants, especially in the border regions (NUTS2) [TRC1-Gaziantep, Adiyaman, Kilis, TRC2-Sanliurfa, Diyarbakir, TRC3-Mardin, Batman, Sirnak, Siirt TR63-Hatay, Kahramanmaras, Osmaniye] (Map 1). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Publications Office of the European Union. Fasani, F., Frattini, T., and Minale, L. (2022). (The Struggle for) Refugee integration into the Labour Market: European Evidence. Journal of Economic Geography, 22(2), 351-393. Feyissa, D. (2015). Power and Its Discontents: Anywaa’s Reactions to The Expansion of The Ethiopia Statae, 1950 – 1991. International Journal of African Historical Studies, 48(1), 31 - 49 FDRE. (2004). Refugee Proclamation No. 409/2004. FDRE. (2019). Refugee Proclamation No. 1100/2019. FDRE. (2020). Investment Proclamation No. 1180/2020. Fuller, S. (2015). Do Pathways Matter? Linking Early Immigrant Employment Sequences and Later Economic Outcomes: Evidence from Canada. International Migration Review, 49(2), 355-405. Gebreegziabher, T., and Regassa, N. (2019). Ethiopia’s High Childhood Undernutrition Explained: Analysis of the prevalence and Key Correlates based on Recent Nationally Representative Data. Public Health Nutrition, 22(11), 2099–2109. .org/10.1017/ S1368980019000569 Gebru, F. K., Haileselassie, M. W., Temesgen, H. A., Seid, O. A., and Mulugeta, A. B. (2019). Determinants of Stunting among under-five Children in Ethiopia: A Multilevel Mixed-effects Analysis of 2016 Ethiopian Demographic and Health Survey Data. In BMC Pediatrics 19 (1). BioMed Central Ltd. .org/10.1186/s12887-019-1545-0 Ginn, T., Resstack, R., Dempster, H., Arnold-Femandez, E., Miller, S., Guerrero, M., and Kanyamanza, B. (2022). 2022 Global Refugee Work Rights Report.", "output": {"entities": {"named_data": ["2016 Ethiopian Demographic and Health Survey Data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Table 2: Ethnic composition of IDPs, refugees, returnees in the North Ethnicity IDPs in Bamako (%) Refugees Niger (%) Refugees Mauritania (%) Returnees (%) Total I + R + R (%) Ethnic composition of the North (%) Songhai 75 21- 71 43 45 Kel Tamasheq 12 56 69 12 38 32 Arab 3- 28 4 11 3 Peulh 4 21- 6 4 7 Other 6 11 3 7 4 12 Total (%) 100 100 100 100 100 100 Total (n) 100 81 100 220 501 1, 268, 009 Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census. The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94 % of returnees were displaced within Mali. Only 6 % returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5 % of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2 % lived in Gao town and 1 % in Kidal town. The remaining 92 % lived in 27 different towns and villages in northern Mali, locations not covered by the survey. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Displaced People Survey", "Housing Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In contrast, districts of origin are distributed widely across the country. This reﬂects the fact that much work migration is from remote rural areas to towns and cities. The main characteristics of work migrants are reported in Table 1, together with those of non- migrant adult males. We see that work migrants are on average younger and better educated. The census contains detailed information about ethnicity, language, and religion. In the Nepal census, the term ‘ ethnicity ’ is used to capture a hodgepodge of caste and tribal distinctions. The census distinguishes up to 103 ethnic categories. Most of these categories only account for a tiny proportion of the total population. In terms of the total adult population, the most common ethnic categories are Chhetri, Brahmin, and Newar who, together, account for 35 % of 13", "output": {"entities": {"named_data": ["Nepal census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "this approach to calculating hunger the HCES-direct method. Of note is that neither the FBS-CV nor the\n\nHCES-direct method actually measures what individuals ate (as in a food intake survey) or ask about\n\nThe FBS-CV and HCES-direct methods both rely on household surveys. In the case of the FBS-CV method,\n\nthe second moment of the calorie distribution comes from the surveys, while the HCES-direct method\n\nrelies on the surveys for all moments. Yet, the design of HCES varies over several key dimensions, such", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "a devastating war, and frequent droughts and floods. SESRE data collection was carried out between November 2022 and January 2023, marked by drought, inflation, and insecurity, posing significant threats to the livelihoods of refugees and host communities in an already fragile economy. The conflict in Northern Ethiopia continued until 2022 and disrupted the socio- economic conditions in the North Gondar Zone, home to over 20,000 refugees. The November 2022 peace agreement between the federal government and Tigrayan authorities offered a glimmer of hope for ending the war in Northern Ethiopia. Still, tensions in other parts of Ethiopia continued. Additionally, following the outbreak of fighting in the Amhara region in 2023, refugees in the Alemwach camp in the Amhara region faced attacks by unidentified armed groups. Moreover, food assistance for refugees has been unstable due to funding shortfalls, resulting in reduced food rations for hundreds of thousands of refugees for several months in 2022 and 2023. Insecurity in the Gambella and Benishangul-Gumuz regions not only undermined refugee and host community livelihoods but also heightened tensions between them. The intensification of conflict in Western Oromia further disrupted humanitarian operations in Eastern Benishangul-Gumuz, blocking the transport of relief and commercial supplies and affecting", "output": {"entities": {"named_data": [], "descriptive_data": ["SESRE data collection"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "To take advan- tage of the much larger ProGres database of nearly 2. 2. million refugees, we compute area averages for the above-mentioned case-level host community conditions, aggregat- ing to the smallest possible geographic unit available (district level for Lebanon and governorate level for Jordan). This information is then matched with all refugees in the ProGres database that have location information in Lebanon and Jordan, yielding a sample of 1. 85 million refugees. Finally, we worry about reporting bias. Respondents may have felt they were more likely to receive assistance if they reported worse living conditions. The bias could also go the other way if refugees want to signal their gratitude for the assistance they receive. The problem is even more acute given our research question. Those who intend 13 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Looking also at the impact of IDPs in Colombia on wages, Morales (2017) uses a labor force survey, census data and registry data to study short and long-term effects as follows: 𝑆ℎ𝑜𝑟𝑡 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ௧ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ௧ ି ଵ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ௧ ൅ 𝜆 ௠ 𝑋 ௠ ௧ ൅ 𝛾௧ ൅ 𝛿 ௠ ൅ 𝛿 ௠ 𝑇 ൅ 𝜀 ௜ ௠ ௧ 𝐿𝑜𝑛𝑔 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ൅ 𝜆 ௠ 𝑋 ௠ ൅ 𝛿 ஽ ௠ ൅ 𝜀 ௜ ௠ where y is the log of wages, i, m, and i are individuals, municipalities and time respectively, 𝑋 ௜ ௠ ௧ are individual controls, 𝑋 ௠ ௧ is the log of total population or other municipality controls, 𝛾௧ and 𝛿 ௠ are time and municipality fixed effects, 𝛿 ௠ 𝑇 are municipality time trends, 𝛿 ஽ ௠ are department fixed effects and d is the inflow of IDPs defined as 𝑑 ௠ ௧ ൌ 100 𝑝𝑜𝑝 ௠ ௧ 𝑓 ௠ ௧ where 𝑓 ௠ ௧ is the total number of IDPs arriving in municipality m at time t. The same variable without the t subscript is used for the long-run effects equation. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey", "census data", "registry data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turk­ish Labour Force Survey to assess their labor market impact. Syrian refugees are overwhelmingly employed informally, since they were not issued work permits, making their arrival a well-defined supply shock to informal labor. Consistent with economic theory our instrumental variable estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupa­tional upgrading, there are increases in formal employment for the Turkish- though only for men without completed high school education. Women and the high-skilled are not in a good position to take advantage of lower cost informal labor. The low educated and women experience net dis­placement from the labor market and, together with those in the informal sector, declining earning opportunities. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "cents. The universe of blocks (“ manzanas ”) was stratified by socioeconomic strata and a representative sample of blocks was selected at random without replacement. To en- sure a sample representative of Colombian children by age group, stratum, and sex, we followed a multi-stage random sampling process. 8 As mentioned earlier, one of the biggest constraints in characterizing the role of forced migration in children ’ s human development within developing countries is the difficulty of finding a representative sample of those migrants. This is specially true in contexts where migrants are not hosted in refugee camps, but are integrated in local communi- ties, which account for 80 % of refugees worldwide (Climate Center 2022). We address these difficulties, leveraging all available information on Venezuelan settlements across the country to construct the largest possible comprehensive listing. The listing included data on Venezuelan settlements from all available sources, such as the 2018 population census, migrant organizations, and settlements identified by iMMAP, a non-profit orga- nization. iMMAP uses multiple sources, including OIM, United Nations, local migrant organizations, and satellite images, to identify Venezuelan settlements geographically. 9 Hence, to create our sampling frame, our field team verified the geographic location of all the Venezuelan settlements in-person and implemented a snowball sampling procedure in all the settlements found. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["2018 population census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "complete census of land parcels in Barafu and Kati, known as the Tanzanian Land Rights\n\n\nSurvey (TLRS). Households were identified using records and maps from the Kinondoni\n\n\nMunicipality, which had created a listing of all households in the area to assist with the\n\n**Notes:** data are from Tanzanian Land Rights survey. Sample restricted to dual-headed households in\ntreatment blocks.\n\n\nlevels of female land ownership: investigating the gender breakdown of land ownership in\n\nusing baseline data from the experimental intervention, which is discussed in more\n\n\ndetail in the following section. Households in two unplanned settlements in Dar\n\n\nes Salaam were asked a series of questions about the _de_ _facto_ ownership of land,\n\n5To avoid priming, households were not asked directly about female ownership. Instead, they were\nasked to list all members of the household that were default owners, must be consulted before a sale, or\nwould be included on a CRO.\n6Section 191(2) of the 1999 Land Act and section 58 of the (1971) Law of Marriage Act.\n7Authors' calculations using data from the Kinondoni municipal data.\n8Section 159(6) of the 1999 Land Act.\n\n\n8\n\n\n\n<!-- PAGE 12 -->\nTable 1: Female land ownership in Dar es Salaam", "output": {"entities": {"named_data": ["Tanzanian Land Rights survey", "Tanzanian Land Rights\n\n\nSurvey (TLRS)"], "descriptive_data": ["baseline data from the experimental intervention", "Kinondoni municipal data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "seekers. 81 There are several challenges associated with using central population registers to estimate refugee and asylum-seeker populations, including: consistency of the definition of refugees with the definition in the 1951 Convention and 1967 Protocol; difficulty and cost of establishing and maintaining a population register (UNSD 2014); and confidentiality safeguards. Compilation of statistics on forcibly displaced populations Several international organizations are involved in the compilation, analysis and dissemination of statistics on forced displacement including UNHCR, 82 Eurostat, IDMC, OCHA, International Committee of the Red Cross (ICRC), 83 WFP84 and IOM. Each of these actors has their own thematic focus and specific objectives, and applies their own methodologies. Asylum-seekers and refugees UNHCR is the principal organization responsible for the compilation, analysis and dissemination of data on asylum-seekers and refugees. UNHCR maintains a publicly available statistical online database85 with data for the period 1951-2014 on refugees (including people in refugee-like situations), asylum-seekers (pending cases), returned refugees, IDPs protected or assisted by UNHCR, returned IDPs previously protected or assisted by UNHCR, stateless persons and others of concern to UNHCR, disaggregated by country of origin and asylum. 86 Data are also provided on demographics, location, asylum-seekers (refugee status determination and monthly data) and resettlement.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["central population registers"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, there is no consensus or agreed best practices on the use of these methods in different contexts or stages of displacement (Brookings 2013). 92 The absence of data on new displacement may simply mean that no displacement has taken place (IDMC 2016). 93 IDMC reports that data, disaggregated by age and sex, were available for 15 of the 60 countries it monitored in 2014, however these data were not comprehensive and are not published. Additionally, in some countries there are data provided by IOM on IDP populations by location from which the urban or rural character of the population may be inferred (e. g. if the camp is located in the capital), but data are not comprehensive and not published. While the majority of humanitarian profile data does not typically cover IDPs living outside of camp or camp-like settings (the large majority of IDPs), IOM ’ s DTM in countries such as Nigeria, Iraq, Yemen and Libya do include information about those residing in host communities. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["humanitarian profile data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "project had to meet a basic minimum literacy level in order to qualify for admission to the program; whereas only half of the 15-29-year-old female respondents to the nationally-representative Core Welfare Indicators Questionnaire (CWIQ) survey report that they can read and write (LISGIS 2007). Fewer than two percent of the girls in the EPAG study responded that they had no education, which is much lower than in the CWIQ survey. Second, the majority of adolescent girls and young women in Liberia reside in rural areas, whereas the survey participants were residing in urban and peri-urban areas, where access to basic social services may be much more improved. Consequently, the results are not representative of adolescent girls and young women in Liberia overall. The results are neither indicative of the average Liberian girl and young woman; nor are they indicative of the average Liberian girl or young woman in the project communities. They are only indicative of the average girl and young woman who are part of the EPAG project. Finally, many of the variables that we examine in this study are measures of self-assessed levels of satisfaction or belief. These are entirely subjective variables, and are subject to significant measurement error. There is considerable evidence that the wording of these questions can affect the answers given, as can the order in which the questions are asked. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Core Welfare Indicators Questionnaire", "CWIQ survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The MHPSS referral pathway was tested through six clinical review sessions conducted jointly by the project's MHPSS technical advisor and the district psychiatric nurses. Case reviews identified bottlenecks at the step between community-based MHPSS workers and facility-based counselors, particularly for cases involving domestic violence survivors. Revised referral procedures were agreed, including a protocol for same-day warm handoffs in urgent cases and the designation of dedicated MHPSS consultation slots in three district hospital outpatient departments.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Using the NLSS data we begin by estimating a regression of the form: yk s = δs + α (ak s − a) + βs (Ek s − Es) + χs (Hk s − Hs) + vk s (4) where yk s is the log of income (or consumption) of household k residing in district s, coeﬃcients δs, βs and χs vary by district, ak s stands for the age and age squared of the household head, Ek s is the education level of the head measured in years of completed education, and Hk s = 1 if the head belongs to what we have earlier classiﬁed as a high caste (i. e., Brahmin, Chhetri or Newar). Since income or consumption are expressed in logs, βs and χs can be thought of as education and high caste premia, respectively. Female headed households are excluded from the regression since the focus is on migrant males. Vector a denotes the average age and age squared of observations across the sample. Variables E and Hs denote the district-speciﬁc averages of Ek s and Hk s. By demeaning regressors, we ensure that eδs measures the unconditional, district- speciﬁc average of yk s. Marital status, household size, and other household characteristics are 15 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["NLSS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "estimates, we also estimate the continuous time, semi-parametric Cox proportional hazard (Cox) model. The main advantage of the Cox model is that it allows us to estimate the relationship between the hazard rate and the determinants of refugee returns without having to make any assumptions about the shape of the baseline hazard function. A continuous time model might also be appropriate in this context, given that we have a long panel of 75 months. Using the same variable deﬁnition as in 3, we estimate h (t) = h0 (t) exp [β1chari + β2regi + β3foodsect + β4housingt + µl + δd + τt] (4) We restrict survival analysis to the Syrian refugees based in Jordan and Lebanon as their host country conditions are proxied by the geographical aggregates computed from the VAF and VASyr surveys. Despite this restriction, more than 85 percent of all refugees in our data set is included in the analysis. It is important to note that the conditions in the country of asylum may be the result of a refugee ’ s anticipated length of stay in the host country. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 displacement. 55 % of the returnees reported to have been employed before the crisis and 36 % in June 2014. Over time the employment situation among the displaced has improved steadily and by December 2014 more people reported being employed than prior to the crisis. All the returnees were able to regain employment after returning. The employment situation of IDPs, returnees, and refugees in Niger is steadily improving; only for refugees in Mauritania does one notice a steady decrease, with 100 % reporting no employment during January and February. Source: Listening to Displaced People Survey, 2014 and 2015. The ownership of livestock and consumer durables was reduced significantly as a consequence of the crisis. Table 7 demonstrates this by showing the Tropical Livestock Units (TLU) 12 owned prior to the crisis and in June 2014 as well as the percentage of ‘ yes ’ responses on a question whether a given asset was owned by the household. 13 The loss on livestock has been enormous particularly amongst IDPs and refugees who lost respectively more than 90 % and 75 % of their animals. 12 TLU is a common unit to describe livestock numbers of various species as a single figure that expresses the total amount of livestock present – irrespective of the specific composition. 13 This was a ‘ yes / no ’ question meaning that if 56 % of the", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "graduates aged 18, 19, and 20. The objective of ENILEMS was to provide information on\n\nThe ENILEMS-ENLACE panel merges information from the respondents of the ENILEMS\n\n2010 survey with their results in the ENLACE Grade 12 taken in May of 2008, 2009, or\n\n2010. Although ENILEMS 2010 did not capture the CURP, it included all the necessary", "output": {"entities": {"named_data": ["ENILEMS\n\n2010 survey"], "descriptive_data": ["ENILEMS-ENLACE panel"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For each of these food items we obtain calorie intake values from TBCA that are based on the descriptions of the food item in POF and TBCA. TBCA has calorie intake values for different preparation forms of the food item.\n\nA second step is applied in order to increase the list of food items that have calorie intake assigned to\nthem-and thus contribute to the cost per calorie estimation. In this step, we create food groups by\naggregating POF's original 7-digit item code to a 5-digit item code. Based on this, we impute calorie intake\nto items that were not mapped to calorie intake in step 1.\n\nFor instance, different types of rice ( _arroz_ _hibrido_, _arroz bica corrida_, _arroz quirera_ ) were not found in the TBCA, but by creating a 5-digit aggregation, we can assign them to the same group as _arroz polido,_ for which we do have a caloric intake estimate.\n\nFor all of these types we assign the average value of calorie intake for rice at this 5-digit item code that had calories assigned from TBCA in step 1 (in this case, _arroz polido_ ). After this step, we have raw calorie intake values for 1,413 items, which account for 86.6 percent of average total expenditures on food consumed at home and 58.1 percent of all food expenditures.", "output": {"entities": {"named_data": ["POF"], "descriptive_data": ["calorie intake values from TBCA"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In order to address these potential sample biases, the CAVR supplemented its documentation with reports produced by Amnesty International and Fokupers (a local NGO). The information contained in these reports was then included into the HRVD database. 9 Note that we do not analyse school completion in 2001 because most children that were of school age in 1999 were still in school in 2001. 10 The questions we used are ― Was [NAME] displaced outside E. Timor in 1999? ‖, and ― Was the [BUILDING] damaged in the violence of 1999? ‖. 14 % of the whole sample surveyed in 2001 report having been displaced, while 26 % report that their house was destroyed. Within our sample of school age children, these figures are 16 % and 25 %, respectively. We have made sure that buildings that are reported to having been destroyed were used for living purposes only. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We measure time-use through the number of hours in the previous day a respondent reports spending idle, as well as the amount of time spent on a variety of other common activities one might do in the camps (including bathing, market, chores, collection of rations, eating, child-rearing, sitting at tea stalls, praying, sleeping, visiting friends / relatives, playing games, playing sport, sitting idle). Finally, we ask respondents how much they consume, borrow and save over the past week. We further consider changes in perceptions on gender and power in two ways. First, we generate a Household Power Index, composed of a set of questions on perceptions of gendered decision-making and intimate partner violence. The questions are drawn from Haushofer and Shapiro (2016), which are themselves adapted from the Demographic Health Surveys. In addition, we produce a Work Rights Index, composed of questions around whether respondents feel that women should be allowed to work inside or outside the home or the camp block. Each outcome is described in greater detail in Appendix C. The frequency with which each outcome is collected is also presented in Appendix C. Multiple hypothesis testing We utilize two approaches to address the issue of multiple hypothesis testing. First, we present our primary outcome, psychosocial well-being, as an inverse-covariance weighted index variable following Anderson (2008). We also generate index variables for other outcomes in which this is possible, such as the cognitive index, the household power index, and the work rights index. Our second strategy is to report the sharpened False Discovery Rate (FDR) q-values for all outcomes within a particular table, which control for the expected proportion of rejections that are type I errors, likewise 12 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Demographic Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 to 32 percent (see Table A1 in appendix). The total number of internally displaced people (IDP) outnumbers the stock of refugees in SSA and in the world but overall has followed a similar trend compared to the number of refugees (by country of origin). 1 Major civil wars in Central Africa mainly explained the peak in 1993 and 1994 and the increase at the end of the 1990s. Figure 1. Refugee population by origin, 1990 ‐ 2013 Note: Authors ’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014. Data from 2007 to 2013 include people in refugee ‐ like situations. Persons in refugee ‐ like situations include “ groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained ” (UNHCR 2014: 39). Refugees in Africa seem to have mainly remained in Africa. Although SSA also hosts refugees from other regions, the closeness of the ‘ blue ’ and ‘ red ’ lines in Figure 2 ‐ representing the number of refugees originating from and hosted in SSA ‐ is an indication that most refugees cross borders within Africa.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A survey was carried out in the health sector (health centers) with the support of the World Bank in 2005, but has not been yet validated. In the rural development sector, where the first expenditure tracking survey between the decentralized center and services was to be carried out in 2005 on PEFA funds, the survey is yet to be carried out. It is critical that adequate management measures are gradually put in place to stop the current waste of resources. These measures include, inter alia, the accounting improvement of material, improvement of the inventory and delivery control, and especially a transparent planning of the deliveries, including, the posting of the received deliveries, their comparison with the planned deliveries and their certification by users within each service. It would also be desirable that in each ministry, an action plan is prepared for the implementation of these measures, on the basis of existing ones, and with target indicators as regards improvement of the arrival of the expenditure at intended destination. The social ministries could usefully open the way in this field, on the basis of some projection already carried out. Without a quantitative and qualitative improvement of the arrival of the expenditure at their final recipient, the increase in the budgetary appropriations to the priority sectors will hardly be translated into substantial concrete results on the ground. 29 It is difficult to make accurate forecasts for the implementation of certain programs or developments in economic parameters such as inflation or interest rate. Some immediate needs that were not foreseen during budget execution may appear during budget execution. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["expenditure tracking survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In short, these contour maps are ShakeMaps from USGS, which are automatically generated maps providing several key parameters following an earthquake, such as peak ground acceleration (PGA), peak ground velocity (PGV) and modified Mercalli intensity (MMI), are used as a base for localized impact.\n\nFor the actual construction of the damage index, two types of data will be used; the intensity data - expressed as PGA - and building inventory data. The building type data stems from the USGS building inventory for earthquake assessment, which provides estimates of the proportions (based on total number of buildings) of building types observed by country; see\n\n\\n<!-- PAGE 17 -->\\nJaiswal and Wald (2008). The data provide the share of 99 different building types within a country separately for urban and rural areas. For Indonesia the building type information was compiled from a World Housing Encyclopedia (WHE) survey.\n\nDamage curves by building type are derived from the curves constructed by the Global Earthquake Safety Initiative (GESI) project; see (International and Regional Development 2001).\n\nThe source for the localized wind speeds is the IBTrACS database that provides the strength and tracks every 6 hours of all typhoons that affected Southeast Asia during the period.", "output": {"entities": {"named_data": ["ShakeMaps from USGS", "World Housing Encyclopedia (WHE) survey", "IBTrACS database"], "descriptive_data": ["USGS building inventory for earthquake assessment"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The objective of the survey was to analyze perceptions and priorities with regard to the Peace Accord, to analyze perceptions of security, to determine access to basic infrastructure and school attendance, to understand nutrition levels, and to measure household asset ownership. In addition to this baseline survey, the World Bank was sponsoring a mobile phone survey among IDPs in Bamako, returnees in Gao, Kidal and Timbuktu town and refugees in camps in Niger and Mauritania. This survey interviewed 500 respondents on a monthly basis. In the August 2015 round of this survey, questions about perceptions and priorities with regard to the Peace Accord were included. This paper also makes use of a subset of the responses obtained from that survey, particularly those from refugees in Niger (n = 80) and Mauritania (n = 100) as these sub-populations who live outside Mali ’ s borders are important stakeholders in the peace process whose opinions risk being ignored. 15 To select a household in a village or neighborhood for the baseline survey, random selection was used: the enumerator divided the locality into two parts and selected five 15 For a more elaborate description of this mobile phone survey, see: Etang Ndip, A., J. Hoogeveen and J. Lendorfer (2016). Socioeconomic Impact of the Crisis in Mali on Displaced People. Journal of Refugee Studies. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["mobile phone survey", "baseline survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "any other population, have varied health-related issues, including noncommunicable and communicable diseases and trauma from injuries and violence. Research shows that conflict inflicts extensive psychological harm on many refugees, particularly youth and children, which often remain unaddressed (Simpson, 2018; Bosqui and Marshoud, 2018; Dong, 2018). Refugee women are specifically vulnerable to sexual and other forms of gender-based violence and require specialized care and access to sexual and reproductive healthcare. 0 10 20 30 40 50 Percent 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Eritrean Somali South Sudanese 8 to 14 years 15 to 18 years Need to work Unable to cover education expenses (fee and materials) School too far Too young Marriage or pregnancy Family not willing Sickness/injury or natural or human calamites Negative perception towards the benefit of education Other Figure 2.14: Reasons for not currently attending school Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 18 Access to essential health services when and where refugees need them is crucial for allowing them to restart their lives. Refugees need access to treatment and preventive care during health emergencies, the importance of which manifested", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In households with more than two children, one child from each age group was chosen through a random selection process to participate, ensuring a broad representation of experiences within the study ’ s scope. Sampling frame. We use the 2018 population census data as a sampling frame for the Colombian sample. It allowed the identification of residential blocks and households with children in the desired age range. With this source of information, it was possible to identify the number of households and residential blocks with children and adoles- 7Although Colombia only grants nationality to children of Colombian nationals, it follows a jus sanguini principle, the Colombian government has introduced reforms, such as the the program Primero la Ni ˜ nez to give Colombian nationality to children of Venezuelan parents born at times when diplomatic relations between Colombia and Venezuela were cut and hence, it was not possible to apply for a Venezuelan nation- ality for this minors in Colombia. 15", "output": {"entities": {"named_data": ["2018 population census data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The paper extends the GTAP 8 database by separating Lebanon, Jordan, Syria, Iraq, and West Bank and Gaza from the rest of the Western Asia aggregate and Algeria and Libya from the rest of North Africa. Kuwait, Qatar, Bahrain, Saudi Arabia, UAE, and Oman are aggregated into a GCC composite group. In addition, the 57 sectors in the GTAP 8 database are aggregated into 22 sectors based on their importance for the countries in the MENA region (Table 1). The resulting MENA-specific database contains 26 countries, among which are the six Levant economies of interest in this paper (Turkey, Lebanon, Syria, Iraq, Jordan, and Egypt) and the rest of the developing MENA countries (Table 1). The procedure used to construct the individual country information employs data from several sources. The UN Statistics Division data for 2007 is the source for the six components of GDP – agriculture, hunting, forestry, and fishing (ISIC A-B); mining, manufacturing, and utilities (ISIC C-E); construction (ISIC-F); transport, storage, and communication (ISIC I); wholesale, retail trade, restaurants and hotels (ISIC G-H); and other activities (ISIC J-P). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["GTAP 8 database"], "descriptive_data": ["MENA-specific database"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In 2008, trained enumerators conducted face-to-face interviews in local languages with 26, 513 respondents across 19 countries. 5 The sample is designed as a representative cross-section of all citizens of voting age in a given country. The dataset used for this paper has a multilevel structure; individuals are nested within primary sampling units (PSUs), which are nested within countries. The PSUs are the smallest, well-deﬁned geographic units for which reliable population data are available and they tend to be socially homoge- nous, thereby producing highly clustered data. In most countries, these will be Census Enumeration Areas (Afrobarometer, 2005, 37-38). Although re- spondents were not sampled based on their ethnic aﬃliation, there is likely to be a high level of clustering in the dataset around ethnicity. In other work, I discuss the advantages of multilevel modeling (Levi and Sacks, 2009). Treating the dependent variable as a binary outcome and taking into account the multilevel nature of our data, I estimate random intercepts for 5I excluded Zimbabwe from the analysis because of missing data on key variables. 9 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Some data challenges common to refugees and IDPs Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities. Sampling. As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay at relatives ’ places, other live in makeshift shacks and others stay in camps. The information available in the UNHCR registry (the census) can also be quite inaccurate, as already discussed, and the degree of accuracy changes for different groups of refugees. Stratification by urban and rural areas, a typical approach in sampling, may mean little for a population that is mostly in urban areas whether in camps or outside camps. Refugees and IDPs are also mobile and more difficult to track over time than other populations. Several statistical institutes worldwide have developed methodologies to track and measure mobile populations such as herders, nomads or homeless people. However, tracking refugees from other countries has been in the Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "7 other disciplines it is relatively rare. A review of published public health literature by Chen et al. (2018) found most surveys use probabilistic designs in the first stage, but random walk or similar methods in the second stage. Lupu and Michelitch (2018) suggest that the combination of random walk and quota sampling is the common approach for political science-themed surveys conducted in the developing world, with 77 percent of respondents to their expert survey using a variation on this design. Diaz de Rada and Martínez (2014) compare a combination of random walk and quota sampling (based on age and gender) to probability designs and find a more accurate estimation of age and educational attainment in the combined method than in the probability methods, but that the probability methods perform better for measuring unemployment. The authors cite the replacement protocols for the probability methods as a reason for the bias and attribute the use of quota sampling for the success in estimating age and education, compared to the gold standard of a high-quality probability sample design. There are also a limited number of papers which directly compare two or three of the methods, but none that consider this wide range of alternatives.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This paper ’ s scope of analysis includes the country as a whole using a nationally representative survey. While regional case studies may reveal salient stresses on public services and job displacement, nationally, there is no significant impact. Over the period of 2009 to 2013, the poverty rates of host community households have stayed relatively stable near the Syrian border; despite the high poverty rates experienced among the recent migrants. By country of origin, the displacement of Syrians is one of the largest in recent history. As a result of the civil war that began in 2011, Syrians started to leave their homes and look for safety in neighboring countries across the region. By November 2015, about 4. 3 million Syrians were seeking refuge in primarily Turkey, Lebanon, Jordan, Iraq, and the Arab Republic of Egypt. 3 The only other time in the last half century that the world experienced a larger group of refugees from a single country is the case of Afghan refugees during the 1980s to 1990s. Refugee displacements of this size are rare. Consequently, they are not well studied and their impacts are not well understood. Moreover, the case of Afghan refugees in Pakistan is different, since they were stigmatized to a larger extent, which limited their movement in Pakistan. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["nationally representative survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**3.5 Environmental and Social Risk Management**\n\nThe ESMF establishes the risk management hierarchy to be applied throughout project implementation: avoidance of adverse impacts is the primary objective, followed by minimization, mitigation, and compensation as successively lower-priority options. The ESMF includes standardized terms of reference for subproject-specific ESMPs, a template grievance intake form, and a matrix of applicable national environmental regulations cross-referenced with the relevant World Bank Environmental and Social Standards.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Children and adolescents ’ long-term exposure to households with a more equalized division of domestic labor, as a result of violent or political conflict, warrants further investigation. 2. 3 Country contexts The countries with subnational regions covered in this study, using data from 2017 or 2018, are Ethiopia, Nigeria, Somalia, South Sudan, and Sudan. All are located in Sub-Saharan Africa, have undergone or are currently involved in armed conflict, and are affected by environmental issues such as drought, famine or flooding. Despite some commonalities, each faces a unique set of social, political and economic challenges, which cannot be accurately covered in this study. However, to contextualize the findings, a brief introduction of the country context is presented alongside the poverty estimates by the $ 1. 90 / day measure and the global Multidimensional Poverty Index (MPI). 3 3 An international measure of acute multidimensional poverty, aligined with the 2030 Agenda, that captures deprivations in health, education, and living standards for more than 100 countries (Alkire and Jahan 2018; Alkire, Kanagaratnam and Suppa 2020). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The SIRRV question on road building clearly notes to exclude \"rehabilitation.\" 10 Later, after discussing our estimation methods, we ascertain that this holds for almost all communes when we predict the counterfactual kilometers of rehabilitated roads in the absence of the project.\n\nAdditional corroborating evidence comes from available SIRRV data on other\n\n(iii) ** and * indicate significance levels of 5 and 10%. (iv) Average commune household consumption is predicted using a consumption model calibrated to the 1998 VLSS.\n\nThe dataset for this analysis comes from an extensive economic survey involving over 9000\nfarmers in ten African countries: Burkina Faso, Cameroon, Egypt, Ethiopia, Ghana, Kenya,\nNiger, Senegal, South Africa and Zambia. Data was gathered from Zimbabwe but the livestock\nobservations had to be dropped because of the turbulent conditions in this country during the\nsurvey period. The data was collected for the GEF project studying the impact of climate change", "output": {"entities": {"named_data": ["SIRRV data", "VLSS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 1. Introduction The most common sampling approach for cross-sectional household surveys in the developing world is a stratified two-stage design (Grosh and Munoz, 1996). Following stratification based on administrative boundaries, clusters are selected in the first stage with probability proportional to size from a national census-based frame. In the second stage, a canvassing operation is conducted in the selected clusters to compile an updated list from which households are randomly selected. While this methodology is straight forward to implement in the field and reliably produces unbiased estimates, there are several downsides. The first downside is cost. The World Bank ’ s Living Standards Measurement Study team, which provides technical assistance on large-scale household surveys around the world, estimates the field listing operation increases the overall budget for data collection by 25 percent. Due to confidentiality concerns, the data collected during a field listing operation, typically the name of the household head and address or location description of dwellings, does not have any analytical applications beyond as a component of the weight calculations. 2 At a time when typical survey costs are in the USD millions, reducing a significant cost component will increase the financial sustainability of data collection. The second drawback to the traditional design relates to timeliness.", "output": {"entities": {"named_data": ["Living Standards Measurement Study"], "descriptive_data": ["national census-based frame"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "CURP, respectively, was able to match 2,820 observations (40 percent of the ENILEMS\n\nuals in the ENILEMS sample were matched to their ENLACE Grade 12 test scores. After\n\neliminating missing observations in the ENLACE score, the panel reaches a total of 3,714", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In this sense, it is necessary to take into consideration that all indicators of prevalence of NSE and its profile will be limited to a subset of this kind of workers. 4. 1 Latin America and the Caribbean This section focuses on the Latin America and the Caribbean region, where a set of 9 countries, that we consider representing the different realities of the region in an exhaustive way, was analyzed. Specifically, the analysis was conducted for Argentina, Brazil, Bolivia, Chile, El Salvador, Mexico, Peru, Dominican Republic and Uruguay. 5O * NET is the successor of DOT (Dictionary of Occupational Titles) which is no longer updated. O * NET was launched in 1998 on the basis of the BLS Occupational Employment Statistics codes. In 2003, it was changed to SOC which implies that the consistent measures of task content are calculated from 2003.", "output": {"entities": {"named_data": ["BLS Occupational Employment Statistics"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "years (Figure ES.1). Even when asked where they would realistically be in the next three years, one-third of refugees believe that they will live in a Western country (Figure ES.2). The intention to migrate abroad is higher for youth. Refugees also perceive they have less control over their lives than hosts, a result driven by South Sudanese refugees. These intentions to migrate combined with low “locus of control” (LOC) may limit refugees’ investment in improving their livelihoods or to integrate. Welfare and Equity In-camp refugees are poorer than their hosts. While monetary poverty appears to be high in refugee- concentrated areas, it is more prevalent among in-camp refugees than their hosts or OCP refugees (Figure ES.3). Welfare varies significantly over the different groups of refugees in Ethiopia, with Eritrean refugees having the lowest poverty incidence and South Sudanese refugees the highest. Although poverty incidence is higher for refugees, the high 0 20 40 60 80 Percent 100 Eritrean (camps) Somali South Sudanese OCP All Refugees Ethiopian refugee camp Ethiopian city Country of birth Other African country Western country Figure ES.1: Desired location in three years Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 Percent 100", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 neighboring community, (iii) residing elsewhere in the Kagera Region and (iv) residing outside the Kagera Region. Table 3 shows that the basic needs poverty rate declined 8 percentage points in the full sample. This figure masks significant differences in changes between subgroups based on migration. For those found residing in the baseline community, poverty rates dropped by 4 percentage points, but rates dropped by 11, 13 and 23 percentage points for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. A similar pattern is found for consumption per capita. While consumption per capita grew by $ 65 overall, it grew by only $ 30 for those found in the same community and by $ 65, $ 100 and $ 287 for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. Dividing consumption into food and non-food components gives the same result. The most basic assessment of welfare changes would have been wrong if we had focused only on individuals still residing in the community, a practice found in many panel data surveys. We would have underestimated the growth in consumption by half of its true increase. The differences in consumption changes of groups in Table 3 are statistically significant, as shown in Table 4.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "7 Annex 9 describes the distribution of refined fuel products by overall consumption decile, based on the 2004 National Household Survey. 8 About 15 percent of the population was defined as poor in 2008.\n\nSubsidies Capital invest't Social programs _Source: Ministry of Finance, 2008 APBN-P, assuming oil at US$95;_ _social program expenditure data from 2008 APBN._\n\nMore detailed data would enable better estimates, for example monthly data on fuel consumption in the provinces near neighboring economies and for otherwise similar provinces where fuel is more likely to only be consumed locally.\n\n_Source: World Bank calculations based on data from the Ministry of Finance._\n\nTo explore how important is such an effect, we use the average em  ployee cost in electricity generation sector (NACE 3511) reported in AMADEUS database   maintained by Bureau Van Dijk as an approximation for labor cost of wind generation.", "output": {"entities": {"named_data": ["2004 National Household Survey"], "descriptive_data": ["social program expenditure data from 2008 APBN"], "vague_data": ["monthly data on fuel consumption", "data from the Ministry of Finance"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "There are 74,210 Sudanese refugees living in\nthe Benishangul Gumuz region out of which\n22,649 arrived during the recent conflict in\nSudan. Long-staying refugees are located at\nthe Sherkole (established in 1996), Bambasi\n(established in 2012), and Tsore (established\nin 2015) camps. Most new arrivals cross from\nSudan into Ethiopia at Kurmuk. A refugee\nsettlement has been established in Ura for\nthe delivery of protection and assistance.\n\ni. Kurmuk Transit Centre and Ura\nRefugee Settlement in\nBenishangul Gumuz (Emergency)\n\nAs per statistical data from Kurmuk Transit\nCentre and Ura Refugee Settlement, 39% of\nthe reported incidents involved rape and\nsexual assault, 47% were physical abuse, and\n14% were psychological and emotional\nabuse. Among the survivors, 89% are women,\nand 11% are girls.", "output": {"entities": {"named_data": [], "descriptive_data": ["statistical data from Kurmuk Transit\nCentre"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "7 convincing instruments and the impossibility of implementing a randomized controlled trial forces conflict researchers to confront the limits of any identification strategy. Panel data, when available, minimize problems related to recall and ex post measures and enable researchers to trace the dynamics of conflict over time (e. g., Guerrero-Serdan 2009). Some studies have constructed panel data by resurveying households that had been surveyed before the conflict, such as Bundervoet, Verwimp, and Akresh ’ s (2009) study in Burundi and Andre and Platteau ’ s (1998) study in Rwanda. Researchers have often empirically addressed the issue of attribution of causality using difference-in-differences, a nonexperimental technique that compares a conflict-affected group (or region) with its preconflict situation and with a control group (or region) that did not experience the conflict. The validity of this method is contingent on no other changes between the two groups (regions) at the same time as the exposure to conflict. Even if there are no other changes, there may be spillover effects from the conflict-affected group to the nonconflict control group, leading to an underestimation of the effects of conflict. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "expect the prices of many manufactures to do as well. The 1995 / 96 NLSS collected information on the quantity and price paid for rice by individual households. From this we compute a unit price per Kg. The log of the district median is used as our price index proxy. To construct an index of housing costs, we take advantage of a section of the 1995 / 96 NLSS survey focusing on housing. The survey collected information on hypothetical and actual house rental values of each household together with house characteristics such as square footage, number and type of rooms, quality of materials, and the availability of various utilities. We use these data to construct an hedonistic index of housing costs for each district. Let rk s be the house rental price paid (or estimated) by household h in district s and let xh s denote a vector of house characteristics. We estimate a regression of the form: log rk s = as + bxh s + ek s to obtain estimates of eas, the housing cost premium in each district s. Regression results are shown in Table A1 in appendix. Many house characteristics are signiﬁcant with the expected sign, e. g., larger, better built houses with better in-house amenities are worth more. District price diﬀerentials are large and jointly signiﬁcant. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["NLSS", "NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "and more immediate impact on food access, we are able to find additional empirical support\n\nfor the last explanation. Given data and information constraints, it is not possible to estimate\n\nSundberg, R., and E. Melander. 2013. \"Introducing the UCDP Georeferenced Event Dataset.\"\n\nNotes: This table compares mobile phone ownership in the November 2017 WFP mobile phone survey and the 2014 Household Budget Survey (HBS), where the 2014 HBS summary statistics are restricted to the share of the population that resides in a household that owns at least one mobile phone.\n\nWe gratefully thank Claudio Montenegro, David Newhouse and Minh Nguyen for their help with the I2D2 database. We gratefully acknowledge the generous support of the World Bank (Office of the Senior Vice-President and Chief Economist and Social Urban Rural and Resilience Global Practice), the Cities Program of the International Growth Center (Grant number 89408), the GWU Institute for International Economic Policy and the GWU Center for International Business Education and Research.\n\nData Used to Estimate the Returns**   **Sources.** The data source for the analysis is the _International_ _Income_ _Distribution_   _Database_ (I2D2) of the World Bank. The database consists of a large number of   individual-level surveys and census samples.", "output": {"entities": {"named_data": ["UCDP Georeferenced Event Dataset", "2014 Household Budget Survey", "I2D2 database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Addis Ababa. Many refugees report that social interactions and sharing resources with hosts is “not easy,” especially in the South Sudanese domain. Overall, 30 percent of refugees say it is not easy to have social interactions with hosts, and 34 percent report that it is challenging to share resources such as water and food. These rates fall to 21 for social interaction and 15 percent for challenge in sharing resources in the South Sudan domain. On the other hand, refuges do not report that it is difficult to conduct market interactions. Country-wide, the relationship between integration outcomes and demographic characteristics is complex. More-educated male refugees are more likely to have Ethiopian friends, but this does not appear to improve ease the creation of social interactions or sharing resources with hosts. The results in Column 2 of Annex D, Table D.19 show—controlling for other characteristics, including region and year of arrival—that refugee men are 6.7 percentage points more likely to have an Ethiopian friend than refugee women, and those who completed secondary education are 22 percentage points more likely to have an Ethiopian friend. However, no significant difference exists in the ease of which these groups find it to have social", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**SIGAF Reconciliation Status**\n\nAs of 31 December, cumulative project expenditures recorded in SIGAF amount to USD 14.7 million across all disbursement categories. This figure reconciles to within USD 12,000 of the Bank's Client Connection disbursement records, a variance attributable to timing differences in exchange rate application. The SIGAF balance will be reconciled in the first week of January following the receipt of the final bank statement for the Designated Account. No unexplained variances were identified in the year-end SIGAF reconciliation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As a result, imputed poverty is measured as income poverty. Another advantage of using the LFS is the availability of CPI at the NUTS2 level in Turkey which allows for spatial deflation of different price levels across the country. Table 1. Survey Comparison and Data Availability Years Available Migration Variables Income or Consumption Geographic Identifier Spatial Deflation HICES 2003-2012 No Consumption, income National, urban / rural No SILC 2009-2012 No Income NUTS1 No LFS 2009-2013 Yes Imputed Income NUTS2 Yes However, there are other issues for consideration when using the LFS. Principally, there is a low number of sample points that are migrant households. Moreover, the study cannot identify migrant households and individuals that are specifically Syrian refugees. Foreign migrants are defined as those who were born abroad and have lived abroad for at least more than 12 months. Some Turkish-born households have also lived abroad for over a year, and these individuals are not considered to be migrants. Amongst foreign-born individuals, only the ones who have been in the country for more than 12 months are included in the sample which underrepresents the actual number of foreign migrants in the region. In addition, no specific procedure is adopted by the enumerators if the household does not speak Turkish. Given that a majority of Syrian refugees do not speak Turkish, the language barrier might result in the removal of Syrian households from the sample. Finally, refugee camps are not included in the sample frame, which limits the study to only examining recent migrants who do not live in refugee camps. 9 Wage income is only available for regular and casual employees in the LFS which accounts for around 60 % of total employment. There is no other monetary income value for the rest of the working population.", "output": {"entities": {"named_data": ["LFS", "SILC", "HICES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situa- tions where the movement is ﬂuid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Sur- veys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difﬁcult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations. To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee ﬂows and stocks by countries of resi- dence and origin dating back to 1951, shortly after the Ofﬁce was established. UNHCR publishes annual statistical reports ranging from “ Global Trends ”, “ Mid-year trends ”, “ Asylum trends ”, to a “ Statistical Yearbook ”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Labor Force Surveys", "Living Standards Measurement Study", "Multiple Indicator Cluster Surveys"], "descriptive_data": ["individual registration of refugees and asylum seekers"], "vague_data": ["administrative records"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The large number of interconnected conﬂicts in these provinces involving non-state armed groups and state actors create a continuous ebb and ﬂow of displacement in eastern DRC (Jacobs & Kyamusugulwa 2018). In June 2020, UNHCR estimated that over 4. 5 million persons were internally displaced in Ituri (1. 6M), North Kivu (1. 9M) and South Kivu (1M) provinces alone (UNHCR 2020). DRC hosts an additional 536, 000 refugees (UNHCR 2020) from neighboring countries with recent experiences of violence, especially Burundi, Uganda, CAR, and South Sudan. Figure 2 plots the trend in the new IDPs in the DRC between 2009 and 2020. 5 Most IDPs in DRC favor staying with host families as opposed to camp displacement (Haver 2008, Rohwerder 2013). In 2017, UNOCHA estimated that around 500, 000 IDPs were in camp- like settings, whereas 3. 3 million sought refuge in host communities (Jacobs & Kyamusugulwa 4The qualitative analysis covers only the Kivu provinces, but the quantitative analysis covers all three. 5Data provided by the Internal Displacement Monitoring Center DRC Page, accessed May 14, 2021. 6", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "DiD estimate is (E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 1ሻ- E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 1ሻ) – (E (𝑌 ௜ ଵ | 𝐷 ௜ ൌ 0ሻ- E (𝑌 ௜ ଴ | 𝐷 ௜ ൌ 0ሻሻ ൌ ሺ 𝛽 ൅ 𝛿ሻ െ 𝛽 ൌ 𝛿. The DiD estimator relies on the “ Equal Trend Assumption ” that does not require both selected and non-selected youth to be on average balanced at baseline on key observable & unobservable characteristics. Table 1 shows that both groups differ on some key characteristics. Non-selected youth are more likely to be older, more educated (hold more academic degrees), come from Beqaa and Nabatiye, and have parents with intermediate education (grade 7 to 9). Selected youth are more likely to be males, younger, students, come from Mount Lebanon and the North, and have mothers with university education. Both groups appear balanced on key outcomes related to soft skills, tolerance values, and labor market outcomes. The exception is that non-selected youth exhibited a better sense of belonging to the Lebanese community and selected youth were more likely to have been unpaid employees (interns) at the time of baseline data collection. In addition to comparing means of observable characteristics, the study also tested for the differences in the statistical distributions of key outcomes using two sample Kolmogorov-Smirnov tests of the equality of distributions. Results indicate that the only key outcome for which there is a statistically significant difference in its distribution between the treatment and comparison groups at baseline is the sense of belonging to the Lebanese community. The largest difference between the distribution functions in the direction that the comparison group contains larger values 10 Standard errors are clustered at the NGO level because that was the unit of allocation into treatment and comparison groups. Abadie et al. 2017 argue that clustering is generally needed even if NGO fixed effects are included in the regression. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "expect the prices of many manufactures to do as well. The 1995 / 96 NLSS collected information on the quantity and price paid for rice by individual households. From this we compute a unit price per Kg. The log of the district median is used as our price index proxy. To construct an index of housing costs, we take advantage of a section of the 1995 / 96 NLSS survey focusing on housing. The survey collected information on hypothetical and actual house rental values of each household together with house characteristics such as square footage, number and type of rooms, quality of materials, and the availability of various utilities. We use these data to construct an hedonistic index of housing costs for each district. Let rk s be the house rental price paid (or estimated) by household h in district s and let xh s denote a vector of house characteristics. We estimate a regression of the form: log rk s = as + bxh s + ek s to obtain estimates of eas, the housing cost premium in each district s. Regression results are shown in Table A1 in appendix. Many house characteristics are signiﬁcant with the expected sign, e. g., larger, better built houses with better in-house amenities are worth more. District price diﬀerentials are large and jointly signiﬁcant.", "output": {"entities": {"named_data": ["NLSS", "NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A significant increase in non-participation among men can be clearly seen in the West Bank in 2020Q2, mostly at the expense of the private infor- mal sector. The labor market then quickly bounces back. By 2020Q3, labor market stocks in the West Bank appear indistinguishable from pre-pandemic periods. Gaza, on the other hand, experienced three consecutive quarters of depressed employment from 2020Q2 to 2020Q4. Non-participation spiked twice, first in 2020Q2 and then in 2020Q4, corresponding respectively to the initial lockdown orders and the subsequent outbreak in Gaza. Recovery also appears to be slow and uneven. Figure (3) shows the labor market flows. We exploit the specific panel structure of the LFS dataset, described in Section 3. 1 by focusing on one cohort of the same respon- dents who were surveyed in 2019Q1, 2019Q2, 2020Q1, 2020Q2, and finally 2020Q4. This cohort of individuals allows us to observe labor market transitions into the pan- demic; to compare with a period over the same quarters in 2019; and, finally, to observe their recovery outcomes in 2020Q4. Overall, Figure (3) shows two labor markets with high levels of churning. On average, 29 % of individuals in the sample would change their labor market states after just one quarter. These churns are especially prominent between informal employment and unemployment, and in Gaza between unemployment and non-participation. The figure also illustrates the significant differences between the West Bank and Gaza in labor market dynamics, differences already observed in the labor market stocks presented in Figure (1). In addition to the significant flows between unemployment 11", "output": {"entities": {"named_data": ["LFS dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Comparing our results with the World Bank poverty lines used for global poverty monitoring, by\nexpressing them in 2011 purchasing power parities (PPP), we find that the total poverty line of R$455 is\nequivalent to approximately US$6.10 a day, and hence in the range of upper-middle income countries. [17]\nMoreover, it is very much in line with the societal poverty line one would obtain for Brazil using POF\n2017/18 data. [18] The food poverty line of monthly R$258 is equivalent to a daily US$3.46 in 2011 PPP,\nabove the international poverty line of US$1.90 per person per day.\n\nIn Brazil, poverty monitoring is not based on POF. Instead, another survey - the Pesquisa Nacional por Amostra de Domicílios Contínua (PNADC), which collects information on income instead of consumption - is used.\n\nDeveloping approaches to use the poverty line estimated here for regular poverty monitoring with the income aggregate based on PNADC is beyond the scope of this paper; however, related work has shown that very\n\n18 The societal poverty line defines someone as suffering from societal poverty if they live on less than $1 plus half of what the median person in their country consumes (see Jolliffe and Prydz, 2017). With median consumption per capita per person per day from POF 2017/18 expressed in 2011 PPP US$ being 10.18, the societal poverty line would be US$6.09 in 2011 PPP per person per day for Brazil.", "output": {"entities": {"named_data": ["POF", "POF\n2017/18 data", "Pesquisa Nacional por Amostra de Domicílios Contínua (PNADC)", "PNADC"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "40 Sub ‐ Saharan Africa 8604000 5895000 7055000 5406100 5068000 N. A. MENA 6230000 8000000 6675000 8592900 10892000 N. A. Asia and Pacific 4325000 2405000 3392000 2128800 5490000 N. A. (excl. Australia, Japan, New Zealand) Americas 1126000 1280000 2176000 2900000 3661000 N. A. (excl. North America) Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). As indicated in Figure A1, these data are much lower compared to those provided from 2003 by IDMC but provide a longer time series. UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In principle, individual registration of IDPs is not used to determine the ‘ status ’ of an IDP, since IDPs have the same rights and entitlements as other citizens and do not need to apply or be granted a special legal status. 66 Rather, registration of IDPs can provide a basis for: (a) establishing the number, location, and key demographic characteristics of displaced populations; (b) providing protection and assistance; (c) keeping track of family relationships; (d) preventing fraudulent access to scarce humanitarian assistance; (e) facilitating the issuance of temporary identity cards to replace lost personal documentation (Brookings 2008); and (f) providing social security benefits. 67 Full IDP registration by international organizations is not 62 By the end of 2014, individual refugee registration was the source of about 77 percent of the data on refugees; estimation accounted for 13 percent of data, combined estimation and registration for 5 percent and other sources for 5 percent (UNHCR 2016). 63 UNHCR may undertake registration activities when national governments do not have the capacity to do so. 64 Additional data can also be recorded such as education and occupation. 65 Insufficient budgetary resources, staff, training or materials. 66 Countries with national legislation that provides a legal status for IDPs are an exception to this international standard. 67 The scope of data collected depends on the objectives of the registration exercise, for example in Kenya, registration of individuals displaced by the 2007 and 2008 post-election violence excluded ‘ integrated ’ IDPs, i. e. those who had Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "household. The dependency ratio— the ratio of dependents of those under age 15 and above age 64—to working members in a household, is also higher for in-camp refugees relative to hosts and highest among South Sudanese refugees. OCP refugees have the smallest average household size and the lowest dependency ratio. Refugee households are more likely to be headed by women. The share is exceptionally high for South Sudanese refugees, where 84 percent are female-headed (Annex D, Table D.1). This reflects a large share of women (71 percent) aged 25 to 44 among South Sudanese refugees (Annex D, Figure D.1). Refugees have younger household heads than hosts, except for Somali refugees, with the youngest household heads in Addis Ababa. -60 -40 -20 0 20 40 60 -60 -40 -20 0 20 40 60 <15 15 to 24 25 to 44 45 to 64 >=65 In camp refugees In camp hosts Percent Percent <15 15 to 24 25 to 44 45 to 64 >=65 Addis refugees Addis hosts Figure 2.4: Age structure Source: World Bank Staff based on SESRE 2023. 49% 48% 45% 45% 47% 47% 51% 52% 55% 55% 53% 53% 0 20 40 60 80 Percent 100 Hosts Refugees Hosts", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "21 Distance from Rebel Group Headquarters We coded the location of the headquarters of the rebel groups participating in the conflicts under study, and calculated the distance from each square to the most proximate rebel group headquarters (we do not know a priori which rebel group or government that will act in a particular square). As the `distance from capital'variable, it was coded as the distance in terms of squares and log-transformed. Border Square We coded squares as border squares if a national border runs through it. Such squares belong to more than one country and are not straightforward to code. We coded national- level information for border squares according to the following rule: A border square was considered to belong to the country that was most frequent among the eight neighboring squares. In tie cases, we assigned nationality randomly between the tied countries. Interaction country-square population This variable was created to test the population settlement pattern hypothesis. It is an interaction between population count at a location (square) as a portion of the country's total population. Road type Road type is a variable by ESRI that is available in the Digitial Chart of the World Data. It is a high resolution dataset at 1: 1, 000, 000 scale and consists of arcs which indicate road mass.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In terms of geographical coverage, this study covers all low-income and middle-income countries with a significant presence of refugees, other Venezuelan refugees and migrants 11 in need of international protection, and / or IDPs. We apply 100, 000 (in terms of the number of FDP as of 2021) as an inclusion threshold for this analysis. This results in the final list of 53 low-income and middle-income countries, which altogether account for 23 million refugees and 58 million IDPs. 12 3. Methodology The methodology we apply to analyze micro-level datasets in the UNHCR and WB MDLs follows a procedure that is systematic, replicable, and scalable (Figure 1). First, we scrape the metadata from all the micro-level datasets found on the UNHCR and WB MDLs. 13 We find 412 datasets from UNHCR MDL and 1, 927 datasets from WB MDL that have been collected in the sample of countries under study. Second, we remove false positive datasets from the UNHCR MDL. While a large majority of datasets in the UNHCR MDL are individual / household-level household survey datasets sampled from refugees or IDPs, some are not. Furthermore, many datasets do not have a clearly defined sampling frame from which a representative sample can be drawn, thus failing to meet our inclusion criteria for this exercise. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The 2024 survey data collection protocol required the field team to conduct back-checks on 15 percent of completed interviews within 72 hours of the original interview. Back-checks were conducted by telephone where network coverage permitted and in person where phone contacts were unavailable. The back-check process identified three enumerators with unusually high rates of interview fabrication; these enumerators were dismissed and their questionnaires invalidated. The affected questionnaires were re-administered to the same households by verified enumerators within five days.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In the case of refugees, host countries rarely facilitate naturalization, only a minority of refugees ever gets resettled in third countries and voluntary repatriation is frequently not a realistic option for several reasons. International law provides for three possible durable solutions for refugees, including integration within the area of displacement, repatriation to their home country or resettlement in a third country; refugee status can also cease when there are no longer compelling reasons for an individual to refuse to avail themselves of the protection of their country of origin. In 2015 only 119, 265 refugees under UNHCR ’ s mandate were either resettled, naturalized53 or ceased to be refugees; and there were only 201, 415 voluntary returns, mostly Afghanistan, Sudan, Somalia and CAR (see Figure 17). These statistics highlight the significant gap between the unprecedented numbers of refugees and the capacity of the international community to provide durable solutions. For the 85 percent of refugees hosted in developing countries, there are only minute prospects for resettlement. Figure 17: Durable Solutions Relative to Refugee Stock 2015 Source: UNHCR Global Trends 2015 Global statistics that show the low rate of refugee returns masks the variation in returns over historical periods and across displacement crises — with significant voluntary returns for some countries.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Challenges around skill mismatch exacerbate exclusion, as many young Lebanese lack the skills and competencies demanded by private sector employers, particularly ‘ soft skills ’. To address some of these challenges, the Lebanese government (GOL) identified volunteerism as a mechanism to enable diverse youth to work together for improved community assets and service delivery as well as increased employability. In September 2012, the GOL issued a Decree (Number 8924 / 2012) that created a new extra curriculum program that requires secondary school students to complete 60 hours of civil work. In addition, the Ministry of Social Affairs (MOSA), through its Volunteering Department, launched annual action plans for the implementation of youth volunteer summer camps across Lebanon. 4 Father ’ s education and residence (region and location of school) are the two largest contributors to inequality of opportunity in students ’ math test scores, accounting for 44 and 23 percent of total inequality, respectively (World Bank, 2016). 5 According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Gallup Poll"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We also use a project level database constructed from provincial and central\n\nHowever, the household survey was designed in view of combining it with the nationally representative 1998 Vietnam Living Standards Survey (VLSS) to predict baseline consumption expenditures for SIRRV households (van de Walle, 2006).\n\nWe then compare this to the independent administrative data on the aggregate allocation.", "output": {"entities": {"named_data": ["1998 Vietnam Living Standards Survey", "VLSS"], "descriptive_data": [], "vague_data": ["project level database", "independent administrative data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "_Source: UNHCR Internal movement of refugees in Sudan as of 16 July 2023_\n\n_4 See Sudan Protection Brief, June 2023 and UNHCR Sudan- Overview of refugees and asylum seekers distribution and_\n\n_movement in Sudan Dashboard as of 16 July 2023_\n\nUNHCR 4", "output": {"entities": {"named_data": ["UNHCR Internal movement of refugees in Sudan"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of data is the National Water Commission; the data consist of daily rainfall measures in\n\nThird, we use the geographic location data of electoral sections obtained from the Department of\n\nelections in 2000 and 2006 by electoral section are public from the Federal Electoral Institute\n\n(IFE) website. In addition to these, we use complementary information on socio-demographic\n\ncharacteristics of municipalities from the 2000 Population Census and the 2005 Short Census or", "output": {"entities": {"named_data": ["2000 Population Census", "2005 Short Census"], "descriptive_data": ["geographic location data of electoral sections"], "vague_data": ["daily rainfall measures"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "To mitigate issues arising from the time interval between the 2007 Population Census and EMDHS 2014, only\n\nEMDHS at regional level. The table shows that measured undernutrition rates in EMDHS and the estimated rates\n\nregression of z-scores is estimated in the EMDHS with addition of the SAE estimates. The regression includes the", "output": {"entities": {"named_data": ["EMDHS 2014", "2007 Population Census", "EMDHS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "destination. Building on a growing literature documenting the relationship between subjective welfare and relative income, Fafchamps and Shilpi (2008) show that Nepalese households care about their consumption level relative to that of others in the same location. If this is the case, it is conceivable that migrants choose their destination not so much for the absolute gain in income it may provide but for the gain in relative status that would ensue. For instance, if returns to education and ability are higher in an urban setting, an educated individual may improve his relative position in society by moving from a rural to an urban setting. To investigate this possibility, we estimate equation (4) using the log of relative income (or relative consumption) as dependent variable and construct a predicted relative income measure using the same formula (5). These are shown in the second panel of Table 1. Theories of work migration predict that individuals move to increase their utility or welfare. The 1995 / 96 NLSS asked respondents a number of questions regarding their subjective satisfac- tion level with various dimensions of consumption — namely, food, clothing, housing, health care, and child schooling. They were also asked their subjective satisfaction with their level of total income.", "output": {"entities": {"named_data": ["NLSS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "migration, and reason for movement including asylum / refugee protection (or conflict-induced internal migration) (UNHCR 2016), or a specific question to identify IDPs or refugees. However, not all censuses cover refugees and asylum-seekers (if foreigners are considered outside the scope of the census or because they are considered a special category), 71 nor is it common practice for national censuses to include questions related to forced displacement. 72 Nevertheless, there are several examples of national censuses that have included relevant questions on forced displacement. 73, 74 In the case of protracted internal displacement situations, IDPs are likely to be included in national censuses; however, census instruments may be subject to manipulation for political purposes. There are several drawbacks of population censuses including their cost, the significant training required for enumerators to ensure consistent answers to questions on forced displacement, impediments to field operations and data processing (such as weather conditions and technical problems), the relative infrequency with which they are carried out, and the long processing time before data and statistics become available, which have consequences for the timeliness of data. Moreover, often censuses are not conducted in contested territory or conflict zones where many displaced persons reside, and this limits the completeness of the data.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population censuses"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts All Refugees All Hosts - 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Predicted total expenditure Predicted poverty rate Figure 5.16: Poverty incidence decreases with education of the household head Source: World Bank Staff based on SESRE 2023. Refugees’ Aspirations 52 A larger proportion of refugees work inside the camp across the distribution. Yet, better-off refugees are more likely to work inside the camp. Regarding location, although working outside the camp is shown to have significant wage effects (see Chapter 3), the data show that the poorest in-camp refugees are more likely to work outside the camp than the richest (Figure 5.18d), an effect apparently driven by refugees from South Sudan and Somalia, who are poorer overall. Regression results show that an increase in the share of employed household members is associated with increased household expenditure for in-camp refugees, their hosts, and out-of-camp refugees (Table D.12 in Annex D). The predicted poverty rate decreases with the share of employed household members, indicating that employment is essential to lowering poverty for in- camp refugees (Figure 5.19). 0 10 20 30 40 50 60 70 80 90 100 Poorest", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of trade with government. Because the dataset used for this study is cross- sectional rather than longitudinal, I am unable to examine whether citizens adjust their beliefs and behavior to relative changes in service delivery. I was only able to test whether there are associations between the absolute service quality across neighborhoods, countries and ethnic groups and deference to the tax department, police and courts. Further, citizens may not be attributing goods and services to the gov- ernment. Rather, citizens may be attributing goods and services, such as roads, electricity grids, sewage systems, health care and education to vari- ous non-state actors including the following: the private sector; NGOs and community-based groups; churches, mosques and other religious institutions; traditional leaders; and, bilateral and multilateral donors. Survey questions on the Afrobarometer only indicate the presence or absence of services and infrastructure, and the quality of these services, but these questions do not probe respondents on who they believe are providing these services. Each of the indicators of perceptions of government performance is sig- niﬁcant at the p < 0. 05 level. Food security is positively associated with a willingness to defer to the tax department. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 people may also gain from conflict in terms of income and wealth and this may explain why some people do not move. The defining attributes of the alternative choices are very different from any other model and the task of economics is to understand what these defining attributes should be. In terms of independent variables, “ push ” factors become more important than “ pull ” factors in forced displacement models. The intensity of a conflict may be more important than the income opportunities in potential destination areas. In addition to the classic socioeconomic variables, risk aversion, stress, anxiety, other traits of personality and behavioral factors in general have to be well understood and measured. Hence, one could think of four essential blocks of independent variables including individual or household socioeconomic characteristics, “ push ” factors, “ pull ” factors and behavioral factors. Also, access to and dissemination of information related to the conflict in the place of origin but also in the potential places of destination may be crucial for people to make choices. This is where social psychology, behavioral economics and neuroeconomics may offer insights into such choices. Forced displacement data are also unusual in their form.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Forced displacement data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 measured by the number of days missed during the academic year, can function as an indicator of school attachment or integration for refugee students. To address potential biases from teacher subjectivity, the analysis of score disparities relies on INVALSI test results. The standardized and anonymized nature of these tests helps mitigate subjectivity in assessment. First, the results section presents some summary statistics of the main outcomes across the different categories of students. Second, we use the administrative data to analyze empirically how Ukrainian refugees and newly arrived foreigners compared to other students as regards their education performance. This estimation is based on an OLS model with the following econometric speciϐication: 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 + 𝛽𝛽1 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 + 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 + 𝑓𝑓𝑔𝑔 + 𝑓𝑓𝑠𝑠 + 𝑓𝑓𝑙𝑙 + ϵigs (1) where 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 represents the outcome of interest (such as test scores, absenteeism, or high-track recommendation) for student i in school s, in grade g, and with language l. The variable 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 is a dummy indicating whether the student is a Ukrainian refugee, and 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 indicates if the student is a newly arrived foreigner. 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 represents the Economic, Social, and Cultural Status of the student, and 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 indicates the student ’ s gender. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As has been demonstrated, jobs are not easy to come by for most refugees, and in order to earn\nmoney some choose to invest in small businesses. Some organisations assist refugees in their\nlivelihoods through allowing them to be able to gain independence and earn their own money. In\n2009-2010 CRAT, in conjunction with the US Embassy in Cameroon, began a project entitled\n“Improving the Coping Status of Urban Refugees”. This project was initiated after a survey and\nresearch project was done with many of the torture victims that CRAT assists, the results of\nwhich showed a strong link between Post-Traumatic Stress Disorder, anxiety and depression,\nwith a lack of livelihood support. According to a psychologist at CRAT, when people are unable\nto meet their basic needs, it is very difficult to treat their mental health issues.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey and\nresearch project"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Significant among these are: (a) the lack of capacity of national statistical agencies in many developing countries to collect robust data on refugees; (b) weak or incomplete monitoring of refugees dispersed within host communities; (c) lack of capacity to maintain up to date information on refugees (reflecting new arrivals, 81 General population registers may also provide opportunities for more elaborate analysis of the integration of refugees in asylum countries, as the data could be linked to other administrative registers, for example on labor and education (UNSD 2014). 82 UNHCR collects, compiles and publishes data on asylum-seekers, refugees and IDPs protected or assisted by UNHCR, including populations in refugee-like or IDP-like situations. 83 Established in 1863, the ICRC ’ s mission is to ensure humanitarian protection and assistance for victims of armed conflict and other situations of violence. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["General population registers"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "likelihood of working outside the camp, or in monthly earnings. Agriculture is an important source of livelihood for host households, but refugee households have low agricultural holdings, reflecting their inability to own land legally. Refugee households are less than half as likely as hosts to report an agricultural holding with crops (19 percent versus 41 percent of host households, Figure 3.14). Refugee livestock ownership is similarly low (22 percent of households own livestock versus 48 percent for host households) but average livestock ownership is higher for Somali households (41 percent) (Figure 3.15). 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 15-24 25-34 35-44 45-54 55-64 Less than Primary Primary Secondary Figure 3.12: Share employed by age – camp refugees Source: World Bank Staff based on SESRE 2023. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 15-24 25-34 35-44 45-54 55-64 Less than Primary Primary Secondary Figure 3.13: Share employed by age – camp hosts Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 32 When refugees own livestock, the value and flock size of this livestock is low. Somali refugees mostly own sheep, goats, and donkeys, and compared to Somali hosts, they", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "11 survey, we construct the asset index based on the same categories of assets captured in every survey wave, ensuring full comparability over time. Most of these assets are recorded as dichotomous variables, taking 0 / 1 values, while the few categorical variables with multiple categories are manually reorganized along an ordinal scale according to costs. The asset variables are first normalized and then transformed into a single wealth index through principal component analysis, following an approach proposed by Kolenikov and Angeles (2009). Scree plots indicate that the first principal component is highly significant in every wave, while further components carry little information, as desired. The wealth-index is likely to indicate the long-term economic well-being, as many durables captured are typically held by households for many years and are not frequently replaced (Sahn and Stifel 2000). In between the first and second survey waves an administrative reform took place in which the definition of urban areas was revised (Megill 2004), among other things. Some communities previously considered rural were now coded as towns, which, to some extent, explains the sharp increase in the proportion of urban population. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 1: Data and Descriptive Statistics: Clusters, Refugee Camps, and Conflicts Revised refugee diversity indices. We first use Afrobarometer data to construct standard indices of diversity, namely the EF and the EP indices (Bazzi et al., 2019; Esteban and Ray, 1994). The EF index describes the probability that two randomly selected individuals from a given location belong to two different ethnic groups (Alesina et al., 2003, 2016; Gomes, 2020b). The EF index can be defined as EFjt = Njt X e = 1 get (1 − get), (2) where Nj is the number of ethnic groups in cluster j at time t and get is the population share of 14 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the West Bank and Gaza. Out of all the estimated coefficients for each period for both outcomes (job loss and job gain), we only find a negative effect on job gain in 2019Q4, which is very small in magnitude (1 percentage point). Taken altogether, the results presented in this section bolster our confidence that we are correctly identifying the effects of the pandemic shock on labor market outcomes. Figure 12: Placebo effect on labor market flows Notes: The figure shows the output of a placebo test with a set-up analogous to Figures 6 and 9. We perform the same regression as specified in Equation (2). Our sample includes data from 2018Q2 to 2020Q1 and assumes that the pandemic started in 2019Q2. Therefore, the post-pandemic period refers to the quarters between 2019Q2 to 2020Q1. The analysis is restricted to men aged 20-59. 7 Conclusion This paper examines the effect of the pandemic on labor markets in the West Bank and Gaza using quarterly labor market data provided by national labor force surveys. With a focus on men ’ s labor market outcomes, this paper sheds light on how labor markets in the West Bank and Gaza adjusted to the COVID-19 shock examining adjustments at the extensive (employment) and intensive (hours of work) margins. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Having returned (early) to Northern Mali did not always lead to a stable condition either: the transition probabilities from returnee to IDP or to refugee are 1. 1 % and 0. 2 % respectively. As we can see from Figure 1, the majority of the sample is Songhai and Kel Tamasheq (almost everybody identified themselves as Muslim). There are clear differences in migration decisions between ethnic groups. The reaction of most of Arab and Kel Tamasheq origin was to leave the country, while most Songhai people preferred to go south, to Bamako, or, by the time of our survey, had already returned to Northern Mali. In fact, as pointed out in (Etang-Ndip et al., 2015), IDPs and returnees have a similar ethnic composition because 94 % of returnees in our sample were IDPs. Far fewer returnees in the sampled cities of Gao, Tombouctou and Kidal returned from refugee camps in the neighboring countries for the simple reason that most refugees used to live in towns and villages outside the regional capitals of Northern Mali. Displaced Refugee Returnee Total Tamasheq Arab Songhai Peulh Bella Other Analytic weights used Source: LDPS 2014-15 Figure 1: Ethnic composition", "output": {"entities": {"named_data": ["LDPS 2014-15"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2017. • 890,825 refugees enrolled in the Level 3 Registration and Biometric Identity Management System (BIMS). Refugee ID cards and proof of registration are issued to 55 and 98 percent of refugees, respectively. Out-of-camp Permit is issued to 48,346 refugees. • One-stop shops have been established in 13 refugee camps (14 are under construction) to provide one-center registration, documentation, and protection services. Annexes 91 • The National Strategy on Violence Against Women and Children (2021 -2026) recognizes refugee women and children. Also, refugees are included in the national Gender Based Violence (GBV). • Digital Request and Compliant System (DRCS) is established and implemented as part of the digitization of refugee protection services. • Refugees are getting mobile courts and free legal aid services. Energy/Environment “Provide market-based sustainable, reliable, affordable, culturally acceptable, environmentally friendly clean/renewable energy solutions for 3 million people. • A National Cooking Fuel Strategy is developed by EEWG to guide and define camp-specific cooking energy options. • In Afar, Gambella, and Melkadida, more than 382,000 refugees and 85,000 hosts have access to alternative cooking fuels and market-based clean electricity from solar-mini grids. • More than 1,739,726 seedlings were planted, 160m3 check dams were built, and 8 km of", "output": {"entities": {"named_data": ["Level 3 Registration and Biometric Identity Management System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The objective of the survey was to analyze perceptions and priorities with regard to the Peace Accord, to analyze perceptions of security, to determine access to basic infrastructure and school attendance, to understand nutrition levels, and to measure household asset ownership. In addition to this baseline survey, the World Bank was sponsoring a mobile phone survey among IDPs in Bamako, returnees in Gao, Kidal and Timbuktu town and refugees in camps in Niger and Mauritania. This survey interviewed 500 respondents on a monthly basis. In the August 2015 round of this survey, questions about perceptions and priorities with regard to the Peace Accord were included. This paper also makes use of a subset of the responses obtained from that survey, particularly those from refugees in Niger (n = 80) and Mauritania (n = 100) as these sub-populations who live outside Mali ’ s borders are important stakeholders in the peace process whose opinions risk being ignored. 15 To select a household in a village or neighborhood for the baseline survey, random selection was used: the enumerator divided the locality into two parts and selected five 15 For a more elaborate description of this mobile phone survey, see: Etang Ndip, A., J. Hoogeveen and J. Lendorfer (2016). Socioeconomic Impact of the Crisis in Mali on Displaced People. Journal of Refugee Studies. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["mobile phone survey", "baseline survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "weather policy, this is deemed a rainfall shock. As the quality of the rainfall data is related to the\n\n11The APHRODITE weather data provides information about how many local weatherstations contributed to a\ncertain rainfall reading. Since some of the rainfall observations are likely to be more accurate than others, I weight\nthem according to accuracy. If there are no rainfall stations contributing to the APHRODITE data within a .75 _[o]_ x.75 _[o]_\n\nWhen the regressions are run with the village characteristics from the 2005 Indian census, the coefficients of interest do not change significantly. Also, most village-level characteristics had insignificant coefficients, with the exception that a more literate population\n\nWhile the BASIX data set does not offer the opportunity to test the direct effects of a cash payment\n\nRobust standard errors in parentheses Obervations weighted by quality of rainfall data", "output": {"entities": {"named_data": ["APHRODITE weather data", "2005 Indian census", "BASIX data set"], "descriptive_data": [], "vague_data": ["rainfall data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "9 Figure 2: The number of FDP micro-level datasets over time Notes: this graph shows a trend in the number of FDP micro-level datasets in the UNHCR / WB MDLs between 1995 and 2022 based on the year of data collection. Note that at the time of this study, many micro-level datasets collected in 2022 were not yet incorporated into the UNHCR / WB MDLs and thus the number for that year should be treated with caution. Project specific datasets Of the 375 FDP datasets, a non-negligible share of them turns out to be project specific, meaning that despite their utility for evaluating and monitoring the impact of a certain project or program, its application for analyzing any broader population of interest is quite limited. We find that 39 percent of the identified datasets sample from a narrow base of beneficiaries from a certain program or project. It is important to draw a representative sample of a broader population of refugees and / or IDPs instead of sampling only from a narrow base. If the sample consists of only beneficiaries from a certain project or program, the generalizability of findings or conclusions drawn from that sample is very much limited and does not extend beyond the small confine of the target population that is relevant only to the project or program itself but not to broader communities of policy makers, development practitioners and scholars alike. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "emotional well-being, as well as local integration prospects. In this sense, some Afghan women\nhave reported a strict control exerted by male family members, which translates into their\nconfinement in the domestic realm.\n\n##### Protection Risk IV\n\n**Trafficking in persons, forced labor or slavery-like practices.** Although asylum seekers, refugees,\nand other forcibly displaced persons have the same labor rights as nationals in Brazil, they\nencounter several obstacles to their economic integration. The experience of displacement,\ncoupled with high levels of unemployment, informal labor, and poverty, heighten the risk of this\npopulation to fall prey of human traffickers. According to official data, between 2021 and 2023,\n355 refugees and migrants of all nationalities were rescued from forced labor or slavery-like\npractices in Brazil from various economic sectors, primarily in timber trade, cassava cultivation,\nclothing manufacturing, road transportation, and tobacco. [33 ]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Notably, while we cannot rule out that time outside due to employment may play a role (e. g., fresh air may boost one ’ s mood), our time-use data indicates that the average refugee already spends at least three hours outside per day, with no measurable diﬀerence between employed and cash arms. As we are powered to detect changes of at least twenty minutes for each activity, our results suggest that large substitutions away from unsavory activities are unlikely to be driving the improvements in psychosocial well-being, insofar as the respondent recalls. 1718 We also investigate whether those who were more idle prior to being employed beneﬁt more from employment. We ﬁnd no impact along this margin, suggesting that the elimination of boredom per se is not the driving force behind the psychosocial value of employment (Appendix Table A10). 17Most respondents do not track their day by time, making collection of reliable time use data challenging (though recent literature documents the broader unreliability of such data). We piloted a variety of strategies, and settled on asking respondents how much time they spent on a set of activities in the previous day.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["time-use data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "SEIS enumerators were instructed to interview only the household's de facto head, defined as the person with primary decision-making authority over household resources, regardless of gender. In cases where the household head was unavailable, the spouse or a proxy respondent aged 18 or above was interviewed. SEIS interviews were conducted in private to minimize social desirability bias on sensitive questions about protection incidents and the household's experiences of discrimination in access to services.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Looking also at the impact of IDPs in Colombia on wages, Morales (2017) uses a labor force survey, census data and registry data to study short and long-term effects as follows: 𝑆ℎ𝑜𝑟𝑡 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ௧ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ௧ ି ଵ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ௧ ൅ 𝜆 ௠ 𝑋 ௠ ௧ ൅ 𝛾௧ ൅ 𝛿 ௠ ൅ 𝛿 ௠ 𝑇 ൅ 𝜀 ௜ ௠ ௧ 𝐿𝑜𝑛𝑔 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ൅ 𝜆 ௠ 𝑋 ௠ ൅ 𝛿 ஽ ௠ ൅ 𝜀 ௜ ௠ where y is the log of wages, i, m, and i are individuals, municipalities and time respectively, 𝑋 ௜ ௠ ௧ are individual controls, 𝑋 ௠ ௧ is the log of total population or other municipality controls, 𝛾௧ and 𝛿 ௠ are time and municipality fixed effects, 𝛿 ௠ 𝑇 are municipality time trends, 𝛿 ஽ ௠ are department fixed effects and d is the inflow of IDPs defined as 𝑑 ௠ ௧ ൌ 100 𝑝𝑜𝑝 ௠ ௧ 𝑓 ௠ ௧ where 𝑓 ௠ ௧ is the total number of IDPs arriving in municipality m at time t. The same variable without the t subscript is used for the long-run effects equation. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5 (UNHCR, 2014), developing countries hosted 10. 1 million refugees or 86 percent of the world ’ s refugees. Contrary to what has been sometimes claimed in popular media, refugees are not “ invading ” the higher ‐ income countries. Actually, SSA has been hosting more refugees than sending them since 1990. The divergence of trends occurring in 2005 is certainly related to large inflows of refugees from North Africa and the Middle East. The second peak in 2011 corresponds to the uprisings that spread across several Arab countries (Egypt, Libya, Syria, Tunisia and Yemen), and the recent one in 2013 to the large outflows of refugees from Iraq, Syria and Yemen. Figure 2. Refugees and Internally Displaced People in SSA, 1990 ‐ 2013 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). Due to changes in classification and estimation methodology in a number of countries, 2007 figures are not fully comparable with pre ‐ 2007 figures (see also footnote 1). Gathering data on internally displaced people is much more challenging since most existing data on IDPs are incomplete or unreliable. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Respondents should be asked to estimate the number of purchases made over a fixed reference period, anchoring questions on consumption rather than on acquisitions (as in the 2016 Barbados Survey of Living Conditions and the 2017/18 Jordan Household Income and Expenditure Survey).\n\n**GDP.** We use GDP data from the Word Development Indicators of the World Bank. It is\n\n\nmeasured using constant 2010 prices in US dollars. [8] For our analysis, GDP series need to be\n\n\nfiltered in order to extract the business cycle component from the trend. We use three different\n\n8We used the data serie called \"NY.GDP.MKTP.KD\"\n9With this setting, we mostly keep fluctuations that have a frequency between 8 and 32 quarters.\n10We keep fluctuations between 32 and 200 quarters, following Comin and Gertler (2006a)\n11The concordance table from SITC Rev2 to BEC can be found on the UN Trade Statistics webpage: `[https:](https://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp)`\n`[//unstats.un.org/unsd/trade/classifications/correspondence-tables.asp](https://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp)` .\n\n**Trade** **Proximity.** We collect data on bilateral trade flows from the Observatory of Economic Complexity (MIT). This database covers 215 countries over the period 1962-2014.\n\nIn this paper, we use the recent GVC indicators from Borin and Mancini (2019). They offer a new toolkit for value-added accounting of trade flows at the aggregate, bilateral, and sectoral levels", "output": {"entities": {"named_data": ["2017/18 Jordan Household Income and Expenditure Survey", "Word Development Indicators", "NY.GDP.MKTP.KD", "Observatory of Economic Complexity"], "descriptive_data": [], "vague_data": ["GVC indicators"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The final steps consist of setting a threshold for when a stream flow is strong enough  to flood the basin and then weighting this with the nightlight data and aggregating up to a  district level.  5  <!-- PAGE 8 --> The earthquake index is constructed from computer generated contour maps by the US  Geological Survey (USGS) of earthquake intensity data, commonly used as potential dam age proxy (De Groeve _et_ _al._, 2008; GeoHazards International and United Nations Centre  for Regional Development, 2001; Federal Emergency Management Agency, 2006).\n\nThe  data are then combined with the nightlight data and aggregated up to district level.  Finally, the 2004 Christmas tsunami has been modeled following a method where Heger  (2016) uses inundation maps to construct a district level damage index assuming uniform  6  <!-- PAGE 9 --> damage across all flooded areas.\n\naggregation of the disaster indices in the main paper. To weight the indices, nightlight data  are used as a proxy for economic activity.  **A.1** **Flood** **Index**  The flood index is made from stream flow modeled in GeoSFM, a software that uses remotely  sensed data as inputs, which are weather and soil and terrain based.\n\nthe contour maps as a base for damage infliction, we combine them with the nightlight and  building type data from the USGS building inventory for earthquake assessment to create  fragility curves by building type; see Jaiswal & Wald (2008) and GeoHazards International  and United Nations Centre for Regional Development (2001).", "output": {"entities": {"named_data": ["USGS building inventory"], "descriptive_data": [], "vague_data": ["nightlight data", "earthquake intensity data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The long-term consequences of these trends are signiϐicant, as diminished educational outcomes and social isolation can hinder successful integration into host communities. Conversely, sustained social and educational integration efforts are vital for positive outcomes. For instance, studies indicate that long-term integration can be hampered by social, economic, and institutional barriers (Chiswick and Miller, 2014), while interventions focused on language support and community engagement can lower these barriers (Ozden and Wagner, 2020). Further, speciϐic interventions aimed at removing obstacles to education for children on the move can contribute signiϐicantly to better integration outcomes (Schuettler and Caron, 2020). This paper beneϐits from key information coming from administrative data on educational records of Ukrainian refugees in Italy for the academic years 2021-2022 to 2023-2024 for grades 6 to 13. This provides a unique opportunity to examine enrollment, attendance, test performance, and other indicators of integration into the Italian educational system. Supplemented by survey data collected in 2023-2024, this study offers an overview of the challenges and opportunities faced by Ukrainian students in secondary schools and highlights areas for potential policy development. This study advances the literature by adding empirical evidence on the short- to medium-term educational impacts of displacement on young refugees within a European host country, offering insights into the role of education policy in mitigating human capital losses. It also contributes to discussions on human development by identifying factors that support or hinder integration, highlighting pathways for improving educational and social outcomes for refugee students. Results highlight that despite gradual improvements, enrollment rates remain signiϐicantly lower among refugees compared to native and other foreign students. Ukrainian refugees also demonstrate higher absenteeism and lower academic performance, particularly in subjects requiring language proϐiciency such as Italian and English. However, good performance in mathematics suggests potential strengths linked to their prior educational backgrounds. Despite these challenges, teachers seem to be more inclined to recommend Ukrainian refugees for high-track education compared to other newly arrived foreigners, indicating potential optimism about their academic capabilities. The Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 as self-confidence, responsibility, and respect. Additionally, the curriculum includes workplace readiness skills, such as interviewing and time management. SECTION 2: STUDY TIMELINE & DATA A quasi experimental impact evaluation design was embedded into the NVSP. As mentioned before, the NVSP received 38 applications from eligible NGOs. Per well-developed selection criteria, 8 the highest 22 ranked proposals were selected to receive funding. Each of the 38 proposals included a list of 50 youth (the minimum number of youth set by the NVSP) who would benefit from the project if selected for funding. However, as mentioned before, the 22 selected projects benefited a total of 1, 296 youth, exceeding the set target of 1, 100 volunteers. Of the 50 volunteers included in each of the 38 proposals, 22 youth per proposal were randomly selected to participate in the impact evaluation study. Therefore, the initial sample size of the study comprised a total of 825 youth: 473 youth who served as the treatment group (representing the 22 selected NGOs that received NVSP funding) and 352 youth who served as the comparison group (representing the 16 non-selected NGOs). However, two NGOs refused to participate in the study once informed that their proposals had not been selected for funding. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "intention to migrate abroad (1) (2) Camp-Based Refugees OCP Refugees Male 0.022 -0.001 (0.019) (0.008) Age Under 30 - - Age 30-44 -0.000 -0.007 (0.020) (0.008) Age 45-64 -0.082*** -0.088** (0.027) (0.036) Education: Primary incomplete - - Education: Completed primary -0.008 0.004 (0.036) (0.009) Education: Completed secondary 0.016 0.006 (0.064) (0.009) Education: Completed post-secondary -0.016 -0.010 (0.067) (0.034) Years in Ethiopia -0.000 -0.000 (0.004) (0.001) Region Fixed Effects Yes No N 3,069 830 Source: World Bank Staff based on SESRE 2023. Note: The outcome positively responds to the question, ‘Do you intend to migrate abroad?’. The sample includes all refugees aged 15 or over who were born abroad. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 112 Results on Welfare and Equity Table D.11: Poverty headcount rate by subgroups Characteristics Subgroups In-camp Addis Ababa Hosts Refugees Hosts Refugees Location or domain Eritrean 16% 73% Somali 38% 76% South Sudanese 36% 89% Addis Ababa 18% 7% Sex of head Female 34% 87% 16% 10% Male 30% 74% 19% 3% Head education No education 38% 87% 35% 16% Primary incomplete 30% 81% 26% 13% Primary complete 29% 79% 14% 6%", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 3: Environmental and Social Risk Assessment**\n\nThe ESMF (Environmental and Social Management Framework) outlines the screening criteria applied to all civil works under Components 1 and 2. Site-specific Environmental and Social Management Plans (ESMPs) will be prepared for each subproject prior to commencement of works. The ESMF also includes a Labor Management Procedures (LMP) section detailing requirements for contractor compliance with national labor law, prohibition of child and forced labor, and worker grievance mechanisms. The LMP will be incorporated by reference into all civil works contracts.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "16 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 6. Gender differences in multidimensional poverty Next we examine differences in multidimensional poverty outcomes by the gender of the household head. Existing literature points out the limitation of household level MPI analysis in masking the intrahousehold distribution of deprivations, and thus being less sensitive to gender based differences in individual outcomes within the family unit, which might lead to underestimation of inequality and gender gaps (Espinoza-Delgado and Klasen 2018; Franco 2017; Klasen and Lahoti 2020, Rodriguez, 2016). However, as the MPI identifies poverty at the household level, our initial analysis focuses on disaggregated results by the gender of the household head. 19 We acknowledge that this approach has several limitations since most women reside in male-headed households, and the composition of households can change after displacement due to separation of family members, and widowhood. Regardless, the analysis at the household level remains relevant given the high prevalence of female-headed households that emerge after displacement, with the analysis showing large differences across countries between households based on the gender of the head. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "First, there is reason to believe that better service delivery may aﬀect citizens ’ willingness to defer to the tax department only through its eﬀect on improved outcomes that matter for citizens ’ livelihoods. Unless improved services and infrastructure have a positive impact on citizens ’ welfare, indi- viduals are unlikely to credit the government for these outputs (Sacks and Levi, 2010). The Afrobarometer ’ s objective measures of service delivery only denote the presence or absence of infrastructure and services. The data do not indicate the condition of the services and infrastructure. Citizens may perceive and reward relative improvements or sanction de- teriorations in services, rather than the absolute level of service quality they receive. If services deteriorate or improve, taxpayers may alter their beliefs about governments ’ performance and should attempt to adjust their terms 10I also tested whether there is a relationship between the presence of a concrete road, health clinic, post oﬃce and electricity grid in the enumeration areas and respondents ’ willingness to pay taxes. None of these objective indicators except for the presence of an electricity grid were signiﬁcant at the p < 0. 05 level. The presence of an electricity grid is negatively associated with the willingness to defer to the tax department. 17 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In other contexts, deregistration signifies not the achievement of a durable solution but rather the end of state or international support (IDMC 2015). 33 The absence of a clear and operational approach to defining the ‘ end ’ of internal displacement may be one of the factors behind the continued overall increase in the global numbers of IDPs. Lack of clarity around when displacement ends also leaves room for political manipulation. Governments may find it politically expedient to artificially prolong IDP status by deterring returns or local integration, for example in Azerbaijan and Georgia to promote claims over territory (Beau 2003). In other contexts, national 28 UNHCR ’ s IDP data focus only on internally displaced populations to which it extends protection or assistance. IDMC coverage of IDP data is more expansive and in 2015 included additional data on: (a) 26 countries accounting for 4. 5 million IDPs including some significant IDP hosting countries (Turkey, India, Ethiopia, Bangladesh and Kenya); and (b) IDPs in countries where UNHCR is active who are not protected or assisted by the agency. In 2015, IDMC ’ s aggregate figure for conflict-induced internal displacement was 3. 3 million higher than UNHCR ’ s aggregate figure for IDPs protected or assisted by the agency. 29 IDMC ’ s 2016 report presents both data sets alongside each other. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["IDP data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "as sold. Annexes 125 Table E.2: Food aid and consumption comparisons Items Quantity (per capita/year) Expenditure (per capita/year) SESRE UNHCR SESRE UNHCR Nonzero All Net of sold ration* Cereals/other cereals 23.2 186.1 175.5 493 12404 Wheat 60.5 133.2 116.6 1427 4710 Maize 39.6 125.5 118.3 312 4267 Rice 21.3 48.5 39.8 560 3392 Sorghum 12.7 132.8 125.7 4 5652 Pulses 7.1 18.9 17.5 248 1514 Vegetable oil/oil 3.7 9.7 8.9 627 1774 Salt 1.6 7.9 7.7 57 232 Biscuits 5.4 4.5 4.4 16 112 Dates 0.0 4.2 3.7 0 . CSB+ 19.3 15.0 14.0 36 532 Other food 565.9 - - 2558 Peas 5.7 - - 137 All cereals 78.2 - - 2994 All pulses 8.2 - - 385 Aggregate ration/month 46.7 - - Source: UNHCR and World Bank Staff based on SESRE 2023. Valuing food aid quantities with prices from SESRE suggests that if UNHCR food aid quantities were received/ reported by refugees, refugees’ food expenditures would be much more comparable to those of hosts (Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research. 1989-2019. 50 (4). Taylor, J., M. Filipski, and M. Alloush (2016). Economic impact of refugees. Proceedings of the National Academy of Sciences 113 (27), 7449 – 53. United Nations High Commissioner for Refugees (2020). Global Trends: Forced Displacement in 2019. Geneva. Verme, P. and K. Schuettler (2021). The impact of forced displacement on host communities a review of the empirical literature in economics. Journal of Development Economics 102606. Verwimp, P. and J. Maystadt (2015, December). Forced Displacement and Refugees in Sub-Saharan Africa. An Economic Inquiry. World Bank Policy Research Working paper 7517. Vogt, Manuel, N.- C. B. S. R. L.- E. C. P. H. and L. Girardin (2015). Integrating Data on Ethnicity, Geography, and Conflict: The Ethnic Power Relations Data Set Family. 51", "output": {"entities": {"named_data": ["Ethnic Power Relations Data Set Family", "UCDP Georeferenced Event Dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A number of different road indicators are available and we choose road line type to use in the analysis. Road type is defined by the following: The reference category (0) points out squares with dual lane / divided highways, other primary roads, or road connectors within urban areas (types 1 or 8 in the ESRI dataset). The second category include secondary roads (type 2), and the third combines squares with informal or tertiary roads (tracks, trails or footpaths) or no road registered at all (types 3 and 0, respectively, in the ESRI dataset). Figure 4 overlays the types of roads in the original dataset before our recategorization. The shaded area represents the portion of Africa for which we code Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["ESRI dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Applications were submitted during the same time span each of these days (8am-12pm CT). 7 The order of applications (grouped by profile) each day was also randomized. The application process is straightforward, it consists of submitting the application and completing an optional pitch. However, some jobs have a mandatory pre-scan questionnaire. For these type of ads we standardized answers and recorded the job ads that implemented these questionnaires. 3. 4 Monitoring Job Applications Job applications were monitored using a web scraping algorithm. For each job application, the following data was scraped from the website every Tuesday, Thursday, and Saturday (between 8am- 12pm CT): 1. Number of times the profile was viewed by the employer 6The locations we classify as Greater Kuala Lumpur are: Kuala Lumpur, Putrajaya, Petaling Jaya, Klang / Port Klang, Kajang / Bangi / Serdang, Subang Jaya, Ampang, Cyberjaya, Seremban, Selangor, Selangor- Others, Selayang, Semenyih, Shah Alam / Subang, and Central. 7If the website is under maintenance, which is common, applications will be delayed until the website is available. 10 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A growing number of low and middle-income countries have tried to improve the law enforcement response to gender-based violence by training professionals, reorganizing police and courts, and trying to provide a more comprehensive response to survivors. Evidence of effectiveness is relatively limited; most well-evaluated initiatives come from high-income countries, and the lessons learned may not be applicable to developing countries. Evaluations of law enforcement reforms in low and middle income countries have typically been limited to case study approaches drawing from police records (notorious for under-reporting), qualitative perspectives from key informant interviews, intermediate outcomes such as changes in attitudes and knowledge among police and judges, and interviews with small numbers of women who have sought legal redress. Population-based data collection, control groups, or follow-up among more than a handful of survivors are rare. Nonetheless, the following initiatives illustrate the types of efforts that have produced important lessons learned. Training personnel in the police and judiciary and other parts of the justice system Throughout the world, organizations have launched efforts to improve the knowledge, attitudes, and practices of justice sector personnel regarding gender-based violence. Some law enforcement institutions organize training internally, as did South Africa following passage of the 1998 Domestic Violence Act (Usdin et al., 2000). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["police records"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes high-income (OECD and non-OECD) countries. Excludes Palestinian refugees under UNRWA ’ s mandate. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "First, I include a measure of whether citizens believe that a large portion of tax administrators is corrupt. Second, I in- clude a variable indicating whether citizens approve of how well their local government is handling the collection of license fees on bicycles, carts and barrows. 8 Third, both the size of a country and the size of the government may aﬀect a government ’ s ability to detect and punish evaders. I include the 7I also include a country-level indicator of government performance, the World Bank Governance indicator of government eﬀectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implemen- tation, and the credibility of the government ’ s commitment to such policies (Kaufmann, Kraay and Mastruzzi, 2006, 4). This variable is not signiﬁcant at the p <. 05 level. 8I included two additional measures in the model neither of which were signiﬁcant at the p < 0. 05 level. One is a measure of citizens ’ approval of how well their local government council provides citizens with the information about the councils budget (i. e. revenues and expenditures). The other, the World Bank governance indicator, control of corruption, measures the extent to which public power is exercised for private gain, as well as capture of the state by elites and private interest (Kaufmann, Kraay and Mastruzzi, 2006, 4). 13 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "When national or international actors provide assistance, there may be an incentive for people to register in camps even if they are staying elsewhere, or to register in multiple locations (Brookings 2011). 69 Furthermore, registration data provide only a snapshot of the stock of IDPs at a particular point in time and may be out of date if registers are not maintained regularly. Registration methodologies can vary across displacement situations. For example, families may be registered rather than individuals and the population estimated based on an assumption of average family size, which can differ among organizations (UNSD 2014). IDPs may be required to present documentation, meet specific criteria or re-register periodically to maintain their benefits, which affects aggregate numbers (IDMC 2015). For example, in Ukraine, pre-requisites for IDP registration (including valid documentation, arrival from a recognized conflict zone and permanent residence registration in recognized conflict zone) means that people displaced within a non-government controlled area, people displaced from a non- recognized conflict zone in a government controlled area, unaccompanied children or people without current / valid identification are not counted as IDPs (IDMC 2015). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["registration data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Survey data, for example from large household survey programs such as the Demographic and Health Surveys (DHS), Multiple Indicators Cluster Survey (MICS), or Living Standards Measurement Study (LSMS) is independent of public record keeping and can also capture vaccination obtained through private or non-governmental providers\n\nThe data that support the findings of this study are available from the from the Central Statistical Office of Poland and the Orbis database of Bureau Van Dijk.", "output": {"entities": {"named_data": ["Demographic and Health Surveys (DHS)", "Multiple Indicators Cluster Survey (MICS)", "Living Standards Measurement Study (LSMS)", "Orbis database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "their earnings on household expenses than men. Most of these studies have focused on adult women, specifically married women with children. It is not known whether the same holds true for young women, who may have other spending priorities, have less experience in managing households, and have younger children. Given the large increases in employment and earnings documented above, the EPAG program serves as a good setting to examine these types of spillovers. The evaluation included detailed interviews with the heads of the household in which EPAG participants were residing. The purpose of the household questionnaires was precisely to examine the hypothesis that investing in young girls would benefit her household. A secondary hypothesis was that EPAG participation may change gender-related attitudes in the participants ’ households. Household data was collected for 1601 out of the 1622 individuals who were interviewed at both baseline and midline; this same sample of 1601 individuals serves as the basis for both the individual and household level analysis in this paper. The estimated impact of the program on a broad range of household outcomes is summarized in Tables 8 and 9. Panel A of Table 8 examines the household size. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Poor households in Bangladesh depend heavily on wood, dung and other biomass fuels\nfor cooking. This paper provides a detailed analysis of the implications for indoor air\npollution, drawing on new monitoring data for respirable airborne particulates (PM10) in a\nlarge number of Bangladeshi households. Concentrations of 300 ug/m [3] or greater are\ncommon in our sample, implying widespread exposure to a serious health hazard. For\ncomparison, Galassi, Ostro, et al. (2000) find substantial health benefits for PM10\nreduction in eight Italian cities whose annual concentrations are far lower: 45-55 ug/m [3] .\n\nhouseholds, using new air monitoring data from Bangladesh. Recent technical advances\n\nIn this paper we have investigated the determinants of indoor air pollution in\n\n\nBangladesh, using monitoring data for a stratified sample of 236 households in the region\n\nof Dhaka. Extrapolating from our results, we have estimated indoor air pollution levels\n\n\nfor a random sample of 600 rural, peri-urban and urban households in six regions:\n\n3 The 24-hour cycle of ambient PM10 concentrations is very similar to the pattern of average hourly\nresiduals from a panel regression that controls for differences in average PM10 concentrations for\nhouseholds that use biomass fuels.\n4\nFor comparison, we cite PM10 concentration measured by the Bangladesh Air Quality Management\nProgram monitor situation at the Parliament building in Dhaka. From March, 2002 to February, 2003, the\nmean daily concentration was 137 ug/m [3] . Our thanks to our colleague Paul Martin for this contribution.", "output": {"entities": {"named_data": [], "descriptive_data": ["new monitoring data for respirable airborne particulates (PM10)", "new air monitoring data from Bangladesh"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "standard ﬁxed or random eﬀect approach is not suﬃcient to ensure correct inference; clustering standard errors by individual is necessary. This is what we do. Having described how the dependent variable is constructed, we turn to regressors. We begin by describing how we construct an estimate of g E [yhs | zh], the level of income (or consumption) yh s that a migrant with characteristics zh can expect to earn in district s. To construct such estimate, we use the 1995 / 96 NLSS data. The reason for using the 1995 / 96 data instead of the 2002 / 3 NLSS survey is to avoid reverse causation, i. e., migration causing a change in income patterns. Migrants are unlikely to be able to accurately predict the evolution of incomes in each district over time. Income and consumption levels observable before migration are thus a reasonable starting point. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["1995 / 96 NLSS data", "NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Normalized Deviation Vegetation Index (NDVI) is used to determine the exposure of households to\nthe drought. The NDVI is derived from satellite images measuring the health of vegetation.\n\nIn controlling for potential confounding factors, we rely on geo-coded conflict fatality data provided by the Armed Conflict Location Event Dataset (ACLED) and on data on the percentage of target beneficiaries reached with aid by pre-war region coming from the Food Security Cluster Somalia (Table A.1).", "output": {"entities": {"named_data": ["Armed Conflict Location Event Dataset (ACLED)"], "descriptive_data": ["geo-coded conflict fatality data provided by the Armed Conflict Location Event Dataset"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the degree to which the global response should include a development element. We ﬁnd that the average stood at around 10. 3 years at the end of 2015, with a median duration of 4 years, and signiﬁcant sensitivity to a few situations. Such numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete protracted situations. To produce these numbers, we rely on the Population Statistics Database compiled and main- tained by UNHCR. The database records the number of “ persons of interest ” to UNHCR in each year since 1951 and for each situation, where a situation consists of a pair host-origin countries. The calculation of duration of exile is obtained under a no-turnover assumption, whereby a de- crease in the number of refugees for any given situation is fully attributed to exits from refugee status, while increases are assumed to be fully accounted for by new cases. Although such ap- proach tends to over-estimate the true duration of exile, the lack of individual-level data on regis- tration precludes reﬁning the estimate further. Attempts to estimate similar statistics have been limited. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Population Statistics Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "77% Proximity to resource hubs Nearest to zone 34% 93% Nearest to woreda 24% 72% Nearest to border 37% 81% Remote 33% 78% Source: World Bank Staff based on SESRE 2023. Annexes 113 Table D.12: Determinants of welfare (total expenditure per capita) (1) (2) (3) (4) In-camp refugees In-camp hosts OCP refugees OCP hosts Female headed 0.00 0.03 -0.01 0.02 (0.03) (0.03) (0.04) (0.05) Age of head (year) 0.00** 0.00 -0.00 -0.00 (0.00) (0.00) (0.00) (0.00) Household size -0.12*** -0.09*** -0.20*** -0.16*** (0.01) (0.01) (0.01) (0.02) Head years of schooling 0.02*** 0.01*** 0.03*** 0.03*** (0.00) (0.00) (0.01) (0.00) Mobile phone 0.10*** 0.19*** 0.39*** 0.17 (0.03) (0.03) (0.11) (0.15) Household has electricity 0.15*** 0.22*** 0.14 0.19 (0.04) (0.03) (0.27) (0.13) HH owns any livestock 0.04 0.03 -0.42* (0.03) (0.04) (0.25) HH member has bank account 0.13*** 0.13*** 0.07 0.16 (0.03) (0.03) (0.10) (0.26) HH has agricultural holding -0.02 -0.08* -0.47* (0.03) (0.04) (0.24) HH operates a nonfarm enterprise 0.07** -0.00 -0.05 0.26*** (0.03) (0.03) (0.10) (0.07) Share of employed members 0.35*** 0.57*** 0.14** 0.06 (0.08) (0.06) (0.06) (0.08) Health shock 0.07 -0.02 -0.04 -0.20 (0.06) (0.06) (0.10) (0.18) Market shock -0.01 -0.09*** -0.04 -0.19*** (0.02) (0.03) (0.04) (0.05) Employment shock 0.01", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 22: Numbers of Refugees in Ongoing Refugee Situations end-2014 Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes Palestinian refugees under UNRWA ’ s mandate. D. Data on asylum-seekers, refugees and IDPs: sources, applications and credibility In this section, a distinction is made between: (a) the collection of source data; and (b) the compilation of data across sources (within a country or across countries). In general, there is a delineation of roles between data collectors and data compilers, however there are organizations, such as UNHCR, IOM and the Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat (OCHA), 59 that are involved in both data collection and compilation activities. Data collection: Sources for refugees, asylum-seekers and IDPs60 Collection of primary data on forcibly displaced persons is generally undertaken by national governments through their national statistical offices, line ministries or immigration agencies. However, where countries lack the capacity to undertake this work, they may rely on international organizations as well as international and local NGOs to collect data or undertake estimates. 61 In general, governments tend to collect data on refugees in developed countries, while UNHCR and NGOs tend to collect data on refugees in developing countries. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "25 less cohesion. The increase in solidarity is less apparent among refugees where the majority (53 %) stated that the crisis had no effect on solidarity. Figure 18: Levels of trust, by group (June) (%) Source: Listening to Displaced People Survey, 2014. Perceptions that different groups have of others are important elements of peace. When asking for the degree to which neighbors, other villagers and people from other ethnic groups can be trusted the survey finds positive outcomes. Although all groups trust people from other ethnic groups slightly less, the general level of trust is high and it remains stable over time. Finally, consider how IDPs, refugees and returnees envision the future of Mali. The majority of refugees in Mauritania vie for an independent or autonomous North, while the majority of IDPs, returnees and refugees in Niger wish to see full government control over the North. 20 20This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**KEY HIGHLIGHTS** **[1]**\n\n Most of the youth in NWS **face protection risks,** with 53% reporting homelessness and 55% experiencing\nexploitation, while 44% face gender-based violence.\n\n 75% of youth **lack access to protection services**, highlighting a significant gap in availability and\naccessibility.\n 77% of respondents indicate **no organizations offer tailored activities or services for youth** in NWS,\n\n**Specific youth groups**, such as those with disabilities 70%, adolescent females 40%, IDPs 48%, and\nhomelessness 36%, are identified as experiencing significant protection risks.\n 82% of **youth believe newly graduated youth have fewer working opportunities**, 66% of Youth actively\nparticipate in community-based structures, with 74% volunteering and 53% involved in outreach teams,\npromoting skill development, relationship building, and community contribution.\n\n 76% of youth in northwest Syria **perceive unequal opportunities for social participation,** with\ndiscrimination (85%), exclusion (34%), and stigmatization (15%) as key contributing factors.\n\n Only 12% of **youth have access to capacity-building or empowerment activities**,\n\n Expanded initiatives for **youth empowerment, leadership, and conflict resolution** are needed, with a\nfocus on essential skills development and resilience building.\n\n 20% of **youth have been threatened or felt afraid**, while 24% sometimes experience such feelings,\nimpacting their well-being.\n\n---\n[1] The Protection Monitoring and Analysis working Group (PMA WG) of the Northwest Syria Protection Cluster, with support from the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Hosts Refugees Eritrean Somali South Sudanese Figure D.24: Household owns livestock Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.23: Household owns crops Source: World Bank Staff based on SESRE 2023. 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.25: Total value of livestock Source: World Bank Staff based on SESRE 2023. 0 5,000 10,000 15,000 20,000 25,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.26: Value per tropical livestock unit Source: World Bank Staff based on SESRE 2023. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.27: Household has non-farm business Source: World Bank Staff based on SESRE 2023. Annexes 110 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.28: Value of productive assets in households with business Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 Percent 70 80 90 100 Hosts Refugees Hosts Refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As in previous sections we follow Henderson et al. (2012) who argue that the relationship between GDP and night light at the country level can be expressed fairly well in a constant elasticity model in which an increase of night light by 1 percent implies an increase of GDP of about 0. 25 percent. Hodler and Raschky (2014) also look at the relationship between log nighttime light intensity and log GDP at the regional level using the panel data of regional GDP per capita assembled by Gennaioli et al. (2013) 48 and they conﬁrm that the relationship is linear and also ﬁnd an elasticity of around 0. 3. Access to political power is ranked on a scale from 1 to 7 in the GROWup dataset. Ethnic groups are\"powerful\"(monopoly of power or dominant group in power), have access to central power through a formal system of power sharing (as\"Senior\"or\"Ju- nior\"partner) or are “ excluded ” from power (self excluded, powerless or discriminated). Strong executive constraint is measured as a dummy indicating whether or not we have executive parity or subordination of the executive at the country level, a value 7 for “ xconst ” variable in Polity IV dataset. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["GROWup dataset", "Polity IV dataset"], "descriptive_data": ["panel data of regional GDP per capita"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Considering that these statistical standards and recommendations were established in recent years, it may not be surprising that the overall level of compliance with EGRISS recommendations is still evolving. Figure 6: Prevalence of Identifying Questions in FDP Datasets A) Refugee identification questions B) IDP identification questions Notes: Panel A shows the percent of FDP datasets containing each of the core identification questions recommended by EGRISS for refugees (in all publicly available datasets designed to sample from refugees that are not project specific and collected after 2010) whereas Panel B shows IDP equivalent of that. For refugees, we do not make a distinction between the question on the country of citizenship and the acquisition of citizenship because many datasets we study often collapse these questions into one by simply asking respondents to indicate their nationality (or nationalities). 5. Conclusion With an ever-growing number of people forced to flee their homes due to conflict, violence, fear of persecution, and human rights violations, data on FDP has become ever more important. A solid evidence base is crucial to inform effective policy responses to address their challenges and to establish such a base, 35 More than 95 percent of the FDP datasets asked questions on either the country of birth or citizenship. 36 While the country of birth does not change, nationality or citizenship can. And it is the country of citizenship or acquisition of citizenship that determines whether refugees and refugee related persons should still be regarded as refugees by the national authorities even though their country of birth may be different from that of their citizenship or nationality and how citizenship interplays with refugee status varies by country (EGRISS 2019, p. 28). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "_returns to education are heterogeneous across countries. The second set of Mincerian returns are those estimated by Montenegro and Patrinos (2014)._\n_Source: The data has been collected from PWT 9.0 (Feenstra, Inklaar and Timmer 2015)._\n\nnatural capital: (i) it represents only the contribution of the physical capital stock from PWT 9.0 in the conventional\n\n<!-- PAGE 12 -->\ndecomposition, and (ii) it shows the contribution of the physical capital stock (from PWT 9.0) and natural capital (Lange\n\n\net al. 2018) in the natural resource decomposition. The average annual rate of growth in output per worker for the\n\nrespectively- are then computed using the estimated relative income shares (which vary across groups) and the PWT   9.0 labor share (which varies across countries and over time).\n\nSince  is calculated from the share of labor force in PWT 9.0 and 𝛼�1 �𝛾� is proxied by the ratio of natural resource", "output": {"entities": {"named_data": ["PWT 9.0"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Polity data - political regime or patterns of authority. Countries with larger positive (negative) polity values have a more democratic (autocratic) system.\n\nThis paper describes an approach to forecasting future climate at the local level using historical weather station and satellite data and future projections of climate data from global climate models (GCMs) that is easily understandable by policymakers and planners.\n\npolicymakers. It draws on historical climate data from weather stations and satellites;\n\nWe begin with monthly temperature and rainfall data for the period 1961-2000", "output": {"entities": {"named_data": [], "descriptive_data": ["historical climate data from weather stations and satellites", "monthly temperature and rainfall data"], "vague_data": ["station and satellite data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Proclamation of Ethiopia provided a long list of areas52 exclusively reserved for Ethiopian nationals, including small businesses that would have been of interest to refugees (FDRE, 2020). Hence, it allows many business activities to go beyond the reach of refugees. A draft MoU signed between RRS and MoLS/MoTRI/ MoR proposed exceptional treatment of refugees engaged in business due to their vulnerability. According to the MoU, refugees can own a business in agriculture, manufacturing, services, small and medium enterprises, handicrafts, and trade sectors through establishing private limited companies or cooperative societies. Hence, refugees can obtain a business license as a private business or association by providing proof of refugee ID, support letter from RRS on the type of business, source of capital, utilization of profits, qualification certification (depending on the sector), and tax identification number. A business license renewal requires a valid refugee ID, tax clearance, and audit report for a limited liability company. 52 Restaurants, tearooms, coffee shops, bars, nightclubs, catering services, producing bakery products and pastries, barbershop and beauty salon services, smithery, tailoring, sawmilling, timber manufacturing, brick and block manufacturing, quarrying, laundry services, translation secretarial services, security services, brokerage services, and attorney and legal consultancy. Annexes 87 Following the", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In contrast, districts of origin are distributed widely across the country. This reﬂects the fact that much work migration is from remote rural areas to towns and cities. The main characteristics of work migrants are reported in Table 1, together with those of non- migrant adult males. We see that work migrants are on average younger and better educated. The census contains detailed information about ethnicity, language, and religion. In the Nepal census, the term ‘ ethnicity ’ is used to capture a hodgepodge of caste and tribal distinctions. The census distinguishes up to 103 ethnic categories. Most of these categories only account for a tiny proportion of the total population. In terms of the total adult population, the most common ethnic categories are Chhetri, Brahmin, and Newar who, together, account for 35 % of 13 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, another regressor indicates that security may still be pivotal: those who owned a weapon were up to 30 percentage points more likely to plan to go back. In addition to this, it is quite surprising that, if the respondent thought that the Northern Mali crisis was improving, he or she was less likely to plan a return to that area. From a technical point of view, we should point out that we have used an LPM even if the dependent variable was a binary outcome. This choice has been made since in this linear model it is straightforward to add fixed-effects. Furthermore, the coefficients can be interpreted as average partial effects. A simple logit or probit model would not have allowed the inclusion of individual fixed-effects because of the incidental parameter problem. An alternative approach would have been to estimate a conditional logit model. However, since the distribution of the fixed effects is unknown, it would not have been possible to estimate the average partial effects in this model, but only the effect of the regressors on the log-odds ratio. 13 We conclude by stressing that the monthly phone interviews were relatively short, so we did not have a rich panel data set. This may have led to omitted variable biases. Indeed, there may still be time varying factors which could have affected both the probability of being employed and the respondents ’ intentions to go back. Nevertheless, we believe that our model managed to control for 13 See (Wooldridge, 2010) page 639. Conclusions from the conditional logit model are qualitatively similar. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data set"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Colombian and Venezuelan migrant children and adolescents. VII Discussion In this study, we analyze novel and unique data on forcibly displaced migrants and hosts, focusing on children and adolescents, to highlight the disparities in human development between them. The structure our analysis in two parts. In the first part, we characterize the main trends in the data. We show that forcibly displaced households have a wealth distribution skewed towards lower values relative to Colombian households. This is likely explained by the assets ownership loss that forcibly displaced households expe- rienced after the migration episode. We also identify meaningful lags in human capital accumulation between Colombian and Venezuelan children and adolescents of approxi- mately 1 year. We further note that the Colombian government ’ s supportive policies for Venezuelan forced migrants are evident through high levels of service access and pro- gram participation for migrants. Nevertheless, it remains surprising that participation is not higher, suggesting significant potential for improvement in increasing sisb ´ en and health insurance enrollments. In a second part of our analysis, we document sizeable lags in physical and cognitive de- velopment of Venezuelan children and adolescents, relative to their Colombian counter- parts. However, we were not able to identify any gaps in the socioemotional and mental health between the two groups. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Challenges around skill mismatch exacerbate exclusion, as many young Lebanese lack the skills and competencies demanded by private sector employers, particularly ‘ soft skills ’. To address some of these challenges, the Lebanese government (GOL) identified volunteerism as a mechanism to enable diverse youth to work together for improved community assets and service delivery as well as increased employability. In September 2012, the GOL issued a Decree (Number 8924 / 2012) that created a new extra curriculum program that requires secondary school students to complete 60 hours of civil work. In addition, the Ministry of Social Affairs (MOSA), through its Volunteering Department, launched annual action plans for the implementation of youth volunteer summer camps across Lebanon. 4 Father ’ s education and residence (region and location of school) are the two largest contributors to inequality of opportunity in students ’ math test scores, accounting for 44 and 23 percent of total inequality, respectively (World Bank, 2016). 5 According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Gallup Poll"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(b) Disaggregation and geo-mapping of data by location (current location and location of habitual residence; urban, peri-urban or rural location), accommodation (organized camp versus non- camp), and demographics (age and sex); (c) Expanded coverage of data collection exercises to include all areas of affected countries (security permitting); (d) Improved coverage and detailed data on displaced populations living outside of organized camps; (e) Improved coverage of ‘ flows ’, i. e. new displacement, durable solutions (returns, integration, resettlement), births, deaths, and in the case of IDPs, the numbers that flee across international borders becoming refugees; (f) Systematic data collection beginning from the earliest moment following displacement, following up as populations disperse, and continuing until sustainable / durable solutions have been achieved; and (g) Better aggregation, analysis and presentation of forced displacement data currently compiled separately by UNHCR, IOM, IDMC and UNRWA. Additional efforts are required to address the gaps in the data required for development policy and planning. These data are critical for informing the design of development policies and assistance programs. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["forced displacement data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The government of Chad continues to ensure the security of refugees and asylum-seekers through the\nHumanitarian Workers and Refugee Detachment (DPHR). Generally, refugees are not more exposed to\n[existing violence and crime. According to protection data collected through Project 21](https://response.reliefweb.int/west-and-central-africa/protection/projet-21) (‘regional protection\nmonitoring’) approximately 15 per cent of refugees and asylum-seekers claim to have been victims of\nphysical assault, but it is noteworthy that these cases occurred in the country of origin at the time of fleeing.\n\nWhile rape and child marriage remain prohibited by Law, other forms of gender-based violence (GBV)\nare insufficiently covered by existing Laws and policies to protect the Chadian and refugee populations.\nThe national policy to respond to GBV, in effect since 2011, applies in refugee camps and refugee hosting\nvillages but faces challenges due to limited resources (human and logistic) to fully implement this policy. The\nreferral mechanism for refugee victims for medical, legal and psychosocial support remains in place but its\neffectiveness depends on the human and financial resources available in the relevant hosting areas.", "output": {"entities": {"named_data": [], "descriptive_data": ["protection data collected through Project 21", "regional protection\nmonitoring"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A map highlighting Medell ´ ın ’ s geographic position and the locations of the households interviewed for this study is provided in Figure 2, offering visual context to our research setting. Representativeness and stratification. VenRepS-Kids is designed to be representative of two groups of youth. The first group consists of Colombian children and adolescents, aged 5 to 17, born to Colombian parents. The second group encompasses Venezuelan migrant children and adolescents of the same age range, born to Venezuelan parents, who mi- grated to Colombia between 2016 and 2020. The sample was further stratified by gender and socioeconomic levels, using Colombia ’ s neighborhood income-based classification system that ranges from 1 to 6, where six indicates the wealthiest neighborhoods. Our 13 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "High-skill occupations include managers, professionals, and associate professionals. Ln Earnings is the log of monthly earnings winsorized at the 1st and 99th percentiles. All other models are linear probability models. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 107 Table D.8: Refugee Household Reliance on NGOs/Donations (1) (2) (3) (4) All Eritrea Somali South Sudan Years in Ethiopia -0.007** -0.017** -0.011** -0.001 (0.003) (0.006) (0.005) (0.005) Member works outside the camp -0.080** -0.335*** -0.180*** -0.001 (0.035) (0.099) (0.051) (0.041) Region Fixed Effects Yes No No No Demographic Controls Yes Yes Yes Yes N 1252 423 412 417 Source: World Bank Staff based on SESRE 2023. Note: Each column is a linear probability model where the outcome is a binary indicator of whether the household relies primarily on donations for income. Demographic controls include the household size and the share within each age and education group. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Table D.9: Determinants of refugee-host earnings gap (1) (2) (3) Earnings Earnings Earnings Refugee (% difference from hosts) -24.9** -22.4** -18.5* (0.137) (0.115) (0.114)", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The MIS was designed to serve as the single source of truth for beneficiary-level data across all three components. It captures individual registration records, service delivery events, transfer payments, and complaint submissions. Each implementing partner has read access to data from their own operation area, while the central PIU has system-wide read and write access. The MIS generates automated exception reports flagging duplicate registrations, inconsistent household composition records, and transfers issued outside the scheduled payment windows.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 as the Demographic and Health Surveys (DHS), though researchers have reduced under-reporting by providing special training to interviewers, placing greater emphasis on respondents ’ privacy and safety, and allowing women multiple opportunities to disclose their experiences (Ellsberg et al., 2001a; Garcia Moreno et al., 2003; Ellsberg et al., forthcoming). Estimates of the magnitude of the problem Population-based surveys have found that between 10-70 % of women report being physically assaulted by an intimate male partner at some point in their lives (Heise, Ellsberg and Gottemoeller, 1999). See Annex A for estimates from many recent population based studies (Ellsberg et al., forthcoming). Findings from a multi-country study on domestic violence and women ’ s health carried out by the World Health Organization in fifteen sites and ten countries found that between 13-62 % of women had experienced physical violence by a partner over the course of their lifetime, and between 3-29 % of women reported violence within the past year (Figure 1. 1). Figure 1. 1.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": [], "vague_data": ["Population-based surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "observations. The ENILEMS-ENLACE panel also includes the information from ENOE\n\nfor all 3,714 matched observations plus information from ENLACE's context question\n\nin means between the observations of ENILEMS that were merged with ENLACE scores", "output": {"entities": {"named_data": ["ENOE", "ENLACE panel", "ENILEMS-ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Deciding on whether to flee or not to flee a conflict (the migration choice) can be an individual or household choice and risk coping strategies may include temporary migration, shuttling between places, migration of only selected members of the households or migration of the whole household. This implies that individuals may stay put throughout the period observed, join or leave the household during the period, or have several episodes of out and immigration. Households may decide to leave and come back several times. In econometric terms, this means that longitudinal data may be left and right censored and have spells within. They are therefore the most complex set of panel data possible and require particular treatment of data and modeling. Survival or duration models can usually accommodate many of these complexities but it is very rare to find similar data sets used in published articles. Collecting such type of data is also not obvious, particularly if conflict is intense and survey areas cannot be reached. This is an issue where empirical economics could provide a real contribution by defining the optimal data format and adapting panel models to this format. Macro models Macroeconomics has attempted to model forced migration using models borrowed from the trade and economic migration literature such as the gravitational model (Echevarria and Gardeazabal, 2016) or used other macro models to test the impact of refugees on trade (White and Tadess, 2010). A more recent body of work is adapting trade models to take into account stochastic shocks in a dynamic framework (Cameron et al., 2007; Artuc et al., 2008). These are rational expectations models that are able to model the unpredictability of shocks, and recent work has tried to adapt these models to the context of violent", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["longitudinal data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Based on specification (1), we estimate total 2013 GDP for Kenya at $26.8 billion (2005 USD), close to its actual level of $26.9 billion (WDI, 2014). On county-level, we find that Nairobi had the highest overall GDP in 2013 ($3.4 billion), followed by Kiambu ($3.0 billion), Nakuru ($2.3 billion), Nyeri ($1 billion), and Kilifi ($1 billion). Counties with the lowest GDP are the sparsely populated counties of Isiolo ($56 million), Lamu ($58 million), Samburu ($67 million), Elgeyo Marakwet ($108 million), and Tharaka Nithi ($109 million). Nairobi has the highest contribution to national GDP (13 percent), followed by Kiambu (11 percent),\n\nBased on specification (1), we estimate total 2013 GDP for Rwanda at $4.58 billion (2005 USD), only slightly higher than the national-accounts estimate of $4.57 billion (WDI, 2014). On the district-level, we find that Gasabo district had the highest GDP in 2013 ($925 million), followed by the two other districts of Kigali (Kicukiro at $538 million and Nyarugenge at $371 million). Districts in the secondary urban centers come next: Rubabu ($219 million), Rusizi ($186 million), Huye ($166 million), and\n\nAs evident from the title of this paper, we also tried to estimate poverty levels and changes based on nightlights data. The estimated associations were however not robust, and more time and effort would need to be invested in examining this relationship in a more detailed fashion.\n\nBased on national labor survey data (EPAM 2010), it appears that mean income from mining activity for each active worker is higher than the average income for all other activities, especially the agricultural and industrial sectors in the Sikasso region.", "output": {"entities": {"named_data": ["WDI, 2014", "national labor survey data (EPAM 2010)"], "descriptive_data": [], "vague_data": ["nightlights data", "national labor survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "18 Figure 3 Job finding, job separation and the change in employment for young men who ever worked between 2016 and 2020 by half year and nationality conditional on informal employment in their most recent job (Seasonally adjusted) Source: Authors ’ calculations using SYPJ data Figure 4 Job finding, job separation and the change in employment for Syrian young men who ever worked between 2016 and 2020 by half year and residency in camp (Seasonally adjusted) Source: Authors ’ calculations using SYPJ data Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1 Introduction The political and economic crisis in Venezuela has given rise to a massive exodus, esti- mated at 4. 3 million people (as of the end of 2019). Many Venezuelans sought refuge in neighboring countries. It is estimated that 1. 5 million Venezuelans had settled in Colombia by the end of 2019, and over 2 million elsewhere in Latin America. Naturally, Venezuelan migrants have sought employment in the host countries but, because of their recent arrival and their transient status, our knowledge of their working conditions is scant. Our paper aims to ﬁll this gap focusing on Ecuador, where almost 400, 000 Venezuelans have settled and many more have transited through the country on the way to other destinations. More speciﬁcally, we provide the ﬁrst analysis of the labor-market conditions of Venezuelan migrants in Ecuador, based on a new nationally representative survey of this population that also includes information on the Ecuadorans living in the same localities. The survey (known by its Spanish acronym EPEC) was promoted by the World Bank and the government of Ecuador and implemented during the summer of 2019. The design and scope of the survey are unique in the context of the countries across Latin America hosting Venezuelan migrants. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The 2023 survey collected data on 1,980 rural enterprises across nine provinces using a stratified random sample drawn from the business registration database maintained by the national statistics office. The 2023 survey questionnaire covered enterprise age, legal form, employment, revenue, access to credit, and adoption of digital technologies. Due to budgetary constraints, the 2023 survey did not include a module on international trade linkages; this limitation is noted where relevant in the interpretation of findings on market integration.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The World Bank's online screening tool classified this operation as High Risk for GBV/SEA/SH due to the presence of civil works in isolated rural communities and a history of gender-based violence documented in the project area. In response, the project's ESMF includes an expanded GBV annex specifying minimum requirements for contractor codes of conduct, confidential reporting channels, survivor support services, and community sensitization activities. These requirements are contractually binding and subject to third-party verification on a biannual basis.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Appendix E: LFS Harmonization Across Survey Rounds**\n\nThe LFS underwent a questionnaire redesign between round 4 (2015) and round 5 (2016), introducing changes to the employment status classification and the self-employment income module. To enable pooled analysis across rounds 1–7, we harmonize the LFS employment status variable using a crosswalk table developed by the national statistics office. The harmonization reduces the number of employment status categories from seven (rounds 5–7) to four (rounds 1–4), which is sufficient for the binary employed/not-employed distinction used in our main analysis.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "in Bangladesh). The data set we use does not cover the 5. 1 million Palestinian refugees who are under the man- date of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UN- RWA). The deﬁnition of Palestinian refugees in international law is distinct from other refugees. Palestinian refugees are people “ whose normal place of residence was Mandatory Palestine be- tween June 1946 and May 1948, who lost both their homes and means of livelihood as a result of the 1948 Arab-Israeli conﬂict ”. Importantly, their patrilineal descendants are also considered refugees regardless of citizenship (UNRWA 2009). For the purpose of the analysis, we do not include Internally Displaced Persons (IDPs), who are deﬁned as “ persons who have been forced or obliged to ﬂee or leave their home or place of habitual residence, in particular as a result of or in order to avoid the effects of armed conﬂict, situations of generalized violence, violations of human rights or natural or human-made disas- ters, and who have not crossed an internationally recognized border ” (United Nations 2004). The categories “ returnees ”, “ Stateless ”, and “ Others of concern ” are also not included. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "protracted nature of refugee hosting in Ethiopia, it is essential to better understand how vocational training programs and cooperatives can become self-sustainable, especially in the more remote border regions with poor market linkages. Box 3.3: Refugee Vocational Training and Cooperatives Jobs and Livelihoods 35 In line with high reliance on remittances, labor force participation and employment rates among OCP refugees are low, with rates similar for male and female refugees. Table 3.2 presents labor force participation (LFP) data; among those aged 15-64, LFP is 46 percent for refugees and 66 percent for hosts. This increases to 67 and 72 percent when you use the relaxed definition of unemployment, reflecting the large number of OCP refugees who say they are ready to work but are not actively searching. The unemployment rate is an astonishing 63 percent for refugees relative to 12 percent for hosts, and it increases to 75 percent under the relaxed definition. Only 17 percent of OCP refugees work, and 23 percent are inactive and not in school. Female refugees have similarly large rates of inactivity and unemployment, and the total share working is similar to male refugees. Female refugees also have high rates of self-employment relative to female", "output": {"entities": {"named_data": [], "descriptive_data": ["labor force participation (LFP) data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "obtained from the Center for Research in Security Prices (CRSP). Stocks with prices less than\n\nof all stocks included in CRSP.\n\nFirm characteristics are obtained from COMPUSTAT. Size is computed as the log of total assets.\n\nof stock returns on the Fama and French (2015) 5 factors.", "output": {"entities": {"named_data": ["Center for Research in Security Prices (CRSP)", "COMPUSTAT"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The LFS is a rotating panel in which households are interviewed for two consecutive quarters, rotated out for two quarters, and then re-interviewed for two further quarters (a 2-2-2 rotation scheme). Because the LFS does not follow individuals across household moves, our analysis is restricted to non-movers — defined as individuals observed in the same dwelling across all four LFS interview waves. This restriction reduces the analytical sample from 18,400 to 14,230 individuals but eliminates the bias that would arise if mobility decisions are correlated with labor market outcomes.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 18: Significant Refugee Returns by Country of Origin 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Countries selected based on their cumulative returns of refugees over the period 1991-2015. Return does not necessarily lead to the full reintegration of a person into their home country or area of origin. In the absence of global data on the success of reintegration following return, data on returns appear to be taken as indication of sustainable return. In reality, many returnees face impediments to reintegration and continue to have specific economic and social vulnerabilities linked to their displacement. They may not be able to reclaim land, access sufficient financial resources (e. g. accumulated during their displacement) or reestablish social networks in areas of origin, which are critical factors for successful reintegration (World Bank 2015). Sustainable refugee return is therefore not a one-off event but a process that provides returnees with adequate safety, housing, livelihoods and services that address their specific vulnerabilities and reduce the likelihood of secondary displacement (World Bank 2015). Figure 19: Voluntary Returns of Refugees 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of refugees and people in refugee- like situations protected or assisted by UNHCR.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 20: Returns of IDPs Protected or Assisted by UNHCR 1993 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of IDPs and people in IDP-like situations assisted and protected by UNHCR. Consequently, the average length of protracted refugee situations has increased over the past two decades according to UNHCR estimates (see Table 2). UNHCR estimates that the average length of ongoing", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 18: Significant Refugee Returns by Country of Origin 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Countries selected based on their cumulative returns of refugees over the period 1991-2015. Return does not necessarily lead to the full reintegration of a person into their home country or area of origin. In the absence of global data on the success of reintegration following return, data on returns appear to be taken as indication of sustainable return. In reality, many returnees face impediments to reintegration and continue to have specific economic and social vulnerabilities linked to their displacement. They may not be able to reclaim land, access sufficient financial resources (e. g. accumulated during their displacement) or reestablish social networks in areas of origin, which are critical factors for successful reintegration (World Bank 2015). Sustainable refugee return is therefore not a one-off event but a process that provides returnees with adequate safety, housing, livelihoods and services that address their specific vulnerabilities and reduce the likelihood of secondary displacement (World Bank 2015). Figure 19: Voluntary Returns of Refugees 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of refugees and people in refugee- like situations protected or assisted by UNHCR. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The effort was inspired by the U.S. Drought Monitor (USDM) and involved USDM experts and experienced officials from Mexico, which had recently implemented a similar drought monitor process.\n\nThe World Bank performed a disaster risk financing diagnostic to gain a more nuanced understanding of the economic impacts of the drought throughout the Eswatini economy.\n\nWorld Development 157, 105932\nAnseeuw, W., Bache, W., Bru, T., Giger, M., Lay, J., Messerli, P., Nolte, K., 2012. Transnational land deals for agriculture in the\nglobal South: Analytical report based on the land matrix database.\n\nby SGC** 2015 1,935 1,530 28,345 0 0.54 2016 2,817 1,916 42,754 0 0.87 2017 3,388 2,139 43,358 0 0.84 2018 5,108 3,519 65,859 0.06 0.85 2019 8,710 5,327 83,269 0.51 0.69 2020 2,431 847 13,408 0 0.50 2021 7,805 5,232 58,442 0.09 0.38 Pre-Prozorro 7,094 4,774 54,425 0 0.41 Prozorro 711 458 4,017 1 0 2022 2,041 1,043 9,964 1 0 Total 34,235 21,553 345,399 0.22 0.61 _Source:_ Own computation from SGC and Prozorro data as described in the text.\n\n0.322 0.255 0.469 0.202 0.174 0.319 0.203 Contract length (years) 8.61 8.33 9.51 8.84 9.04 7.98 7.08 _**Land use**_ Crops 0.48 0.48 0.47 0.65 0.66 0.61 0.69 Pasture 0.35 0.34 0.36 0.29 0.27 0.37 0.38 Forest 0.15 0.15 0.13 0.09 0.09 0.08 0.06 _**Distance in km to**_ Main road 8.65 8.48 9.22 9.31 9.30 9.32 9.38 Nearest city 15.64 15.66 15.58 16.58 16.59 16.57 17.16 Grain elevator 13.39 13.55 12.84 13.56 13.50 13.84 14.43 Kyiv 297.22 295.17 304.04 312.33 309.85 323.26 334.55 _Source:_ Own computation from SGC and Prozorro data as described in the text.", "output": {"entities": {"named_data": ["U.S. Drought Monitor", "land matrix database"], "descriptive_data": ["SGC and Prozorro data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Both treatment and control group respondents report equally high confidence in their ability to return to school “ should [she] decide to do so. ” These findings from the quantitative impact evaluation complement the results from a set of qualitative focus group discussions that were held with participants at the end of the 6 months of classroom training. Twenty-five percent of the trainees from Round 1 participated in a total of 34 focus group discussions that covered a variety of topics including their satisfaction with the program and their empowerment in both social and economic realms. The trainees overwhelmingly voiced a high degree of satisfaction with the training, and trainers commented on how the motivation or “ seriousness ” of the participants grew over the 6 month period. The trainees credited the transport allowance and free childcare in particular as features that facilitated their full participation; as one trainee commented, 22 Questions adapted from the Adolescent Self-Regulation Inventory, developed and validated for youth in the United States by Moilanen, 2006. The questions were revised and translated into an 11-item for the Liberian context. In the future, we plan to conduct basic testing on this scale on internal consistency and reliability. 17", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Our evaluation explicitly investigated these spillovers by including household-level indicators such as food security and attitudes of the household head toward gender norms. Our results show strong impacts on economic outcomes, including large and statistically significant increases in employment and earnings of EPAG participants. We show mixed results on empowerment- related outcomes, and very little evidence of spillovers on non-participants. Self-assessed measures of self-confidence show huge gains, as does ownership and control over monetary resources such as savings. The remainder of the paper is organized as follows: Section 2 describes the EPAG project including some of its innovative design features and implementation details. Section 3 reviews the methodology of the evaluation and Section 4 presents results on the three groups of outcomes discussed above: economic, empowerment, and spillovers. Section 5 includes a short discussion of cost-effectiveness. Section 6 presents a series of robustness checks and Section 7 concludes with a discussion of next steps and policy implications. 2. The EPAG Project The EPAG project is part of a larger Adolescent Girls Initiative (AGI) administered by the World Bank with support from the Nike Foundation and the Governments of Australia, the United Kingdom, Norway, Denmark, and Sweden. Launched in Washington DC in October 2008, the AGI was spearheaded by President Ellen Johnson Sirleaf, who signed on to undertake the initiative ’ s first pilot project in Liberia. The Liberian pilot was launched in March 2010 and has served as a role model to seven subsequent pilot projects in Rwanda, South Sudan, Nepal, Afghanistan, Haiti, Jordan, and Lao PDR. Under the global AGI, young women and adolescent girls are given a package of skills training and complementary services in order to facilitate their successful transition to employment. In the case of EPAG, the intervention consisted of a six month phase of classroom-based training, followed by a six 4 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "To complete this analysis with information on how these losses are distributed in the population and especially on the poorest, a survey was conducted with informal dwellers affected by the 2005 floods. It was found that the aggregated losses they suffered from are about $250 million (for total losses due to the 2005 flood of about $2 billion), but the relative impact on their savings and consumption was extremely large, with average capital losses of the same order of magnitude than their average total savings (in other terms, their savings were totally wiped out by the event).\n\n<!-- PAGE 12 --> _Figure 2: Roofer wages in an area where losses have been significant after the 2004 hurricane season in_ _Florida. Data from the Bureau of Labor Statistics, Occupational Employment Surveys in May 03, Nov 03,_ _May 04, Nov 04, May 05, May 06, May 07._\n\nThe box figure illustrates the effect of insurance penetration [13] on the household's budget, for a July 2005 like flood estimated using the ARIO model. Three scenarios are included: (i) γ=0, equivalent to the absence of insurance system, but with an access to credit; (ii) the current value of flood insurance penetration estimated by RMS (γ=0.08 for households, γ=0.15 for firms); and (iii) γ=1, representing the situation where all the reconstruction is paid for by insurance.\n\n<!-- PAGE 5 --> methodologies and approaches, and they often reach quite different results. In the US, for instance, a systematic analysis by (Downton and Pielke, 2005) showed that loss estimates differ by a factor of 2 or more for half of the floods that cause less than $50 million in damages.\n\nOne of the appealing features of night lights data is their availability on every level. Night lights\ndata are measured on the so-called 30 arc sec level, corresponding to roughly 1 square kilometer\nat the equator.", "output": {"entities": {"named_data": ["Bureau of Labor Statistics, Occupational Employment Surveys"], "descriptive_data": [], "vague_data": ["night lights data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 as the Demographic and Health Surveys (DHS), though researchers have reduced under-reporting by providing special training to interviewers, placing greater emphasis on respondents ’ privacy and safety, and allowing women multiple opportunities to disclose their experiences (Ellsberg et al., 2001a; Garcia Moreno et al., 2003; Ellsberg et al., forthcoming). Estimates of the magnitude of the problem Population-based surveys have found that between 10-70 % of women report being physically assaulted by an intimate male partner at some point in their lives (Heise, Ellsberg and Gottemoeller, 1999). See Annex A for estimates from many recent population based studies (Ellsberg et al., forthcoming). Findings from a multi-country study on domestic violence and women ’ s health carried out by the World Health Organization in fifteen sites and ten countries found that between 13-62 % of women had experienced physical violence by a partner over the course of their lifetime, and between 3-29 % of women reported violence within the past year (Figure 1. 1). Figure 1. 1. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": [], "vague_data": ["Population-based surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "most land cover by built-up and shops, and nearest to Zone capital city, respectively. All estimates are controlled for refugee camps. Standard errors in parentheses. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001 Variables Basic Model Local Market Proximity Market access Model I Model II Model I Model II Annexes 119 Table D.16: Proximity and market accessibility effects on engagement in agriculture activity: logit model Variables Proximity Market access Model I Model II Individual feature Male 0.1979*** 0.2016*** 0.1998*** (0.0289) (0.0286) (0.0297) Age -0.0307*** -0.0294*** -0.0296*** (0.0083) (0.0081) (0.0088) Age squared 0.0004*** 0.0004*** 0.0004** (0.0001) (0.0001) (0.0001) Some primary -0.1403*** -0.1436*** -0.1484*** (0.0305) (0.0298) (0.0307) Household feature HH size: member age [15,29] -0.0157 -0.0171 -0.0186 (0.0118) (0.0113) (0.0117) HH size: member age (30,44] 0.0422* 0.0414* 0.0320+ (0.0179) (0.0178) (0.0185) HH size: member age (45,64] 0.0187 0.0278 0.0314 (0.0221) (0.0226) (0.0237) HH access electricity -0.2980** -0.2655** -0.2658** (0.0929) (0.0884) (0.0974) Proximity and market access Level two 0.2065*** (0.0607) Level three 0.2358** (0.0788) Level four 0.3559*** (0.0769) Distance to Zone city (Km) 0.0022*** (0.0006) Market accessibility indicator -0.0792* (0.0330) Observations 742 737 742 Chi-square test 0.0000 0.0000 0.0000 R2 0.2517 0.2435 0.2128 Source: World Bank Staff based on SESRE 2023. Standard errors in", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Regrettably, until now almost all enrollment numbers cited have been based on establishment surveys which do just that. These data sources show that around 200, 000 children were enrolled full-time in madrassas before 2001. Since 2001, our school census suggests that these numbers may have increased somewhat, although the experience varies across districts. To put this number in context, total primary enrollment (grades 1-5) in public and private schools stood at 17. 4 million in 2003 (Government of Pakistan, Ministry of Finance, 2003). The choice of madrassa schooling viewed as either the percentage of eligible children or the percentage of enrolled children, is statistically insignificant for the average Pakistani household. Enrollment in madrassas accounts for approximately 0. 3 percent of all children between the ages of 5 and 19. Given that the overall enrollment rate for this age group is roughly 42 percent, this represents less than 0. 7 percent of all enrolled children, an order of magnitude less than the 33 percent cited by the International Crisis Group report (2002). 3 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 to provide statistics for private versus public enrollment. 4The PIHS is the equivalent of the widely used Living Standard Measurement Surveys (LSMS) implemented in various countries. See http: / / www. worldbank. org / lsms for extensive notes on the 1991 PIHS. See also www. statpak. gov. pk for information on the census and the Federal Bureau of Statistics data. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["PIHS", "Living Standard Measurement Surveys"], "descriptive_data": ["census of private schools"], "vague_data": ["school census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 19: Trust and Conﬂict in the Cross Section The problem with a lack of trust and strong group identities is that they penetrate and pervert formal institutions. The ethnic politics analyzed by Burgess et al. (2015) is just one example. Shayo and Zussman (2011), for example, use data from Israeli small claims courts to show that Arab and Jewish judges displayed signiﬁcant judicial ingroup bias. Furthermore, this bias is strongly associated with terrorism intensity in the vicinity of the court in the year preceding the ruling. Conﬁdence-building is also a crucial ingredient for the establishment of a fertile investment climate, which in turn is a trigger of economic development post conﬂict. This is the core message of the World Bank Report by Mills and Fan (2006). An important role of increasing trust doubtlessly goes to the media. It has been shown, for example, that hate radio in Rwanda played a critical role in the extent of ethnic violence during the genocide. 59 Other research has shown that media coverage can have strong eﬀects on political preferences more generally. 60 Perhaps the most direct proof of the crucial role played by the media in the post-conﬂict situation comes from DellaVigna et al. (2014). The authors exploit variation in radio reception of na- tionalistic Serbian radio in border regions in Croatia. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from Israeli small claims courts"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "NGOs. Initiatives such as JIPS, a collaborative project of UN and NGO actors, have also been established to support governments and operational organizations to design and implement data collection processes. There are a variety of data sources for generating statistics on forced displacement, each of which has strengths and weaknesses. Despite the significant challenges, large amounts of data are collected and disseminated every year. The main data sources and methods for the generation of statistics on forcibly displaced populations include: (a) registration of refugees and asylum-seekers; (b) registration of IDPs; (c) profiling of IDPs; (d) population movement tracking systems; (e) national population censuses; (f) sample surveys; (g) border crossings; (h) administrative records and registers; (i) general population registers; and (j) a variety of estimation methods for producing statistics when adequate and reliable data on individuals are unavailable (UNSD 2014). Several of these data sources might be used together to triangulate estimates of stocks and flows for a particular displacement situation. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "data are collected from the World Bank's World Development Indicators (WDI). In this context, TFP growth is:\n\nOil Metal **Trade Openness** Percent of GDP 1990 - 94 64 95 65 56 2010 - 14 77 94 93 70 **Herfindahl Index** Product Concentration 1990 - 94 0.41 0.84 0.69 0.29 2010 - 14 0.30 0.67 0.46 0.21 Market Concentration 1990 - 94 0.20 0.30 0.08 0.19 2010 - 14 0.20 0.15 0.28 0.18 **Natural Resources** Percent of GDP 1990 - 94 17.0 46.9 26.8 9.8 2010 - 14 13.2 43.0 16.3 8.9 Percent of total exports 1990 - 94 0.82 0.99 0.95 0.78 2010 - 14 0.73 0.89 0.71 0.72 _Sources: WDI, and author's calculations using WITS, COMTRADE_", "output": {"entities": {"named_data": ["World Bank's World Development Indicators (WDI)", "WDI", "WITS", "COMTRADE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 INTRODUCTION In 2015, an estimated 2. 2 million Syrians Under Temporary Protection (SUTPs) were residing in Turkey, the majority arriving in the country over the last 4 years. 2 Turkey ’ s national population is roughly 75 million; recent refugees account for approximately 3 percent of the population. For a country that has never experienced such a large-scale, sudden inflow of foreigners, demographic changes in the composition of the population and labor force will yield unprecedented implications. This paper examines, as data allows, the relationship between the size of the foreign-born population and host community poverty rates in Turkey. First, this paper finds the poverty rates of ‘ recent migrants ’ near the Syrian border (NSB) significantly increased from 2009 to 2013. Second, the number of foreign-born households being captured by the Labor Force Survey (LFS) is expanding, which suggests a growing number of foreign households that are likely to be Syrians. Third, with respect to poverty, the results show no negative impacts on the host community as a result of the increasing size of the foreign-born population. The impact of SUTPs has been both positive and negative. Overall, a significant negative impact on host communities ’ welfare is not observed in the data. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "There is a clear increase in the entry of new foreign ﬁrms which may be driven by refugees ’ enterpreneurship. Cheaper low-skilled labor may have helped all businesses cutting costs. Balkan and Tumen (2016) had also found a decline in prices and attribute their ﬁnding to lower labor costs, which may also be one of the mechanisms driving our results. Gross proﬁts and sales also appear to have gone up, which would be consistent with an increase in demand. As noted by Maystadt and Verwimp (2014), heterogeneous eﬀects on speciﬁc subgroups of the native population should be expected from refugee crises. In case of the Syrian refugee crisis in Turkey, the business activity in hosting region appears to have beneﬁted. For a complete picture of the eﬀects of the Syrian refugee crisis on local economies in Turkey, further research will be needed on market activity, health and longer term eﬀects. More speciﬁcally for the line of research this study focused on, further analysis using micro-level ﬁrm data would be needed to understand how ﬁrms adjust their activity, 24 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "and Fonden threshold information for 1,745. As shown in table 1, using 2000 census data, we\n\nvarious administrative records that allow us to observe at the municipal level: the number of\n\n**Keywords:** Disaster Risk Financing and Insurance; Financial Institutions; Climate Change and Disaster Risk; Shocks and Vulnerability to Poverty   _∗_ We would like to thank Artemio Couti˜no of SAGARPA who provided administrative CADENA data, as well as information about program rules.\n\nTo determine the effect of   insurance payments on yields and area sowed, we use agricultural production data from SAGARPA   detailing the annual hectares sowed, hectares harvested, and total production in metric tons at the   municipality-crop level.\n\nNote. Standard errors are clustered at the state level. Asterisks indicate statistical significance: _∗_ _p_ _<_ 0 _._ 10, _∗∗_\n_p_ _<_ 0 _._ 05, _[∗∗∗]_ _p_ _<_ 0 _._ 01. Observations are at the municipality-year-level. Hectares sowed are defined as the total\nhectares growing any rainfed agricultural crop as reported in SAGARPA production data.", "output": {"entities": {"named_data": ["administrative CADENA data", "SAGARPA production data"], "descriptive_data": ["2000 census data", "agricultural production data from SAGARPA"], "vague_data": ["administrative records"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "29 evaluations have been conducted (Renton et al., 2000; Shaw, 2000; Shaw, 2002a; Shaw, 2002b; Paine et al., 2002; White, Greene and Murphy, 2003; Interagency working Group, 2003). For example, the Medical Research Council (from the United Kingdom) conducted an external evaluation of program effectiveness in the Gambia using key informant interviews, focus groups, and a KAP (knowledge, attitudes and practices) survey in four villages, including two program sites and two controls. That study found that the Gambia program improved self-reported attitudes and behaviors related to violence against women. Specifically, the program reduced the social acceptability of wife-beating at the community level and appeared to produce a corresponding drop in that behavior. Qualitative findings from other Stepping Stones sites suggest similar benefits. Program H (Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru) is being carried out by four NGOs. It aims to change gender norms and sexual behaviors in Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru (Barker, 2003; White, Green and Murphy, 2003; Guedes, 2004). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "9 Bank Account No member has a bank or mobile money account. 1 / 12 Many of the indicators align with goals identified in the 2030 Agenda for Sustainable Development, such as no hunger, good health, access to quality education, clean water and sanitation, and decent work, as well as indicators that are especially relevant for displaced people, such as possession of legal identification, physical safety, and food security. The focus on gendered dynamics justifies health indicators related to pregnancy care, combining information on prenatal care, assisted delivery, and early marriage. A full discussion of the MPI ’ s indicator selection can be found in Admasu et al. (2021). We focus on six of these 15 indicators that use individual-level data – viz years of schooling, school attendance, pregnancy care, early marriage, legal identification, and unemployment. Our intrahousehold analysis drops the two health indicators due to data limitations; the question about age at marriage was only asked to the household head in Ethiopia, Nigeria, and South Sudan, whereas in Somalia and Sudan, it was applied to more members than the head. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "All regression models are estimated using ordinary least squares with standard errors clustered at the DHS cluster level. Clustering at the DHS cluster level is appropriate because households within the same DHS cluster are subject to common shocks — including local health facility quality, crop yield variation, and community-level program exposure — that generate within-cluster correlation in error terms. Alternative specifications clustering at the administrative district level are presented in Appendix Table A4; point estimates are unchanged and standard errors are slightly wider.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This database lists seven categories: refugees, asylum-seekers, returned refugees, internally displaced persons (IDPs), returned IDPs, stateless persons and others of concern. For each group the database provides yearly information about their composition by loca- tion of residence and origin. We exploit only the data on “ refugees ”. 22 In particular, we are interested in the annual stock of refugees for each country of residence, i. e. how many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number of IDPs is extremely high in some instances but cannot be captured with the same level of conﬁdence as refugees generally. 23 Cross-country data about conﬂict is provided by the UCDP / PRIO. As for the index of country-level economic activity, we use again information provided by the Penn World Table and World Bank databases. As mentioned above, our aim is to explore the dynamics of refugees during conﬂicts. In other words, we attempt to answer several questions.", "output": {"entities": {"named_data": ["Penn World Table", "World Bank databases"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "required to purchase other basic staple foods such as salt and vegetables. Given that the WFP provisions are the only reliable rations that refugees receive, we approximate a cash transfer of 450 taka per week to at least double potential weekly consumption. Relative to the wealth refugees possess, 450 taka per week is likewise sizeable: average baseline savings is 195 taka, with the median refugee reporting zero taka in savings. Average baseline borrowing (typically in the form of store credit) is 1, 600 taka, with a median of 600 taka. Refugees have no economically meaningful assets that may be more common among the rural poor, such as land or cattle, given the unanticipated displacement which forced them from their homes. Relative to other employment opportunities, average reported pay is 300 taka per day for less than three days. The monthly cash transfer is therefore more than double what a refugee might expect from alternative employment if he or she is fortunate enough to secure a job. 4 Data Collection and Survey Instruments Timeline and survey instruments We collected data via a baseline, commencing in November 2019, and endline survey, commencing in February 2020, as well as seven midline surveys conducted prior to payment disbursal each week. These weekly surveys collected a small subset of well-being outcomes.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5 does not have migration or geographic identifiers. The SILC contains geographic identifying variables, (at the NUTS1 level) but still lacks migration variables. The data set this study uses is the Labor Force Survey (LFS) since there is an adequate availability of both migration and geographic variables. The LFS is representative at the NUTS2 level which corresponds to 26 regions in Turkey. One caveat is that income in the LFS refers to only wage income from employment, 9 and is an insufficient measure of income that should be used for welfare measurement. For example, important sources of income such as social assistance, asset liquidation, or remittances are missing. Therefore, income in the LFS is imputed with a few assumptions using information from the SILC. The NUTS1 spatial effects of the SILC are a good proxy for NUTS2 welfare dynamics in the LFS which increases the accuracy of the imputation model. However, since the original sample frame of the LFS does not account for the recent influx of foreign migrants in Turkey, the labor market characteristics of recent migrants might not be representative of the actual SUTP population. Therefore, results of the imputation could be interpreted as upper bound estimates for recent migrants. More details of survey techniques used to complete this exercise are available in the Annex.", "output": {"entities": {"named_data": ["SILC", "Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 6832 This paper presents findings from the impact evaluation of the Economic Empowerment of Adolescent Girls and Young Women (EPAG) project in Liberia. The EPAG project was launched by the Liberian Ministry of Gender and Development in 2009 with the goal of increasing the employment and income of 2, 500 young Liberian women by providing livelihood and life skills training and facilitating their transition to productive work. The analysis in this paper is based on data collected during two rounds of quantitative surveys in 2010 and 2011, the second of which was conducted six months after the classroom-based phase of the training program ended. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "more than five consecutive years—increased over time, accounting for 40 percent of all refugees in 2021 (World Bank, 2023). In Ethiopia, 95 percent of all refugees live in a protracted situation, according to SESRE data. 11 According to UNHCR mid-2022 statistics, South Sundanese, Somali and Eritrean refugees constitute 46, 29 and 18 percent of the total refugee populations in Ethiopia. (https://www.unhcr.org/refugee-statistics/download/?url=2bxU2f) 12 https://www.unhcr.org/refugee-statistics/insights/explainers/children-born-into-refugee-life.html Sociodemographic Profile 10 53% 30% 5% 12% South Sudanese Somali Eritrean Addis Refugees Figure 2.1: Refugees by survey domain Source: World Bank Staff based on SESRE 2023. 68% 62% 63% 88% 31% 38% 32% 11% 0 10 20 30 40 50 60 70 80 90 100 Percent Eritrean Somali South Sudanese Addis Refugees Country of origin Ethiopia Other Figure 2.2: Country of birth Source: World Bank Staff based on SESRE 2023. Refugees fled from their country of birth mainly due to conflict and violence. Almost all South Sudanese refugees, or 99 percent, left their country because of conflict and violence, as did 91 percent of Somalis, and 73 percent of Eritrean refugees. On the other hand, OCP13 refugees came to Ethiopia with the hope of going to a Western country (35 percent), to escape from conflict and", "output": {"entities": {"named_data": ["SESRE data", "UNHCR mid-2022 statistics"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 Last, specific adjustments are made in the case of Germany and the Republic of Korea. For Germany, bilateral data are available only by nationality. However, these data fail to take adequate account of the large number of ethnic Germans who arrived from other countries between 1944 and 1950 (mainly expellees) and those who arrived after1950 (mainly resettlers). Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3). In the case of Korea, data by nationality are readily available for each census round. However, these data fail to account for the large numbers of migrants from the People ‘ s Democratic Republic of Korea living in the Republic of Korea. Since the United Nations Trends in International Migrant Stock details the total migrant stock in the Republic of Korea by the country of birth definition and because citizenship is rarely granted to people from outside, it is simply assumed that the nationality data were comparable to the foreign-born definition. The nationality total was then subtracted from the UN total and the remaining migrants were assigned to the People ‘ s Democratic Republic of Korea. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["United Nations Trends in International Migrant Stock", "German 2005 micro-census"], "descriptive_data": [], "vague_data": ["nationality data", "bilateral data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "percent for refugees and 57 percent for hosts if you include all available to work regardless of whether they are searching (relaxed unemployment). The remaining 57 percent of refugees are inactive, and just over half are currently in school, leaving 23 percent of refugees neither working nor studying, compared to 20 percent of hosts. In-camp refugees have high unemployment rates relative to hosts. Figure 3.2 shows that only 25 percent of in-camp refugees performed paid work in the week before the survey, compared to 48 percent for hosts. At the household level, only 54 percent of refugee households have any workers, relative to 86 percent for hosts. The strict unemployment rate is 21 percent for refugees and 7 percent for hosts, while the relaxed unemployment rate is significantly higher at 43 percent for refugees and 15 percent for hosts. Across camp domains, Eritreans have the highest rate of relaxed unemployment at 55 percent, while it is 45 percent and 40 percent for Somalis and South Sudanese, respectively. South Sudanese have the highest rates of inactive workers remaining in school, reflecting that they have a younger population and that more young adults stay in school (mainly primary) after age 15.40 During", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "project had to meet a basic minimum literacy level in order to qualify for admission to the program; whereas only half of the 15-29-year-old female respondents to the nationally-representative Core Welfare Indicators Questionnaire (CWIQ) survey report that they can read and write (LISGIS 2007). Fewer than two percent of the girls in the EPAG study responded that they had no education, which is much lower than in the CWIQ survey. Second, the majority of adolescent girls and young women in Liberia reside in rural areas, whereas the survey participants were residing in urban and peri-urban areas, where access to basic social services may be much more improved. Consequently, the results are not representative of adolescent girls and young women in Liberia overall. The results are neither indicative of the average Liberian girl and young woman; nor are they indicative of the average Liberian girl or young woman in the project communities. They are only indicative of the average girl and young woman who are part of the EPAG project. Finally, many of the variables that we examine in this study are measures of self-assessed levels of satisfaction or belief. These are entirely subjective variables, and are subject to significant measurement error. There is considerable evidence that the wording of these questions can affect the answers given, as can the order in which the questions are asked.", "output": {"entities": {"named_data": ["Core Welfare Indicators Questionnaire", "CWIQ survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Table 1 provides descriptive statistics for the years 2011 and 2014. 18 Labor force participation is very low in Turkey, around 54 percent of the working-age population in 2011, though it has been rising. The reason is that female labor force participation is particularly low at about one-third. The majority of employment is private sector, around one-third of the working-age population, compared to 6 percent employed in the public sector. There are a large number of unpaid workers (7 percent) and unemployment is at 5 percent of the working-age population, an unemployment rate of about 10 percent. School attendance has been rising over the period, from 12 to 16 percent of the working-age population, and the fraction retired has been steady at about 5 percent. Correspondingly, educational attainment has been rising though still 13 percent of the working-age population has no formal education, 57 percent at least completed primary education but not high school, and high school completion has risen from 30 to 34 percent. 17 Of those who have an irregular workplace 60 percent are agricultural workers, 14 percent work in construction, 7 percent in transportation and 5 percent in retail and in manufacturing each, and 3 percent as household employees. 18 Note that in 2014 new regulations for the Household LFS were carried out within the framework of European Union criteria. Consequently, statistics are not necessarily entirely comparable across years. Since we do not use aggregate time-series variation for identification this does not affect our empirical strategy, see Section 3. For those interested, the Turkish Statistical Institute provides consistent time-series on their website.", "output": {"entities": {"named_data": ["Household LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "in Bangladesh). The data set we use does not cover the 5. 1 million Palestinian refugees who are under the man- date of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UN- RWA). The deﬁnition of Palestinian refugees in international law is distinct from other refugees. Palestinian refugees are people “ whose normal place of residence was Mandatory Palestine be- tween June 1946 and May 1948, who lost both their homes and means of livelihood as a result of the 1948 Arab-Israeli conﬂict ”. Importantly, their patrilineal descendants are also considered refugees regardless of citizenship (UNRWA 2009). For the purpose of the analysis, we do not include Internally Displaced Persons (IDPs), who are deﬁned as “ persons who have been forced or obliged to ﬂee or leave their home or place of habitual residence, in particular as a result of or in order to avoid the effects of armed conﬂict, situations of generalized violence, violations of human rights or natural or human-made disas- ters, and who have not crossed an internationally recognized border ” (United Nations 2004). The categories “ returnees ”, “ Stateless ”, and “ Others of concern ” are also not included.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 household displacement and the destruction of household dwellings during the violent events. We are also able to measure conflict intensity across time – including peaks of violence at various stages of the conflict – at the district level from event data on violence intensity during the conflict in Timor Leste. We focus on primary school effects because only a small percentage of the Timorese population attended secondary school. Our results show mixed evidence for the impact of violent conflict on educational outcomes. Mirroring some of the findings of Bellows and Miguel (2006) and others, we find evidence for a rapid recovery of the education sector in Timor Leste, and of educational outcomes, particularly for girls. However, in line with emerging results in the micro-level literature, we find that the 1999 wave of violence in Timor Leste – as well as peaks of violence in the 1970s and 1980s – resulted in negative effects on primary school attendance and attainment. This effect is particularly strong for boys. We attribute the first result to a process of educational catch-up among girls in Timor Leste that started before the conflict and continued despite the conflict. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["event data on violence intensity during the conflict"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 19: Trust and Conﬂict in the Cross Section The problem with a lack of trust and strong group identities is that they penetrate and pervert formal institutions. The ethnic politics analyzed by Burgess et al. (2015) is just one example. Shayo and Zussman (2011), for example, use data from Israeli small claims courts to show that Arab and Jewish judges displayed signiﬁcant judicial ingroup bias. Furthermore, this bias is strongly associated with terrorism intensity in the vicinity of the court in the year preceding the ruling. Conﬁdence-building is also a crucial ingredient for the establishment of a fertile investment climate, which in turn is a trigger of economic development post conﬂict. This is the core message of the World Bank Report by Mills and Fan (2006). An important role of increasing trust doubtlessly goes to the media. It has been shown, for example, that hate radio in Rwanda played a critical role in the extent of ethnic violence during the genocide. 59 Other research has shown that media coverage can have strong eﬀects on political preferences more generally. 60 Perhaps the most direct proof of the crucial role played by the media in the post-conﬂict situation comes from DellaVigna et al. (2014). The authors exploit variation in radio reception of na- tionalistic Serbian radio in border regions in Croatia. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from Israeli small claims courts"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Columns 2, 3, and 4 show that age, school attendance, and employment status at baseline are all correlated with survey attrition. To further investigate whether these characteristics lead to differential attrition between the treatment and control groups, we interact treatment with particular characteristics at baseline. Columns 5 and 6 show that, conditional on being in the control group, the most likely predictor of attrition is having a child. By itself, being a mother does not predict attrition, but when interacted with treatment, we see that control group mothers are more likely to have dropped out of the panel than their treated counterparts. Employment at baseline, while positively correlated with attrition, does not differentially affect treated and untreated individuals. Because the differential attrition between treatment and control groups may bias our results, we use Inverse Probability Weighting (IPW) as outlined in Wooldridge (2002) to adjust the estimates of our key outcomes, using the inverse probability of inclusion in the panel as a probability weight. As a first step, we use the probit model in Column (6) of Table 10 to regress the likelihood of being observed twice on baseline individual characteristics, including those likely to affect attrition, such as employment and parental status. In the second step, we use the inverse of the predicted values from that probit model as probability weights to redo the difference-in-difference regressions for our key outcomes of interest. This method gives more weight to the individuals with the highest chance of attrition, giving them more influence on the estimate of the impact than those with a low probability of attrition. The results are reported in Table 11. The results show a high degree of similarity between the original (unadjusted) and the adjusted estimates. Across all outcomes, the point estimates and standard errors vary only slightly. 27 Of these 305 cases, nine are dropped from the attrition analysis because the household head was not interviewed at baseline, hence the household level control variables are not available. 23", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "7 Gibson and Kim (2012) use a HCES with direct measures of consumption from food stocks and find an error of up to 300 KCal per person per day from ignoring destocking of one major calorie-source (rice) that is subject to bulk buying and storage.\n\nthese reports when made by people who never transact in metric units. A typical HCES consumption\n\nOnce the food reported in an HCES is converted into calories, the household's calorie intake is compared\n\nWhile the potential sources of mismeasurement in HCES are numerous, this study systematically\n\nusing a survey experiment conducted in Tanzania. There were a total of eight alternate designs, which", "output": {"entities": {"named_data": ["HCES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "9 this oversubscribed list. Selection into the training group was based on a “ vulnerability score ” that gave priority to younger, female and unemployed individuals. Despite this approach, intake was “ fuzzy ”- participants were ordered by their vulnerability score, with the most vulnerable entering up until capacity. In some intakes, individuals with comparatively high scores were not taken into the program. In others, individuals with comparatively low scores were included. We construct our treatment and control groups from these intake decisions. Data were collected from members of the host and refugee communities in each country. 4 In both Jordan and Lebanon, the intervention was implemented on a rolling basis. As soon as one training cycle was completed, another would begin. Data were collected in three waves during each training cycle. First, during an “ outreach ” phase, where data were collected in order to assign treatment status. Second, at “ baseline ”, which occurred before the training had begun but after treatment assignment was known. Third, data were collected at “ endline ”, immediately following the end of the training. Data collection for those assigned to the treatment and control followed the same pattern. 5 Outreach and baseline data collection took place less than a week apart and were collected between July 2018 and September 2019.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This rate decreases as we move away from mining areas (figure 12a and 12b). Not only are the rates higher in the first two types of communes mentioned, but the rates in those communes also increased faster from 1998 to 2009, the two years being the years of the last two General Population and Housing Censuses in Mali.\n\n|Figure 12a Net primary enrollment (%)|Figure 12b Net primary enrollment (%) mining<br>communes|\n|---|---|\n|<br>|<br>|\n|_Source:_RGPH (General Population and Housing Census) 1998 and 2009.|_Source:_RGPH (General Population and Housing Census) 1998 and 2009.|\n\n5 We could not use the 1987 census data because communes were not created at that time.", "output": {"entities": {"named_data": ["General Population and Housing Censuses", "General Population and Housing Census", "RGPH (General Population and Housing Census)"], "descriptive_data": ["1987 census data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Population Data Analysis – September 2022\n\nOverview\n\nAs of the end of September 2022, Southern Africa hosts around **8.6 million persons of concern (PoCs) to UNHCR** .\nThis includes 1.1 million refugees and asylum-seekers and 6.9 million internally displaced persons (IDPs), as well as\nothers of concern, refugee returnees and IDP returnees. **The Democratic Republic of Congo (DRC) represents 77**\n\n**per cent of the regional data.**\n\nRefugees, Asylum-Seekers and Others of concern\n\nThe region hosts **785,000 refugees, 278,000**\n\n**asylum-seekers** **and** **36,000** **others** **of**\n\n**concern** . Among those 1.1 million PoCs, 74 per\ncent of them are from the countries outside of\nthe Southern Africa region. [1] The top five\ncountries of origin are Central African Republic\n(243,000), Rwanda (242,000), DRC (228,000),\nBurundi (84,000) and Ethiopia (61,000).\n\nInternally Displaced Persons\n(IDPs)\n\nIn Southern Africa, there are **6.9 million**\n\n**internally displaced persons (IDPs)** . Most of\nthem are conflict-induced, 6.4 million, but there\nare also natural disaster-induced IDPs, 0.5\nmillion. The data on IDPs are reported in DRC,\nCongo, Mozambique and Zimbabwe (see\nFigure 1).\n\nDurable Solutions\n\nFigure 1. Number of IDPs in RBSA by Cause as of 30 September 2022\n\n---\n[1] The Southern Africa region refers to the 16 countries covered by the Regional Bureau for Southern Africa of UNHCR including Angola,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**6.2 Baseline and Endline Survey Design**\n\nThe 2024 survey data constitute the project's baseline. They were collected using computer-assisted personal interviewing (CAPI) on Android tablets, with GPS coordinates recorded for each interview to enable geographic analysis. The 2024 survey data cover consumption expenditure, agricultural production, access to markets, and child schooling outcomes, structured to mirror the indicators in the results framework. A pre-analysis plan was registered with the AEA RCT Registry prior to data collection to protect against specification searching in the impact evaluation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We use the sum of conflict events occurring in the historic homeland of ethnic group e in the previous year t − 1, denoted as Conflictet − 1, and we use the mean distance between the historic homeland of ethnic group e and the border of country d to predict the number of refugees of a certain ethnic group e moving from country o to d at time t. 22 In order to be consistent with EPR-ER data construction, we restrict our analysis to all origin – destination country pairs that are at a maximum distance ≤ 950 km from each other. Predicted numbers of refugees are then transformed into predicted shares for the three largest groups to follow the logic used by the EPR-ER dataset. We then plug in these predicted shares in the following way: X \\PredictedRefcet = Refocdt ∗ \\Shareodet. (6) The predicted shares of refugees per camp c are then used to compute (as documented above) refugee diversity indices to be used as instrumental variables. The first-stage equations corresponding to the 2SLS-equivalent of Equation 1 can be expressed as 20We conduct a robustness check on Equation 5, replacing the dyadic origin – destination fixed effects with separate origin and destination fixed effects (Section 5. 4). 21More information on LEDA can be found in Appendix A. 1. 22The construction of the IV follows a long tradition in using the gravity model to predict bilateral migration flows (Ravenstein, 1985, 1989; Crozet, 2004; Mayda, 2010; Garcia et al., 2015; Beine et al., 2016). In our analysis, a major difference is that we have an additional dimension: the ethnic group e. 20", "output": {"entities": {"named_data": ["EPR-ER data", "EPR-ER dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5 (UNHCR, 2014), developing countries hosted 10. 1 million refugees or 86 percent of the world ’ s refugees. Contrary to what has been sometimes claimed in popular media, refugees are not “ invading ” the higher ‐ income countries. Actually, SSA has been hosting more refugees than sending them since 1990. The divergence of trends occurring in 2005 is certainly related to large inflows of refugees from North Africa and the Middle East. The second peak in 2011 corresponds to the uprisings that spread across several Arab countries (Egypt, Libya, Syria, Tunisia and Yemen), and the recent one in 2013 to the large outflows of refugees from Iraq, Syria and Yemen. Figure 2. Refugees and Internally Displaced People in SSA, 1990 ‐ 2013 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). Due to changes in classification and estimation methodology in a number of countries, 2007 figures are not fully comparable with pre ‐ 2007 figures (see also footnote 1). Gathering data on internally displaced people is much more challenging since most existing data on IDPs are incomplete or unreliable. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "refugees’ social integration (ReDSS, 2018). Box 7.1: Socio-political tensions in the Gambella Region Figure 7.3: Host response to “Refugees are good people” by gender Source: World Bank Staff based on SESRE 2023. Figure 7.4: Host response to “Would you feel comfortable having a refugee as a neighbor?” by gender Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Male Female Male Female Male Female Male Female Eritrean Somali South Sudanese Addis Ababa Strongly agree Agree Disagree Strongly disagree Percent Markets and Opportunities 66 Most hosts do not think they have experienced adverse effects from refugees. However, a sizeable minority are concerned about the effects on employment, inflation, security, and deforestation in their communities, with significant differences across domains. The most consistent perceived effects are economic competition and price increases. Across domains, 29 to 39 percent of hosts think they have experienced either wage or employment competition due to refugees.50 Beliefs that refugees have increased prices are especially prevalent in the Eritrea and Addis Ababa domains (70 and 81 percent, respectively), possibly reflecting concerns over housing costs in Addis Ababa. Other studies have shed light on this phenomenon in more detail.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5 narrative around the regressions and aims to explain why not more people migrate when benefits of doing so are so high. 2. The Setting: Tanzania and Kagera, 1994-2004 In the last decade, Tanzania has experienced a period of relatively rapid growth, attributed to liberalization, a renewed trade orientation, a stable political context, and a relatively positive business climate to boost economic performance. Real GDP growth was of the order of 4. 2 % per year between 1994 and 2004, while annual population growth was around 3. 2 % in the same period (URT, 2004). There is also evidence that growth had accelerated in the last few years compared to the 1990s. However, this growth has not been sufficiently broad-based to result in rapid poverty reduction. On the basis of the available evidence, poverty rates have declined only slightly and most of the poverty reduction progress has been made in urban areas. According to the Household Budget Survey (HBS), between 1991 and 2000 / 01, poverty declined from 39 percent to 36 percent in mainland Tanzania. The decline in poverty was steep in Dar es Salaam (from 28 % to 18 %) but minimal in rural Tanzania (from 41 % to 39 %). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Household Budget Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Those who had moved out of the Kagera Region by 2004 experienced consumption growth that was 10 times higher compared to those who remained in their original community. These averages translate into very different poverty dynamics patterns for the physically mobile and immobile. For those who stayed in the community, poverty rates drop by about 4 percentage points over these 13 years. For those who moved elsewhere within the region, poverty rates drop by about 12 percentage points, and for those who moved out of the region, they drop by 23 percentage points. Had we not tracked and interviewed people who moved out of the community – a practice found in many panel surveys – we would have seriously underestimated the extent to which poverty has gone down over the past 13 years in the Kagera Region; we would have reported poverty reduction at about half of its true value. Clemens and Pritchett (2007) raise similar concerns in the context of income growth and international migration. In addition, the data would omit the part of the population with a high information content on pathways out of poverty. Still, these statistics are not evidence that moving out of the community leads to higher income growth. As noted above, we cannot observe the counterfactual: What would income growth have been for migrants had they not migrated? We exploit some unique features of these data to address concerns about unobserved heterogeneity. First, individual fixed effects regressions for movers and stayers produce a difference-in-difference estimation of the impact of physical movement, controlling for any fixed individual factors that affect consumption. Second, we can control for initial household fixed effects in the growth rate of consumption since we observe baseline households in which some individuals migrate Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "13 Proposition 7 State-Level Effects of Population Size: The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects. 3 Research Design 3. 1 Unit of Analysis To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8. 6 km x 8. 6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope'and `location'variables in the Uppsala / PRIO dataset. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["ACLED dataset", "Uppsala / PRIO dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "ministries to achieve better outcomes for refugees and their hosts. ◆ Improve the coverage, accuracy, reliability, quality, and comparability of data to provide the analytical underpinning for policy decisions. Executive Summary 1 C onflict, political unrest, environmental disruption, and economic instability has forcibly displaced millions of people globally (Ferris, 2010; Black, 2001). Over the last decade, the number of forcibly displaced persons has continuously increased. In mid-2023, there were 36.4 million refugees worldwide (UNHCR, 2023d). As development reduces global poverty, extreme poverty is increasingly concentrated among vulnerable groups; refugees are among these vulnerable groups (World Bank, 2017). Therefore, the plight of the forcibly displaced poses significant challenges to broad development efforts to eradicate extreme poverty and achieve the Sustainable Development Goals (SDGs). Ethiopia has a long history of hosting refugees and has one of the largest refugee populations in Africa. The refugee situation in Ethiopia is characterized by both complex humanitarian emergencies and protracted refugee status. Forced displacement is a pressing issue in the country, a result of conflict, drought, flood, economic instability, and political instability in neighboring countries (Martin, 2010; UNHCR, 2020d; IPCC, 2019). As of year-end 2023, more than 922,000 refugees and asylum seekers 1. Introduction 0 200,000", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Maternal depression was measured by the Self-Reporting Questionnaire (SRQ-20), which has previously been used in Pakistan and has been found to be valid and reliable for screening for depressive disorders in rural Pakistan (Husain et al., 2006). The SRQ-20 contains 20 yes / no questions, with higher scores indicating higher levels of depression, and was found to have a high internal consistency reliability, with a Cronbach ’ s alpha of. 89 in this sample. Anxiety. Maternal anxiety was assessed using the General Anxiety Disorder 7-item questionnaire (GAD-7). This 7-item questionnaire has been found to be valid in the Gilgit province of Pakistan (Ahmad et al., 2017) and has subsequently been used in other assessments of anxiety in Pakistan (Yasmin et al., 2021). The GAD-7 was found to have a high internal consistency reliability, with a Cronbach ’ s alpha of. 80 in this sample. Parenting Stress. Parenting stress was measured using an adapted version of the Parental Stress Scale (PSS), which has previously been validated in Urdu in Punjab (Bilal et al., 2021). The adapted version used in this survey was designed by Ugarte et al., 2024 for Rohingya refugees in Bangladesh. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "-0.000* -0.001*** -0.001*** -0.000 0.000 -0.000 0.001 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) Educ: < Primary - - - - - - - - Educ: Primary -0.028 0.058*** 0.127 -0.042* 0.077** -0.104 -0.172 0.121 (0.020) (0.021) (0.107) (0.022) (0.032) (0.069) (0.122) (0.361) Educ: Secondary 0.115*** 0.506*** 0.700*** -0.045 0.460*** -0.200** 0.098 0.849*** (0.031) (0.046) (0.100) (0.045) (0.107) (0.080) (0.212) (0.311) Educ: Post-sec 0.231*** 0.716*** 0.812*** 0.004 0.493*** -0.073 0.222 0.528** (0.024) (0.037) (0.078) (0.117) (0.121) (0.149) (0.367) (0.232) Years in Ethiopia 0.009*** 0.001 0.001 0.003 0.006 (0.003) (0.002) (0.004) (0.012) (0.015) Work outside camp 0.352*** (0.128) Region fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Restrict to workers outside camp Yes N 3321 1626 494 3,069 830 975 238 73 Source: World Bank Staff based on SESRE 2023. Note: Columns 2 and 5-6 are restricted to working respondents. Columns 3 and 7-8 are restricted to employees. High-skill occupations include managers, professionals, and associate professionals. Ln Earnings is the log of monthly earnings winsorized at the 1st and 99th percentiles. All other models are linear probability models. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "global-trends-report-2021.html. UNHCR. (2022c). Ethiopia Country Refugee Response Plan (ECRRP) Jan Dec 2022. https://data.unhcr.org/en/documents/ details/94099 UNHCR. (2022d). Serdo Refugee Camp Profile April 2022. https://data.unhcr.org/fr/documents/details/92436 UNHCR. (2022e). “Global Report 2021.” http://reporting.unhcr.org/globalreport2021/pdf. 81 UNHCR. (2023). Ethiopia Global Refugee Forum Pledge Progress Report. UNHCR. (2023a). Addis Ababa Quarterly Urban Factsheet, April 2023. UNHCR. (2023b). UNHCR Resettlement Handbook. UNHCR. (2023c). Culture, Context and Mental Health and Psychological Well-being of Refugees and Internally Displaced Persons from South Sudan. UNHCR. (2023d). Mid-year Trends. Geneva. https://www.unhcr.org/mid-year-trends-report-2023 UNHCR. (2023e). Ethiopia Country Refugee Response Plan 2023. https://reporting.unhcr.org/ethiopia-country-refugee- response-plan-summary UNHCR. (2024). Global Refugee Forum 2023 Pledges. https://globalcompactrefugees.org/pledges-contributions UNHCR. (2024a). Press Release (Ethiopia Launches Inclusive ID System for Refugees, Boosts Access to National Services). https://www.unhcr.org/africa/news/press-releases/ethiopia-launches-inclusive-id-system-refugees-boosts-access- national-services UNHCR, and World Bank Group. (2020). Understanding the Socioeconomic Conditions of Refugees in Kenya Volume A: Kalobeyei Settlement. Results from the 2018 Kalobeyei Socioeconomic Survey. Washington, D.C. UNICEF. (2018). Situation and Access to Services of Persons with Disabilities in Addis Ababa. Briefing Note. UNICEF. (2019). Birth Registration for Every Child by 2030: Are WE on Track? New York. UNICEF. (2021). Education in South Sudan. Briefing Note. Vemuru, V., Sarkar, A., and Woodhouse, A.F. (2020). Impact of Refugees on Hosting Communities in Ethiopia: A Social Analysis. World", "output": {"entities": {"named_data": ["2018 Kalobeyei Socioeconomic Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "To further control for geological and agro-ecological characteristics at the community level, elevation figures were included, taken from the GLOBE 1 kilometer elevation database (GLOBE Task Team et al. 1999). Elevation differences were also incorporated as a measure of terrain roughness. These were computed using the SRTM3 data (Jarvis et al. 2006) by subtracting the minimum elevation in a 1 kilometer grid from the maximum elevation in the 1 kilometer grid. Finally, urban/rural and regional indicator variables help capture unobserved (time invariant) location specific characteristics related to the urban and rural livelihood systems as well as those related to regional variation in the agro-ecological, economic and political environment. Year dummies were included to control for the survey year.\n\nGiven the focus here on riverine and coastal floods and to better pinpoint the locations that actually experienced riverine or coastal floods, the DFO flood maps were overlaid with the location of the rivers taken from the Hydroshed project (Lehner et al., 2008). Only the major rivers were mapped as these will have sufficiently large catchment areas exposing the downstream areas they traverse to (non-localized) flooding. Riverine and coastal flood bands were then derived by considering DFO flooded areas within 2, 5 and 8 meters elevation difference from the closest point on the major river or the coast using a Digital Elevation Model from GLOBE (GloBe Task team, 1999).\n\nTo reflect access to public services, indicator variables are included that take the value of one if clean water [32], sanitary latrines, [33] or electricity are present in the community, and zero otherwise. To proxy a household's integration in the overall economy, the community data were further augmented with the distance of the centroid of the community (in meters) to the nearest part of the nearest primary or secondary road, as designated in the VMAP0 dataset (NIMA 1997). These measures were computed that using Arc View 3.2. In addition, the estimated travel time to the nearest town or city of at least 25,000 people, the nearest city of at least 100,000 people, and the nearest city of at least 500,000 people were included. The travel times were computed in Arc View from a friction grid that assigned differing travel times to various classes of roads, urban areas, water bodies, off-road, and crossing international boundaries. [34]\n\nThe expenditure data are expressed in January 2002 prices using the CPI provided by the Government Statistical Office. They are also corrected each year for regional and rural/urban differences across the 8 regions.", "output": {"entities": {"named_data": ["GLOBE 1 kilometer elevation database", "SRTM3", "Hydroshed project", "VMAP0"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "9 Visits (JD-HV). ProGres records demographic information of each individual refugee and their household at the time of registration with UNHCR including the relationship of each household member with the principal applicant. In this paper, we refer to the self-identified principal applicant as the household head – the term used in the poverty research literature. Self-identification is commonly used as a means of identifying the household head in household surveys of income, consumption and expenditure surveys which are used to estimate poverty rates (see Hanmer et al, (2020)). Home visits began at the same time as the UNHCR cash assistance program was launched. The program aimed to assist vulnerable Syrian households to meet their basic needs. During the home visits, UNHCR determined whether a household met the criteria to qualify for assistance, considering economic poverty and the presence of members in the household requiring protection. Since 2013, these visits have been made to every registered Syrian refugee household in Jordan outside the camps (UNHCR, 2014). At registration, each household is assigned a unique registration number that serves to cross- reference the initial registration data with Home Visits data. Home Visits data are collected for about one-third of the registered households. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Religious School Enrollment in Pakistan A Look at the Data Tahir Andrabi1 Pomona College Jishnu Das The World Bank Asim Ijaz Khwaja Harvard University Tristan Zajonc Harvard University Abstract Bold assertions have been made in policy reports and popular articles on the high and increasing enrollment in Pakistani religious schools, commonly known as madrassas. Given the importance placed on the subject by policy makers in Pakistan and those internationally, it is troubling that none of the reports and articles reviewed based their analysis on publicly available data or established statistical methodologies. This paper uses published data sources and a census of schooling choice to show that existing estimates are inflated by an order of magnitude. Madrassas account for less than 1 percent of all enrollment in the country and there is no evidence of a dramatic increase in recent years. The educational landscape in Pakistan has changed substantially in the last decade, but this is due to an explosion of private schools, an important fact that has been left out of the debate on Pakistani education. Moreover, when we look at school choice, we find that no one explanation fits the data. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["census of schooling choice"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Electricity** **Capital** **GDP** **Consumption** **Stock** **Population** Median 3.84 3.65 3.45 1.44 Average 3.48 4.10 3.88 1.43 Standard Deviation 4.37 6.87 2.77 0.97 Skewness -1.58 1.56 0.81 0.11 Kurtosis 9.83 20.48 1.10 -0.33 _Sources: World Development Indicators, Penn World Tables Version 8, and author's calculations._\n\n<!-- PAGE 19 -->\n#### **VII. Hypothesis Testing**\n\nWe explore two basic questions. First, could night-time lights data serve as a good proxy for any of the\neconomic variables considered in this paper?\n\nA new satellite is generating superior night-time lights data. The data are generated by the Suomi National Polar-orbiting Partnership (SNPP) satellite series operated by the NASA - NOAA Joint Polar Satellite System that was launched in late 2011.\n\nobserved in cross-section surveys - even when controlling for other observable characteristics -   cannot directly be interpreted as reflecting a change in attitudes over the life-cycle.", "output": {"entities": {"named_data": ["World Development Indicators"], "descriptive_data": [], "vague_data": ["cross-section surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Given that we control for the effects of conflict in addition to the usual socio-economic determinants of fertility and given that conflict may also affect these other variables, the estimated coefficient of the conflict variable can be interpreted as the ‘ pure conflict ’ effect. This makes our calculations conservative estimates of the total effects of conflict on fertility. For example, conflict is likely to reduce the educational attainment of children exposed to violence during their school-age (Akresh and de Walque 2008) and of girls in particular (Shemyakina 2006), hence inter alia raising their fertility. In both (1a) and (1b), measures of each woman ’ s socio-economic characteristics include three age categories (young: 15-24 years, middle: 25-34 years, and old: 35-49 years), the number of sons and daughters born before the time span of interest, her education (no education, some primary education, and some secondary or higher education), a dummy variable indicating whether she is currently in a union, 9 a dummy variable indicating whether she has had more than one union, a continuous household wealth index (see Section 4. 1), a dummy variable indicating whether she has always lived in the same community, a dummy variable indicating whether the current location is urban, mortality rates of children under five years at the district 9 This variable is not included in the estimation of equation (1b), given that widowhood is strongly correlated with being in union. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "school report cards and a related online platform. Still, few schools viewed ENLACE\n\nBy design, ENLACE had a national mean score of 500 and a standard deviation of 100\n\nand excellent. ENLACE's methodology followed item response theory (IRT), allowing\n\nIn 2008, SEP decided to use ENLACE scores to measure teacher performance in\n\nsubject to yearly evaluations. In 2008, ENLACE scores among their students were given", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "ratings. Where GDP per capita growth data have been recorded, it appears that FCS countries have stronger growth trends than non-FCS countries and in both groups of countries growth is correlated with stronger outcome ratings.\n\ncategorization is derived from the FCS lists for FY06 to FY13. [1] Partial FCS countries are those classified as FCS in at least two years during this period.\n\n7For example that would be detected through the implementation status reports (ISRs).\n\nwe rely on the data from Melecky and Podpiera (2013) and the 2003, 2007, and 2012 Bank Regulation\n\n\nand Supervision Surveys of the World Bank. For banking crisis classification, we rely on Laeven and\n\n\nValencia's (2013) database and cross-check our results against the crisis classification by Reinhart and\n\ncountries.\n\n\n8\n\n\n\n<!-- PAGE 11 -->\n_**Regression Model**_\n\n\nWe use the systemic banking crisis database of Laeven and Valencia (2013) to identify banking", "output": {"entities": {"named_data": [], "descriptive_data": ["systemic banking crisis database"], "vague_data": ["GDP per capita growth data", "FCS lists"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "### 4.2 GLSS Cluster-Level Aggregation\n\nTo construct community-level outcome variables from GLSS household data, we aggregate household-level observations to the GLSS enumeration area level using survey-weighted means. We restrict this aggregation to GLSS enumeration areas with at least 10 interviewed households to limit noise from small-cluster estimates. The resulting GLSS cluster-level dataset comprises 378 enumeration areas distributed across all ten regions, with an average of 18 interviewed households per cluster.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We only found reliable approximations from the International Displacement Monitoring Center (IDMC) between 2003 and 2013. According to IDMC, there were about 12. 5 million internally displaced people in SSA at the end of 2013 (IDMC 2014), more than one third of the total number of IDPs and more than tripling the number of refugees in SSA. Although the number of IDPs in SSA is the highest since 2007, the share of IDPs in SSA has been decreasing from 53 % in 2003. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "prior to the pandemic, a period characterized by less distinct time trends in the labor market. Since the focus of the paper is on the impact of COVID on labor market stock and dynamics, we also restrict the sample of interest to include only prime-aged working adults (aged 20 to 59). The dataset contains standard variables expected of a labor force survey, including those denoting employment, unemployment, and inactivity. It also contains information on the intensive margin of the labor supply, including hours worked and full-time and part-time status. Information on employment sector, industry, contract status, health insurance coverage, and mode of work (distinguishing between employees and self-employed, for example) is also available, allowing us to construct indicators of formality and to differentiate different modes of employment. Information on occupation is also available, but only at the level of 2-digit ISCO-08 classification. This information is enough to distinguish between white- and blue-collar occupations but it is not enough to observe additional relevant pandemic-related job characteristics such as the degree of contact with the public. 3. 2 Descriptive statistics Figure (1) tracks the evolution of labor market stocks in the West Bank and Gaza respectively over time. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Data and Variables Definition The data used to conduct this study are the ARIS-REDS data from the National Council of Applied Economic Research (NCAER). Since 1971, the NCAER has been conducting surveys on a _sample_ of households in 232 villages in the 17 major states of India.\n\ndevelopment improves. According to FAO data, in the last five decades per capita milk\n\ndevelopment partners) as part of the Living Standard Measurement Study - Integrated Survey  on Agriculture (LSMS-ISA) program [2] . The paper aims to contribute to building an evidence\n\n2 More information on the program is available at www.worldbank.org/lsms-isa.", "output": {"entities": {"named_data": ["ARIS-REDS", "Living Standard Measurement Study - Integrated Survey  on Agriculture (LSMS-ISA) program", "LSMS-ISA", "Living Standard Measurement Study - Integrated Survey  on Agriculture (LSMS-ISA)"], "descriptive_data": [], "vague_data": ["FAO data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Chew et al. (2018) use a baseline convolutional neural network model on a gridded population sampling frame to select a sample of households in Nigeria and Guatemala. The authors found this technique to be on par with human canvassing in terms of accuracy, and to outperform other machine learning models based on crowdsource or remote sensing data. Grais et al (2007) compared an unweighted random point selection methodology to a random walk in their study of vaccination rates in urban Niger. The authors do not find statistically significant differences between the methods, though the sample size was limited and both methods were non-probabilistic. 3. Design and Field Protocols 3. 1. Experiment Design This paper makes use of a dataset from the purposefully designed methodology experiment conducted in one section of the Protection of Civilians site 1 (PoC1, Figure 1), one of the largest IDP camps in Juba, South Sudan. To generate a gold standard as the basis of comparison, a household census was conducted between August and September 2017. During this exercise, 2, 655 households were interviewed using a questionnaire designed to collect demographic information, dwelling characteristics, household consumption, and perception data. At the end of each census interview, households received a unique barcode that could be used to identify them later in the experiment. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "To avoid changes in camp composition, immediately following the completion of the census fieldwork, the interviewers returned to the field to implement the experiment. Teams used each of the sample selection methods to identify which households would have been selected had that method been used for a survey. To avoid respondent fatigue, instead of re-asking the questionnaire, the interviewers simply scanned the unique bar code of the selected household. Once scanned, the barcodes created an observation in the method-specific dataset with the information captured in the census. Each sampling technique targeted about 322 interviews so that comparisons could be made between the methods using an identical sample size. There was, however, some non-response for each method if interviewers were not able to contact a household member who could provide access to the barcode, if the barcode had not been retained by the household, or if the barcode was not scanned correctly. Protocols for each individual method are listed below. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure A. 2: Injective relations We also isolate many-to-one (bijective) relations. In this case, we have to aggregate the Afro- barometer ethnicities with their unique and more aggregated correspondence in the UNHCR refugee camps data (See Figure A. 3). Figure A. 3: Bijective relations The remaining correspondences are either (i) one-to-many (bijective) but opposite to Figure A. 3 (i. e., many ethnicities from the UNHCR refugee camps data correspond to one ethnicity from the Afrobarometer) or (ii) many-to-many relations. For both cases, we apply a more pragmatic approach: a. In both cases, we disregard ethnicities that do not appear either in the Afrobarometer or in the UNHCR refugee camps data. This means that for the remaining ethnicity that has no counterpart in either the Afrobarometer or the UNHCR refugee camps data, we simply keep the name of the ethnicity as such, i. e., this information is not dropped. b. Then, after ignoring ethnicities that have no occurrence in our datasets, we check whether the one-to-many or the many-to-many relation has not boiled down to a one-to-one resp. many-to- one relation again. If so, we can treat them as above. c. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "At present, the FAO hunger estimates rely on CVs which are not always country specific, do not vary\n\nranging from 19% when the personal diary is used to 72% when the estimates come from an HCES that\n\nhousehold surveys (HCES-direct method), as opposed to calculating them from a combination of food\n\nbalance sheets and household surveys (FBS-CV method) as currently done by the FAO. The FBS-CV", "output": {"entities": {"named_data": ["HCES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Compared to the baseline year of 2001, residents were more fearful of crime by 0. 17 sd. These fears are not necessarily unfounded, as we ﬁnd some weak evidence of changes in levels of violence in their parishes. Compared to 2001, a 1-SD increase in refugee presence in 2011 is associated with a 1. 26 percentage point increase in likelihood of a violent event, and in 2016, a 1. 11 percentage point increase. These ﬁndings are in line with our predictions for Uganda. As in Global North contexts, we recognize that out-group members can elicit fears and insecurity. It is unclear from the ACLED dataset, however, whether these violent events involve refugees. It is also possible that ACLED underreports violent incidences that are not reported by the media. Recent scholarship on the relationship between refugees and violent conﬂict ﬁnds that hosting generally has null eﬀects on conﬂict (Zhou and Shaver, 2021); when conﬂicts do occur, refugees tend to be the victims (Savun and Gineste, 2019). Nevertheless, by 2020, like with Afrobarometer respondents reporting feeling more safe, these fears of crime reverse with a negative eﬀect of- 0. 13 sd. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We focus on two policy actions: legalization (in the sense of providing legal work and residence permits to all migrants) and measures aimed at improving the quality of employment for highly educated Venezuelan migrants. 6. 1 The Eﬀects of Legalization Based on the EPEC survey, approximately 90 % of Venezuelan migrants in Ecuador lack legal status, in the sense that they are not authorized to work. This has important consequences for Venezuelan workers. As shown in Section 4. 3, lack of legal status is associated with a higher likelihood of informal employment (column 5 in Table 7). Inspired by Clemens et al. (2018), the goal of this section is to use the estimates of these eﬀects to simulate the consequences of providing legal work permits to Venezuelans on the quality of their employment (measured by informality) and, through this channel, on their productivity (measured by wages). The relevant information is collected in Table 10. We classify Venezuelan workers that lack legal status on the basis of their education level (primary, secondary or tertiary) and the quality of their employment (formal or informal). Based on the data in column 3 15 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "11 There may be grounds for skepticism about these estimates for madrassa enrollment. Since the data were collected prior to 2001, geopolitical changes after September 11 could have led to greater madrassa enrollment. In addition, the household-based survey faces the usual problems of accurately estimating a low-probability event — although enrollment is less than 1 percent in these surveys, the sampling error is large (see Bauman, 2001, for a description of similar problems in estimating home-schooling in the United States). Finally, while the census of populations does not face the problem of small samples, it is not that recent (1998) and some may have reservations regarding the quality of government data. 10 The LEAPS census of schooling choice conducted in 2003 provides a rough check on these numbers (see appendix for details). This census was conducted in three districts of Punjab and villages were chosen randomly based on the criterion that each village must have at least one private school. Typically, this means that the villages lies somewhere between fully urban and fully rural populations and are not representative of the districts that they are in. Estimates from the LEAPS census show that as a percentage of enrolled children, the numbers in two of the three districts are slightly higher than those of the population census. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": [], "vague_data": ["household-based survey", "census of populations"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "33 In school: an indicator for whether the respondent currently attends regular education (schooling). This does not preclude also being employed. Retired: an indicator for a respondent who declares that they are not engaged in job search because they are retired. Full / part-time employment: an indicator for whether a person works full or part-time for all people in private sector employment (see above definition of employment). Full-time employment is defined as usual working hours of 30 or more hours per week, part-time employment as usual working hours of less than 30 hours per week. We do not use the indicator provided in the LFS data since there seems to be some confusion in which category 30 hours per week falls (with these evenly divided between full and part-time). Education: we classify people into three education categories. Low education is defined as those with no completed formal education. Medium education is defined as those with at least completed primary education but no high school completion. Higher education is defined as people who have at least completed high school. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["LFS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 3. Wealth Index Distribution (a) Wealth Index- Total 0. 05. 1. 15. 2. 25 k-Density- 5 0 5 10 15 Wealth Index Colombian Venezuelan Venezuelan: Before migrating (b) Adequate Housing Materials (% of Total) 0 1 2 3 4 k-Density 0. 2. 4. 6. 8 1 Average- Dwelling Material Colombian Venezuelan Venezuelan: Before migrating (c) Asset Ownership (# Total) 0. 05. 1. 15 k-Density 0 10 20 30 40 Total Assets Colombian Venezuelan Venezuelan: Before migrating (d) Access to Services (% of Total) 0 1 2 3 k-Density 0. 2. 4. 6. 8 1 Average- Access to Services Colombian Venezuelan Venezuelan: Before migrating Notes: Panel (a) presents the distribution of the wealth index for Colombian and Venezuelan households in our sample in 2022 and pre-migration. Wealth Index is an index measure of the household ’ s cumula- tive living standard constructed following The Demographic and Health Surveys (DHS) methodology. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A number of different road indicators are available and we choose road line type to use in the analysis. Road type is defined by the following: The reference category (0) points out squares with dual lane / divided highways, other primary roads, or road connectors within urban areas (types 1 or 8 in the ESRI dataset). The second category include secondary roads (type 2), and the third combines squares with informal or tertiary roads (tracks, trails or footpaths) or no road registered at all (types 3 and 0, respectively, in the ESRI dataset). Figure 4 overlays the types of roads in the original dataset before our recategorization. The shaded area represents the portion of Africa for which we code Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["ESRI dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Internally Displaced Persons (IDPs)** : Over 652,600 Afghans (approximately 96,000 families) were\nnewly displaced due to conflict in 2016, adding to a protracted IDP population of over 1 million. Most\nIDPs found refuge with host families in neighboring communities, already facing extreme poverty.\nFood, adequate shelter, WASH, and health care remain high priority needs, while efforts to raise\nawareness of mines and ordnance risks are also ongoing. A majority of IDPs live an insecure existence\nin makeshift shelters and informal squatter settlements with irregular access to services, poor\n\n8 8.8% of women aged 20-24 were married or in a union before the age of 15 and 45% before the age of 18 (AfDHS15)\n\nPage **7** of **12**", "output": {"entities": {"named_data": ["AfDHS15"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In certain contexts, there can be significant overlaps in these two groups; however data systems may be maintained separately for conflict-induced displacement and natural disasters (e. g. in Afghanistan) leading to possible gaps or double counting if these categories are combined. 30 The IOM Displacement Tracking Matrix (DTM) is a system to track and monitor displacement and population mobility. It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route. It has been active in over 40 countries since its inception in 2004. See http: / / www. globaldtm. info /. 31 This is typically defined as nomads not having access to their traditional routes, but routes can vary. 32 IDMC has recently adjusted their methodology to facilitate greater comparability across situations and improvements are reflected in IDMC ’ s end-2015 data. 33 This is not necessarily a problem if the purpose of the registration system is to delineate entitlements to assistance rather than to determine status. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Linking Ethnic Data from Africa", "Afrobarometer", "EPR-ER dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "discussed earlier, the expenditure of in-camp refugees is almost half that of hosts despite sizeable food aid and significant investments made by the WFP and UNHCR in cash transfers (in selected camps). The significantly lower expenditures (food and non-food) among refugees compared to the host population led to higher poverty rates. The team cross-checked the food aid received by in-camp refugees based on administrative data from the UNHCR and WFP with food consumption data from SESRE. The analysis looks at both separately for information provided by UNHCR and WFP. While WFP is not responsible for distributing non-food items such as mattresses, cooking, feeding utensils, etc., UNHCR provides non-food items; maybe mattresses were distributed in Alemwach since it is a relatively new camp. WFP’s food and cash assistance targets all individuals in refugee households. The information received from UNHCR on food aid provided to refugees in each camp includes quantities per food item per month and cash transfers per person per month for each camp and period. The food items include cereal, wheat, maize, rice, sorghum, CSB/famex (CSB+), pulse, biscuit, date biscuit, dates, oil, vegetable oil, salt, and cash (Table E.4). We have computed the per person per month in-kind aid", "output": {"entities": {"named_data": [], "descriptive_data": ["administrative data from the UNHCR", "food consumption data from SESRE"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The data shows that cohorts that were of school age during the Indonesian occupation achieved higher level of education than older cohorts (i. e. those born in the 1970s compared to those born in the 1960s or before) testifying for an increasing trend as expected. The figure shows that despite the increasing trend a large fraction of individuals have low education levels. Interestingly, all curves start to drop after the 1987 cohort. This decreasing trend is observed among individuals aged 20 or younger in 2007 and provides evidence of a mismatch between the grade attended and the grade that they should have achieved at their age. 4 This is caused by a persistent sluggishness in grade achievement due to the high level of delayed entry to school and high rates of repetition. 5 The impact of the conflict in its different phases and the subsequent reconstruction efforts on schooling levels of children in Timor Leste is therefore unclear. The early years of violence coincided with an education for all policy in which quantity was preferred to quality. In addition, the 1999 violence that followed the withdrawal of Indonesian troops led to the destruction of schools and the removal of children from school. The reconstruction program implemented after 1999 tried to counteract this destruction, and achieved fast progress. However, the education sector was still in very poor shape. In the next section, we investigate in more detail the effects of the conflict on educational outcomes of boys and girls in Timor Leste. 4. Identification strategy and data description 4 Those born in 1992 are 15 in 2007. So they might have at most completed grade 9 and this justifies part of the drop in the curves as the grade completed is right censored. 5 The high levels of school delay are also confirmed by the figures on gross and net enrolment ratios calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5.4 Dependency ratio 1.1 1.5 0.5 0.4 0.7 1.3 Female-headed 44% 73% 45% 58% 44% 69% Head’s age 42.3 39.6 42.0 30.9 42.1 37.6 Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 12 2.2 Education Integrating refugee children into educational programs soon after arrival14 avoids the loss of valuable years of education and human capital accumulation that can hinder future prospects. Integrating refugees into national education systems can improve future outcomes for refugee children and their hosts (UNHCR, 2020; Piper et al., 2020; Abu-Ghaida and Silva, 2020; Crawford et al., 2015; Bilgili et al., 2019). Investment in human capital development can enable refugees to contribute to local economies to benefit refugees and hosts alike, and it can contribute to the recovery of countries of origin and the hosting communities. Despite the positive externalities of integrating refugees into public-school systems, a large influx of refugee children can exacerbate existing inefficiencies. Where there are large inflows, the national education system might require additional human and financial resources to integrate newly arrived children. Increasing the supply and improving the quality of schools in affected areas, supported by external assistance and financing, can avoid tension between refugees and host community populations over", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Lack of livelihood opportunities in areas of displacement combined with wide spread insecurity, lack\nof freedom of movement, and limited humanitarian access to AGEs controlled areas is generating an\nalarming humanitarian and protection crisis that requires immediate attention and a new strategic\napproach by the APC.\n\n## **PATTERNS OF ABUSE**\n\nAnalysis of protection risks needs to be improved to better prioritize and mitigate risks in 2017-2018.\nThe APC will dedicate further time and resources to producing a comprehensive protection risk\nanalysis. Information available suggest the following patterns of abuse:\n\n1 REACH PIDP study, p. 17\n\n2 IDMC. Afghanistan: New and long-term IDP risk becoming neglected as conflict intensifies.\n\n3 The findings of the Protection community assessment in the East indicate that additional influx of people into communities, that already\nhave limited service providers’ capacities, overstretches the resources, like water, health and education, leading to the overcrowded\nschools, hospitals facing challenges with the number of people and water sources being not sufficient to meet the needs of the local and\ndisplaced population.", "output": {"entities": {"named_data": ["REACH PIDP study"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Evidence for Sub-Saharan Africa shows that female-headed households that have previously been highly dependent on male income – notably widow-headed households – are particularly prone to poverty, although other types of female-headed households may not be more likely to be poor than male-headed households (Brown & Van der Walle, 2020). For this reason, while it is important to analyze differences in poverty risk between households with female and male heads, it is also important to understand how different routes to female headship, and the changes in the household composition more broadly, impact poverty risks and expenditure levels. This paper explores level and distributional changes in expenditure among Syrian refugee households in Jordan between 2013 and 2018 using a gender lens. By the end of 2018, UNHCR had registered more than 671, 000 Syrians1 who had fled their homes and settled in Jordan. At the end of the time period covered by this research, UNHCR ’ s 2019 Vulnerability Assessment Framework (VAF) 1 UNHCR ’ s data portal records 671, 551 registered Syrian refugees residing in Jordan on January 13, 2019. See UNHCR (2022b) available at https: / / data. unhcr. org / en / situations / syria / location / 36. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These surveys are part of a long-term data collection effort by the research team (Vinck & Pham 2014) and were collected separately from the focus group discussions. The focus group discussions thus did not directly inﬂuence the design of the surveys, but rather directed the analysis strategy of the surveys that our team has collected at regular intervals in eastern DRC. The survey data are analyzed in two ways. First, 11 surveys collected between 2017 and 2021 8Eastern DRC is a site of ongoing violence, raising a number of ethical, methodological, and practical concerns about collecting data. We discuss the ethical protections we implemented when collecting this survey data in the Appendix, Section B. 9Territoires are sub-provincial administrative units. Additional details on the structure of administrative units are available in the Appendix, Section A. 1. 21 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Do people run away from their home country when a conﬂict breaks out? Does the seriousness of the conﬂict matter in this decision? In which phase of the conﬂict do they leave? When do refugees come back to their home country? In order to answer these questions, we look at the impact of conﬂict incidence on 21We will return to these issues in the policy section. 22According to the UNHCR deﬁnition, this category includes “ individuals recognized under the 1951 Convention relating to the Status of Refugees; its 1967 Protocol; the 1969 OAU Convention Governing the Speciﬁc Aspects of Refugee Problems in Africa; those recognized in accordance with the UNHCR Statute; individuals granted complementary forms of protection; or those enjoying temporary protection; and people in a refugee-like situation ”. 23The UNHCR Global Trends Report 2014 provides evidence that conﬁrms this hypothesis. About 59. 5 million people were forcibly displaced worldwide by the end of year 2014. Among them, 19. 5 million were refugees and 38. 2 million were IDPs. 28 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Deciding on whether to flee or not to flee a conflict (the migration choice) can be an individual or household choice and risk coping strategies may include temporary migration, shuttling between places, migration of only selected members of the households or migration of the whole household. This implies that individuals may stay put throughout the period observed, join or leave the household during the period, or have several episodes of out and immigration. Households may decide to leave and come back several times. In econometric terms, this means that longitudinal data may be left and right censored and have spells within. They are therefore the most complex set of panel data possible and require particular treatment of data and modeling. Survival or duration models can usually accommodate many of these complexities but it is very rare to find similar data sets used in published articles. Collecting such type of data is also not obvious, particularly if conflict is intense and survey areas cannot be reached. This is an issue where empirical economics could provide a real contribution by defining the optimal data format and adapting panel models to this format. Macro models Macroeconomics has attempted to model forced migration using models borrowed from the trade and economic migration literature such as the gravitational model (Echevarria and Gardeazabal, 2016) or used other macro models to test the impact of refugees on trade (White and Tadess, 2010). A more recent body of work is adapting trade models to take into account stochastic shocks in a dynamic framework (Cameron et al., 2007; Artuc et al., 2008). These are rational expectations models that are able to model the unpredictability of shocks, and recent work has tried to adapt these models to the context of violent Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["longitudinal data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Sources of information This work includes a descriptive analysis that allows us to identify how the prevalence of non- standard employment (NSE) has evolved in the last two decades in different regions of the planet, as well as the evolution of the profile of workers in those roles, in terms of their educational level, salary per hour, and type of tasks performed. This analysis was based mainly on periodic surveys of households that included information regarding the employment and educational situation of individuals. Although the denomination of this type of surveys varies from country to country, in all the cases analyzed there is usually a survey of annual or higher frequency that includes information required to identify the labor status of the individuals as well as to analyze the salary profile and education of the employed. However, it should be noted that the identification of the type of work relationship (standard or non- standard) is frequently limited in these data sources. Indeed, it is only possible to identify part-time employment and temporary employment (not in all cases) within the non-standard forms of employment mentioned in the previous section.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["periodic surveys of households"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(a) High frequency sample surveys using smartphone and cellular technologies. For example, a high-frequency survey initiative in Somalia employs a dynamic questionnaire loaded onto smartphones, which enables data to be collected from household interviews in 60 minutes. This approach was developed to overcome the challenges of insecurity, limited data gathering capacity and budgetary constraints. (b) The use of mobile phones to conduct surveys or follow up interviews following face-to-face household surveys. For example, a Bank paper on the impact of the 2012 crisis in Mali on IDPs, refugees and returnees used information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. This combination provided a mechanism to monitor the impact of conflict on hard-to-reach populations who at times live in areas inaccessible to enumerators. And in Sierra Leone and Liberia, the Bank supported the use of mobile phones to collect key socio-economic data on the effects of the Ebola virus. (c) Crowdsourcing data on displacement. Platforms such as the Kenyan Ushahidi has crowd- sourced data on displacement in Kenya and eastern DRC by encouraging IDPs and host communities to report incidents using their mobile phones or the internet, including information about living conditions. The platform references these reports geo-spatially. (d) Geo-mapping of data on displaced populations and affected host communities. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["face-to-face household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Second, the Turkish LFS asks respondents whether they had previously lived in a different province (one of Turkey ’ s 81 NUTS 3 regions), and if so in what year they moved to their current province. We estimate the impact of refugees on the probability a native moved to a subregion in the past year. Table 9 reports OLS and IV estimates of the impact of refugee on net population growth in subregion (Columns 1 and 2) and gross population inflows (Columns 3 and 4). Net population growth is estimated at the level of NUTS 2 subregions. Population inflows to a subregion are estimated at the individual level (and standard errors clustered by subregion- year). All regressions include subregion and year fixed effects and a year-specific control for log distance from the Syrian border. The first column presents the estimates for the whole sample, subsequent columns for different sub-samples by gender, age and education. For the full sample the net population growth in a subregion is positively correlated with refugee flows, while the IV point estimate is negative (though neither estimate is statistically significant). The probability of a Turkish person migrating to a subregion is negatively correlated with refugee flows (the OLS estimate is highly statistically significant). The IV estimate is of a similar magnitude, but no longer statistically significant. This same pattern broadly holds for both women and men. The only other statistically significant IV estimates are a decrease in the population aged 15 – 24, an age group that is likely more mobile, and of those with medium educational attainment. There is also a decrease in the inflow of low Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "that are at a maximum distance of 80 km from the 7, 547 clusters. 12 Figure 1 shows the locations of these refugee camps and clusters. Clusters are represented in green, while clusters in the vicinity of a refugee camp are represented in red. Refugee camps are designated with a red + sign. There are some important limitations associated with this data. First, the data only provides information on refugees residing in camps monitored by the UNHCR. In Figure B. 7, we combine the UNHCR refugee camp data on the annual number of refugees and the UNHCR official statistics on refugees (which includes people in refugee-like situations) at the country level. 13 Although the overall trends match, our constructed dataset clearly underestimates the true refugee population in Africa, which is not surprising since our camp-specific data does not contain dispersed refugees or refugees living outside of camps. While our data seem to represent quite fairly the number of refugees in camps, there is significant heterogeneity across countries. Based on the visual inspection of Figure B. 8, the quality of the refugee data appears to be less reliable for the following countries in our sample: Gabon, Mali, Senegal, and Togo. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR official statistics on refugees"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Road data from the provincial transport authority's annual condition census were used to validate the project's prioritization model. The road data include pavement condition index scores, GPS coordinates of assessed segments, and traffic count estimates for 312 road links in the target area. Cross-checking the road data against community consultations confirmed that communities with the lowest road condition scores also reported the highest frequency of missed health facility appointments due to inaccessibility during the rainy season.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "40% of the respondents reported that their community **38%**\nor living area is not currently experiencing elevated\ntensions and frequent clashes. This perception is\nconsistent with the available surveys and assessments\nrun by the NWS PC, on the escalation of hostilities\nstarted in October 2023 and still ongoing.\n The survey also explored the **presence of Unexploded Ordnance (UXO)** in the living areas of youth in\nnorthwest Syria, along with their experiences of receiving support for clearance and awareness, 13%\nreporting presence in their area. Among those who reported the presence of UXO, 25% received help or\nsupport for clearance, and 79% of those who received support reported that it included an awareness\nsession about explosive ordnance exchange.\n\n**Is your community experiencing elevated**\n\n**tensions and frequent clashes?**\n\n**38%**\n\n**7**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "6 3. The Data The Kagera Health and Development Survey (KHDS) was originally conducted by the World Bank and Muhimbili University College of Health Sciences (MUCHS), and consisted of about 915 households interviewed up to four times from fall 1991 to January 1994 (at 6-7 month intervals) (see World Bank, 2004, and http: / / www. worldbank. org / lsms /). The KHDS 1991-1994 serves as the baseline data for this paper. Initially designed to assess the impact of the health crisis linked to the HIV-AIDS epidemic in the area, it used a stratified design to ensure relative appropriate sampling families with adult mortality. Comparisons with the 1991 HBS suggest that in terms of basic welfare and other indicators, it can be used as a representative sample for this period for Kagera (results not shown but available upon request). The objective of the KHDS 2004 survey was to re-interview all individuals who were household members in any round of the KHDS 1991-1994 and who were alive at the last interview (Beegle, De Weerdt and Dercon, 2006). This effectively meant turning the original household survey into an individual longitudinal survey. Each household in which any of the panel individuals live would be administered the full household questionnaire. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["KHDS 1991-1994", "KHDS 2004 survey", "Kagera Health and Development Survey"], "descriptive_data": [], "vague_data": ["household survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We develop and use a model to estimate expected asset losses and expected welfare losses, and quantify socioeconomic resilience in 117 countries. The model builds on Hallegatte et al. (2016b), which only considered river floods and 90 countries. Here we add new countries thanks to new socioeconomic data, and we introduce additional hazards - coastal floods and storm surge, windstorm, earthquakes, and tsunamis - using the risk assessment provided in the Global Assessment Report (UN-ISDR 2015). We also simplify the model - focusing on the factors that were found to have most influence on our previous assessment and disregarding other factors - and improve our modeling of insurance and social protection.\n\nIn our national indicator, we include the role of early warning system using data reported in the context of the Hyogo Framework for Action [7] (UN-ISDR, 2015a). The priority for action #2 (\"Identify, assess and monitor disaster risks and enhance early warning\") includes an indicator (P2-C3) related to \"Early warning\n\n<!-- PAGE 16 --> overlays flood maps from the GLOFRIS global model and poverty maps from the World Bank. For countries where this study does not provide data, we use an older, similar study by (Winsemius et al. 2015). This other World Bank study overlays the same GLOFRIS flood maps as above with geo-localized household surveys (using the Demographic and Health Surveys [8] ) to assess the exposure of poor people to river floods relative to the exposure of non-poor people.\n\nWe calculate the average capital productivity as output-side GDP divided by total reproducible capital within a country, both variables from Penn World Tables.", "output": {"entities": {"named_data": ["GLOFRIS", "Demographic and Health Surveys", "Penn World Tables"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "According to Council on Foreign Relations, the estimated number of people killed since December 2013 is over 50, 000, and over 1. 6 million people are internally displaced66. Economic Opportunity South Sudan has abundant natural resources. Before its oil production fell sharply, the government relies on oil for its revenue. It also has very fertile soils and abundant water supplies. South Sudan has struggled with economic development since its independence and its economic conditions have deteriorated since January 2012 when the government decided to 61 IMF, Six Things to Know about Somalia ’ s Economy, April 11, 2017, http: / / www. imf. org / en / News / Articles / 2017 / 04 / 11 / NA041117 ‐ Six ‐ Things ‐ to ‐ Know ‐ About ‐ Somalia ‐ Economy 62 UNHCR, Somalia, http: / / reporting. unhcr. org / node / 2550? y = 2016 # year 63 Human Rights Watch, World Report, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / south ‐ sudan 64 Human Rights Watch, World Report, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / south ‐ sudan 65 UNHCR, South Sudan http: / / reporting. unhcr. org / node / 2553 66 Council on Foreign Relations (CFR) https: / / www. cfr. org / global / global ‐ conflict ‐ tracker / p32137 #! / conflict / civil ‐ war ‐ in ‐ south ‐ sudan", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We have constructed two different variables that try to account for the degree of severity of the conflict. 10 The first variable identifies individuals belonging to households that were displaced due to the 1999 wave of violence (all members displaced). The second variable identifies individuals in households that report having their house completely destroyed by the violent attacks in 1999. The TLSS 2001 contains also useful retrospective information on school attendance and grade attained across three different academic years: 1998 / 99, 1999 / 00 and 2000 / 01. We are 7 Commission for Reception, Truth and Reconciliation & Benetech Human Rights Data Analysis Group. ― Human Rights Violations Database. ‖ 9 February, 2006. Website: http: / / www. hrdag. org / resources / timor-leste_data. shtml. 8 There may be potential sample biases in the statement taking procedure given the voluntary nature of the process. It is possible that those living in more remote or mountainous areas, those living far away from the areas where the statements were taken, the sick, old and disabled and those with no access to the media or means of mass communication have a lower probability of being part of the sample. By contrast, those more active in local communities are more likely to have provided a testimony.", "output": {"entities": {"named_data": ["Human Rights Violations Database", "TLSS 2001"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "### Financial Covenants\n\nThe Loan Agreement includes the following financial covenants: (i) the borrower will maintain the SIGAF accounting module for the project active and updated within five business days of any transaction; (ii) annual project financial statements will be prepared in accordance with International Public Sector Accounting Standards (IPSAS) on a cash basis; (iii) external auditors will be appointed within four months of project effectiveness; and (iv) the external audit report, together with the management letter, will be submitted to the Bank within six months of each fiscal year-end.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "All civil works contracts under the project include as a condition precedent to payment the submission of a workers' accommodation standard certification and a signed code of conduct from contractor supervisory staff, consistent with the LMP. The PIU's E&S officer has developed a compliance checklist aligned with the LMP requirements, which is used during quarterly site inspections. Instances of non-compliance identified during inspections are recorded in a corrective action register and followed up within 30 days.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "V. B Impacts of exposure to Taliban territorial control on educational attainment Table 4 reports estimates for the effects of refugees ’ exposure to Taliban, contested terri- tories, and U. S. allies on refugees ’ educational attainment. First, this reveals that the im- pact of Taliban exposure is negative across four variables we examined: literacy, formal schooling attendance, primary education completion, and secondary education comple- tion. However, it is worth noting that the estimated effects do not reach statistical signif- icance for the completion of primary education. Nevertheless, when we combine these four variables into an education index (computed as their average), we observe enhanced precision, allowing us to document the significant negative effects of Taliban exposure on the education index. These effects are both substantial and meaningful. For instance, the coefficients in column (3) and Panel A indicate that a one-unit increase in the aver- age share of exposure to Taliban presence (representing 100 % presence in the province of birth during the five years between 2017 and 2021) is associated with an 18. 5 percentage point decrease in the share of individuals with some level of education. The estimates further demonstrate strong and adverse effects of refugee exposure to con- tested territories on educational attainment. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "widespread among Shona households, and access to external support was minimal. Although employment levels\n\nbegan to recover a year later, they remained below pre-pandemic levels, underscoring the prolonged adverse\n\nimpact of the pandemic on this already vulnerable community. [[9]]\n\n_Figure 1: Labor force status after the COVID-19 outbreak (18-64 years)_\n\n_Source: Kenya COVID-19 Rapid Response Phone Surveys (RRPS)_ _[[9]]_\n\n**Two rounds of household surveys taking place over five years before and after the transition of the Shona in**\n\n**Kenya from statelessness to citizenship help us to understand this community – and present one of the first**\n\n**socioeconomic pictures of the impact of citizenship on the welfare of stateless persons.** Between 2019 and 2024,\n\nthe median age of Shona household members remained unchanged at 18 years, underscoring a predominantly\n\nyouth demographic profile. Gender distribution is even, showing only a marginal change, with the female population\n\nincreasing slightly from 49 percent to 50 percent. Average household size has risen modestly from 4.9 to 5.4\n\nmembers, which may reflect greater household consolidation or socio-economic stability following their\n\nnaturalization. The most notable shift is the significant reduction in the proportion of female-headed households,", "output": {"entities": {"named_data": ["Kenya COVID-19 Rapid Response Phone Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "which collected heretofore unavailable expenditure data on each type of subsidized food\n\n4. New 1997 Household Budget Survey\n\nEgyptian food subsidy system.' [8] This study used expenditure data from the 1990/91\n\nHousehold and Income Expenditure Survey (HIES), which was a large, nationally\n\nand Statistics (CAPMAS).1 [9] One of the main problems with this 1990/91 HIES survey", "output": {"entities": {"named_data": ["1997 Household Budget Survey", "Household and Income Expenditure Survey (HIES)", "HIES"], "descriptive_data": [], "vague_data": ["expenditure data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 4. 1 Variation across Districts Pakistan is divided administratively into four provinces with 102 districts — Punjab, Balochistan, North-West Frontier Provinces (NWFP), and Sindh — plus the federal capital Islamabad, the Federally Administered Tribal Areas (FATA), the federally administered Northern Areas and Azad Jammu and Kashmir (AJK). The four provinces or Punjab, Balochistan, Sindh and NWFP, together with Islamabad, account for more than 97 percent of the population. Geographically, parts of Balochistan, the NWFP and FATA border Afghanistan. Sindh and Balochistan are sparsely populated provinces, with the exception of Karachi in Sindh, which is the single biggest metropolis in the country with a population approaching 10 million. We use data from the population census, 1998, as well as the census of private schooling, 2000, to provide estimates of madrassa, private, and government school enrollment in each district except for those in the province of FATA. The geographical dispersion of madrassa enrollment depends on how we define madrassa prevalence. There are three alternatives. We could present a geographical breakdown of the total number of children enrolled in madrassas. This number is related to the total population of the district, and may thus reflect only the size of the district relative to others. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["population census"], "descriptive_data": ["census of private schooling, 2000"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Surveys, 76 Demographic and Health Surveys (DHS), 77 and Multiple Indicator Cluster Surveys (MICS). 78 The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner (UNSD 2014). There are only a few cases where modules or questions on forced displacement have been integrated into survey instruments (most notably in Somalia, Uganda, West Bank and Gaza, Azerbaijan, Bosnia and Herzegovina, Serbia, Ghana, as well as health surveys in Albania, Ukraine and Moldova). There are several challenges associated with ‘ mainstreaming ’ forced displacement into household surveys: (a) there is huge demand for adding sector-specific or thematic modules to international surveys; (b) it is relatively difficult to convince national statistical agencies to modify their county-specific surveys; (c) disaggregating survey results by specific vulnerable groups (e. g. refugees, IDPs, migrant populations) requires these distinctions to be integrated into the sampling frame and sometimes there is insufficient information to do this or a lack of resources to expand the sample size; (d) lack of access to displacement- affected areas; and (e) difficulties associated with integrating an inherently political topic into less controversial surveys. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Multiple Indicator Cluster Surveys", "Demographic and Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "18 household socioeconomic information. In some cases, socioeconomic information is collected at the individual or household level but more often information is collected at the community level. It is extremely rare to have unit record data sets on IDPs, which explains why we found only 18 studies in the Econpaper repository as already documented. Recognizing the problem of scarcity and availability of information on IDPs, international organizations have set up in several countries coordination mechanisms to count IDPs usually coordinated by IOM, UN ‐ OCHA or the UNHCR. There are also global efforts to centralize this information on the part of organizations such as the UNHCR, UN ‐ OCHA, the international Displacement Monitoring Centre (iDMC) or the Joint IDP Profiling Services (JIPS). These efforts are making good progress on harmonizing counts of IDPs but remain short of establishing proper data collection systems that could deliver in the years to come unit data of quality for research. Hence, research on IDPs remains constrained by lack of data, lack of a blueprint on how to collect data and lack of an organization dedicated to IDP data collection. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 7 of 51 In this sense, it is necessary to draw attention to the fact that the non-standard employment statistics presented below are not homogeneous among countries. In countries where both forms of non- standard employment were identified, we define non-standard employment as those occupations that satisfy at least one of the conditions, that is, either corresponds to a part-time occupation or a temporary job. In countries where temporary employment was not identified in the data, our non- standard employment category will coincide with part-time employment. Note that in either case, as other non-standard employment modalities are not identified, the indicators presented in this paper indicate a lower level with respect to the true dimension of the phenomenon. The only aspect addressed that required the use of additional information was the analysis linked to the profile of tasks that are developed in the framework of non-standard jobs. To carry out this analysis, the information available in the O * NET (Occupational Information Network) database was used in conjunction with the Household surveys. This database provides information referring to the content of tasks of the occupations. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In addition to these categories, the 2018 home visit survey reports expenditure data on water, transportation, infant needs, essential household items, hygiene items, debt payment, and telecommunications. This difference in expenditure categories could inflate reported expenditure in the second wave. This is because survey respondents tend to reveal more accurate information when asked item by item, than if asked for an expenditure or income aggregate. In part, this may be due to an intention to withhold information to obtain the monetary assistance, and in part, it has to do with the recall error that this type of questionnaire elicits. To aggregate expenditure, we restrict the number of categories to those in both waves which represent a sizeable portion of a household expenditure; that is, rent, utilities, food, water, and transportation. These five categories average 97. 5 percent and 77 percent of household expenditure in the first and the second waves, respectively. While spending on healthcare, education, and household items were also recorded in both waves, the amounts were not large and were excluded because health care and education expenditures may reflect a vulnerability (for example, caused by illness or disability in the case of health care), or a luxury for those who can access the service, and 8 The monthly per capita cash assistance Syrian refugees receive is roughly 60 %- 65 % of the Survival Minimum Expenditure Basket (SMEB) and is intended to cover rent, water and sanitation costs. Assistance levels are mainly determined by family size. Since 2014, PAs often make the withdraws or their allocated cash assistance using iris scan enabled ATM machines (UNHCR, 2018b). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "11 however, since they cannot distinguish between a country where the population is concentrated in one cluster covering 10 % of the territory and one where the population is concentrated in two clusters of 5 % each, but with a considerable geographical distance between them. Population and conflict geography in the Democratic Republic of Congo (DRC) corresponds to these arguments regarding population concentration and dispersion. Concentrations of language-based minorities are evident throughout eastern DRC. Due to the limited access of the government, the close proximity to international borders, and the dense population concentrations, these concentrated minorities have a higher potential of conflict than other, more accessible, sparsely populated areas of DRC. Figure 1 shows the population concentrations in 1990 (CIESIN data) for Central Africa. Heavily populated areas are shaded in deeper tones of red / grey. Civil conflict in DRC has overwhelmingly occurred in the eastern portion of the state, which is the most densely populated area and also geographically peripheral to the capital, Kinshasa. Of the eleven Congolese rebel groups accounted for in the dataset used in this paper, all have operated either exclusively or partially in the eastern and southern areas of DRC. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["CIESIN data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "MPI of IDP communities is considerably higher than that of the host communities, so it follows that the censored headcount ratios display a similar gap. The difference by indicator is particularly noticeable in the living standards dimension, where the electricity, cooking fuel, housing, and bank account indicators show over 34 percentage point difference between the censored headcount ratios for the IDP and host communities. Figure 1. Censored headcounts of each indicator in the MPI, by displacement status in Sudan (2018)", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "20 between 1948 and 1965, with increases in the percentage of adults with religious education in the cohorts born after this date. There is also wide geographical dispersion in the prevalence of madrassa education in Pakistan. Although all districts report that less than 2. 5 percent of children in the relevant age group (children between the ages of 5 and 19) are going to madrassas, the Pashto speaking belt that borders Afghanistan stands out in terms of the popularity of madrassas as an educational choice. The notion that the madrassa movement coincided with resistance to the Soviet invasion of Afghanistan is supported by the 1998 data from the population census. The increase in the stock of religiously educated individuals starts with the cohort that came of age in 1979 (the year of the Soviet invasion of Afghanistan) and the largest increase is for the cohort co-terminus with the rise of the Taliban. Combined with the fact that the largest enrollment percentage in Pakistan is in the Pashtun belt bordering Afghanistan, this suggests events in neighboring Afghanistan influence madrassa enrollment. Is there something intrinsic about Pashtun sensibility or tribal culture that leads to higher madrassa enrollment? The differentiation of the Pashtun and non-Pashtun districts does not extend to Pashtun and non-Pashtun households in the LEAPS data. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LEAPS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Impacts on the labor market are unclear; initial research suggests that there has been a supply shock to informal labor markets. This has had a large-scale impact on the employment of natives in the informal sector. At the same time, research suggests there has been a boost to formal employment for the Turks, but this has been uneven: the low educated and women experience net displacement from the labor market and, together with those in the informal sector, declining earning opportunities.  The “ Socio-economic Assessment of the Impact of Syrians under Temporary Protection (SuTPs) on Turkish Hosting Communities ” [ongoing], to be undertaken in partnership with the Government of Turkey, will include a nationally representative household survey with SuTP and local Turkish households including camp and non-camp environments. The questionnaire will cover welfare (assets, income, expenditure), municipal services, labor and employment, education, social networks and quality of life. F. Options to improve forced displacement statistics Significant efforts are needed to enhance the reliability, comparability, quality and scope of the global data on forced displacement. In particular, more robust estimates are needed of the scale (stocks, flows and locations) and typology (demographics, location and accommodation) of forced displacement crises. This requires substantial improvements in the rigor of data collection and compilation methodologies including: (a) Harmonization of definitions and methodologies used in the collection and analysis of statistical data on forced displacement — covering stocks and flows of refugees, asylum- seekers and IDPs — to ensure comparability across regions and countries; Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "percentage points less overall and 34 and 18 percentage points less in Eritrean and Somali camps, respectively. There is little benefit to working outside the camp to reduce aid reliance in South Sudanese households. On the other hand, years spent in Ethiopia are only associated with a gradual decrease in dependence on donations, with only a 1 percentage point reduction for every year spent in the country. The lack of employment outcomes contrasts with other refugee-hosting countries in East Africa, highlighting the lack of labor market access for Ethiopian refugees. Evidence suggests that refugees 0 5,000 10,000 15,000 20,000 25,000 Camp Hosts Camp Refugees Figure 3.19: Value of productive assets among households with non-farm business 0 0.05 0.1 0.15 0.2 0.25 Camp Hosts Camp Refugees Figure 3.18: Household has non-farm business Source: World Bank Staff based on SESRE 2023. Productive assets include the subset of assets with production value, such as farm tools and water pumps, sewing and building equipment, and commercial cars. Outliers are treated, and values are adjusted for inflation. 0 20 40 60 80 100 Camp Hosts Camp Refugees Current Camp Refugees COB Other (rental income, PSNP, pension) Remittances (local/international) Donations (NGO/gov) Crops/livestock Salary (employment/casual labor) 0", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "16 { Tables 4 and 5 here} Among the first six categories that are based on raw census data, three categories (raw scaled, R & R not scaled, and R & R scaled) are constructed through the summation of bilateral raw numbers and disaggregations of some aggregate categories in the original censuses. Since these categories together constitute around 45 percent of migrants in each census round, the original bilateral portion of each cell was compared with the final number assigned to them after the various calculations as a check on accuracy. For each decade, therefore, the overall percentage contribution of the raw bilateral data to the total is calculated (table 6). 23 In each census round, at least 92 percent of all those categories are derived from the raw data. { Table 6 here} Simulating Missing Data Finally, to examine the reliability of the estimated missing census data and test the methodologies, several scenarios are assumed. All bilateral observations for a single year for four countries (Australia, United States, Switzerland, and Chile) in different parts of the world are deleted and the missing cells are filled using one of five methods. 24 The first simulation assumes that all bilateral data for 2000 are missing but that the total number of migrants is available. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["raw census data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "to severe based on the score. Moreover, depression is screened with the Patient Health Questionnaire, a 9-item tool administered directly to adolescents. These instruments collectively gauge a broad spectrum of psychological and emotional states, including post-traumatic stress, emotional disturbances, behavioral issues, inat- tention, peer relationships, prosocial behavior, anxiety, and depression, offering a com- prehensive view of the mental health and socioemotional development of the minors in our study. Figures B. 3 and B. 4 depict the distribution of the raw scores for Venezuelan and Colom- bian minors for each of the four scales. Surprinsingly, we do not observe any stinking differences on the distribution of any of these scores across groups. We are also not able to distinguish statistical differences between Colombian and Venezuelan children in any of the scales, when we estimate the specification highlighted in equation (1) as illustrated in Table 7. This is an unexpected result considering that typically, forcibly displaced pop- ulations have a high prevalence of socioemotional and mental health issues, but might be related to the fact that Venezuelan migrants have not faced war (as many forced migrants have in other contexts) directly and as such, these issues are less prevalent.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "sexual encounter. Finally, about 10 % of study respondents have experienced a forced sexual encounter in their lifetimes, which is consistent with the national figure of 13 % for this age group (DHS 2007). 17 Table 2B presents baseline balance tests for the household characteristics of respondents in the study sample. More than 40 % of households are headed by females, and the average household has slightly fewer than five members. The mothers of EPAG respondents tended to have very low education levels (almost 60 % had never been to formal school), while the fathers had more variance in their education (about a quarter had no schooling, but over 60 % had at least some secondary education). In both treatment and control households, a high proportion of school-aged children are in fact enrolled in school. Less than half of young people aged 13-30 in study households have any employment. Housing conditions are also similar across experimental groups. Tables 2A and 2B include a representative, but not exhaustive, list of indicators that were tested for balance by the authors. We conclude that the presence of very few significant differences between the individual or household characteristics between the two experimental groups indicates a high degree of internal validity for the study. 3. 5. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The MHPSS program evaluation conducted at 18 months assessed changes in functional impairment, social participation, and perceived support among beneficiaries who received structured psychosocial support compared with a waitlist control group. Facilitators documented session attendance and content coverage using a standardized session log. The evaluation found meaningful reductions in psychological distress scores among participants, though the absence of clinical diagnostic instruments limited the ability to measure changes in diagnosable mental health conditions.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A significant increase in non-participation among men can be clearly seen in the West Bank in 2020Q2, mostly at the expense of the private infor- mal sector. The labor market then quickly bounces back. By 2020Q3, labor market stocks in the West Bank appear indistinguishable from pre-pandemic periods. Gaza, on the other hand, experienced three consecutive quarters of depressed employment from 2020Q2 to 2020Q4. Non-participation spiked twice, first in 2020Q2 and then in 2020Q4, corresponding respectively to the initial lockdown orders and the subsequent outbreak in Gaza. Recovery also appears to be slow and uneven. Figure (3) shows the labor market flows. We exploit the specific panel structure of the LFS dataset, described in Section 3. 1 by focusing on one cohort of the same respon- dents who were surveyed in 2019Q1, 2019Q2, 2020Q1, 2020Q2, and finally 2020Q4. This cohort of individuals allows us to observe labor market transitions into the pan- demic; to compare with a period over the same quarters in 2019; and, finally, to observe their recovery outcomes in 2020Q4. Overall, Figure (3) shows two labor markets with high levels of churning. On average, 29 % of individuals in the sample would change their labor market states after just one quarter. These churns are especially prominent between informal employment and unemployment, and in Gaza between unemployment and non-participation. The figure also illustrates the significant differences between the West Bank and Gaza in labor market dynamics, differences already observed in the labor market stocks presented in Figure (1). In addition to the significant flows between unemployment 11 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["LFS dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1 Introduction Company owners and managers make two decisions with important implications in the labor market: what skills are demanded and who to hire. On the demanded skills, a driver of trends in employment is the changing demand for soft skills (Heckman and Kautz (2012), Weidmann and Deming (2021)). However, we know relatively little about what kinds of soft skills employers value in modern entry- level jobs (Heller and Kessler, 2022). On the decision of who to hire, it is in the best interest of companies to hire based on workers productivity. However, several studies have documented the existence of labor market discrimination in a wide range of contexts (Bertrand and Duflo (2017), Neumark (2018)) and it remains unclear how discrimination operates throughout the hiring process and how the existent empirical evidence on discrimination is linked to economic theory (Bertrand and Duflo, 2017). We conducted a correspondence study in 2023 using a large online job platform to assess demand for soft skills in the context of hiring discrimination in Malaysia. Malaysia is a particularly interesting setting because it is an upper-middle-income economy, home to multiple ethnicities representing large shares of the population, and previously documented gender gaps in labor force participation and wages.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["large online job platform"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "by the migration episode. These include the minors age and sex, the parent ’ s and grand- parent ’ s education pre-migration, and a wealth index constructed with retrospective in- formation on the household conditions pre-migration. 14 Grandparent ’ s education is used a proxy for living standards that is unaffected by the migration episode from Venezuela to Colombia and the Venezuelan crisis, which intensified in 2016. ϵij depict the standard errors clustered at the household level to correct for intra-household correlation. For robustness, we will present the estimates of equation 1 with and without controls. As further robustness, we use propensity-score weights (Hirano and Imbens 2001, Hirano, Imbens and Ridder 2003). 15 V. A Physical development: Body Mass Index and health status In our study, we examine disparities in nutritional and health status among Colombian and Venezuelan children aged 5 to 10 years, focusing on standardized body mass index (BMI), instances of overweight and underweight, and overall health status. The BMI serves as an indicator of nutritional status for both adults and children, calculated as an individual ’ s weight in kilograms divided by their height in meters squared, according to World Health Organization (WHO) guidelines. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 inflow). We then use the composite variable as an instrument to identify the causal impact of refugee presence on livelihood diversification and commercialization in host communities. Figure 2: Household livelihood strategy framework under refugee inflow Source: Adapted from Nielsen et al. (2013) and Walelign and Jiao (2017) 4. Data sources The major data source of the current study is the World Bank ’ s Development Response to Displacement Impacts Project (DRDIP) 12 baseline survey from Ethiopia. The Ethiopia DRDIP survey was administered between September 2017 and August 2018. The survey covers 113 Kebeles (wards) in 16 Woredas (districts) from the top five refugee-hosting regions in Ethiopia. The selection of the sample households follows stratified random sampling with proportion to size (the number of households) using Woredas as a geographic stratum. The sample originally comprised a total of 3, 390 households, who were selected using systematic random sampling within each Woreda. We used data from 3, 375 households, as 15 of them were excluded due to missing location information (GPS). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As with the earlier two matching estimations, we matched the children within each DHS year.\n\nThis is around 7, 6, and 5 percentage points for the 2007, 2011, and 2014 DHS, respectively.\n\n\"The Effect of Water and Sanitation on Child Health: Evidence from the Demographic and Health Surveys 1986-2007.\n\n16 According to the Joint Monitoring Programme (JMP), an improved sanitation facility is defined as \"one that hygienically separates human excreta from human contact.\n\n\" The JMP considers the following categories as improved sanitation: flush to piped sewer system, flush to septic tank, flush to pit (latrine), flush to unknown place, ventilated improved pit latrine, pit latrine with slab, and composting toilet.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": ["DHS year"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Most papers with few exceptions use standard OLS estimators or some of its variants (Table 2). Two papers use general equilibrium models (Bodvarsson, Van den Berg, and Lewer 2008; Hercowitz and Yashiv 2002) and two papers simply compare means between treated and non-treated groups resulting in simple difference estimations (Card, 1990 and Alix-Garcia and Bartlett, 2015). [Table 2] The unit of observation varies depending on the data at hand. Most studies rely on household survey data where individuals or households are the unit of observations and most studies include some regional dimension (more frequently administrative areas). Where longitudinal or panel data are available time is also included. Other choices for unit of observations include skills or education level, various types of population groups (based on gender, age etc.), and, in a few cases, economic sectors, industry or labor market segments. The use of fixed effects varies. Some papers use the full set of parameters depicting units of observation (for example, household, region and time fixed effects in equations where the unit of observation is constructed using household, region and time). Other papers use subsets of these parameters whereas other papers introduce variables that are not used to identify the unit of observation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Programs designed to enhance employment among young people can focus on the supply side of the labor market, including skills training programs for both wage employment and entrepreneurship training; or programs to augment demand, such as wage subsidies, public works, and community service programs; or programs to help the labor market clear, such as job search assistance and placement services. In addition, it may be that the constraints facing young people are not in the labor market itself, but in other markets, such as for credit. The vast majority of jobs programs have focused on the supply side: training programs make up about 79 percent of over 600 cases included in the World Bank ’ s Youth Employment Inventory database. 3 Although rigorous and general evidence of success is limited, it seems that successful skills training programs share a few key features: they are responsive to local market conditions, they provide more than just technical skills in a specific area (including, for example, “ life skills ”), and they include ancillary services that alleviate other constraints preventing successful labor market integration (e. g., access to credit). Among the most celebrated are the Jovenes programs that provide demand-driven technical training, plus social skills that help in the labor market, plus internships. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Youth Employment Inventory database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Another way to view the connection between low return on education and youth frustrations is that the rapid intergenerational mobility in education has failed to yield similar mobility in either income or social status. This delinking between educational and occupational mobility has been documented for Egypt by Binzel and Carvalho (2013). While there is no similar work on Jordan, this article contributes to this agenda by documenting the first step in this process, which is the link between public investment in schooling and the educational mobility across generations. 1. This is based on version 2. 0 of the Barro-Lee dataset for educational attainment among the total population 15 and older.", "output": {"entities": {"named_data": ["Barro-Lee dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We have constructed two different variables that try to account for the degree of severity of the conflict. 10 The first variable identifies individuals belonging to households that were displaced due to the 1999 wave of violence (all members displaced). The second variable identifies individuals in households that report having their house completely destroyed by the violent attacks in 1999. The TLSS 2001 contains also useful retrospective information on school attendance and grade attained across three different academic years: 1998 / 99, 1999 / 00 and 2000 / 01. We are 7 Commission for Reception, Truth and Reconciliation & Benetech Human Rights Data Analysis Group. ― Human Rights Violations Database. ‖ 9 February, 2006. Website: http: / / www. hrdag. org / resources / timor-leste_data. shtml. 8 There may be potential sample biases in the statement taking procedure given the voluntary nature of the process. It is possible that those living in more remote or mountainous areas, those living far away from the areas where the statements were taken, the sick, old and disabled and those with no access to the media or means of mass communication have a lower probability of being part of the sample. By contrast, those more active in local communities are more likely to have provided a testimony. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Human Rights Violations Database", "TLSS 2001"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Usual) which would lead to even higher emissions.\n\n\nWe rely on four climate models: CNRM (Gueremy et al. 2005), ECHAM\n\n\n(Cubasch et al 1997), GFDL (Manabe et al. 1991), and MIROC (Hasumi and Emori\n\nshown in Figure 1. Note that a range of temperature changes are predicted for this\n\n\nemission scenario.\n\n\nThis study\n\n\nUsing a tropical cyclone generator in each ocean basin, the climate data is used to\n\n\nproject 17,000 tropical cyclone tracks (Emanuel et al. 2008). There are 3,000 tracks in", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "6 3. Data Sources and Methods 3. 1. Administrative data on educational outcomes in Italy Two administrative data sources represent the backbone of this paper. These are the administrative data obtained from the Ministry of Education (MoE) for academic years 2021-22, 2022-23 and 2023- 24, and standardized test score data from the Italian National Institute for the Evaluation of the Educational System (INVALSI) for the 2022-2023 academic year. These datasets offer valuable insights into the educational outcomes of students in Italy, including Ukrainian refugees who entered the Italian school system following the invasion in 2022. Deϐinitions. In both datasets, students are categorized into ϐive demographic groups based on their nationality and timing of entry into the Italian educational system. These groups are Italian students, Ukrainian refugee students, non-refugee Ukrainian students, newly arrived foreign students, and other foreign students. Among Ukrainian students, the distinction between refugees and non- refugees is based on their enrollment date in the Italian education system. Ukrainian refugees are deϐined as Ukrainian students who enrolled in Italian schools after February 2022. In this paper, Ukrainian refugees are labeled “ Ukr post-Feb 2022 “, while non-refugee Ukrainians are labeled “ Ukr pre-Feb 2022 “.", "output": {"entities": {"named_data": [], "descriptive_data": ["standardized test score data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "UNICEF (2017) reports that for boys, child labor, school violence, and the high costs of schooling (for transportation and stationery supplies) are the main barriers to their enrollment. For girls, barriers include the distance to the nearest school, the high cost of transportation, the need to help with household chores, health problems, and families refusing to educate their daughters. According to the JD ‐ HV database, the most common reasons parents gave for their children not attending school were financial constraints (35 percent), lack of capacity in schools (29 percent), or that children were required to work to support their family (14 percent). By 2016, the enrolment rate of Syrian refugee school ‐ age children in Jordan was 83 percent, 54 percent in formal education, and 29 percent in nonformal education (World Bank 2017b). 10 Refugee Employment It has been hard for refugees to find work in Jordan. The slowdown in growth in Jordan pre ‐ dates the arrival of Syrian refugees and the economy has been increasingly unable to absorb new labor market entrants. Between 2010 and 2016, labor force inactivity increased, employment decreased, and unemployment increased in Jordan (Malaeb and Wahba, 2018). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["JD ‐ HV database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "conomic characteristics. The data suggests that both groups were comparable and that those referred by forced migrant organizations were not more vulnerable before migration. Of 15 characteristics analyzed, only the time of settlement in Colombia was statistically different between groups. While this difference is mechanical (because RAMV migrants migrated earlier in general and likely referred other forced migrants who migrated around the same time), it is also small (less than one month). Moreover, in Figure C. 1, we show that date of arrival was uncorrelated with an index constructed with baseline socioeconomic character- istics of migrants in our sample during our period of analysis. We address concerns related to biases introduced by the characteristics of migrants sam- pled through different sources by estimating the local effects for RAMV and non-RAMV migrants who migrated around the RAMV cutoff date. First, we checked the internal valid- ity of this empirical strategy by showing that RAMV and non-RAMV migrants who arrived around the cutoff date were comparable based on a rich set of baseline observables (Table 1). Second, we checked for the comparability of RAMV and non-RAMV referrals from or- ganizations and the comparability of RAMV and non-RAMV referrals from other migrants (Tables B. 2 – B. 3). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The comparison between ∆ h is for actual and hypothetical destinations tells us whether the actual district of destination is more densely populated than alternative destinations. Results are presented in Table 3 for all variables used in the analysis. We begin with district log income eδs. We have two estimates of eδs, one obtained using reported income data, and the other based on reported consumption data. Given that most respondents to the NLSS survey are self-employed, measurement error is typically larger for income than for consumption. We see that our estimates of log income and consumption eδs are on average 20 % and 8 % higher in 20", "output": {"entities": {"named_data": ["NLSS survey"], "descriptive_data": [], "vague_data": ["reported consumption data", "reported income data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The 2005 insurance policy's surety bond requirement created delays for three smaller contractors who lacked access to the commercial surety market at competitive rates. To resolve this, the PIU negotiated a framework agreement with two national insurance companies approved by the Insurance Regulatory Authority to provide project performance bonds under the 2005 insurance policy at standardized premium rates. This arrangement reduced average bond issuance time from 22 days to 4 days and has been incorporated into the standard bidding documents for subsequent civil works contracts.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of the groups and the distances between them. For instance, (Bazzi et al., 2019) shows that polariza- tion increases ethnic attachment. Others have highlighted the reduction in trust, either interpersonal trust or institutional trust Alesina and Ferrara (2002); Beugelsdijk and Klasing (2016). To assess the importance of alternative explanations, we first replicate our analysis using individual data on violence. In addition to participation in protests, we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault. We then assess the relationship between the revised refugee diversity indices and alternative individual outcomes such as ethnic vs. national identity, generalized trust, trust in neighbors, and institutional trust (trust in government). The questions from the Afrobarometer mentioned below are used as a proxy for these outcomes: 32 1 Attack: Over the past year, how often (if ever) have you or anyone in your family: Been physically attacked? 2 Crime: Over the past year, how often (if ever) have you or anyone in your family: Feared crime in your own home? 3 National identity: Let us suppose that you had to choose between being a [Ghanaian / Kenyan / etc.] and being a [respondent ’ s identity group].", "output": {"entities": {"named_data": ["Afrobarometer survey data"], "descriptive_data": [], "vague_data": ["individual data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Although much more suited to geographically disaggregated analysis than other datasets, this location dataset has some limititations, It does not record changes over time in the center location and extent of conflicts, and it reports the total extent of the conflict zone without distinguishing between areas that saw repeated and extensive fighting and those that only experienced scattered activities or individual events far from the center of the conflict. 3. 2 Disaggregated Dependent Variable: ACLED The ACLED dataset (Raleigh & Hegre, 2005) deals with these problems. The dataset takes the PRIO / Uppsala Armed Conflicts Dataset as its point of departure. The dataset is limited to events within conflicts that fall within the Uppsala conflict definition; conflicts involving two parties, one of which is a government, and fighting resulting in at least 25 battle deaths. 3 ACLED is designed to parse out both the temporal and spatial actions of rebels and governments within civil wars. 3See the PRIO / Uppsala Armed Conflict Data codebook for more information (Strand, Wilhelmsen & Gleditsch, 2004). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["PRIO / Uppsala Armed Conflicts Dataset", "ACLED dataset"], "descriptive_data": [], "vague_data": ["location dataset"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Our analysis exclusively uses data from the 1951-2015 UNHCR Population Statistics Reference database (extracted September 18 2015). Data were provided for 173 countries: 77 percent of these data were based on individual refugee registration, 13 percent on estimates, 5 percent on combined estimation and registration, and 5 percent on other sources. The data are structured as follows: for each situation, the database records annual numbers of persons of concern, which comprise “ Refugees (including refugee-like situations) ”, “ Asylum seekers ”, “ Internally Displaced Persons ”, “ Returnees ”, “ Stateless ” persons, and “ Others of concern ”. A situation is a pair country of origin / country of destination. For example, Somali refugees in Kenya account for one situation, Somali refugees in Ethiopia for another, and South Sudanese refugees in Kenya for yet another. Furthermore, a situation is considered major if it involves more than 25, 000 people. It is referred to as protracted if it is major for at least 5 continuous years. The database, and therefore our analysis, is limited to refugees under UNHCR protection. It does not include asylum seekers, i. e. individuals who have sought international protection under the 1951 Convention but whose claims for refugee status have not yet been determined, and persons in “ refugee-like situations ”, i. e. individuals outside their country or territory of origin who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained (e. g., undocumented Rohingya originating from Myanmar 4 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The MPI can be decomposed by any groups for which the data are representative and broken down by indicator to show the composition of multidimensional poverty, adding to the policy relevance of the analysis. To tackle individual-level and intrahousehold analyses, we build on the work of Alkire, Ul Haq and Alim (2019). The focus is on individual deprivations, and we call the persons with individual-level data in each indicator the eligible household members. For example, children aged 6-16 years might be eligible for deprivations in terms of school attendance, but not those older or younger. For individual-level indicators, we identify who and how many household members are deprived: their gender and their age, and what proportion of eligible household members are deprived. This is a powerful and potentially informative steppingstone for analysis. Consider two households, each of which has five eligible members with data on nutrition. The aggregation rule in this example is that if any household member is undernourished then the household is undernourished. So, both households are deprived in terms of Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The study will conduct an ex-ante micro- simulation using pre-crisis household data (IHSES 2012) and macroeconomic projections for 2014 to gauge the distributional impact of the crises across groups (e. g. individuals and / or households, sectors, IDPs and host communities) and space (e. g. urban / rural, governorates).  The Economic and Social Impact Assessment for Kurdistan Region of Iraq [completed in 2015] provides an analysis of the impact of displaced people on access to and quality of service delivery across several sectors.  The Lebanon Economic and Social Impact Assessment of the Syria Conflict [completed in 2013] provides an analysis of the impact of displaced people on access to and quality of service delivery across several sectors.  The Bank and UNHCR undertook a welfare assessment of Syrian refugees living in Jordan and Lebanon [completed in 2016] focusing on welfare, poverty and vulnerability. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["IHSES 2012"], "descriptive_data": ["pre-crisis household data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Production of soybeans QSB 41,000 metric tonnes Faostat [a] Imports of soybeans MSB 27 metric tonnes Faostat [b] Export of soybeans XSB 14,445 metric tonnes Faostat [b] Diesel consumption D 596.84 mil. liters Energy Sector Report [c]\n\nDiesel price PD 0.92 $/liter Energy Sector Report [c]\n\n\nReport of the Status of\nDiesel tax t 0.30 $/liter\nPetroleum Industry [d]\n\nMPG biodiesel/MPG diesel λ 0.91 de Gorter et al. (2011)\nLocal price of soybeans at Beira linked to Argentian price PSB 370.24 $/metric tonne HighQuest Partners (2011)\n\nBiodiesel price in Germany PB 1.64 $/liter UFOP [e]", "output": {"entities": {"named_data": ["Faostat"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Geospatial data from the national land registry, including parcel boundary polygons, ownership classification codes, and historical transaction records, were reviewed during project preparation to assess land tenure risks in the areas earmarked for infrastructure works. The geospatial data revealed that 23 percent of subproject sites intersected with unresolved customary land claims, triggering the preparation of individual resettlement plans for the affected sites. Geospatial data will be updated following the completion of land demarcation activities planned under Component 1.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Component 3: Institutional Strengthening and Capacity Building (US$2.1 million)**\n\nThis component will finance: (i) a functional review of the national disaster risk management agency's M&E systems, including an assessment of the SIGAF modules currently in use for project accounting; (ii) training of 120 national and subnational staff in financial management, procurement, and environmental and social risk management; and (iii) development of a communications strategy to disseminate project results to beneficiaries and the public. Capacity building activities will be designed around the competency gaps identified through a training needs assessment completed during project preparation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 crop products (e. g., wheat, potatoes) and the value of five livestock products (i. e., milk, egg, butter, hides, and honey) sold in the market. 14 Several other data sources were utilized. First, the Ethiopian refugee camps location data set from the Humanitarian Data Exchange (HDX) 15 and the total number of refugees by camps from the United Nations High Commissioner for Refugees (UNHCR), Addis Ababa office. We use data from 26 official UNCHR refugee camps in Ethiopia that were operational in 2018 (see Figure 1; 3). Second, we use administrative data sets for Ethiopia and refugee source countries from the database of Global Administrative Areas (GADM). 16 We also use the conflict data set from the Armed Conflict Location and Event Data Project (ACLED) 17 and the population data from the Gridded Population of the World (GPW) data set. 18 On the basis of these data sets and the location of sample households from Ethiopia DRDIP data set, we generated the following variables: i) distance of sample households to the nearest refugee camp, the nearest region (administration level 1 in GADM) to the refugee camps, ii) distance of the refugee camps to the nearest border of the refugee source country, iii) Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Gender Perceptions- Violence (IPV) The standardized total score of ﬁve questions regarding norms for inti- mate partner violence (IPV) from the Demographic and Health Survey (DHS) (The important decisions in the family should be made only by the men of the family. How often would you agree? The wife has the right to express her opinion even when she disagrees with what her husband is saying. How often would you agree? A wife should tolerate being beaten by her husband in order to keep the family together. How often would you agree? A husband has the right to beat his wife. How often would you agree? It is more important to send a son to school than it is to send a daughter. How often would you agree?). Financial Well-being Savings Response to the question “ How much money do you currently have in savings? ” During the collection surveys (midlines) this question instead asked “ How much money did you save in the past week? ” Borrowing Total amount of money the household has borrowed. Economic Decision Making Risk Preference Measured using incentivized responses to the multiple price list deci- sions adapted from Holt-Laury and Sprenger (2002).", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Procurement Performance**\n\nAs of the midterm review, all prior review contracts have been awarded and counter-signed, and 83 percent of goods and non-consulting services contracts have been awarded against the Year 2 procurement plan. Delays in two civil works contracts are attributed to a late ESMF clearance for one subproject and to difficulties in obtaining land access certification for a second. Both contracts are now unblocked, and the STEP system has been updated to reflect the revised award dates. No misprocurement has been identified during Bank reviews of contract documentation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Our nightlights data set is composed of 22 satellite-year composites for the period 2000 2013. Each composite covers Central America and contains information on 604,473 one kilometer square grid-cells.\n\nTo measure the distribution of surface winds from hurricanes in Central America during our sample period, we use the wind field model developed by Pita et al. (2015). This model uses an asymmetric Holland equation that has been specifically calibrated for Central America.\n\nData on the distribution of mangroves come from two sources. The first is a collection of harmonized maps, 1960 to 1996, that was assembled for the Mangrove World Atlas (Spalding et al., 1997). We use this map to identify areas that have historically supported mangrove habitats.", "output": {"entities": {"named_data": ["Mangrove World Atlas"], "descriptive_data": ["nightlights data set"], "vague_data": ["harmonized maps"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "standard ﬁxed or random eﬀect approach is not suﬃcient to ensure correct inference; clustering standard errors by individual is necessary. This is what we do. Having described how the dependent variable is constructed, we turn to regressors. We begin by describing how we construct an estimate of g E [yhs | zh], the level of income (or consumption) yh s that a migrant with characteristics zh can expect to earn in district s. To construct such estimate, we use the 1995 / 96 NLSS data. The reason for using the 1995 / 96 data instead of the 2002 / 3 NLSS survey is to avoid reverse causation, i. e., migration causing a change in income patterns. Migrants are unlikely to be able to accurately predict the evolution of incomes in each district over time. Income and consumption levels observable before migration are thus a reasonable starting point.", "output": {"entities": {"named_data": ["1995 / 96 NLSS data", "NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "24 According to Table 12, all countries but Sudan show a significant relationship for school- age children ’ s experience of intrahousehold inequality and their displacement status, although sample sizes mean that only Nigeria and Somalia ’ s results are robust. In Nigeria, most children experiencing intrahousehold inequality reside in non-displaced households – although it is crucial to note that these children constitute most of the school-age children (71. 1 %), and the levels of intrahousehold inequality are nonetheless far higher than anticipated if displacement status had no effect. In Somalia, displaced children are significantly more likely to experience intrahousehold inequality in school attendance, as they constitute 63. 5 % of school-age children experiencing intrahousehold inequality even though they only make up 35. 3 % of the school-age children population in the sample. For the years of schooling indicator, the overall lack of intrahousehold inequality among the MPI poor in years of schooling obscures meaningful or robust differences by displacement status. Gender and displacement status appear to jointly have significant impacts in school attendance in Ethiopia, Somalia, and South Sudan. In Northeast Nigeria, it appears that displacement status has larger effects than gender. In Somalia, forcibly displaced school children experience intrahousehold inequality more often than non-displaced children, to the disadvantage of girls. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2017 SESRE 2023 (In camp) Hosts Refugees Hosts Refugees Country of origin South Sudanese 23% 53% Somali 24% 30% Eritrean 25% 5% Sudan 28% Demographics Female headed 35% 66% 44% 73% Children (0 to 14) 55% 61% 47% 53% Youth (15 to 24) 13% 17% 19% 21% Adults (25 to 64) 29% 22% 31% 24% Elderly (>=65) 2% 1% 3% 2% Education Net primary enrollment 74% 79% 75% 69% Net secondary enrollment 35% 13% 39% 22% Living conditions Own a house 72% 5% 69% 14% Overcrowding 32% 59% 36% 56% Improved sources of water 96% 98% 91% 100% Access to electricity (grid) 46% 8% 41% 3% Improved toilet facility (shared/not shared) 51% 69% 38% 43% Employment Employed 61% 22% 48% 25% Unemployed 2% 6% 9% 19% Inactive, not in school 23% 44% 20% 23% Inactive, in school 14% 27% 24% 33% Poverty incidence US$1.9 per capita per day 27% 65% National Poverty line 32% 84% Food security High food insecurity 26% 67% Food insecurity scale 4.0 8.1 Social cohesion Economic competition 33% 49% Increased insecurity 37% 39% Source: Pape et al. (2018) and World Bank Staff based on SESRE 2023.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Landscan Population Data. Oak Ridge National\nLaboratory.\nHallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D., Vogt-\nSchilb, A., 2016.\n\nAt the national-level analysis, we overlay the flood hazard maps developed for this study with spatial socioeconomic data. For Vietnam, the World Bank has produced estimates of the number of people within each district who live below the poverty line: this \"poverty map\" is displayed in Map 4a, and the full methodology can be found in (Lanjouw, Marra, and Nguyen 2013). In addition, we use gridded population density data with a 1km resolution from Landscan (Geographic Information Science and Technology 2015). This \"population map\" is displayed in Map 4b.\n\nThe spatial socioeconomic data set used for Ho Chi Minh City is a data set of potential slum areas and of urban expansion from 2000 to 2010, from the Platform for Urban Management and Analysis (PUMA), a city-level data set developed by the World Bank (World Bank 2015).This data was collected via satellite in the year 2012, through a combination of visual interpretation of various sources and vintages of imagery.\n\nThe inundation maps were used in an earlier flood risk study of HCMC (Lasage et al. 2014), and were\ncomposed with the MIKE 11 hydraulic modeling software (DHI 2003).", "output": {"entities": {"named_data": ["Landscan Population Data", "Platform for Urban Management and Analysis (PUMA)"], "descriptive_data": [], "vague_data": ["inundation maps"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel. 19 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["EPR-ER data", "Murdock Atlas", "EPR-ER"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The instrument becomes even weaker if we use refugee to population ratios rather than the number of refugees, therefore we use the absolute number of refugees throughout. 4Including 2014 data in the DD estimations generally results in more statistically signiﬁcant and larger coeﬃcients but does not change the direction of the results. 5The weights are calculated using the Stata package synth provided by the authors. We use the option nested to calculate the weights through the nested optimization procedure described in Abadie et al. (2011). 6Tunceli ’ s 2011 export and import values are missing for 2011, therefore only 2009 and 2010 values could be used in calculating Tunceli ’ s average. 7The Chamber of Commerce also provides information on the number of ﬁrms that shut down. However, reporting exits is not mandatory and the indicator is therefore less reliable. We found no signiﬁcant eﬀects in both the IV and DD estimates on the number of ﬁrms that shut down. 8Since the number of new foreign ﬁrms is 0 in several observations, we add 1 to the value. As an alternative, we used the hyperbolic inverse sine transformation which does not have the same problem with 0s as log transformation and found similar results (Burbidge et al., 1988). 9Publicly available data from the Ministry of Science, Industry and Technology indicate that less than 10 % of total revenue is from micro-establishments. Most of the net sales and gross proﬁts reported stem from larger ﬁrms that should be included in the Chamber of Commerce data. 10All ﬁrms exceeding 200, 000 Turkish Liras (ca. $ 85, 000) in sales are obligated to report detailed balance sheets. Smaller ﬁrms may still report their balance sheets but would be doing so on a voluntary 25 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Oseni and Pollitt (2013) use cross-sectional data on electricity outages collected by the 2007 World Bank Enterprise Survey of 6,854 firms in 12 African countries.\n\n** **Data** We used the social accounting matrix (SAM) of year 2007.\n\nWe use a choice experiment study to fill this knowledge gap and identify preferences for\n\ngoods, when market data are not available for assessing these valuations. A choice experiment\n\n\nsurvey presents the respondent with choice scenarios where each choice scenario has\n\nThe enumerators spoke local languages and were trained in conducting choice   experiment surveys (and most had previous experience with collecting choice experiment data", "output": {"entities": {"named_data": ["2007 World Bank Enterprise Survey", "social accounting matrix (SAM) of year 2007"], "descriptive_data": [], "vague_data": ["choice experiment\n\n\nsurvey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "After a short respite in November, Israel imposed a third lockdown from December to February the next year. Gradual easing continued throughout March. During this lockdown, vaccination was rolled out, including to commuters with valid permits. Given this context, we expect the main shock to occur in 2020Q2 in the West Bank, with negative effects of smaller magnitudes to persist over the next quarters. In Gaza, we expect another major shock in 2020Q3 that carries over to Q4. We expect the commuters ’ outcomes to instead track events in Israel and occupied territories more closely, with a major shock in 2020Q2, recovery in Q3, and another dip in Q4. 3 Data 3. 1 Data sources and definitions We employ data from the Labor Force Surveys (LFS) of the West Bank and Gaza, collected by the Palestinian Central Bureau of Statistics and prepared by the Economic Research Forum. The surveys are conducted on a quarterly basis, covering periods from 2000 onwards. The LFS is meant to represent all households whose ordinary residence is in the West Bank and Gaza, though their place of work need not be. The LFS is representative at the region level (respectively of the West Bank and Gaza), as well as at the level of locality types (urban, rural, and refugee camps) (Palestine- Labor Force Survey, LFS, 2021). Importantly for the purpose of our analysis, the data have a panel dimension, en- abling the study of labor market transitions. The sample rotation scheme is described 9 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Labor Force Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Surveys, 76 Demographic and Health Surveys (DHS), 77 and Multiple Indicator Cluster Surveys (MICS). 78 The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner (UNSD 2014). There are only a few cases where modules or questions on forced displacement have been integrated into survey instruments (most notably in Somalia, Uganda, West Bank and Gaza, Azerbaijan, Bosnia and Herzegovina, Serbia, Ghana, as well as health surveys in Albania, Ukraine and Moldova). There are several challenges associated with ‘ mainstreaming ’ forced displacement into household surveys: (a) there is huge demand for adding sector-specific or thematic modules to international surveys; (b) it is relatively difficult to convince national statistical agencies to modify their county-specific surveys; (c) disaggregating survey results by specific vulnerable groups (e. g. refugees, IDPs, migrant populations) requires these distinctions to be integrated into the sampling frame and sometimes there is insufficient information to do this or a lack of resources to expand the sample size; (d) lack of access to displacement- affected areas; and (e) difficulties associated with integrating an inherently political topic into less controversial surveys.", "output": {"entities": {"named_data": ["Multiple Indicator Cluster Surveys", "Demographic and Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Refugees In camp Addis Ababa Total Attending school Primary school Secondary school 0 10 20 30 40 50 60 70 80 90 100 Primary school (7 to 14 years) Secondary school (15 to 18 years) Percent Percent Figure 2.10: Children currently attending school Source: World Bank Staff based on SESRE 2023. a. Attended education b. Education level a. All children b. School-age children 20 The Primary Gross Enrollment Rate (GER) is the ratio of the number of children enrolled in primary school irrespective of age to the number of children of primary school age (age 7 to 14). 21 The Primary Net Enrollment Rate (NER) is the ratio of the number of children of primary school age enrolled in primary school to the number of children of primary school age (age 7 to 14). 22 The secondary Gross Enrollment Rate (GER) is the ratio of the number of children enrolled in secondary school irrespective of age to the number of children of secondary school age (age 15 to 18). 23 Secondary Net Enrollment Rate (NER) is the ratio of the number of children of secondary school age enrolled in secondary school to the number of children of secondary school age", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The UNHCR supported in engaging refugee communities and leaders. The RRS facilitated access to all camps for the survey teams; this is the first time that the RRS facilitated access to all camps for such an extensive survey. Notably, the UNHCR and RRS facilitated the collaboration of the field workers with refugee leaders in each camp and Addis Ababa to support the teams in identifying sampled households and maintaining the safety of the field workers, and the sampled households during the entire survey period. The World Bank team led the collaboration between ESS, UNHCR, and RRS and provided technical support to the ESS since the project’s inception. The SESRE used a logistics plan similar to HoWStat. Six ESS branches were responsible for administering the survey: the Asayita, Gondar, Jigjiga, Negele, Gambella, Assosa, and Addis Ababa branches. Twenty-four field teams carried out the fieldwork, each consisting of one statistician, one team supervisor, and four enumerators. All field staff involved in the SERSE participated in the HoWStat survey. Enumerators were knowledgeable about local cultures and languages and could detect inconsistencies and misunderstandings during interviews to ensure high-quality data. Supervisors were additionally trained on how to troubleshoot standard technical issues with tablets. The", "output": {"entities": {"named_data": ["HoWStat survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Another ex- planation of these results could be the vulnerability of the Colombian population who in many cases has also a long history of internal forced displacement and violence. V. D Social Cohesion We also delve into the differences in secondary outcomes among Colombian and Venezue- lan adolescents concerning social cohesion. We focus on assessing altruism, trust, iden- tity towards specific domains, networks, and experiences of discrimination. To measure altruism and trust, we employ the questions from the Global Preference Survey, a tool developed by Falk et al. (2022) to elicit risk, time, and social preferences. Specifically, to measure altruism we ask the adolescents how much of a fictional endowment would they be willing to donate to a good cause. To measure trust, we include the 7-itme ques- 38", "output": {"entities": {"named_data": ["Global Preference Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This assessment is made possible through the use of state of the art transportation modeling techniques and by combining three data sets: 1) An innovative General Transit Feed Specification (GTFS) data set collected for the purpose of this analysis under normal (dry) and flooded (wet) conditions, 2) a travel survey data set with travelers' socioeconomic attributes and Origin-Destination (OD) information, as well as 3) a set of high-resolution global flood maps that capture both the extent and depth of pluvial and fluvial floods.\n\n**3.2** **General Transit Feed Specification (GTFS) Public Transit Feed**\n\nWong, J. C. (2013). _Use of the general transit feed specification (GTFS) in transit performance measurement_ [Thesis,\nGeorgia Institute of Technology]. https://smartech.gatech.edu/handle/1853/50341\n\nTo understand the impact of flooding on public transit, we compared the GTFS transit feed mapped under dry and wet conditions and evaluated changes in headways, blockages of roads and rerouting, and travel speeds.", "output": {"entities": {"named_data": ["General Transit Feed Specification (GTFS)", "General Transit Feed Specification (GTFS) Public Transit Feed", "GTFS transit feed"], "descriptive_data": ["travel survey data set"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Sudan domain (.48 standard deviations below the mean). It is higher for female than male hosts in both domains, by .14 standard deviations from average in the Somali domain and .19 standard deviations in the South Sudan domain. The difference in attitudes between male and female hosts is not statistically significant, and inter-group attitudes vary little by age or education. We can study the characteristics associated with attitudes by putting this index into a regression 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Addis Ababa All Hosts All Refugees Neither are trustworthy Only my group Only the other group Both are trustworthy Percent Figure 7.9: Are most Ethiopians/refugees in Ethiopia trustworthy? Source: World Bank Staff based on SESRE 2023. Note: Combines two questions regarding trust in Ethiopians and refugees, with identical wording to both Ethiopians and refugees. 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 Eritrean Somali South Sudanese Addis Ababa All Hosts Figure 7.10: Host attitudes Index Source: World Bank Staff based on SESRE 2023. Note: This index is an average of ten questions regarding beliefs about refugees’ character, the rights they should", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "their earnings on household expenses than men. Most of these studies have focused on adult women, specifically married women with children. It is not known whether the same holds true for young women, who may have other spending priorities, have less experience in managing households, and have younger children. Given the large increases in employment and earnings documented above, the EPAG program serves as a good setting to examine these types of spillovers. The evaluation included detailed interviews with the heads of the household in which EPAG participants were residing. The purpose of the household questionnaires was precisely to examine the hypothesis that investing in young girls would benefit her household. A secondary hypothesis was that EPAG participation may change gender-related attitudes in the participants ’ households. Household data was collected for 1601 out of the 1622 individuals who were interviewed at both baseline and midline; this same sample of 1601 individuals serves as the basis for both the individual and household level analysis in this paper. The estimated impact of the program on a broad range of household outcomes is summarized in Tables 8 and 9. Panel A of Table 8 examines the household size. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "#### 4. RESPONSE\n\nThe Protection Sector in Sudan comprises General Protection, Child\nProtection, GBV and Mine action AoRs. There is also active Housing\nLand and Property and Durable Solutions Working groups that work\nclosely with Protection Cluster. These structures meet every month\nat the national and state level (five Darfur, South Kordofan and Blue\nNile). The Protection Sector developed its strategy and work plan.\nThe Sector developed the Protection of Civilians’ Incidents Tracking\ntool in consultation with other agencies. It also publishes maps of\n\nhealth care providers and called on Sudanese authorities to enforce\ninternational”, Report of the Secretary-General, 2 March 2022", "output": {"entities": {"named_data": [], "descriptive_data": ["maps of\n\nhealth care providers"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Excluding respondents who have relocated would omit those with the higher rates of income growth and poverty reduction. Table 5 reports confidence intervals for the incremental samples (which are not mutually exclusive); it gives a more detailed picture of how inference on consumption growth and poverty reduction would have changed if we had not tracked movers. It is apparent that inference from a ‘ simple ’ panel survey of respondents continuing to reside within the original communities would have produced underestimates of actual consumption growth and poverty reduction in this population. These conclusions are robust across the distribution of consumption, as well as at the mean and poverty line. Panel A in Figure 2 depicts the cumulative density function for consumption per capita for those people who remained living in the same community. Panels B, C and D make the same graph for respondents found residing in neighboring communities, elswehere in Kagera Region and outside Kagera Region. As respondents were located further from their location in 1991, so the difference between the 1991 and 2004 graphs becomes more pronounced. Note how, for people who remained in the baseline community, the 1991 and 2004 distributions lie close to each other under the poverty line and diverge above it, while for other mobility categories there is more divergence at the bottom of the graph. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Those who had moved out of the Kagera Region by 2004 experienced consumption growth that was 10 times higher compared to those who remained in their original community. These averages translate into very different poverty dynamics patterns for the physically mobile and immobile. For those who stayed in the community, poverty rates drop by about 4 percentage points over these 13 years. For those who moved elsewhere within the region, poverty rates drop by about 12 percentage points, and for those who moved out of the region, they drop by 23 percentage points. Had we not tracked and interviewed people who moved out of the community – a practice found in many panel surveys – we would have seriously underestimated the extent to which poverty has gone down over the past 13 years in the Kagera Region; we would have reported poverty reduction at about half of its true value. Clemens and Pritchett (2007) raise similar concerns in the context of income growth and international migration. In addition, the data would omit the part of the population with a high information content on pathways out of poverty. Still, these statistics are not evidence that moving out of the community leads to higher income growth. As noted above, we cannot observe the counterfactual: What would income growth have been for migrants had they not migrated? We exploit some unique features of these data to address concerns about unobserved heterogeneity. First, individual fixed effects regressions for movers and stayers produce a difference-in-difference estimation of the impact of physical movement, controlling for any fixed individual factors that affect consumption. Second, we can control for initial household fixed effects in the growth rate of consumption since we observe baseline households in which some individuals migrate Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "impression of the state ’ s role in development and give credit to the state for helping to leverage external resources. Citizens are also likely to give the state credit where mechanisms to voice complaints about non-state actors exist and where bureaucrats are able to eﬀectively respond to complaints. Under these conditions, non-state service provision is likely to strengthen the ﬁscal contract. 5 Data and Methods I explore the relationship between external service provision and deference to government using Afrobarometer survey data from 19 Sub-Saharan African countries (see Table 1). Africa is an especially good place to examine these issues because of the large amount of variation both within and across African countries in the extent to which non-state actors, donors and other states are active in service provision and the extent to which governments are relatively eﬀective and fair. Government responsiveness, corruption and reliance on non-public resources vary considerably among localities with consequences for citizen understanding of and relationship to government (Gibson and Hoﬀman, 2005). This project relies on the fourth round of Afrobarometer data that surveys Africans ’ views towards democracy, economics, and civil society with random, stratiﬁed, nationally representative samples. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Chinese Exports in Agriculture and Food Products**   Value of Exports in Billion USD        _Source_ : COMTRADE Database, Authors calculation    19     <!-- PAGE 23 -->  **Table 1.\n\n6 [http://faostat.fao.org/](http://faostat.fao.org/)   6     <!-- PAGE 10 -->  standards and heavy regulation (Chen and Findlay 2008).Chinese exports in agricultural and  food products are increasing.\n\nAlthough there is no data on the implementation of  voluntary standards, the China Statistical Yearbook of Certification and Accreditation (cited in  Jin et al.", "output": {"entities": {"named_data": ["COMTRADE Database", "faostat", "China Statistical Yearbook of Certification and Accreditation"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "access for refugees by easing restrictions and providing work permits. ◆ Integrate refugee children into national education system to improve their long-term prospects. ◆ Strengthen inclusive healthcare systems to address the health needs of refugees. Focus on place-based interventions: ◆ Invest in refugee hosting areas to benefit both refugees and host communities. ◆ Direct additional educational resources to districts hosting refugees to support integration. ◆ Expand access to social safety nets for vulnerable refugees and hosts. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Comfortable Neutral Not comfortable Percent Figure ES.7: Host response to “Would you feel comfortable having a refugee as a neighbor?” Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Strongly agree Agree Disagree Strongly disagree Percent Figure ES.6: Host response to “Refugees are good people” Source: World Bank Staff based on SESRE 2023. Executive Summary viii Continue implementation of progressive policies: ◆ Implement concrete actions to fulfill Government pledges and proclamations to move away from encampment toward mobility based on economic opportunities. ◆ Harmonize national and sub-national", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 crop products (e. g., wheat, potatoes) and the value of five livestock products (i. e., milk, egg, butter, hides, and honey) sold in the market. 14 Several other data sources were utilized. First, the Ethiopian refugee camps location data set from the Humanitarian Data Exchange (HDX) 15 and the total number of refugees by camps from the United Nations High Commissioner for Refugees (UNHCR), Addis Ababa office. We use data from 26 official UNCHR refugee camps in Ethiopia that were operational in 2018 (see Figure 1; 3). Second, we use administrative data sets for Ethiopia and refugee source countries from the database of Global Administrative Areas (GADM). 16 We also use the conflict data set from the Armed Conflict Location and Event Data Project (ACLED) 17 and the population data from the Gridded Population of the World (GPW) data set. 18 On the basis of these data sets and the location of sample households from Ethiopia DRDIP data set, we generated the following variables: i) distance of sample households to the nearest refugee camp, the nearest region (administration level 1 in GADM) to the refugee camps, ii) distance of the refugee camps to the nearest border of the refugee source country, iii) Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Humanitarian Data Exchange (HDX)", "database of Global Administrative Areas", "Gridded Population of the World (GPW) data set", "Ethiopia DRDIP data set"], "descriptive_data": ["Ethiopian refugee camps location data set"], "vague_data": ["administrative data sets"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "By highlighting socioeconomic interactions and outcomes, SESRE aims to guide development interventions and facilitate refugee integration. The survey covers various aspects, including demographic profiles, livelihoods, welfare patterns, and social cohesion, offering valuable insights for policymakers and humanitarian actors. SESRE is a separate but integrated survey alongside the Ethiopian Household Welfare Statistics Survey (HoWStat),1 the national household survey to measure poverty and other socio-economic outcomes. Like most national poverty surveys, HoWStat excludes displaced populations—Internally Displaced People (IDPs) or refugees—including in Ethiopia. To have up-to-date information on the socio-economic outcomes and poverty levels of refugees and to allow comparison to Ethiopian host communities, the SESRE applied the same questionnaire and data collection methods as the HoWStat, with some modifications. The World Bank, Ethiopia’s RRS, Ethiopia’s Statistical Service, and UNHCR collaborated to implement SESRE and was the first of its kind. This report uses data from the SESRE extensively to analyze the Ethiopian refugee situation and to devise policy directions. The SESRE covers three types of groups: (i) refugees in camps; (ii) refugees out-of-camps in Addis Ababa; and (iii) host communities; all of which require a distinct sampling procedure. The sampling frame for refugee camps is based on UNHCR’s proGRES database. SESRE is", "output": {"entities": {"named_data": ["Ethiopian Household Welfare Statistics Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "ICRC ’ s work is based on the Geneva Conventions of 1949, their Additional Protocols, its Statutes — and those of the International Red Cross and Red Crescent Movement — and the resolutions of the International Conferences of the Red Cross and Red Crescent. 84 WFP is the food assistance branch of the United Nations and the world's largest humanitarian organization addressing hunger and promoting food security. 85 See: popstats. unhcr. org. 86 IDP data are only included from 1998 onwards. 87 See: http: / / data. unhcr. org. Currently the Burundi situation, Yemen (regional refugee and migrant response plan), DRC regional refugee response, Mediterranean (refugees / migrants emergency response), CAR, Côte d ’ Ivoire, Syria Emergency, Sahel Emergency, South Sudan Situation, Horn of Africa Emergency, and the Liberia Portal. 88 IOM ’ s new Global Migration Data Analysis Centre provides limited data on global migration trends such as data on asylum application in Europe and selected countries (including demographics, country of origin, and country of asylum).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["IDP data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 permits and subsequently for the right to work. In practice, this is a long and cumbersome process and by late 2015 at most several thousand had been issued. 14 The economic impact of Syrian refugees in Turkey extends beyond changes in the potential labor supply of informal workers in important ways. There has been extensive humanitarian aid provided to the refugees, overwhelmingly by the Turkish government. Reportedly, by early 2015 the Turkish state had spent $ 6 billion (with total outside contributions $ 300 million). 15 Much of these funds have been spent on food, various services, non-food items such as medicines, clothing, shelter, and housing-related goods. In particular, there are 20 accommodation centers (camps) in 10 cities in Turkey. 2. 2 Data Sources We use the Turkish Household Labor Force Survey (LFS) micro-level data sets compiled and published by the Turkish Statistical Institute. The data contains a rich set of labor market variables along with individual-level characteristics and the region of residence. We primarily rely on two years of LFS data: 2011 (just before the arrival of the refugees) and 2014 (the last year available). 16 By design the LFS does not contain any information on Syrian refugees.", "output": {"entities": {"named_data": ["Turkish Household Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "migration, and reason for movement including asylum / refugee protection (or conflict-induced internal migration) (UNHCR 2016), or a specific question to identify IDPs or refugees. However, not all censuses cover refugees and asylum-seekers (if foreigners are considered outside the scope of the census or because they are considered a special category), 71 nor is it common practice for national censuses to include questions related to forced displacement. 72 Nevertheless, there are several examples of national censuses that have included relevant questions on forced displacement. 73, 74 In the case of protracted internal displacement situations, IDPs are likely to be included in national censuses; however, census instruments may be subject to manipulation for political purposes. There are several drawbacks of population censuses including their cost, the significant training required for enumerators to ensure consistent answers to questions on forced displacement, impediments to field operations and data processing (such as weather conditions and technical problems), the relative infrequency with which they are carried out, and the long processing time before data and statistics become available, which have consequences for the timeliness of data. Moreover, often censuses are not conducted in contested territory or conflict zones where many displaced persons reside, and this limits the completeness of the data. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population censuses"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The study: (i) compares the socio- economic profile of refugees with that of the Syrian population before the crisis and with the hosting populations of Jordan and Lebanon; (ii) provides a welfare and vulnerability assessment of Syrian refugees including a poverty profile, the socio-economic characteristics of higher poverty and where pockets of deep poverty are located; (iii) analyzes key drivers of welfare and poverty; and (iv) models monetary and non- monetary vulnerability.  In Lebanon, Jordan and Iraq, the Bank is leading an initiative to evaluate the socio-economic impact of the regional crises on the welfare of Syrian refugees and host communities in neighboring countries [ongoing]. Data on living conditions, access to services and economic opportunities, coping strategies and economic status are to be collected via a specialized household survey and a sub-component of the survey will be carried out on a semi-annual basis to continue to monitor and adapt support as needed.  A recent Bank paper, “ Turkey ’ s Response to the Syrian Refugee Crisis and the Road Ahead ” [completed in 2015] assessed the impact of Syrian refugees on host areas in various sectors. It found that the presence of Syrian refugees is placing a strain on municipal services, housing rental markets, social relations, and education services for Turkish households.", "output": {"entities": {"named_data": [], "descriptive_data": ["specialized household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Another ex- planation of these results could be the vulnerability of the Colombian population who in many cases has also a long history of internal forced displacement and violence. V. D Social Cohesion We also delve into the differences in secondary outcomes among Colombian and Venezue- lan adolescents concerning social cohesion. We focus on assessing altruism, trust, iden- tity towards specific domains, networks, and experiences of discrimination. To measure altruism and trust, we employ the questions from the Global Preference Survey, a tool developed by Falk et al. (2022) to elicit risk, time, and social preferences. Specifically, to measure altruism we ask the adolescents how much of a fictional endowment would they be willing to donate to a good cause. To measure trust, we include the 7-itme ques- 38 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Global Preference Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Women experience particularly pronounced displacement in the informal sector and no formal job gains. In contrast, for men displacement in the informal sector is fully offset by employment growth in formal sector, with no net job losses. Clearly, our main findings do not depend on the particular sample of subregions we analyze, and importantly are robust to restricting the analysis to a more homogenous group of regions. 6. CONCLUSIONS This paper combines newly available data on the 2014 distribution of 1. 6 million Syrian refugees across subregions of Turkey and the Turkish LFS, to assess the impact on Turkish labor market conditions. The Syrian refugees in Turkey are overwhelmingly employed informally, since they were not issued work permits, and so their arrival was a well-defined supply shock to informal labor. Consistent with economic theory our IV estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupational upgrading, there are increases in formal employment for the Turkish. This increase though only occurs among men without completed high school education. The employment patterns of women and the high-skilled mean they are not in a good position to take advantage of lower cost 36 Results are also robust to dropping all subregions with close to no refugees.", "output": {"entities": {"named_data": ["Turkish LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In certain contexts, there can be significant overlaps in these two groups; however data systems may be maintained separately for conflict-induced displacement and natural disasters (e. g. in Afghanistan) leading to possible gaps or double counting if these categories are combined. 30 The IOM Displacement Tracking Matrix (DTM) is a system to track and monitor displacement and population mobility. It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route. It has been active in over 40 countries since its inception in 2004. See http: / / www. globaldtm. info /. 31 This is typically defined as nomads not having access to their traditional routes, but routes can vary. 32 IDMC has recently adjusted their methodology to facilitate greater comparability across situations and improvements are reflected in IDMC ’ s end-2015 data. 33 This is not necessarily a problem if the purpose of the registration system is to delineate entitlements to assistance rather than to determine status. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["IOM Displacement Tracking Matrix"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The database provides information on international bilateral migrant stocks (by citizenship2 or place of birth), sex, and age. There is considerable variation, however, in how destination countries collect, record, and disseminate immigration data. Meaningful comparison of destination country records over time is thus often confounded. In constructing global bilateral migration matrices, several challenges arise. First, destination countries typically classify migrants in different ways — by place of birth, citizenship, duration of stay, or type of visa. Using different criteria for a global dataset generates discrepancies in the data. Second, many geopolitical changes occurred between 1960 and 2000, with many international borders redrawn as new countries emerged and others disappeared. In addition to creating millions of migrants overnight — as when the Soviet Union collapsed — these events complicate the tracking of migrants over time. Third, even when national censuses of destination countries include data on international migrant stocks, the data are presented along aggregate geographic categories rather than by country of origin. Data therefore need to be disaggregated to the country level. Finally, the greatest hurdle is dealing with omitted or missing census data. Very few destination countries — especially developing countries — have conducted rigorous censuses or population registers during every census round over the second half of the twentieth century. Wars, civil strife, lack of funding, and political intransigence are but a few reasons why records may be discontinuous. 1 Of the 3, 500 sources detailed in the overarching UN Global Migration Database, 1, 107 were suitable for analysis, once repeated censuses had been removed or combined. Global Migration Database should not be confused with the Trends in International Migrant Stock Database, which lists aggregate migrant stocks for each destination country in the world at five year intervals (United Nations 2006) 2 The article treats the concepts of nationality and citizenship as analogous and uses the terms interchangeably.", "output": {"entities": {"named_data": ["UN Global Migration Database", "Trends in International Migrant Stock Database"], "descriptive_data": [], "vague_data": ["census data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As explained in section 4, the HRVD dataset contains data on the number of human rights violations occurred since the start of the conflict in 1975 until its end in 1999 for each district. The types of violations recorded are killings, deaths due to deprivation and disappearances. We use only the number of killings to identify years and districts affected by the conflict. We exclude the deaths due to deprivation because the districts in which this occurred may very likely not be those where the conflict was most intense, but were simply places were the victims were hiding as a consequence of escaping from the troops, and died for starvation. In addition, since killings are less likely to affect entire families than deaths due to deprivation, there is a lower underreporting bias attached to the former measure relative to the latter one (Silva and Ball 2006). We also exclude disappearances as, according to HRVD data, they do not show enough time and geographical variation in order to identify individuals more or less exposed to the conflict. We believe that the number of killings proxies quite well the intensity of the conflict across time and space as their occurrence largely tracked the movements of the Indonesian military operations. The other two types of violations do not seem to show the same pattern (Silva and Ball 2006). For the same reason, we believe that it proxies quite well the destruction of houses and infrastructure and the displacement of people given the way in which the last wave of violence occurred (i. e. the scorch-earth technique employed by Indonesian troops as they moved towards West Timor).", "output": {"entities": {"named_data": ["HRVD dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Box 3: The Role of GIS in Project Site Selection**\n\nDuring project preparation, GIS was used to overlay three spatial datasets: (i) displacement hotspot maps produced by UNHCR's Field Information and Coordination Support section; (ii) a poverty concentration index derived from the national household budget survey; and (iii) road network accessibility scores from the Ministry of Transport's infrastructure database. The GIS analysis identified 47 communities meeting the dual threshold of high displacement concentration and limited infrastructure access. These communities form the initial targeting list for Component 1 activities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "–– Ensuring that a **situation analysis and mapping of relevant actors**\n(Government, UN agencies, NGOs, multilaterals and donors) in the health\nsector is done in collaboration with the line ministry to inform the design of a\nresponse in each area of public health and nutrition and for every stage of the\nresponse.\n\n–– **Facilitating data driven responses** Facilitate and support the collection,\ncompilation, analysis, interpretation and dissemination of health program\ndata. Support inclusion of refugees in national data systems and tools\nincluding disaggregation of data by nationality to the extent possible.\n\n–– **Refugee participation and consultation** : wherever possible, continue to\ndevelop and support consultative processes that enable refugees and host\ncommunity members to assist in designing appropriate, accessible and\ninclusive responses.\n\n–– **Providing technical expertise & support** : UNHCR will seek to provide\nor facilitate technical and general support to partners on program\nimplementation and support for inclusion of refugees in national data\nsystems..", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["health program\ndata"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5 narrative around the regressions and aims to explain why not more people migrate when benefits of doing so are so high. 2. The Setting: Tanzania and Kagera, 1994-2004 In the last decade, Tanzania has experienced a period of relatively rapid growth, attributed to liberalization, a renewed trade orientation, a stable political context, and a relatively positive business climate to boost economic performance. Real GDP growth was of the order of 4. 2 % per year between 1994 and 2004, while annual population growth was around 3. 2 % in the same period (URT, 2004). There is also evidence that growth had accelerated in the last few years compared to the 1990s. However, this growth has not been sufficiently broad-based to result in rapid poverty reduction. On the basis of the available evidence, poverty rates have declined only slightly and most of the poverty reduction progress has been made in urban areas. According to the Household Budget Survey (HBS), between 1991 and 2000 / 01, poverty declined from 39 percent to 36 percent in mainland Tanzania. The decline in poverty was steep in Dar es Salaam (from 28 % to 18 %) but minimal in rural Tanzania (from 41 % to 39 %).", "output": {"entities": {"named_data": ["Household Budget Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "18 Figure 11: Timing of Return (%) Source: Listening to Displaced People Survey, 2014. 94 % of the returnees were displaced inside Mali and 6 % outside the country. 67 % went to Bamako, 11 % in Koulikoro, 9 % to Ségou, 5 % to Mopti and 3 % went elsewhere (Kidal, Gao and Sikasso). The majority returned between June and October 2013 a period that followed the signing of a peace deal between the interim government and rebel factions to allow presidential elections to be held in July (first round) and August (second round) 2013. In October security in the North worsened again and ever since the number of people returning has been very limited. The main challenges reported by returnees in June 2014 were (i) poverty and food insecurity; (ii) lack of infrastructure (including lack of safe drinking water) and (iii) unemployment. 11 % of the returnees stated not to be facing any challenges (Figure 12). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "After a short respite in November, Israel imposed a third lockdown from December to February the next year. Gradual easing continued throughout March. During this lockdown, vaccination was rolled out, including to commuters with valid permits. Given this context, we expect the main shock to occur in 2020Q2 in the West Bank, with negative effects of smaller magnitudes to persist over the next quarters. In Gaza, we expect another major shock in 2020Q3 that carries over to Q4. We expect the commuters ’ outcomes to instead track events in Israel and occupied territories more closely, with a major shock in 2020Q2, recovery in Q3, and another dip in Q4. 3 Data 3. 1 Data sources and definitions We employ data from the Labor Force Surveys (LFS) of the West Bank and Gaza, collected by the Palestinian Central Bureau of Statistics and prepared by the Economic Research Forum. The surveys are conducted on a quarterly basis, covering periods from 2000 onwards. The LFS is meant to represent all households whose ordinary residence is in the West Bank and Gaza, though their place of work need not be. The LFS is representative at the region level (respectively of the West Bank and Gaza), as well as at the level of locality types (urban, rural, and refugee camps) (Palestine- Labor Force Survey, LFS, 2021). Importantly for the purpose of our analysis, the data have a panel dimension, en- abling the study of labor market transitions. The sample rotation scheme is described 9", "output": {"entities": {"named_data": ["Labor Force Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 The article employs a unique data source from Jordan, the 2010 Jordan Labor Market Panel Survey (JLMPS 2010), which includes information on parents ’ schooling for every adult in the sample, along with the 2010 School Census produced by the Jordanian Ministry of Education (Hashemite Kingdom of Jordan, 2010). The school census provides the subdistrict, type, and date of establishment of every school in Jordan, allowing us to measure the local supply of each type of schools in each subdistrict in every year (under the presumption that there were no significant school closures or changes in type over time, which is likely the case). The exposure of an individual in the JLMPS 2010 sample to the supply of public schooling is then determined by the number of sex-appropriate basic (or secondary) public schools (per 1, 000 individuals) that were available to them in their subdistrict of birth at the time they were of age to enroll in that school level (six years of age for basic and 15 years for secondary). The richness of the data set makes it the first in the Middle East to allow such a study.", "output": {"entities": {"named_data": ["2010 School Census", "2010 Jordan Labor Market Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**7.1 Summary of Key M&E Instruments**\n\n| Instrument | Frequency | Managed By | Output |\n|---|---|---|---|\n| MIS routine data | Continuous | PIU M&E | Monthly dashboard |\n| Annual surveys | Yearly | External firm | PDO indicator updates |\n| Protection monitoring | Quarterly | UNHCR / NRC | Protection situation reports |\n| GRM reports | Monthly | PIU coordinator | Grievance resolution log |\n| ESMF field audits | Semi-annual | PIU E&S officer | Compliance report |\n\nAll monitoring instruments feed into the annual progress report submitted to the Association by April 30 of each year.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The imputation of income poverty is done using the Turkish Labor Force Survey (LFS), and with information and modeling parameters determined from the Survey of Income and Living Conditions (SILC). More details and validation of this methodology is discussed throughout this paper. While explicit identification of Syrians in available surveys is not feasible, there is evidence of an increase in the amount of foreign-born individuals that is being captured in the LFS. The arrival year of foreign-born migrants is available in the data which allows for identification of “ Settled Migrants ” and “ Recent Migrants ”. The latter is used as a proxy for Syrian refugees for the purposes of this paper. National official surveys that are conducted under-report the refugee population. Yet, since about 10 percent of Syrian refugees are in camps and the remaining are residing throughout the country, it is not surprising that they are accessible to interviews by the LFS. Despite data limitations, there are strong and significant trends in the poverty rates for the recent foreign- born, especially for those near the Syrian border. In 2013, recent migrants near the Syrian border were the poorest group4 in Turkey. While this statistic in itself is not initially surprising, fluctuating welfare trends of recent migrants over time is noteworthy. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Turkish Labor Force Survey", "Survey of Income and Living Conditions"], "descriptive_data": [], "vague_data": ["National official surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "7 Database. Where both censuses and population registers are available, censuses receive priority. Censuses, generally conducted decennially, are retrospective tools for surveying an entire population (or in some cases, a representative sample) at a single point in time. In addition to their universal coverage, their greatest strength is the inclusion of questions on place of birth and nationality. Censuses also typically aim to enumerate the resident population, whether documented or undocumented (Bilsborrow and others 1997). So although some migrants have a strong incentive to provide false information to enumerators, many undocumented migrants will be captured in these matrices. 7 The size and scope of the census questionnaires vary enormously, both over time and in different destination countries. And there is potential variation in the quality of censuses both across countries and over time. Richer countries have many resources at their disposal to design questionnaires, train interviewers, employ statisticians, and disseminate results. Researchers have little choice but to accept the data at face value. However, where the underlying census is clearly substandard (when there are errors that are obviously not coding errors or not easily corrected), these data are omitted from the analysis. Popular in many parts of Europe, population registers are continuous reporting systems providing up-to-date demographic and socioeconomic information for everyone surveyed.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["censuses", "population registers"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "- **Physical Constraints:** Poor infrastructure, damaged roads, and impassable routes during the rainy season make it difficult\nfor humanitarian vehicles to access remote areas. Shortages of clean water, fuel, and electricity also affect operations.\n\n- **Lack of Awareness on Inclusion:** Specific needs of diverse at-risk groups, such as people with disabilities and older\nindividuals, are often considered under one group of ‘the vulnerable’ without an analysis of underlying causal factors.\nThe limited awareness on inclusion and the specific risks is likely to translate to insufficient support for disability, gender\nand age inclusion in humanitarian settings and requires enhanced efforts to include disability inclusion as part of the\nexisting coordination structures.\n\n**CRITICAL GAPS IN FUNDING AND POPULATION REACHED**\n\n- Limited resources due to conflicts and instability.\n\n- Coordination structures are still in need of awareness in relation to the needs and barriers of people with disabilities and\nolder persons.\n\n- Accessibility and inclusion are seen as an add on or specific requirements which are not included in budgeting from the\non-set due to perceived high costs of technical support for disability and age inclusion.\n\n- Data gaps that hinder advocacy with donors and humanitarian actors.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 2. Sample and Distribution of Venezuelans in Colombia Notes: The left-hand panel of the ﬁgure illustrates in shades the number of Venezuelans registered in the RAMV census; the red circles depict the surveys carried out per municipality. The right-hand panel illustrates the number of Venezuelans per municipality reported in the 2018 Colombian census, a proxy of the overall distribution of migrants in the country. The correlation between the sample and the 2018 Colombian census registry is 0. 93. 39 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "E Nigeria Somalia Sudan Host community Refugees Host community IDP Host community IDP Host community IDP No earners 40 % 51 % 62 % 50 % 32 % 64 % 7 % 45 % Remittance recipients only 7 % 8 % 76 % 69 % 16 % 37 %-- Female single earner 12 % 52 % 23 % 37 % 35 % 70 % 24 % 55 % Male single earner 8 % 23 % 4 % 14 % 24 % 50 % 8 % 39 % Majority female earners 3 % 57 % 9 % 28 % 9 % 28 % 15 % 45 % Equal contribution 10 % 24 % 17 % 16 % 23 % 63 % 8 % 41 % Majority male earners 5 % 16 % 15 % 19 % 20 % 52 % 9 % 39 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Overall, the results show that besides gender, displacement status and the number of household contributors plays a key role in the identification and level of poverty. In comparison with female-headed non-displaced households, more female-headed displaced households are classified as multidimensionally poor.", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 agreements that did not fully satisfied either party (Ndayirukiye and Takeuchi 2014). This tension related to land, and who has a claim to the land, can lead to social tensions in communities with higher levels of return. Figure 3 – Refugees in Tanzania in 2005 by province of origin in Burundi Note: The number in brackets is the number of refugees in Tanzania in 2005 which was originally from the given province in Burundi. This information comes from (UNHCR 2021b). The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census. An important question for our hypotheses is the degree to which there is evidence of migration-related societal divisions in the country. There is no direct quantitative evidence on identity issues (i. e. returnees versus stayees), but we have data on attitudes towards emigration, remittances and return that can provide insights on these identities and even be a proxy for migration-related identity in some cases. Overall, attitudes towards emigration and return are mixed and show that there is scope for the existence of migration-related divisions. In Table 1 we report the share of respondents who agreed with different statements regarding emigration, remittances and return. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["1990 Burundi Census"], "descriptive_data": ["data on attitudes towards emigration, remittances and return"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The SEIS sampling frame was validated against UNHCR's registration database prior to fieldwork. SEIS clusters that could not be matched to valid registration records — typically due to secondary displacement between registration and survey fieldwork — were replaced using a random draw from the list of eligible replacement clusters. The SEIS documentation records 31 cluster replacements, representing 5.3 percent of the original SEIS sample, and replacement clusters were confirmed to be statistically comparable to the dropped clusters on observed baseline characteristics.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Over 11,000 indigenous Venezuelans currently residing in Brazil have been identified by UNHCR\nand partners. Compared to the overall Venezuelan population, they face compounded challenges\naccessing basic rights and services, including higher rates of food insecurity (58% vs 52%), health\ncare needs (75% vs 59%) and out of school children (21% vs 15%). [3] Language barriers and limited\nformal education of adults (indigenous refugees are 5 times more likely to have no formal\neducation when compared to the general Venezuelan population in Brazil), significantly affect their\nprospects for successful integration. [4]\n\n### Protection brief in graphics\n\nPopulation category Population category\n\nBreakdown by nationality\n\n_2 World Bank and UNHCR (2021), Integration of Venezuelan Refugees and Migrants in Brazil,_\n\n_[https://documents1.worldbank.org/curated/en/498351617118028819/pdf/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf;](https://documents1.worldbank.org/curated/en/498351617118028819/pdf/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf)_\n_ACNUR & Ministerio do Trabalho e Emprego (2024), Informe sobre o mercado de trabalho formal para Haitianos no Brasil,_\n_[https://www.acnur.org/br/sites/br/files/2024-11/informe-mercado-trabalho-formal-haitianos-brasil-jun-2024.pdf](https://www.acnur.org/br/sites/br/files/2024-11/informe-mercado-trabalho-formal-haitianos-brasil-jun-2024.pdf)_\n_ACNUR & Ministerio do Trabalho e Emprego (2024), Informe sobre o mercado de trabalho formal para pessoas refugiadas afegãs no Brasil_\nhttps://www.acnur.org/br/sites/br/files/2024-11/informe-mercado-trabalho-formal-pessoas-afegas-no-brasil-junho-2024.pdf\n\n---\n[3] R4V (2023), Refugee and Migrant Needs Analysis, https://rmrp.r4v.info/rmna2023/](https://rmrp.r4v.info/rmna2023/) p. 85", "output": {"entities": {"named_data": ["Refugee and Migrant Needs Analysis"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Income and consumption index: includes the logarithm of monthly household income and consumption. Because these variables are continuous the index is constructed following the methodology outlined in Kling, Liebman and Katz (2007). Labor market index: includes a dichotomous variable for employment, and the logarithm of monthly wages and weekly hours worked. Descriptive statistics for each of the outcomes and their components variables are de- scribed in Table 1. III. B Data for the Taliban ’ s Geographical Presence To obtain information on the Taliban ’ s geographical presence, we scraped online data from the Foundation for Defense of Democracies ’ Long War Journal on the districts con- trolled by Taliban, by U. S. Allies, and contested territories between the two groups. 9 The data includes a yearly evolution of the territorial control in Afghanistan. The classification is based on open-source information from NATO ’ s data in Afghanistan, press reports, in- formation provided by government agencies, and the Taliban. To categorize the districts, they identify who provides government services, ensures security within the district, ad- ministers the district openly, and oversees local courts (see Appendix B. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1 Introduction Company owners and managers make two decisions with important implications in the labor market: what skills are demanded and who to hire. On the demanded skills, a driver of trends in employment is the changing demand for soft skills (Heckman and Kautz (2012), Weidmann and Deming (2021)). However, we know relatively little about what kinds of soft skills employers value in modern entry- level jobs (Heller and Kessler, 2022). On the decision of who to hire, it is in the best interest of companies to hire based on workers productivity. However, several studies have documented the existence of labor market discrimination in a wide range of contexts (Bertrand and Duflo (2017), Neumark (2018)) and it remains unclear how discrimination operates throughout the hiring process and how the existent empirical evidence on discrimination is linked to economic theory (Bertrand and Duflo, 2017). We conducted a correspondence study in 2023 using a large online job platform to assess demand for soft skills in the context of hiring discrimination in Malaysia. Malaysia is a particularly interesting setting because it is an upper-middle-income economy, home to multiple ethnicities representing large shares of the population, and previously documented gender gaps in labor force participation and wages. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["large online job platform"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "To estimate the fraction of income of the poor and non-poor that comes from social protection and transfers, we calibrate �� using the \"average per capita transfer from all social protection and labor (for beneficiaries)\" and the coverage in each quintile from the ASPIRE database [10] :\n\n<!-- PAGE 18 --> that quintile. Poor and non-poor correspond to the bottom 20% and the top 80% respectively. ASPIRE at present does not cover developed countries, so we use data from the US Consumer Expenditure Survey (CES, 2015), Canadian National Household Survey (CNHS, 2015), Australian Household Wealth and Wealth Distribution Survey (AWWDS, 2015), and European Union Survey of Income and Living Conditions (EU- SILC, 2015).", "output": {"entities": {"named_data": ["ASPIRE database", "US Consumer Expenditure Survey", "Canadian National Household Survey", "Australian Household Wealth and Wealth Distribution Survey", "European Union Survey of Income and Living Conditions"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Not all cross-sectional studies have multiple rounds of comparable data, covering the period before and after the crisis. When comparing impacts between locations within a country, cross-sectional data also usually does not allow to capture impacts on those who moved out and to differentiate impacts between those who were already there before the shock and those who moved in afterwards. Some of the models based on administrative areas qualify as spatial econometrics models in that they use estimation methods that derive from this literature and are published in spatial econometrics journals. Studies that compare different areas within a country are not only confronted with the potential endogeneity of the size and skill composition of the inflow and the choice of destination, but also with the endogenous reactions of the host community. Local workers might respond to the labor supply shock by dropping out of the labor force, investing in education, occupational upgrading or moving to other areas and diffusing the impact of the inflow. Even if local workers do not respond to wage variations, capital flows may equalize capital / labor ratios within the country, labor-intensive industries might move towards the regions with a high refugee or IDP influx or firms might use more labor-intensive production technologies. The reactions of the host country workers, investors and firms are medium-to long-term in nature and will play less of a role in the short-term if there are large, sudden and geographically concentrated inflows. Some of the papers explicitly analyze these potential channels, notably migration of local workers, and, to a lesser extent, occupational upgrading. Outmigration of hosts is a critical complement to the labor market analysis and excluding this outcome can lead to an underestimation of the impacts of forced displacement on the labor market outcomes of natives. The papers we reviewed that looked at tasks complexities and the question of substitution vs complementarities between refugees and natives found occupational upgrading among", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Total Poverty** **Food Poverty** **Lower Total**\n**Estimation** **Poverty**\n\nParametric R$287 R$507\nNonparametric R$287 R$503\n_Source:_ Own calculations using POF 2017/18.\n\n**Total Poverty** **Food** **Lower Total**\n**Estimation** **Poverty** **Poverty**\n\nParametric R$251 R$443\nNon-parametric R$251 R$441\n_Source:_ Own calculations using POF 2017/18.\n\nDeaton and Zaidi (2002) advert to the fact that implicit rent, as elicited in POF and used here, is a hypothetical concept that could lead to estimations that are not usable.\n\nIn this paper we have presented our estimate of a poverty line for Brazil, using the CBN approach and based on the most recent data (POF 2017/18). Our preferred specification results in a food poverty line, accounting only for nutritional requirements, of R$258 (in 2018 Southeast urban prices) per person per month.\n\nIn comparison with earlier work, mainly based on POF 2003, our poverty lines are generally similar in real\nvalues, although methodologies differ, and consumption patterns have likely changed over time.\nConverted to January 2018 prices and considering São Paulo (mostly metropolitan) lines in the case of\nregional lines, previous estimates range from R$485 to R$532 (Rocha, 2007; Silveira et al., 2007). Ferreira\net al. (2003) used POF 1996 and estimated a lower poverty line of R$477 in January 2018 metropolitan\nSão Paulo prices. Only World Bank (2007) estimated a considerably lower poverty line of R$272 in January\n2018 metropolitan São Paulo prices.", "output": {"entities": {"named_data": ["POF 2017/18", "POF"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "School feeding programs are largely implemented by the World Food Programme, reaching a total of 13,000\nrefugees in 14 schools in the provinces of Lake Chad and Logone Oriental.\n\nThe Unified Social Registry (RSU), launched in 2019, has made limited progress in capacity, governance and\nfinancing, despite continuous support from WFP and NGOs.\n\n**4.4** **Protection for vulnerable groups**\n\nIn practical terms, the national protection system for the most vulnerable has not undergone any changes\nand the measures in place to support victims of human trafficking are still insufficient. This holds true for\nvictims of Gender-Based Violence (GBV) and at-risk children. The national protection services aimed at\nthese vulnerable groups continue to be underdeveloped and underfunded.\n\n12 R E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D**", "output": {"entities": {"named_data": ["Unified Social Registry"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "20 of intense conflict. We examine also the overall impact of the 25 years of conflict on educational outcomes. This analysis allows us to consider the full long-term impact of the conflict on educational outcomes of different generations of children in Timor Leste. 4. 2. 1. The educational impact of the 1999 wave of violence We exploit variation in the number of killings over time and across districts to identify conflict affected individuals. Our intention here is to analyze whether individuals exposed to the violence during their primary school age show different primary school completion rates eight years after the end of the war, relative to those not affected by the conflict. The outcome variable in which we are interested is whether individuals completed primary school in 2007. Figure 7 shows average primary school attainment for all individuals in our sample. The graph shows an increasing trend in primary school completion across cohorts and a progressive reduction of the gender gap. The gap among the younger cohort (those born after 1987) is almost zero. The drop in the curve for the younger cohort confirms the presence of significant delays in school attendance. For the purpose of this analysis, we use the TLSS 2007 dataset and the HRVD dataset contained in the CAVR data publication. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["TLSS 2007 dataset", "HRVD dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9826 Despite the many simultaneous deprivations faced by forcibly displaced communities, such as food insecurity, inadequate housing, or lack of access to education, there is little research on the level and composition of multi­dimensional poverty among them, and how it might differ from that of host communities. Relying on house­hold survey data from selected areas of Ethiopia, Nigeria, Somalia, South Sudan, and Sudan, this paper proposes a Multidimensional Poverty Index (MPI) that captures the overlapping deprivations experienced by poor individuals in contexts of displacement.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The weight given to each province in constructing the synthetic control is based on pre-treatment outcomes. We use the pre-treatment average of the outcome dimension, Y, the unemployment rate, employment rate and the import and export per capita of the province to determine the degree of similarity between control group provinces and the treated provinces, which in turn determines the weight assigned to control provinces. The unemployment and employment rates are included to control for the general economic performance while trade values are added to control for the degree of ’ openness ’ of the province. 6 The treated unit i = 1 is constructed by taking the mean of the outcome variables in the provinces hosting refugees in 2012 or 2013. 5 Data We use several data sources for the analysis. The IV estimations use data from years 2011 and 2014 while the DD estimations use data from 2009 to 2014. The numbers of refugees up to 2012 are treated as 0. The refugee data for 2012 and 2013 are obtained from UNHCR ’ s oﬃcial weekly statements in December. Data on the number of refugees in 2014 is from Erdo ˘ gan (2014), who uses statements released by the Ministry of the Interior to compile his data. All refugee data we use in the analysis is provided at the level of 81 provinces. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The third proxy for economic activity is given by per capita growth of night light, computed using satellite data from the National Oceanic and Atmospheric Administration (NOAA). 6 Night light data has the beneﬁt 6Satellite data is available for a shorter time period, 1992-2013. 9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["satellite data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Further, as Jordan and the international community develop new approaches that respond holistically to the specific needs of refugee and host communities, more evidence is needed about how gender ‐ based constraints and vulnerability affect refugee women ’ s ability to take up economic opportunities and to access the services and resources they need to enable their families to move out of poverty. Our analysis applies a gender lens to a rich set of microdata on Syrian refugees in Jordan collected by UNHCR between 2011 and 2014. As these data do not capture how the changes in policies affect refugees and the constant evolution of their situation since 2014, the analysis is not intended to directly inform current policy choices and decisions. Instead, our aim is to devise an approach that can provide greater insights into gender ‐ specific barriers, based on the premise that the experiences and potential vulnerabilities of women, men, and children are significantly different in refugee settings. We use household ‐ level data to examine the relationship between poverty and gender for Syrian refugees. Our approach is informed by a body of work in the academic literature that has used household survey data to examine the relationship between the gender of the household head and household 2 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 3 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 4 https: / / data2. unhcr. org / en / documents / download / 64568, accessed August 2, 2018. 5 https: / / reliefweb. int / sites / reliefweb. int / files / resources / 64114. pdf, accessed August 2, 2018. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["microdata on Syrian refugees in Jordan"], "vague_data": ["household survey data", "household ‐ level data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 Finally, we divide the communities based on land scarcity before the 1993 conflict in order to explore the possible role of posterior rules regarding land provision to returnees. Respondents in communities that had more and less pre-war land available have broadly similar attitudes towards return. 4. Research design 4. 1 The survey We collected the data for this project during January to March 2015 as part of a nationwide survey on issues related to migration for the Labour Market Impacts of Forced Migration (LAMFOR) project. The survey had two components. First, a household survey in which 15 households were interviewed in 100 communities (i. e. sous-collines) across the 17 provinces of the country. Second, a community survey in which a local leader was interviewed in each of the 100 communities. The number of communities selected in each province was based on information from the 2008 Census. Figure 4 indicates the location of the communities surveyed. Figure 4 – Location of communities surveyed in Burundi Note: Geolocation of the 100 communities (i. e. sous-collines) sampled in the survey. Each community corresponds to a dot. Fifteen households and a local leader were interviewed in each community. The number of communities selected in each province was based on information from the 2008 Census. In the analysis below we focus on rural areas. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["2008 Census"], "descriptive_data": [], "vague_data": ["household survey", "community survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The household and family dynamics in Liberia, as in other African settings, can be complex, with families often sending their children to live with relatives who are more able to provide for their schooling and basic needs. Young parents, in particular, often leave home to migrate for work, leaving their children back home with relatives until they are able to establish themselves and send for their children. If the economic success of the EPAG participant allowed her to bring non-resident family members into her household (including but not limited to her own children), then overall household size may have been expected to increase as a result of the program. This does not appear to have happened, at least in the short term. The results in Table 8 show that overall household size was not affected by the program. It is possible that the increase in earnings due to EPAG was too small, or too short-lived, to have induced the kinds of migrations described above. Other measures of household well-being, including food security and asset ownership, reflect shorter- term investments that might be influenced by the economic success of EPAG participants. Panel B of Table 8 shows the impact of EPAG on a broad range of food security measures. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 3. Wealth Index Distribution (a) Wealth Index- Total 0. 05. 1. 15. 2. 25 k-Density- 5 0 5 10 15 Wealth Index Colombian Venezuelan Venezuelan: Before migrating (b) Adequate Housing Materials (% of Total) 0 1 2 3 4 k-Density 0. 2. 4. 6. 8 1 Average- Dwelling Material Colombian Venezuelan Venezuelan: Before migrating (c) Asset Ownership (# Total) 0. 05. 1. 15 k-Density 0 10 20 30 40 Total Assets Colombian Venezuelan Venezuelan: Before migrating (d) Access to Services (% of Total) 0 1 2 3 k-Density 0. 2. 4. 6. 8 1 Average- Access to Services Colombian Venezuelan Venezuelan: Before migrating Notes: Panel (a) presents the distribution of the wealth index for Colombian and Venezuelan households in our sample in 2022 and pre-migration. Wealth Index is an index measure of the household ’ s cumula- tive living standard constructed following The Demographic and Health Surveys (DHS) methodology. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This assistance is crucial and has been reported to support a meal a day, a better roof, and dignity for Syrians who have fled to Jordan. 8 The JD ‐ HV database has detailed information on expenditure, sources of income, and indicators of household ‐ level welfare, for example, as reflected by recourse to coping mechanisms, standard of accommodation, or access to water, sanitation, and hygiene (WASH). JD ‐ HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need. Verme et al. (2016) draw attention to the precarious circumstances of Syrian refugees in Jordan and Lebanon. Around 55 percent of refugees in Jordan are vulnerable to monetary poverty and more than half are vulnerable to food shocks. Family size increases the probability of being poor, with the poverty rate almost doubling if the size of the family goes from one to two members and increasing by 17 percent when the number of children increases from one to two. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["JD ‐ HV database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 INTRODUCTION In 2015, an estimated 2. 2 million Syrians Under Temporary Protection (SUTPs) were residing in Turkey, the majority arriving in the country over the last 4 years. 2 Turkey ’ s national population is roughly 75 million; recent refugees account for approximately 3 percent of the population. For a country that has never experienced such a large-scale, sudden inflow of foreigners, demographic changes in the composition of the population and labor force will yield unprecedented implications. This paper examines, as data allows, the relationship between the size of the foreign-born population and host community poverty rates in Turkey. First, this paper finds the poverty rates of ‘ recent migrants ’ near the Syrian border (NSB) significantly increased from 2009 to 2013. Second, the number of foreign-born households being captured by the Labor Force Survey (LFS) is expanding, which suggests a growing number of foreign households that are likely to be Syrians. Third, with respect to poverty, the results show no negative impacts on the host community as a result of the increasing size of the foreign-born population. The impact of SUTPs has been both positive and negative. Overall, a significant negative impact on host communities ’ welfare is not observed in the data. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Estimation Procedures We thus propose the following estimation procedures to predict the poverty rate in period 2, where consumption data are missing but the relevant characteristics x are available. Step 1: Check that Assumption 1 is satisfied, which involves verifying that key features of the two surveys such as the sampling frames and the questionnaires are (essentially) the same. If data from earlier survey rounds are available, check that the regression model that is used for imputation satisfies Assumption 2 on these data. 23 The difference is that we use a random effects probit model to estimate equations (1) and (2) instead of the linear random effects model, that is, the estimating equation is)'() (j j j x j j y P ε µ β + + Φ =, with j = 1, 2, where (.) Φ is the cumulative normal distribution. 26 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**in order to meet housegold food needs?**\n\n**Sell HH assets/goods (radio, furniture, jewellery)**\n\n**Reduce food expenditure**\n\n**Spent household savings**\n\n**Sold house/land**\n\n**Accept degrading unsuitable high risk dangerous or exploitative work**\n\n**Some members return to homeland**\n\n##### **b) Food coping strategies**\n\n**79%**\n\nThe reduced Coping Strategies Index (rCSI) includes the five most commonly used food-related coping strategies and\ntheir order of severity as a proxy indicator to measure access to food. The higher the rCSI, the more coping strategies\nhouseholds had to endure. The reduced food coping index score was the highest in the North with a value of 24.66,\nfollowed by BML 21.4, South 16.58, and Bekaa was the lowest with the value of 10.25.\n\n**Figure 9: Food coping reduced index score per area**\n\n**24.66**\n\n**21.41**\n\n**18.74**\n\n**16.58**\n\n**10.25**\n\n**Bekaa** **BML** **North** **South** **All**", "output": {"entities": {"named_data": ["reduced Coping Strategies Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "food consumption expenditure in Tanzania based on the Household Budget Survey 2000/01. To make\n\nrepresentative 2006-07 Household Budget Survey (the comparison results are not presented here but\n\nThis model is estimated using a sample of 206,414 children from house holds in rural villages of India from the District Level Household Survey 2007-08 (DLHS-3).\n\nThe most relevant for the current study is a recent paper that  used propensity score matching to estimate the effect of access to improved sanitation on  diarrhea in children under five using the District Level Household Survey (DLHS-3) (Ku\n\nAnother  study (Bose, 2009) using similar methods from Nepal looks at access to sanitation using  a 2006 Demographic and Health Survey (DHS) finding reductions of 5 percent from mean", "output": {"entities": {"named_data": ["Household Budget Survey 2000/01", "2006-07 Household Budget Survey", "District Level Household Survey 2007-08 (DLHS-3)", "District Level Household Survey (DLHS-3)", "2006 Demographic and Health Survey (DHS)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Notes: The graph plots local means of secondary school outcomes by ENLACE test score\npercentile in grade 6 in 2007. The solid line shows a linear fit estimated using the grouped\ndata. Panel (a) reports the probability of on-time graduation from grades 9 and 12, proxied\nby siting in the Enlace exam in those grades. Panel (b) reports Enlace test scores in grades\n9 and 12 (normalised with mean 0 and SD 1) conditional on taking the Enlace exam in 2010\nand 2013. Data: ENLACE panel.\n\n\n38\n\n\n\n<!-- PAGE 41 -->\nFigure 4: ENLACE test scores and post-secondary school outcomes\n\n(a) College Enrollment<br>1<br>(%)<br>College .8<br>in<br>Enrolled<br>.6<br>.4<br>0 10 20 30 40 50 60 70 80 90 100<br>Enlace Test Score Percentile in Grade 12\n\nd<br>not .5<br>if<br>(%),<br>Employed .4<br>.3<br>0 10 20 30 40 50 60 70 80 90 100<br>Enlace Test Score Percentile in Grade 12|\n|---|---|\n|2.7<br>2.8<br>2.9<br>3<br>3.1<br>3.2<br>Wage hourly earnings (ln)<br>0<br>10<br>20<br>30<br>40<br>50<br>60<br>70<br>80<br>90<br>100<br>Enlace Test Score Percentile in Grade 12<br>(c) Wage Earnings|.2<br>.3<br>.4<br>.5<br>.6<br>Employed in formal firm (%), if employed<br>0<br>10<br>20<br>30<br>40<br>50<br>60<br>70<br>80<br>90<br>100<br>Enlace Test Score Percentile in Grade 12<br>(d) Employed in Formal Firm|\n\n\n\nNotes: The graph plots local means of post-secondary school outcomes by ENLACE test\nscore ventile in grade 12.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(henceforth CRU), provided by the Climatic Research Unit of the University of East\n\nand satellite-based observations. The data enable us to characterize historical climate\n\n<!-- PAGE 6 -->\n**2.1 Assignment of reliability weights to the eight GCMs, based on their**\n**historical \"goodness of fit\" to the CRU data**", "output": {"entities": {"named_data": ["CRU data"], "descriptive_data": [], "vague_data": ["satellite-based observations"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The most recent labor force survey from Liberia mirrors these statistics: there are roughly 1. 1 million people in the workforce, of whom 195, 000 (about 18 percent) are engaged in wage employment; the remaining 900, 000-plus workers (82 percent) are considered in vulnerable employment, working for themselves or working unpaid for their own households (LISGIS 2010). Among young women (15-24) in Liberia, the unemployment rate is 8 percent, double the rate among young men (LISGIS 2010). Most of these gaps can be explained by differences across individuals, especially in educational attainment, skills training, and years of experience. But segregation, market segmentation, and discrimination do play a role in determining these individual characteristics. Women have fewer opportunities for education or training, less access to credit, a larger share of domestic responsibilities, and less independence and control over their own lives. In Liberia, women comprise half of the employed, but only about one-quarter of paid employment (LISGIS 2011). Fourteen years of civil war in Liberia devastated the country ’ s infrastructure and institutions, and left a generation of young people with very low levels of education and training. Girls were particularly disadvantaged. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the sample frame were surveyed. In this way, all the camps in the sample frame were selected in the sample and were surveyed. For host households, areas within 5-kilometer radius of the camps were divided into EAs of 300 by 300 meters, with only residential EAs as per Open Street Maps included in the sample frame. SESRE, on the other hand, used a stratified, two-stage cluster sample design. Initially, camps were divided into EAs, and pseudo EAs were created from the proGRES database by grouping 150-200 households consecutively. EAs and households within those EAs were then selected. For host households, EAs adjacent to refugee camps were used as the sampling frame. While the definition of host households differed between SPS and SESRE, both surveys shared similarities in the selection of EAs and the random sampling of households within those EAs. In SPS, all households within the selected EAs for host community sampling were listed, and 12 households were randomly chosen and surveyed per EA. SESRE also selected 12 refugee and host households per EA, treating EAs as the Primary Sampling Unit and households as the Secondary Sampling Unit. (i) The distinct sampling designs and objectives of the two surveys render", "output": {"entities": {"named_data": ["proGRES database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**7.3 Environmental and Social Commitment Plan (ESCP)**\n\nThe ESCP sets out legally binding commitments for the borrower related to environmental and social management, including: (i) operationalization of the grievance redress mechanism within 60 days of project effectiveness; (ii) disclosure of the ESMF and LMP on the borrower's website within 30 days of Board approval; (iii) submission of semi-annual E&S progress reports to the Bank; and (iv) maintenance of the MHPSS referral pathway documentation and making it available to Bank staff upon request during supervision missions.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Due to our research design and the complexity of introducing a third grouping who would play outgroup in the behavioral experiments regardless of partner identity, we exclude PRL from the main analyses presented here. 5 Data were also collected six months after the end of the training but this was heavily disrupted due to the outbreak of the COVID-19 pandemic. In Lebanon, this resulted in a change to the method of data collection (from in-person to telephone) and in Jordan, an end to data collection entirely. In Jordan, this had a more pronounced effect on the control group, due to the scheduling of data collection and implementation of restrictions in Jordan. Given these complexities, we do not present results from these analyses. 6 In addition, we attempted to collect information on the extent of social and economic interactions between hosts and refugees. At baseline, almost 95 % of respondents in both the treatment and control group reported such interactions. For this reason, we do not include this information in these analyses. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A number of different road indicators are available and we choose road line type to use in the analysis. Road type is defined by the following: The reference category (0) points out squares with dual lane / divided highways, other primary roads, or road connectors within urban areas (types 1 or 8 in the ESRI dataset). The second category include secondary roads (type 2), and the third combines squares with informal or tertiary roads (tracks, trails or footpaths) or no road registered at all (types 3 and 0, respectively, in the ESRI dataset). Figure 4 overlays the types of roads in the original dataset before our recategorization. The shaded area represents the portion of Africa for which we code Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "If registration is linked to the provision of services or other entitlements, there may be strong incentives to register births and new arrivals and weak incentives to deregister, leading to the inflation of the register over time or even instances of fraud and abuse (e. g. multiple registration, “ borrowing children ” etc.) (UNHCR 2003). Consequently, data from a refugee register may overestimate the number of refugees, requiring periodic corrective action through the verification of records. For example, in 2014 a verification of registration records for Somali refugees in the Dadaab camps in Kenya led to the deactivation of tens of thousands of records for individuals that are believed to have returned spontaneously to Somalia (UNHCR 2015). Additional problems with refugee registers include security concerns or inclement weather preventing refugees from accessing registration sites (UNHCR 2003) and the application of data protection principles. Registration of IDPs Individual registration is not as common a method of estimating numbers of IDPs as it is for refugees.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee register"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "General population registers In a small but growing number of countries, information from the central population register is the main source of migration statistics. 80 While population registers may generate statistics on both internal and international migration (if they record changes of residence, and international arrivals and departures) they do not typically record reasons for movement. However, it may be possible to link data from the central population register to those from immigration or border authorities to identify refugees and asylum- 76 Using standard ILO definitions, Labor Force Surveys collect data on work-related issues and provide a basis for measuring employment and unemployment indicators. They are typically conducted monthly in developed countries and quarterly or annually in developing countries. 77 Supported by USAID and implemented by ICF International, the DHS Program has collected, analyzed and disseminated data on population, health, HIV and nutrition through more than 300 surveys in over 90 countries. 78 MICS is an initiative of UNICEF that assists countries in collecting and analyzing health and education data in order to fill data gaps for monitoring the situation of children and women. 79 Many refugee hosting countries issue a form of identification, either specific to refugees or based on national identification documents or those issued to non-national residents. In many cases where such documents are not issued, refugee identity cards are issued in collaboration with UNHCR. 80 A population register provides a mechanism for the continuous recording of selected data on the resident population including a unique identification number, date of birth, sex, marital status, place of birth, place of residence, citizenship and language and possibly also socio-economic data, such as occupation or education. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Labor Force Surveys", "DHS Program"], "descriptive_data": ["central population register"], "vague_data": ["national identification documents", "population register"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "While both sons and daughters of the head may be expected to be more likely to stay in the community than other initial household members, patri-locality would make this probability higher for boys than for girls. In sum, this means we are using a set of six instruments. Although we can show that statistically convincing and close to identical results can be obtained by only using a subset of these instruments, we use the full set of instruments in the reported results. While our main measure of migration (Mi) is an indicator for having moved, we also substitute this for the log of the distance moved (kilometers from the original community of the location in which the individual was found in 2004, ‘ as the crow flies ’, set to 0 for non-movers). We will also extend the multivariate analysis to explore the role of moving to more urbanized areas and the role of sector movement in raising consumption growth. 6. Regression Results Table 9 presents the basic results for the initial household fixed effects (IHHFE) and 2SLS estimates (means for covariates are in Appendix Table 1). For each we estimate using an indicator for having moved and a measure of distance of the move. The 2SLS estimates in column (3) and (4) use the six instruments defined above. In Table 10, we present the first stage results of regressions explaining migration or the distance traveled in migration.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes high-income (OECD and non-OECD) countries. Excludes Palestinian refugees under UNRWA ’ s mandate.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "refugees lived in poverty, compared to 17 percent of hosts, in 2018 (World Bank, 2019). In Kalobeyei Settlement in Turkana County in Kenya, more than half of refugees are poor (58 percent), higher than the national poverty rate of 37 percent, lower than the poverty rate in Turkana County but comparable to the average poverty rate of the 15 poorest counties in Kenya (UNHCR and World Bank Group, 2020). About 72 percent of registered Venezuelans in Brazil live in extreme poverty, compared to 48 percent of Brazilians (Shamsuddin et al., 2021). Similarly, this report finds that poverty rates in Ethiopia’s refugees in camps are much higher than for host communities. Yet, refugee inflows can significantly affect host communities. Governments have been preoccupied with whether the arrival of large numbers of people in specific locations creates risks or opportunities for decades. Experience has shown that opportunities typically result if the influx of refugees is managed well and brings benefits to host communities similar to those of voluntary migrants. A recent review of the literature on refugee effects on host communities showed that most studies find a positive or non- significant effect of forced displacement on hosts’ employment, wages, and household well-being.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For example, IDPs who subsequently cross international borders may be counted as both IDPs and refugees (e. g. in the case of the Syrian displacement crisis). Table 4: Stocks and Flows Stock Increases Decreases Asylum- seekers  New applications for asylum, separately identifying individuals who were previously IDPs  Positive decisions (convention status, complementary protection status)  Rejected  Otherwise closed Refugees  Spontaneous arrivals (group recognition, temporary protection, individual recognition), separately identifying individuals who were previously IDPs  Resettlement arrivals  Births  Administrative corrections  Repatriation  Resettlement  Cessation  Naturalization  Deaths  Administrative corrections IDPs  New internal displacement  Births  Administrative corrections  Cross border flight, becoming an asylum-seeker or refugee  Return  Settlement elsewhere in the country  Local integration  Administrative corrections Source: UNHCR Global Trends, IDMC Forced Displacement Data Model", "output": {"entities": {"named_data": ["UNHCR Global Trends"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Of all countries in the World Bank's WDI, only 60%\n(40% of the analyzed countries) had an inflation figure for the most recent 2019-2020 period. The IFS\ndata reported similarly on only half of the countries. This was last checked on August 31, 2021; WDI\ndata identifier FP.CPI.TOTL.ZG, and IFS data identifier PCPI ~~P~~ C ~~P~~ P ~~P~~ T.\n\nThe paper highlights the new price monitoring capabilities using surveys from the World Food Programme (WFP) gathered in 25 fragile and conflict-affected countries.\n\nSubnational food prices have been surveyed in many countries for years by\nhumanitarians to inform their country operations. Well-known data bases are\nthose from the WFP, FEWS NET and the Food and Agricultural Organization\n(FAO). [6] The paper focuses on raw monthly data from the WFP, but parts of the\ndiscussion, and particularly the methods developed here, could apply to similar\ndata sets. [7]\n\nThe paper gathered all end-of-August data available from the WFP Vulnerability Analysis and Mapping (VAM) unit as of September 21, 2021.", "output": {"entities": {"named_data": ["World Bank's WDI", "WFP Vulnerability Analysis and Mapping (VAM) unit"], "descriptive_data": ["surveys from the World Food Programme"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Challenges around skill mismatch exacerbate exclusion, as many young Lebanese lack the skills and competencies demanded by private sector employers, particularly ‘ soft skills ’. To address some of these challenges, the Lebanese government (GOL) identified volunteerism as a mechanism to enable diverse youth to work together for improved community assets and service delivery as well as increased employability. In September 2012, the GOL issued a Decree (Number 8924 / 2012) that created a new extra curriculum program that requires secondary school students to complete 60 hours of civil work. In addition, the Ministry of Social Affairs (MOSA), through its Volunteering Department, launched annual action plans for the implementation of youth volunteer summer camps across Lebanon. 4 Father ’ s education and residence (region and location of school) are the two largest contributors to inequality of opportunity in students ’ math test scores, accounting for 44 and 23 percent of total inequality, respectively (World Bank, 2016). 5 According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job.", "output": {"entities": {"named_data": ["Gallup Poll"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The cross-dimensional poverty cut-off is defined as k = 50 %, with those deprived in half or more of the weighted indicators identified as multidimensionally poor. 4. Data Data on forcibly displaced populations are scarce, with many household surveys excluding refugees and IDPs from the sample framework. To ensure that MPI results are representative of these communities and that they can be disaggregated for comparative analysis, an initial review of possible data sets was conducted. Feasibility was determined based on the availability of sufficient sample sizes for forcibly displaced persons for quantitative analyses, as well as inclusion of many of the indicators (on health, education, living standards, etc.) 14 A household is deprived if the respondent reports feeling moderately or very unsafe when alone at home, walking alone after dark, or walking around during the day. In Sudan, the indicator on the ‘ feeling safe from crime and violence when at home ’ was not available, and the indicator only considers answers to the questions on safety when walking alone. 15 Unprotected dug well, unprotected spring, carts with tank, tanker-truck, surface water, or other are considered as unsafe water sources according to international guidelines. See https: / / washdata. org / monitoring / drinking-water. 16 Pit latrine without slab, bucket, hanging toilet, and no facility (open defecation) are considered as unimproved sanitation facilities according to international guidelines. See https: / / washdata. org / monitoring / sanitation. 17 According to the ILO definition, those who did not participate in employment in the last four weeks (and have no work to return to) are actively looking for work and are available to start, or those currently waiting to start work are classed as unemployed. See https: / / www. ilo. org / ilostat-files / Documents / description_UR_EN. pdf. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "NGOs. Initiatives such as JIPS, a collaborative project of UN and NGO actors, have also been established to support governments and operational organizations to design and implement data collection processes. There are a variety of data sources for generating statistics on forced displacement, each of which has strengths and weaknesses. Despite the significant challenges, large amounts of data are collected and disseminated every year. The main data sources and methods for the generation of statistics on forcibly displaced populations include: (a) registration of refugees and asylum-seekers; (b) registration of IDPs; (c) profiling of IDPs; (d) population movement tracking systems; (e) national population censuses; (f) sample surveys; (g) border crossings; (h) administrative records and registers; (i) general population registers; and (j) a variety of estimation methods for producing statistics when adequate and reliable data on individuals are unavailable (UNSD 2014). Several of these data sources might be used together to triangulate estimates of stocks and flows for a particular displacement situation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["general population registers", "sample surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The long-term consequences of these trends are signiϐicant, as diminished educational outcomes and social isolation can hinder successful integration into host communities. Conversely, sustained social and educational integration efforts are vital for positive outcomes. For instance, studies indicate that long-term integration can be hampered by social, economic, and institutional barriers (Chiswick and Miller, 2014), while interventions focused on language support and community engagement can lower these barriers (Ozden and Wagner, 2020). Further, speciϐic interventions aimed at removing obstacles to education for children on the move can contribute signiϐicantly to better integration outcomes (Schuettler and Caron, 2020). This paper beneϐits from key information coming from administrative data on educational records of Ukrainian refugees in Italy for the academic years 2021-2022 to 2023-2024 for grades 6 to 13. This provides a unique opportunity to examine enrollment, attendance, test performance, and other indicators of integration into the Italian educational system. Supplemented by survey data collected in 2023-2024, this study offers an overview of the challenges and opportunities faced by Ukrainian students in secondary schools and highlights areas for potential policy development. This study advances the literature by adding empirical evidence on the short- to medium-term educational impacts of displacement on young refugees within a European host country, offering insights into the role of education policy in mitigating human capital losses. It also contributes to discussions on human development by identifying factors that support or hinder integration, highlighting pathways for improving educational and social outcomes for refugee students. Results highlight that despite gradual improvements, enrollment rates remain signiϐicantly lower among refugees compared to native and other foreign students. Ukrainian refugees also demonstrate higher absenteeism and lower academic performance, particularly in subjects requiring language proϐiciency such as Italian and English. However, good performance in mathematics suggests potential strengths linked to their prior educational backgrounds. Despite these challenges, teachers seem to be more inclined to recommend Ukrainian refugees for high-track education compared to other newly arrived foreigners, indicating potential optimism about their academic capabilities. The", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The LFS reference week design captures the contemporaneous employment status of respondents at the time of interview, making it sensitive to the specific week in which households are interviewed within a quarter. To address seasonality, the LFS staggers interviews evenly across the 13 weeks of each quarter, ensuring that agricultural and other seasonal employment cycles are represented proportionally in the quarterly sample. We account for residual seasonality in our models by including a full set of quarter-of-year fixed effects and their interactions with the rural indicator.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "7 All data collection is done by GISSE a research institute in Bamako. The anonymized unit record data of the baseline and the monthly surveys can be downloaded from www. gisse. org. The response rate for the phone interviews has been very high (Table 1): after 6 rounds of monthly interviews the original sample is almost entirely intact. The low level of attrition demonstrates that mobile phone samples can be maintained over prolonged periods without being unduly affected by (non-random) respondent drop-out. 3. Characteristics of the Displaced and Returnee Population According to the 2009 population census, the two most sizeable ethnic groups in northern Mali are the Songhai (45 %) and Kel Tamasheq (32 %)-- see Table 2. The crisis brought about an ethnic divide, which is reflected in the composition of the three sub-samples. The majority of IDPs and returnees are Songhai (75 % and 71 % respectively), while the majority of refugees are Kel Tamasheq. Results suggest that the decision of where to flee was determined by ethnicity: Kel Tamasheq and Arabs left the country; Songhai fled towards Bamako.", "output": {"entities": {"named_data": ["2009 population census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The curriculum included entrepreneurship principles, market analysis, business management, customer service, money management, and record-keeping. The EPAG program was implemented by four NGOs who were selected by the Liberian Ministry of Gender and Development through a competitive bidding process: the Community Empowerment Program (CEP), Liberia Entrepreneurial and Economic Development (LEED), the International Rescue Committee (IRC), and the American Refugee Committee (ARC). Two of these organizations (ARC and IRC) further subcontracted to four Liberian NGOs. 4 The service providers were responsible for developing training curricula, identifying training venues, 5 making arrangements for childcare services, assisting with the mobilization of the nine target communities, and participating in the recruitment of training participants. The EPAG program differed from many training programs in a number of ways. First, performance bonuses were awarded to training providers that successfully place their graduates in jobs or micro- enterprises. The bonus was the last payment that the service providers received under their contracts. These were paid about 12 months after the start of training, or around the same time as the midline survey. Second, a variety of contests and competitions were also held among EPAG trainees (such as attendance prizes, quizzing contests, business plan competitions, etc.).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, another regressor indicates that security may still be pivotal: those who owned a weapon were up to 30 percentage points more likely to plan to go back. In addition to this, it is quite surprising that, if the respondent thought that the Northern Mali crisis was improving, he or she was less likely to plan a return to that area. From a technical point of view, we should point out that we have used an LPM even if the dependent variable was a binary outcome. This choice has been made since in this linear model it is straightforward to add fixed-effects. Furthermore, the coefficients can be interpreted as average partial effects. A simple logit or probit model would not have allowed the inclusion of individual fixed-effects because of the incidental parameter problem. An alternative approach would have been to estimate a conditional logit model. However, since the distribution of the fixed effects is unknown, it would not have been possible to estimate the average partial effects in this model, but only the effect of the regressors on the log-odds ratio. 13 We conclude by stressing that the monthly phone interviews were relatively short, so we did not have a rich panel data set. This may have led to omitted variable biases. Indeed, there may still be time varying factors which could have affected both the probability of being employed and the respondents ’ intentions to go back. Nevertheless, we believe that our model managed to control for 13 See (Wooldridge, 2010) page 639. Conclusions from the conditional logit model are qualitatively similar. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the degree to which the global response should include a development element. We ﬁnd that the average stood at around 10. 3 years at the end of 2015, with a median duration of 4 years, and signiﬁcant sensitivity to a few situations. Such numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete protracted situations. To produce these numbers, we rely on the Population Statistics Database compiled and main- tained by UNHCR. The database records the number of “ persons of interest ” to UNHCR in each year since 1951 and for each situation, where a situation consists of a pair host-origin countries. The calculation of duration of exile is obtained under a no-turnover assumption, whereby a de- crease in the number of refugees for any given situation is fully attributed to exits from refugee status, while increases are assumed to be fully accounted for by new cases. Although such ap- proach tends to over-estimate the true duration of exile, the lack of individual-level data on regis- tration precludes reﬁning the estimate further. Attempts to estimate similar statistics have been limited.", "output": {"entities": {"named_data": ["Population Statistics Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The upcoming section provides an overview of the situation facing Venezuelan migrants in Colombia, setting the stage for understanding the context within which our study is situated. Section three offers a comprehensive description of the VenRePS-Kids study, covering aspects such as the sampling frame, the instrument used for data collection, the representativeness of the study, its implementation process, and an overview of descriptive statistics. Section four delves into the human development disparities observed among forcibly displaced chil- dren and adolescents, providing detailed insights into the nature of these gaps. In section 9 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A recent global hazard analysis generated a comprehensive database of hazard events during 1975- 2007 from observed data and of event probabilities from geo-physical models (ISDR 2009). We combined this hazard information with city-specific population projections 2 It should be noted that well-documented evidence for such externalities is quite scarce. Their importance is usually taken as given: “ A building collapse may create externalities in the form of economic dislocations and other social costs in addition to the economic loss suffered by the owner. The owners may not have taken these consequences into account when evaluating specific mitigation measures. Consider the following example. A building toppling off its foundation after an earthquake could break a pipeline and cause a major fire, which would damage other homes that had not been affected by the earthquake in the first place. “ Kuenreuther and Roth (1998). See also www. quakesmart. org / index. php? option = com_content & view = article & id = 92 & Itemid = 209. But some experiences have been documented: “ As shown by research on the Great Hanshin-Awaji Earthquake, including that conducted by the Architectural Institute of Japan, Architectural Institute of Japan (1997), houses with inferior earthquake-resistant quality triggered large negative externalities in the neighborhood. For example, broken fragile houses blocked transportation networks, thereby preventing effective fire fighting and, by severing lifelines, they made recovery more difficult. ” (Nakagawaa et al. 2007).", "output": {"entities": {"named_data": [], "descriptive_data": ["city-specific population projections"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Humanitarian organizations such as UNHCR and OCHA as well as international organizations such as IOM are also involved in the collection of data on IDPs, often involving international and local 59 OCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. See http: / / www. unocha. org /. 60 This section draws heavily on the “ Report of Statistics Norway and the Office of the United Nations High Commissioner for Refugees on statistics on refugees and IDPs ” presented at the UNSD in March 2015. 61 The number of countries where UNHCR exclusively collects data on refugees declined from 76 in 2010 to 72 in 2014, while the proportion of countries where refugee data were exclusively provided by governments gradually increased over the same period from 33 to 38 percent. In 2014, the proportion of countries where data were provided through collection conducted jointly by governments and UNHCR was 15 percent, while in the remaining proportion (13 percent), refugee data were provided exclusively by NGOs and other organizations. In 2014, more than 173 countries and territories provided data on refugees. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "[2 Available at http://faostat3.fao.org/home/index.html (accessed 11 June 2013)](http://faostat3.fao.org/home/index.html) 3 Smith (1998) reports that, at the time, for 18 out of the 99 countries, the CVs are estimated based on analysis of nationally representative HCES. The rest of the countries' CVs are predicted either from measures of income distribution or as the mean CV estimated for other countries in the same region.\n\n4 In 2012 the FAO revised its distribution to be the skew-normal distribution (Azzalini, 1985), which generalises the normal distribution to allow for skewing.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["HCES"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5. Time Use and Child Labor: The fifth module examined how children spend their leisure time, their involvement in child labor, and interactions with peers. 6. Pro-social Preferences and Migration Outlook: The sixth module concentrated on adolescents ’ pro-social behaviors, such as altruism and trust, and explored their expectations and intentions regarding migration. 7. Socio-emotional and Mental Health: The final module involved the administration of various scales to assess socio-emotional well-being and mental health, includ- ing trauma, behavioral problems, anxiety, and depression. The scales include the Trauma Symptom Checklist for Young Children (TSCYC), Strengths and Difficulties Questionnaire (SDQ), General Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). All these scales and the corresponding outcomes that we evaluated are described in the next subsection. The survey also employed the Peabody vocabulary test to evaluate the cognitive devel- opment of all participating children and adolescents. A summary of the survey modules is depicted in Table A. 1. III. C Sample comparability While Medell ´ ın ranks as the third city with the highest migration in Colombia, it is crucial to recognize the degree to which migrants arriving in the city differ from those migrating to other regions in Colombia. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Trauma Symptom Checklist for Young Children", "Strengths and Difficulties Questionnaire", "General Anxiety Disorder Scale", "Patient Health Questionnaire"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1. Introduction Sub-Saharan Africa is the youngest region in the world, and the world ’ s fastest-growing. Between 2010 and 2025, the number of people between 15 and 24 will grow to 250 million – a net increase of nearly 50 percent. Over the next decade, roughly one million young people will enter the labor market each month in Sub-Saharan Africa. However, these young people often enter the labor market too early and unprepared. Although access to education is growing, illiteracy remains high and schooling low: among the 32 Sub-Saharan African countries in the Barro-Lee (2010) data set, nearly 40 percent of women aged 15 and above have received no education at all; and the most recent (2007-2011) statistics in the World Bank ’ s Edstats data reveal that female literacy is less than 60 percent, on average. 1 This lack of preparedness contributes to a growing problem of youth unemployment. Quantifying the level of unemployment is bedeviled by lack of data and measurement issues. Household and labor force surveys throughout Africa usually record unemployment rates of less than 10 percent, 2 but those figures belie the extent of underemployment and vulnerability. Those same surveys indicate that the vast majority of working adults have insecure work in the informal sector, on the family farm, or in less- productive or unremunerated labor. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Barro-Lee", "Edstats data"], "descriptive_data": [], "vague_data": ["Household and labor force surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These calculations conﬁrm our earlier assessment. 5 Conclusion Combining data from a household survey and an 11 % census of the population, we have estimated destination choice regressions for Nepalese internal migrants. Results show that population density, social proximity, and access to amenities exert a strong inﬂuence on migrants ’ choice of destination. These results conﬁrm earlier work on the factors aﬀecting the subjective welfare cost of isolation (Fafchamps and Shilpi, 2008). 29 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey", "11 % census of the population"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Addis Ababa All Hosts Increased economic opportunity Increased insecurity Are taking our land Figure 7.6: Host beliefs about refugee impact in Ethiopia Source: World Bank Staff based on SESRE 2023. 50 The numbers are combined here, but mainly measures employment competition since very few are concerned about wage competition. 0 5 10 15 20 25 30 35 40 Eritrean Somali South Sudanese Addis Ababa All Hosts Improved Infrastructure Improved Services Figure 7.8: Positive experience due to refugees Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 67 Overall, hosts and refugees show similar trust rates in each other; still, refugees are generally more trusting. Questions about the trustworthiness of hosts and refugees reveal that most people either trust both (hosts and refugees) or neither group. Only 17 percent of hosts trust other Ethiopians but not refugees, and only 10 percent of refugees trust other refugees but not Ethiopians. In comparison, 39 percent of hosts and 55 percent of refugees trust both groups. Once again, hosts’ trust towards refugees is highest in the Somali domain (67 percent) and lowest in the South Sudanese domain (29 percent). On the other hand, refugee trust towards hosts is lowest in the Eritrean", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The vulnerability of the Internally Displaced People in SSA has certainly been overlooked for too long, but the increased support provided by UNHCR is an encouraging but challenging sign in that respect. Figure 3. Refugees and Internally Displaced People in SSA, 2003 ‐ 2013 Source: Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). The annual number of IDPs is collected from the IDMC (2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2012, 2013, 2014) annual reviews. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(b) Disaggregation and geo-mapping of data by location (current location and location of habitual residence; urban, peri-urban or rural location), accommodation (organized camp versus non- camp), and demographics (age and sex); (c) Expanded coverage of data collection exercises to include all areas of affected countries (security permitting); (d) Improved coverage and detailed data on displaced populations living outside of organized camps; (e) Improved coverage of ‘ flows ’, i. e. new displacement, durable solutions (returns, integration, resettlement), births, deaths, and in the case of IDPs, the numbers that flee across international borders becoming refugees; (f) Systematic data collection beginning from the earliest moment following displacement, following up as populations disperse, and continuing until sustainable / durable solutions have been achieved; and (g) Better aggregation, analysis and presentation of forced displacement data currently compiled separately by UNHCR, IOM, IDMC and UNRWA. Additional efforts are required to address the gaps in the data required for development policy and planning. These data are critical for informing the design of development policies and assistance programs.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["forced displacement data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "obtained from the multilevel logistic model. Thus, I am conﬁdent that I did not lose substantial statistical information by dichotomizing this variable. 6. 2 Independent Variables 6. 2. 1 Socio-Demographic Variables I control for standard socio-demographic variables that can aﬀect citizens ’ acceptance of government ’ s right to make people pay taxes. A question prob- ing respondents on their household income was not included in the fourth round of Afrobarometer surveys. Asking respondents to quantify their in- come can be problematic in the context of developing economies, where in- dividuals are often embedded in barter or commodity exchange, rather than, market economies. There are, however, reasonably good proxies including whether respondents own a television, radio, car, and mobile phone, and use the internet. Age, education, employment, and urban or rural residence are demographic factors that also aﬀect household resources. 6. 2. 2 Experience with Paying Taxes or Fees It is diﬃcult to assess just how ubiquitous taxes are in ordinary Africans ’ lives. There has not been any systematic eﬀort to take stock of the types and amount of taxes citizens pay across Africa. Similar to pre-modern European states, African states ’ revenue raising capacity is generally low. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Afrobarometer surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 population study2 (Brown et al, 2019) revealed that 78 percent of the population of registered refugees lived below the Jordanian poverty line, and between 2017 and 2019, there was only a 2- percentage point reduction of highly vulnerable cases. 3 UNHCR (2018a) also reports that close to 37 % of the registered Syrian refugees in Jordan are separated from a family member. Household structures have often been fluid. Many Syrian families sent members ahead to Jordan to settle and sometimes, after the reunification of the rest of the family in Jordan, male family members traveled on to Türkiye or to Europe (UNHCR, 2018a). These separations likely affected families emotionally and economically. However, little is known about the changes in economic well-being over time or how gender inequality has shaped poverty and expenditure outcomes. We use two waves of data from a unique household survey, the United Nations High Commissioner for Refugees (UNHCR) Home-Visits survey for 2013-14 and 2017-18. First, we create comparable measures of expenditure per capita over time. Then, we assess whether certain types of households are more likely to be below the median of the per capita expenditure distribution in each time period. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "and 6 percent for hosts, and in the camps is 13 percent for refugees and 12 percent for hosts. 0.05 0 0.1 0.15 0.2 0.25 0.3 0.35 Elementary Occupations Craf/Related Trades Service/Sales Machine Operators Associate Prof. Clerical Support Professionals Share of Refugees Observed Predicted according to observables Figure 3.26: Refugee occupation concentration Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 38 In Addis Ababa, refugee girls—like boys—are much less likely than host counterparts to work or attend school, and they are more likely to be unemployed or NEET. They are even less likely than refugee boys to be in school and more likely to be NEET (23 percent for girls relative to 15 percent for boys—Figure 3.28). In camps, refugee girls look more like their host counterparts regarding schooling and labor force participation. However, among those in the workforce, their relaxed unemployment rate is much higher (55 percent for female refugees and 36 percent for female hosts). Boys in camps have higher schooling rates (75 percent relative to 65 percent for hosts), reflecting their higher propensity to stay enrolled in primary or secondary schooling after the typical completion age, especially in South Sudanese camps where 84 percent", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "There were in total 1, 6 million refugees by November 2014. Data on the number of new ﬁrms and their ownership characteristics and value are provided by the Turkish Chamber of Commerce. 7 Data on total sales and gross proﬁts are obtained from the Turkish Ministry of Science, Industry and Technology. Other economic indi- cator variables, such as population and unemployment rates, are obtained from Turkish Statistics. Since Syrians usually have guest status rather than resident status during the period of analysis, they are not counted in oﬃcial statistics such as province population and unemployment rates. Turkey is oﬃcially divided into 81 provinces and that is the level of our analysis and variables throughout. The Chamber of Commerce provides data on the number of new ﬁrms and the num- ber of new foreign-owned ﬁrms at the provincial level. Enterprises deﬁned as ﬁrms do 11 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Survey of Refugees in Ethiopia (SESRE) contribute to the debate on policies? The Socio-Economic Survey of Refugees in Ethiopia (SESRE) is a representative survey of the refugee population in Ethiopia and their host communities, the first of its kind.9 Ethiopia made significant progress over the past few years in articulating more progressive and comprehensive refugee responses. In 2023, the GoE included new pledges and commitments made in the 2023 Global Refugee Forum, including a significant shift in its refugee management policies. This includes improving the socio and economic opportunities for refugees through an agenda to transform camps into human settlements and including refugees in national services for education, including secondary education and health (UNHCR, 2024). Systematically collecting high-quality data on refugees and their hosts in one survey is pertinent to inform the GoE’s roadmap and programs to address the development needs of refugees and hosts. The national household survey of Ethiopia–Household Welfare Statistics Survey (HoWStat)—excludes the majority of displaced populations (Internally Displaced People [IDPs] or refugees) from its sample of households. Thus, we have limited in-depth information on the socio-economic outcomes—including on poverty— for refugees across all camps in Ethiopia to compare with Ethiopian hosts. SESRE collected data from November", "output": {"entities": {"named_data": ["Socio-Economic Survey of Refugees in Ethiopia"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Most importantly for the purpose of the analysis is the fact that the survey provides individual-level data on own schooling and parents ’ schooling for all adults in the sample, which is quite rare in household surveys from developing countries. Also, the survey provides the actual years of schooling completed and not only the highest educational degree attained, which allow observing the schooling variable with precision.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This database lists seven categories: refugees, asylum-seekers, returned refugees, internally displaced persons (IDPs), returned IDPs, stateless persons and others of concern. For each group the database provides yearly information about their composition by loca- tion of residence and origin. We exploit only the data on “ refugees ”. 22 In particular, we are interested in the annual stock of refugees for each country of residence, i. e. how many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number of IDPs is extremely high in some instances but cannot be captured with the same level of conﬁdence as refugees generally. 23 Cross-country data about conﬂict is provided by the UCDP / PRIO. As for the index of country-level economic activity, we use again information provided by the Penn World Table and World Bank databases. As mentioned above, our aim is to explore the dynamics of refugees during conﬂicts. In other words, we attempt to answer several questions. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Without army protection most parts of the North, especially Kidal remain inaccessible to those working for the central and local government. 3 Armed bandits are active and IED explosions as well as violent attacks on the MINUSMA peacekeeping forces are regular occurrences. Under these circumstances, data collection is very difficult. INSTAT, the National Bureau of Statistics, has not been in a position to collect information from northern Mali since the beginning of the crisis. To our knowledge, our surveys implemented by a private survey entity, GISSE, are the only systematic and representative effort to collect data in north Mali since the crisis. They offer a unique database providing a crucial perspective that would otherwise not be reflected in academic analyses and policy level decision-making, and a perspective that is indispensable in any attempt at understanding the situation in Northern Mali. 4 Preceding the Accord on Peace and Reconciliation in Mali (Accord pour la paix et la réconciliation au Mali) (hereafter, the Peace Accord) of May and June 2015, four peace accords had been signed between the government and Toureg and Arab armed groups in 1 Kel Tamasheq (those who speak Tamasheq) is synonymous for Tuareq. 2 Francis David (2013): The regional impact of the armed conflict and French intervention in Mali. NOREF, Norwegian Peacebuilding Resource Centre. 3 Assessing Recovery and Development Priorities in Mali ’ s Conflict-Affected Regions. Draft Report of the Joint Assessment Mission for Northern Mali (January 2016), p. 15-16. 4 All data can be downloaded from www. gisse. org. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Firms listed in the NBS registry without a contact telephone number were also excluded from the sampling\nframe. Hence, all survey results should be interpreted as representative of more formal business activities.\n\nTo ensure that robust estimation results can be obtained for different subpopulations, the survey used a\ndedicated sampling strategy. Based on information from the NBS, all registered firms were divided into\nfive distinct strata, depending on their reliance on transport systems. Ordered from low to high transport\nreliance, these strata contain firms from the following sectors:\n\nDirect asset losses are the most tangible impact of disasters on firms. In high-income countries, a range\nof institutions, such as insurance companies or governmental organizations, collect data on direct losses\n(for the United States, see, for example, Smith and Katz 2013). In developing countries, where insurance\nmarkets and data collection are limited, less is known about the direct losses that firms incur. While\ndatabases such as EM-DAT provide some data on aggregate direct disaster losses, few quantitative firmlevel studies have been conducted. One exception is De Mel et al. (2012), who analyze the impact of the\n\nFirms incur a wide range of losses due to disasters, both in terms of direct damages and indirectly\ntransmitted costs. Based on the data collected for this study, Appendix B offers a full discussion of the\nscale and type of firms' disaster losses.\n\n\nThe survey data reveals that flood risks are high throughout most of Tanzania and confirm that firms face\nsubstantial recurring losses.", "output": {"entities": {"named_data": ["NBS registry", "EM-DAT"], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "restricted to workers with completed secondary education. Among workers with completed secondary, 30 percent of refugees and 50 percent of hosts are in high-skill occupations (Figure 3.25). Among only women, these numbers are 20 percent and 50 percent, respectively. Instead, refugee men and women in Addis Ababa are over- represented in crafts and related trades (typically classified as medium-skill occupations). 0 20 40 60 80 100 Addis Hosts Addis Refugees Current Addis Refugees COB Salary (employment/casual labor) Crops/livestock Donations(NGO/gov) Remittances (local/international) Other (rental income, PSNP, pension) Percent 0 20 40 60 80 100 Percent Addis Male-Headed Addis Female-Headed Figure 3.21: Household primary income source Source: World Bank Staff based on SESRE 2023. Note: “COB” refers to livelihood strategies in their country of birth. a. Pre-post migration b. By gender of head Table 3.2: Labor force statistics Addis Hosts Addis Hosts Labor force participation rate (strict) 66% 46% Unemployment rate (strict) 12% 63% Labor force participation rate (relaxed) 72% 67% Unemployment (relaxed) 19% 75% Employment-to-population ratio 58% 17% Source: World Bank Staff based on SESRE 2023. Note: Labor force participation ratio is the share of working-age people who are engaged in the labor market, either employed or unemployed. Unemployment is the", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "FE remove the effect of individual-specific time-invariant characteristics so as the net effect of the predictor variable on outcome variables can be assessed, per the following equation: 𝑌 ௜ ௧ ൌ 𝛼 + 𝜇 ௡ + 𝛽𝑇௧ + 𝛾𝐷 ௜ + 𝛿ሺ𝑇 ∗ 𝐷ሻ ௜ ௧ + 𝜃ଶ𝐸ଶ ൅.. ൅ 𝜃 ௡ 𝐸 ௡ ൅ 𝜀 ௜ ௧ (3) where 𝐸 ௡ is entity n (i. e. the individual volunteer). Since they are binary (dummies), there are n-1 included in the model (i. e. 758 individual volunteers). 𝜃ଶ is the coefficient for the binary regressors (the 758 volunteers). Additionally, we propose dealing with attrition in two ways. First, we utilize the standard “ Manski Bounds ” approach (Horowitz and Manski, 2000) by imputing upper and lower bound estimates for missing data on estimated outcomes of interest at follow-up, where lower bound estimates take the lowest possible value and upper bound estimates take the highest possible value for individuals who could not be tracked over time. This allows us to provide the two extreme possible scenarios for estimated impacts had data been successfully collected for attritors. Second, we use the Inverse Probability Weighting (IPW) procedure to establish narrower bounds that might provide a better sense of whether there is a robust treatment effect. This entails first estimating a probit model that predicts the probability of data being observed (i. e. not attrition) using a set of covariates at", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Due to the variations in weight and height ratios among children and adolescents according to gender and age, the benchmarks for determining nutritional status are specifically tailored to these factors. We adhere to WHO guidelines to calculate the standardized BMI values for minors. A BMI exceeding one standard deviation (SD) from the mean suggests overweight, while a BMI less than minus one SD indicates underweight. 16 Health status is assessed through a binary variable, assigned a value of one if the caregiver has reported any health issues such as disease or chronic pain, accidents, dental pain, surgical interventions, or preg- 14For the Colombian households the wealth index is measured with contemporaneous data. 15This procedure restricts the sample to the common support of the propensity score for being a forced migrant and weights observations for Colombian kids by a non-parametric function of the propensity score. This procedure has been shown to increase the estimate ’ s efficiency. 16Furthermore, a BMI greater than 2SD is indicative of obesity risk, and less than- 2SD signals a risk of severe thinness. 33", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Data on the structural and cyclical characteristics of national economies comes from the World Bank's World Development Indicator Database [9] and UNCTAD-STAT; [10] the figures on education are sourced from Barro & Lee (2013); [11] those on the real effective exchange rate (REER) are taken from Darvas (2012); [12] and metal- and oil-price figures are sourced from the IMF's Primary Commodity Prices database. [13] Referring to the original datasets will provide further details on the methodology and sources used.\n\nWe need first to describe inequality and poverty levels in the affected region. We define the poor as the individuals in the bottom quintile in terms of consumption or income. Therefore, the parameter �� is equal to 20%. To estimate �� and ��, we use the World Development Indicators database, which provides the income share of the bottom 20%. Figure 3 shows the result, highlighting the large variability - and lack of correlation - between inequality and national income level.\n\nIn this national-level analysis, and consistent with our focus on the socioeconomic drivers of resilience, we use a very simple methodology. We use the Global Building Inventory database from PAGER, by USGS, which provides a distribution of building types (buildings only, not contents) within countries across the world (USGS 2015). This typology has been developed to assess vulnerability to earthquakes but here we use it for all hazards.", "output": {"entities": {"named_data": ["UNCTAD-STAT", "Primary Commodity Prices database", "World Development Indicators database"], "descriptive_data": ["Global Building Inventory database from PAGER"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "III THE VENREPS-KIDS STUDY In this section, we offer a detailed overview of the VenRePs-kids Study, covering its de- sign, implementation, the questionnaire utilized, and the primary outcomes that will be employed to evaluate the human development disparities between forcibly displaced children and adolescents and their peers in host communities. III. A Design Location. Our study is conducted in Medell ´ ın, Colombia ’ s second-largest city, following Bogot ´ a. Medell ´ ın was chosen for this study because it hosts the third-largest Venezue- lan migrant population in the country, trailing only Bogot ´ a and C ´ ucuta, as indicated by the 2018 population census data. Additionally, previous research has demonstrated that survey response rates among migrants in Medell ´ ın are notably high. For instance, a na- tionally representative survey of Venezuelan migrants conducted in 2018 — which was representative across Colombia — revealed that Medell ´ ın had the highest response rates among migrants, whereas Bogot ´ a recorded the lowest (Ib ´ a ˜ nez et al. 2022). This finding supports the decision to focus our study exclusively on Medell ´ ın, also considering the challenges and high costs associated with tracking a highly mobile population longitu- dinally in previous research efforts.", "output": {"entities": {"named_data": [], "descriptive_data": ["2018 population census data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The top 5 protection incidents reported in the region are homicide (30 per cent), assault and battery (29 percent),\nextortion of property (16 per cent), destruction of properties (13 per cent), and looting (12 per cent). Also, those\nprotection incidents affected 99.88 percent of Internally displaced persons (IDPs), 0.11 percent of refugees, and 0.01\nper cent of asylum-seekers. The protection incidents affected more males (53 per cent) than females (47 per cent),\nand the protection incidents affected the age group (0 to 17), children, who represent 44 percent of all victims\nreported in September 2022. In addition, the perpetrators of those protection incidents are armed groups (46 per\ncent), host communities (26 per cent), others (23 per cent), and unknown (5 per cent). It is worth mentioning that 1.3\npercent of the regional population, compared to September 2022 regional data, was affected by the protection\nincidents reported in September 2022.", "output": {"entities": {"named_data": [], "descriptive_data": ["September 2022 regional data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 consumption or expenditure is measured in per-capita terms and has implications for other indicators of well-being such as housing, rents, or crowding. The Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and Living Standards Measurement Surveys (LSMS) are the most common household surveys used to produce comparative statistics on well-being across time and countries. The DHS and MICSs define household members as (i) usual residents or people who slept in the dwelling the previous night and who (ii) share living arrangements and (iii) share food (ICF International, 2012; UNICEF, 2013). The LSMSs define household members as (i) people who slept in the dwelling three or more months of the last 12 months and (ii) share food (Grosh and Munoz, 1996). 7 While the definitions differ, they are defined on similar concepts and are unlikely to lead to major differences in key household characteristics. The UNHCR has definitions for household that resemble the definitions used by MICSs and LSMSs but uses the concept of “ case ” as unit of observation. The UNHCR defines a case as: “ A processing unit similar to a family headed by a Principal Applicant. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 agreements that did not fully satisfied either party (Ndayirukiye and Takeuchi 2014). This tension related to land, and who has a claim to the land, can lead to social tensions in communities with higher levels of return. Figure 3 – Refugees in Tanzania in 2005 by province of origin in Burundi Note: The number in brackets is the number of refugees in Tanzania in 2005 which was originally from the given province in Burundi. This information comes from (UNHCR 2021b). The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census. An important question for our hypotheses is the degree to which there is evidence of migration-related societal divisions in the country. There is no direct quantitative evidence on identity issues (i. e. returnees versus stayees), but we have data on attitudes towards emigration, remittances and return that can provide insights on these identities and even be a proxy for migration-related identity in some cases. Overall, attitudes towards emigration and return are mixed and show that there is scope for the existence of migration-related divisions. In Table 1 we report the share of respondents who agreed with different statements regarding emigration, remittances and return. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["1990 Burundi Census"], "descriptive_data": ["data on attitudes towards emigration, remittances and return"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "`o` They also pushed for career rotation and continuous support for **education** as a foundation for\nsociety. While both groups shared a commitment to youth empowerment, the female group\nleaned more toward social and psychological well-being, whereas the male group prioritized\nleadership, structure, and legal safeguards.\n\n## **HEALTH AND DISABILITY**\n\n**Health and disability**\n\nA small percentage (7%) identify as having a disability [2] according to the\nWashington Group classification. Within youth with disabilities, **males**\n\n**constitute the majority** (72%) compared to females (28%).\nDisproportionate impact of disability on young men should be the driver\nof additional data collection to develop specific programs to address\ntheir needs and exposure to risks.\n\n**Person with disabilities by**\n\n**gender**\n\n**Male**\n\n**72%**\n\n The majority of young people in northwest Syria, (84%), do not\nface any health problems conditions. However, a significant 16%\nreported facing health issues, with varying levels of access to\nhealth services and medication.\n Regarding access to health services, the results indicate that only 47% of those with health problems,\nconditions or disabilities have access to health services, while 53% do not.\n 56% of youth with health problems, conditions or disabilities **do not have access to adequate medication** .\n\n---\n[2] https://www.washingtongroup-disability.com/question-sets/", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "month placement and support phase in which the trainees were supported in their transition to self or wage employment. Upon recruitment, the participants are assigned to a\"Job Skills (JS)\"track or a\"Business Development Services (BDS)\"track. When possible, the participant's track preference was honored; however, the demand for the Job Skills track greatly exceeded the supply, so the remaining trainees were placed into the BDS track. In the first round of training, the proportion of Job Skills track places was limited to 35 % of the total training places available given the expectation that few wage jobs will be available in the Liberian job market. The Job Skills track provided training in six areas: 1) hospitality, 2) professional cleaning / waste management, 3) office / computer skills, 4) professional house / office painting, 5) security guard services, and 6) professional driving. These areas were determined based on independent labor market assessments, a review of the available market data, and input from EPAG ’ s private sector partners. All Job Skills trainees received training in entrepreneurship skills as well. The BDS training taught young women how to identify micro-enterprise opportunities based on an assessment of market needs, and how to grow and manage any existing businesses they already had. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["available market data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Second, there are cases where the current study reports data by nationality, but the corresponding figure in the Trends in International Migrant Stock refers to the foreign born. This situation generally arises when a census does not report the number of foreign-born migrants on a bilateral basis. Examples include Austria and Côte d ‘ Ivoire. Third, differences in the years to which the data refer can generate large disparities. For example, this study uses the 1966 data for Australia, whereas Trends in International Migrant Stock reports data for 1970. Overall, however, the fact that the totals are remarkably close in every decade adds credence to the estimates here. IV. THE EVOLUTION OF GLOBAL BILATERAL MIGRATION The greatest strengths of the global migration matrices are their bilateral coverage, the number of decades covered, and the disaggregation by gender. These data are too rich for a full analysis of all movements between all pairs of countries. Instead, this section summarizes the major trends in the evolution of bilateral migrant stocks, based primarily on World Bank regions. 25 Global Trends The migration matrix for the 1960 census round reflects a realigning world in the postcolonial era. Over the 1960-2000 period, the composition of world migration 25 Appendix 1 details the World Bank regions: South Asia, East Asia and Pacific, Sub-Saharan Africa, Latin America and the Caribbean, Europe and Central Asia, and Middle East and North Africa. High-income Middle East and North Africa refers to the predominantly oil producing countries in the Persian Gulf (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) and to Israel. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "reason to believe that the provision of services by donors and non-state actors could either strengthen or weaken citizens ’ deference to government. I will identify the conditions under which these two scenarios are likely to occur. On the one hand, citizens may be less likely to support the government with deference to its laws and regulations when they credit non-state actors or donors for service provision. The provision of services by donors and non- state actors is likely to prompt citizens to question why they should pay taxes to a government that is not providing them with anything in exchange. On the other hand, the provision of goods and services by donors and non-state actors might strengthen citizens ’ legitimating beliefs and their willingness to defer to governmental laws and regulations if citizens view their government as essential to leveraging and managing these external resources. I assess these competing hypotheses using multi-level analyses of Afro- barometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows us to analyze associations between donor and non- state actor service provision and the sense of obligation to comply with the tax authorities. Third, I assess the relationship between the provision of ser- vices by donors and non-state actors and citizens ’ willingness to defer to two additional authorities, the police and courts. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afro- barometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "### 7.2 Monitoring and Evaluation Framework\n\nThe M&E framework distinguishes between process indicators, tracked through the MIS on a monthly basis, and outcome indicators, measured through periodic surveys. Process indicators cover outputs such as number of beneficiaries registered, cash transfers disbursed, and community infrastructure works completed. Outcome indicators are measured at baseline, midterm, and endline using a structured household survey instrument administered to a representative sample drawn from the beneficiary and comparison populations.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The observations of positive events contain more information than the non-event observations We therefore sample asymmetrically: We sample all of the transition events and 1. 0 % of the non-transition events. 3. 4 Disaggregated Independent Variables Local level data on land, population, and elevation is available in the geospatial format of raster files with a resolution of 1km. Using Geographic Information systems (GIS), attributes from raster and point data are associated with the grid square in which they lie. In this way, spatial data is georeferenced to a location that is defined by the grid cell. This process results in a data structure in which each row has within it combined information on a square defined by the grid, the national level information in which is it located, and () () () ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ ∑ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ = = ∉ = t X t X t d d j j p j R i d j j p j w w t β β 1 1 exp exp at out breaks war a | square a in war Pr Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Using the NLSS data we begin by estimating a regression of the form: yk s = δs + α (ak s − a) + βs (Ek s − Es) + χs (Hk s − Hs) + vk s (4) where yk s is the log of income (or consumption) of household k residing in district s, coeﬃcients δs, βs and χs vary by district, ak s stands for the age and age squared of the household head, Ek s is the education level of the head measured in years of completed education, and Hk s = 1 if the head belongs to what we have earlier classiﬁed as a high caste (i. e., Brahmin, Chhetri or Newar). Since income or consumption are expressed in logs, βs and χs can be thought of as education and high caste premia, respectively. Female headed households are excluded from the regression since the focus is on migrant males. Vector a denotes the average age and age squared of observations across the sample. Variables E and Hs denote the district-speciﬁc averages of Ek s and Hk s. By demeaning regressors, we ensure that eδs measures the unconditional, district- speciﬁc average of yk s. Marital status, household size, and other household characteristics are 15", "output": {"entities": {"named_data": ["NLSS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 Internal Conﬂicts and Refugees A particularly serious aspect of internal conﬂicts is the human suﬀering they generate. This is not only those who are killed or injured in conﬂict but the large number of people who are forced to leave their homes. The issue of refugees has received particular attention in Western media in recent years as refugee ﬂows from Northern Africa, the Middle East and Afghanistan are increasingly reaching Europe. These refugee streams are linked to a severe humanitarian crisis with considerable funding needs for international donors and heavy strains on host countries. 21 The current refugee crisis, however, is in no way unique. Civil war has always been closely linked to humanitarian crisis and refugee streams are one way to capture this. In this section we provide a cross-country analysis aimed at investigating how the stock of refugees evolves when a civil conﬂict hits a country. In the analysis we will focus entirely on showing changes in the stock of refugees across time to illustrate the dimensions involved. We will base our later analysis on these population movements. We exploit country-level data gathered from several sources. Data about refugees is provided by the UNHCR Population Statistics Database. The database provides in- formation about UNHCR ’ s populations of concern from the year 1951 up to 2014. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["UNHCR Population Statistics Database"], "descriptive_data": [], "vague_data": ["country-level data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "representative sample of undocumented migrants in Colombia ’ s major cities as of 2020. 10 This survey encompasses information on the socioeconomic status, health, well-being, access to services, and labor market outcomes of adult undocumented migrants. Second, we compare our sample with the Administrative Venezuelan Migrant Registry (RAMV), a nationwide census of undocumented Venezuelan migrants conducted by the Colom- bian government in 2018. 11 This census surveyed Venezuelan households regarding their socioeconomic conditions and the labor market characteristics of the household head. We compare the household characteristics and the labor market outcomes of the house- hold heads in our sample with those in VenRePS and RAMV surveys in Table A. 3. 12 We observe that households in the VenRePS-Kids survey are smaller on average and have a greater number of children living in the household. The latter is anticipated since one of the eligibility criteria to participate in our survey is the presence of at least one child in the household. Furthermore, the household heads in our sample are disproportionately female and more likely to be married, aligning with the family structure targeted in our sampling frame. Regarding labor outcomes, household heads in our sample are more likely to be employed and engaged in the informal sector compared to those surveyed in VenRePS and the RAMV census. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Administrative Venezuelan Migrant Registry", "VenRePS-Kids survey", "RAMV surveys", "Administrative Venezuelan Migrant Registry (RAMV)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Bank branches in El Fasher have limited capital for small businesses as their headquarters in Khartoum regard the area as too great a risk and IDPs themselves as riskier investments than their host community peers (UN- HABITAT 2009: p. 8). Exposure to unclean cooking fuels and inadequate housing can lead to poor health outcomes, while lack of access to electricity and a bank account further excludes individuals from labor market integration and livelihood opportunities that would empower forcibly displaced persons to overcome their multiple, overlapping deprivations. Clearly, displacement status puts individuals at a greater risk of poverty than their host community neighbors, and we can unpack those risks in greater detail using the MPI. Results can also be broken down to show the percentage contribution of each indicator to multidimensional poverty (see Figure 2). Among refugees in Ethiopia, lack of a bank account is the largest contributor to poverty, while among host communities, the largest contributor is years of schooling. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "One of the main contributions of this paper is the use of panel data which allows us to examine the effect of the pandemic on labor market transitions — job loss and job gain rates — in addition to the effect on labor market stocks. Studying both stocks and flows provides a comprehensive framework to analyze the impact of the pandemic on labor markets and allows for a better understanding of the underlying mechanisms 30", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "21 Lebanon. It was the obvious step forward in overcoming these problems and the answer to the increasing demand for evidence around the Syrian crisis. The study benefitted from a unique and unprecedented set of data. The UNHCR registry data in Jordan and Lebanon were among the better quality registry data available worldwide and the UNHCR also conducted home visits in Jordan that, at the time of the study, covered over a third of all refugees. There were also sample surveys in both Jordan and Lebanon that were small in size but representative of the population present in the registry. The home visits and the surveys included questions on income and expenditure that could be used for the welfare assessment. Using these data, the study addressed ten questions defined as follows: 1) Who are the refugees?; 2) How different are refugees from “ regular ” populations?; 3) How poor are refugees?; 4) What are the main predictors of refugees ’ welfare and poverty?; 5) How vulnerable are refugees from a monetary and non ‐ monetary perspective?; 6) Do poverty and vulnerability statuses overlap?; 7) How effective are refugee assistance programs?; 8) What is the potential for alternative policies?; 9) How does welfare compare across countries and data sets?; 10) How transferable are Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR registry data"], "vague_data": ["sample surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We measure livelihood diversification using two main variables: the degree of diversification of activities as a primary occupation and the degree of diversification of activities as a secondary occupation. 3 The degree of agricultural commercialization is also measured using two variables: the value from the sale of crop and livestock products. We measure refugee inflow (presence) as the number of refugees (population) in the nearest refugee camp to the household location weighted by the household's inverted distance to the camp. The impact of refugee inflow on household livelihood strategies can be causal if there are no confounding factors that affect livelihoods in host communities when refugee inflow changes. This is unlikely as refugee flow and the location of refugee camps are not random (see e. g., Baez 2011). Refugee camps are often situated close to international borders, among others, to allow for easy repatriation of the refugees when stability is restored in their countries of origin. In addition, refugees often seek shelter in the nearest refugee camp once they arrive in the host country, which is arguably true in most hosting countries as refugees often travel on foot for 2 According to UNHCR, a protracted refugee situation is a situation in which at least 25, 000 refugees from the same nationality have been in exile for at least five years in a given host country. 3 Diversification of activities is calculated using the inverse Simpson diversity index. In constructing the index, we considered both agricultural and non-agricultural livelihood activities. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The author may be contacted at asacks @ worldbank. org. and managing these resources. The author assesses these competing hypotheses using multi-level analyses of Afrobarometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows for analysis of associations between donor and non-state actor service provision and the sense of obligation to comply with the tax authorities, the police and courts. The findings yield support for the hypothesis that the provision of services by donors and non-state actors is strengthening, rather than undermining, the relationship between citizens and the state.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Electricity (meter) Electricity (meter, generator, solar) Percent Figure 2.24: Source of lighting Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Improved source of drinking water Improved bathing facilities Percent Figure 2.22: Access to drinking water and hygiene Source: World Bank Staff based on SESRE 2023. 37 Improved toilet facility includes toilets flush to septic tank/pit latrine/piped sewer system, pit latrine with slab, or composting toilet. 38 Improved waste disposal refers waste not thrown to field or yard, into river and burnt. 24 T his chapter presents findings on labor market outcomes and livelihood choices of refugees and hosts. It discusses how sociodemographic characteristics such as age, gender, education level, and location of residence relate with labor market outcomes. Ethiopia has experienced steady economic growth for much of the last two decades, but even before the country’s concurrent crises, economic growth did not transform the labor market structure. Between 2004 and 2020, Ethiopia’s GDP annual growth averaged 10 percent, helping to reduce the poverty by about ten percentage points.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Annual surveys funded under the previous project cycle provided panel data tracking changes in household income, agricultural production, and child schooling from 2017 to 2021. The annual surveys were administered by the same field firm across all rounds, ensuring consistent interviewer training and questionnaire interpretation. The final annual survey in 2021 achieved a response rate of 96 percent, with non-response concentrated among households that had migrated seasonally for employment, which may slightly understate income recovery among the most economically mobile households.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "21 Lebanon. It was the obvious step forward in overcoming these problems and the answer to the increasing demand for evidence around the Syrian crisis. The study benefitted from a unique and unprecedented set of data. The UNHCR registry data in Jordan and Lebanon were among the better quality registry data available worldwide and the UNHCR also conducted home visits in Jordan that, at the time of the study, covered over a third of all refugees. There were also sample surveys in both Jordan and Lebanon that were small in size but representative of the population present in the registry. The home visits and the surveys included questions on income and expenditure that could be used for the welfare assessment. Using these data, the study addressed ten questions defined as follows: 1) Who are the refugees?; 2) How different are refugees from “ regular ” populations?; 3) How poor are refugees?; 4) What are the main predictors of refugees ’ welfare and poverty?; 5) How vulnerable are refugees from a monetary and non ‐ monetary perspective?; 6) Do poverty and vulnerability statuses overlap?; 7) How effective are refugee assistance programs?; 8) What is the potential for alternative policies?; 9) How does welfare compare across countries and data sets?; 10) How transferable are", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR registry data"], "vague_data": ["sample surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "26 education Turkish to a subregion. In sum, there is some evidence that the inflow of Syrian refugees results in a decrease in the number of Turkish living in a subregion. The evidence, however, is weak and the impact unlikely to be very large. 5. PLACEBO TESTS AND ROBUSTNESS CHECKS 5. 1 Placebo Tests The key threat to the validity of our instrument is that there are subregion specific economic trends that are correlated with the instrument, and not fully controlled for by the inclusion of the log distance of a Turkish subregion from the Syrian border. A priori this seems unlikely since the instrument is also based on travel distances, but we can test for the existence of such trends in a pre-period. Specifically, we run regressions that are analogous to those reported in Tables 5, 6 and 7 using data from the LFS 2009 and 2011. As a placebo test we pretend that the Syrian refugees had arrived between 2009 and 2011, rather than between 2011 and 2014, to see if the instrument is correlated with Turkish outcomes in this pre-period. Table 10a presents the results of our placebo tests. For the overall sample there is no statistically significant trend that is correlated with subsequent (instrumented) refugee flows in formal or informal employment, or in log wages. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For rainfall data, I use a historical daily grid of rainfall, which is interpolated based on readings\n\nthat suddenly drop to zero purchasers is suspicious, especially since the BASIX data does not contain\n\nerror I run simulations where I assume that the BASIX data has been matched completely correctly,\n\nI start by examining whether there is actual autocorrelation in the rainfall data. To test for", "output": {"entities": {"named_data": ["BASIX data"], "descriptive_data": ["historical daily grid of rainfall"], "vague_data": ["rainfall data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "location in the past, interacted with the year of observation. This is a semi-parametric ver- sion of the traditional migrant network instrument as recommended by Goldsmith-Pinkham et al. (2020) and it allows the strength of the network eﬀect to potentially vary in each year. An overidentiﬁcation test can be used to examine whether the instrument has a consistent relationship over time. We ﬁnd robust evidence that increased immigration from Venezuela has a positive impact on labor market outcomes for Peruvians, with increased employment rates, incomes and expenditure in locations that receive more Venezuelans. Additionally, locations that receive more immigrants have lower levels of reported non-violent crime, improved reported quality of local services, greater reported trust in neighbors and higher reported community quality. On the other hand, we ﬁnd evidence that in locations with more Venezuelans, Peruvians report that their community likes diversity less. There are a number of potential explanations for these ﬁndings that we plan to explore in future work. 1 The arrival of Venezuelans may have expanded the economic opportunities for Peruvian because of their higher levels of potential productivity, due to higher human capital, and their concentration in low wage jobs. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "tionnaire from Falk et al. (2022), along with a question on how much do the subjects in our sample trust the Colombian government. Additionally, we inquired about the ado- lescents ’ social networks by asking about their number of friends of each nationality and whether they have felt discriminated in school. While these outcomes were not the pri- mary focus of the VenReps Kids survey, they collectively provide a general assessment of social cohesion, a crucial aspect in understanding the overall well-being of children and adolescents. We measure the average differences in altruism between Venezuelan and Colombian ado- lescents following the estimation of equation 1. Table 8 presents the results for altruism, trust, discrimination and social ties in panels A, B and C respectively. As in the previous tables, the first column for each outcome reports the results of the estimates of equation 1 without controls, and the second and third columns report the results of the estimation with all controls and the propensity score matching respectively. As for altruism, we see that Venezuelan adolescents are on average willing to donate 17 % more of their imagi- nary money endowment to a good cause relative to Colombian adolescents. As for trust, the results are mixed when analyzing the trust items separately.", "output": {"entities": {"named_data": ["VenReps Kids survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "They contain a rich source of information on various attributes of datasets, including the country and dates of data collection, sampling strategy, survey modules, and other related aspects. While submission of datasets to the libraries is voluntary and thus does not guarantee an exhaustive list of all publicly available existing datasets on FDP, they are considered to be among the largest databases of microdata concerning development and forced displacement (Thompson 2010; EGRISS 2023). For the purpose of this study, microdata are defined as primary data collected from household surveys. Our focus lies in identifying micro-level datasets that are publicly available and designed to have a representative sample6 of FDP so that collected data can be disaggregated for refugees and / or IDPs specifically. By studying the geographical and thematic coverage of existing FDP microdata, we seek to also shed light on critical data gaps that remain to be filled with further data collection efforts. The paper identifies critical gaps in geographical and thematic coverage as well as compliance with international recommendations for proper identification of displacement status. The paper highlights that microdata is comparatively rich in Sub-Saharan Africa in contrast to other regions. However, data scarcity is notably pronounced in countries facing fragility and conflict and also among IDPs. There are also certain topics that are relatively lacking in the FDP microdata. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "16 Investors actively trade-off disaster risk with gains from economic density. In addition to the city or location specific analysis, we examine how investors value risk from natural disasters. Data from a recently compiled dataset of a sample of global cities provides some insights. Gomez-Ibañez and Ruiz Nuñez (2006) constructed a dataset of central business district office rents for 155 cities around the world in 2005 to identify cities where rents seem elevated or depressed by poor land use or infrastructure policies. Their dataset also includes information on many factors that determine the supply and demand for central office space such as construction wage rates, steel and cement prices, geographic constraints, metropolitan populations and incomes. We link this information to the natural disasters hotspot dataset (Dilley et al. 2005), and examine if city demand – as reflected in office rents, is sensitive to risk from natural disasters. Gomez-Ibañez and Ruiz Nuñez (2006) focus on offices in the primary business district, which they define as the district having the highest density of employment; a very large, if not the largest, concentration of offices; and the highest rents in the metropolitan area. As we are interested in the tradeoff between economic density and disaster risk, using the central business district works well for our analysis.", "output": {"entities": {"named_data": [], "descriptive_data": ["dataset of central business district office rents for 155 cities around the world", "natural disasters hotspot dataset"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The LFS reports employment status for individuals aged 15 to 64. The LFS does not cover institutionalized populations, individuals in collective living arrangements (dormitories, military barracks), or the de facto homeless population. These exclusions are standard for household-based labor force surveys and are unlikely to materially affect our estimates, since the excluded populations represent a small fraction of the working-age total and their labor market outcomes are not correlated with the shocks we study.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "18 the local data on physical geography and population from the raster data. These data can then be imported into statistical programs for analysis. We aggregate all data up to a grid of 8. 6x8. 6km squares. Each grid square is assigned attributes of the country it is in along with information from data disaggregated to the level of the individual squares. Figure 3 illustrates this grid as a fictive country somewhat smaller than the average size in our dataset (50x50 squares, or 430x430 km) with a fairly representative but stylized population distribution. The country has three major cities, one of which is the capital, and two smaller ones. A rebel group has its headquarters at the Eastern border. The ACLED data for the Central African conflicts were aggregated up to the 8. 6x8. 6km squares and merged with information on other explanatory variables aggregated to the same level.", "output": {"entities": {"named_data": ["ACLED data"], "descriptive_data": [], "vague_data": ["raster data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": " Despite the challenges, the survey also reveals a positive trend of **youth engagement in their communities**\n(20%), however, 17% are not accessing any form of work-related activity, situation that can also lead to\nrisks of recruitment or engagement in illegal activities (including smuggling), as well as drug abuse.\n\nThe results also show that a significant majority of young people, (82%), **do not believe that newly graduated**\n\n**youth have the same working opportunities** as those who have already graduated or have previous work\nexperience. Lack of job opportunities represent the biggest barriers for recently graduated youth, most likely, in\nthe case of NWS, humanitarian organizations constitute the biggest employment alternative, however, lack of\nexperience can also represent a barrier for youth to access.\n\nFDGs showed that:\n\n **Young women are especially vulnerable to sexual exploitation, harassment, and early or forced**\n\n**marriages**, leading to social exclusion and lack of support, particularly for divorced women. Femaleheaded households and displaced individuals also face discrimination.\n\n The male group highlighted that **corruption, regionalism, and favoritism in employment** and resource\ndistribution exacerbate discrimination, with young people often being excluded from opportunities. They\nalso noted that the continuation of armed conflict increases vulnerability to exclusion.\n\n**10**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Return intention survey**\n\n**Shalozan Tangi, Kurrum agency**\n\n**June 2014**\n\n**I.** **Background**\n\nKurrum Agency is the only tribal region in the country’s semi-autonomous seven tribal\nterritories which has a large number of Shiites - the rest of the six tribal agencies are\noverwhelmingly inhabited by Sunni Muslims. According to official figures, its total\npopulation is 500,000, with 58 percent Sunni and 42 percent Shiite. The majority of the\nShiites live in the upper part of the Kurrum Agency, while Sunnis inhabit lower and central\nKurrum. The population of Kurrum valley consists of a number of tribes, namely Turi,\nBangash, Parachamkani, Massozai, Alisherzai, Zaimusht, Mangal, Kharotai, Ghalgi and\nHazara. There was also a sizeable Sikh population but most of them have left the valley.\nSectarian violence is not a new phenomenon in Kurrum Agency where well over 4000\npeople have been killed in clashes between the Sunni and Shia tribes since the decade of\n1980s. Kurrum Agency is divided into three tehsils: upper Kurrum, lower Kurrum and central\nKurrum.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Return intention survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Table 1 provides descriptive statistics for the years 2011 and 2014. 18 Labor force participation is very low in Turkey, around 54 percent of the working-age population in 2011, though it has been rising. The reason is that female labor force participation is particularly low at about one-third. The majority of employment is private sector, around one-third of the working-age population, compared to 6 percent employed in the public sector. There are a large number of unpaid workers (7 percent) and unemployment is at 5 percent of the working-age population, an unemployment rate of about 10 percent. School attendance has been rising over the period, from 12 to 16 percent of the working-age population, and the fraction retired has been steady at about 5 percent. Correspondingly, educational attainment has been rising though still 13 percent of the working-age population has no formal education, 57 percent at least completed primary education but not high school, and high school completion has risen from 30 to 34 percent. 17 Of those who have an irregular workplace 60 percent are agricultural workers, 14 percent work in construction, 7 percent in transportation and 5 percent in retail and in manufacturing each, and 3 percent as household employees. 18 Note that in 2014 new regulations for the Household LFS were carried out within the framework of European Union criteria. Consequently, statistics are not necessarily entirely comparable across years. Since we do not use aggregate time-series variation for identification this does not affect our empirical strategy, see Section 3. For those interested, the Turkish Statistical Institute provides consistent time-series on their website. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Household LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the degree to which the global response should include a development element. We ﬁnd that the average stood at around 10. 3 years at the end of 2015, with a median duration of 4 years, and signiﬁcant sensitivity to a few situations. Such numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete protracted situations. To produce these numbers, we rely on the Population Statistics Database compiled and main- tained by UNHCR. The database records the number of “ persons of interest ” to UNHCR in each year since 1951 and for each situation, where a situation consists of a pair host-origin countries. The calculation of duration of exile is obtained under a no-turnover assumption, whereby a de- crease in the number of refugees for any given situation is fully attributed to exits from refugee status, while increases are assumed to be fully accounted for by new cases. Although such ap- proach tends to over-estimate the true duration of exile, the lack of individual-level data on regis- tration precludes reﬁning the estimate further. Attempts to estimate similar statistics have been limited. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Population Statistics Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Furthermore, the Ministry of Education continues to collect data on refugee and asylum-seekers students for the\neducation management information system. The same applies with the Ministry of Health; data on refugee and\nasylum-seekers are included in the national health information system. The government is working on releasing\nbreakdowns of pupils by legal status (nationals and refugees). Additionally, the Government continues to work\nto include refugees in the national civil registry database. However, the technical and financial prerequisites\nfor this are not yet in place. Furthermore, UNHCR has long been advocating for refugees and asylum-seekers\nto be included in the future national population and household census, and there is now apparent agreement\nfrom the government on this principle.", "output": {"entities": {"named_data": [], "descriptive_data": ["national population and household census"], "vague_data": ["education management information system", "national health information system", "national civil registry database"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "If many people migrate to a speciﬁc location, such as the capital city, this is likely to aﬀect wages, incomes, and access to amenities in that location. 7 This would generate a potential endogeneity bias due to the fact that incomes and amenities in that location result in part from the decision of many migrants to locate there. To eliminate this bias, we use past data to estimate the income regression. More precisely, let T be the period for which we have income information and T + t the period at which we 5The dropped observation corresponds to the location of origin M h ii which, as explained earlier, we do not include in the analysis since including M h ii would mean de facto including the decision of whether to migrate or not. 6McFadden (1974) has shown that, in multiple choice problems of the kind studied here, the application of logit estimation is justiﬁed if (1) the errors in each latent choice equation follow the extreme value distribution and (2) errors are independent across choices. See Train (2003), Chapter 3 for a detailed discussion. The estimation of models with correlated errors across choices requires either multiple integration or the use of Bayesian estimation techniques relying on Gibbs sampling. With a choice of over 70 possible destinations, multiple integration is out of the question. Gibbs sampling remains a possibility but would require extensive programming. We choose instead to keep the logit approach but to correct the standard errors for possible correlation in errors across choices. In our case the possible eﬃciency gain achieved by Bayesian methods does not appear to justify the programming cost. 7The eﬀect could be negative — e. g., congestion — or positive — e. g., agglomeration externalities. 10 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["past data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The data for this paper come from the European Social Survey (ESS) for the survey years 2002,\n\nobservations per country/year. The ESS covers 36 countries, 24 of which are included in our\n\nCard et al (2012) investigates the drivers of attitudes towards immigrants in Europe using,\n\n\namong others, the following variables of the ESS:", "output": {"entities": {"named_data": ["European Social Survey (ESS)", "ESS", "European Social Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "An alternative version of the database that has been mapped to the United Nations (2006, 2009) Trends in International Migrant Stock database is available from the authors. These data are standardized over time in terms of the years to which they refer. { Table 3 here} Calculating Missing Gender Splits Although common in the underlying data, bilateral migration data disaggregated by gender are sparser than aggregate migrant totals (see table 1). An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits; they are described in appendix 5. Combining Migrant Definitions Only a single definition of a migrant (foreign born or foreign citizen) can be applied to each destination country in the final matrices. Switching definitions over time 17 The subregions used for the disaggregations are the 21 UN regions (see http: / / unstats. un. org / unsd / methods / m49 / m49regin. htm, with the countries of Oceania aggregated into a single subregion. They do not match the large World Bank regions used in the analysis in section IV. 18 While this propensity measure is clearly inappropriate, less than 1 percent of all migrants and observations are assigned on this basis. This method is included so that every migrant in the underlying data is accounted for. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock", "Global Migration Database"], "descriptive_data": [], "vague_data": ["bilateral migration data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These findings were also echoed in the context of other developing countries such as India (Allard et al., 2022) and Zimbabwe (Mabugu, Maisonnave, Henseler, Chitiga-Mabugu, & Makochekanwa, 2023). In the MENA economies ’ context, the evidence is considerably sparcer. Early work on the impact of the COVID-19 pandemic on labor markets in the MENA region re- lied on high-frequency phone surveys and highlight important job losses among wage workers and an uneven impact across industries (Krafft, Assaad, & Marouani, 2021, 2022). Providing evidence from Labor Force Surveys in the Islamic Republic of Iran, Dang and Salehi-Isfahani (2023) find that the pandemic exacerbated the pre-existing low participation of females in the labor force. Wahby and Assaad (2023), on the other hand, focus on the impact of the pandemic on Syrian refugees in Jordan and find a divergence in job finding and separation rates of Syrian refugees relative to their Jorda- nian hosts after the onset of the pandemic. Focusing on cross-border commuters in the West Bank and Gaza, Adnan and Etkes (2022) find that undocumented commuters benefited relative to their documented peers after the pandemic, as Israeli policies inadvertently created incentives for employers to favor the former. This sharply con- trasts the results by Borjas and Cassidy (2020) on the impact of the pandemic on immigrants in the United States. The rest of this paper is organized as follows. Section 2 provides background information on labor markets in the West Bank and Gaza, as well as background in- formation on the COVID-19 pandemic and government responses. Section 3 describes the data. Section 4 discusses our methodology. Section 5 presents the main regression results and investigates heterogeneous effects. Section 6 provides robustness checks. Finally, we provide concluding remarks in Section 7. 4 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Labor Force Surveys", "high-frequency phone surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Typically, registers have evolved over time (from parish records, for example). They were never developed specifically to record international migration information, and they vary considerably across countries. For example, the laws under which individuals are classified as migrants and the conditions under which they are inscribed or deregistered differ greatly (Bilsborrow and others 1997). The Raw Data The Global Migration Database is a vast collection of destination country data sources detailing migrant stocks from numerous origin countries and regions (United Nations [2008]). Compiling and maintaining the underlying primary sources require herculean efforts to scour the key census collections of the world and enter the data manually. In total, the database comprises records from some 3, 500 separate censuses from more than 230 migrant destination countries and territories, by sex and age.", "output": {"entities": {"named_data": ["Global Migration Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "such as education and caste. We also construct measures of social proximity between a migrant ’ s place of birth and each possible destination, using detailed available data on ethnicity, caste, language, and religion. We also investigate a number of factors that may inﬂuence the choice of migration destination but have not received much attention in the existing literature. Fafchamps and Shilpi (2009) have shown that the subjective welfare cost of geographical isolation is high. To investigate this issue, we include regressors controlling for population density and for the average distance to various amenities. Fafchamps and Shilpi (2008) have further shown that migrants are concerned with their welfare relative to that of their birth district as well as to that in their destination location. We examine whether relative welfare considerations inﬂuence the choice of migration destination. Additional controls include distance and prices. The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal. The diverse terrain of Nepal along with geographical variation in amenities makes it ideal for our study. The mountainous nature of Nepal means that the country faces daunting challenges in the provision of transport and energy infrastructure. These challenges are unique to Nepal, however. Similar constraints are faced by many developing countries — or regions within such countries. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["2001 population Census data"], "descriptive_data": ["LSMS survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 The exact list of socioeconomic variables in the DHS is not identical to that on the JFPR application form. We\nderive the index of socioeconomic status in the DHS from variables describing: the ownership of a bicycle, cart,\nboat, motorbike, car, truck, radio, television; the conditions of the dwelling such as hard roofing and finished\nflooring; the availability of electric lighting; the main source of drinking water; the type of toilet facilities; and the\nmain type of cooking fuel used.\n\nbeneficiaries was done by the Local Management Committee (LMC) of a JFPR _secondary_ school. In\n\nThe JFPR scholarship program established a cut-off in the maximum number of scholarships\n\nFinally, the paper presents evidence of heterogeneity in the JFPR program effects. For this", "output": {"entities": {"named_data": ["DHS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "this way, profiling of IDP situations aims to underpin advocacy, protection and assistance activities as well as support the achievement of durable solutions by informing joint strategies between government, humanitarian and development actors. Profiling provides an overview of displacement-affected populations through the collection and analysis of minimum core data (number of IDPs, disaggregated by location, age and sex) and where possible additional quantitative and qualitative data (causes of displacement, patterns of displacement, protection concerns, humanitarian needs, vulnerabilities, and aspirations and prospects for durable solutions). Profiling may utilize data collection techniques at individual, household and community levels, often combining population estimation methods, a review of secondary data, focus group discussions, household surveys and key informant interviews targeted specifically at forcibly displaced populations (UNSD 2014). 70 Profiling methods focus on displacement situations, rather than only on displaced populations, and therefore includes comparisons to conditions in the host population. IDMC estimates that humanitarian profiling data forms the basis for 18 of their 60 country estimates and around 63 percent of their annual estimates (IDMC 2015), with the largest volume of data on conflict-induced internal displacement provided by OCHA followed by IOM. There are several practical challenges associated with IDP profiling exercises in displacement situations. Insecurity or terrain may impede access to displaced populations in conflict-affected or hard to reach areas.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["humanitarian profiling data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12% 63% Labor force participation rate (relaxed) 72% 67% Unemployment (relaxed) 19% 75% Employment-to-population ratio 58% 17% Source: World Bank Staff based on SESRE 2023. Note: Labor force participation ratio is the share of working-age people who are engaged in the labor market, either employed or unemployed. Unemployment is the share of people participating in the labor force who are not employed. The “relaxed” definition of labor force participation includes anyone who is available to work. The “strict” definition of labor force participation includes only those who are available to work and also actively searching for work. Employment-to-population ratio is the share of working-age people who are employed. Jobs and Livelihoods 36 Occupational downgrading among the small share of OCP refugees who work is not explained by education, gender, and age. Another way to visualize the scale of occupational downgrading among refugees in Addis Ababa is to calculate how they should be distributed across occupations if they were in the same occupations as hosts within their education, gender, and age group. For example, suppose that among male hosts under age 25 with primary education, 70 percent work in elementary occupations and 30 percent in crafts and related trades. Imagine taking", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "22 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). To uncover the main drivers of the observed gender based differences in multidimensional poverty at country level, we study the absolute contribution of the gender difference in each indicator to the overall household gender gap (see Figure 6), calculated as the difference between the censored headcount ratio for males versus females. We find that in Ethiopia, the gender gap that disadvantages female-headed households is mostly driven by the difference in financial insecurity measures (lack of legal ID and bank account) and health measures (early marriage, physical safety, and food insecurity), which is further reinforced by the differences in the living standard and education measures. Female-headed refugee households are more food insecure, live in unimproved housing, have lower access to electricity, are more likely to be married at an early age, and have lower access to legal identification and a bank account. In South Sudan, gender gap that disadvantages female-headed households is mainly explained by the differential in the financial insecurity and health measures, but cumulative gaps in the living standard and education indicators also contribute to the overall gap at the household level. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "displaced children and adolescents residing in host communities, rather than in refugee camps. Particularly, VenRePs-Kids collects data on 2, 556 households including 1, 338 Colombian and 918 Venezuelan households, respectively. The study collects rich and comprehensive data on children ’ s and adolescent ’ s development including anthropometric measures, vo- cabulary ability tests, and socio-emotional and mental health assessments. It also collects data on risk behaviors, time use, social integration measures, prosocial behaviors, and parents and caregivers sociodemographics, among other dimensions. 2 The study includes Colombian children and adolescents as the comparison group to high- light the developmental differences of Venezuelan forcibly displaced children. This choice stems from the fact that comparing Venezuelan children in Colombia with their counter- parts remaining in Venezuela is impractical due to the latter ’ s exposure to a severe eco- nomic and humanitarian crisis, marked by limited access to services and food. This envi- ronment severely hampers their potential for normal human development. Additionally, many Venezuelan children and adolescents have spent more of their lives in Colombia than in Venezuela. Therefore, Colombian children and adolescents serve as the most ap- propriate benchmark for assessing the developmental gaps of their Venezuelan peers. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["VenRePs-Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location. Area stratifi- cation reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analyzed. ” Afrobarometer provides geocoded data for 6 rounds, which correspond to the 1991 – 2016 period, with the information on an individual ’ s ethnicity available from round 3 (corresponding to 2005 – 2006). We therefore restrict our analysis to the 2005 – 2016 period. The selection of countries is driven by data availability. Among the 33 countries with available Afrobarometer data, we exclude Botswana, Cape Verde, Lesotho, Madagascar, Mauritius, Sao Tome and Principe, South Africa, and Swaziland, for which no data is available on refugee camps or from the EPR-ER. We also exclude Sudan since the question on individual ethnicity is not asked in this country ’ s survey. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, there is no consensus or agreed best practices on the use of these methods in different contexts or stages of displacement (Brookings 2013). 92 The absence of data on new displacement may simply mean that no displacement has taken place (IDMC 2016). 93 IDMC reports that data, disaggregated by age and sex, were available for 15 of the 60 countries it monitored in 2014, however these data were not comprehensive and are not published. Additionally, in some countries there are data provided by IOM on IDP populations by location from which the urban or rural character of the population may be inferred (e. g. if the camp is located in the capital), but data are not comprehensive and not published. While the majority of humanitarian profile data does not typically cover IDPs living outside of camp or camp-like settings (the large majority of IDPs), IOM ’ s DTM in countries such as Nigeria, Iraq, Yemen and Libya do include information about those residing in host communities. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["humanitarian profile data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In  recent years enrollment rates have increased sharply and are higher for girls than boys in Bangladesh's urban areas, according to UNICEF.\n\nFemale children are more likely to be enrolled than male children in primary  schools, a result in line with recent UNICEF findings.\n\nThe data for the Netherlands is taken from the Dutch National Institute for Public Health and Environment (RIVM). [2] The data for Germany is from the Robert Koch Institute. [3] The data for Italy can be viewed via a live dashboard, [4] and the raw data is well organized and available on a github page. [5] The Spanish data was taken from this link. [6]\n\nThe COVID-19 data is taken from the RIVM. [8] The first data snapshot includes all confirmed\ncases as of March 22 (a total of 4,004 with known residence out of 4,157 confirmed cases).", "output": {"entities": {"named_data": ["Dutch National Institute for Public Health and Environment (RIVM)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The LMP commits the project to applying the principle of equal pay for work of equal value and to prohibiting any deduction from wages without explicit written worker consent. Contractors are required to keep payroll records in a format compatible with SIGAF's payroll reporting template and to make these records available to the PIU's social development officer upon request. The PIU conducted unannounced payroll audits at three construction sites in the quarter and found that wage payment was consistent with LMP requirements at all three sites.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "as farm tools and water pumps, sewing and building equipment, and commercial cars. Outliers are treated, and values are adjusted for inflation. 0 20 40 60 80 100 Camp Hosts Camp Refugees Current Camp Refugees COB Other (rental income, PSNP, pension) Remittances (local/international) Donations (NGO/gov) Crops/livestock Salary (employment/casual labor) 0 20 40 60 80 100 Camps-Male-Headed Camps-Female-Headed Salary (employment/casual labor) Crops/livestock Donations(NGO/gov) Remittances (local/international) Other (rental income, PSNP, pension) Percent Percent Figure 3.20: Household primary income source Source: World Bank Staff based on SESRE 2023. Note: “COB” refers to livelihood strategies in their country of birth. a. Pre-post migration b. By gender of head Jobs and Livelihoods 34 arrive in Uganda with few assets, in a state of high poverty, and with similarly low employment rates. However, unlike Ethiopia, employment rates for refugees in Uganda improve over time, approximately doubling after five years or more (World Bank, 2023b). Uganda is also notable for providing work rights to refugees in practice (Ginn et al., 2022) (Box 3.3). 3.2 Labor market outcomes of OCP refugees and their hosts Refugee households in Addis Ababa rely heavily on remittances as their primary source of income. This reflects the fact that the Eritrean OCP refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "months before their survey interview compared to any other group. Of the South Sudanese refugees and hosts that had health issues, 65 percent of refugees and 59 percent of hosts were ill due to malaria. Somali refugees and their hosts have the lowest share of illness (Annex D, Table D.3). Of the ill, most hosts and refugees received the necessary treatment in health institutions. However, in-camp refugees (91 percent) are more likely to get treatment than OCP refugees (71 percent). Refugees access medical services in health institutions located inside and outside of camps. Most in-camp refugees get medical assistance in health centers and health institutions implemented by RRS or NGOs within and outside camps. Refugees in Addis Ababa—who, due to their OCP, have to access healthcare without support from the international community—get medical services from private sources (58 percent) and government 25 Currently, there is only one refugee settlement site in Ethiopia: Alemwach in the Amhara region. 0 5 10 15 20 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Percent Figure 2.15: Faced any health problem Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The SIGAF system's fixed assets module was used to register all project-procured equipment at point of delivery. Each item received a unique SIGAF asset code printed on a tamper-proof label, and asset records were updated to reflect depreciation annually using the straight-line method over the estimated useful life. The SIGAF fixed assets register is reconciled against the physical inventory count conducted by the Ministry's internal auditors at the end of each fiscal year.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": " The survey results indicate that a significant\nmajority of youth in Northwest Syria, 92% **Which services are requiered by youth to**\n\n**improve their situation?**\n\nindicated that **youth require various services to**\n\n**address their needs and aspirations** . The most\nidentified services required by youth include\nimproving **livelihoods and mental wellbeing** .\n\nhumanitarian aid. **Literacy** (23%) is also a\nsignificant finding of this study that should be\neasily address by humanitarian actors.\n\nDuring the FDGs youth also mentioned:\n\n**Do the Youth actively participate in Community-**\n\n**Based Structures?**\n\n**Which services are requiered by youth to**\n\n**improve their situation?**\n\nFunding for small projects\n\n**84%**\n\nVocational Training\n\nGroup PSS\n\nLiteracy\n\nIndividual PSS\n\nMHPSS\n\nSexual and Reproductive…\n\nCyber Security\n\n **Shared Commitment to Youth Empowerment** : Both male and female participants emphasized the critical\nrole of education, training, and job opportunities in fostering community engagement and societal growth.\n\n**11**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In other settings, NGOs such as Rozan in Pakistan (Rashid, 2001), Profamilia in the Dominican Republic (Guedes et al., 2002), and the Musasa Project in Zimbabwe have trained law enforcement personnel on issues related to gender-based violence. Elsewhere, governments have collaborated with the United Nations to provide training and support for the police and judiciary. For example, ILANUD is a joint institute of the government of Costa Rica and the United Nations that works with governmental agencies throughout Latin America to improve the work of prosecutors, judges, lawyers, police and other professionals in criminal justice generally, and gender-based violence specifically (Villanueva, 1999; ILANUD, n. d.). Most of these initiatives have been evaluated using key informant interviews and pre and post questionnaires before and after training-if they have been evaluated at all. Nonetheless, training appears to be both constructive and urgently needed (Rashid, 2001; Villanueva, 1999). Other lessons learned include the finding that changing attitudes of law enforcement is a challenging, long-term process. The quality of the trainings ’ content and the skills of the trainer are essential. Training appears to be most effective when all levels of personnel (especially high-level officials) participate, and when training is backed up with changes throughout the institution, such as policies, procedures, adequate resources, and continual monitoring and evaluation. Special police stations or cells for crimes against women All-women police stations began in Brazil and were later tried in other countries in Latin America and Asia. As of 2003, for example, Nicaragua had 17 police stations for women and children (called", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "According to data collected by the GBV – Information Management System in South Sudan in 2016, approximately 98% of\nreported GBV incidents affected women and girls. Half (51%) are survivors of intimate partner violence are women. A third of\nwomen (33%) have experienced sexual violence from a non-partner, primarily during attacks or raids. Almost half (48%) of\ngirls between 15 and 19 are married ‘to reduce financial burdens’ or to secure much-needed assets for families, which result\nin higher risks of early pregnancy, complex birth etc. The risk of child marriage remains constant due to conflict, the country’s\neconomic situation and harmful social norms. These figures do not disaggregate based on disability. There is also a lack of agedisaggregated data that might highlight certain types of GBV faced by older women - especially older women with disabilities.\nYoung and older women with and without disabilities face multiple and diverse forms of oppression and this increases their\nrisk of exposure to GBV and the barriers to accessing services.", "output": {"entities": {"named_data": ["GBV – Information Management System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The SEIS income module collects data on four income sources: formal wage employment, self-employment net earnings, transfers from households in the country of origin, and humanitarian cash assistance. Households reporting income from all four sources are rare (fewer than 3 percent of the SEIS sample), and the total household income variable is dominated by humanitarian cash assistance in the early post-displacement period. As the length of stay increases, the SEIS data show a gradual shift from cash assistance dependency toward self-employment income, consistent with the livelihood integration trajectory documented in the qualitative literature.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10044 Most refugee hosting communities are characterized by high levels of poverty with precarious livelihood conditions, low access to public services, and underdeveloped infra­structure. While the unexpected inflow of refugees might bring both constraints and opportunities for improving and maintaining local livelihoods in these communities, the understanding of these effects remains limited. Using a household level micro data set from a 2018 baseline survey of the Ethiopia Development Response to Displacement Impacts Project, this paper assesses the impact of refugee inflow on the livelihood strategies of host communities with respect to diversification and agricultural commer­cialization. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["household level micro data set"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "refugee households living under OCP, 10 percent moved to Addis Ababa to access basic social services, such as education and health. Thus, some refugees from the above may receive medical services free of charge. Based on SESRE data, most OCP refugees (58 percent) rely primarily on private healthcare services, while 40 percent access healthcare through the national system (Annex D, Figure D.7). This can help explain why OCP refugees’ average annual per capita expenditure on health is almost twice that of hosts. In-camp refugees have access to healthcare through the international community, so their out-of-pocket spending on health is thus very low and much lower than hosts’ out-of-pocket health expenditures. The difference in per capita health expenditure is large between Eritrean and South Sudanese refugees and their hosts but low among Somali refugees and their hosts (Annex D, Figure D.12). Child Nutrition and Health Outcomes Child nutrition and health represent a significant challenge for both hosts and refugees. The nutritional status of children under age five is based on anthropometry measures; that is, stunting, underweight, and wasting. A child is identified as 26 Driven by Eritrean refugees in Alemwach camp who do not have a health facility inside the refugee", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "According to 2017 data from GSO, these 864 schools represent about 12 percent of all schools in coastal provinces (General Statistics Office of Vietnam, n.d.).\n\nData on power plants was sourced from the free and open Global Power Plant Database created and maintained by the World Resources Institute (WRI) (Global Energy Observatory et al. 2019).\n\nA digital spatial representation of Vietnam's electricity transmission network in vector format was prepared by a team at the World Bank and is freely and openly available online (World Bank, 2017).\n\nTo approximate the economic impact of hazards on the tourism sector, macroeconomic estimates were\nsourced from World Bank open data [4] and publications. National tourism GDP was obtained by applying\nthe contribution of tourism to GDP in 2017 (WTTC, 2018) to the national GDP in 2017 (World Bank, 2019).\nThe number of direct jobs in the tourism industry in 2017 are obtained from numbers published by the\nWorld Travel and Tourism Council (WTTC, 2018).\n\n\nIn the fisheries sector, aquaculture output in tons per province in 2017 was sourced from the General\nStatistics Office of Vietnam (GSO, 2019).", "output": {"entities": {"named_data": ["Global Power Plant Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "ES.6: Host response to “Refugees are good people” Source: World Bank Staff based on SESRE 2023. Executive Summary viii Continue implementation of progressive policies: ◆ Implement concrete actions to fulfill Government pledges and proclamations to move away from encampment toward mobility based on economic opportunities. ◆ Harmonize national and sub-national laws to support the full implementation of refugee protection. ◆ Coordinate efforts among stakeholders to track progress and share best practices. ◆ Redesign the out-of-camp policy (OCP) to encourage mobility to realize greater socioeconomic opportunities for refugees while accelerating and automating issuance of work authorizations to enable sustainable improvements in refugees’ lives. ◆ Address challenges in accessing business licenses for refugee self-employment, including access to finance. Improve cooperation and coordination ◆ Invest in and accelerate inclusive approaches to economic opportunities and self-reliance to support the GoE in implementing the Refugee Proclamation of 2019. ◆ Define better coordination and engage line ministries to achieve better outcomes for refugees and their hosts. ◆ Improve the coverage, accuracy, reliability, quality, and comparability of data to provide the analytical underpinning for policy decisions. Executive Summary 1 C onflict, political unrest, environmental disruption, and economic instability has forcibly displaced millions of people globally (Ferris, 2010;", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "in Ethiopia, as well as from host communities. The sample frame for the survey was derived from the list of all refugee camps, sites, and locations provided by UNHCR-Ethiopia as of January 2017, covering the four main regions that host refugees: Tigray, Afar, Gambella, Benishangul-Gumuz, and Somali. The SPS specifically excludes refugee households living out of camp, thereby making it representative of the refugee population residing in camps in Ethiopia. In contrast, SESRE, carried out in 2023, expanded its data collection to include out-of-camp refugees living in Addis Ababa. The SPS and SESRE both utilized stratified sampling designs but with different methodologies and definitions of the host households. The SPS employed a multi-stage stratified random sampling approach, dividing refugee camps into EAs of 150 by 150 meters using GIS technology. The number of EAs selected from each camp was proportional to the size of the camp, ensuring all camps in the sample frame were surveyed. In this way, all the camps in the sample frame were selected in the sample and were surveyed. For host households, areas within 5-kilometer radius of the camps were divided into EAs of 300 by 300 meters, with only residential EAs as per Open Street", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The respondent ’ s current location is iden- tiﬁed down to the centro poblado level, which roughly corresponds to an urban neighborhood or a rural town. Encuesta Nacional de Hogares (ENAHO) is the Peruvian version of the Living Standards Measurement Survey, e. g. a nationally representative household survey collected monthly on a continuous basis. For our analysis, we use data from January 2007 to December 2020. The survey covers a wide variety of topics, including basic demographics, educational back- ground, labor market conditions, crime victimization, and a module on respondent ’ s percep- tions about the main problems in the country and trust on diﬀerent local and national level institutions. Observations are also spatially identiﬁed at the municipality level, but here we focus on variation in the Venezuelan share of the population at the province level, of which there are 196, as these are best representative of local labor markets. Latin American Public Opinion Project (LAPOP) is a opinion survey conducted bi-annually in all countries in Latin America and designed to be representative of urban populations. This was ﬁelded in Peru in 2010, 2012, 2014, 2017 and 2019 and consists of about 2, 000 observations from mostly urban areas. The survey questions are centered around politics, 8 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Moreover, some refugees may not register because they are unaware that they should, and others may be reluctant to do so because they are skeptical of the integrity of the registration process (e. g. fair access to entitlements or opportunities for durable solutions) or lack confidence in protection measures. Individuals in irregular migration flows may also choose not to apply for asylum due to fear of declaring themselves to the authorities. A significant challenge with refugee registers is keeping them up to date. Individual registration can provide a robust snapshot of the stock of refugees and asylum-seekers, but registers need to be updated regularly to reflect flows, i. e. increases in refugee and asylum-seeker numbers (births, new arrivals) and decreases (deaths, departures, durable solutions). In situations of sudden mass influxes, existing registration capacity may not be adequate and the scope of registration data is then rationalized. 65 Additionally, it may not be possible to capture all demographic changes in the case of highly mobile populations. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee registers"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "neighbouring countries disclose episodes of violence and cite the risk of conflict-related\nsexual violence as one of the main reasons for flight. Accounts of combatants looting\ncivilians’ homes and deliberately targeting women and girls, as well as cases of harassment\nat checkpoints, and of sexual violence and exploitation during their journeys to\nneighbouring countries are emerging in Chad, Ethiopia, South Sudan and Egypt.\n\nWithin Sudan, the rising number of survivors have limited possibilities to approach service\nproviders and report incidents, given the lack of public health services, closure of many\nfacilities and the unavailability of specialised staff and health personnel, from both the\ngovernment and the humanitarian community sides. Consequently, there is considerable\ndelay in providing medical services to survivors, including clinical management of rape and\nadministration of PEP kits, with detrimental effects on possible HIV transmission and on\nthe rate of unwanted pregnancies.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Poverty remains widespread and social indicators are well below the average for Sub-Saharan Africa. Chad is ranked 173 among the 177 countries surveyed in the 2006 UNDP Human Development Report. The incidence of poverty (defined as the proportion of households with annual spending below what is necessary to meet minimal needs) is estimated at 55 percent according to a 2003 household survey; an estimated four-fifths of the population of about 8. 8 million is living on less than a dollar a day3. Of the population over 15 years old, more than 73 percent (and 76 percent of women) are illiterate. Access to potable water has improved over past years, but is still limited to one out of three people in 2005. Less than two percent of the population has access to electricity and only 1021 kilometers of roads has been paved on a surface area of over 1. 2 million square kilometers. As already mentioned, Chad has recently become oil producing country; however, the economy remains largely agricultural and pastoral. About 80 percent of the country ’ s population lives in rural areas and continue to make their living4 from agriculture and livestock. Cotton is the principal cash crop, employing about 300, 000 families.", "output": {"entities": {"named_data": [], "descriptive_data": ["2003 household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The second source is Giri et al. (2011), who use 1997 to 2000 Landsat data, together with supervised and unsupervised digital image classification, to construct a 30 square meter resolution map of the global distribution of mangrove.\n\n4Nordhaus (2006) considers areas with elevation less than 8 meters as vulnerable to storm surge. We\nuse a less stringent definition because Shuttle Radar Topography Mission (SRTM) elevation estimates\nbelow 10 meters are not considered reliable (McGranahan et al., 2007). We thank Eric Strobl for\nproviding us with GIS boundaries for global coastal lowlands constructed from STRM data.\n\n\n7\n\n\n\n<!-- PAGE 10 -->\nshortest path to the coast. We find that there are 3,853 cells (49% of cells in storm surge\n\nWe measure local economic activity using remote sensing data on nightlights; potential hurricane de struction using a damage index derived from a wind field model calibrated for Central America; and mangrove protection by calculating the cumulative width of mangroves along the closest path to the coast.", "output": {"entities": {"named_data": ["Shuttle Radar Topography Mission"], "descriptive_data": [], "vague_data": ["remote sensing data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**SOUTH SUDAN** | October 2023\n\n- Adapt modalities of GBV services to address and response to needs of persons with disabilities and GBV actors know how\nto access support services, for example for interpretation. Local OPDs, in particular women led OPDs, are trained in how\nto safely identify and refer GBV survivors.\n\n- Ensure that Child Safeguarding Policy training is systematically provided to as many humanitarian workers as possible especially those in close contact with children - and communities to ensuring that all humanitarian actions are properly\nimplemented to protect all children including children with disabilities from the increasing deliberate or unintentional acts\nof abuse and exploitation registered in the last quarter.\n\n- Ensure the meaningful participation of older women and persons with disabilities in awareness raising campaigns and\nother community-based activities.\n\n- Adopt strategies to prevent and address discrimination against older women and support the full inclusion of women from\nall age groups in empowerment activities and interventions.\n\n- Continue collection of GBV data that is age and disability disaggregated to ensure that data is inclusive of older women\nwith and without disabilities and use the data to inform responses.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["GBV data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "be imputed into the LFS, it may be unclear, ex-ante, if SUTPs are adequately included in the survey. Despite a small sample of foreigners and other concerns, there is evidence that the LFS sample does include some “ recent foreign ” migrants, especially in the border regions (NUTS2) [TRC1-Gaziantep, Adiyaman, Kilis, TRC2-Sanliurfa, Diyarbakir, TRC3-Mardin, Batman, Sirnak, Siirt TR63-Hatay, Kahramanmaras, Osmaniye] (Map 1).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This paper analyzes data from the initial wave of VenRePS-Kids, conducted from Oc- tober to December 2022, to outline key demographics and stylized facts about forcibly displaced children and adolescents. Initially, we examine the human development dis- parities of forcibly displaced Venezuelan children and adolescents in comparison to their Colombian counterparts. Our approach to human development is broad, covering physi- cal, cognitive, socio-emotional, and mental health aspects. Additionally, we complement our analysis by exploring differences in food security, social cohesion, and the economic status of parents. Although our analysis is descriptive, it represents a crucial initial step 2Venezuelan households are defined as those where both parents and their children have a Venezuelan nationality. Colombian households are composed of Colombian citizens only. 3 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["VenRePS-Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**awareness sessions (90%), community networks (53%) protection analysis (44%). The** main gap remains\non specific strategies to mitigate impacts on **the safety, evacuation routes and well-being** .\n According to the survey findings, a significant majority of respondents (77%) indicated **that there are**\n\n**currently no organizations**, institutions, agencies, or entities offering tailored and specialized activities or\nservices specifically designed for youth in northwest Syria.\n\n 79% of the respondents believe some groups within youth are facing **disproportionate impacts** :\n\nYoung parents, the only segment of this\npopulation groups with some level of access to\nservices and programs, was not identified by\nrespondents as one of the groups at heightened\nrisk. The attribution of 15% to **young people**\n\n**belonging to minorities** is relevant finding,\nconsidering that, it is a high percentage for a\nsmall group. **IDPs** as the second group exposed\nto higher impact represents a need to better\nunderstand the specificities of the risks faced by\nthem; also, relevant to see that adolescent men\nseem to face increased risks than adolescent\nwomen. Absence of data on the particularities\nof these risks faced by both genders limits the\naccuracy of the programmatic response.\n\nThose that belong to minorities", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey findings"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 7 of 51 In this sense, it is necessary to draw attention to the fact that the non-standard employment statistics presented below are not homogeneous among countries. In countries where both forms of non- standard employment were identified, we define non-standard employment as those occupations that satisfy at least one of the conditions, that is, either corresponds to a part-time occupation or a temporary job. In countries where temporary employment was not identified in the data, our non- standard employment category will coincide with part-time employment. Note that in either case, as other non-standard employment modalities are not identified, the indicators presented in this paper indicate a lower level with respect to the true dimension of the phenomenon. The only aspect addressed that required the use of additional information was the analysis linked to the profile of tasks that are developed in the framework of non-standard jobs. To carry out this analysis, the information available in the O * NET (Occupational Information Network) database was used in conjunction with the Household surveys. This database provides information referring to the content of tasks of the occupations. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "REGIONAL BUREAU OF SOUTHERN AFRICA\n\nPROTECTION MONITORING DASHBOARD ON INCIDENTS IN DRC AND MOZAMBIQUE\n\nAs of 30 September 2022\n\nMAP SHOWING THE NUMBER OF REPORTED PROTECTION INCIDENTS PER\n\nProvince in Mozambique\n\n(MOZ) covered by\nprotection monitoring", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "- Significant number of new IDPs (141,775 as of June 2017) due to the recurrent military clashes,\ndecrease of 25% compared to 2016;\n\n- Increase number of refugee returnees as of June 2017 compared to the previous year due to\nshrinking protection space in Pakistan and Iran;\n\n- Increased trends in short term displacement due to the lack of humanitarian access, with\npeople often returning to their place of origin after the engagement has finished (ex. Situation\nin Kunduz in 2016: 118,166 people were displaced from Kunduz in September-October, and\n69,916 Kunduz IDPs that were displaced within the Northern and North-Eastern provinces\nreturned to their places of origin within several weeks – a month period);\n\n- Pattern of secondary and multiple displacement in rural and urban centers (according to the\nREACH study on prolonged displacement, some 23% IDPs were displaced twice or more [1] );\n\n- Pattern of return in unsafe areas due to limited livelihood opportunities in urban centers and\nthe need to tend to crops [2] ;\n\n- Pattern of local integration in urban center of protracted IDPs due to lack of security in area\nof origin;", "output": {"entities": {"named_data": [], "descriptive_data": ["REACH study on prolonged displacement"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In a 2004 note to its Executive Com- mittee, UNHCR established the average at 17 years at the end of 2003 (Executive Committee of the High Commissioner ’ s Programme 2004). This number has been widely quoted by media, ac- tivists, humanitarian agencies, and development institutions (Milner 2014; United Nations 2016; UNHCR 2015). The rest of the paper is organized as follows. Section 1 gives some deﬁnitions and background information on the refugee population. In section 2, we provide some summary statistics from our main source of data, the UNHCR Population Statistics Database. Section 3 describes the method followed to construct duration statistics and presents a few stylized facts. The results of our anal- ysis are presented in section 4. Section 5 concludes. 1 Background: Deﬁnitions and Data Under the terms of the 1951 Convention Relating to the Status of Refugees – henceforth the Convention – later amended by the 1967 Protocol, a refugee is a person, who “ owing to a well-founded fear of being persecuted for reasons of race, religion, nationality, membership of a particular social group or political opinion, is outside the country of his nationality, and is unable to, or owing to such fear, is unwilling to avail himself of the protection of that country. ” Data on refugees and asylum seekers are collected by individual countries, international orga- 3 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR Population Statistics Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In this sense, it is necessary to take into consideration that all indicators of prevalence of NSE and its profile will be limited to a subset of this kind of workers. 4. 1 Latin America and the Caribbean This section focuses on the Latin America and the Caribbean region, where a set of 9 countries, that we consider representing the different realities of the region in an exhaustive way, was analyzed. Specifically, the analysis was conducted for Argentina, Brazil, Bolivia, Chile, El Salvador, Mexico, Peru, Dominican Republic and Uruguay. 5O * NET is the successor of DOT (Dictionary of Occupational Titles) which is no longer updated. O * NET was launched in 1998 on the basis of the BLS Occupational Employment Statistics codes. In 2003, it was changed to SOC which implies that the consistent measures of task content are calculated from 2003. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["BLS Occupational Employment Statistics"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Border crossings The registration of people crossing internationals borders is conducted in many countries, and in some cases these data are used to estimate migration flows. Identifying refugees among people crossing borders is a significant challenge, particularly if individuals decide not to apply for asylum or refugee status (UNSD 2014). Additional problems associated with the collection of data on border crossings include: (a) difficulties distinguishing migrants from other people crossing a border, such as tourists, commuters, traders and truck drivers; (b) lack of capacity of many border posts and officials to handle large migration flows; (c) less scrutiny and diligence of emigration flows compared with immigration flow; and (d) lack of tight controls at most borders and the high incidence of undocumented or irregular crossings (UNSD 2014). Administrative records and registers Many countries have administrative records or registers of immigrants that could generate statistics on asylum-seekers and refugees. In particular, data on residence permits issued to refugees or asylum- seekers could be used to generate statistics on both flows and stocks of refugees. 79 For example, Eurostat collects and disseminates data on residence permits granted to those with refugee status and subsidiary protection (UNSD 2014). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data on residence permits granted to those with refugee status and subsidiary protection"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "It is diﬃcult to draw causal inference from observational data. This study is no exception. The results presented here are nevertheless suﬃciently suggestive to cast doubt on the theory that the choice of migration destination is driven primarily by income diﬀerentials. Other factors seem to play a strong — and probably more important — role. References 1. Adams, Richard, Remittances, Investment, and Rural Asset Accumulation in Pakistan, 30 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["observational data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "9 Bank Account No member has a bank or mobile money account. 1 / 12 Many of the indicators align with goals identified in the 2030 Agenda for Sustainable Development, such as no hunger, good health, access to quality education, clean water and sanitation, and decent work, as well as indicators that are especially relevant for displaced people, such as possession of legal identification, physical safety, and food security. The focus on gendered dynamics justifies health indicators related to pregnancy care, combining information on prenatal care, assisted delivery, and early marriage. A full discussion of the MPI ’ s indicator selection can be found in Admasu et al. (2021). We focus on six of these 15 indicators that use individual-level data – viz years of schooling, school attendance, pregnancy care, early marriage, legal identification, and unemployment. Our intrahousehold analysis drops the two health indicators due to data limitations; the question about age at marriage was only asked to the household head in Ethiopia, Nigeria, and South Sudan, whereas in Somalia and Sudan, it was applied to more members than the head.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["health indicators"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The LFS employment module follows ILO definitions: a person is classified as employed if they performed at least one hour of paid or unpaid work during the reference week, or if they were temporarily absent from their regular job. The LFS therefore captures both formal wage employment and informal self-employment in subsistence agriculture, a distinction that is important for understanding the full scope of labor market adjustment to the economic shocks we study. The LFS does not collect wage information for self-employed individuals, which limits our ability to estimate wage effects for that subgroup.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "challenge in some cases, notably among South Sudanese, refugees generally believe they are culturally similar to their hosts. This rate averages 78 percent for all refugees. It is lowest in South Sudanese camps at 68 percent and highest in Somali camps at 87 percent. Many of these refugees who say they are culturally similar to their hosts respond that they have no Ethiopian friends and that social interactions with Ethiopians are complex. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Refugees Figure 7.16: Share of refugees who agree they are “culturally similar to hosts” Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 Age Under 30 Age 30-44 Age 45-64 Age Over 64 Female Male Figure 7.18: Share or refugees engaged in a community representative body by demographic group Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 Eritrean Somali South Sudanese Addis Ababa All Refugees Figure 7.17: Share or refugees involved in a community representative body Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 71 Low refugee social integration is not", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Africa. The Afro Barometer Working Papers No. 200. Woldehanna, T., Hoddinott, J., and Dercon, S. (2008). Poverty and Inequality in Ethiopia:1995/96 – 2004/05. May. World Bank (2017). Forcibly Displaced: Toward a Development Approach Supporting Refugees, the Internally Displaced, and Their Hosts. Washington, D.C: World Bank. https://doi.org/10.1596/978-1-4648-0938-5. World Bank. (2019). Informing the Refugee Policy Response in Uganda. Results from the Uganda Refugee and Host Communities 2018 Household Survey. World Bank. (2019b). Better Opportunities for All: Vietnam Poverty and Shared Prosperity Update Report. Washington DC: World Bank Group. World Bank (2020). Ethiopia Regional Poverty Report: Promoting Equitable Growth for All Regions. Washington, D.C.: World Bank Group. World Bank (2020b). Ethiopia Poverty Assessment: Harnessing Continued Growth for Accelerated Poverty Reduction. Washington, D.C.: World Bank Group. World Bank (2023). World Development Report 2023: Migrants, Refugees, and Societies. Washington, D.C: World Bank Group. https://doi:10.1596/978-1-4648-1941-4 World Bank. (2023a). Social Cohesion and Forced Displacement: A Synthesis of New Research. World Bank. (2023b). Welfare in Forcibly Displaced Populations: From Measuring Outcomes to Building Capabilities. Leveraging Harmonized Data to Improve Welfare among Forcibly Displaced Populations and their Hosts: A Technical Brief Series. World Bank. (2023c). Do Legal Restrictions Affect Refugees’ Labor Market and Education Outcomes? Evidence from Harmonized Data", "output": {"entities": {"named_data": ["Uganda Refugee and Host Communities 2018 Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "0.10, ** p < 0.05, *** p < 0.01 Annexes 114 The regression specification used is: where is the vector of control variables that include demographic characteristics (sex of household head, age of household head, family size, years of schooling completed by the head), assets and wealth (mobile phone ownership, livestock ownership in tropical livestock units, land ownership, bank account, non-farm business ownership, electricity access), employment (share of employed members), resource and market access (market accessibility and proximity to resource hubs), and shocks (health, market, employment, drought, political). The regression also controls for survey domain and survey time (month) fixed effects to account for the effects of location and time on welfare. Table D.13: Determinants of welfare for in-camp refugees (1) In-camp refugees Head years since refugee status (from 2022) 0.00 (0.00) Head wants to go back to own/parents 0.02 (0.03) Ration change -0.43*** (0.10) Head: has any relative in own/parents’ COB 0.05 (0.04) HH received humanitarian food aid in past 12 months -0.00 (0.04) Distance to woreda capital (log) -0.10*** (0.01) Distance to border (log) -0.03 (0.02) Medium market accessibility -0.02 (0.06) High market accessibility 0.29*** (0.05) Constant 11.10*** (0.34) Survey domain Yes Survey time Yes Observations 1,266 Source:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "to the Venezuelan migratory crisis, whereas the VenRepPS survey was conducted during the pandemic in 2020. These temporal inconsistencies result in varying sample composi- tions across surveys, diverging from the landscape we observe in 2022. Notably, forced migrants in the VenReps Kids survey migrated during the crisis but have since remained in the country for several years, potentially leading to disparities in household integration outcomes. IV GENERAL DESCRIPTIVE STATISTICS IV. A Key characteristics of adults Table 2 provides descriptive statistics for the adults in our study, encompassing the pri- mary caregiver, mother and father (if residing with the child), and the individual finan- cially responsible for the child (should they be different from the aforementioned per- sons). Typically, the roles of primary caregiver and financial provider are fulfilled by either the mother or the father. The table is organized into three panels for clarity: Panel A details key individual characteristics, Panel B outlines adults ’ access to services, and Panel C focuses on labor market characteristics. Within the table, columns (1) and (2) present average values for adults from Colombia and Venezuela, respectively, while the final column displays the results of mean difference tests between these two groups, with standard errors noted in brackets. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Overall, 27 % of the transitions that occurred had an imputed month. Two things to keep in mind when considering this table. This does not show all the respondents-time periods in our sample (8, 240 observations). Since imputation will only occur when there is a transition in or out of a job, we are only including in the table those transitions and not all the periods where the individual is staying in a certain job. The total number of transitions is 726 which is less than the total number of individuals in the sample. That is because for some individuals all transitions occur prior to 2016 so they are not captured in our synthetic panel. Table 2 Transitions in and out of jobs by half, nationality, camp residency and imputation status of the month where the transition occurred Half Jordanian Syrian camp = 0 camp = 1 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "prior to the pandemic, a period characterized by less distinct time trends in the labor market. Since the focus of the paper is on the impact of COVID on labor market stock and dynamics, we also restrict the sample of interest to include only prime-aged working adults (aged 20 to 59). The dataset contains standard variables expected of a labor force survey, including those denoting employment, unemployment, and inactivity. It also contains information on the intensive margin of the labor supply, including hours worked and full-time and part-time status. Information on employment sector, industry, contract status, health insurance coverage, and mode of work (distinguishing between employees and self-employed, for example) is also available, allowing us to construct indicators of formality and to differentiate different modes of employment. Information on occupation is also available, but only at the level of 2-digit ISCO-08 classification. This information is enough to distinguish between white- and blue-collar occupations but it is not enough to observe additional relevant pandemic-related job characteristics such as the degree of contact with the public. 3. 2 Descriptive statistics Figure (1) tracks the evolution of labor market stocks in the West Bank and Gaza respectively over time.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "recently introduced the first version of their Global Internal Displacement Database (GIDD) that allows users to explore, filter and sort IDMC ’ s data to produce graphs and tables, and export underlying data. 102 Such platforms need to incorporate safeguards to protect the privacy and confidentiality of individuals ’ data. UNHCR and Statistics Norway are currently leading an initiative to improve forced displacement statistics with the participation of national statistical agencies. This process began with the presentation of the “ Report on Statistics on Refugees and IDPs ” at the 46th session of the UN Statistical Commission in March 2015, 103 followed by an international conference in Turkey in October 2015. 104 The conference set in motion a process for national statistical agencies to collaborate to develop a set of recommendations that both countries and international organizations can use to improve data collection, reporting, data disaggregation, and overall quality, including the preparation of International Recommendations for Refugee Statistics (IRRS). Progress on this agenda was discussed at the 47th session of UNSD held in New York in March 2016, where it was recommended that the expert group should also include IDPs in its scope of work (UNSD 2016). 105 The current initiative is focused on refugees, asylum-seekers and IDPs but would ideally be extended to host communities and returnees.", "output": {"entities": {"named_data": ["Global Internal Displacement Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Consequently, they may fear detection by authorities when asked to respond to surveys or participate in public initiatives. 1 Furthermore, collective repre- sentative and longitudinal data on forcibly displaced migrants, a population with high mobility rates, is difficult and costly (Ib ´ a ˜ nez et al. 2024). This complexity is compounded when focusing on children and adolescents, given the need for enumerators to receive specific training to interact with such a vulnerable demographic and for migrant parents to authorize their children ’ s involvement despite prevailing distrust issues. To address this knowledge gap, we launched the Venezuelan Refugee Panel Study for Kids (VenRePs-Kids) in Medell ´ ın, Colombia. VenRePs-Kids is a longitudinal study repre- sentative of forcibly displaced Venezuelan and Colombian children and adolescents aged 5 to 17. To our knowledge, it is the first study to gather panel data specifically on forcibly 1This concern is also prevalent among undocumented migrants in the United States, as highlighted by Amuedo-Dorantes and Lopez (2015). 2 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["VenRePs-Kids", "Venezuelan Refugee Panel Study for Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Statistical Note: Clustering in DHS-Based Analyses**\n\nIn all regression tables, _t_-statistics are computed using standard errors clustered at the DHS cluster level. This clustering is essential because: (a) the DHS sampling design introduces within-cluster homogeneity that violates the assumption of independent errors; and (b) our treatment variable — mine proximity — is constant within a DHS cluster, so ignoring within-cluster correlation would produce artificially small standard errors. The effective sample size after clustering is substantially smaller than the nominal number of DHS interviews.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "26 value of consumption flow of durable goods. 22 While monetary poverty can measure temporal resource holdings, multidimensional poverty, as a more comprehensive measure, includes chronic and exacerbating sources of poverty. This difference explains the existence of mismatches between individuals identified as monetary versus MPI poor, which are often more prominent in poorer countries (Evans et al 2020). This section examines these differences in the contexts of displacement. Table 9. Percentage of the sample in each poverty category: Rows sum to 100 % Non-poor by both measures Only Monetary Poor Only Multidimensional Poor Monetary and multidimensional poor Ethiopia 38 % 23 % 12 % 27 % N. E Nigeria 13 % 69 % 4 % 15 % Somalia 20 % 32 % 14 % 34 % Sudan 33 % 47 % 4 % 17 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). This table presents the distribution of households in each of the categories in the columns. Thus, each row adds up to 100 %. South Sudan is excluded from this analysis as monetary data is not available for the country.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Staff based on SESRE 2023. Most of the analysis presented in this chapter is based on detailed consumption data from the Socioeconomic Survey of Refugees in Ethiopia (SESRE) conducted between October 2022 and February 2023. All consumption of food and non-food items is included, regardless of whether these items are purchased on the market, come from own production, or received as gifts. For own-consumption and gifts, the quantities consumed are valued at prevailing prices in the enumeration area. Although consumption is expressed annually, the reference period used during data collection varies based on the nature of the items. For example, questions related to information on food and food-related items was asked by visiting households twice a week using the “last three days” and “last four days” as reference periods. For house rent, durable goods, clothing, health and education expenditures, and some other categories, the survey questions used the “last three months” and “last 12 months” as references. Imputed rent for owner-occupied houses is calculated by the Ethiopian Statistical Service (ESS) team and is included in the consumption expenditure data shared with the Bank team. Spatial and temporal price deflators adjust for price variations across time and space. First, nominal consumption", "output": {"entities": {"named_data": ["Socioeconomic Survey of Refugees in Ethiopia"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 employment status and education level also significant at 10 %. Marital status and education are important predictors of attrition. As we might expect, these imbalances suggest some threats that, if left uncorrected, could undermine the parallel trends assumption of difference-in-difference estimators. That said, we see no sign of differences between treatment and control, or attritors and non-attritors, over the key GRIT personality features. This suggests that members of the treatment group are not, for example, more motivated to succeed than members of the control group. To account for these biases, we generate a series of inverse probability weights to balance the data. These weights define the probability of an individual with particular characteristics (e. g. host or refugee status) being in each of the treatment and control groups at baseline and endline and are used to rebalance the data in order to closer support the parallel trends assumption. Results are shown in Column 3 of Table 4. Following weighting, data balances on all key factors, including nationality. This suggests that the parallel trends assumption is more reasonable under the weighted dataset than in the raw treatment / control data. 11 Based on these analyses, we conclude that it is safe to use weighted OLS-based approaches.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The GIS-based beneficiary targeting map was reviewed during the midterm assessment to verify that the communities selected for Component 2 infrastructure investments remained those with the highest unmet need. Updated displacement figures from UNHCR and poverty scores from the Ministry of Social Welfare were overlaid in the GIS to check whether the original targeting ranks had shifted. The GIS reanalysis confirmed that 41 of the original 47 target communities remained in the highest-need tertile; the six communities that fell below the threshold were replaced through a targeted consultation process.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, an endline survey was conducted after the second round of the EPAG program as per the timeline depicted in Figure 1. Examination of this endline survey data will permit a descriptive analysis of the outcomes of the first group of trainees 12 months after they completed the EPAG program, as well as examination of the outcomes of the second batch of trainees. The second round included not only the control group from this impact evaluation but also newly recruited participants who were offered brief basic literacy and numeracy training program prior to program entry. Work is already underway to design and implement the third round of EPAG, with a substantial redesign of the Job Skills track, an emphasis on reaching younger girls with lower literacy, and expansion to communities outside of Monrovia. If the high success rates found in this study are replicated for these future cohorts, the EPAG program should serve as a model for policy makers in Africa and the world seeking to improve lives and livelihoods of all youth, male and female. 25 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["endline survey", "endline survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the standard ethnic diversity indices to include the annual variation in refugee ethnicities. 8 We then construct a measure of proximity between the clusters in the host country and refugees in surrounding camps by defining an 80-km buffer around each cluster. 9 To control for unobserved heterogeneity and changes within a given cluster, we introduce cluster and year fixed effects, αj and δt. To minimize the risk of confounding the refugee-induced changes in diversity with the annual changes in refugee numbers, we also control for the presence of refugees based on the same buffer as the one used to construct the refugee-induced change in diversity. More specifically, the variable Refugeesjt − 1 counts the number of refugees present in cluster j at year t − 1 within the predefined buffer. The variable is also transformed into an inverse hyperbolic sine to ease interpretation. Finally, Qjt controls for yearly shocks at the cluster level, such as weather shocks. In particular, we control for rain and temperature anomalies. Standard errors are clustered at the Afrobarometer cluster level. 4. 2 Data and descriptive statistics Our analysis combines various sources of data: Afrobarometer, UNHCR refugee camp data, Armed Conflict Location and Event Data (ACLED), Uppsala Conflict Data (UCDP), and the Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Production data for the agricultural cooperative subcomponent were obtained from the Ministry of Agriculture's crop monitoring unit, which collects yield estimates from extension officers at the district level following each harvest season. These production data cover 14 crop types and are disaggregated by farm size category and irrigation status. Baseline production data will be compared against endline estimates to assess whether project-supported cooperatives achieved productivity gains consistent with the expected rate of return in the economic analysis.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A joint study conducted by HI and IOM in Bentiu in 2023 highlights the limited capacity of the South Sudanese authorities to\naddress the needs and rights of persons with disabilities, compounding the challenges. The gaps in disability disaggregated\ndata also hinder effective assistance, although at times there may be valid reasons for not reporting on specific vulnerabilities\nwhen providing mass assistance like awareness raising or information sharing activities. The reported numbers of persons with\ndisabilities benefiting from individualized assistance services through the 5W system are very low. Together with the reliance\non the global estimate as the basis for planning, this indicates a need for improved data collection, analysis, and utilization to\nbetter support persons with disabilities.\n\nData from the Protection Cluster's Protection Monitoring System (PMS) collected between October 2022 and March 2023\nfurther emphasizes the severe impact on persons with disabilities. Approximately 76% of key informants consistently report\nviolations of persons with disabilities' ability to access humanitarian aid, especially in crucial areas such as food, shelter, and\nhealth services. Moreover, according to the PMS data, they face disproportionate challenges in accessing justice and\naddressing Housing, Land, and Property issues.", "output": {"entities": {"named_data": ["Protection Cluster's Protection Monitoring System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "food item per month and cash transfers per person per month for each camp and period. The food items include cereal, wheat, maize, rice, sorghum, CSB/famex (CSB+), pulse, biscuit, date biscuit, dates, oil, vegetable oil, salt, and cash (Table E.4). We have computed the per person per month in-kind aid quantities into annual values using the household size and prices from SESRE and mapped them to the closest food item in SESRE (this was not straightforward as the items are different) considering food ration change periods. The food ration scaling factor is 50 percent vs. 84 percent. Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food aid admin data for every item except Biscuits. Refugee households still report lower quantities, even correcting for shares indicated as sold. Annexes 125 Table E.2: Food aid and consumption comparisons Items Quantity (per capita/year) Expenditure (per capita/year) SESRE UNHCR SESRE UNHCR Nonzero All Net of sold ration* Cereals/other cereals 23.2 186.1 175.5 493 12404 Wheat 60.5 133.2 116.6 1427 4710 Maize 39.6 125.5 118.3 312 4267 Rice 21.3 48.5", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": ["UNHCR food aid admin data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The UNHCR data identify principal applicants for each household and our analysis examines differences in household poverty between households with a female rather than male principal applicant. 6 The principal applicant is the person who receives assistance from UNHCR for the family and is self ‐ selected or selected by the family. This definition of female headship has advantages over the way that household headship is commonly identified in household surveys. An often ‐ noted drawback of the headship variable is that female headship may reflect the enumerators ’ perception about who should be considered a family head rather than who has the most responsibility for the family ’ s welfare in practice. 7 Social norms can also affect whether female respondents self ‐ identify as household heads. For example, some Eritrean returnees who would in other cultural settings be regarded as de jure female headed (single mothers, widows, divorcees, separated women) reported being male ‐ headed. Other Eritrean female returnees who would be considered de facto heads reported headship by absent husbands or male relatives (Kibreab, 2003). Our approach is therefore to distinguish between different types of female and male principal applicant households, using a typology that reflects some of the indicators of vulnerability used by UNHCR. We find that distinguishing between different types of female principal applicant households is important in the setting of Syrian refugees in Jordan. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "29 should be entered immediately into the accounts; and (c) Payment must be recorded as soon as they are made. Budget implementation should be reviewed periodically to ensure that programs are implemented effectively and to identify any financial or policy derailment. The review of budget execution should cover financial, physical and other performance indicators. Development budgets are often beset by implementation problems because of insufficient implementation capacities and other factors such as delays in mobilizing external financing, overoptimistic implementation schedules or difficulties in importing supplies. It is thus important to have in place mechanisms for reviewing the most significant or problematic projects. These could consist of a regular monthly or quarterly review of projects within the line ministries and a midyear review involving line ministries and central agencies29. The government has taken steps to improve the tracking of budget expenditure until the intended destination, particularly investments spending, for which a tracking survey was entrusted in 2005 with the Ministry of infrastructures. In addition, the ministries took themselves certain internal initiatives, in particular in health and education sectors, but the action plans of these ministries were not updated as envisaged in 2005, and there are neither reliable benchmark, nor quantitative targets as regards improvement of the arrival of the expenditure at intended destination in these sectors.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["tracking survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 Figure 3: Level of education age 25 +; compared with regional average (%) (*) INSTAT refers to census 2009 Source: Listening to Displaced People Survey, 2014. With regards to ownership of consumer durables, IDPs, refugees and returnees were better endowed than the average citizen of the North (see Figure 4). As was the case for education, they are more comparable to the average citizen in Bamako than to the average citizen in the regions of Gao, Timbuktu and Kidal. Figure 4: Asset ownership compared with regional average Source: Listening to Displaced People Survey, 2014 and EMOP 2011 (INSTAT). The main occupation of IDPs, refugees and returnees before the crisis was commerce (Table 5). This held for over half of the IDPs, 37 % of refugees and 34 % of returnees. 18 % of the refugees 51 85 60 47 85 89 87 15 6 18 29 11 8 8 34 9 22 25 5 3 5 IDPs Refugees Returnees Bamako (INSTAT) Gao (INSTAT) Timbuktu (INSTAT) Kidal (INSTAT) Secondary or Higher Primary None 0 100 200 300 400 500 600 IDPs Refugees Returnees Bamako (Instat) Gao (Instat) Timbuktu (Instat) Kidal (Instat) Percentage Mobile Phone Car / Motorized Vehicle Motorbike / scooter Bicycle Refridgerator TV CD", "output": {"entities": {"named_data": ["Displaced People Survey", "Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Recall that, by definition, attritors are not enrolled in a JFPR school. Arguably, the probability of\n\n6 Smith and Welch parametrize this as the enrollment ratio between attritors and non-attritors. We report this ratio\nin columns 2 and 5 of Table 3.\n\n\n11\n\n\n\n<!-- PAGE 13 -->\nprobability of other girls who were turned down for scholarships and did not enroll in a JFPR school, but\n\nscholarships and did not enroll in a JFPR school, but whose enrollment status could be established; this\n\nenrollment of attrited recipient and non-recipient girls, the estimated JFPR program effect is 0.191. Note,\n\ngirls who received JFPR scholarships and other girls-they correspond to the raw difference in", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 as the Demographic and Health Surveys (DHS), though researchers have reduced under-reporting by providing special training to interviewers, placing greater emphasis on respondents ’ privacy and safety, and allowing women multiple opportunities to disclose their experiences (Ellsberg et al., 2001a; Garcia Moreno et al., 2003; Ellsberg et al., forthcoming). Estimates of the magnitude of the problem Population-based surveys have found that between 10-70 % of women report being physically assaulted by an intimate male partner at some point in their lives (Heise, Ellsberg and Gottemoeller, 1999). See Annex A for estimates from many recent population based studies (Ellsberg et al., forthcoming). Findings from a multi-country study on domestic violence and women ’ s health carried out by the World Health Organization in fifteen sites and ten countries found that between 13-62 % of women had experienced physical violence by a partner over the course of their lifetime, and between 3-29 % of women reported violence within the past year (Figure 1. 1). Figure 1. 1. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": [], "vague_data": ["Population-based surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Poverty remains widespread and social indicators are well below the average for Sub-Saharan Africa. Chad is ranked 173 among the 177 countries surveyed in the 2006 UNDP Human Development Report. The incidence of poverty (defined as the proportion of households with annual spending below what is necessary to meet minimal needs) is estimated at 55 percent according to a 2003 household survey; an estimated four-fifths of the population of about 8. 8 million is living on less than a dollar a day3. Of the population over 15 years old, more than 73 percent (and 76 percent of women) are illiterate. Access to potable water has improved over past years, but is still limited to one out of three people in 2005. Less than two percent of the population has access to electricity and only 1021 kilometers of roads has been paved on a surface area of over 1. 2 million square kilometers. As already mentioned, Chad has recently become oil producing country; however, the economy remains largely agricultural and pastoral. About 80 percent of the country ’ s population lives in rural areas and continue to make their living4 from agriculture and livestock. Cotton is the principal cash crop, employing about 300, 000 families. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["2003 household survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "34 Any statistics on the imputed welfare will based on the set of imputed welfares for each household. The estimator takes the form, with R denotes the number of simulation: ܪ ෡ ൌ 1 ܴ ෍ ݄ ሺݕ ෤ ௥ ሻ ோ ௥ ୀ ଵ where ݄ ሺݕሻ is a function that converts the vector y with (log) incomes for all households into a poverty measure (such as the head-count rate or bottom 40 %), and where ݕ ෤ ௥ denotes the r-th simulated imputed welfare. Figure 6. Survey-to-Survey Imputation Methodology, an illustration For the case of Turkey, we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey. Income is used instead of consumption for this paper ’ s analysis. The model included variables related to: household demographics (age, gender, age composition, etc.), household characteristics (education, labor activity, etc.), household head ’ s characteristics (age, gender, labor, education, marital status, etc.) and household assets holding (both livestock and durables). Based on that model the simulated values of consumption (at household level) were imputed for the households in the corruption survey.", "output": {"entities": {"named_data": ["Labor Force Survey", "Survey on Income and Living Conditions survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "displaced due to armed conflict, situations of generalized violence and violations of human rights. 28 Data on IDPs monitored by IDMC are disaggregated and currently published separately for conflict-induced displacement and disaster-induced displacement. 29 At the country level the IOM ’ s Displacement Tracking Matrix (DTM) 30 provides data on IDPs in both conflict and natural disaster settings (activated in all major natural disaster contexts in recent years). Global data on conflict-induced internal displacement reflect variations in how IDPs are defined across situations. There is no consensus on how far a person must flee in order to be considered internally displaced. The definition of internal displacement for nomadic populations, which account for a significant share of IDPs in the Horn of Africa and increasingly in the Sahel, is open to controversy. 31 Moreover, while some countries register IDP children born in displacement (e. g. Azerbaijan, Cyprus and Georgia), other countries do not (IDMC 2015). The crafting of a definition for IDPs and its application in a particular context may be heavily influenced by local and national politics in conflict and post-conflict countries, as well as the direct link between estimates of displaced populations and humanitarian assistance, which can lead to both over- and under-reporting. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Displacement Tracking Matrix"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Ethiopia. Box 1.1: Comparison of SPS 2017 and SESRE 2023 Sociodemographic Profile 9 2. Sociodemographic Profile T his chapter presents results on sociodemographic outcomes of refugees and hosts. It provides the context for refugees and their hosts, covering demographic characteristics, human capital, living conditions, and displacement experience, which are crucial to understand refugees’ context in Ethiopia. These results are presented across the eight domains: Eritrean, Somali, South Sudanese, and refugees in Addis Ababa and their hosts, as well as broad categories between in-camp and out-of-camp refugees and hosts. 2.1 Demographic characteristics In the SESRE sample, most refugees are from South Sudan, accounting for 53 percent of all refugees.11 South Sudanese refugees reside in camps in Gambella and Benishangul-Gumuz regions. Somali refugees living in camps in the Somali region constitute 30 percent of the refugee sample. Eritrean refugees who reside in camps in the Amhara and Afar regions and Addis Ababa under the Out-of-Camp Policy (OCP) constitute 5 and 12 percent of the sample, respectively (Figure 2.1). More than 30 percent of refugees in camps are born in Ethiopia (Figure 2.2). Somali refugees have a higher share of refugees born in Ethiopia (38 percent). According to UNHCR estimates (2023)12, around 1.9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In 2008, trained enumerators conducted face-to-face interviews in local languages with 26, 513 respondents across 19 countries. 5 The sample is designed as a representative cross-section of all citizens of voting age in a given country. The dataset used for this paper has a multilevel structure; individuals are nested within primary sampling units (PSUs), which are nested within countries. The PSUs are the smallest, well-deﬁned geographic units for which reliable population data are available and they tend to be socially homoge- nous, thereby producing highly clustered data. In most countries, these will be Census Enumeration Areas (Afrobarometer, 2005, 37-38). Although re- spondents were not sampled based on their ethnic aﬃliation, there is likely to be a high level of clustering in the dataset around ethnicity. In other work, I discuss the advantages of multilevel modeling (Levi and Sacks, 2009). Treating the dependent variable as a binary outcome and taking into account the multilevel nature of our data, I estimate random intercepts for 5I excluded Zimbabwe from the analysis because of missing data on key variables. 9 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The data on total gross proﬁts and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key diﬀerence from the Chamber of Commerce data is that the sales and proﬁts data include all businesses including self- proprietorships. 9 Data were provided for the years between 2010 and 2014 and are re- ported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by deﬁnition and they are likely to be less accurate and complete for smaller ﬁrms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and proﬁt ﬁgures. 10 We scale the variables according to province size by dividing sales and proﬁts by the pop- ulation of the provinces. If we use sales and proﬁts in absolute terms, we get qualitatively similar results. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "13 genocide in 1994, with estimates that approximately 300, 000 to 400, 000 women suffered rape; Somalia in the early 1990s; the conflict in Kashmir; the 15-year-long civil war in Peru; and the recent civil war in Sudan (See, for example, McGinn 2000; El Jack 2003; Human Rights Watch 1995, 1996; McGinn 2000; Swiss and Giller 1993). A global review of 50 countries found significant increases in gender-based violence following major wars (World Bank 2011). Estimates of sexual and gender-based violence can suffer in both wartime and peacetime from serious underreporting (i. e., because people are unwilling or afraid to report gender-based violence, especially when the perpetrator is a family member) or overreporting, when incidence statistics are inflated because reporting improves with time (Nordås and Cohen 2011). This situation also occurs in peacetime, making it very difficult to accurately assess the increases in sexual and gender-based violence that are associated with conflict. Recent evidence highlights variations in the prevalence of sexual and gender-based violence in war situations and relates this variation to combatant norms and group cohesion. This evidence shows that Bosnia and Rwanda are anomalous cases of wartime rape being used as a war weapon for ethnic cleansing. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 Internal Conﬂicts and Refugees A particularly serious aspect of internal conﬂicts is the human suﬀering they generate. This is not only those who are killed or injured in conﬂict but the large number of people who are forced to leave their homes. The issue of refugees has received particular attention in Western media in recent years as refugee ﬂows from Northern Africa, the Middle East and Afghanistan are increasingly reaching Europe. These refugee streams are linked to a severe humanitarian crisis with considerable funding needs for international donors and heavy strains on host countries. 21 The current refugee crisis, however, is in no way unique. Civil war has always been closely linked to humanitarian crisis and refugee streams are one way to capture this. In this section we provide a cross-country analysis aimed at investigating how the stock of refugees evolves when a civil conﬂict hits a country. In the analysis we will focus entirely on showing changes in the stock of refugees across time to illustrate the dimensions involved. We will base our later analysis on these population movements. We exploit country-level data gathered from several sources. Data about refugees is provided by the UNHCR Population Statistics Database. The database provides in- formation about UNHCR ’ s populations of concern from the year 1951 up to 2014. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR Population Statistics Database"], "descriptive_data": [], "vague_data": ["country-level data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "arise. The field visits helped to understand the camp administrative structure and environment of the teams facilitating the camp and to test the accessibility of sampled refugee households inside the camp, in Addis Ababa, and the identification of the host. ESS provided detailed feedback on the fieldwork procedures and adjustments made before the fieldwork began. For instance, in Afar (Asayita), the visit helped to identify challenges in tracing sampled refugee households and to take the necessary corrective measures. Likewise, in Addis Ababa, the visit assisted in designing an appropriate strategy to select host communities. The survey created a good opportunity for a collaborative effort between different government institutions and development partners. This collaboration allowed the sharing of experiences across institutions and knowledge for ESS to implement such unique surveys in the future. The survey process, from preparation to implementation, focused on ensuring data quality for refugee data collection. During preparation, ESS translated the survey instrument into different main languages and undertook an in- depth training of supervisors and enumerators for enumerators to understand better the concepts of the questions related to the refugee context. Moreover, a close follow-up and coordination in the field helped to get better quality data and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 5. 2 Composition of poverty Unpacking the headline numbers further, important patterns emerge about the composition of multidimensional poverty among forcibly displaced and host communities in these countries. Overall, the censored headcount ratios (proportion of people who are poor and deprived in a given indicator) are lower among non-displaced communities than among refugees and IDPs, but there are large differences in which indicators are the most salient in different countries. The indicators with the largest difference between the two populations are bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria. These findings reinforce the need for policies and programming that take into account the measured experiences of IDPs and refugees. In this way, the MPI can function both as tool to monitor, track, and bear witness to the lived experiences of forcibly displaced communities, as well as advise on evidence-based interventions that address the needs of the local population. Figure 1 shows the censored headcounts of each indicator in Sudan ’ s MPI, with large differences appearing by displacement. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Ethiopia–Household Welfare Statistics Survey (HoWStat)—excludes the majority of displaced populations (Internally Displaced People [IDPs] or refugees) from its sample of households. Thus, we have limited in-depth information on the socio-economic outcomes—including on poverty— for refugees across all camps in Ethiopia to compare with Ethiopian hosts. SESRE collected data from November 2022 to January 2023, from a nationally representative sample of 3,452 refugee households and their hosts. The SESRE covers all currently operating refugee camps of major refugee groups: Eritreans, South Sudanese, and Somalis, as well as the out-of- camp refugees of Addis Ababa and their respective host communities. The survey was aligned with the HoWStat methodology, allowing comparability between refugees and their host communities. The World Bank, Ethiopia’s RRS, Ethiopia’s Statistical Service, and UNHCR collaborated to implement SESRE10 and was the first of its kind, building on the “Skills Profile Survey 2017, A Refugee and Host Community Survey” conducted in Ethiopia in 2017. The SPS 2017 was conducted in refugee camps and host communities in four regions in Ethiopia. The survey was used to draw a profile for skills and potential opportunities for refugees and host 9 See Annex C for detailed information on survey design and methodology. 10 Financial", "output": {"entities": {"named_data": ["Skills Profile Survey 2017", "Ethiopia–Household Welfare Statistics Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The paper extends the GTAP 8 database by separating Lebanon, Jordan, Syria, Iraq, and West Bank and Gaza from the rest of the Western Asia aggregate and Algeria and Libya from the rest of North Africa. Kuwait, Qatar, Bahrain, Saudi Arabia, UAE, and Oman are aggregated into a GCC composite group. In addition, the 57 sectors in the GTAP 8 database are aggregated into 22 sectors based on their importance for the countries in the MENA region (Table 1). The resulting MENA-specific database contains 26 countries, among which are the six Levant economies of interest in this paper (Turkey, Lebanon, Syria, Iraq, Jordan, and Egypt) and the rest of the developing MENA countries (Table 1). The procedure used to construct the individual country information employs data from several sources. The UN Statistics Division data for 2007 is the source for the six components of GDP – agriculture, hunting, forestry, and fishing (ISIC A-B); mining, manufacturing, and utilities (ISIC C-E); construction (ISIC-F); transport, storage, and communication (ISIC I); wholesale, retail trade, restaurants and hotels (ISIC G-H); and other activities (ISIC J-P).", "output": {"entities": {"named_data": ["GTAP 8 database"], "descriptive_data": ["MENA-specific database"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 crop products (e. g., wheat, potatoes) and the value of five livestock products (i. e., milk, egg, butter, hides, and honey) sold in the market. 14 Several other data sources were utilized. First, the Ethiopian refugee camps location data set from the Humanitarian Data Exchange (HDX) 15 and the total number of refugees by camps from the United Nations High Commissioner for Refugees (UNHCR), Addis Ababa office. We use data from 26 official UNCHR refugee camps in Ethiopia that were operational in 2018 (see Figure 1; 3). Second, we use administrative data sets for Ethiopia and refugee source countries from the database of Global Administrative Areas (GADM). 16 We also use the conflict data set from the Armed Conflict Location and Event Data Project (ACLED) 17 and the population data from the Gridded Population of the World (GPW) data set. 18 On the basis of these data sets and the location of sample households from Ethiopia DRDIP data set, we generated the following variables: i) distance of sample households to the nearest refugee camp, the nearest region (administration level 1 in GADM) to the refugee camps, ii) distance of the refugee camps to the nearest border of the refugee source country, iii) Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["database of Global Administrative Areas", "Gridded Population of the World (GPW) data set", "Humanitarian Data Exchange (HDX)", "Ethiopia DRDIP data set"], "descriptive_data": ["Ethiopian refugee camps location data set"], "vague_data": ["administrative data sets"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Since the set of household members at baseline have subsequently moved, and usually not as a unit, the 2004 round results in more than 2, 700 household interviews (from the baseline sample of 912 households). Although the KHDS is a panel of respondents and the concept of a ‘ household ’ after 10-13 years is a vague notion, it is common in panel surveys to consider re-contact rates in terms of households. Excluding households in which all previous members are deceased (17 households with 27 people), the field team managed to re-contact 93 % of the baseline households. This is an excellent rate of recontact compared to panel surveys in low-income countries and high-income countries. The KHDS panel has an attrition rate that is much lower than that of other well-known panel survey summarized in Alderman et al. (2001) in which the rates ranged from 17. 5 % attrition per year to the lowest rate of 1. 5 % per year. Most of these surveys in Alderman et al. (2001) covered considerably shorter time periods (two to five years). Figure 1 charts the evolution of households from baseline to 2004. One-half of all households interviewed were tracking cases, meaning they did not reside in the baseline communities. Of those households tracked, only 38 % were located nearby the baseline community. Overall, 32 % of all households were not located in or relatively nearby the baseline communities. While tracking is costly, it is an important exercise because migration and dissolution of households are often hypothesized to be important Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["KHDS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Significant among these are: (a) the lack of capacity of national statistical agencies in many developing countries to collect robust data on refugees; (b) weak or incomplete monitoring of refugees dispersed within host communities; (c) lack of capacity to maintain up to date information on refugees (reflecting new arrivals, 81 General population registers may also provide opportunities for more elaborate analysis of the integration of refugees in asylum countries, as the data could be linked to other administrative registers, for example on labor and education (UNSD 2014). 82 UNHCR collects, compiles and publishes data on asylum-seekers, refugees and IDPs protected or assisted by UNHCR, including populations in refugee-like or IDP-like situations. 83 Established in 1863, the ICRC ’ s mission is to ensure humanitarian protection and assistance for victims of armed conflict and other situations of violence. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["General population registers"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Second, the Turkish LFS asks respondents whether they had previously lived in a different province (one of Turkey ’ s 81 NUTS 3 regions), and if so in what year they moved to their current province. We estimate the impact of refugees on the probability a native moved to a subregion in the past year. Table 9 reports OLS and IV estimates of the impact of refugee on net population growth in subregion (Columns 1 and 2) and gross population inflows (Columns 3 and 4). Net population growth is estimated at the level of NUTS 2 subregions. Population inflows to a subregion are estimated at the individual level (and standard errors clustered by subregion- year). All regressions include subregion and year fixed effects and a year-specific control for log distance from the Syrian border. The first column presents the estimates for the whole sample, subsequent columns for different sub-samples by gender, age and education. For the full sample the net population growth in a subregion is positively correlated with refugee flows, while the IV point estimate is negative (though neither estimate is statistically significant). The probability of a Turkish person migrating to a subregion is negatively correlated with refugee flows (the OLS estimate is highly statistically significant). The IV estimate is of a similar magnitude, but no longer statistically significant. This same pattern broadly holds for both women and men. The only other statistically significant IV estimates are a decrease in the population aged 15 – 24, an age group that is likely more mobile, and of those with medium educational attainment. There is also a decrease in the inflow of low", "output": {"entities": {"named_data": ["Turkish LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Treatment Randomization: The housing subsidy program was randomized geographically at the community level. 8 In the ﬁrst step, 158 communities in Irbid and Mafraq were randomized into treatment or control for HSP assistance, stratifying on governorate and district population quartile. One third of communities were randomly assigned to treatment, while the remaining two thirds were randomly assigned to control. All eligible applicants living in the treatment communities were assigned to treatment. In all analysis below, error terms are clustered at the community level. There were several reasons for the cluster randomized design, including the ability to streamline implementation (and thus lower program costs), to reduce conﬂict among refugee households as- signed to treatment versus control, and to improve the analysis of treatment spillovers onto host community members by boosting the local saturation of program assistance, thereby making it more salient to neighbors. Refugee Sample: The survey sample consists of all refugees assigned to treatment and an 7See AER RCT Registration Number AEARCTR-0006141. 8Communities correspond to the Jordanian government ’ s administrative unit of “ localities ”. Communities in Mafraq have 3, 866 people on average, while those in Irbid are somewhat larger at 14, 626 individuals on average. 10 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The paper assembles a new, regionwide panel data set that measures local economic activity using nightlights, potential hurricane damages using a detailed hurricane windstorm model, and mangrove protection by mapping the width of mangrove forests on the path to the coast.\n\nSpecifically, we use remote sensing data on nightlights to measure local eco nomic activity. Nightlights have been shown to be a good proxy of economic activity (see Donaldson and Storeygard (2016) for a review), and their high spatial resolution is ideal because the economic impact of tropical cyclones has been shown to be highly localized\n\nWe measure potential hurricane damages using predicted wind speed from the wind field model of Pita et al. (2015). The model is calibrated for Central America and has been validated with historical data.\n\nWe measure local economic activity using imagery from four weather satellites that are part of the United States Air Force Defense Meteorological Satellite Program. These satellites record daily cloud formation by measuring the amount of moonlight reflected by clouds at night.\n\nSpecifically, we use the annual composites produced by the National Oceanic and Atmospheric Administration (NOAA). These composites predominantly measure man-made lights because they only use information from cloudfree days and because NOAA's methodology filters transient sources of light.", "output": {"entities": {"named_data": [], "descriptive_data": ["annual composites", "remote sensing data on nightlights", "composites produced by the National Oceanic and Atmospheric Administration (NOAA)"], "vague_data": ["panel data set"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Bank branches in El Fasher have limited capital for small businesses as their headquarters in Khartoum regard the area as too great a risk and IDPs themselves as riskier investments than their host community peers (UN- HABITAT 2009: p. 8). Exposure to unclean cooking fuels and inadequate housing can lead to poor health outcomes, while lack of access to electricity and a bank account further excludes individuals from labor market integration and livelihood opportunities that would empower forcibly displaced persons to overcome their multiple, overlapping deprivations. Clearly, displacement status puts individuals at a greater risk of poverty than their host community neighbors, and we can unpack those risks in greater detail using the MPI. Results can also be broken down to show the percentage contribution of each indicator to multidimensional poverty (see Figure 2). Among refugees in Ethiopia, lack of a bank account is the largest contributor to poverty, while among host communities, the largest contributor is years of schooling. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We have constructed two different variables that try to account for the degree of severity of the conflict. 10 The first variable identifies individuals belonging to households that were displaced due to the 1999 wave of violence (all members displaced). The second variable identifies individuals in households that report having their house completely destroyed by the violent attacks in 1999. The TLSS 2001 contains also useful retrospective information on school attendance and grade attained across three different academic years: 1998 / 99, 1999 / 00 and 2000 / 01. We are 7 Commission for Reception, Truth and Reconciliation & Benetech Human Rights Data Analysis Group. ― Human Rights Violations Database. ‖ 9 February, 2006. Website: http: / / www. hrdag. org / resources / timor-leste_data. shtml. 8 There may be potential sample biases in the statement taking procedure given the voluntary nature of the process. It is possible that those living in more remote or mountainous areas, those living far away from the areas where the statements were taken, the sick, old and disabled and those with no access to the media or means of mass communication have a lower probability of being part of the sample. By contrast, those more active in local communities are more likely to have provided a testimony. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Bank branches in El Fasher have limited capital for small businesses as their headquarters in Khartoum regard the area as too great a risk and IDPs themselves as riskier investments than their host community peers (UN- HABITAT 2009: p. 8). Exposure to unclean cooking fuels and inadequate housing can lead to poor health outcomes, while lack of access to electricity and a bank account further excludes individuals from labor market integration and livelihood opportunities that would empower forcibly displaced persons to overcome their multiple, overlapping deprivations. Clearly, displacement status puts individuals at a greater risk of poverty than their host community neighbors, and we can unpack those risks in greater detail using the MPI. Results can also be broken down to show the percentage contribution of each indicator to multidimensional poverty (see Figure 2). Among refugees in Ethiopia, lack of a bank account is the largest contributor to poverty, while among host communities, the largest contributor is years of schooling.", "output": {"entities": {"named_data": ["MPI"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We sourced bilateral trade value data from WITS and bilateral tariff data from a medley of sources, presented in Appendix Table A1. As part of this procedure, all entries in the two composite regions (rest of Western Asia and rest of Northern Africa) were split and assigned the split values to the newly created economies, while all entries for the two composite regions from the GTAP database were removed from the database. Each entry was split using the most thematically relevant external source. Sectoral GDP shares were used to split consumption and production values, trade data were used to split export and import values, and tariff information was used to assign tariff values. Export shares were used to split further production and consumption information into the final set of industries presented in Table 1. For internal consistency purposes, the required accounting relationships were imposed on the split database 8", "output": {"entities": {"named_data": ["WITS", "GTAP database"], "descriptive_data": ["bilateral tariff data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Staff based on SESRE 2023. Note: Columns 3-4 are restricted to workers, and Columns 5-6 are restricted to workers in camps. High-skill occupations include managers, professionals, and associate professionals (around 7% of refugees). Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 124 Table E.1: Food aid data/information received from UNHCR Item Remark Assumptions Cereal Not clear Other cereals Wheat Matched Maize Matched Rice Matched Sorghum Matched CSB/famex (CSB+) Not in SESRE Average of other cereals/pulses Pulse Not clear Peas Biscuit Matched Date biscuit Not in SESRE Merged with biscuits Dates Matched Oil Merged with edible oil Vegetable oil Merged with edible oil Salt Matched Cash - - Source: UNHCR Annex E: Robustness Checks of Refugees’ Consumption This Annex discusses assessing the disparity between refugee ration aid and reported consumption quantities. This is reported as a robustness check. As discussed earlier, the expenditure of in-camp refugees is almost half that of hosts despite sizeable food aid and significant investments made by the WFP and UNHCR in cash transfers (in selected camps). The significantly lower expenditures (food and non-food) among refugees compared to the host population led to higher poverty", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In the recent past, the number of persons affected by drought has been comparable to\nthat of victims of hurricanes and floods (146 million, on average, between 2000 and\n2005 according to the EM-DAT). The latest report of the IPCC predicts increased\nwater shortages in Africa (74 to 250 million people affected in 2020) and Asia:\n\"Freshwater availability in Central, South, East and Southeast Asia particularly in\nlarge river basins is projected to decrease due to climate change which, along with\npopulation growth and increasing demand arising from higher standards of living,\ncould adversely affect more than a billion people by the 2050s.\" (Intergovernmental\nPanel on Climate Change 2007a, 10). Case studies, however, paint a contrasting\npicture. The effect of a lack of drinking and irrigation water on migration is actually\nless sudden than that of the meteorological events mentioned in the previous chapter,\nand only generates progressive departures.", "output": {"entities": {"named_data": ["EM-DAT"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the main government department responsible for refugee affairs, housed within the former National Intelligence and Security Services (NISS). The former ARRA was elevated to an agency level in accordance with Proclamation No. 1097/2018, which defines the Powers and Duties of the Executive Organs of the Government and established the Agency for Refugees and Returnees Affairs (ARRA) under the Ministry of Peace in 2018. In 2021, a new government announced through the Definition of Powers and Duties of the Executive Organs Proclamation No. 1263/2021, during which it reestablished ARRA as Refugees and Returnees Service (RRS). The RRS became one of the executive organs accountable to the NISS which is accountable to the Prime Minister’s Office and oversees the Immigration and Citizenship Service other than RRS. Introduction 4 Refugees in Ethiopia have been severely affected by ongoing conflict and unrest across Ethiopia. The country has dealt with multiple crises, including rampant inflation, a devastating war, and frequent droughts and floods. SESRE data collection was carried out between November 2022 and January 2023, marked by drought, inflation, and insecurity, posing significant threats to the livelihoods of refugees and host communities in an already fragile economy. The conflict in Northern Ethiopia continued until 2022", "output": {"entities": {"named_data": [], "descriptive_data": ["SESRE data collection"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "analysis of social integration outcomes (1) (2) (3) (4) (5) Has in Ethiopia: Easy to do: Family Friend Market Interactions Social Interactions Sharing Resources Male 0.007 0.067** 0.033 0.010 -0.076** (0.017) (0.033) (0.027) (0.036) (0.035) Age Under 30 - - - - - Age 30-44 -0.030 0.033 0.007 0.004 -0.048* (0.021) (0.025) (0.025) (0.029) (0.028) Age 45-64 -0.041 0.024 0.002 0.011 0.012 (0.025) (0.038) (0.038) (0.050) (0.041) Age Over 64 -0.009 -0.062 0.100** 0.041 0.046 (0.046) (0.058) (0.043) (0.089) (0.073) Educ: Primary incomplete - - - - - Educ: Completed primary -0.004 0.121*** 0.008 0.028 0.063** (0.027) (0.033) (0.035) (0.041) (0.032) Educ: Completed secondary -0.026 0.218*** -0.020 0.061 0.060 (0.028) (0.054) (0.051) (0.060) (0.057) Educ: Completed post-sec. 0.003 0.238* 0.100** -0.020 0.238 (0.045) (0.131) (0.040) (0.095) (0.155) Years in Ethiopia 0.003** 0.006* 0.008*** -0.001 0.006 (0.002) (0.004) (0.003) (0.004) (0.004) Agrees hosts culturally similar 0.021 0.047 0.020 -0.010 -0.030 (0.016) (0.039) (0.037) (0.051) (0.036) Region Fixed Effects Yes Yes Yes Yes Yes N 1667 1667 1667 1667 1667 Source: World Bank Staff based on SESRE 2023. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 123 Table D.20: Regression", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "National Poverty line 32% 84% Food security High food insecurity 26% 67% Food insecurity scale 4.0 8.1 Social cohesion Economic competition 33% 49% Increased insecurity 37% 39% Source: Pape et al. (2018) and World Bank Staff based on SESRE 2023.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "21 Results suggest that 66 % of the returnees trust the Malian police and army most when it comes to providing security in the North. Almost half believe that the Malian army is brave and well trained. The vast majority of returnees believe that the government ’ s policies regarding reconciliation, security and social cohesion are good or very good. They also support the government ’ s approach towards decentralization and providing infrastructure such as access to potable water and electricity. As the next section will illustrate this differs strongly with the opinions of refugees. 6. Prospects for Peace IDPs, refugees and returnees have comparable opinions with regard to the requirements for peace: (i) addressing the ongoing crisis, (ii) improving security and (iii) reconciliation. Although there is agreement on what needs to be done, there is little consensus on what happened during the crisis, who the culprits are and who the main victims. Figure 15: What is the most important problem the Government needs to resolve today? (%) Source: Listening to Displaced People Survey, 2014. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Gender Perceptions- Violence (IPV) The standardized total score of ﬁve questions regarding norms for inti- mate partner violence (IPV) from the Demographic and Health Survey (DHS) (The important decisions in the family should be made only by the men of the family. How often would you agree? The wife has the right to express her opinion even when she disagrees with what her husband is saying. How often would you agree? A wife should tolerate being beaten by her husband in order to keep the family together. How often would you agree? A husband has the right to beat his wife. How often would you agree? It is more important to send a son to school than it is to send a daughter. How often would you agree?). Financial Well-being Savings Response to the question “ How much money do you currently have in savings? ” During the collection surveys (midlines) this question instead asked “ How much money did you save in the past week? ” Borrowing Total amount of money the household has borrowed. Economic Decision Making Risk Preference Measured using incentivized responses to the multiple price list deci- sions adapted from Holt-Laury and Sprenger (2002). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The study will conduct an ex-ante micro- simulation using pre-crisis household data (IHSES 2012) and macroeconomic projections for 2014 to gauge the distributional impact of the crises across groups (e. g. individuals and / or households, sectors, IDPs and host communities) and space (e. g. urban / rural, governorates).  The Economic and Social Impact Assessment for Kurdistan Region of Iraq [completed in 2015] provides an analysis of the impact of displaced people on access to and quality of service delivery across several sectors.  The Lebanon Economic and Social Impact Assessment of the Syria Conflict [completed in 2013] provides an analysis of the impact of displaced people on access to and quality of service delivery across several sectors.  The Bank and UNHCR undertook a welfare assessment of Syrian refugees living in Jordan and Lebanon [completed in 2016] focusing on welfare, poverty and vulnerability.", "output": {"entities": {"named_data": ["IHSES 2012"], "descriptive_data": ["pre-crisis household data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Our results suggest that any comparative assessment of hunger prevalence using HCES should clearly\n\nfrom HCES, such as poverty counts and inequality measures (Beegle et al. 2012). The reason is that the\n\nexpected when measuring hunger directly from HCES and how some of these errors likely differ by\n\nhunger numbers across the various arms of the survey experiment and verifies whether the magnitude\n\nAgriculture Organization (FAO) of the United Nations in a series of reports tracking world hunger, with", "output": {"entities": {"named_data": ["HCES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Table 2 presents hunger estimates derived from HCES alone. The calorie measure displays a great\n\nOne of the arguments for favoring the HCES-direct method over the FBS-CV method is that it allows for\n\npatterns of hunger are sensitive to the type of HCES that is used. For example, for each standard\n\nour experiment to calculate a CV of calorie availability, following FAO (1996, Appendix 3). [11] Specifically,\n\nwe collapse the data to 10 deciles of daily per capita kilocalories available and then calculate the CV\n\nFAO motivates these manipulations, which serve to lower the CV, by the desire to purge the CV of", "output": {"entities": {"named_data": ["HCES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3. Secondary outcomes among refugees: These include a broader set of variables that were grouped into 14 broad families in the PAP including: (1) dwelling characteristics and house- hold structure, (2) consumption and expenditure, (3) ﬁnancial participation, (4) earnings, labor, and occupational choice, (5) migration, (6) physical, mental health, and sleep, (7) marriage and fertility, (8) child outcomes, (9) social capital, (10) political attitudes, (11) time use, (12) education and cognition, (13) behavioral games and preferences, and ﬁnally, (14) speciﬁc COVID-19 related outcomes. The three primary outcomes noted above are a subset of these measures. Certain outcomes were collected in each of the three survey rounds, for instance, the food security measures (the number of meals eaten and the frequency of go- ing to bed hungry). Respondents also reported information on child school attendance, and completion of learning activities when schools were closed due to the COVID-19 pandemic. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We use the FAO Food Composition Tables for the Near East to convert daily food quantities into kilocalories; we then divide by the effective household size to get per capita daily caloric intake. [6]\n\nThe amount of assistance delivered during conflict is increas ing to support the growing share of the extreme poor living in these settings. However, little is known about how well assistance is targeted, which can have implications for conflict itself. Using a novel data source in Yemen that tracks food assistance, which accounts for the largest share of assistance in the country, the authors find that the share of households receiving food assistance significantly increased following the U.N. announcement of a food emergency and that the increases were larger in regions identified by the U.N. as being closer to famine. Furthermore, the increases in\n\nThis article investigates the change in humanitarian assistance following a particularly in teresting event- the announcement of a food emergency in Yemen in 2017. The United Na tions announced the 2017 Integrated Food Security and Phase Classification (IPC) in March\n\nutilizes a novel mobile phone survey conducted by the World Food Programme (WFP) that pro vides high-frequency data on food assistance and food access that are regionally disaggregated.\n\n_∗_ The authors thank the World Food Programme for sharing the data from the November 2017 Yemen mVAM survey and commend the team on the impressive data they have collected over the course of the conflict in Yemen. We thank the editor and two anonymous reviewers for excellent comments and suggestions.", "output": {"entities": {"named_data": ["FAO Food Composition Tables for the Near East", "Integrated Food Security and Phase Classification (IPC)", "November 2017 Yemen mVAM survey"], "descriptive_data": ["novel data source in Yemen that tracks food assistance", "mobile phone survey conducted by the World Food Programme"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Notably, while we cannot rule out that time outside due to employment may play a role (e. g., fresh air may boost one ’ s mood), our time-use data indicates that the average refugee already spends at least three hours outside per day, with no measurable diﬀerence between employed and cash arms. As we are powered to detect changes of at least twenty minutes for each activity, our results suggest that large substitutions away from unsavory activities are unlikely to be driving the improvements in psychosocial well-being, insofar as the respondent recalls. 1718 We also investigate whether those who were more idle prior to being employed beneﬁt more from employment. We ﬁnd no impact along this margin, suggesting that the elimination of boredom per se is not the driving force behind the psychosocial value of employment (Appendix Table A10). 17Most respondents do not track their day by time, making collection of reliable time use data challenging (though recent literature documents the broader unreliability of such data). We piloted a variety of strategies, and settled on asking respondents how much time they spent on a set of activities in the previous day. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["time-use data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "To this eﬀect, we replace yh i with yh i / yi in equation (1) and proceed as outlined above. If migration decisions are based on relative rather than absolute income, then the coeﬃcients of eδs − eδi and (eηs − eηi) zh should be positive and signiﬁcant only when they are computed using yh i / yi. In addition to relative and absolute income diﬀerences, the analysis also examines the re- spective roles of various location characteristics such as housing and food prices, availability of public services, and density of human settlement. 8An alternative strategy for the estimation of pre-migration income distribution in cross-section data is sug- gested by Bayer, Khan and Timmins (2008). 11 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1 POST-CONFLICT TRANSITIONS WORKING PAPER NO. 16 Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis * Håvard Hegre Centre for the Study of Civil War, PRIO (CSCW) Clionadh Raleigh CSCW, PRIO & University of Colorado at Boulder Abstract Why do larger countries have more armed conflict? This paper surveys three sets of hypotheses forwarded in the conflict literature regarding the relationship between the size and location of population groups: Hypotheses based on pure population mass, on distances, on population concentrations, and some residual state-level characteristics. The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events. The analysis covers 14 countries in Central Africa. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper develops a statistical method to analyze this type of data. The analysis confirms several of the hypotheses. World Bank Policy Research Working Paper 4243, June 2007 The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post- Conflict Transitions Project can be found at http: / / econ. worldbank. org / programs / conflict). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Armed Conflict Location and Events Dataset"], "descriptive_data": [], "vague_data": ["conflict event data", "geographically disaggregated data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, there is no consensus or agreed best practices on the use of these methods in different contexts or stages of displacement (Brookings 2013). 92 The absence of data on new displacement may simply mean that no displacement has taken place (IDMC 2016). 93 IDMC reports that data, disaggregated by age and sex, were available for 15 of the 60 countries it monitored in 2014, however these data were not comprehensive and are not published. Additionally, in some countries there are data provided by IOM on IDP populations by location from which the urban or rural character of the population may be inferred (e. g. if the camp is located in the capital), but data are not comprehensive and not published. While the majority of humanitarian profile data does not typically cover IDPs living outside of camp or camp-like settings (the large majority of IDPs), IOM ’ s DTM in countries such as Nigeria, Iraq, Yemen and Libya do include information about those residing in host communities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["humanitarian profile data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "South Sudanese Addis Ababa All Refugees Discrimination/harassment in past year Victim of crime in past 2 weeks Figure 7.19: Discrimination and harassment Source: World Bank Staff based on SESRE 2023. 72 A ddressing the challenges refugees face in Ethiopia requires a concerted effort to promote their self-reliance, economic integration, and access to education. By leveraging data from initiatives like SESRE and adopting a comprehensive approach that considers the needs of both refugees and host communities, Ethiopia can maximize the benefits of hosting refugees while minimizing associated costs. The GoE has proven its strong commitment to protecting refugees, but the progressive policy framework has not yet translated into tangible socioeconomic outcomes for refugees. The encampment model previously followed in Ethiopia neglected how refugees affect socio-economic and environmental conditions of hosting communities, including the untapped potential for refugees to contribute to the local economy. Despite strong improvements in Ethiopia’s underlying legal framework to benefit refugee inclusion, and a strong international aid response, refugees still face various challenges accessing services and improving their socioeconomic outcomes. Refugees are unable to move to locations with better economic opportunities and require a work permit (which is difficult to get for work outside of refugee camps) to", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "thirty years of autocratic rule that followed the Congolese independence from Belgium colonial rule paved the way to a violent transition, culminating in two internationalized wars from 1996 to 1997, in the aftermath of the Rwandan genocide, and from 1998 to 2003. Since the end of the Second Congo War in 2003, eastern Congo has remained unstable and violent, with many domestic and foreign-backed armed groups – including elements of the state military, FARDC – using violence against civilians and each other (Autesserre 2010). The violence and instability have resulted in poor living conditions and regular forced displacement for Congolese civilians. This project focuses on three provinces of eastern DRC that are especially impacted by forced displacement and political violence: North Kivu, South Kivu, and Ituri. 4 These three provinces account for 4. 5 million out of an estimated 5. 268 million total (85 %) IDPs in DRC 2020 (UNHCR Operational Data Portal: Democratic Republic of Congo 2021). Other provinces not included in this study but hosting IDPs include southern and central provinces such as Kasai, Kasai-Central, Kasai-Oriental, Lomani, Sankuru, and Tanganyika. The analysis in this paper focuses exclusively on dynamics in eastern Congo where the authors collected data.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Moreover, some refugees may not register because they are unaware that they should, and others may be reluctant to do so because they are skeptical of the integrity of the registration process (e. g. fair access to entitlements or opportunities for durable solutions) or lack confidence in protection measures. Individuals in irregular migration flows may also choose not to apply for asylum due to fear of declaring themselves to the authorities. A significant challenge with refugee registers is keeping them up to date. Individual registration can provide a robust snapshot of the stock of refugees and asylum-seekers, but registers need to be updated regularly to reflect flows, i. e. increases in refugee and asylum-seeker numbers (births, new arrivals) and decreases (deaths, departures, durable solutions). In situations of sudden mass influxes, existing registration capacity may not be adequate and the scope of registration data is then rationalized. 65 Additionally, it may not be possible to capture all demographic changes in the case of highly mobile populations. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee registers"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**3.3 Production Data for Economic Rate of Return Estimation**\n\nProduction data covering the five-year period from 2018 to 2022 were sourced from the Ministry of Agriculture's administrative records and validated through rapid field surveys covering 120 farms in the project area. The production data are available at commune level and capture planted area, harvested area, and yield by crop variety. A 12 percent farm-gate price increase was applied to the production data in the economic model to reflect the projected reduction in post-harvest losses attributable to improved storage infrastructure financed under the project.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Outcome Variable Collection Periods Basline Midline Weekly Endline Psychological Well-being PHQ9 X X Life Satisfaction Index X X Stress Index X X X Sociability (Total) X X X Sociability (Positive) X X X Self-Worth Index X X Locus of Control X X Allocation Decision Game X X Stability Index X X Physiological Well-being Index X X Gender Dynamics Gender Perceptions- Work X X Gender Perceptions- Violence (IPV) X X Financial Well-being Savings X X ∗ X Borrowing X X Economic Decision Making Risk Preference X X Time Preference X X Other Outcomes Cognitive Ability X X ∗ X Physical Health X X ∗ X Notes: The “ Baseline ” survey was conducted with respondents before treatment assignment was revealed. The “ Midline ” survey were questions asked immediately after treatment assignments were disclosed after the baseline survey, but before the work task had begun. “ Weekly ” surveys were conducted after each week of work (if any). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The relation- ships between displacement dynamics and perceptions of each manifestation of social cohesion are analyzed separately by running 12 models for each independent variable (6 regressions for each sample). The regressions are correlations and should not be interpreted causally. Hosting status and displacement ﬂows are likely related to perceptions of social cohesion in indirect ways and the structure of the survey data limit the ability to specify the channels through which these relation- ships run. Each regression controls for characteristics that may inﬂuence respondents ’ perceptions of social cohesion outside of the presence of IDPs or refugees in the local community such as province, gender, age, marital status, level of education, employment, and exposure to violence. 11Poll numbers correspond to the number wave in our larger project, as described and shown in Table 2. 28 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "26 Source: Listening to Displaced People Survey, 2014. 95 % of the refugees in Mauritania envision an independent or autonomous North and 26 % of the refugees in Mauritania even state the independence of Azawad (= the north) as a main condition for returning home. Stark differences can also be observed with regard to the discussion around a possible federalist solution for the North that was ongoing when the monthly phone interviews were conducted in October. As illustrated in the Figure 20 below, 80 % of the refugees in Mauritania support a federalist solution, while the majority of IDPs, returnees and refugees in Niger are not in favor. Of those who do not support a federalist solution (96 % of the IDPs, 88 % of the refugees in Niger and 95 % of the returnees), the majority of IDPs (61 %) and returnees (70 %) as well as 38 % of the refugees in Niger suggest decentralization as a possible solution to resolve the conflict. 13 % of the refugees in Niger also mention war and 27 % the integration of the North. Nonetheless, 49 % of IDPs, 86 % of returnees and 89 % of refugees believe a stable and sustainable peace accord can be achieved. by 4 % of the population living Timbuktu, 2 % in Gao and nobody in Kidal. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "which increases signiﬁcantly under both treatment arms. In other words, the psychosocial value to employment appears to be driven largely by the non-pecuniary dimensions of the employment experience. 5. 4 Impacts of employment on reported physical health, cognitive function, and economic decision-making The positive eﬀects of employment extend to other measures beyond psychosocial health. Table 3 presents results on reported physical health, cognitive function, and incentivized measures of risk and time preference. We observe a signiﬁcant increase in the days reported healthy. This eﬀect may be due to ‘ real ’ health improvements from increased exercise (which has also been documented to translate to improved mental health (Herbert et al. (2020))) from the employment task or ‘ perceived ’ health improvements in which improved psychoso- cial well-being translates into feeling less physically ill. Should the channel be exercise, we may expect health improvements to grow over time. Our weekly data on days healthy sug- gests this is not the case: we observe the treatment eﬀect on health from the ﬁrst week of working, and the gap remains steady throughout the following two months (Appendix Figure A3).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["weekly data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "supervisor, and four enumerators. All field staff involved in the SERSE participated in the HoWStat survey. Enumerators were knowledgeable about local cultures and languages and could detect inconsistencies and misunderstandings during interviews to ensure high-quality data. Supervisors were additionally trained on how to troubleshoot standard technical issues with tablets. The supervisors conducted reinterviews, consistency, spot-checking, and data syncing to the head office. Also, the statisticians from ESS branch offices were with the team all the time to support and monitor the fieldwork. The data collection system consisted of encrypted Android devices for prolonged usage in the field equipped with the chosen survey application and a GPS tracking application for EA delineations. Electronic data files were transferred daily to the ESS central office in Addis Ababa via the Annexes 96 secured link. The core team from ESS undertook field supervision and was responsible for the day-to- day field management. Also, the World Bank team undertook field supervision, providing on-time and on-the-spot guidance for the field teams whenever and wherever they encountered a challenge. (e) Challenges faced and lessons learned The SESRE served as a learning experience for including refugees in future rounds of the official household survey (HoWStat). Given the unique", "output": {"entities": {"named_data": ["HoWStat survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Since the set of household members at baseline have subsequently moved, and usually not as a unit, the 2004 round results in more than 2, 700 household interviews (from the baseline sample of 912 households). Although the KHDS is a panel of respondents and the concept of a ‘ household ’ after 10-13 years is a vague notion, it is common in panel surveys to consider re-contact rates in terms of households. Excluding households in which all previous members are deceased (17 households with 27 people), the field team managed to re-contact 93 % of the baseline households. This is an excellent rate of recontact compared to panel surveys in low-income countries and high-income countries. The KHDS panel has an attrition rate that is much lower than that of other well-known panel survey summarized in Alderman et al. (2001) in which the rates ranged from 17. 5 % attrition per year to the lowest rate of 1. 5 % per year. Most of these surveys in Alderman et al. (2001) covered considerably shorter time periods (two to five years). Figure 1 charts the evolution of households from baseline to 2004. One-half of all households interviewed were tracking cases, meaning they did not reside in the baseline communities. Of those households tracked, only 38 % were located nearby the baseline community. Overall, 32 % of all households were not located in or relatively nearby the baseline communities. While tracking is costly, it is an important exercise because migration and dissolution of households are often hypothesized to be important Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["KHDS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For this study, data on the four types of capital are obtained for three periods - 1995, 2000 and 2005 and for 210 countries, from an updated database underpinning the wealth estimates of nations (World Bank, 2006; G. Ruta and K. Hamilton, personal communication, 2009). The data are measured in per capita values at 2005 constant prices. An econometric analysis is conducted for a panel data of capital, along with the data on the magnitude of natural disasters for the same 2 <!-- PAGE 5 --> periods. Because of limitations in the data on human capital related to education (HS), the intangible capital residual (HR) is used as a proxy measure of human capital in this study.", "output": {"entities": {"named_data": [], "descriptive_data": ["updated database underpinning the wealth estimates of nations", "data on the magnitude of natural disasters"], "vague_data": ["data on human capital related to education"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The report focuses on intent-to-treat (ITT) estimates, measuring the impact of offering volunteering opportunities and soft skills training independently of actual take-up. 9 We estimate the following individual-level intent-to-treat regression: 𝑌 ௜ ௧ ൌ 𝛼 + 𝜇 ௡ ൅ 𝛽𝑇௧ + 𝛾𝐷 ௜ + 𝛿ሺ𝑇 ∗ 𝐷ሻ ௜ ௧ + 𝜀 ௜ ௧ (1) where 𝑌 ௜ ௧ is the outcome of interest for respondent i in period t, 𝑇௧ is a post-treatment year binary variable, 𝐷 ௜ is a binary variable for being assigned to the treatment, and 𝜇 ௡ is a fixed effect for NGOs. 𝛼 represents the baseline average for the outcome of interest for non-selected youth. 𝛽 is the difference in after-and- before intervention in outcomes for non-selected youth. 𝛽 ൅ 𝛿 is the difference in after-and- before intervention in outcomes for selected youth. 𝛾 is the difference in 9 Due to some procurement delays that caused a big time-lag between baseline data collection and actual NGO project implementation, many of the volunteers who belonged to selected NGOs and who were randomly selected to participate in the impact evaluation study dropped out after their baseline data were collected and were replaced by other volunteers. Project monitoring data reveal that 23 percent of volunteers assigned to treatment did not actually end up participating in the NVSP. Given the relatively high number of non-compliance, we are unable to perform Local Average Treatment Effects (LATE) analyses to understand the impact of participating in NVSP on outcomes of interest. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Project monitoring data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Following the approach used for the Global Costing of Refugee Inclusion, successful inclusion is defined as earning sufficient income to be no longer poor and to consume more than the (international) poverty line. This definition opens two tracks for investigation: first, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this question is found by identifying the poverty gap for refugees. This opens the second track which explores the factors that determine, or at least that are associated with, the size of the poverty gap. The note is organized as follows. The next section discusses data and presents some key descriptive statistics on refugees and host communities in Uganda. This is followed by a methodological section discussing how own income and aid are complements and how an analysis of poverty gaps informs about the need for assistance. This is followed by two analytical sections. The first identifies refugee poverty gaps, and assistance needs for refugees with distinct characteristics. The following section estimates how much has been saved by including refugees in the economy and explores how more could be saved. Conclusions follow. 2 The poverty numbers in World Bank (2019) are based on the official poverty line adopted in Uganda in 1997. There was a need to update this line as it was too old and producing a very low poverty rate. For example, using this line produced a national poverty rate of about 21 percent in 2019 / 20 compared to more the than 40 percent international poverty rate using the USD 2. 15 2017 PPP daily poverty line. In order to address this criticism, the poverty line was revised by the Uganda Bureau of Statistics in 2021, but it is not available for the 2018 Refugee and Host Communities Household Survey used in this note. Instead, we are using the international poverty line throughout.", "output": {"entities": {"named_data": ["2018 Refugee and Host Communities Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "An open empirical question remains on the relative importance of the three initial conditions (i. e., unemployment, co-ethnic enclaves and attitudes) when examined together. Moreover, we expect the effect of the individual factors to vary in the short, medium and long run over the 20 years examined. In the next section we empirically investigate these issues, and examine whether these assumptions are supported by the empirical evidence. 4 Research Design 4. 1 Data and Descriptive Statistics In our analysis we construct a unique longitudinal dataset which covers the universe of refugees and migrants in Switzerland over 1998-2018. It allows us to follow refugees over the life-cycle for 20 years, and allows us to follow them even after they change residence permit and status. To construct this longitudinal dataset we are combining three administrative datasets. The first dataset, that covers all asylum seekers is AUPER (Automatisierte Personen Registratursystem) which is provided by the State Secretariat for Migration. The data includes information about the residence permit, year of arrival, country of origin, canton of allocation and socio-demographic characteristics. Once an asylum seeker has obtained a residence permit other than permit N (asylum seekers) or F (provisionally admitted foreigners), he or she is registered in ZAR (Zentrales Ausländerregister). This longitudinal dataset is also coming from the SEM. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Increasing the schooling attainment of girls is a challenge in much of the developing world. In\nthis paper we evaluate the impact of a program that gives scholarships to girls making the transition\nbetween the last year of primary school and the first year of secondary school in Cambodia. We show\nthat the scholarship program had a large, positive effect on the school enrollment and attendance of girls.\nOur preferred set of estimates suggests program effects on enrollment and attendance at program schools\nof 30 to 43 percentage points; scholarship recipients were also more likely to be enrolled at any school\n(not just program schools) by a margin of 22 to 33 percentage points.\n\nThe impact of the JFPR program\nappears to have been largest among girls with the lowest socioeconomic status at baseline. The results we\npresent are robust to a variety of controls for observable differences between scholarship recipients and\nnon-recipients, to unobserved heterogeneity across girls, and to selective attrition out of the sample.\n\nto the 2000 Demographic and Health Survey (DHS), 85 percent of 15 to 19 year olds had completed", "output": {"entities": {"named_data": ["2000 Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Although the OCO-2 satellite platform provides the best available database, its coverage for our 25-km grid cells is limited by its 16-day repeat cycle, relatively narrow observation track, and the frequent occurrence of cloud cover over some areas.\n\nFor the translation of regression residuals to emissions deviations, we use the EDGAR gridded database of CO2 emissions estimated from sectoral activity data and standard emissions parameters (Crippa et al.\n\nWe translate these residuals to emissions using the EDGAR global database of gridded CO2 emissions estimated from local activity measures and standard emissions parameters (Crippa et al.\n\n2020). The current EDGAR database terminates in 2018, so we perform the conversion using data for 2015 - 2018, the period of overlap with our OCO-2 database.", "output": {"entities": {"named_data": ["EDGAR gridded database of CO2 emissions", "EDGAR global database", "EDGAR database", "OCO-2 database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "are combined. The surveys follow a repeated cross-sectional design and are not panels (i. e. the same administrative units, not the same people, are re-sampled), so responses are aggregated to the groupement level, the lowest level at which the project consistently collect representative data. Table 2 provides a summary of the dates, sizes, and percent of respondents who report being displaced within each survey wave. This aggregated temporal analysis can show, associations between ﬂuctuations in displacement and perceptions of social cohesion over space and time at the groupement level. Question coverage varies across survey waves, but a battery of core questions enables consistent observation of how many individual respondents self-report being displaced at the time of the survey and being involuntarily moved within the past year. Poll Date N % Currently Displaced % Displaced Last Yr % Hosting Displacees # 11 July 2017 5834 4. 35 7. 42 – # 12 September-October 2017 4013 1. 62 2. 62 – # 13 December 2017 4883 3. 50 7. 97 – # 14 March-April 2018 1933 4. 97 8. 85 31. 35 # 15 June-July 2018 5951 3. 70 8. 35 30. 33 # 16 October 2018 1112 6. 47 4. 68 – # 17 December 2018 5918 5. 86 11. 20 – # 19 July-August 2019 5961 5 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This distinction is also applied to foreign students, with those entering the system after February 2022 referred to as “ Migrant post-Feb 2022 ” and others referred as “ Migrant pre-Feb 2022 ”. 7 Administrative data from MoE. The Ministry data includes information for all students who enrolled at any point during the academic year. 8 This information covers academic year, grade, gender, birth date, birthplace, citizenship. They also include school-speciϐic information such as the name and identifying code of the institution where the student is enrolled. Furthermore, the dataset includes a variety of school outcome variables, including grades in English, Italian, Mathematics, overall GPA calculated as the average across all subjects, behavior scores from grade 9 to grade 12, guidance council evaluations from lower secondary school, records of absences, late entries, and early exits. Given the timing of this study, data for academic year 2022-23 are the most complete. For academic year 2022-2023, school enrollment data at the provincial level was provided for 4, 269, 348 enrolled students across the 8 years of Italian lower and upper secondary school, encompassing both public and private institutions. The dataset includes nearly all students in the country irrespective of their citizenship. 9 Table 1 shows the distribution of the different groups of students by grade. In the 7 For ease of reference, we refer to non-Italian and non-Ukrainian students as migrants. However, we acknowledge that some of these students may be refugees or displaced students. 8 At the time of writing this paper, both MoE and INVALSI data were not fully available and, as such, only the information on enrollment was used for the academic year 2023-24. 9These numbers do not include students enrolled in Provincial centers for adult education (CPIA). See footnote 5. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["INVALSI data"], "descriptive_data": [], "vague_data": ["school enrollment data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The decision to link it with CM made ENLACE a de facto high-stakes test encouraging\n\nin grade inflation (Contreras and Backoff, 2014). Anomalies in ENLACE and the creation\n\n<!-- PAGE 10 -->\n###### **2.2 The ENLACE Panel**\n\nENLACE created a unique personal identifier ( _Clave_ _Única_ _de_ _Registro_ _Poblacional_ or\n\nthe ENLACE dataset included a school identifier, socioeconomic information for each", "output": {"entities": {"named_data": ["ENLACE dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(vi) ran out of food, (vii) adults were hungry but did not eat, (viii) went without eating for a whole day, (ix) restricted consumption so kids could eat, and (x) borrowed food or relied on friend/relative for help. 7.0 8.4 7.8 6.0 8.2 6.5 In Camp Addis Ababa Total Hosts Refugees 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Poor (0-21) Borderline (21-35) Acceptable ( > 35) Percent Figure 5.10: Dietary diversity and food consumption status Source: World Bank Staff based on SESRE 2023. Note: Dietary diversity score is calculated as the total number of food groups (out of 12) consumed by the household in the last seven days before the survey. The food groups are cereals, roots and tubers, vegetables, fruits, meat (including poultry and offal), eggs, fish and seafood, pulses and legumes and nuts, milk and milk products, oils and fats, sugar/honey, and others. Food consumption status is determined based on food consumption score. a. Dietary diversity score (out of 12 groups) b. Food consumption status Refugees’ Aspirations 49 Moreover, insecurity and displacement-related shocks are common for Eritrean refugees, with 14 percent having experienced", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "future year by the counterpart observation in the CRU benchmark dataset (from the most\n\nrepresentative temperature/rainfall combination, derived from CRU data for 1980-2000).\n\ndeparture: the benchmark series from the CRU data. This translation step is necessary", "output": {"entities": {"named_data": ["CRU benchmark dataset"], "descriptive_data": [], "vague_data": ["CRU data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 8 of 51 In the 9 cases mentioned, it was possible to identify part-time workers, while temporary workers were only identified in the cases of Argentina, Brazil, Chile, Mexico and El Salvador. 4. 1. 1 Variation of the NSE as a percentage of total employment The prevalence of NSE in the total employment has not shown very significant variations in the countries considered in the last two decades (Figure 1). Indeed, most of the countries analyzed show non-standard employment registers similar to those observed in the mid-1990s. The exceptions where the variation is a little more relevant are Brazil and Uruguay, where there are contractions in the incidence of the NSE of the order of 10 and 5 percentage points respectively and Mexico, where there is an increase of 5 percentage points. Figure 1: Prevalence of NSE among salaried employees. (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the prevalence of NSE by types of occupations, considering the ISCO classification at one digit, we find a quite similar pattern across countries. Indeed, in most of the considered countries, the “ Elementary Occupations ” are the category where the prevalence of NSE is higher. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of dependability (Heller and Kessler, 2022). In a recent study, Karpowitz et al. (2023) show how these soft skills interact with discriminatory practices. They find that assigning leadership roles to women in a classroom setting reduces gender discrimination. Similarly, Kaas and Manger (2012) use a correspondence study to show that presenting soft information, such as reference letters with information on conscientiousness and agreeableness, seems to mitigate discrimination. Second, unlike most correspondence studies, we exploit data on firm characteristics and decisions at multiple stages of the hiring process to conduct a rich heterogeneity analysis. Most studies are only able to observe if the candidate receives an interview offer. Hangartner et al. (2021) is an interesting exception, which tracks online employers ’ actions and collects information that allows them to study hiring decisions. We instead observe five stages in the hiring process: 1) if employers reject an application, 2) if they visit an applicant ’ s profile, 3) the number of visits to each profile, 4) if employers contact the candidate, and 5) if they offer an interview. These five outcomes provide a rich preview into the hiring decision. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Following the approach used for the Global Costing of Refugee Inclusion, successful inclusion is defined as earning sufficient income to be no longer poor and to consume more than the (international) poverty line. This definition opens two tracks for investigation: first, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this question is found by identifying the poverty gap for refugees. This opens the second track which explores the factors that determine, or at least that are associated with, the size of the poverty gap. The note is organized as follows. The next section discusses data and presents some key descriptive statistics on refugees and host communities in Uganda. This is followed by a methodological section discussing how own income and aid are complements and how an analysis of poverty gaps informs about the need for assistance. This is followed by two analytical sections. The first identifies refugee poverty gaps, and assistance needs for refugees with distinct characteristics. The following section estimates how much has been saved by including refugees in the economy and explores how more could be saved. Conclusions follow. 2 The poverty numbers in World Bank (2019) are based on the official poverty line adopted in Uganda in 1997. There was a need to update this line as it was too old and producing a very low poverty rate. For example, using this line produced a national poverty rate of about 21 percent in 2019 / 20 compared to more the than 40 percent international poverty rate using the USD 2. 15 2017 PPP daily poverty line. In order to address this criticism, the poverty line was revised by the Uganda Bureau of Statistics in 2021, but it is not available for the 2018 Refugee and Host Communities Household Survey used in this note. Instead, we are using the international poverty line throughout. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["2018 Refugee and Host Communities Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "report). Box 5.2: Disparity between refugee ration aid and reported consumption quantities 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Own production Market purchase Transfers/gifs Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Percent 0 10 20 30 40 50 60 70 80 90 100 Percent Own production Market purchase Transfers/gifs Figure 5.5: Food and non-food expenditures shares by sources Source: World Bank Staff based on SESRE 2023. a. Food expenditure b. Non-food expenditure Refugees’ Aspirations 46 5.1.3 Multidimensional poverty Refugees are more vulnerable to multidimensional poverty than hosts. The multidimensional poverty rate is relatively high among refugees, driven primarily by low living standards and poor access to education. Trends in monetary poverty are mirrored using the Multidimensional Poverty Index (MPI)—an index measuring deprivations across three dimensions of well-being: education, health, and standard of living (see Box 5.3). MPI provides a general picture of the extent of deprivation (Alkire et al., 2021). The results show that 50 percent of refugees and 23 percent of hosts are multidimensionally poor (Figure 5.7). Looking at in-camp refugees and their hosts, multidimensional poverty is 64 percent for", "output": {"entities": {"named_data": ["Multidimensional Poverty Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "JDC Joint Data Center LFPR Labor Force Participation Rate LFS Labor Force Survey LoC Locus of Control LSMS Living Standards Measurement Study MoE Ministry of Education MoLS Ministry of Labor and Skills MoLSA Ministry of Labor and Social Affairs MoR Ministry of Revenue MoTRI Ministry of Trade and Regional Integration MoU Memorandum of Understanding MPI Multidimensional Poverty Index NEET Not in Employment, Education or Training NER Net Enrollment Rate NGO Non-governmental Organization OAU Organization of African Unity OCP Out-of-Camp Policy PPP Purchasing Power Parity RRS Refugees and Returnees Service SESRE Socio-economic Study of Refugees in Ethiopia TVET Technical and Vocational Education and Training UNHCR The United Nations Refugee Agency UNICEF The United Nations International Children’s Emergency Fund UPSNJP Urban Safety Net and Jobs Project WFP World Food Programme WHO World Health Organization i The report titled \"Expanding development approaches to refugees and their hosts in Ethiopia\" was prepared by a team of the Poverty and Equity Global Practice at the World Bank led by Christina Wieser (Senior Economist, World Bank), including Wondimagegn Mesfin Tesfaye (Economist, World Bank), Fikirte Girmachew (Consultant, World Bank), Jeremey Aaron Lebow (Young Professional, World Bank), and Manex Bule Yonis (Economist, World Bank), under the adept guidance", "output": {"entities": {"named_data": ["LFS Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "It is diﬃcult to draw causal inference from observational data. This study is no exception. The results presented here are nevertheless suﬃciently suggestive to cast doubt on the theory that the choice of migration destination is driven primarily by income diﬀerentials. Other factors seem to play a strong — and probably more important — role. References 1. Adams, Richard, Remittances, Investment, and Rural Asset Accumulation in Pakistan, 30 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["observational data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2.0 percentage points more likely to be enrolled at any school, JFPR or otherwise (p-value: 0.29). Note\n\nscholarship receipt, therefore provide further evidence of a JFPR program effect.\n\nthe 2000 Demographic and Health Survey; here too there is a clear schooling gradient. [3] Figure 1 shows,\n\n2 Comparisons for the full sample of girls, not just those with completed applications, also suggest that scholarship recipients were more likely to be enrolled and attending school: In the full sample, the difference in the probability of enrollment at a JFPR school is 22.1 percentage points; the difference in the probability of attending is 21.9 percentage; and the difference in the probability of enrollment at any school, not just a JFPR school, is 12.1 percentage points.", "output": {"entities": {"named_data": ["2000 Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "<!-- PAGE 16 -->\n**Appendix 1: Comparing applicants with full application forms to those with only partially**\n**completed forms**\n\n**All applicants** **Full application form**\n**Outcomes**\nEnrolled in JFPR school 0.81 0.82\nAttending on the day of school visit 0.74 0.75\nEnrolled in any school 0.87 0.87\n\nDas, Jishnu, Quy-Toan Do, and Berk Özler. 2005. \"Reassessing Conditional Cash Transfer Programs.\"\n_World Bank Research Observer_ 20(1): 57-80.\n\nDeolalikar, Anil. 1993. \"Gender Differences in the Returns to Schooling and School Enrollment Rates in\nIndonesia.\" _Journal of Human Resources_ . 28(4): 899-932.\n\n\"Educational Attainment and Enrollment Profiles: A Resource Book based on an Analysis of Demographic and Health Survey Data.\" Development Research Group.\n\n<!-- PAGE 19 -->\n**Figure 1: Attendance and enrollment status by decile, JFPR application data**\n\nJFPR applicants: Enrollment at a JFPR school, by JFPR applicants: Attendance on day of school visit, scholarship status and decile by scholarship status and decile", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As summarized by Robinson (2017), “ fortunately, Afrobarometer respondents comprise stratified random samples at all levels, making population estimates based on them unbiased: thus, the major concern with using Afrobarometer sample data to construct demographic measures is unbiased measurement error. ” Based on a comparison of census-based and survey-based diversity indexes across five African countries, Robinson (2017, 224) found that a “ sample-based measure tends to underestimate the overall degree of diversity compared to census data ”. In theory, this should make it more difficult to observe the true relationship between ethnic diversity and some outcomes at the local level. Diversity indices are more likely to be measured with noise in highly diverse communities at the local level. We nonetheless argue that such a concern should not be overestimated, for three reasons. First, such noise cannot easily explain the contrast between the coefficients corresponding to the pre-revised and revised indices and the opposite results found for the revised refugee fractionalization and the revised polarization. This set of results can be explained by the fact that our identification comes from the annual changes in refugees flows. Second, the IV approach is likely to deal with the measurement errors if they are correlated with our main variables of interest. Our IV estimates therefore capture a local average treatment effect coming from the plausibly exogenous increase in annual refugee flows of particular ethnic groups. The similarity of the IV results to the OLS results supports this interpretation. Third, at the cost of introducing attenuation bias30, we also aggregate the number of conflict events at the regional level. Lines B and C of Table 7 confirm the negative and positive effects found for the revised fractionalization and polarization indexes, respectively, whether or not 30Another risk highlighted by Robinson (2017) is the fact that ethnic diversity may also capture different theoretical mechanisms at aggregated levels. 34 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer sample data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 displacement. 55 % of the returnees reported to have been employed before the crisis and 36 % in June 2014. Over time the employment situation among the displaced has improved steadily and by December 2014 more people reported being employed than prior to the crisis. All the returnees were able to regain employment after returning. The employment situation of IDPs, returnees, and refugees in Niger is steadily improving; only for refugees in Mauritania does one notice a steady decrease, with 100 % reporting no employment during January and February. Source: Listening to Displaced People Survey, 2014 and 2015. The ownership of livestock and consumer durables was reduced significantly as a consequence of the crisis. Table 7 demonstrates this by showing the Tropical Livestock Units (TLU) 12 owned prior to the crisis and in June 2014 as well as the percentage of ‘ yes ’ responses on a question whether a given asset was owned by the household. 13 The loss on livestock has been enormous particularly amongst IDPs and refugees who lost respectively more than 90 % and 75 % of their animals. 12 TLU is a common unit to describe livestock numbers of various species as a single figure that expresses the total amount of livestock present – irrespective of the specific composition. 13 This was a ‘ yes / no ’ question meaning that if 56 % of the Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "refugees and their host communities, this work aims to inform policies and operations (humanitarian actors, development partners, and government) to facilitate refugee integration and their lives, along with hosting communities. This report’s eight chapters aim to comprehensively provide an overview of SESRE results, with the final chapter highlighting policy implications. This Chapter 1 introduces the refugee situation in Ethiopia. Chapter 2 presents the sociodemographic profile of refugees and their hosts, including demographic characteristics, education, health, and living conditions. Chapter 3 provides an in-depth profile of jobs and livelihoods of refugees and their hosts, covering labor market outcomes for refugees inside and outside of camps and those of hosts living in the vicinity of refugees, as well as a subsection on labor market outcomes of youth. Chapter 4 dives deeper into refugees’ future aspirations and their feeling of personal control over their lives. Chapter 5 describes the welfare situation of refugees and their hosts by: (i) understanding different dimensions of welfare, such as monetary poverty, inequality, multidimensional poverty, food security, and shocks; and (ii) understanding determinants of welfare and estimating the cost to meet basic needs through a combination of assistance and some economic inclusion of refugees into national systems. Chapter", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location. Area stratifi- cation reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analyzed. ” Afrobarometer provides geocoded data for 6 rounds, which correspond to the 1991 – 2016 period, with the information on an individual ’ s ethnicity available from round 3 (corresponding to 2005 – 2006). We therefore restrict our analysis to the 2005 – 2016 period. The selection of countries is driven by data availability. Among the 33 countries with available Afrobarometer data, we exclude Botswana, Cape Verde, Lesotho, Madagascar, Mauritius, Sao Tome and Principe, South Africa, and Swaziland, for which no data is available on refugee camps or from the EPR-ER. We also exclude Sudan since the question on individual ethnicity is not asked in this country ’ s survey. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["EPR-ER"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In other contexts, deregistration signifies not the achievement of a durable solution but rather the end of state or international support (IDMC 2015). 33 The absence of a clear and operational approach to defining the ‘ end ’ of internal displacement may be one of the factors behind the continued overall increase in the global numbers of IDPs. Lack of clarity around when displacement ends also leaves room for political manipulation. Governments may find it politically expedient to artificially prolong IDP status by deterring returns or local integration, for example in Azerbaijan and Georgia to promote claims over territory (Beau 2003). In other contexts, national 28 UNHCR ’ s IDP data focus only on internally displaced populations to which it extends protection or assistance. IDMC coverage of IDP data is more expansive and in 2015 included additional data on: (a) 26 countries accounting for 4. 5 million IDPs including some significant IDP hosting countries (Turkey, India, Ethiopia, Bangladesh and Kenya); and (b) IDPs in countries where UNHCR is active who are not protected or assisted by the agency. In 2015, IDMC ’ s aggregate figure for conflict-induced internal displacement was 3. 3 million higher than UNHCR ’ s aggregate figure for IDPs protected or assisted by the agency. 29 IDMC ’ s 2016 report presents both data sets alongside each other. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["IDP data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Women ’ s Empowerment in Agriculture Index (WEAI) (Alkire et al. 2012) uses individual-level data, and the linked Gender Parity Index reflects inequalities across women and men ’ s deprivation scores within the same household. Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer. Espinoza-Delgado and Klasen (2018) create an individual-level MPI to understand differences in poverty between women and men in Nicaragua, finding similar overall incidence, but much higher intensity of poverty among women. Bessell (2015) and Pogge and Wisor (2016) explore deeply contextual gendered poverty measures and elucidate the ways that participatory consultations can inform the design and uses of gendered measures. Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa. Alkire, Ul Haq, and Alim (2019) use individual-level data alongside MPI data to expose gendered and intrahousehold differences among MPI poor and non-poor children. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["EU-SILC data sets"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The GLWD Coastal Wetlands area identified along the northern boundary of the project zone supports mangrove ecosystems that provide fish nursery habitat and protect the coastline from storm surge erosion. A coastal ecological assessment conducted during project preparation confirmed that proposed coastal access infrastructure would not directly encroach on the GLWD Coastal Wetlands perimeter. However, cumulative sedimentation risks from upstream construction activities were identified as a concern, prompting inclusion of erosion control and runoff management requirements in the ESMF.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The cross-dimensional poverty cut-off is defined as k = 50 %, with those deprived in half or more of the weighted indicators identified as multidimensionally poor. 4. Data Data on forcibly displaced populations are scarce, with many household surveys excluding refugees and IDPs from the sample framework. To ensure that MPI results are representative of these communities and that they can be disaggregated for comparative analysis, an initial review of possible data sets was conducted. Feasibility was determined based on the availability of sufficient sample sizes for forcibly displaced persons for quantitative analyses, as well as inclusion of many of the indicators (on health, education, living standards, etc.) 14 A household is deprived if the respondent reports feeling moderately or very unsafe when alone at home, walking alone after dark, or walking around during the day. In Sudan, the indicator on the ‘ feeling safe from crime and violence when at home ’ was not available, and the indicator only considers answers to the questions on safety when walking alone. 15 Unprotected dug well, unprotected spring, carts with tank, tanker-truck, surface water, or other are considered as unsafe water sources according to international guidelines. See https: / / washdata. org / monitoring / drinking-water. 16 Pit latrine without slab, bucket, hanging toilet, and no facility (open defecation) are considered as unimproved sanitation facilities according to international guidelines. See https: / / washdata. org / monitoring / sanitation. 17 According to the ILO definition, those who did not participate in employment in the last four weeks (and have no work to return to) are actively looking for work and are available to start, or those currently waiting to start work are classed as unemployed. See https: / / www. ilo. org / ilostat-files / Documents / description_UR_EN. pdf. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "to the Venezuelan migratory crisis, whereas the VenRepPS survey was conducted during the pandemic in 2020. These temporal inconsistencies result in varying sample composi- tions across surveys, diverging from the landscape we observe in 2022. Notably, forced migrants in the VenReps Kids survey migrated during the crisis but have since remained in the country for several years, potentially leading to disparities in household integration outcomes. IV GENERAL DESCRIPTIVE STATISTICS IV. A Key characteristics of adults Table 2 provides descriptive statistics for the adults in our study, encompassing the pri- mary caregiver, mother and father (if residing with the child), and the individual finan- cially responsible for the child (should they be different from the aforementioned per- sons). Typically, the roles of primary caregiver and financial provider are fulfilled by either the mother or the father. The table is organized into three panels for clarity: Panel A details key individual characteristics, Panel B outlines adults ’ access to services, and Panel C focuses on labor market characteristics. Within the table, columns (1) and (2) present average values for adults from Colombia and Venezuela, respectively, while the final column displays the results of mean difference tests between these two groups, with standard errors noted in brackets.", "output": {"entities": {"named_data": ["VenRepPS survey", "VenReps Kids survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Environmental Baseline: Wetland Mapping**\n\nField survey results confirmed that the GLWD Coastal Wetlands boundary as currently mapped by the national environment authority is consistent with the extent of active mangrove and brackish marsh ecosystems observed on the ground. Three small encroachments of the GLWD Coastal Wetlands boundary by agricultural clearings were identified and reported to the environment authority. The project's ESMP requires monitoring of the GLWD Coastal Wetlands boundary at six-month intervals to detect any further encroachment attributable to project-induced land use change.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Due to our research design and the complexity of introducing a third grouping who would play outgroup in the behavioral experiments regardless of partner identity, we exclude PRL from the main analyses presented here. 5 Data were also collected six months after the end of the training but this was heavily disrupted due to the outbreak of the COVID-19 pandemic. In Lebanon, this resulted in a change to the method of data collection (from in-person to telephone) and in Jordan, an end to data collection entirely. In Jordan, this had a more pronounced effect on the control group, due to the scheduling of data collection and implementation of restrictions in Jordan. Given these complexities, we do not present results from these analyses. 6 In addition, we attempted to collect information on the extent of social and economic interactions between hosts and refugees. At baseline, almost 95 % of respondents in both the treatment and control group reported such interactions. For this reason, we do not include this information in these analyses.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Table 5: Coverage of Published Data on Location, Accommodation and Demographics 2015 Population end- 2015 (millions) Urban or Rural Location Accommodatio n Age Sex Refugees and people in refugee-like situations 16. 1 85 % 83 % 58 % 68 % Other people of concern to UNHCR 47. 8 (including 37. 5 IDPs) 70 % 47 % 25 % 39 % UNHCR ’ s total population of concern 63. 994 73 % 56 % 33 % 46 % IDPs monitored by IDMC but not included in UNHCR's data 3. 3 0 % 0 % 0 % 0 % Source: UNHCR Global Trends 2015 Note: Other people of concern to UNHCR include asylum-seekers, IDPs and people in IDP-like situations protected or assisted by UNHCR, stateless persons, and ‘ other ’. Overall robustness of current data The robustness of data is difficult to estimate. A review of data collection and compilation methodologies shows broad variations in terms of the accuracy and reliability of the global estimates of forced displacement that are widely used. Headline figures on forced displacement are significant in shaping public opinion and are critical for sound decision making, both to inform the allocation of resources and to design effective humanitarian and development responses. However, the available estimates are potentially misleading and should not be referred to without appropriate caveats and qualifiers. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This is used to analyze the impact of changes in conﬂict and luminosity patterns on return in an aggregate manner using ordinary least squares (OLS) and Poisson quasi maximum likelihood (PQML) count models. Second, we use the detailed information on refugee characteristics provided by ProGres together with conditions in countries of asylum, 4 to analyze individual return decisions. Given that we have arrival and- where applicable- return dates for each refugee, we can study their likelihood of return for a given month using both discrete 4Since the conditions in countries of asylum are only captured for a small sample of registered refugees in Lebanon and Jordan, we approximate host country conditions with district averages for the full sample. 3 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "measures of the adequacy of calorific intake for a survey sample or before the CV needed for the FAO\n\n(2012b) present a useful list of various HCES in low and middle income countries highlighting substantial differences in their design across a select number of\n\nHCES in all their relevant dimensions (requiring a 22-page form to cover all variations). Drawing on\n\nConsequently, HCES with different methods of data capture (diary versus recall questionnaires), levels of\n\ncomparable. The survey experiment we use in this paper was designed in part to assess the extent to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["HCES"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Flood maps generated by the disaster risk reduction directorate were integrated into the project's site assessment tool to automate flood-risk flagging for proposed subproject locations. When a proposed site's coordinates fall within the 50-year inundation boundary on the flood maps, the tool generates an automatic alert requiring sign-off from the PIU's environmental specialist before the subproject can proceed to the design phase. Since rollout of the tool in June, 17 alerts have been generated, of which 12 were resolved through minor site adjustments and 5 required full site relocation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Average share of primary school age children in primary education Average share of secondary school age children in secondary education Percent Figure D.3: Share of school-age children in education per household Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 Eritrean Somali South Sudanese Addis refugees Availability of education document Able to verify education document Percent Figure D.2: Refugees’ education document Source: World Bank Staff based on SESRE 2023. Annexes 101 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Primary school (7 to 14 years) Boys Primary school (7 to 14 years) Girls Secondary school (15 to 18 years) Boys Secondary school (15 to 18 years) Girls Percent Figure D.4: School-age children currently attending school by gender Source: World Bank Staff based on SESRE 2023. Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In Camp Addis Ababa Total Average household expenditure on education (children in school) Average household expenditure on education per child (school age (4 to 18 years)) - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18.000 Figure D.6: Average annual household education expenditure", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The DFIL confirms that the project's Designated Account will be held at the National Bank and operated jointly by the PIU coordinator and the Ministry of Finance's project accountant. Withdrawals from the DA exceeding USD 500,000 require dual authorization and supporting documentation submitted through the Bank's Client Connection system. The DFIL also specifies that the project may request a one-time advance from the Bank prior to project effectiveness under the project preparation advance mechanism, subject to the conditions set out in the relevant Board approval.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 25 of 51 higher followed by the “ Professionals ” and “ Skilled agricultural, forestry and fishery workers ”. On the contrary, “ Managers ”, “ Technicians ”, “ Craft and related trades workers ” and “ Plant and machine operators and assemblers ” are the types of occupations with a lower incidence of NSE in the region. Figure 16: Prevalence of NSE among salaried employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Figure 17: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys. As in the case of Latin American countries, the observed prevalence of NSE across types of occupations suggests a strong heterogeneity across non-standard employees. Actually, as we will analyze in detail in next sections, the productivity and task profile of workers in the categories of “ Professionals ” and “ Elementary workers ” is very different, even though both types of occupations are characterized by a higher prevalence of non-standard employment arrangements. 0 % 5 % 10 % 15 % 20 % 25 % 30 % Russia Georgia Kyrgyz Republic Turkey Armenia Albania Moldova starting point ending point 0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % 40 % 45 % 50 % Managers Professionals Technicians Clerical Support Workers Services and Sales Workers Skilled", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "changes) and reducing the number of meals eaten daily (negative food intake). Borrowing food or cash from friends and relatives and purchasing food on credit second represent the second and third most common coping strategies. Refugees in camps and in Addis Ababa are more likely to rely on these coping strategies than their hosts (Figure 5.12). The results further show that both refugee and host households do not engage in adverse coping strategies, such as the sale of (productive) assets that would make them vulnerable to poverty. This could be because either they do not have enough assets to sell or because the strategies they utilize are enough to cope with the effects of shocks. 5.2 Determinants of welfare The poverty profile in this section compares the characteristics of poor compared to non-poor people. The previous section presents refugees’ and host communities’ poverty and welfare patterns. This section substantiates the earlier discussions on poverty levels by describing the demographic, geographic, and socioeconomic characteristics by expenditure quintiles for each refuge and host group separately, along with the poverty headcount rate across grouping variables (see Annex D, Table D.11). The descriptive statistics are substantiated by results from a regression analysis examining correlates", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "children (under age 15). The data further show that the poorest refugees and hosts are more likely to have married and older household heads compared with the richest counterparts (Figure 5.14). Richest in-camp and out of camp refugee households are more likely to have female- headed households compared to the poorest. There is no difference in the gender of the household head among the poorest and richest host households (Annex D, Table D.11). Location is an essential determinant of monetary poverty. Monetary poverty is highest among South Sudanese refugees (89 percent) (Figure 5.15). There is a significant difference in poverty rates between in-camp refugees and their hosts, the gap being the highest in the Eritrean domain. As discussed in Chapter 2, refugee households have larger household sizes than hosts, except in Addis Ababa. In light of the discussion above, the highest poverty incidence among South Sudanese refugees could be associated with their high dependency ratio and high number of female-headed households. In-camp refugees working inside the camp tend to exhibit lower poverty incidence (81 percent) than those working outside the camp (88 percent). The poor tend to live in households headed by individuals with limited education. This trend is evident", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Geospatial data compiled from satellite imagery, field GPS surveys, and administrative boundary shapefiles were integrated into the targeting framework for rural livelihood subgrants. The geospatial data were processed using open-source tools and validated against paper maps held by district land offices. Where discrepancies between the geospatial data and official cadastral records were identified, the cadastral records were treated as authoritative and the geospatial data updated accordingly.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 woman becomes a widow, it is likely that she does not marry again given a lack of suitable partners of similar age. 8 Finally, we calculate a demographic conflict proxy that captures the extent of deaths across age groups (SEXRATIO). The data on sex ratio comes from two secondary sources: the 1991 Census (which is matched with the 1992 RDHS) and the 2002 Census (which is matched with the 2000 RDHS and the 2005 RDHS). Women are assigned the average sex ratio (defined as the ratio of males to females) in the cohort of their potential partners in a given province, taking into account the typical age difference between spouses in Rwanda. More precisely, sex ratios in a woman ’ s five-year age group, one younger age group and two older age groups were averaged. These provincial, age group-specific sex ratios are the closest approximation to the local marriage market possible with publicly available data. Still, sex ratios derived from census data overestimate the number of men potentially available on the marriage market, as tens of thousands of male perpetrators of genocide were in jail (Ministry of Finance and Economic Planning et al. 2003). It is important to note that our analysis is based on a sample of survivors. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "environment. Any issues discovered during these monitoring visits were brought to the attention of service providers and resolved swiftly in conjunction with the project coordination team at MoGD. Eligibility: The EPAG program was targeted to young women who: i) were age 16 to 27, ii) possessed basic literacy and numeracy skills, iii) were not enrolled in school within several months prior of the program initiation, and iv) resided in one of nine target communities in and around Monrovia. 6 These eligibility criteria stemmed from the project's objectives to reach young women at an early enough age to significantly improve the trajectory of their working years, to focus on girls who already had the basic literacy and numeracy skills needed to succeed in the labor market, and to avoid incentivizing applicants to drop out of school. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The data shows that cohorts that were of school age during the Indonesian occupation achieved higher level of education than older cohorts (i. e. those born in the 1970s compared to those born in the 1960s or before) testifying for an increasing trend as expected. The figure shows that despite the increasing trend a large fraction of individuals have low education levels. Interestingly, all curves start to drop after the 1987 cohort. This decreasing trend is observed among individuals aged 20 or younger in 2007 and provides evidence of a mismatch between the grade attended and the grade that they should have achieved at their age. 4 This is caused by a persistent sluggishness in grade achievement due to the high level of delayed entry to school and high rates of repetition. 5 The impact of the conflict in its different phases and the subsequent reconstruction efforts on schooling levels of children in Timor Leste is therefore unclear. The early years of violence coincided with an education for all policy in which quantity was preferred to quality. In addition, the 1999 violence that followed the withdrawal of Indonesian troops led to the destruction of schools and the removal of children from school. The reconstruction program implemented after 1999 tried to counteract this destruction, and achieved fast progress. However, the education sector was still in very poor shape. In the next section, we investigate in more detail the effects of the conflict on educational outcomes of boys and girls in Timor Leste. 4. Identification strategy and data description 4 Those born in 1992 are 15 in 2007. So they might have at most completed grade 9 and this justifies part of the drop in the curves as the grade completed is right censored. 5 The high levels of school delay are also confirmed by the figures on gross and net enrolment ratios calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["TLSS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "using purchasing power parity rates. It comes from the World Development Indicators\n\nThe governance indicators come from the International Country Risk Guide (ICRG)\n\nData on the Gini coefficient were drawn from a more complete World Bank source\n\n100 km of ice-free coast) come from Gallup et al. (1999). Data on total forest area (km [2] ),\n\nprecipitation come from WDI (2010). Latitude (in absolute value), mean elevation (meters", "output": {"entities": {"named_data": ["World Development Indicators", "International Country Risk Guide"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These people are then expected to return to their place of origin once the conflict is over and governments are typically over optimistic about the duration of civil conflicts and about return of IDPs. In some cases, governments also have an interest in denying the very existence of IDPs for political purposes. Therefore, little time is spent in surveying IDPs or trying to find durable solutions in the place where they migrated. Moreover, national censuses are usually conducted every ten years and statistical agencies have little incentives to revise censuses, master samples and sample survey structure for situations that are perceived as short ‐ term. In most cases, new surveys are suspended or carried out under the pre ‐ crisis frameworks and, in either case, information on IDPs is not collected or poorly collected. This leaves specialized government agencies or international organizations in charge of IDP statistics (and care). However, unlike refugees, the IDPs do not benefit from a specialized international agency such as the UNHCR. IDP assistance is currently provided by a multitude of organizations including ministries of interior, specialized government agencies, the UNHCR, the International Organization for Migration (IOM), the UN Office for Humanitarian Affairs (UN ‐ OCHA), specialized NGOs and others. Some of these organizations collect information on IDPs and make this information public while others collect information that is not published and others do not collect information and focus on providing assistance. Most data collected are for the simple purpose of counting IDPs and do not include individual or", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national censuses"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "There is a clear increase in the entry of new foreign ﬁrms which may be driven by refugees ’ enterpreneurship. Cheaper low-skilled labor may have helped all businesses cutting costs. Balkan and Tumen (2016) had also found a decline in prices and attribute their ﬁnding to lower labor costs, which may also be one of the mechanisms driving our results. Gross proﬁts and sales also appear to have gone up, which would be consistent with an increase in demand. As noted by Maystadt and Verwimp (2014), heterogeneous eﬀects on speciﬁc subgroups of the native population should be expected from refugee crises. In case of the Syrian refugee crisis in Turkey, the business activity in hosting region appears to have beneﬁted. For a complete picture of the eﬀects of the Syrian refugee crisis on local economies in Turkey, further research will be needed on market activity, health and longer term eﬀects. More speciﬁcally for the line of research this study focused on, further analysis using micro-level ﬁrm data would be needed to understand how ﬁrms adjust their activity, 24 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "From 23 to 25 May 2014, 137 interviews were conducted with the Shalozan Tangi displaced\npopulation. The Return Intention Survey (RIS) was conducted using a specific tool/\nquestionnaire developed in 2013 for previous consultations and slightly adapted to the\ncurrent situation.\n\nFurthermore, during an inter cluster mission from 26 to 28 May, protection cluster\nrepresentative conducted additional consultations with populations displaced from\nShalozan Tangi (12 male key informants) and in areas of return in Shalozan Tangi (two male\nkey informants from amongst the population already returned to the area of origin in 2012).\n\n**III.** **Profile of respondents**\n\nOut of the 137 respondents, one was women (a female headed household) and 126 men. 69\n% of the respondents were heads of households (including one female respondent) and also\nincluded three community leaders.", "output": {"entities": {"named_data": ["Return Intention Survey (RIS)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The LFS question on usual occupation was re-coded using the International Standard Classification of Occupations (ISCO-08) at the two-digit level for comparability with other countries' labor force surveys. Occupational coding was conducted by the national statistics office's coding team using an automated coding software trained on prior LFS data. For unusual occupational descriptions, manual coding was applied after a verification call with the interviewer. Approximately 4.2 percent of the LFS occupation responses required manual review.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In contrast, districts of origin are distributed widely across the country. This reﬂects the fact that much work migration is from remote rural areas to towns and cities. The main characteristics of work migrants are reported in Table 1, together with those of non- migrant adult males. We see that work migrants are on average younger and better educated. The census contains detailed information about ethnicity, language, and religion. In the Nepal census, the term ‘ ethnicity ’ is used to capture a hodgepodge of caste and tribal distinctions. The census distinguishes up to 103 ethnic categories. Most of these categories only account for a tiny proportion of the total population. In terms of the total adult population, the most common ethnic categories are Chhetri, Brahmin, and Newar who, together, account for 35 % of 13 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Nepal census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "PREPAREDNESS\nEMERGENCY\nTRANSITION\nLONG-TERM INCLUSION\nOTHER ACTIVITIES (PUBLIC HEALTH, NUTRITION, FOOD SECURITY)\nReview national health plans and policies \nand advocate that activities for refugees \nare included and equitably provided as \nnationals.\nMeasles, polio vaccination on arrival and vitamin A \nsupplementation.\nExpand vaccination to national immunization schedule (EPI) and supplementary immunization activities (SIA).\nAdditional support for vaccines and human resources may be needed and may be sought as per the GAVI, Fragility, \nEmergencies, and Refugees Policy.\nUNICEF may support cold chain capacity, training of health workers and vaccine related activities\nAs above\nEssential primary health care.\nIntegrated primary health services.\nGradually integrate refugees into national health services, support services if need be with human resources for \nhealth, medications, medical supplies and equipment.\nEngage other UN agencies (UNICEF, UNFPA and WHO) to support efforts to include refugees in national program/\nsystems.\nIf refugee standalone facilities, aim for accreditation and inclusion in national system.\nEngage supervision from Ministry of Health especially for malaria, TB, HIV, nutrition, reproductive health and \nimmunization.\nUse national clinical management protocols.\nInternational (and in exceptional situations, local) procurement of medicines in line with UNHCR policy. Review of \nand support to MoH procurement protocols/systems and integrate where applicable (context-specific and based on \nquality assurance assessment).\n8\nPUBLIC HEALTH AND NUTRITION", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Woreda Nearest to Zone Share of individuals (%) Trade Industry Other service Agriculture Trade Industry Other service Agriculture Low accessibility Medium accessibility High accessibility Share of individuals (%) 0 20 40 60 80 100 Markets and Opportunities 61 6.2 Effects of local factors on refugees’ labor market outcomes This section estimates the effect of local factors on refugees’ employment outcomes. It shows how local factors matter for employment opportunities by looking at refugees aged 18 to 64 not currently studying. More specifically, it sheds light on the importance of accessible locations and proximity to economic and resource hubs for refugees to perform better in local labor markets and to access sustainable economic opportunities. The analysis uses household and individual information from SESRE data and geospatial information. The estimation applies logistic regressions to predict the effects of the various indicators on the probability of being employed and working in different sectors of employment (see Annex D, Table D.14). Annex D, Table D.15 shows the average marginal effects of the explanatory variables. We discuss the results using predicted marginal probabilities of being employed based on various local factors. The local labor market structure affects49 the possibility of refugees finding jobs. Consistent with", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, the program itself was designed to fit within the young women ’ s lives, specifically with regard to their employment, education, and childcare duties. Training schedules were flexible, to allow participants to continue with their pre-existing educational and income-generating activities (and many participants did report continuing with these activities) and free childcare was provided. Hence, at least for these three dimensions of life, there would not have been much incentive to change one ’ s behavior prior to starting the program. While these explanations do not erase concerns about anticipatory behavior, they at least mitigate them. The generalizability of these results is also limited by the differences between the EPAG target group and the population of young women in Liberia. First, a high proportion of adolescent girls and young women in Liberia are illiterate or have very low literacy, while the participants recruited for the EPAG 17 Gender-based violence questions were administered in line with international ethical protocols, with additional informed consent procedures and referral mechanisms as needed. 18 See Ashenfelter (1978). 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Section A.1, [9] we verify that the I2D2 database generates   global patterns that are broadly consistent with patterns observed when using other   global databases such as the _World Development Indicators_ (WDI) database of the World   Bank. More precisely, we find that our samples are globally representative in terms of   per capita incomes, age structure, education, and self-employment.\n\n10For example, we find that log mean earnings in I2D2 are strongly correlated across country-years with log per capita GDP in WDI. We show that wage variance is in line with what is expected.\n\nIn our case, I2D2 calculates their wage as the amount of salary taken from the business; and (iii) public sector workers because they receive nonwage compensation and their wages may not reflect the full payment for their labor. 23Results hold if we use 12 or 14 years or information about high school graduation (Web Appx.\n\nIf   34Since most low-income countries are in sub-Saharan Africa and to ensure that our results are not driven by a lower quality of surveys from the region, we verify that the patterns that we obtain for subSaharan Africa in I2D2 are consistent with the patterns observed when using other global databases such as the _World Development Indicators_ database of the World Bank (see Web Appx. Section A.1).\n\nWe set the schooling transition probabilities _πs_ ( _·_ ) to match the schooling  distribution in developed and developing economies (I2D2). [49] We assume agents may   only accumulate up to 25 years of schooling as some countries only record schooling   up to 25.", "output": {"entities": {"named_data": ["I2D2 database", "I2D2"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Both the EHCVM sample and the COVID-19 phone survey include each of the eight FIES questions, allowing us to get a measure of food insecurity for both periods.\n\n_Notes:_ These figures come from Google's COVID-19 Community Mobility Data. With the same kind of aggregated and anonymized data used in Google Maps, these data show changes for each day in time spent at specific types of places relative to the baseline period.\n\nBaqueano, F., Christensen, C., Ajewole, K. and Backman, J. (2020). International food security\n\n\nassessment, 2020-30. _GFA-31,_ _U.S._ _Department_ _of_ _Agriculture,_ _Economic_ _Research_ _Service_ .\n\n\nBarrero, J., Bloom, N. and Davis, S. (2020). Covid-19 is also a reallocation shock. _NBER_ _Working_\n\nThe Kinshasa Commuter Travel Survey (CTS), conducted by the Japan International Cooperation Agency (JICA, 2018), reports information on local commuting patterns as well as socio-economic attributes at both household (income/expenditure, number of vehicles, members, etc.) and individual (age/sex, work/school type and place, industrial category, income, vehicle availability, etc.) levels.\n\nBuilding on the resulting wet network, we then ran travel simulations for a total of 8,866 commuters [13] in Kinshasa using the origin and destination location information provided in the JICA commuter travel survey under both dry and wet conditions for five flood return periods.", "output": {"entities": {"named_data": ["EHCVM", "Google's COVID-19 Community Mobility Data", "Kinshasa Commuter Travel Survey (CTS)", "JICA commuter travel survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Table 4: Financial Management Capacity Assessment**\n\n| Dimension | Rating | Key Weakness | Mitigation |\n|---|---|---|---|\n| Budgeting | Moderate | Delays in budget releases | Contingency drawdown procedure |\n| Accounting | Moderate | Partial SIGAF rollout | PIU parallel records |\n| Internal Controls | Substantial | Limited segregation of duties | Additional FM staff |\n| Reporting | Moderate | IFR delays in prior project | Monthly reporting schedule |\n| External Audit | Low | N/A | Competitive audit firm selection |\n\nThe overall financial management risk is rated **Moderate** following agreed mitigation measures. SIGAF will be fully operational within six months of project effectiveness.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "35 Inputs: 1. Household Survey with consumption or income welfare aggregates 2. Project data / Other survey data without welfare aggregates 3. Set of harmonized common variables in both surveys Outputs: 1. Set of imputed welfare variables for project data / other survey for each household in the data 2. Imputed welfare variables can be used for poverty, distributional analysis (quintiles or more), profiling of the poor or group of interest Models: 1. Ordinary Least Squares (OLS) 2. Probit 3. Multiple Imputation (MI) Table 18. Model Specification Variables Demographic Share of children, share of adults, share of adults squared and share of old (omitted) Characteristics of head Age, gender, and level of education Interactions with urban dummy variable Level of education of the head, age of the head Geography Dummies for regions at NUTS 1 level (12 regions) Interactions with Geography Level of education of the head, age of the head interacted with regions at NUTS 1 level (12 regions) and urban-rural division 1. Validation and Robustness Check Figure 7. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 10 of 51 Source: Own calculations based on Household surveys Figure 4: Prevalence of Temporary employment among salaried employees (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the evolution of non-standard employment according to their age profile, we found a slight increase in the share of the older groups (Figure 5 and 6). This slight aging in the profile of non- standard workers is observed in both part-time and temporary employment. This finding is striking since, in principle, it was expected that the non-standard modalities of employment would show an increasing participation of the younger groups of the population. However, this change in the age composition of NSE is consistent with the age profile observed in total employment. In fact, the 0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % Argentina Brazil Peru Dominican Republic El Salvador Starting point 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % Argentina Brazil Chile Mexico El Salvador Starting Point Ending Point Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "28 In section three of this study, we could not find taxes and regulations among the potential barriers to sales and employment growth that clearly separated the ups from the downs in the formal sec- tor. We also showed that the large sector has become a smaller share of the economy rather than a bigger share due to self-employment growth and due to the process of de-industrialisation. In substance, the CIS-7 may fit the developing countries scenario better than the transitional countries scenario in the Schneider and Klinglmair (2004) regressions. If this is the case, we should expect that the growth of the informal sector negatively contributes to growth. The data we have do not contradict this hypothesis given that the shadow economy has been on the rise during the recession period and has stabilised during the growth period. This digression on informality suggests that self-employment may partially act as an host to in- formal and illegal activities especially during recessions where self-employment may constitute a refuge for small informal and illegal businesses. Self-employment is also evidently a sector of ne- cessity for those who wish to keep health and pension records alive and do not want to formally register anywhere else. In times of growth this sector may instead function as a first step to for- mality, an entry gate to the formal sector given its lower entry barriers and taxes.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in these papers are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http: / / econ. worldbank. org. * Contact author: Håvard Hegre; CSCW, PRIO, Hausmanns gate 7, N-0187 Oslo, Norway. Email: hhegre @ prio. no. Thanks to Joachim Carlsen for writing a program to create the dataset used in the analysis, to Siri Aas Rustad for research assistance, and to Kristian Gleditsch, Anke Hoeffler, Pat Regan, Mike Ward, Nils Weidmann and Jen Ziemke for valuable comments. The research has been funded by the Research Council of Norway, grant no. 163115 / V10. WPS4243 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "displaced children and adolescents residing in host communities, rather than in refugee camps. Particularly, VenRePs-Kids collects data on 2, 556 households including 1, 338 Colombian and 918 Venezuelan households, respectively. The study collects rich and comprehensive data on children ’ s and adolescent ’ s development including anthropometric measures, vo- cabulary ability tests, and socio-emotional and mental health assessments. It also collects data on risk behaviors, time use, social integration measures, prosocial behaviors, and parents and caregivers sociodemographics, among other dimensions. 2 The study includes Colombian children and adolescents as the comparison group to high- light the developmental differences of Venezuelan forcibly displaced children. This choice stems from the fact that comparing Venezuelan children in Colombia with their counter- parts remaining in Venezuela is impractical due to the latter ’ s exposure to a severe eco- nomic and humanitarian crisis, marked by limited access to services and food. This envi- ronment severely hampers their potential for normal human development. Additionally, many Venezuelan children and adolescents have spent more of their lives in Colombia than in Venezuela. Therefore, Colombian children and adolescents serve as the most ap- propriate benchmark for assessing the developmental gaps of their Venezuelan peers. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["VenRePs-Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 neighboring community, (iii) residing elsewhere in the Kagera Region and (iv) residing outside the Kagera Region. Table 3 shows that the basic needs poverty rate declined 8 percentage points in the full sample. This figure masks significant differences in changes between subgroups based on migration. For those found residing in the baseline community, poverty rates dropped by 4 percentage points, but rates dropped by 11, 13 and 23 percentage points for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. A similar pattern is found for consumption per capita. While consumption per capita grew by $ 65 overall, it grew by only $ 30 for those found in the same community and by $ 65, $ 100 and $ 287 for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. Dividing consumption into food and non-food components gives the same result. The most basic assessment of welfare changes would have been wrong if we had focused only on individuals still residing in the community, a practice found in many panel data surveys. We would have underestimated the growth in consumption by half of its true increase. The differences in consumption changes of groups in Table 3 are statistically significant, as shown in Table 4. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This paper ’ s scope of analysis includes the country as a whole using a nationally representative survey. While regional case studies may reveal salient stresses on public services and job displacement, nationally, there is no significant impact. Over the period of 2009 to 2013, the poverty rates of host community households have stayed relatively stable near the Syrian border; despite the high poverty rates experienced among the recent migrants. By country of origin, the displacement of Syrians is one of the largest in recent history. As a result of the civil war that began in 2011, Syrians started to leave their homes and look for safety in neighboring countries across the region. By November 2015, about 4. 3 million Syrians were seeking refuge in primarily Turkey, Lebanon, Jordan, Iraq, and the Arab Republic of Egypt. 3 The only other time in the last half century that the world experienced a larger group of refugees from a single country is the case of Afghan refugees during the 1980s to 1990s. Refugee displacements of this size are rare. Consequently, they are not well studied and their impacts are not well understood. Moreover, the case of Afghan refugees in Pakistan is different, since they were stigmatized to a larger extent, which limited their movement in Pakistan.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["nationally representative survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Refugees Hosts Refugees Age group <15 39% 46% 54% 50% 47% 56% 15 to 24 19% 19% 17% 22% 21% 21% 25 to 44 26% 25% 21% 17% 21% 16% 45 to 64 13% 8% 7% 9% 8% 5% >=65 4% 2% 2% 2% 3% 1% Gender Male 48% 51% 50% 49% 48% 46% Female 52% 49% 50% 51% 52% 54% Marital Status Never Married 22% 27% 21% 30% 22% 25% Married 62% 56% 69% 52% 66% 57% Other 16% 17% 10% 18% 12% 18% Household characteristics Household size 4.3 4.7 5.7 6.0 5.4 6.5 Dependency ratio 0.8 1.1 1.5 1.4 1.1 1.7 Female-headed 38% 43% 43% 61% 51% 84% Head’s age 45.4 39.3 41.0 43.6 40.7 37.1 Source: World Bank Staff based on SESRE 2023. Annexes 98 Table D.2: Education outcomes by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Education level No education 38% 49% 52% 56% 31% 43% Incomplete primary 20% 30% 13% 20% 24% 27% Complete primary 20% 17% 21% 18% 27% 25% Complete secondary 12% 4% 6% 4% 10% 3% Complete post-secondary 10% 0% 9% 2% 8% 2% Education level (youth -15 to 24 years) No education 4% 17% 20% 18%", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(b) Disaggregation and geo-mapping of data by location (current location and location of habitual residence; urban, peri-urban or rural location), accommodation (organized camp versus non- camp), and demographics (age and sex); (c) Expanded coverage of data collection exercises to include all areas of affected countries (security permitting); (d) Improved coverage and detailed data on displaced populations living outside of organized camps; (e) Improved coverage of ‘ flows ’, i. e. new displacement, durable solutions (returns, integration, resettlement), births, deaths, and in the case of IDPs, the numbers that flee across international borders becoming refugees; (f) Systematic data collection beginning from the earliest moment following displacement, following up as populations disperse, and continuing until sustainable / durable solutions have been achieved; and (g) Better aggregation, analysis and presentation of forced displacement data currently compiled separately by UNHCR, IOM, IDMC and UNRWA. Additional efforts are required to address the gaps in the data required for development policy and planning. These data are critical for informing the design of development policies and assistance programs. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["forced displacement data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Quality Metrics A standard Measure in the assessment of a sampling designs is the Root Mean Squared Error (RMSE) and calculated as: 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = ∑ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑠𝑠𝑠𝑠𝑠𝑠 1000 𝑠𝑠𝑠𝑠𝑠𝑠 = 1 1000 = ൥ 1 1000 × ඥ (𝑌𝑌 ෠ − 𝑌𝑌) 2 𝑌𝑌 ൩ × 100 Expressed here as percentage deviation from the population mean Y and calculated for each parameter of interest. Equation.. is only the empirical representation though and a result of rearranging the definition of the Mean squared Error, 𝑀𝑀𝑀𝑀𝑀𝑀൫𝑌𝑌 ෠ ൯ = 𝐸𝐸൫𝑌𝑌 ෠ − 𝑌𝑌൯ 2 = 𝐸𝐸 ൣ ൫𝑌𝑌 ෠ − 𝑌𝑌෨൯ + ൫𝑌𝑌෨ − 𝑌𝑌൯൧ 2 = 𝐸𝐸 (𝑌𝑌 ෠ − 𝑌𝑌෨) 2 + 2𝐸𝐸൫𝑌𝑌 ෠ − 𝑌𝑌෨൯൫𝑌𝑌෨ − 𝑌𝑌൯ + ൫𝑌𝑌෨ − 𝑌𝑌൯ 2 And decomposing it into 𝑀𝑀𝑀𝑀𝑀𝑀൫𝑌𝑌 ෠ ൯ = 𝑉𝑉𝑉𝑉𝑉𝑉൫𝑌𝑌 ෠ ൯ + 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 (𝑌𝑌 ෠) with 𝑌𝑌 ෠, 𝑌𝑌෨ and 𝑌𝑌 being the estimate from the sample, the mean of this estimate and the true value in the population respectively. Var is the corresponding variance, and Bias the resulting bias component, which is defined as: 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵 ൫𝑌𝑌෨൯ = 𝑌𝑌෨ − 𝑌𝑌 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A growing number of low and middle-income countries have tried to improve the law enforcement response to gender-based violence by training professionals, reorganizing police and courts, and trying to provide a more comprehensive response to survivors. Evidence of effectiveness is relatively limited; most well-evaluated initiatives come from high-income countries, and the lessons learned may not be applicable to developing countries. Evaluations of law enforcement reforms in low and middle income countries have typically been limited to case study approaches drawing from police records (notorious for under-reporting), qualitative perspectives from key informant interviews, intermediate outcomes such as changes in attitudes and knowledge among police and judges, and interviews with small numbers of women who have sought legal redress. Population-based data collection, control groups, or follow-up among more than a handful of survivors are rare. Nonetheless, the following initiatives illustrate the types of efforts that have produced important lessons learned. Training personnel in the police and judiciary and other parts of the justice system Throughout the world, organizations have launched efforts to improve the knowledge, attitudes, and practices of justice sector personnel regarding gender-based violence. Some law enforcement institutions organize training internally, as did South Africa following passage of the 1998 Domestic Violence Act (Usdin et al., 2000).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["police records"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Having seen that mandatory e-auctions significantly increased land prices, we estimate (1) using data from   the entire 2015-22 period as an additional robustness check and using data from 2018/19 when e-auctions\n\nthe nearest main road, grain elevator, and city. A land use map, constructed based on remotely sensed data\n\nAdding data on (offline and online) auctions conducted in the 2015 to 2020 period serves not only as a", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["data from", "remotely sensed data", "data on (offline and online) auctions"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "in primary compared to their hosts, refugees struggle to attend secondary education. Secondary GER22 and NER23 for refugees are almost half those of hosts. For example, only 23 percent of secondary school-aged refugee children and youth attend secondary school. This share is higher (41 percent) for hosts. Refugee Secondary NER also is very high compared to the national 5 percent in 2021/22 (MoE, 2022). By country 0 20 40 60 80 100 Eritrean Somali South Sudanese Addis refugees South Sudanese Addis refugees 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali Incomplete primary Complete primary Complete secondary Complete post-secondary Percent Percent Figure 2.9: Refugees’ education outside of Ethiopia (18 years and above) Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Attending school Primary school Secondary school 0 10 20 30 40 50 60 70 80 90 100 Primary school (7 to 14 years) Secondary school (15 to 18 years) Percent Percent Figure 2.10: Children currently attending school Source: World Bank Staff based on SESRE", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "fact that they are time consuming and therefore expensive to collect. At least three other less resource\nintensive alternatives to the FAO approach have been suggested to derive hunger numbers:\n\nanthropometric data, self-assessments, and direct use of HCES.\n\nquicker and cheaper to collect than full HCES efforts. However, how well they correlate with other\n\nThe third approach, and the one that we concentrate on here, is to use HCES to derive hunger statistics\n\nHCES are positioned between the single subjective hunger question and the intensive 24-hour recall.", "output": {"entities": {"named_data": ["HCES", "direct use of HCES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Section 6 presents the differing views of IDPs, refugees and returnees on what happened during the crisis and prospects for peace. Section 7 concludes the paper. 2. The Listening to Displaced People Survey The Listening to Displaced People Survey (LDPS) combines a baseline face-to-face survey with mobile phone follow-up interviews. During the baseline survey respondents were identified and information on household and respondent characteristics was collected. Once the baseline interview was completed, respondents were given a mobile phone and started to receive, at monthly intervals, phone interviews from a call center in Bamako. During these phone interviews structured questions were asked about welfare of the household. Phone interviews are standard practice in developed countries and they are increasingly being used in less developed countries, as the coverage of cell phone networks expands. Not only do these kinds of surveys allow for low cost, high frequency representative data collection (Hoogeveen et Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(1) GDP growth, (2) agricultural GDP value added, (3) industrial GDP value added, and (4) poverty\n\ngrowth, (2) agricultural GDP value added (%), (3) industrial GDP value added (%), and (4) poverty\n\n<!-- PAGE 28 -->\n###### **_References_**\n\nAlley, William: The Palmer Drought Severity Index: Limitations and Assumptions, Journal of Climate and Applied\nMeteorology, 1984, 23,1100 - 1109.\n\n16 For this research, the model was updated by the Institute of Water Modeling (IWM) with river alignments of GBM basins using available physical maps of India, Nepal, Tibet; and sub catchments of the GBM basins were redelineated using the Digital Elevation Model (DEM) based on SRTM-version 3. The model has been first calibrated using round the year hydrological feature of 2005 and validated for two subsequent years 2006 and 2007. Then the model was further updated using most recent data of last hydrological year 2009. 17 Bathymetries of the rivers have been updated incorporating the available latest cross-sections and bathymetries of floodplain routing channels are taken from national land terrain model developed in FAP (Flood Action Plan) 19.\n\nchannels. It is based on the existing national DEM for Bangladesh and model simulations", "output": {"entities": {"named_data": ["Palmer Drought Severity Index"], "descriptive_data": [], "vague_data": ["agricultural GDP value added"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We use the sum of conflict events occurring in the historic homeland of ethnic group e in the previous year t − 1, denoted as Conflictet − 1, and we use the mean distance between the historic homeland of ethnic group e and the border of country d to predict the number of refugees of a certain ethnic group e moving from country o to d at time t. 22 In order to be consistent with EPR-ER data construction, we restrict our analysis to all origin – destination country pairs that are at a maximum distance ≤ 950 km from each other. Predicted numbers of refugees are then transformed into predicted shares for the three largest groups to follow the logic used by the EPR-ER dataset. We then plug in these predicted shares in the following way: X \\PredictedRefcet = Refocdt ∗ \\Shareodet. (6) The predicted shares of refugees per camp c are then used to compute (as documented above) refugee diversity indices to be used as instrumental variables. The first-stage equations corresponding to the 2SLS-equivalent of Equation 1 can be expressed as 20We conduct a robustness check on Equation 5, replacing the dyadic origin – destination fixed effects with separate origin and destination fixed effects (Section 5. 4). 21More information on LEDA can be found in Appendix A. 1. 22The construction of the IV follows a long tradition in using the gravity model to predict bilateral migration flows (Ravenstein, 1985, 1989; Crozet, 2004; Mayda, 2010; Garcia et al., 2015; Beine et al., 2016). In our analysis, a major difference is that we have an additional dimension: the ethnic group e. 20 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["EPR-ER data", "EPR-ER dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This means that night lights data can be used to estimate economic activity at levels that are not usually captured in national accounts, such as subnational administrative units (provinces, districts, counties, cities, etc.) or regions not coinciding with national borders (coastal vs inland regions, connected vs unconnected regions, etc.). For instance, Henderson, Storeygard and Weil (henceforth HSW) estimate, counter to intuition, that coastal areas in Sub-Saharan Africa are growing slower than the hinterland.\n\nData on night lights are provided by the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). The DMSP satellites circle the earth 14 times a day and record the intensity of Earth-based lights.\n\nWe used stable light imagery of SSA derived from scores of orbits of the DMSP OLS in from 1992-2013 since this product inter-calibrated where fires and other ephemeral lights have been removed, although there are noteworthy blunders associated with over-glow effects where lighting spreads to neighboring pixels (and hence economic activity is wrongfully attributed to certain places). The stable lights imagery has annual quantized pixels with values (Digital Numbers) with integers ranging from 0 to 63.\n\nData on GDP and other indicators used in the analysis (surface of country territory, electricity consumption, etc.) are provided by the World Development Indicators (2014). Following HSW, we use constant GDP in local currency units.", "output": {"entities": {"named_data": ["Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS)", "DMSP OLS", "World Development Indicators"], "descriptive_data": [], "vague_data": ["night lights data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "refugees, as defined under the 1951 UN Convention. It is consequently not surprising\nthat surveys of refugee studies only give very limited attention to environmental\naspects, except for the degradation that refugees might cause themselves (Black and\nRobinson 1993; Richmond 1988; Zolberg, Suhrke, and Arguayo 1986).\n\n**A problematic concept**\n\nOne last element that may have curbed the study of links between environment and\nmigration is that several researchers have rejected the very concept of environmental\nrefugees (Black 2001). Rightly highlighting the shaky empirical character and sloppy\nnature of most work on the subject, they have brought to the fore problems arising\nfrom a unidirectional link between environmental changes and migrations in the face\nof well-established results from research on population flows. For Castles, \"the term\nenvironmental refugee is simplistic, one-sided and misleading. It implies a\nmonocausality which very rarely exists in practice (…) [Environmental and natural\nfactors] are part of a complex pattern of multiple causality, in which [they] are closely\nlinked to economic, social and political ones.\" (Castles 2002, 5).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["surveys of refugee studies"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "154 P. B. Spiegel & P. V. Le\n\nDiscussion\n\nOverall, the majority of surveys that met the inclusion criteria were of insufficient\nmethodological rigor to be reproducible. Other important methodological issues,\nsuch as households or persons who were absent or refused to participate, as well\nas whether replacement of such households or persons occurred, were not stated\nin most of the reports. Sample sizes had a wide range and some may have been of\ninsufficient size to be precise enough to interpret results or make meaningful\ncomparisons with future surveys. The majority of questionnaires were not field\ntested or back-translated nor were qualitative methods used to complement the\nquantitative methodology used in many of these surveys. Only a minority of\nsurveys obtained informed consent from participants. Few provided definitions\nof essential terms, such as high risk sex or non-regular partners. Although most\nof the surveys included training of surveyors, few reported gender balance of\ninterviewers. All of the above methodological flaws inject sufficient biases into\nthese surveys to make the most of them unacceptable.\nThe majority of the written reports lacked sufficient detail and structure to be\nanalysed and interpreted by the reader in a meaningful way. Most reports\nprovided objectives for the survey as well as conclusions and recommendations.\nHowever, few stated limitations and biases and only half appended the\nquestionnaire to the report. Important methodological details were missing in\nmany surveys, including information on how the sample size was chosen, the\nspecifics of the sampling methodology and whether replacement was used. Most\nof the analyses were descriptive in nature with few reports using comparative\nstatistics. The majority of reports used some variation of the internationallyaccepted HIV indicators for knowledge (e.g. prevention and misconceptions) but\nonly a minority used some variation of these indicators for practice and attitudes.\nThe disaggregation by age or gender varied considerably among reports which led\nto difficulties in comparing results; furthermore, few studies reported indicators\naccording to both gender and age.\nThere are limitations to this article. Despite an attempt to search as widely as\npossible in the published and grey literature, as well as to contact organizations\nknown to undertake BSSs in conflict and post conflict settings, some surveys will\nhave been missed. In addition, those included are not just BSSs but reproductive\nhealth KAP surveys with an HIV component. The latter may not contain as much\ndetail on HIV as BSSs, however, this would neither affect the basic methodological weaknesses nor the absence of key internationally-accepted standardized\nHIV indicators. Some of the BSSs examined in this report were conducted before\n2002 when the internationally accepted UNGASS indicators were developed; this\ntogether with the changing of indicators over time makes it difficult to interpret\nthe usage of internationally-accepted indicators. Misclassification may have\noccurred because results were based on findings written in the reports reviewed.\nFor those reports that omitted key methodological issues or results, the data were\nrecorded in a negative fashion (e.g. if type of sampling was not mentioned, the\nsurvey was recorded as not employing random sampling).", "output": {"entities": {"named_data": ["internationally accepted UNGASS indicators"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "[8] Daewoo Securities (1988 and 1999) _Handbook of Listed Companies,_ Daewoo Securities: Korea.\n\nscope data were missing or incomplete, we collected additional company information from Wisenet\n\nKorea. [7] This internet server provides detailed financial information, including income statements\n\nSecond, using data on foreign ownership from 1998 and 1999 and firm characteristics from the\n\n'° Of course, a drawback of this approach is that the estimates of elements of E will not be very precise, as the crosssectional dimension in these regressions is large relative to the time dimension. For this reason, we use only 20 states\nbecause annual data for gross state product is only available for 23 years, from 1963-1986. To give some idea of the\nmagnitude of the spatial correlations, note that the average cross-sectional correlation for the U.S. state data is .193, with\na maximal value of .629, while for the O.E.C.D., the corresponding figures are .312 and .761.", "output": {"entities": {"named_data": [], "descriptive_data": ["annual data for gross state product"], "vague_data": ["data on foreign ownership"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "While camps have been recognized as posing serious challenges (Jacobsen and Crisp 1998), it is quite striking to observe that this organizational feature is not as spread in other regions of the world as in SSA. At best, only 28, 25 and 15 percent refugees are hosted in planned / managed camps in Asia, Americas, and the MENA region, respectively. Such figures are based on the most recent year available (2013) and may change significantly following the large inflows of Syrian refugees into Egypt, Lebanon, Iraq, Jordan and Turkey. Nonetheless, the differences are sufficiently striking to believe that this is a distinct feature of refugee hosting in SSA. 2 UNHCR defines a protracted refugee situation as “ one in which 25, 000 or more refugees of the same nationality have been in exile for five years or longer in a given asylum country ” (2012: 23). 3 The figures are based on refugees (including those in refugee ‐ like situation). Persons in refugee ‐ like situations include “ groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained ” (UNHCR 2014: 39). The number of refugees and people in refugee ‐ like situation for which demographic data is available does not necessarily equal the total number of refugees. However, for SSA, there is little difference between the two. We also restrict the number of refugees to those whose accommodation is known by the UNHCR (approximately 19 % in the world and 8 % for SSA).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["demographic data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 Data Analysis All analyses were conducted with Stata / SE 14. 0 (StataCorp LP, College Station, TX). A bivariate model was also used to examine the relationship between the main predictor (fatalities) and each outcome. For the final model, a multilevel approach was used to account for the nested structure of the data, with clustering of women within districts. Model Specification Multilevel logistic regression models were used to quantify the effect of district level conflict on the odds of IPV after sequentially adding blocks of independent variables as described in Table 2. The models included a random intercept for district, to account for the geographic clustering of the sample and systematic differences between districts that would not otherwise be captured in a simple logistic regression. This approach has been used in similar analysis in past research (Kelly et al, 2018; Kelly, 2019). Multilevel Model- Dichotomous Exposure: In the regression equation above, i indexes the district and j indexes the individual. 𝑌𝑌𝑖𝑖𝑖𝑖 is the indicator for whether a woman (j) in district (i) has reported experiencing violence in the last 12 months. β_0 + b0i defines the district level odds of a woman experiencing violence in district i given no conflict holding the individual-level covariates fixed. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "• The government, UNHCR, and GIZ are working together to develop a national roadmap for including refugees in the national TVET system. Protection/Capacity “Strengthening Asylum System and Social Protection: (i) Refugee Status Determination (RSD), refugee registration, civil documentation, and permits; (ii) National social protection system in refuge hosting areas-particularly for vulnerable individuals.” • The 2019 Refugee proclamation improved provisions related to registration, documentation, and protection of refugees and asylum seekers as well as refugee status determination, and three implementation directives adopted. • RSD procedures are simplified for asylum seekers from Syria and Sudan. • Refugees are included in a Civil Registration and Vital Statistics Systems (CRVS) and the National Social and Behavior Strategy (awareness raising about the need for vital events registration). • A backlog of birth registration of 120,000 refugee children is cleared, and 72,286 vital events (62,816 birth, 8,177 marriage, and 757 divorce ) have been registered since 2017. • 890,825 refugees enrolled in the Level 3 Registration and Biometric Identity Management System (BIMS). Refugee ID cards and proof of registration are issued to 55 and 98 percent of refugees, respectively. Out-of-camp Permit is issued to 48,346 refugees. • One-stop shops have been established in 13 refugee camps", "output": {"entities": {"named_data": ["Level 3 Registration and Biometric Identity Management System", "Civil Registration and Vital Statistics Systems"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 Figure 3: Level of education age 25 +; compared with regional average (%) (*) INSTAT refers to census 2009 Source: Listening to Displaced People Survey, 2014. With regards to ownership of consumer durables, IDPs, refugees and returnees were better endowed than the average citizen of the North (see Figure 4). As was the case for education, they are more comparable to the average citizen in Bamako than to the average citizen in the regions of Gao, Timbuktu and Kidal. Figure 4: Asset ownership compared with regional average Source: Listening to Displaced People Survey, 2014 and EMOP 2011 (INSTAT). The main occupation of IDPs, refugees and returnees before the crisis was commerce (Table 5). This held for over half of the IDPs, 37 % of refugees and 34 % of returnees. 18 % of the refugees 51 85 60 47 85 89 87 15 6 18 29 11 8 8 34 9 22 25 5 3 5 IDPs Refugees Returnees Bamako (INSTAT) Gao (INSTAT) Timbuktu (INSTAT) Kidal (INSTAT) Secondary or Higher Primary None 0 100 200 300 400 500 600 IDPs Refugees Returnees Bamako (Instat) Gao (Instat) Timbuktu (Instat) Kidal (Instat) Percentage Mobile Phone Car / Motorized Vehicle Motorbike / scooter Bicycle Refridgerator TV CD Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 32 of 51 Figure 25: Education profile of Temporary employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Note: SP indicates the starting point of the analysis and EP states de ending point. It should be noted, however, that, like in the case of Latin American countries, the improvement in the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in the Annex of the paper. When we analyze what has happened at the salary level in the period of consideration (Figure 26), two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by the economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases. 10 10As mentioned above, standard employment is calculated as the total number of employees who do not identify with any of the non-standard categories (temporary or part-time). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Percentage of women who reported sexual violence by an intimate partner (ever), physical violence by an intimate partner (ever), and physical violence by an intimate partner in the past 12 months. 50 % 59 % 30 % 27 % 34 % 13 % 31 % 50 % 62 % 34 % 33 % 47 % 23 % 41 % 37 % 20 % 10 % 14 % 6 % 17 % 23 % 47 % 23 % 29 % 31 % 6 % 23 % 40 % 42 % 49 % 3 % 18 % 19 % 16 % 8 % 19 % 16 % 8 % 13 % 29 % 3 % 17 % 25 % 13 % 15 % Bangladesh (Urban) Bangladesh (Province) Brazil (Urban) Brazil (Province) Ethiopia (Province) Japan (Urban) Namibia (Urban) Peru (Urban) Peru (Province) Thailand (Urban) Thailand (Province) Tanzania (Urban) Tanzania (Province) Serbia Samoa sexual violence ever physical violence ever physical violence past 12 months Source: Unpublished data from the WHO Multi-Country Study on Women ’ s Health and Domestic Violence Against Women. The final published comparative report is forthcoming. Cited with permission. Prevalence data on sexual violence is even more limited than physical violence. However, evidence suggests that a substantial proportion of girls and women have experienced child sexual abuse, forced sex and other forms of sexual coercion in virtually every setting of the world. For example, population-based studies have asked about “ forced ” sexual debut among sexually experienced young people and found rates from 7 % (New Zealand), to 46 % (in the Caribbean) (Heise and Garcia Moreno, 2002). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["WHO Multi-Country Study on Women ’ s Health and Domestic Violence Against Women"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Finally, Gunther and Fink (2010) analyze data from 172 DHS  surveys and find that households having flush toilets have 13 percent lower odds of diarrhea\n\nWe use a subsample of 209,762 children between 0 to 48 months of age that live in ru ral areas from the Third Round of the District Level Household Survey (DLHS-3). The\n\nThe sample  design of the DLHS-3 survey makes this measure possible since in rural areas the DLHS-3  uses census villages as PSU (International Institute for Population Sciences (2010)).\n\nDLHS is a nationwide survey with district level representation of India's households, which\n\ncollects information on family planning, maternal and child health, reproductive health of\n\nThese ratios are reasonably comparable with the estimations of JMP for rural  areas in India. Table 2 presents the summary statistics of these variables for the 209,762", "output": {"entities": {"named_data": ["Third Round of the District Level Household Survey (DLHS-3)", "DLHS-3 survey", "District Level Household Survey (DLHS-3)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "### **Registration and Documentation**\n\nUNHCR Indonesia undertakes registration of persons seeking asylum in Indonesia on behalf of the\nGovernment of Indonesia and issues UNHCR identity documentation. Registration interviews are\nprimarily conducted in-person for verification of biometric data and relevant records. Most\nregistration interviews take place at the Reception Center in Jakarta or during accommodation\nvisits/missions by the respective field team for individuals residing outside the greater Jakarta area. In\n2023, 2,547 individuals (1,324 cases) were registered by UNHCR Indonesia, which includes 1,225\nRohingya refugees registered during emergency registration missions following boat disembarkations\nin Aceh in November and December 2023. UNHCR Indonesia provides continuous registration services\nto registered refugees and asylum-seekers and maintains updated personal data in our internal\ndatabase to ensure vulnerable refugees and asylum-seekers are identified and assisted with\nprotection interventions and solutions.\n\n### **Refugee Status Determination**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["internal\ndatabase"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "11 Figure 3: Location of refugee camps in Ethiopia and the Ethiopia Development Response to Displacement Impacts Project (DRDIP) sample households Source: Authors ’ compilation using the database of the Global Administrative Areas (GADM) (https: / / gadm. org / data. html, accessed November 20, 2020) and the Humanitarian Data Exchange (HDX) database for the refugee location (https: / / data. humdata. org / dataset / ethiopia- refugee-camp-locations, accessed November 20, 2020). From the Ethiopia DRDIP data set, we derive two measures of livelihood diversification and two measures of agricultural commercialization (all at household level). The measures of diversification include: (i) the degree of labor diversification in different productive livelihood activities (e. g., farming, wage employment) as a primary activity (occupation), and (ii) the degree of labor diversification in different livelihood activities as a secondary activity (occupation). 13 These two outcomes were constructed using the inverse Simpson diversity index as 1 ∑ 𝑛𝑛 𝑖𝑖 𝑆𝑆𝑖𝑖 2, where 𝑆𝑆𝑖𝑖 is the share of the number of adult labor engages in 𝑖𝑖𝑡𝑡ℎ livelihood activity to total active adult household labor and 𝑖𝑖 ranges from 1 to the number of livelihood activities that a household engages in (Valdivia et al. 1996). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": " In Somalia, the Mogadishu Household Survey covered both residential areas and IDP camps, collecting data on expenditures, demographics and living conditions [survey completed; analysis forthcoming]. As part of the Somalia Knowledge for Operations and Political Economy (SKOPE) initiative, the Puntland Household Survey will also cover both residential and IDP populations [ongoing].  An IDP study in South Sudan [ongoing] aims to assess the economic needs of IDPs and host communities in urban areas, covering livelihoods, water and sanitation, infrastructure as well as intentions and conditions to return. Basic information about education, employment and general health variables will also be collected.  The Iraq Crisis Response Study [ongoing] will assess the impact of the Islamic State and oil price-related crises on IDPs and households left behind in IS controlled areas. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9826 Despite the many simultaneous deprivations faced by forcibly displaced communities, such as food insecurity, inadequate housing, or lack of access to education, there is little research on the level and composition of multi­dimensional poverty among them, and how it might differ from that of host communities. Relying on house­hold survey data from selected areas of Ethiopia, Nigeria, Somalia, South Sudan, and Sudan, this paper proposes a Multidimensional Poverty Index (MPI) that captures the overlapping deprivations experienced by poor individuals in contexts of displacement. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 The Government of Chad subscribes to this logic as evidenced by the application decree of Chad ’ s Asylum Law signed in 2023, 1 and the National Response Plan to the Impact of the Sudanese Crisis which is under preparation. The Law and the Plan promote the local integration of refugees, aim to avoid settling refugees in permanent camps and promote self-sufficiency. They offer refugees the right to own land, to engage in formal employment and commercial activities, to move freely, and to access to banking services. While participation is the stated policy objective, the reality is that previous arrivals are almost exclusively living in camps, and that the new arrivals live in “ organized sites ” (as humanitarians now call them), presumably to cope with massive arrivals but with little concrete evidence for the onward movement of refugees. This note explores the size of this four-way benefit (for refugees, hosts, the Chadian state and the international community) by estimating how much could be saved on aid for basic needs consumption by enabling refugees from Sudan to realize their economic potential. For its empirical work the note draws primary on data from the ECOSIT4 survey. 2 In 2018 – 19, Chad became one of the first countries in Africa to capture refugees and host communities in its national household survey.", "output": {"entities": {"named_data": ["ECOSIT4 survey"], "descriptive_data": [], "vague_data": ["national household survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 The only publicly available sources of data to document patterns of enrollment and available educational options for Pakistani families are household-based surveys. These are the official 1998 Census of Population (Government of Pakistan) 3, the 1991, 1998, and 2001 rounds of the Pakistan Integrated Household Survey4, and a 2003 census of schooling choice conducted by our research team. The fact that three sources use different definitions of madrassa enrollment, and were collected at different times by individuals with very different institutional affiliations provides independent verification of enrollment estimates and allows us to determine the sensitivity of our results. The household data tell us whether a child is enrolled full-time in a madrassa, but not whether a child goes for an hour on any given day to study the Quran. Therefore this data does not confound full-time with part-time attendees — a child who attends a public school during the day and a madrassa in the evening is recorded as enrolled in a public school. This is an important distinction since parents might use a modicum of madrassa or mosque based education to teach their children about religion. Consequently, if we contrast these household-based numbers with numbers from establishment-based reports, discrepancies can arise. From virtually any policy perspective, including evening quran classes in enrollment figures seems misguided. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "9 3. 1 Data Sources We use three different types of data to verify our estimates and determine how sensitive they are to changes in definition and the year of the survey. Two sources are nationally representative, but date from 2001 or before, the third is data from a census of households carried out by the authors in 2003 as part of a project on educational choice. The first source is the “ long ” form of the population census in 1998, which is a large sample-based survey with information on enrollment. This survey is representative at the level of the district and region (rural or urban) and provides comprehensive coverage of the entire country. 7 We use this data to examine enrollment patterns across districts. The second type of data, based on household surveys, are different rounds of the Pakistan Integrated Household Survey (PIHS) carried out in 1991, 1998 and 2001. While the data is not as extensive as the census, it contains detailed household information on schooling and income, and has been used extensively by researchers both in Pakistan and the United States. Finally, we use the census of schooling choice among households that our research team conducted in August 2003 (referred to as the project on “ Learning and Educational Achievement in Punjab Schools ”, or LEAPS).", "output": {"entities": {"named_data": ["Pakistan Integrated Household Survey"], "descriptive_data": ["census of schooling choice among households"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 Background Information 2. 1 Characteristics of West Bank and Gaza ’ s labor markets The labor markets of the West Bank and Gaza exhibit features typical of the broader Middle East and North Africa (MENA) region, but also have attributes that are highly unique. Additionally, important differences exist between the West Bank and Gaza. This section provides an overview of these characteristics. We use data from the Labor Force Surveys (LFS) of the West Bank and Gaza and we focus on 20-59 years old men. In Section 3. 1, we provide more information about the data sources and sample selection. We divide each labor market into five mutually exclusive and jointly exhaustive states: public sector employment, private formal sector employment, private informal sector employment, unemployment, and out of labor force. 1 We focus our discussion exclusively on men, as women ’ s labor force participation in both the West Bank and Gaza is very low, never reaching values above 25 %. This low participation rate is common in MENA countries and makes the role of the pandemic on women ’ s labor market outcomes relatively less important than other, more relevant structural factors. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Labor Force Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Next, we evaluate firm level data on ownership to get a better understanding of what foreigners\n\n2Aitken and Harrison (1999) argue a similar point. Using plant level data on productivity in Venezuela, they find\n\nhypotheses testing. Data on mergers and acquisitions in Korea are from Securities Data Corporation\n\nfrom Securities Data Corporation (2000) or have a foreign ownership share exceeding 25 percent.\n\nHandbook of Listed Companies, Daewoo Securities (1998 and 1999). In cases where the World", "output": {"entities": {"named_data": ["Securities Data Corporation"], "descriptive_data": ["plant level data on productivity in Venezuela"], "vague_data": ["firm level data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We contribute to this literature by exploring the eﬀect of refugees ’ proximity on the migration policy preferences of host community populations in a low-income country. 5 Focused almost exclusively on the Global South, the second strand of research explores whether the presence of refugees is associated with a greater risk of conﬂict (Jacobsen, 2002). Earlier studies highlighted possible tensions with local citizens that are exacerbated by resource competition or ethnic rivalry (Salehyan and Gleditsch, 2006; Rüegger, 2017). Much of this scholarship recognizes that refugees are often victims of conﬂict (Onoma, 2013; Fisk, 2018; Böhmelt, Bove and Gleditsch, 2019; Savun and Gineste, 2019). Recent research suggests that conﬂict between host communities and refugees may be avoided if refugees ’ presence attracts aid and economic activity that beneﬁts both (Lehmann and Masterson, 2020). We complement this literature on the refugee – conﬂict nexus, which thus far has relied on cross-country analysis, by exploiting — following Zhou and Shaver (2021) — within-country variation in exposure to refugee settlements. The third strand explores the welfare consequences of refugees ’ presence on host communities in developing countries. However, without auxiliary data (such as survey data on policy preferences regarding refugee policies), these studies cannot tell us how the economic consequences of refugee hosting aﬀect social cohesion (if at all). Moreover, almost all studies in this research domain strand of the literature concentrate on a single domain, such as labor market outcomes (Fallah, Kraﬀt and 5See also Zhou (2018), which examines how the presence of refugees can change local citizens ’ opposition to citizenship inclusion in sub-Saharan Africa. 6 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "PAGE 25 --> similar income aggregates can be obtained between POF and PNADC (Paffhausen et al., 2021), which could be exploited in further work in this direction.\n\nMyanmar Ministry of Planning and Finance & World Bank Group. (2017). Technical Poverty Estimation\nReport: Myanmar Poverty and Living Conditions Survey. World Bank, Yangon.\n\nOliveira, L. S., De Souza, D. F., Dos Santos, L. A., Antunes, M., Brendolin, N. C., & Quintaes, V. C. (2016). \"Construction of a Consumption Aggregate Based on Information from POF 2008-2009 and Its Use in the Measurement of Welfare, Poverty, Inequality and Vulnerability of Families.\" _Review of Income and_ _Wealth_, 62, 179-S210.\n\nRodrigues, C.T., & Helfand, S., & Lima, J.E. (2018). Novas linhas de pobreza para o Brasil: Uma análise a partir das Pesquisas De Orçamentos Familiares (POF) 2002-2003 e 2008-2009.", "output": {"entities": {"named_data": ["PNADC", "Myanmar Poverty and Living Conditions Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "impression of the state ’ s role in development and give credit to the state for helping to leverage external resources. Citizens are also likely to give the state credit where mechanisms to voice complaints about non-state actors exist and where bureaucrats are able to eﬀectively respond to complaints. Under these conditions, non-state service provision is likely to strengthen the ﬁscal contract. 5 Data and Methods I explore the relationship between external service provision and deference to government using Afrobarometer survey data from 19 Sub-Saharan African countries (see Table 1). Africa is an especially good place to examine these issues because of the large amount of variation both within and across African countries in the extent to which non-state actors, donors and other states are active in service provision and the extent to which governments are relatively eﬀective and fair. Government responsiveness, corruption and reliance on non-public resources vary considerably among localities with consequences for citizen understanding of and relationship to government (Gibson and Hoﬀman, 2005). This project relies on the fourth round of Afrobarometer data that surveys Africans ’ views towards democracy, economics, and civil society with random, stratiﬁed, nationally representative samples. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For example, the baseline year for children sampled in the 2007 DHS will be the year 2000.\n\nWe do not  have any particular expectations regarding the relationship between DHS years and the likelihood of  enrollment (Year [2011], Year [2014]).\n\n[14] Clusters from  DHS 2000 were geomatched with DHS 2007 to estimate the exposure to unimproved sanitation when children in the 2007 sample were 1 or 2 years old.\n\nOur data set is a pseudo-panel with children ages 6-9 sampled from three cohort pairs of  independent repeated cross-sections of DHS 2000-07, 2004-11, and 2007-14, following Deaton (1985),  Magadi (2016), and Ncube and Shimeles (2012), respectively.\n\nWe matched the children within each DHS year, using the Stata user-written program _cem_ introduced in Blackwell et al.", "output": {"entities": {"named_data": ["2007 DHS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "assignment process was conducted to assign the trainees to the first or second round of training. 9 Of those entered into the random selection, 1273 young women were assigned to the first round of training, with the remaining 808 to serve as a control group (the control group would participate in the second round of training starting in July 2011). Of the 1273 assigned to treatment, 118 women were not found or chose not to participate after they were selected. 10 In order to fill at least some of these slots, 39 young women from the control group were randomly issued as replacements, resulting in a modified control group of 769 individuals. In the end, 1191 young women entered the first round of training. 11 The assignment process and all post-randomization modifications are summarized in Figure 2. Table 1 reports the baseline and midline survey response rates leading to the sample used for the analysis in this paper. The target sample for both the baseline and midline survey consisted of the original 2106 EPAG recruits, of which 1989 were successfully interviewed during the baseline survey. 12 At midline, 1736 were interviewed, including 56 who were not interviewed at baseline. For our analysis, we drop individuals who were excluded from the randomization or who were manually re-assigned from control to treatment as replacements after the randomization. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "<!-- PAGE 9 -->\n|Figure 6a Share of population working primarily<br>in agriculture (%) in mining communes|Figure 6b Share of population working primarily<br>in extractives (%) in mining communes|\n|---|---|\n|||\n|_Source:_ RGPH (General Population and Housing Census) 1998 and 2009.|_Source:_ RGPH (General Population and Housing Census) 1998 and 2009.|", "output": {"entities": {"named_data": ["RGPH (General Population and Housing Census)", "General Population and Housing Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The pregnancy care indicator is also excluded from the intrahousehold inequalities analysis as the reference populations for the analysis did not permit rigorous statistical testing. 3. 3 Limitations With a few exceptions, gendered MPIs have been designed using indicators that are present in standard survey instruments, which themselves struggle with normative challenges (Alkire 2018). To create improved gendered MPIs, in which people ’ s poverty can be compared across gender and age or the life cycle, research must develop “ comparable ” definitions of capability deprivation that matter to people in different age cohorts or different life situations. Reliable indicators comparing men and women ’ s income, ownership of assets, and decision-making powers in the same household are difficult, as are those measuring decent work. Health indicators also differ by gender, change across the life cycle, and vary across family structures and disability status. The MPI constructed in Admasu et al. (2021) has the same weaknesses as these measurement paradoxes, but it remains a step in the right direction. We aim to mitigate the limitations of this household-level measure by unpacking the deprivations of indicators available at the individual level, disaggregating those deprivations by gender Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Percent <15 15 to 24 25 to 44 45 to 64 >=65 Addis refugees Addis hosts Figure 2.4: Age structure Source: World Bank Staff based on SESRE 2023. 49% 48% 45% 45% 47% 47% 51% 52% 55% 55% 53% 53% 0 20 40 60 80 Percent 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Male Female Figure 2.5: Gender composition Source: World Bank Staff based on SESRE 2023. 22% 27% 36% 60% 30% 33% 66% 55% 54% 28% 59% 50% 12% 18% 9% 12% 11% 17% 0 20 40 60 80 Percent 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Never Married Married Other Figure 2.6: Marital status (18 years and above) Source: World Bank Staff based on SESRE 2023. Table 2.1: Household characteristics In camp Addis Ababa Total Hosts Refugees Hosts Refugees Hosts Refugees Household size 5.2 6.2 3.5 2.7 4.2 5.4 Dependency ratio 1.1 1.5 0.5 0.4 0.7 1.3 Female-headed 44% 73% 45% 58% 44% 69% Head’s age 42.3 39.6 42.0 30.9 42.1 37.6 Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 12 2.2 Education Integrating refugee children into educational programs soon after arrival14 avoids the loss of", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10099 Situations of forced displacement create unique challenges for social cohesion because of the major disruption of social dynamics among both displaced persons and host communi­ties. This paper uses a sequential mixed method approach to analyze the relationship between hosting displaced persons and perceptions of social cohesion in eastern Democratic Republic of Congo. First, participatory research methods in focus groups empowered participants to pro-duce a locally driven definition of social cohesion. The results from these exercises inform the quantitative assessment by dictating measurement strategies when analyzing original surveys.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These systems employ a combination of data collection techniques including key respondent interviews, focus group discussions, registration, observations and physical counts, samplings and other statistical methodologies. For examples, UNHCR ’ s population tracking systems identifies and trains local NGOs to monitor key locations such as IDP settlements, bus stations and roads to report on movements. The accuracy of data from movement tracking systems is subject to several caveats. These include: limited access to locations and routes due to insecurity; vast geographical areas to monitor; mixed population flows that include refugees, IDPs, pastoral and seasonal movements and economic migrants; massive population flows that overwhelm monitoring capacity; disinclination of individuals to provide information when there is no assistance being offered; pressures from communities to inflate figures to maximize future assistance; and political pressures to suppress accurate reporting on IDP movements. Additionally, due to the fluid nature of displacement in many contexts and the likelihood of recurring displacements, it is not possible to use movement data to provide estimates of population stocks. Population censuses National population and housing censuses often provide the most comprehensive source of population data and offer the potential for estimating numbers of forcibly displaced people. To estimate the size of displaced populations a census would need to include questions on country (and / or place) or birth, year of (internal) 70 Other data collection methods may be used such as movement tracking systems, registration, big data etc. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["movement tracking systems", "population tracking systems"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Looking also at the impact of IDPs in Colombia on wages, Morales (2017) uses a labor force survey, census data and registry data to study short and long-term effects as follows: 𝑆ℎ𝑜𝑟𝑡 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ௧ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ௧ ି ଵ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ௧ ൅ 𝜆 ௠ 𝑋 ௠ ௧ ൅ 𝛾௧ ൅ 𝛿 ௠ ൅ 𝛿 ௠ 𝑇 ൅ 𝜀 ௜ ௠ ௧ 𝐿𝑜𝑛𝑔 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ൅ 𝜆 ௠ 𝑋 ௠ ൅ 𝛿 ஽ ௠ ൅ 𝜀 ௜ ௠ where y is the log of wages, i, m, and i are individuals, municipalities and time respectively, 𝑋 ௜ ௠ ௧ are individual controls, 𝑋 ௠ ௧ is the log of total population or other municipality controls, 𝛾௧ and 𝛿 ௠ are time and municipality fixed effects, 𝛿 ௠ 𝑇 are municipality time trends, 𝛿 ஽ ௠ are department fixed effects and d is the inflow of IDPs defined as 𝑑 ௠ ௧ ൌ 100 𝑝𝑜𝑝 ௠ ௧ 𝑓 ௠ ௧ where 𝑓 ௠ ௧ is the total number of IDPs arriving in municipality m at time t. The same variable without the t subscript is used for the long-run effects equation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey", "census data", "registry data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This question is examined in Jordan using a unique dataset that links individual data on own schooling and par­ents ’ schooling for adults, from a household survey, with the supply of schools in the subdistrict of birth at the time the individual was of age to enroll, from a school census. The identification strategy exploits the variation in the supply of basic and secondary public schools across cohorts and subdistricts of birth in Jordan, controlling for year and subdistrict-of-birth fixed effects and interactions of gov­ernorate and year-of-birth fixed effects. The findings show that the local availability of basic public schools does, in fact, increase intergenerational mobility in education. For instance, a one standard deviation increase in the supply of basic public schools per 1, 000 people reduces the father- son and mother-son associations of schooling by 18 – 20 percent and the father-daughter and mother-daughter asso­ciations by 33 – 44 percent. However, an increase in the local supply of secondary public schools does not seem to have an effect on the intergenerational mobility in education.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey", "school census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "two partial indices: the headcount ratio (H) and the intensity of poverty (A) i.e. (MPI = H*A). The headcount ratio is the share of poor people in the population, while the intensity shows how much deprivation poor people experience on average. A cut-off point of 0.33 is used for the multidimensional poverty headcount ratio; that is, a household is multidimensionally poor if the MPI is greater than 0.33. The population vulnerable to poverty is defined as those who experience 20-32.9 percent intensity of deprivation, and the population in severe poverty are those with an intensity of 50 percent or higher (that is, if the MPI is 0.50 or higher). Box 5.3: MPI methodology 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Worse Same Better Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Percent 0 10 20 30 40 50 60 70 80 90 100 Percent Worse Same Better Figure 5.8: Perceived changes in household living standards Source: World Bank Staff based on SESRE 2023. Note: The survey asks how the household living standard has changed compared to last year and the last five", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The data shows that cohorts that were of school age during the Indonesian occupation achieved higher level of education than older cohorts (i. e. those born in the 1970s compared to those born in the 1960s or before) testifying for an increasing trend as expected. The figure shows that despite the increasing trend a large fraction of individuals have low education levels. Interestingly, all curves start to drop after the 1987 cohort. This decreasing trend is observed among individuals aged 20 or younger in 2007 and provides evidence of a mismatch between the grade attended and the grade that they should have achieved at their age. 4 This is caused by a persistent sluggishness in grade achievement due to the high level of delayed entry to school and high rates of repetition. 5 The impact of the conflict in its different phases and the subsequent reconstruction efforts on schooling levels of children in Timor Leste is therefore unclear. The early years of violence coincided with an education for all policy in which quantity was preferred to quality. In addition, the 1999 violence that followed the withdrawal of Indonesian troops led to the destruction of schools and the removal of children from school. The reconstruction program implemented after 1999 tried to counteract this destruction, and achieved fast progress. However, the education sector was still in very poor shape. In the next section, we investigate in more detail the effects of the conflict on educational outcomes of boys and girls in Timor Leste. 4. Identification strategy and data description 4 Those born in 1992 are 15 in 2007. So they might have at most completed grade 9 and this justifies part of the drop in the curves as the grade completed is right censored. 5 The high levels of school delay are also confirmed by the figures on gross and net enrolment ratios calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007.", "output": {"entities": {"named_data": ["TLSS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 1. INTRODUCTION Refugees pose a massive moral, political and economic challenge for potential host countries. 1 The scale of the challenge is larger than ever, with 60 million people forcibly displaced by conflicts across the world (UNHCR, 2014). War in Syria has produced more refugees than any other conflict of the past two decades: around 4. 6 million have fled the country, with an additional 7. 6 million internally displaced. 2 About 2. 5 million Syrians have found refuge in Turkey, making it the largest refugee-hosting country worldwide. This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turkish Labour Force Survey to assess the impact on Turkish employment and wages. The large majority (85 percent) of Syrians have left the refugee camps and entered the Turkish labor market. 3 They are overwhelmingly employed informally, since they were not issued work permits. This makes their arrival a well-defined supply shock to informal labor, and a particularly good context in which to test the predictions of basic economic theory. We instrument for refugee flows using travel distance between 13 origin governorates in Syria and 26 Turkish subregions (338 origin-destination pairs). This allows us to also control for distance from the Syrian border, and thus any confounding factors that are correlated with proximity to Syria. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Turkish Labour Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page | 7\n\nPriority 4 of the CBP and \nOutreach strategy aims to \ncontribute to prevention of \nviolence through Community \nsafety action plans to reduce \nviolence in the community \ncommunity makes contributions to pay the salary of the Imam and may pool together funds to provide \nsupport to persons living in poverty, vulnerable persons (i.e. widows, orphans, PWSN), and persons \nseeking financial assistance for medical treatment or to pursue educational opportunities.  \nPositive Coping Mechanisms [Questions about how people cope with stress and what activities in the community help persons \novercome traumatic events].  \nFor stress relief, males referenced different coping mechanisms such as: discussing their issues with \nelders and/or religious scholars, engaging in prayer to seek help and guidance from their faith, and \ngathering with their friends or relatives to discuss issues (peer counseling). Those who can afford it, \nengage in sports, go to recreational spaces or restaurants with \nfriends, or gather in common places (Hujras) to play cards or \nother games. Negative coping mechanisms reported by males \ninclude domestic violence against women and children, or \n“unusual religious activities” such as going beyond the required \nnumber of prayers and excessive citation of the Qur’an.  \nDue to movement restrictions, females do not have the same \navailability of stress relief opportunities outside the home. As a \nresult, females reported that to cope with stress they may isolate themselves in the home and cry, seek \nmedication, and talk to relatives to find support and solutions (peer counseling). Females also mentioned \nthat due to stress, a negative coping mechanism is emotional and physical abuse of children. The findings \nreinforce previous PA findings and lend strong support for Community safety plans as a means to \nempower the community to address the negative coping mechanisms.  \nVoluntary Repatriation [Questions about the decision making process of return to Afghanistan, how persons receive information on \nreturn to Afghanistan and how return to Afghanistan has affected community dynamics].  \nThe majority of community members report that they have relatives, friends or know of community \nmembers that have returned to Afghanistan, for the following reasons3: \n \nPolice harassment in Pakistan \n \nUncertainty of POR card extension in Pakistan \n \nPoverty and lack of employment opportunities in Pakistan \n \nStrict border crossing control between Pakistan and Afghanistan \n \nDesire to join relatives and/or communities back in Afghanistan  \nCommunity members report they receive information on repatriation from4: \n \nUNHCR/partner helplines \n \nPrint and electronic media \n \nContact with friends and relatives back in Afghanistan via phone and internet \n \nInformation from persons that have recently traveled to Pakistan from Afghanistan \nAs a result of return, the community reports a decrease in livelihoods5 opportunities and increased \nexposure to security issues as community numbers reduce and government authorities actively support \nreturns. Furthermore, loneliness and sadness due to friends and family departing Pakistan is reported as a \nnegative impact as well as decreased educational opportunities for children due to teachers returning.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR/partner helplines"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006. 1 A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large. 2 Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010). 3 In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese 1 On the quality of public services indicator, Lebanon ’ s score declined from 2. 5 in 2006 to 0. 8 in 2012 on a 4-point scale. On the territorial coverage indicator, its score went down from 2. 7 in 2006 to 1. 5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men.", "output": {"entities": {"named_data": ["SWMENA survey", "Middle East and North Africa Survey Project", "Gallup World Poll"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "and purchase goods at better prices. In Ethiopia, refugees’ locations differ vastly in terms of proximity to resource and economic hubs. About 37 percent of refugees live within 10 kilometers of the nearest Woreda city and another 43 percent live within 10 to 20 kilometers. Zone capital cities are farther away, but almost half (45 percent) of in-camp refugees live within 20 kilometers of the nearest Zone capital city (Figure 6.1a). Borders seem farther, with 18 percent of refugees living within 30 46 Labor Force and Migration Survey 2021. Markets and Opportunities 58 kilometers of the nearest border. When defining mutually exclusive location categories to measure proximity to resource hubs, we see that 44 percent of refugees live closest to the nearest Zone capital city. Another 28 percent live closest to a Woreda City, which is not a Zone capital city. About 11 percent live close to a border but not the Zone capital or Woreda city, and 17 percent of in-camp refugees live in remote areas far from a Zone capital city, Woreda city, or a border. When looking at accessibility, as defined by a market accessibility index, more than one-third of the refugees are located in areas with", "output": {"entities": {"named_data": ["Labor Force and Migration Survey 2021"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Specifically, the monthly mVAM began in August 2015 and has been conducted in nearly every month since. [7] The 10-minute survey is conducted by mobile phone and reaches approxi mately 2400 households every month through random digit dialing. The survey is stratified by\n\nEvery month, the survey collects information necessary to construct the Food Consumption Score (FCS) [10], information on food coping strategies necessary to construct the Reduced Coping Strategy Index (rCSI) [11], information on whether the household received food assistance in\n\npublicly available. However, the WFP publicly shares each month the governorate-level averages and confidence intervals of all key variables collected [13], and these monthly governorate-level  estimates are used in the empirical analysis to analyze changes in food assistance and food access following the 2017 IPC announcement. [14] We further merge the mVAM data with data", "output": {"entities": {"named_data": ["Food Consumption Score (FCS)", "Reduced Coping Strategy Index (rCSI)"], "descriptive_data": ["monthly governorate-level  estimates"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Disaggregated data on IDPs who are not protected or assisted by UNHCR are collected by other agencies, including IOM, but data are not comprehensive and therefore not published in IDMC ’ s global reports. 93 The difficulties of collecting disaggregated data on locations of IDPs are compounded by the fluidity of IDP movements — IDPs might suffer multiple displacements or they might resort to changing locations as a coping strategy (e. g. moving between their homes and place of displacement or testing different locations before deciding where to stay) (Brookings 2011). In recent years, efforts have been made to improve data collection for IDPs living outside of camps by employing a range of techniques including: (a) profiling; (b) household surveys; (c) collecting information on IDPs who come to camps to visit family members or collect relief items; and (d) community outreach programs (Brookings 2013). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Disaggregated data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As explained in section 4, the HRVD dataset contains data on the number of human rights violations occurred since the start of the conflict in 1975 until its end in 1999 for each district. The types of violations recorded are killings, deaths due to deprivation and disappearances. We use only the number of killings to identify years and districts affected by the conflict. We exclude the deaths due to deprivation because the districts in which this occurred may very likely not be those where the conflict was most intense, but were simply places were the victims were hiding as a consequence of escaping from the troops, and died for starvation. In addition, since killings are less likely to affect entire families than deaths due to deprivation, there is a lower underreporting bias attached to the former measure relative to the latter one (Silva and Ball 2006). We also exclude disappearances as, according to HRVD data, they do not show enough time and geographical variation in order to identify individuals more or less exposed to the conflict. We believe that the number of killings proxies quite well the intensity of the conflict across time and space as their occurrence largely tracked the movements of the Indonesian military operations. The other two types of violations do not seem to show the same pattern (Silva and Ball 2006). For the same reason, we believe that it proxies quite well the destruction of houses and infrastructure and the displacement of people given the way in which the last wave of violence occurred (i. e. the scorch-earth technique employed by Indonesian troops as they moved towards West Timor). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["HRVD dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We complete this data with exact geographic location data from MineAtlas (2013), where satellite imagery shows the actual mine boundaries, which allows us to identify and update the center point of each mine. The production data and ownership information are double-checked against the companies ’ annual reports. For Ghana, this exercise results in 17 industrial mines tracked over time. We have annual production levels from 1990 until 2012. As mentioned, Table 1 shows the mining companies active in Ghana during recent decades, with opening and closing years (although some were closed in between, and are not presented in the table). Figure 2 shows the geographic distribution of these mines. Figure 2 Gold mines and DHS clusters in Ghana Panel A Gold mines and 20 km buffer zones Panel B Gold mines, DHS clusters, and 100 km buffer zones 4 The distances are radii from mine center point, and form concentric circles around the mine. 5 The DHS and the GLSS data are representative at the regional level, and not at the district level. Since the regional level is too aggregated, we do the analysis at the district level, but note that the sample may not be representative. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5. Time Use and Child Labor: The fifth module examined how children spend their leisure time, their involvement in child labor, and interactions with peers. 6. Pro-social Preferences and Migration Outlook: The sixth module concentrated on adolescents ’ pro-social behaviors, such as altruism and trust, and explored their expectations and intentions regarding migration. 7. Socio-emotional and Mental Health: The final module involved the administration of various scales to assess socio-emotional well-being and mental health, includ- ing trauma, behavioral problems, anxiety, and depression. The scales include the Trauma Symptom Checklist for Young Children (TSCYC), Strengths and Difficulties Questionnaire (SDQ), General Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). All these scales and the corresponding outcomes that we evaluated are described in the next subsection. The survey also employed the Peabody vocabulary test to evaluate the cognitive devel- opment of all participating children and adolescents. A summary of the survey modules is depicted in Table A. 1. III. C Sample comparability While Medell ´ ın ranks as the third city with the highest migration in Colombia, it is crucial to recognize the degree to which migrants arriving in the city differ from those migrating to other regions in Colombia.", "output": {"entities": {"named_data": ["Trauma Symptom Checklist for Young Children", "Strengths and Difficulties Questionnaire", "Patient Health Questionnaire", "General Anxiety Disorder Scale"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(see Annex E for detailed information). Food aid information received from WFP includes five food items and their quantities distributed to refugees: cereal (mainly wheat but in some camps rice), pulses (mostly yellow split peas), CSB+, vegetable oil, and salt. We have computed the per person, per month in-kind aid quantities into annual values using the same prices as other food items based on SESRE data, mapping them to the closest food item in SESRE (this was not straightforward as the items are different). We further considered the changes in quantities of food rations that took place across survey months due to funding shortages, which can significantly affect the overall wellbeing of refugees in Ethiopia. Based on this information, we compare items refugees should have received with what refugees reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food aid admin data for every item except biscuits. Refugee households still report lower quantities, even when valuing the food ration quantities indicated as sold in markets. Possible explanations for lower food quantities are that food rations are only received once a month, which may not coincide with the interview date. Moreover, SESRE asks what food people", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": ["UNHCR food aid admin data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(0.0814) Distance to Zone city (Km) -0.0030* (0.0013) Market accessibility indicator 0.4056** (0.1381) Observations 2024 2024 2024 2024 2205 2024 Chi-square test 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 R2 0.1373 0.1373 0.1373 0.1373 0.1172 0.1373 Source: World Bank Staff based on SESRE 2023. Note: Average marginal effects are estimated. a refers whether a household ran out of food in the last 12 months. b refers if most of the land is covered in the community by agriculture activities i.e., less built-up and shops. c refers recent (5 years) internal migrants from rural to urban centers. Distance to the nearest bank variable is excluded from proximity model II, as it is captured by effect of distance to the nearest zone city. The left side for years in exile, Head education level, HH size, most land cover, and proximity level is less than 15 years, Head with no education, non-working age members, most land cover by built-up and shops, and nearest to Zone capital city, respectively. All estimates are controlled for refugee camps. Standard errors in parentheses. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001 Variables Basic Model Local Market Proximity Market access Model I Model II Model I Model II Annexes 119", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "sectoral distribution of employment, the poorest refugees—in- and out-of- camp—tend to be employed in the service sector. While employment in the industry sector is low for refugees, the poorest are less likely to be employed in the industry sector than the richest. The poorest hosts of in-camp refugees are more likely to be employed in agriculture, and the richest appear to be employed in the industry or service sectors (Figure 5.18b). Not surprisingly, the poorest hosts of in-camp refugees and the poorest refugees work in low (or medium)-skilled occupations, while the richest are employed in high-skill occupations (Figure 5.18c). - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 No education Primary incomplete Primary complete Secondary incomplete Secondary complete Post- secondary No education Primary incomplete Primary complete Secondary incomplete Secondary complete Post- secondary In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts All Refugees All Hosts In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts All Refugees All Hosts - 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Predicted total expenditure Predicted poverty rate Figure 5.16: Poverty incidence decreases with education of the household head Source: World Bank Staff based on SESRE", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turk­ish Labour Force Survey to assess their labor market impact. Syrian refugees are overwhelmingly employed informally, since they were not issued work permits, making their arrival a well-defined supply shock to informal labor. Consistent with economic theory our instrumental variable estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupa­tional upgrading, there are increases in formal employment for the Turkish- though only for men without completed high school education. Women and the high-skilled are not in a good position to take advantage of lower cost informal labor. The low educated and women experience net dis­placement from the labor market and, together with those in the informal sector, declining earning opportunities.", "output": {"entities": {"named_data": ["Labour Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "seekers. 81 There are several challenges associated with using central population registers to estimate refugee and asylum-seeker populations, including: consistency of the definition of refugees with the definition in the 1951 Convention and 1967 Protocol; difficulty and cost of establishing and maintaining a population register (UNSD 2014); and confidentiality safeguards. Compilation of statistics on forcibly displaced populations Several international organizations are involved in the compilation, analysis and dissemination of statistics on forced displacement including UNHCR, 82 Eurostat, IDMC, OCHA, International Committee of the Red Cross (ICRC), 83 WFP84 and IOM. Each of these actors has their own thematic focus and specific objectives, and applies their own methodologies. Asylum-seekers and refugees UNHCR is the principal organization responsible for the compilation, analysis and dissemination of data on asylum-seekers and refugees. UNHCR maintains a publicly available statistical online database85 with data for the period 1951-2014 on refugees (including people in refugee-like situations), asylum-seekers (pending cases), returned refugees, IDPs protected or assisted by UNHCR, returned IDPs previously protected or assisted by UNHCR, stateless persons and others of concern to UNHCR, disaggregated by country of origin and asylum. 86 Data are also provided on demographics, location, asylum-seekers (refugee status determination and monthly data) and resettlement. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "### INTRODUCTION\n\nWithin UNHCR public health covers various areas including primary health\ncare, nutrition and food security, reproductive health and HIV, mental\nhealth and integrated refugee Health Information Systems (iRHIS). Sectoral\nprogramming in a comprehensive response context means applying a wholeof-government (i.e. relevant national and local authorities for health and\nnutrition response) multi-stakeholder approach and planning with relevant\npartners. The overall responsibility of coordinating the health sector\nresponse in refugee-only situations will be with the Ministry of Health, with\nsupport of UNHCR and relevant partners. A wide range of partners play\na role in planning and delivering public health interventions in different\nareas and at different stages of the refugee response. For an effective and\ncomprehensive response it is therefore essential to know how and when to\nengage these various partners. Though the establishment of refugee-specific\nservices may be needed in the early phases of a refugee situation, longer\nterm solutions are required to ensure that refugees have access to services\nthrough the national health system. Host countries may require assistance\nfrom other partners, including international organizations but also local\npartners, to make the necessary adjustments to comprehensively include\nrefugee health needs into national development and local health plans,\nto strengthen/reinforce national and local resilience of national and local\nhealth systems to meet the health needs of refugees and host communities.", "output": {"entities": {"named_data": ["integrated refugee Health Information Systems"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The rapid humanitarian assessments of newly conflict-induced displaced populations often detect\nchildren amongst those injured by the armed clashes. Aside from material hardships, the psychological\nimpact of the conflict and subsequent flight is deemed to be severe. Recruitment and use of children\nby armed forces and armed groups remains a significant risk in light of the fragmentation of NSAG and\nvarying degrees of interest in compliance with IHL. Active conflict led to 11,418 civilian casualties [5 ] in\n2016 - approximately 11% were women and 31% were children. In 2016, the Country Taskforce on\nMonitoring and Reporting (CTFMR) verified 57 incidents of recruitment and use of children in the\nconflict (89 boys) who were recruited and mainly used for planting IEDs, transporting explosives,\ncarrying out suicide attacks and spying. Forced recruitment is primary reason given by Afghan asylum\nseekers in Sweden and Norway. Poverty, coercion and lack of livelihood opportunities, including\nduring the more prolonged phases of displacement, is also a factor that contributes to the recruitment\nof children, particularly adolescents. Access to education in displacement is generally hindered by\nseveral factors: Poverty and destitution, with a loss of assets and means of livelihood, often forces\ndisplaced families to engage children in support of family resilience and interim livelihood strategies.\nLack of civil documentation, cultural and social norms, threats and intimidation, social status and\npoverty are significant obstacles. In 2016, 423 schools were intermittently closed due to conflict and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "only 12 percent relied on crops or livestock. This contrasts with the Eritreans in camps, who previously relied primarily on agricultural and labor income, and demonstrates the large differences between Eritrean households that could and could not acquire OCP status. This, coupled with the finding that OCP refugees have much higher levels of education compared to in- camp Eritrean refugees (as highlighted in Chapter 2), again indicates that OCP refugees were relatively well-off before displacement, and they still have family members or other support systems. Refugees across all regions of Ethiopia have benefited from livelihood training interventions provided by RRS, domestic and international NGOs, and humanitarian organizations. For example, the Ikea Foundation, through UNHCR, invested around US$100 million in the Dollo Ado camps in Somalia between 2012 and 2019. Much of this funding supported economic development and livelihood opportunities for refugees and the host community, including creating livelihood cooperatives in agriculture, livestock value chain, energy, firewood, and microfinance (Betts et al., 2020). In the north of the country, UNHCR worked with various partners to provide Eritrean refugees with vocational skills training, tools, and start-up capital for crafts, such as leather products, weaving, and tailoring. UNHCR records indicate that more than", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR records"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "related to education, gender, and age. The results in Figure 3.26 show that refugees are under-represented in high-skill occupations and services and sales relative to what we would expect based on their age, gender, and education, and substantially over-represented in crafts and related trades. Among employee workers, OCP refugees also earn less than hosts, though the wage gap is smaller than for in-camp refugees. Annex D, Table D.9 shows that refugees in Addis earn 25 percent less than their hosts. Even after adjusting for demographic characteristics, occupation, and sector of work, this wage gap remains at around 19 percent. 0 20 40 60 80 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female - Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure 3.22: Work status by gender Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure 3.23: Work type by gender Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Addis Hosts Male", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location. Area stratifi- cation reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analyzed. ” Afrobarometer provides geocoded data for 6 rounds, which correspond to the 1991 – 2016 period, with the information on an individual ’ s ethnicity available from round 3 (corresponding to 2005 – 2006). We therefore restrict our analysis to the 2005 – 2016 period. The selection of countries is driven by data availability. Among the 33 countries with available Afrobarometer data, we exclude Botswana, Cape Verde, Lesotho, Madagascar, Mauritius, Sao Tome and Principe, South Africa, and Swaziland, for which no data is available on refugee camps or from the EPR-ER. We also exclude Sudan since the question on individual ethnicity is not asked in this country ’ s survey.", "output": {"entities": {"named_data": ["EPR-ER"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "to documentation: Enhance digital infrastructure in refugee hosting areas to facilitate refugee inclusion to the digital economy including digitally enabled livelihood opportunities and financial inclusion as well as to foster their access to socio- economic e-services, including standardized travel documents. Source: Ethiopia GRF Pledge Progress Report (RRS & UNHCR, 2021 and 2023) Pledges Progress as of 2023 Annexes 92 S ESRE is a separate but integrated survey alongside the Ethiopian Household Welfare Statistics Survey (HoWStat),54 the national household survey to measure poverty and other socio-economic outcomes. Like most national poverty surveys, HoWStat excludes displaced populations—Internally Displaced People (IDPs) or refugees—including in Ethiopia. To have up-to-date information on the socio-economic outcomes and poverty levels of refugees and to allow comparison to Ethiopian host communities, the SESRE applied the same questionnaire and data collection methods as the HoWStat, with some modifications. Training of the enumerator team and implementation arrangements of the survey followed the same standards and procedures as the HoWStat. SESRE data was not collected alongside HoWStat due to security concerns at the time of data collection for HoWStat, especially in the refugee areas. The SESRE aimed to solve two problems: (i) gaps in data on the socioeconomic dimensions of refugees,", "output": {"entities": {"named_data": ["Ethiopian Household Welfare Statistics Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "to the Venezuelan migratory crisis, whereas the VenRepPS survey was conducted during the pandemic in 2020. These temporal inconsistencies result in varying sample composi- tions across surveys, diverging from the landscape we observe in 2022. Notably, forced migrants in the VenReps Kids survey migrated during the crisis but have since remained in the country for several years, potentially leading to disparities in household integration outcomes. IV GENERAL DESCRIPTIVE STATISTICS IV. A Key characteristics of adults Table 2 provides descriptive statistics for the adults in our study, encompassing the pri- mary caregiver, mother and father (if residing with the child), and the individual finan- cially responsible for the child (should they be different from the aforementioned per- sons). Typically, the roles of primary caregiver and financial provider are fulfilled by either the mother or the father. The table is organized into three panels for clarity: Panel A details key individual characteristics, Panel B outlines adults ’ access to services, and Panel C focuses on labor market characteristics. Within the table, columns (1) and (2) present average values for adults from Colombia and Venezuela, respectively, while the final column displays the results of mean difference tests between these two groups, with standard errors noted in brackets. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["VenRepPS survey", "VenReps Kids survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Aldo Morri for his excellent editorial support. Finally, we extend our deepest gratitude to the survey respondents for their willingness to share their experiences which has been instrumental in deepening our understanding of the challenges and needs faced by both refugees and host households. ACKNOWLEDGEMENTS ii Introduction E thiopia, with its long history of hosting refugees, is grappling with the complex challenges of accommodating close to 1 million refugees and asylum seekers. These come primarily from neighboring countries like South Sudan, Somalia, Eritrea, and Sudan housed in camps in mostly rural areas spread around the country near border areas. While Ethiopia has adopted progressive refugee policies, including the Comprehensive Refugee Response Framework (CRRF), challenges persist in translating these policies into tangible socioeconomic outcomes for refugees. Despite Ethiopia’s efforts to shift from a camp-based approach to a more inclusive model promoting self-reliance and integration, refugees live largely in camps, are reliant on humanitarian aid, and face barriers to accessing employment and education. The country’s new Refugee Proclamation grants refugees the right to basic services, work, and freedom of movement, but implementation delays hinder their realization. To address these challenges and achieve better development outcomes for both refugees and host communities, a", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The LFS's rotating panel design introduces attrition that is potentially non-random: households that relocate between LFS interview waves are excluded from panel analyses, and mobile households may differ systematically from stayers in their labor market outcomes. We test for selective attrition by comparing baseline characteristics of households observed in all four LFS waves with those lost to follow-up due to residential relocation. Attritors are somewhat younger and have lower educational attainment on average, but observable baseline differences are small in magnitude.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Furthermore, in some contexts, registration as an IDP can expire after a prescribed timeframe without regard to whether the person achieved a durable solution (e. g. after five years in Russia). The accuracy of IDP registers is greatly impacted by political considerations, particularly a government ’ s willingness to acknowledge internal displacement and to enable the humanitarian community to respond. Some countries may be reluctant to acknowledge the presence of IDPs, or may be inclined to understate numbers to demonstrate progress in military operations or limit assistance provided to IDPs. For example, in Kenya, registration of individuals displaced by the 2007 and 2008 post-election violence excluded ‘ integrated ’ IDPs, i. e. those who had sought refuge with host communities or rented accommodation in urban areas, as assistance was limited to registered IDPs. Alternatively, aggregate numbers of IDPs in particular countries may be inflated to suggest a deterioration of the situation or to maximize humanitarian assistance. Therefore, access to IDP areas and the willingness of IDPs to be counted may be largely dependent on government policies. These political considerations can lead to disagreements on the data, undermine cooperation and in some cases even lead to reduced humanitarian funding. Profiling of IDP situations Profiling of IDP situations is a collaborative process aimed at generating reliable data that can be broadly agreed upon. As a collaborative process, it can be a crucial tool for generating agreement on persistent questions such as who is recognized as internally displaced within a given context, what are the most prevalent vulnerabilities caused by displacement, and how do IDPs fare compared to host populations. In sought refuge with host communities or rented accommodation in urban areas, as assistance was limited to registered IDPs. 68 However, in many conflict-affected countries, governments lack the basic capacity to maintain Civil Registration and Vital Statistics (CRVS) systems including the registration of births and deaths in non-displacement situations, let alone the registration of IDPs displaced due to natural disasters or conflict. 69 IOM has introduced biometric registration systems in South Sudan, Sudan, DRC and Nigeria to circumvent these problems.", "output": {"entities": {"named_data": ["Civil Registration and Vital Statistics"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "While there is a large literature on the role of immigrants on the native-born population in terms of labor market competition, there is a limited amount of studies that examine the effect from displaced populations. Many conclusions from the traditional literature on the study of immigrants ’ impact on natives cannot be applied to the case of Syrians in Turkey. There are many differences between the inflow of Syrians and other flows of extended family and economic immigrants. First, the sheer volume of Syrian refugees and the short time- frame in which they entered Turkey is unprecedented. For the case of Syrians in Turkey, or displaced populations in general, large movements of refugees are not restricted due to humanitarian reasons. Second, formal immigration processes are controlled, limited, and regulated by destination countries. Therefore, results from literature on “ immigrants ” are very different than a focus on displaced or refugee populations. Recent literature on the labor market effects of SUTPs estimates negative impacts on host community employment rates. The negative displacement results are largest for the young, women, informal workers, 2 United Nations High Commissioner for Refugees (UNHCR) – Syrian Regional Refugee Response, Inter-agency Information Sharing Portal 3 (UNHCR) – Syrian Regional Refugee Response, Inter-agency Information Sharing Portal Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Inter-agency Information Sharing Portal 3"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 2**\n\n**FGDs Questionnaire**\n\nFGDs Questions :\n\n1. Torture, Arbitrary detention, and Forced Disappearance, are some of the protection risks faced by youth\nin NWS, from your point of view What solutions or actions would help mitigate/overcome these\nprotection risks?\n2. The survey results conducted earlier in NWS indicated that 66% of youth are facing discrimination, if you\nagree with that statement, can you please clarify and elaborate the factors contributing to\ndiscrimination, exclusion, and stigmatization among youth in the region?\n3. What challenges do young people encounter when trying to acquire official documents?\n4. Which are the top 5 risks you believe young males face? and which are the tops 5 protection risks young\nfemales face?\n5. How can organizations engage young people in NWS in the rebuilding of their communities?\n6. 54% of the surveyed youth in NWS who themselves or their family have properties and 83% of them\nhave property documents, could you explain more by whom it is occupied?\n7. What are the main protection risks related to your/your family's properties?\n\nDemographics:\n\nFemale Participants: Total of 50 across all FGDs, 25 in Aleppo/ A'zaz and 25 in Idleb/Idleb.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey results"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "tegrated in Tajik labor markets relative to their previous situation in Afghanistan. This is a typical characteristic of forcibly displaced populations as it naturally take time for them to recover their livelihood. Moreover, females have lower labor market participa- tion rates, hours worked, and wages relative to men. We also see that student enrollment for individuals of schooling age is dramatically lower in Tajikistan relative to Afghanistan. Finally, not surprisingly, household size for refugees is smaller in Tajikistan, which likely originates from family separation due to forced displacement from Afghanistan. Outcome variables. Using the census data, we construct five index variables for each of the outcomes of interest: refugee integration, educational attainment, mental health, income and consumption, and labor market outcomes. Indexes for dichotomous vari- ables were constructed as the average of all outcomes. Indexes that include continuous variables were constructed standardizing each variable, averaging all variables, and stan- dardizing the average once again. The methodology to construct indexes was adopted from Kling, Liebman and Katz (2007). Appendix B. A describes in detail all the variables included in each of the indexes. In sum, each of the indexes comprises the following: Integration index: Includes four variables that measure the feelings of belonging of refugees in Tajikistan. These are measured using a Likert scale. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Papaioannou, 2016; Berman et al., 2017; Harari and Ferrara, 2018; Eberle et al., 2020; McGuirk and Burke, 2020b). As a further robustness check, we also use data on conflict incidence and intensity from the UCDP, which uses a more conservative definition of conflict. The UCDP dataset is manually curated and compiled with automated computer assistance (Sundberg and Melander, 2013). The UCDP defines an armed conflict event as “ an incident where armed force was used by an organized actor against another organized actor, or against civilians, resulting in at least one direct death at a specific location and a specific date ” (Pettersson et al., 2020). We extract daily event observations from the UCDP dataset if the location of the actual event is exactly known, the event location is within a radius of less than 25 km around a known point, or at least the administrative district where the event happened is known. As pointed out by Eberle et al. (2020), the UCDP events are more likely to capture violence between large-scale and more structured groups. Table B. 2 shows that on average, conflict events seem to occur more in refugee-hosting areas. This is of course not a causal interpretation but a simple correlation.", "output": {"entities": {"named_data": ["UCDP dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The cooperative medical scheme's benefit package was reviewed during project preparation to assess its adequacy for the health care needs of the target population. The review found that the scheme covered primary care visits and generic medicines at contracted facilities but excluded dental care, mental health outpatient services, and optical care. The project will support a targeted benefit expansion for enrolled refugees and host community members in the lowest two wealth quintiles, covering MHPSS outpatient consultations at no additional premium cost.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "groupement level data cannot measure the character of the displacement in any meaningful way, but analysis at the individual level can. 5. 2 Individual Level Relationships Two survey waves posed additional questions on local displacement dynamics. While these ad- ditional questions restrict comparison with other survey waves, they provide the opportunity to unpack the mixed results found in the aggregate analysis. Poll 14 is a special survey that only sam- ples cities (Ville de Goma, Ville de Beni, Ville de Butembo, Ville de Bukavu, Ville d ’ Uvira, Ville de Bunia and Irumu in particular) while Poll 15 is a representative sample of all territoires in the three provinces. 11 These survey waves are labeled as “ Cities ” and “ General ” samples in the indi- vidual analysis. Analyzing these two surveys together enables the comparison of relationships by the local context, which may distort the impact that hosting has on perceptions of social cohesion. Figure 5 plots the coefﬁcients from series of logistic regressions to account for the binary na- ture of the dependent variables. The regressions include Province ﬁxed effects and groupement clustered standard errors to account for unmeasured context-speciﬁc dynamics. Responses are weighted by the inverse proportion of selection at the territoire in each regression.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey waves"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The resulting migration matrices should be viewed as work in progress, but they are an important step in an ongoing global effort to improve migration data. The matrices can be readily updated as additional or superior information surfaces, and they can easily be extended to include future census rounds. Bilateral datasets of international migration are rare. Attempts to create them have focused almost exclusively on industrialized countries as destinations because these countries have more accurate and more frequently produced data. Harrison and others (2003) calculate bilateral remittances for the countries of the Organisation for Economic Co-operation and Development (OECD) together with the 27 largest nonmembers. These estimates are based on international bilateral migrant stock data that the authors also provide, although many of the data are derived from the Trends in International Migration (OECD 2002). This report, published annually since 1973, was arguably the most comprehensive guide to international migration for many years and has been the basis for many studies (see, for example, Mayda 2007). More recently, the OECD has developed a database that provides a comprehensive overview of migration to OECD countries in 2000 (OECD 2008). These data are disaggregated by a number of covariates including age, gender, educational attainment, and place of birth. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Trends in International Migration"], "descriptive_data": ["international bilateral migrant stock data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We, therefore, first examine whether or not there is structure, both, to selection into the treatment group and attrition, which could undermine our econometric approach, where we rely on difference-in-difference estimators. Imbalance between treatment and control groups could undermine the key assumption of parallel trends. For example, given that men and women face different barriers in the labor market, we should not expect employment to evolve in the same way for men and women after the treatment. We would expect to observe a difference-in-differences for a treatment group where women are more common than in the reference group, even without the program. To test for imbalances, we run a simple regression of treatment and attrition indicators at baseline on the socio-economic and demographic controls, GRIT indicators, self-reported optimism, employment status and risk. Table 4 (Column 1 for the treatment analysis, Column 2 for the attrition analysis) shows some signs of structure. In particular, host status and risk preferences are significantly different between treatment and control, with 9 For example, “ hummus ” is used to refer to chickpeas in general but can also be used for the dish involving mashed chickpeas, tahini, lemon and garlic in Lebanon. In other dialects, some qualifiers are required to specify this dish (e. g. hummus ne ’ em, or smooth hummus). This is akin to identifying a British or American individual using similar variations in foodstuffs such as courgette / zucchini; coriander / cilantro; etc. 10 Specifically, we regress variables with missing observations on the list of all variables with a complete record. We then use the predicted values from this regression to populate the missing variables. Where appropriate, predicted values are rounded to the nearest integer and within answer codes of that variable. In a second round, this process is repeated on the full set of actual and predicted values from the first stage.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["GRIT indicators"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The MPI can be decomposed by any groups for which the data are representative and broken down by indicator to show the composition of multidimensional poverty, adding to the policy relevance of the analysis. To tackle individual-level and intrahousehold analyses, we build on the work of Alkire, Ul Haq and Alim (2019). The focus is on individual deprivations, and we call the persons with individual-level data in each indicator the eligible household members. For example, children aged 6-16 years might be eligible for deprivations in terms of school attendance, but not those older or younger. For individual-level indicators, we identify who and how many household members are deprived: their gender and their age, and what proportion of eligible household members are deprived. This is a powerful and potentially informative steppingstone for analysis. Consider two households, each of which has five eligible members with data on nutrition. The aggregation rule in this example is that if any household member is undernourished then the household is undernourished. So, both households are deprived in terms of Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The descriptive characteristics of foreign-born and host community households are still comparable, and therefore allow for comparisons between 2013 and 2014. It is possible to make some inferences on welfare changes by looking at changes in host community employment rates and labor market characteristics. Overall, this paper finds no negative effects on host community welfare from an increasing population of SUTPs. As other authors have stated, the influx of SUTPs has had both positive and negative impacts. It seems on average, the host community has been strong and adaptive, and not negatively impacted. This is not to disregard that real strains do exist in some regions where the SUTP population is very large. Nor do these results undermine findings of displacement effects in the labor market that certain types of workers are experiencing. However, on average nationally, we do not see a systematic decline in the welfare of the host community between 2011 and 2013. The remainder of the paper is organized as follows. Section 2 outlines the data availability and technical issues. Section 3 discusses descriptive statistics of the foreign-born and host community. Section 4 explores the impact of the foreign-born population on host community welfare. 1. DATA AND TECHNICAL ISSUES Data Sets Data availability limits which data set can be used to identify the foreign-born population and geographic location while measuring poverty. The Turkish Statistical Institute (TUIK) has been conducting three nationally representative surveys annually since 2005; the Household Income and Consumption Expenditure Survey (HICES), the Survey on Income and Living Conditions (SILC) and the Labor Force Survey (LFS). However the HICES, which is the national survey that is used to measure official poverty, 5 UNCHR, 2013pg 13 6 UNHCR (22 November 2013), UNHCR (15 September 2014) 7 UNHCR (22 March 2013), Erdogan (2014), pg 14 8 This technical issue will be discussed further later in the paper.", "output": {"entities": {"named_data": ["HICES", "Survey on Income and Living Conditions", "Household Income and Consumption Expenditure Survey", "Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Ethiopia in 2017. The SPS 2017 was conducted in refugee camps and host communities in four regions in Ethiopia. The survey was used to draw a profile for skills and potential opportunities for refugees and host 9 See Annex C for detailed information on survey design and methodology. 10 Financial support was provided by the World Bank and UNHCR Joint Data Center on Forced Displacement. Introduction 7 communities to design a better mix of approaches that could help the government in designing livelihood opportunities for these communities. Due to differences in scope, sampling design, and methodology, results based on the SPS cannot be directly compared with those of SESRE. Box 1.1 summarizes the similarities and differences between SPS 2017 and SESRE 2023. The Skills Profile Survey (SPS), conducted in 2017, is a household survey focused on collecting data on refugees from South Sudan, Somali, Eritrea, and Sudan living in camps in Ethiopia, as well as from host communities. The sample frame for the survey was derived from the list of all refugee camps, sites, and locations provided by UNHCR-Ethiopia as of January 2017, covering the four main regions that host refugees: Tigray, Afar, Gambella, Benishangul-Gumuz, and Somali. The SPS specifically", "output": {"entities": {"named_data": ["SPS 2017"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Wages: the earnings measure we use is the response to the question “ how much did you earn from your main job activity during the last month? ” In the LFS 2011 there is further information on how much of that income was irregular, for example a bonus payment, but the LFS 2014 no longer provides that breakdown. There is also a measure of the “ number of hours per week worked in the main job ” (both usual and total hours), which can be used to construct hourly wages. Since the hours worked measure does not correspond exactly to the earnings measure and introduces additional measurement error, our preferred wage measure is the monthly wage. We exclude wage observations were respondents report having usual working hours of less than 14 or more than 84 hours per week. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LFS 2014", "LFS 2011"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Disaggregated data on IDPs who are not protected or assisted by UNHCR are collected by other agencies, including IOM, but data are not comprehensive and therefore not published in IDMC ’ s global reports. 93 The difficulties of collecting disaggregated data on locations of IDPs are compounded by the fluidity of IDP movements — IDPs might suffer multiple displacements or they might resort to changing locations as a coping strategy (e. g. moving between their homes and place of displacement or testing different locations before deciding where to stay) (Brookings 2011). In recent years, efforts have been made to improve data collection for IDPs living outside of camps by employing a range of techniques including: (a) profiling; (b) household surveys; (c) collecting information on IDPs who come to camps to visit family members or collect relief items; and (d) community outreach programs (Brookings 2013).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Disaggregated data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "impression of the state ’ s role in development and give credit to the state for helping to leverage external resources. Citizens are also likely to give the state credit where mechanisms to voice complaints about non-state actors exist and where bureaucrats are able to eﬀectively respond to complaints. Under these conditions, non-state service provision is likely to strengthen the ﬁscal contract. 5 Data and Methods I explore the relationship between external service provision and deference to government using Afrobarometer survey data from 19 Sub-Saharan African countries (see Table 1). Africa is an especially good place to examine these issues because of the large amount of variation both within and across African countries in the extent to which non-state actors, donors and other states are active in service provision and the extent to which governments are relatively eﬀective and fair. Government responsiveness, corruption and reliance on non-public resources vary considerably among localities with consequences for citizen understanding of and relationship to government (Gibson and Hoﬀman, 2005). This project relies on the fourth round of Afrobarometer data that surveys Africans ’ views towards democracy, economics, and civil society with random, stratiﬁed, nationally representative samples.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "I expect citizens who perceive tax collection to be the responsibility of non-state actors to be less likely to be willing to pay taxes to the state ’ s tax department than citizens who perceive tax collection to be the responsibility of the state. Perceptions of the eﬀectiveness of donor and non-state actor provision of services are assessed using the following items. Respondents were probed on how much they believe the following non-state actors and donors do to help their country: the United Nations; international donors and NGOs; international businesses and investors; China; and the United States. 9 I also include Freedom House ’ s political liberties and civil rights ratings for the 19 countries in the sample. These two variables should capture the relative equality of inﬂuence in making policy. They indicate whether citizens are able to express their voice without fear of repression and whether elections are free and fair. Neither of these variables are signiﬁcant at the p < 0. 05 level. 14", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Outcome Variable Collection Periods Basline Midline Weekly Endline Psychological Well-being PHQ9 X X Life Satisfaction Index X X Stress Index X X X Sociability (Total) X X X Sociability (Positive) X X X Self-Worth Index X X Locus of Control X X Allocation Decision Game X X Stability Index X X Physiological Well-being Index X X Gender Dynamics Gender Perceptions- Work X X Gender Perceptions- Violence (IPV) X X Financial Well-being Savings X X ∗ X Borrowing X X Economic Decision Making Risk Preference X X Time Preference X X Other Outcomes Cognitive Ability X X ∗ X Physical Health X X ∗ X Notes: The “ Baseline ” survey was conducted with respondents before treatment assignment was revealed. The “ Midline ” survey were questions asked immediately after treatment assignments were disclosed after the baseline survey, but before the work task had begun. “ Weekly ” surveys were conducted after each week of work (if any).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Component 4: Project Management, Monitoring, and Evaluation (US$3.8 million)**\n\nThis component will finance the operating costs of the Coordination Unit and support the rollout of the Management Information System (MIS) across all twelve participating district offices. The MIS will track beneficiary registration, service delivery milestones, and grievance redress outcomes, providing real-time dashboards for national coordinators. Quarterly progress reports generated from the MIS will form the basis of implementation support missions. The component also covers the cost of the endline evaluation, contracted to an independent firm, and capacity-building workshops for district-level M&E officers.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Women ’ s Empowerment in Agriculture Index (WEAI) (Alkire et al. 2012) uses individual-level data, and the linked Gender Parity Index reflects inequalities across women and men ’ s deprivation scores within the same household. Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer. Espinoza-Delgado and Klasen (2018) create an individual-level MPI to understand differences in poverty between women and men in Nicaragua, finding similar overall incidence, but much higher intensity of poverty among women. Bessell (2015) and Pogge and Wisor (2016) explore deeply contextual gendered poverty measures and elucidate the ways that participatory consultations can inform the design and uses of gendered measures. Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa. Alkire, Ul Haq, and Alim (2019) use individual-level data alongside MPI data to expose gendered and intrahousehold differences among MPI poor and non-poor children. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["EU-SILC data sets"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "include the rice price — which appears with the wrong sign but is only marginally signiﬁcant — and elevation and population density — which are no longer signiﬁcant. Comparing Tables 7 and 5, we ﬁnd that in the smaller NLSS 2002 / 3 dataset none of the anticipated consumption variables is statistically signiﬁcant. Other results are as before. 4. 4 Magnitude To assess the relative magnitude of our results, we multiply coeﬃcients estimated in Tables 4 and 5 by the standard deviation of their respective regressors. We then average over the various regressions reported in Tables 4 and 5. Calculations are summarized in Table 8. The larger the value, the more inﬂuence the regressor has on the choice of a destination district. We see that the most important regressors in terms of magnitude are travel time to the near- est road, elevation, language similarity, and the price of rice. Consumption variables have an eﬀect on migration destination that is smaller in magnitude: a one standard deviation increase in anticipated relative consumption, for instance, has an eﬀect on destination that corresponds to a third of the eﬀect of a one standard deviation in elevation — and one-sixth of a one stan- dard deviation in distance from the nearest road. Income variables have a negligible eﬀect on migration decisions. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["NLSS 2002 / 3 dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "25 less cohesion. The increase in solidarity is less apparent among refugees where the majority (53 %) stated that the crisis had no effect on solidarity. Figure 18: Levels of trust, by group (June) (%) Source: Listening to Displaced People Survey, 2014. Perceptions that different groups have of others are important elements of peace. When asking for the degree to which neighbors, other villagers and people from other ethnic groups can be trusted the survey finds positive outcomes. Although all groups trust people from other ethnic groups slightly less, the general level of trust is high and it remains stable over time. Finally, consider how IDPs, refugees and returnees envision the future of Mali. The majority of refugees in Mauritania vie for an independent or autonomous North, while the majority of IDPs, returnees and refugees in Niger wish to see full government control over the North. 20 20This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Displaced People Survey", "Afrobarometer perception survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the Refugee Proclamation of 2019, while encampment undermines achieving the goals set out in the Proclamation. The GoE pledged to transform camps into settlements and facilitate mobility for refugees to take advantage of opportunities in the labor market and increase work authorization to allow for the formalization of working conditions. Humanitarian and development partners should strongly support this pledge by swiftly investing in and accelerating inclusive approaches through the engagement of line ministries. Yet, large gaps in financing remain to fill the needs of refugees and host communities. Better coordination and engaging line ministries can achieve better outcomes for refugees and their hosts. Implementing an overarching coordination mechanism across line ministries to track investments and progress on refugee inclusion could leverage the existing humanitarian resources to deliver the first mile investment into inclusive development approaches, led by development actors. Improved communication, collaboration, and connections between RRS and line ministries could support initiatives seeking to mainstream refugees into existing governance structures. Encouraging these collaborative efforts of departments and agencies of the GoE can achieve a successful implementation of development solutions. Efforts to improve the coverage, accuracy, reliability, quality, and comparability of data can provide the analytical underpinning for policy decisions. Better", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5 implemented in six areas: Bamako, the regional capitals of Gao, Timbuktu, and Kidal as well as one refugee camp in Mauritania and one in Niger. Bamako was selected because it is home to a large number of IDPs. The refugee camps were selected to obtain a sample of refugees. Returnees were identified in the regional capitals of Timbuktu, Gao and Kidal where the phone network was (still) functional. The approach to selecting respondents differed by location and depended on the availability of pre-existing population information.  Bamako: Listing information of all households with IDPs was obtained from the International Organization for Migration (IOM). Based on this data 10 districts were selected and in each district 10 households were randomly identified.  Gao, Timbuktu and Kidal: No listing data was available and the cities were divided into different sectors. The enumerator was assigned a starting point in a sector, a direction (North, South, East, West) and based on the code of the day 9 the enumerator selected the first household. If the code of the day was 4, the enumerator would choose the 5th house to conduct the first interview. No more than 6 houses were to be interviewed from one starting point.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Protection monitoring interviews conducted in the three northern border districts revealed persistent gaps in access to civil documentation, with 42 percent of respondents reporting that at least one household member lacked a valid national identity document. Barriers cited most frequently included costs of administrative fees, distance to documentation offices, and limited awareness of entitlements. Protection monitoring interviews were conducted with 1,840 households across 18 communities between January and April, with samples drawn using systematic random sampling from updated household registration lists.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Total Population**\n\n**11,735 Individuals**\n\n**(6,548 Cases)**\n\n**Vulnerabilities***\n\nUnaccompanied or\n\nseparated child\n\nWoman at risk\n\nSingle parent\n\nChild at Risk\n\nDisability\n\nChronic Illness\n\n**136**\n\n_*One individual may have multiple specific needs_\n\n**2,288**\n\n2020 2021 2022 2023 2024\n\n_*Pre-registration data refers to a headcount upon arrival or at disembarkation sites. Some individuals departed prior to registration with UNHCR._\n\nUNHCR / 1 November 2024 3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Pre-registration data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "cereal production (in kilograms) per residential [household member reported by the] household head in the immediate post harvest [period as the measure of agricultural] production.\n\nindex. [8] Here, we construct a food variety score (FVS) to combine the diversity of a\n\n\nperson's diet into a single index (Hatloy, _et al.,_ 1998). The FVS is based on the number\n\n\nof different food items eaten over a registration period. We evaluate two versions: 1) a\n\nassociated with food shortages into a numerical index. Our third alternative indicator is\n\n\nan index of these 'coping strategies'. We asked the most knowledgeable woman within\n\n1996 and the World Bank's forthcoming World Development Report on Poverty 2000/1, the demand for such comprehensive measures is more urgent than ever.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "each country. However, while we are aware of this limitation, the ESS has been extensively\n\n(2012) mentions that these questions in the ESS eliminate ambiguities by referring to _people_   _who come to live in a country_, rather than to _immigrants_ . In countries where citizenship is   based on blood ancestry such as Germany, a translation of immigrants would include people   who were born in the country but are not citizens.\n\nsection survey data. The second specification incorporates age, year of birth and survey year   dummy variables as explanatory variables (blue line). According to the model without any   cohort controls, older people are less likely to exhibit positive attitudes toward immigrants than   their younger peers in almost all countries considered.\n\n_Is the period of time long enough to capture life cycle effects?_\n\n\nIt could be argued that the period of time covered by the ESS - from 2002 to 2012 - may not be\n\n\nlong enough to capture life cycle patterns. Preferences may change over a longer period of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "There is one limit to our approximation in Equation 4. The ethnic composition of refugees in each year t for a given origin – destination pair of countries obtained from the EPR-ER database is assumed to be homogeneous across camps of the same origin – destination pair of countries for the refugees at year t. This may seem to be a strong assumption; however, the risk of misallocating refugees is reduced as the annual variation in the EPR-ER is generated by just a few dominant groups for a given origin – destination pair and the geographical distribution of refugees by country of origin is highly influenced by the proximity to their countries of origin. 16 As can be seen from panel A of Table B. 2, in refugee-hosting areas, on average, both EF and the EP seem to increase quite significantly when they are revised by incorporating the number of refugees in an 80-km buffer: the mean value of the standard EF index is 25. 58 %, while the mean value of the revised refugee EF index is 37. 90 %. The mean value of the standard EP index is 10. 11 %, while the mean value of the revised refugee EP index is 14. 07 %. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The countries in our sample are Benin, Burkina Faso, Burundi, Cameroon, Gabon, Ghana, Guinea, Ivory Coast, Kenya, Liberia, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, Tanzania, Togo, Uganda, Zambia, and Zimbabwe. As described in Table B. 1, we also incorporate information on the quality of our refugee data, which is determined by comparison with official UNHCR bilateral data. Below we describe how these data have been used to define our main variables of interest and present some descriptive statistics in Table B. 2. 11 Conflict. In Equation 1, we first relate variation in ethnic diversity with data on conflict from ACLED (Linke et al., 2010). Two main definitions are used: the incidence of conflict and the intensity of conflict. Incidence is captured by an indicator equal to one if conflict occurred in a particular year within a pre-defined buffer around cluster j. Intensity is measured by summing the number of conflict events occurring in a particular year within the same buffer area. A conflict event is defined as a single altercation wherein force is used by one or more groups for a political end (Linke et al., 2010). We further describe events (non-exclusively) as violent events, non-violent events, violence against civilians, and riots. In our main analysis, we focus on violent conflicts (Section 5. 1) and report results for other outcomes as robustness tests (Section 5. 3). In doing so, we follow a recent and large literature that has combined the ACLED dataset with geographically disaggregated data in Africa (Besley and Reynal-Querol, 2014; Berman and Couttenier, 2015; Michaelopoulos and 11Panel A of Table B. 2 shows descriptive statistics for the data from refugee-hosting areas specifically, whereas panel B of Table B. 2 shows descriptive statistics for our data in all covered areas. 11 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data on conflict from ACLED"], "vague_data": ["geographically disaggregated data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "for the exam in 2007, we were able to identify 71 percent three years later in ENLACE\n\nwe use administrative data from the annual school census ( _Formato_ _911_ ) to estimate the\n\nENLACE panel has a survival rate that is 6 and 7 percentage points lower vis-a-vis the\n\n<!-- PAGE 11 -->\nmatching. Take-up rate in the ENLACE test is high, 87.3 percent of the population\n\nsecondary in 2013. The difference in graduation or survival rates between the ENLACE", "output": {"entities": {"named_data": [], "descriptive_data": ["annual school census"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "restrictions on their employment. Legal restrictions (i.e., not having work permits) and location often prevent refugees from working, which limits their ability to generate income and improve their economic situation. As a result, many refugees rely on food aid and have limited access to necessities such as housing and electricity. Multidimensional poverty tends to be high among refugees. Low living standards and low education primarily drive multidimensional poverty. Standard of living indicators, low-quality cooking fuel, inadequate housing and low asset ownership, contribute half to non-monetary poverty. Moreover, deprivation in education and child malnutrition also contribute most to multidimensional poverty among refugees. Refugee households tend to have worse food security than hosts. In-camp refugees have less diverse diets, suffer food insecurity, and have low consumption status compared to hosts (Figure ES.4). Broadly, there is a need to enhance the economic self-sufficiency and food security of in-camp refugees and host communities by improving their livelihood opportunities. For in-camp refugees, consumption (expenditures) tends to increase with certain characteristics. These include higher education, access to mobile phones, owing a non-farm business, possessing a bank account, and being closer to a market town or and Woreda capitals. Education (of the household head) and employment strongly", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "to inform decisions such as the size of schools to ensure progress toward inclusive systems that support refugees and their hosts can be made. Strengthening the use of statistics includes facilitating access to data and disseminating results. The SESRE is an excellent start to this initiative. Yet, the need to systematically integrate refugees in every round of the national household surveys and other data collection activities is key to allowing for evidence-based policy making. 77 Abdelhady D., and Al Ariss, A. (2023). How Capital Shapes Refugees’ Access to the Labour Market: The Case of Syrians in Sweden. The International Journal of Human Resource Management, 34(16), 3144-3168, DOI: 10.1080/09585192.2022.2110845 Abu-Ghaida, D., and Silva, K. (2020). Forced Displacement and Educational Outcomes: Evidence, Innovations, and Policy Indications. Second issue. Quarterly Digest on Forced Displacement. Washington, D.C.: World Bank Group, UNHCR and JDC. Adda, J., Dustmann, C., and Görlach, J. S. (2022). The Dynamics of Return Migration, Human Capital Accumulation, and Wage Assimilation. The Review of Economic Studies, 89(6), 2841-2871. Alix-Garcia, J., Walker, S., Bartlett, A., Onder, H., and Sanghi, A. (2018). Do Refugee Camps Help or Hurt Hosts? The Case of Kakuma, Kenya. Journal of Development Economics, 130, 66-83. Alkire, S., Kanagaratman, U.,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "and their hosts by: (i) understanding different dimensions of welfare, such as monetary poverty, inequality, multidimensional poverty, food security, and shocks; and (ii) understanding determinants of welfare and estimating the cost to meet basic needs through a combination of assistance and some economic inclusion of refugees into national systems. Chapter 6 aims to understand how the location of camps determines labor market outcomes, highlighting the importance of refugees’ location as part of the development strategy for refugees in Ethiopia. Chapter 7 looks at social cohesion by showcasing attitudes between refugees and hosts and the level of social integration of refugees. Chapter 8 highlights policy directions based on the conclusions of the report to maximize the benefits of hosting refugees while minimizing the costs. (vi) Differences in poverty estimation: The poverty measurement methodology is distinct for each survey with SESRE applying the same methodology as HoWStat. Despite the difference in methodology used in the Skills Profile Survey (SPS) and SESRE, we find similar patterns in some of the indicators common in both surveys among in camp refugees such as demographic composition, primary and secondary net enrollments, housing condition, access to basic infrastructures, employment and attitude of hosts toward refugees. Moreover, both", "output": {"entities": {"named_data": ["Skills Profile Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "effects is Calahorrano (2011). Using panel data for Germany between 1999 and 2008, she finds   that immigration concerns decrease over the life-cycle.\n\nsimilar to Calahorrano (2011). However, given the lack of comparable panel data surveys for a   large group of countries, we use pooled cross-sections from the European Social Survey (ESS)", "output": {"entities": {"named_data": ["European Social Survey (ESS)"], "descriptive_data": [], "vague_data": ["panel data", "panel data surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Because the DHS cluster displacement algorithm can misassign households at cluster boundaries to the wrong treatment group, we conduct a robustness check in which we exclude all DHS clusters located within 10 km of the treatment threshold. This boundary exclusion sample represents 74 percent of the original sample. Point estimates in this restricted sample are almost identical to those in the full sample, and the exclusion of boundary clusters reduces rather than inflates the estimated treatment effect, allaying concerns about bias from spatial misassignment.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 Data from the EM-DAT database, created and maintained by the Center for Research on the Epidemiology of Disasters (CRED) at the Catholic University of Louvain. 3 There are multiple formal definitions.\n\nFor a disaster to be listed in the EM-DAT database, at least one of the following criteria should be met: (i) 10 or more people are reported killed; (ii) 100 people are reported affected; (iii) a state of emergency is declared; (iv) a call for international assistance is issued.\n\nHazard maps for countries can provide this information<br>(with uncertainty) based on historical data; hazard maps<br>can be adjusted based on climate models to investigate<br>future conditions (even larger uncertainty)\n\nnd evacuation<br>schemes.<br>Information and education<br>campaigns on risk maps|\n|Vulnerability|The vulnerability of the<br>exposed capital𝑉 (or<br>equivalently, total asset<br>losses )|Fraction of the population covered by an early warning<br>system and with ability to prepare and evacuate|Fraction of the population covered by an early warning<br>system and with ability to prepare and evacuate|\n|Macro-economic<br>resilience ( )|The interest rate and<br>marginal<br>capital<br>productivity ( );|Macroeconomic data provide this information|Policies<br>to<br>improve<br>the<br>macroeconomic context|\n\n_Figure B1: Roofer wages in an area where losses have been significant after the 2004 hurricane season in_\n_Florida. Data from the Bureau of Labor Statistics, Occupational Employment Surveys in May 03, Nov 03,_\n_May 04, Nov 04, May 05, May 06, May 07._\n\nFigure B2 is a classical quantity-price plot, showing the long-term demand and supply curves for a goods\nor service aggregated at the macroeconomic level. The green line is the demand curve: it shows how the", "output": {"entities": {"named_data": ["EM-DAT database", "Occupational Employment Surveys"], "descriptive_data": [], "vague_data": ["historical data", "Macroeconomic data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15 causes of death. The Danish Epidemiology Science Centre (1999) found severe malnutrition and high mortality in a survey of 422 refugee children in Guinea ‐ Bissau. They report higher malnutrition and higher mortality for children living in a non ‐ camp setting, compared to children living in a camp. The Goma epidemiology group (1995) found high prevalence of child mortality as well as acute malnutrition among children in refugee camps in Eastern Zaire, especially in female headed households. The magnitude of the difference between ‘ normal ’ mortality in the country under study, in the absence of conflict and the mortality in a refugee camp, depends on several parameters: the health infrastructure in the country as well as in the camp, the food available to camp and non ‐ camp residents, the frequency of visits by nurses or doctors, the intensity of the conflict (e. g. attacks on camps), and so on. Thus, the results are highly dependent on the context. For example, Singh et al (2005) do not find a difference in under 5 mortality among refugee versus non ‐ refugee households in western Uganda and South Sudan, whereas Verwimp and Van Bavel (2005) find higher child mortality and fertility among Rwanda refugees in Congo versus Rwandan women who did not became a refugee. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of 422 refugee children in Guinea ‐ Bissau"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "6 3. The Data The Kagera Health and Development Survey (KHDS) was originally conducted by the World Bank and Muhimbili University College of Health Sciences (MUCHS), and consisted of about 915 households interviewed up to four times from fall 1991 to January 1994 (at 6-7 month intervals) (see World Bank, 2004, and http: / / www. worldbank. org / lsms /). The KHDS 1991-1994 serves as the baseline data for this paper. Initially designed to assess the impact of the health crisis linked to the HIV-AIDS epidemic in the area, it used a stratified design to ensure relative appropriate sampling families with adult mortality. Comparisons with the 1991 HBS suggest that in terms of basic welfare and other indicators, it can be used as a representative sample for this period for Kagera (results not shown but available upon request). The objective of the KHDS 2004 survey was to re-interview all individuals who were household members in any round of the KHDS 1991-1994 and who were alive at the last interview (Beegle, De Weerdt and Dercon, 2006). This effectively meant turning the original household survey into an individual longitudinal survey. Each household in which any of the panel individuals live would be administered the full household questionnaire.", "output": {"entities": {"named_data": ["KHDS 1991-1994", "KHDS 2004 survey", "Kagera Health and Development Survey"], "descriptive_data": [], "vague_data": ["household survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "33 Ratha, D., and W. Shaw. 2007. ― South-South Migration and Remittances. ‖ World Bank Working Paper 102, World Bank, Washington, DC. United Nations Statistics Division. 1998. Recommendations on Statistics of International Migration Revision 1. New York: United Nations. United Nations, Department of Economic and Social Affairs, Population Division. [2008]. United Nations Global Migration Database. New York: United Nations. http: / / esa. un. org / unmigration — — —. 2006. Trends in Total Migrant Stock 1960 – 2000, 2005 Revision. Database. POP / DB / MIG / Rev. 2005 / Doc. New York: United Nations. — — —. 2009. Trends in International Migrant Stock: The 2008 Revision. Database. POP / DB / MIG / Stock / Rev. 2008. New York: United Nations. http: / / www. un. org / esa / population /. — — —. 2010. ― World Population Prospects: The 2009 Revision, Highlights ‖, Working Paper No. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["United Nations Global Migration Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The lack of certainty means children are trying to prepare for further studies (e. g., in universities) in two separate systems with varying requirements. The survey results indicate that students enrolled in both systems spend as much time in Italian schools as those attending only Italian schools, averaging 31 hours per week. However, students participating in both systems spend an additional 8 hours per week on online Ukrainian classes. This puts an extra burden on these children. Connectedness to Italy is correlated with demographic characteristics and social environment of refugee children. Additionally, Table 6 shows that making new friends in the country of destination and speaking Italian are strongly associated with higher connection to Italy. The mental distress resulting from displacement is a key barrier to educational integration for many Ukrainian refugees in Italy. The link between poor mental health and low school attendance and performance is widely acknowledged in the literature (see Fiining et al., 2019 for a systematic review). In the World Bank survey data, children and caregivers reported signs of mental distress, with 16 % of children and 24 % of refugee caregivers reported experiencing psychological distress 61 % 35 % 31 % 68 % 50 % 36 % 23 % 68 % 59 % 26 % 26 % 59 % Would like to continue living in Italy Would like to move back to Ukraine Feel strongly connected to Italy Feel strongly connected to Ukraine Caregivers (N = 283) Children between 9 and 14 years old (N = 141) Children between 15 and 20 years old (N = 96) Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["World Bank survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The study is based on two-period panel data collected by the IHDS, which was jointly carried out by researchers from the University of Maryland and the National Council of Applied Economic Research (NCAER) in New Delhi. This nationally representative survey covers a wide-ranging set of topics, including energy use, income, expenditure, education, health, and employment. The survey covers all of India's key states and union territories except Andaman and Nicobar Islands and Lakshadweep. The first round of the survey was carried out in 2004-05 (mostly in 2005) and collected information on 41,554 households in 33 states and union territories, 383 districts, 1,503 villages, and 971 urban blocks. The second one, conducted in 2011-12 (mostly in 2012), re-interviewed 83 percent of the original households and split households (if located within the same village or town), and interviewed 2,134 new households, for a total of 42,152 households.\n\nAccording to IHDS 2012 data, the average duration of power outages of households who have less than 24 hours of power supply is about 12.5 hours a day. Increasing the supply of electricity to 24 hours a day would lead to an estimated income gain for the rural population of US$6.5 billion annually.\n\n2 In the following, we use the terms \"access\" and \"grid-connection\" interchangeably.\n3 World Bank Enterprise Surveys (http://www.enterprisesurveys.org).", "output": {"entities": {"named_data": ["IHDS", "World Bank Enterprise Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Beneficiary assessments completed during the project preparation phase established that the primary constraint to economic inclusion among the target population was lack of legal work authorization, cited by 74 percent of respondents, followed by limited language proficiency in the dominant national language (58 percent) and absence of recognized educational credentials (51 percent). These beneficiary assessments were conducted through a combination of individual household interviews and focus group discussions with community representatives, using a standardized protocol developed with the Ministry of Labor.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 INTRODUCTION In 2015, an estimated 2. 2 million Syrians Under Temporary Protection (SUTPs) were residing in Turkey, the majority arriving in the country over the last 4 years. 2 Turkey ’ s national population is roughly 75 million; recent refugees account for approximately 3 percent of the population. For a country that has never experienced such a large-scale, sudden inflow of foreigners, demographic changes in the composition of the population and labor force will yield unprecedented implications. This paper examines, as data allows, the relationship between the size of the foreign-born population and host community poverty rates in Turkey. First, this paper finds the poverty rates of ‘ recent migrants ’ near the Syrian border (NSB) significantly increased from 2009 to 2013. Second, the number of foreign-born households being captured by the Labor Force Survey (LFS) is expanding, which suggests a growing number of foreign households that are likely to be Syrians. Third, with respect to poverty, the results show no negative impacts on the host community as a result of the increasing size of the foreign-born population. The impact of SUTPs has been both positive and negative. Overall, a significant negative impact on host communities ’ welfare is not observed in the data. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Night-time lights data are a beneficial by-product of a meteorological satellite program. The data are\ncollected by the United States Air Force Defense Meteorological Satellite Program (DMSP). DMSP\nsatellites have been circling the earth since the 1970s in a polar orbit that allows observations of every\n\n1. Number of illuminated pixels with _DN_ >=6 within the borders of a country.\n2. Average _DN_ within all illuminated pixels.\n_Sources:_ NGDC v4, World Development Indicators for land area, and author's calculations.\n\nLong-run patterns in _AoL_ and _R_ are consistent with some country circumstances. For example, as shown\nin Table 1, a country with a rapidly expanding _AoL_ is more likely to be a country with a high urban\npopulation growth rate. China, Indonesia, Malaysia, Vietnam, and Yemen are examples. A country with\nshrinking _AoL_ could be in the early, painful stages of transition from a planned economy to a market\neconomy. Azerbaijan, Tajikistan and Ukraine are examples. Countries with growing average radiance, _R_,\n\nSouknilanh et al (2015) find their night-light based _GDP_ estimates are improved when supplemented by ground cover data from a second satellite (MODIS).\n\n16 Normally there is a quasi-fixed ratio of intermediates to gross output which slowly falls as productivity improves. Countries that import most of their intermediates will be subject to external shocks (trading partner demand, terms of trade) that disrupt this relationship. 17 From a sample of 166 countries in 2010, from the World Development Indicators.\n\n[26] The result is the v.4 DMSP stable lights data set with between 20 and 100 observations per year per pixel depending upon circumstances (Baugh et al.", "output": {"entities": {"named_data": ["Defense Meteorological Satellite Program", "World Development Indicators"], "descriptive_data": [], "vague_data": ["stable lights data set"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "than 500, 000 refugees, together accounting for half of all refugees and people in refugee-like situations (excluding Palestine refugees). Major refugee hosting countries are typically the neighbors of countries of origin. For example, Syria ’ s neighbors (Turkey, Lebanon, and Jordan) together accounted for 27 percent of total refugee numbers; Afghanistan ’ s neighbors (Pakistan and the Islamic Republic of Iran) together accounted for 16 percent; and Somalia ’ s and South Sudan ’ s neighbors (Ethiopia, Kenya and Uganda) together accounted for 11 percent. Some countries (Lebanon, Jordan and Turkey) are hosting a particularly large share of refugees relative to their population (see Figure 10). 45 However, in all other countries, the number of refugees as a percentage of the population is 3 percent or lower, and most often below 1 percent. Figure 7: Top 15 Host Countries as a Share of Total Refugees and Asylum-Seekers 1991 – 2015 Source: UNHCR Statistical Online Population Database Note: Includes refugees, people in refugee-like situations and asylum-seekers. Excludes Palestinian refugees under UNRWA ’ s mandate. 45 Nauru is a special case since the Australian government funds the offshore processing center where refugees and asylum-seekers intercepted at sea are detained pending determination of their status.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The firm-level financial data for 1997 are primarily from the Worldscope database. The World\n\nin Australia and Canada, respectively. Using industry data from Mexico, Blomstrom and Persson\n\nCommission on an annual basis. We use [group-affiliation data from the 1994-1997 lists of business]\n\nby the level of growth of a sector or a country. Indeed, using firm level data, Haddad and Harrison", "output": {"entities": {"named_data": ["Worldscope database"], "descriptive_data": ["industry data from Mexico"], "vague_data": ["group-affiliation data", "firm level data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "9 Figure 10. Selected characteristics across Ugandan and refugee households, % Source: RHCS 2018, WB staff calculations. A simple comparison between refugees and Ugandan households demonstrates that refugees lag with regards to selected characteristics found to narrow the poverty gap. For example, refugees are less likely to have access to land than Ugandans. If refugees have access to land, the majority do not own it, but have user rights. The size of land also differs a lot among Ugandan and refugee households. Most Ugandans have at least 0. 05 hectare per capita, while the majority of refugees have less than 0. 05 hectare per capita. Refugee heads of household are also less likely to work and less likely to be literate compared to their Ugandan counterparts. Refugees have higher shares of children and elderly in household size compared to Ugandans. For example, among almost 60 percent of refugee households, more than half of the household members are children and elderly compared to 42 percent of households among Ugandans. Economic inclusion dividend When a development approach to hosting refugees is followed and refugees earn incomes, there are two key beneficiaries. Refugees themselves, who gain dignity, financial autonomy and pathways to self-reliance. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["RHCS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In the alternative specification, an additional step has been taken to assign calories to food items registered in the POF household expenditure questionnaire on frequent purchases (POF 3). The cost per calorie in this alternative scenario is minimally higher, leading to a food poverty line of R$259 in 2018 urban Southeast prices.\n\n_Source:_ Own calculations using POF 2017/18.\n_Note:_ Main specification estimates the cost per calorie based on assigning food items in POF to the TBCA and imputation for food\nitems at the 5-digit level (i.e., steps 1 and 2 described above). The alternative specification extends the imputation to larger food\ngroups based on categorization by IBGE (2020).\n\n**Lower** **Upper**\n**Total poverty line** R$455 R$1,061\n𝒔 [𝑭𝑭] 0.235 0.243\n_Source:_ Own calculations using POF 2017/18 data.\n_Note:_ These total poverty lines are based on the food poverty line of R$258, coming from our main specification, which is based\non the minimum calorie requirement of 2,100 calories per person per day and average cost per calorie of the bottom 40 percent\nof the welfare distribution, with calories having been assigned in a two-step procedure and deflated by household-specific foodprice Paasche indices, as described earlier in this section.", "output": {"entities": {"named_data": ["POF household expenditure questionnaire"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "New Research. World Bank. (2023b). Welfare in Forcibly Displaced Populations: From Measuring Outcomes to Building Capabilities. Leveraging Harmonized Data to Improve Welfare among Forcibly Displaced Populations and their Hosts: A Technical Brief Series. World Bank. (2023c). Do Legal Restrictions Affect Refugees’ Labor Market and Education Outcomes? Evidence from Harmonized Data World Bank. (2023d). Ethiopia Economic Opportunities Program: Aide Memoire. World Bank Group. 2021. “IDA19 Mid-Term Refugee Policy Review.” Washington, D.C.: World Bank Group. Zanfrini L., and Giuliani, C. (2023). Look at Me, but Better: The Experience of Young NEET Migrant Women between Vulnerability and Stifled Ambitions. Social Sciences 12, 110. https://doi.org/10.3390/socsci12020110. Zetter R., and Ruaudel, H. (2016). Refugees’ Right to Work and Access to Labor Markets – An Assessment, Part 1. World Bank Global Program on Forced Displacement (GPFD) and the Global Knowledge Partnership on Migration and Development (KNOMAD) Thematic Working Group on Forced Migration. http://bit.ly/KNOMAD-Zetter-Ruaudel-2016-1 Zhou, Y. Y., Grossman, G., and Ge, S. (2022). Inclusive Refugee Hosting in Uganda Improves Local Development and Prevents Public Backlash. References ANNEXES Annexes 83 E thiopia hosts refugees from some 24 countries. By far the largest groups are refugees from South Sudan, Somalia, and Eritrea. Each of these groups is described below, including", "output": {"entities": {"named_data": ["Harmonized Data World Bank"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "It will also be useful to compare the main labor market outcomes (employment, informality, temporality, work hours, earnings and hourly wages) between Venezuelan migrants and Ecuadoran workers with the same (nominal) education levels. This com- parison also allows us to examine whether the outcomes of highly educated Venezuelans are relatively better or worse than the outcomes for the less educated ones. 13 Table 4 reports this information. As before, we restrict the sample to the working- age population (age 15-70). The ﬁrst column summarizes the values for Ecuadoran natives, column 2 reports the data for the Venezuelan migrants, and column 3 presents the ratio of the value in column 2 relative to column 1. Several points are worth noting. As noted earlier, Venezuelan workers are much less likely to have low education levels (26 percentage points) and much more likely to have a college degree (25 percentage points) than the average native. In terms of employment rates, we observe that among 13 We have not succeeded in obtaining a systematic comparison of the quality of education in Venezuela and Ecuador. The best assessment is based on an analysis by Juan Maragall (Inter-American Development Bank) based on a 2009 PISA study conducted in the state of Miranda in Venezuela. The data show that students in Venezuela have lower reading and math levels than the average for Latin America. More speciﬁcally, the gap is estimated to be 13 percentage points for public schools and 6 percentage points for private schools. Given that Ecuador ’ s scores are in line with the average for Latin America, these data suggest that the quality of the Venezuelan education system is somewhat below the Ecuadoran counterpart. However, we also note that many Venezuelan migrants were schooled prior to the recent deterioration of educational institutions in Venezuela and that migrants are typically pos- itively selected in regards to their origin populations. As a result, it seems reasonable to assume that the educational credentials of Venezuelans are comparable to those of Ecuadoran workers. 8 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "7 Figure 1: Map of regions in Ethiopia, location of refugee camps, and refugee source countries. Source: Database of the Global Administrative Areas (GADM) (https: / / gadm. org / data. html, accessed on November 20th, 2020) In Ethiopia, the Refugees and Returnees Services (RRS, former Agency for Refugees and Returnees Affairs (ARRA)) is responsible for managing refugee camps and its oversight by making sure that the commitment of the federal government is met (Nigusie and Carver 2019). Except for Eritrean refugees, most of whom are eligible for out of camp policy, arriving refugees, at the time the data was collected, were allocated to one of the 26 refugee camps spanning the five refugee hosting regions. Refugees living outside of camps represent about 10 percent of the refugees in Ethiopia (Abebe et al. 2018). The allocation tends to be based on shared identity between the refugee and the host communities and the distance of the refugee camps from the border of the source country. The South Sudanese refugees are hosted in the refugee settlements in Gambella, except the few who were relocated to the refugee camps in Benishangul-Gumuz region. Most of these refugees arrived during the civil conflict in South Sudan in 2013 (Nigusie and Carver 2019). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Database of the Global Administrative Areas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The United Nations Economic Commission for Europe ’ s (UNECE) guidelines include a question on reason for migration, population with a refugee-like background and IDPs as non-core topics / questions (UNHCR 2016). 72 While most countries include questions on country of birth and citizenship, only about 40 percent include a question on year of migration, less than a quarter include a question on reason for international migration, and about a fifth include a question on the reason for internal migration (UNHCR 2016). 73 E. g. Kyrgyz Republic 1999 (refugee status), West Bank and Gaza 2007 (refugee status), Zambia 2000 and 2010 (purpose of stay), Germany 1970 (federal refugee identity card), Greece 2001 (reason for settling in Greece), Sudan and South Sudan 2008 (type of household including IDP and refugee), Liberia 1990 (ever displaced by war since 1990), Uganda 2014 (refugees). 74 UNHCR is collaborating with the Statistics Norway on systematically embedding forcibly displaced peoples in national statistics exercises and collaborates with national authorities and with UNFPA in various countries on the design of census exercises that include refugees, IDPs, returnees and stateless persons. 75 LSMS is a household survey program housed in the Bank's Development Research Group that provides technical assistance to national statistical offices in the design and implementation of multi-topic household surveys covering household behavior, welfare and interactions with government policies. All data gathered through the LSMS is published online in the Bank ’ s Central Microdata Catalog. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LSMS", "Central Microdata Catalog"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As can be seen from both panel A and panel B, non-violent conflicts seem to occur slightly more than violent conflicts. On average, the likelihood of violent conflict stands at about 48 %, while this figure increases to 52 % in refugee-hosting areas. Conflicts among more structured and large groups, as captured by the UCDP data, appear to be less frequent. UNHCR refugee data. To exploit the variation in ethnic diversity induced by the annual variation in refugees (and also to control for the direct effect of refugees on our outcomes), we use data on refugee camps provided by the UNHCR. The dataset contains detailed time-series information on the locations and sizes of 1, 453 refugee camps across the world and 821 refugee camps in Sub-Saharan Africa over the 2000 – 2016 period. To the best of our knowledge, the UNHCR currently provides the most comprehensive information available on refugees at the subnational level, allowing us to assess the ethnic composition of camps, which is key to our research question. First, we use the country of origin of refugees recorded for each year at the camp level to approximate the ethnic composition each camp. Second, we restrict the data on refugees to those aged 18 and above in order to make it comparable to the Afrobarometer- based individual data. Third, we only use data on refugees hosted within the boundaries of the host country. Merging data on refugee camps with the Afrobarometer, we end up with information on 172 camps 12 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["UCDP data"], "descriptive_data": ["data on refugee camps"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10100 This paper explores the impact of refugee return on social cohesion using data from Burundi, a country that expe­rienced high levels of repatriation during the 2000s. It uses a nationwide survey conducted in 2015 and relies on geographic features of the communities for identification purposes. The results suggest varying impacts of refugee return on different aspects of social cohesion. The stronger effects, suggest that refugee return has a negative impact on the feeling that community members help each other, could borrow money for emergencies from non-household members and feeling that the community is peaceful.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["nationwide survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Responding to the evolving situation, in line with the Return SOP endorsed by the\nHumanitarian Country Team (HCT) in February 2012, but also in accordance with the\n“Return Policy Framework for IDP from FATA” endorsed by the FATA authorities in 2010, the\nProtection Cluster agreed to conduct a series of consultations with the Shalozan Tangi\npopulation to capture their intentions and position vis-à-vis the return process.\n\n**II.** **Methodology**\n\nAfter the crosscheck of UNHCR data on Shalozan Tangi IDPs, 359 records were found. Out of\nthose, telephone numbers were available for 171 families. The return intention survey was\nconducted through IVAP call centre (enumerators trained by protection cluster on 20 May\n2014 on the return intention survey form) and 137 of IDPs responded to the calls. The\nquantitative data collection was facilitated by the use of Personal Data Assistants (PDAs)\nprogrammed with ODK system software. The RIS was conducted by 6 enumerators who\ncontacted displaced families from a call centre using the contact information available\nthrough the IVAP records. This strategy was chosen due to limited time constraint before\nthe start of returns.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR data on Shalozan Tangi IDPs", "return intention survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The descriptive characteristics of foreign-born and host community households are still comparable, and therefore allow for comparisons between 2013 and 2014. It is possible to make some inferences on welfare changes by looking at changes in host community employment rates and labor market characteristics. Overall, this paper finds no negative effects on host community welfare from an increasing population of SUTPs. As other authors have stated, the influx of SUTPs has had both positive and negative impacts. It seems on average, the host community has been strong and adaptive, and not negatively impacted. This is not to disregard that real strains do exist in some regions where the SUTP population is very large. Nor do these results undermine findings of displacement effects in the labor market that certain types of workers are experiencing. However, on average nationally, we do not see a systematic decline in the welfare of the host community between 2011 and 2013. The remainder of the paper is organized as follows. Section 2 outlines the data availability and technical issues. Section 3 discusses descriptive statistics of the foreign-born and host community. Section 4 explores the impact of the foreign-born population on host community welfare. 1. DATA AND TECHNICAL ISSUES Data Sets Data availability limits which data set can be used to identify the foreign-born population and geographic location while measuring poverty. The Turkish Statistical Institute (TUIK) has been conducting three nationally representative surveys annually since 2005; the Household Income and Consumption Expenditure Survey (HICES), the Survey on Income and Living Conditions (SILC) and the Labor Force Survey (LFS). However the HICES, which is the national survey that is used to measure official poverty, 5 UNCHR, 2013pg 13 6 UNHCR (22 November 2013), UNHCR (15 September 2014) 7 UNHCR (22 March 2013), Erdogan (2014), pg 14 8 This technical issue will be discussed further later in the paper. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Household Income and Consumption Expenditure Survey", "Survey on Income and Living Conditions", "Labor Force Survey", "HICES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, permit-holding status is neither sufficient for, nor necessary to, working in Israel. As of 2019Q4, just under a fifth of West Bank residents had the right to work in Israel and the occupied territories, but almost a quarter among them were not commuting across the border for work. The vast majority of such individuals are holders of Israeli or Jerusalem IDs. Conversely, among those who do commute to Israel and occupied territories, 17 % do not hold valid permits or IDs. Likewise, permit-holding status does not logically affect the formality status of the commuter. The frequent border crossings between the West 2This is derived from authors ’ own calculations using the 2016 Jordan Labor Market Panel Survey. 3It is worth noting that the LFS is representative of the residents of the West Bank and Gaza, whose work may not lie in the country. 6", "output": {"entities": {"named_data": ["LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Data on municipal level requests and approvals for disaster declarations were constructed from\n\nthe archives of Mexico's official diary. Data on municipal level Fonden expenditures, and\n\nlevel. Specifically, we calculate by municipality, from the 2005 population conteo and the 2010 population census, the number of dwellings with the following characteristics: dwelling has non\n\n12The source of state level GDP data is INEGI. GDP is measured in constant 2008 pesos.\n\nUNDP estimates of municipal GDP per capita in 2000 by municipal population. Third, we", "output": {"entities": {"named_data": [], "descriptive_data": ["municipal level Fonden expenditures", "2005 population conteo and the 2010 population census"], "vague_data": ["state level GDP data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In principle, the positive attitudes among many hosts towards refugees, the high degree of cultural similarity between groups, and the willingness of refugees to be engaged in their community are all promising signs for social integration. Yet, even in the Somali domain, where cultural similarity and host attitudes are greatest, more than two-thirds of refugees do not have an Ethiopian friend, and more than half do not find social interactions with hosts easy. Better employment outcomes for refugees with Ethiopian friends indicate the benefits of facilitating social integration for refugee livelihoods and economic integration. 0 5 10 15 20 25 30 Age Under 30 Age 30-44 Age 45-64 Age Over 64 Female Male Discrimination/harassment in past year Victim of crime in past 2 weeks Figure 7.20: Discrimination and harassment by demographic group Source: World Bank Staff based on SESRE 2023. 0 5 10 15 20 25 30 35 Eritrean Somali South Sudanese Addis Ababa All Refugees Discrimination/harassment in past year Victim of crime in past 2 weeks Figure 7.19: Discrimination and harassment Source: World Bank Staff based on SESRE 2023. 72 A ddressing the challenges refugees face in Ethiopia requires a concerted effort to promote their self-reliance, economic integration, and", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "It also increases women ’ s household decision making autonomy but decreases women ’ s ability to decide how their earnings are deployed. The results imply that to be successful, pro­grams to mitigate these adverse effects of conflict on women need to be context specific and rely on data-driven evidence from situations of conflict whenever possible. Policy makers are called to design programs that address harmful gender norms and intimate partner violence at the individual / household and community levels, especially for women residing in areas with high-intensity conflict. Measurement of women ’ s empowerment should consistently include sev­eral domains of women ’ s lives to gauge progress in voice and agency, financial autonomy, and violence reduction. This paper is a product of the Gender Global Theme. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at uekhator @ worldbank. org, jkelly @ hsph. harvard. edu, Amalia. h. rubin @ gmail. com, and darango @ worldbank. org. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The notion that volunteering may affect positively youth ’ s sense of social cohesion has gained policy relevance since the publication of the World Development Report 2013: On Jobs, which stresses that in countries affected by conflict situations, creating the types of productive opportunities that strengthen social cohesion can help reduce the volatility of economic growth and achieve international development goals by defusing tensions and building trust among the different communities involved. This paper provides novel empirical evidence on the impact of volunteering on enhancing social cohesion values in Lebanon, a country with a fragile and highly complex political, religious and social landscape, as well as high degrees of social and economic exclusion among its young population. To our knowledge, this is the first impact evaluation that rigorously addresses this research question in Lebanon and in the Middle East and North Africa (MENA) region. The main results show that youth who were selected to participate in a volunteering program that consisted of 80 hours of inter-community volunteering activities and 20 hours of soft skills training were more likely to report higher and improved values of social cohesion in the short term. In specific, they were more likely to report higher tolerance values as well as a stronger sense of belonging to Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For a detailed definition see section 4 Household size Number of people included in the case records of each PA in Individual ProGress dataset Wage Income 1 if the household receives income from employment and / or daily or irregular work Income from remittances 1 if the household receives income from remittances Income per capita Raw sum of household income from all sources; work, pension, assets in Syria transfers, donations, other organizations'humanitarian aid, and other divided by household size Male Adults Number of males above 18 (inclusive) in the household Marital Status Categorical variable. The classification includes married PAs with spouse in the household, married PAs without spouse in the household, widowed, single or engaged, and divorced or separated. Proportion of female Number of female divided by the household size Location Categorical variable for 11 Governorates / cities. Ajloun City, Aqaba, Balqa, Irbid Jerash, Karak, Maan, Madaba, Mafraq, Tafilah, Zarqa. In Camp 1 if the household is located in a refugee camp Poverty before UNHCR and WFP assistance 1 if household expenditure before UNHCR plus WFP assistance is below the poverty line (JD50) Poverty before UNHCR assistance 1 if household expenditure after WFP assistance but before UNHCR assistanc is below the poverty line (JD50) Source: Authors ’ elaboration. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Individual ProGress dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Refugees and Host Communities Household Survey expanded the national Household Consumption and Informal Sector Survey to include a representative sample of refugees and host communities, including Sudanese and host communities located in the east of the country. The remainder of this note is organized as follows. Section 2 presents a short discussion of the literature on the economic participation of refugees. Section 3 compares the characteristics of newly arrived refugees from Sudan with previous arrivals for whom survey data is available, to find that both groups are highly comparable. Section 4 uses the existing data to explore how the basic needs refugees are covered from own-income. Sections 5 and 6 dig deeper by exploring econometrically the correlates of higher incomes of refugees. A discussion of the results and their policy implications follows in section 7, after which section 8 concludes. 2. Benefits of economic participation of refugees Whether or not the arrival of Sudanese refugees in Chad contributes to economic growth is of limited immediate relevance as concerns about the safety of fellow humans drive the response. Nor does any decision maker suggest that hosting refugees is a development strategy Chad should pursue.", "output": {"entities": {"named_data": ["Refugees and Host Communities Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In most other cases, sexual and gender-based violence is a crime of opportunity that is often committed by relatives rather than strangers (Wood 2009, 2006). A study of sexual violence in a pediatric ward in Goma, Democratic Republic of the Congo, showed the predominance of domestic sexual violence over militarized rape. Of 500 pediatric cases treated for sexual violence at the hospital (2006 – 2008), nearly all were females between the ages of 10 and 18 (Kalisya and others 2011). Also in the Democratic Republic of the Congo, the results of a population-based household survey with a randomly assigned module on sexual violence (a subsample of 3, 436 women) yielded a very high prevalence of rape — an estimate of more than 400, 000 women were raped in the 12 months prior to the 2007 survey — and showed that the most pervasive form of sexual violence was from intimate partners and that the most conflict-affected provinces were at higher risk of sexual violence (Peterman, Palermo, and Bredenkamp 2011). A population-based random cluster survey of adults in Liberia (conducted in 2008) is one of the few quantitative studies on the legacy of sexual violence in conflict situations. The study showed Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The household head interviews also provided an opportunity to gather useful household-level information (including assets, housing characteristics, and household head characteristics) to be used as control variables in our analysis. The baseline survey was conducted before participants were informed of the outcome of the randomization, and the midline survey was conducted one year after the baseline survey, one month after completion of the first round of training and before the start of the second round. The results in this paper are based on a panel data set that includes data from the baseline and midline surveys as well as administrative and monitoring data from the program implementation team. Note that although the midline data collection occurred just one month after the completion of the intervention, the intervention itself includes 6 months of post-training follow-up; hence the midline survey captures outcomes of the first cohort 7 months after they exited the classroom-based training. This timing was necessary to prevent too long of a delay between cohorts; the second cohort of trainees started immediately after the midline survey was completed. Future analysis using endline data will examine the trends in outcomes of both groups after Round 1 completion, but will not involve comparison against a pure control group of non-participants. 3. 2. Sample and attrition Following the communications and outreach campaigns in each of the nine target communities, during which 2, 106 young women were originally recruited to be a part of the EPAG program, 8 a randomized 8 The original recruitment effort fell short of the target of enrolling 2500 participants. Hence, before the second round of training, another recruitment effort was launched and an additional 617 girls were enrolled. These girls are not included in the impact evaluation since they were not subject to the initial random assignment. 7", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data set"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In the absence of objective measures of these factors at the commune level, we leverage the Afrobarometer [15] surveys to investigate at the individual level the effect of the proximity to gold-mining activities on perceptions of governance and quality of public services.\n\nWe use the 2005, 2012, and 2014 rounds of the nationally representative Afrobarometer survey in\nMali. We focus on variables of political responsiveness, corruption, and quality of public services\nthat are similar across the three rounds. Geocoded enumeration areas are combined with location\nof industrial mine sites to construct our treatment variable, which is the cumulative gold production\nwithin 20 kilometers of the mine at the time of the survey. This assumes that both distance to mines\nand intensity of production matter for the effect. We then estimate the following logistic regression\nof the form:\n\n<!-- PAGE 24 -->\n**Figure 17 Locations of Afrobarometer enumeration areas**", "output": {"entities": {"named_data": ["Afrobarometer survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We find no evidence that Pashtun households are more likely to send their children to Madrassas compared to the rest of the sample, suggesting that geopolitical factors and geographical proximity to Afghanistan matter more than cultural preferences. 16 Similarly there is no evidence for religiosity or household preference-based models of madrassa enrollment. The radical religiosity argument suggests that children are more likely to be sent to madrassas when the family favors a radical brand of Islam. If true, what are we to make of the fact that more than 75 percent of all households with a child in a madrassa also send a child to a public or private school? In a multivariate context we checked whether households identified as “ radically Islamic ” were more likely to send their child to a madrassa. 17 Again, we found no 16 The data from the LEAPS census asked about ethnic and caste identity, and households that classified themselves as “ Pathan ” or “ Afghani ” were used to represent Pashtun households. In line with the usual residential patterns of individuals with Pashtun backgrounds, most of these households are in district Attock in the North of Punjab. 17 In a largely Islamic country it is difficult to find good measures of religiosity.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Moreover, some refugees may not register because they are unaware that they should, and others may be reluctant to do so because they are skeptical of the integrity of the registration process (e. g. fair access to entitlements or opportunities for durable solutions) or lack confidence in protection measures. Individuals in irregular migration flows may also choose not to apply for asylum due to fear of declaring themselves to the authorities. A significant challenge with refugee registers is keeping them up to date. Individual registration can provide a robust snapshot of the stock of refugees and asylum-seekers, but registers need to be updated regularly to reflect flows, i. e. increases in refugee and asylum-seeker numbers (births, new arrivals) and decreases (deaths, departures, durable solutions). In situations of sudden mass influxes, existing registration capacity may not be adequate and the scope of registration data is then rationalized. 65 Additionally, it may not be possible to capture all demographic changes in the case of highly mobile populations.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee registers"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "While most existing theories of madrassa enrollment are based on household attributes (for instance, a preference for religious schooling or the household ’ s access to other schooling options) the data show that among households with at least one child enrolled in a madrassa, 75 percent send their second (and / or third) child to a public or private school or both. Widely promoted theories simply do not explain this substantial variation within households. 1 Corresponding Author: Tahir Andrabi (tandrabi @ pomona. edu). This study would not have been possible without the enthusiasm and continuous support we received from Tara Vishwanath. Charles Griffin first encouraged us to look at the data. We thank Veena Das, Shehla Andrabi, Sehr Jalal, Ritva Reinikka and Carolina Sánchez for their encouragement and to Hedy Sladovich for her excellent editorial suggestions. The paper has also benefited from comments by Ismail Radwan, Naveeda Khan, Shahzad Sharjeel and Shanta Devarajan. The research department of the World Bank provided funding for this study through the Knowledge for Change trust fund. The findings, interpretations and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent. Working papers describe research in progress by the authors and are published to elicit comments and to further debate. Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized WPS3521", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 % of respondents think that a **go and see visit** prior to the return would be useful. From\nthose who responded positively, 63 % would like to send community leaders for the go and\nsee visit, while remaining 37 % male family members.\n\n9 % were aware about some kind of information campaign on the return. It is to be noted\nthat at the time of the interviewed conducted, the information campaign on return has not\nyet been officially launched.\n\nIn 64 % of interviewed families, the response on **who decides on the return** was political\nadministration **.** This percentage is relatively high compared to other return intention\nsurveys conducted by protection cluster. In 23 % it is the community elders who reportedly\nmake the decision on return, followed by family members in 13 % of cases (which is usually\nthe highest category reported). Only 49 % of respondents felt that they **participate in the**\n\n**decision making process** on the return.\n\n**Despite the fact that IDPs wish to return to their area of origin, 26 % indicated that not**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["return intention\nsurveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "integrate into the surrounding community and labor market. Among Eritreans in Afar and Amhara, the share working is 43 percent and 9 percent, respectively. Among workers, Eritreans in Amhara are three times as likely to work for NGOs or RRS, and very few work outside the camp. Box 3.1: Eritrean refugee sample in the SESRE Jobs and Livelihoods 27 Ethiopia relies on a camp-based model, with 88 percent of refugees hosted in camps. As outlined, in-camp refugees generally do not have work permits or business licenses and largely depend on work inside the camp or informal work outside the camp. On the other hand, the GoE introduced an out-of- camp policy (OCP)39 in 2010 that provides refugees the opportunities to live in Addis Ababa and different non-camp locations of their choice. Roughly 71,000 Eritreans were under the OCP regime as of 2022, with over 90 percent living in Addis Ababa. In practice, most of those approved for the OCP have family and friends in Ethiopia who support them with remittances—fewer than 1,500 OCP work permits were issued by 2022. The permit allows refugees to freely move and establish residence in all areas of the country except restricted areas. OCP refugees", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "41 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Since some adjustments could take place over time, we always use the figures from the last available report. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "positive long-term integration as it limits long-term scarring effects, such as long- term unemployment or inactivity (Fasani et al., 2022; Slotwinski et al., 2019). Refugee employment after arrival depends on policies in the host country concerning work permits and mobility (Fuller, 2015; World Bank, 2017). On the other hand, arrival of refugees may have a complex range of positive and negative effects on local labor markets in host communities, including on sectoral employment, wages, and prices. Studies in Ethiopia show that 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Addis Refugees Camp Refugees Lack of work/business opportunities High cost of living Poor services or institutional support Lack of freedom or mobility Lack of community/family networks Insecurity or discrimination Figure 3.1: Top 3 difficulties with being a refugee Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 26 refugees may decrease employment among hosts in rural areas (Ayenew, 2021), while other studies find no effect on employment and increases in consumption (von der Goltz, 2023) and product diversification and livestock sales (Walelign et al., 2022) as refugees increase consumer demand for agricultural products, with variations in effects across the different regions of Ethiopia. Most refugees in Ethiopia do", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The sample households are located within varying distance from the nearest refugee camp (approx. 67 to 76, 665 meters) (see Figure 3). 12DRDIP aims to improve access to basic social services, expand economic opportunities, and enhance environmental management for communities hosting refugees through providing funding for community driven projects.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "13 Second, each individual in the JLMPS restricted sample is matched to the 2010 Jordanian school census. The matching process determines for each individual the number of sex- appropriate public basic and secondary schools per 1, 000 individuals available in the individual ’ s subdistrict of birth when the individual was of age to accede to this educational level (six years of age for the basic level and 15 years of age for the secondary level). 7 A school is considered sex-appropriate for a female if it is a girls ’ or a mixed school and for a male if it is a boys ’ or mixed school. The empirical analysis is also performed by entering boys ’, girls ’, and mixed schools separately. 8 Measuring the local supply of public schools at the subdistrict of birth of the individual (i. e., the child) mitigates potential endogeneity originating from parents who had a higher taste for schooling moving to subdistricts where public schooling was more abundant when their child was of school age, although it is not possible to rule out that parents might have moved across subdistricts prior to the birth of their child.", "output": {"entities": {"named_data": ["JLMPS restricted sample", "2010 Jordanian school census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The study: (i) compares the socio- economic profile of refugees with that of the Syrian population before the crisis and with the hosting populations of Jordan and Lebanon; (ii) provides a welfare and vulnerability assessment of Syrian refugees including a poverty profile, the socio-economic characteristics of higher poverty and where pockets of deep poverty are located; (iii) analyzes key drivers of welfare and poverty; and (iv) models monetary and non- monetary vulnerability.  In Lebanon, Jordan and Iraq, the Bank is leading an initiative to evaluate the socio-economic impact of the regional crises on the welfare of Syrian refugees and host communities in neighboring countries [ongoing]. Data on living conditions, access to services and economic opportunities, coping strategies and economic status are to be collected via a specialized household survey and a sub-component of the survey will be carried out on a semi-annual basis to continue to monitor and adapt support as needed.  A recent Bank paper, “ Turkey ’ s Response to the Syrian Refugee Crisis and the Road Ahead ” [completed in 2015] assessed the impact of Syrian refugees on host areas in various sectors. It found that the presence of Syrian refugees is placing a strain on municipal services, housing rental markets, social relations, and education services for Turkish households. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["specialized household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "40 Sub ‐ Saharan Africa 8604000 5895000 7055000 5406100 5068000 N. A. MENA 6230000 8000000 6675000 8592900 10892000 N. A. Asia and Pacific 4325000 2405000 3392000 2128800 5490000 N. A. (excl. Australia, Japan, New Zealand) Americas 1126000 1280000 2176000 2900000 3661000 N. A. (excl. North America) Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). As indicated in Figure A1, these data are much lower compared to those provided from 2003 by IDMC but provide a longer time series. UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "ICRC ’ s work is based on the Geneva Conventions of 1949, their Additional Protocols, its Statutes — and those of the International Red Cross and Red Crescent Movement — and the resolutions of the International Conferences of the Red Cross and Red Crescent. 84 WFP is the food assistance branch of the United Nations and the world's largest humanitarian organization addressing hunger and promoting food security. 85 See: popstats. unhcr. org. 86 IDP data are only included from 1998 onwards. 87 See: http: / / data. unhcr. org. Currently the Burundi situation, Yemen (regional refugee and migrant response plan), DRC regional refugee response, Mediterranean (refugees / migrants emergency response), CAR, Côte d ’ Ivoire, Syria Emergency, Sahel Emergency, South Sudan Situation, Horn of Africa Emergency, and the Liberia Portal. 88 IOM ’ s new Global Migration Data Analysis Centre provides limited data on global migration trends such as data on asylum application in Europe and selected countries (including demographics, country of origin, and country of asylum). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["IDP data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Throughout the war, HLP rights violations has been one\nof the main impacts, particularly related to\n\n**secondary/unauthorized occupation, destruction,**\n\n**and confiscation** . Absence of ownership documents\n(either due to loss or verbal agreements) increased the\nexposure to house, land, and property related\nviolations. In the case of youth, these risks are\nenhanced, due to **lack of procedures to secure tenure**\n\n**for youth when parents have died** .\n\nOccupied I don't\nknow\n\n**What is the status if your familys property?**\n\n**54%**\n\nAbandoned Destroyed Rented Confiscated\n\nFamily separation, lack of access to legal services,\ninformality on tenure, ethnic background,\ndiscrimination, are some of the existing drivers for **additional HLP violations for youth** . The percentage of destroyed\nproperties documented by the survey raised additional concerns related to future restitution of HLP rights for young\npeople and the need to safeguarding these documents for transitional justice purposes.\n\nYouth participating in the FDGs also mentioned:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Bank and Israel and occupied territories strongly suggest that commuters would be differently affected during the pandemic when border closures were enacted. Another unique feature is the presence of a large refugee population in the West Bank and Gaza. However, it is important to note that refugees in this context are de- fined quite differently from other contexts. Here, not only the individuals immediately displaced are considered refugees, but also their patrilineal descendants, even if born many decades later. In particular, the LFS dataset follows the United Nations Relief and Works Agency (UNRWA) definition of refugees, which is “ persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948, and who lost both home and means of livelihood as a result of the 1948 conflict, ” as well as “ the descendants of Palestine refugee males, including adopted children ” (UNRWA, 2023). Consequently, most refugees are indistinguishable in socio-economic outcomes and labor market behavior from non-refugees. However, residence in refugee camps does make a significant difference. As of 2019Q4, 5 % of the West Bank ’ s residents live in refugee camps, as do 14 % in Gaza. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "tanker water. 36 Improved bathing refers private or shared bathtub, shower, separate room for bathing. 0 20 40 60 80 100 120 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Overcrowded Improved wall Improved roof Percent Figure 2.21: Housing quality Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Owned Rented UN/NGO temporary UN/NGO permanent Other Percent Figure 2.20: Dwelling type Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 23 Refugees’ access to improved sanitation facilities is similar to hosts. Refugees’ access to improved toilet facilities37 is the same as hosts, or even better in some cases. Eritrean, Somali, and OCP refugees and their hosts have similar toilet facilities. Even though the percentage of households with access to improved toilet facilities is lower among South Sudanese refugees than other refugees, it is higher compared to their hosts. Moreover, refugees have higher access to improved waste disposal methods38 than hosts. Both refugee and host households have low access to electricity, except for hosts of Eritrean refugees. Hosts around Eritrean refugees have better access to electricity (meter private or shared) for", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "16 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 6. Gender differences in multidimensional poverty Next we examine differences in multidimensional poverty outcomes by the gender of the household head. Existing literature points out the limitation of household level MPI analysis in masking the intrahousehold distribution of deprivations, and thus being less sensitive to gender based differences in individual outcomes within the family unit, which might lead to underestimation of inequality and gender gaps (Espinoza-Delgado and Klasen 2018; Franco 2017; Klasen and Lahoti 2020, Rodriguez, 2016). However, as the MPI identifies poverty at the household level, our initial analysis focuses on disaggregated results by the gender of the household head. 19 We acknowledge that this approach has several limitations since most women reside in male-headed households, and the composition of households can change after displacement due to separation of family members, and widowhood. Regardless, the analysis at the household level remains relevant given the high prevalence of female-headed households that emerge after displacement, with the analysis showing large differences across countries between households based on the gender of the head. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 3. Wealth Index Distribution (a) Wealth Index- Total 0. 05. 1. 15. 2. 25 k-Density- 5 0 5 10 15 Wealth Index Colombian Venezuelan Venezuelan: Before migrating (b) Adequate Housing Materials (% of Total) 0 1 2 3 4 k-Density 0. 2. 4. 6. 8 1 Average- Dwelling Material Colombian Venezuelan Venezuelan: Before migrating (c) Asset Ownership (# Total) 0. 05. 1. 15 k-Density 0 10 20 30 40 Total Assets Colombian Venezuelan Venezuelan: Before migrating (d) Access to Services (% of Total) 0 1 2 3 k-Density 0. 2. 4. 6. 8 1 Average- Access to Services Colombian Venezuelan Venezuelan: Before migrating Notes: Panel (a) presents the distribution of the wealth index for Colombian and Venezuelan households in our sample in 2022 and pre-migration. Wealth Index is an index measure of the household ’ s cumula- tive living standard constructed following The Demographic and Health Surveys (DHS) methodology.", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Death Rates, Adolescent Mothers, and\n\nChildren and Adolescents out of school\n\nusing primary data from the MSNA and\n\nsecondary data sources.\n\nCalculations were refined using qualita\ntive information obtained from experts\n\nduring needs assessments validation\n\nworkshops conducted in Miranda, Zulia,\n\nFalcón, Bolívar, Delta Amacuro, Sucre,\n\nAmazonas, Apure, Táchira, Lara, and\n\nthe Capital District. The overall PIN for\n\nthe Protection Cluster is 4,425,466.00,\n\nrepresenting a 5% increase compared\n\nto the calculation for the Humanitarian\n\nResponse Plan (HRP) 2022-2023.\n\nFurther, throughout October, the national\n\nProtection Cluster participated in the\n\nfacilitation of extended OCHA Local\n\nCoordination Fora meetings in the states\n\nof Miranda, Sucre, Bolivar, Delta Ama\ncuro, Apure, Amazonas, Táchira, Zulia,\n\nFalcón, and Lara. The primary objective\n\nof these workshops was to validate data\n\ncollected from the MNSA on the human\nitarian situation of the respective state\n\nand, to gather complementary informa\n\ntion to contribute to the narrative of the\n\nHNO. Finally, it helped to validate the\n\nprioritization of municipalities in collabo\nration with Cluster partners and OCHA.\n\n**II.** **Coordination** **with** **other**\n\n**platforms, subnational Clusters,**\n\n**ICCG, and Partners**\n\n**Coordination with the Response for**\n\n**Venezuela (R4V) continued through-**\n\n**out September and October through**\n\n**bilateral meetings and common work-**", "output": {"entities": {"named_data": ["MSNA"], "descriptive_data": [], "vague_data": ["secondary data sources"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Indeed, despite the recent literature rejecting the conflictive impact of refugees in hosting areas (Zhou and Shaver, 2021), the magnitude of our coefficients might be explained by the confounding presence of refugees. Columns (5) and (6) further introduce climatic controls. Column (6) corresponds to Equation 1 and refers to our benchmark specification. Columns (1) and (2) show that without incorporating the changes in ethnic diversity induced by refugees we would not be able to identify a relationship between diversity and violent conflicts. In column (3), the revised refugee fractionalization index has a negative and significant coefficient, while the revised refugee polarization index has a positive and significant effect on the incidence of violent conflicts. In columns (2), (4), and (6), our coefficients of interest are of the same order of magnitude when the number of refugees is controlled for. Our results are not altered by incorporating rainfall and temperature anomalies (columns (5) and (6)), but the estimates become slightly more precise. 21", "output": {"entities": {"named_data": ["refugee fractionalization index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "natural log of a country ’ s population size and government consumption as a share of GDP; the latter is the most widespread measure of government size (Adsera and Boix, 2002; Alesina and Wacziarg, 1998; Rodrik, 1998). Tax compliance is also related to the government ’ s ability to eﬀectively detect and punish tax avoiders, tax evaders, and tax arrears. Although an imperfect measure of states ’ deterrent capacity, Afrobarometer includes two survey questions on perceptions of government enforcement and monitoring capacities. One question probes respondents on how often ordinary people who break the law go unpunished. The other probes respondents on how often oﬃcials who commit crimes go unpunished. This latter question is also a measure of perceived government fairness- the extent to which a government implements the law evenly across all social groups. 6. 2. 5 Procedural Justice I include two indicators of procedural justice. The ﬁrst probes respondents on how often people are treated unequally under the law. The next taps citizens ’ perceptions of the government ’ s treatment of their ethnic group. Speciﬁcally, respondents were asked how often their ethnic group is treated unfairly by their government. 9 6. 2. 6 Donor and Non-State Actor Provision of Services I include a measure of who citizens believe is responsible for collecting income taxes. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In other contexts, deregistration signifies not the achievement of a durable solution but rather the end of state or international support (IDMC 2015). 33 The absence of a clear and operational approach to defining the ‘ end ’ of internal displacement may be one of the factors behind the continued overall increase in the global numbers of IDPs. Lack of clarity around when displacement ends also leaves room for political manipulation. Governments may find it politically expedient to artificially prolong IDP status by deterring returns or local integration, for example in Azerbaijan and Georgia to promote claims over territory (Beau 2003). In other contexts, national 28 UNHCR ’ s IDP data focus only on internally displaced populations to which it extends protection or assistance. IDMC coverage of IDP data is more expansive and in 2015 included additional data on: (a) 26 countries accounting for 4. 5 million IDPs including some significant IDP hosting countries (Turkey, India, Ethiopia, Bangladesh and Kenya); and (b) IDPs in countries where UNHCR is active who are not protected or assisted by the agency. In 2015, IDMC ’ s aggregate figure for conflict-induced internal displacement was 3. 3 million higher than UNHCR ’ s aggregate figure for IDPs protected or assisted by the agency. 29 IDMC ’ s 2016 report presents both data sets alongside each other.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["IDP data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "13 Commission for Reception, Truth and Reconciliation (CAVR). 7 This information has been collected from deponents to the Commission ‘ s statement-taking process. 8 We make use of data on the number of killings that occurred during the war in order to derive patterns and variation of violence in Timor Leste over time and across space. We use this data to identify districts and years that experienced high and low violence-intensity, both at the start of the occupation and following the withdrawal of Indonesian troops in 1999. This allows us to estimate both the impact of the first years of the conflict and the impact of the last wave of violence in 1999. 4. 1. Identification strategy: The impact of violence on school attendance in 2001 We first investigate the short-term impact of the 1999 violence. The empirical questions being addressed are: (i) whether the violence in 1999 imperiled school attendance9 and school grade deficit, and (ii) whether different channels of exposure to conflict – displacement and house destruction – affected boys and girls and different age groups differently. 4. 1. 1. Primary school attendance and grade deficit rates in 2001 We make use of information in TLSS 2001 collected at the individual and household levels on displacement and house destruction to identify conflict-affected individuals. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["TLSS 2001"], "descriptive_data": ["data on the number of killings that occurred during the war"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10100 This paper explores the impact of refugee return on social cohesion using data from Burundi, a country that expe­rienced high levels of repatriation during the 2000s. It uses a nationwide survey conducted in 2015 and relies on geographic features of the communities for identification purposes. The results suggest varying impacts of refugee return on different aspects of social cohesion. The stronger effects, suggest that refugee return has a negative impact on the feeling that community members help each other, could borrow money for emergencies from non-household members and feeling that the community is peaceful. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["nationwide survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "years) ......... 103 Figure D.12: Average annual per capita health expenditure ....................... 103 Figure D.13: Types of disability..................................................................... 104 Figure D.14: Rent expenditure (Refugees and hosts in Addis Ababa) ........ 104 Figure D.15: Hand washing facility................................................................. 104 Figure D.16: Top 3 difficulties with being a refugee by survey domains ..... 108 Figure D.17: Work status by survey domains ������������������������������������������������ 108 Figure D.18: Type of work by survey domains ���������������������������������������������� 108 Figure D.19: Occupation by survey domains ������������������������������������������������� 108 Figure D.20: Work location by survey domains ��������������������������������������������� 108 Figure D.21: Hours per week by survey domains ����������������������������������������� 108 Figure D.22: Hourly earnings by survey domains ����������������������������������������� 109 Figure D.23: Household owns crops ............................................................. 109 Figure D.24: Household owns livestock......................................................... 109 Figure D.25: Total value of livestock ............................................................ 109 Figure D.26: Value per tropical livestock unit ������������������������������������������������ 109 Figure D.27: Household has non-farm business ����������������������������������������� 109 Figure D.28: Value of productive assets in households with business ........ 110 Figure D.29: Primary source of income pre-post migration by survey domains......................................................................... 110 Figure D.30: Youth work status by survey domains �������������������������������������� 110 Figure D.31: In-camp refugee locations by ecological Zone ........................ 115 Figure D.32: Refugee’s labor market performance ��������������������������������������� 115 Figure D.33: Economic sector........................................................................", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Therefore, the final sample size consisted of 759 youth, of which 473 treatment and 286 comparison. Detailed baseline data were collected through face-to-face interviews from July to September 2015 prior to implementation. The actual implementation varied between projects and ranged between the second half of August and end of December 2015. Sampled youth from both selected and non- selected NGOs were invited to fill out a questionnaire with detailed information on volunteers ’ socio-economic backgrounds, education levels, interests and attitudes towards volunteering, employment, soft skills, as well as social cohesion values. Follow-up data were collected between November 2016 and March 2017, approximately one year following the start of implementation, through phone and face-to-face interviews. The questionnaire contained the same modules asked and collected at baseline. Despite the high 8 Proposals were ranked based on four main selection criteria: institutional appraisal (25 points), technical appraisal (40 points), project impact (25 points), and financial appraisal (10 points). There was also a fifth criterion related to sustainability of volunteering activities, which was assigned a bonus score (5 points). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The only question included in the WVS related to attitudes towards immigrants - and available   for a sufficient number of survey years - is: _On this list are various groups of people.\n\nUsing household surveys for 24 countries between 2002 and 2012, we find that in most\n\n<!-- PAGE 24 -->\nDeaton, Angus. The analysis of household surveys: a microeconometric approach to\n\nSource: Attitudes toward migrants were estimated from ESS (see data section for more details). The gap reflect the difference between the positive attitudes of\n\ngroup as the majority (ESS). _Proimmigdiff_ is the average pop. share (%) who would like many or some immigrants of a _different_", "output": {"entities": {"named_data": ["WVS", "ESS"], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 Finally, we divide the communities based on land scarcity before the 1993 conflict in order to explore the possible role of posterior rules regarding land provision to returnees. Respondents in communities that had more and less pre-war land available have broadly similar attitudes towards return. 4. Research design 4. 1 The survey We collected the data for this project during January to March 2015 as part of a nationwide survey on issues related to migration for the Labour Market Impacts of Forced Migration (LAMFOR) project. The survey had two components. First, a household survey in which 15 households were interviewed in 100 communities (i. e. sous-collines) across the 17 provinces of the country. Second, a community survey in which a local leader was interviewed in each of the 100 communities. The number of communities selected in each province was based on information from the 2008 Census. Figure 4 indicates the location of the communities surveyed. Figure 4 – Location of communities surveyed in Burundi Note: Geolocation of the 100 communities (i. e. sous-collines) sampled in the survey. Each community corresponds to a dot. Fifteen households and a local leader were interviewed in each community. The number of communities selected in each province was based on information from the 2008 Census. In the analysis below we focus on rural areas. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "throughout the day prior to the survey day. We develop our own questions around self- worth rather than employing the more standard Rosenberg Self-Esteem Scale, which we found inappropriate given the Rohingya ’ s recent experiences. Speciﬁcally, we construct an index of self-worth from two questions designed to elicit respondents ’ beliefs about how they contribute to their family and community. Finally, we adapt the Cantril Self-Anchoring Striving Scale (Cantril, 1965) to measure how stable respondents feel in their present lives and in the future. We additionally examine the impacts of each treatment on physical health, cognitive function, economic decision making, time-use, and consumption. We capture respondents ’ sense of physical health by asking how many days they have fallen sick in the past thirty days and cognitive function by employing a digit-span memory test and a series of basic arithmetic problems. We explore economic decision making along two dimensions: incentivized time preferences (Andreoni and Sprenger, 2012; Gin ´ e et al., 2018) and incentivized risk preferences (Holt and Laury, 2002).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "NGOs. Initiatives such as JIPS, a collaborative project of UN and NGO actors, have also been established to support governments and operational organizations to design and implement data collection processes. There are a variety of data sources for generating statistics on forced displacement, each of which has strengths and weaknesses. Despite the significant challenges, large amounts of data are collected and disseminated every year. The main data sources and methods for the generation of statistics on forcibly displaced populations include: (a) registration of refugees and asylum-seekers; (b) registration of IDPs; (c) profiling of IDPs; (d) population movement tracking systems; (e) national population censuses; (f) sample surveys; (g) border crossings; (h) administrative records and registers; (i) general population registers; and (j) a variety of estimation methods for producing statistics when adequate and reliable data on individuals are unavailable (UNSD 2014). Several of these data sources might be used together to triangulate estimates of stocks and flows for a particular displacement situation. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["sample surveys", "general population registers"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**SOUTH SUDAN** | October 2023\n\n#### **CONTEXT**\n\n**YOUNG POPULATION AND PREVALENCE OF DISABILITIES**\n\nSouth Sudan is a young nation with an average age of 18.8 years. In the absence of an updated census, it is estimated that 1.2\nmillion people, or 16% of the population have disabilities. The most commonly identified disabilities include visual\nimpairments, hearing impairments, and physical disabilities. [iv] Additionally, the proportion of older people in South Sudan is on\nthe rise, accounting for 5.1% of the population in 2016, an increase from 3.9% in 2008, and an indication that the number is\ngrowing each year. [v] Notably, most older people in South Sudan reside in impoverished rural areas, where they play a crucial\nrole as caregivers for children whose parents have often perished in the conflict.\n\nThe country has a troubled history marked by decades of armed conflict, leading to displacement, economic instability, and\nincreased climate hazards such as flooding and droughts. These factors have further weakened an already fragile state and\nlimited governance capacity, disproportionately impacting older individuals and those with disabilities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["updated census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In all speciﬁcations, conﬂict incidence correlates nega- tively with country-level economic performance. The estimated coeﬃcients of conﬂict incidence are statistically signiﬁcant and negative. We also ﬁnd that the coeﬃcients 7For a discussion see Henderson et al. (2012). In order to calculate light per capita we use popu- lation data that is provided by a World Bank dataset. 8The standard reference here is Miguel et al. (2004). Ciccone (2011) shows that high rainfall levels three years earlier seem to be best predictors of conﬂict in the reduced form. Miguel and Satyanath (2011) argue that lagged negative growth shocks are a predictor of conﬂict onset. In any case, there is no evidence from this literature that contemporaneous growth declines cause conﬂict. Bazzi and Blattman (2014) corroborate the view that the relationship between income shocks and conﬂict is not straightforward. They do not ﬁnd evidence of an eﬀect of price shocks on conﬂict onset and only weak evidence on incidence. 9Results from this are presented in the Appendix. 10We take the threshold from Mueller (2016) who shows that a threshold like this leads to a similar number of coded civil wars as the threshold of 1000 battle-related deaths often used in the conﬂict literature. In the context here, this is a conservative approach as it is not the threshold which yields the biggest diﬀerence between conﬂict and non-conﬂict countries. 10 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["World Bank dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Despite notable disparities in the characteristics of our sample compared to the VenRePS and RAMV samples, understanding the interaction of these attributes within a frame- work of self-selection among migrants into Medellin is challenging. Moreover, it is cru- cial to note two primary distinctions between our survey and the VenRePS and RAMV surveys. Firstly, the inherent differences in the sampling frames of each survey stem from their distinct measurement objectives. Second, both surveys were conducted at different times compared to our survey. The RAMV survey was undertaken in 2018 in response 10See Ib ´ a ˜ nez et al. (2022) for specific survey and sampling details. 11Refer to Ib ´ a ˜ nez et al. (2022) for further details. 12Since the VenRePS and RAMV surveys lack information regarding children and adolescents within households, our analysis concentrates only on the household and household head characteristics that are available in all three surveys. 20", "output": {"entities": {"named_data": ["RAMV survey", "VenRePS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 3. Refugees as Agents of Their Own Destiny 3. 1 The Composition of Africa ’ s Refugee Population and Its Consequences One of the first elements that catch the eye in Figure 6 is the difference in the composition of the refugee population in Africa compared to the rest of the world. The share of children and women among refugees is higher in Africa than elsewhere, in particular East and West Africa stand out here. This is, at least partly, a consequence of Africa ’ s younger, general population, but other forces could be at work as well, e. g. higher mortality of adult males in Africa or adult males staying behind or being separated from the rest of the household. It does mean however that, relative to other areas, more attention should be going to the needs and capacities of women and children in Africa. This means, for example, adaption of and increased supply of schooling and health services. Figure 6. The composition of refugees by age and gender, 2013 Source: Note: UNHCR statistics (UNHCR 2014). Asia excludes Australia, Japan and New Zealand. Americas exclude Canada and the United States. These percentages have been calculated by country when demographic data are available for at least 30 % of the total. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["demographic data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(Dercon et al 2005; Woldehanna et al 2008)—market shocks related to rising food prices, food shortage, and health shocks appear to be most prevalent (Figure 5.11). High food prices drive the market shocks. Food shortage seems to represent a crucial economic shock among refugees—roughly 31 percent of in-camp refugees are affected by food shortage—but not for their host communities. 4.0 2.1 2.9 8.1 3.4 7.0 In Camp Addis Ababa Total Hosts Refugees Figure 5.9: Food insecurity scale for refugees and hosts Source: World Bank Staff based on SESRE 2023. 44 Food insecurity experience is measured based on a scale that ranges between 0 and 10 and calculated by adding household’s experience related to the following events in the past year: (i) worried about having enough food, (ii) unable to eat healthy/nutrition food, (iii) only ate a few kinds of food, (iv) had to skip a meal, (v) adults ate less, (vi) ran out of food, (vii) adults were hungry but did not eat, (viii) went without eating for a whole day, (ix) restricted consumption so kids could eat, and (x) borrowed food or relied on friend/relative for help. 7.0 8.4 7.8 6.0 8.2 6.5 In Camp Addis Ababa Total Hosts", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant ’ s for these households remain after humanitarian assistance is received. To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV). The ProGres database for Jordan includes information on refugees ’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘ proof of registration ’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Profile Global Registration System", "Jordan Home Visits round 3"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5.1.4 Food security Refugees and hosts perceive that household living standards have deteriorated over time. To capture subjective well-being, the survey asks if the living standard of the household or their community has improved or worsened in the past five years (Figure 5.8a) and in the past 1 year (Figure 5.8b). Overall, most households feel that their living standards have deteriorated. While in-camp refugees are more pessimistic about the changes in their households’ living standards, there is no significant difference in perceptions among OCP refugees in Addis Ababa and their hosts. The considerably high negative perception about changes in household living standards indicates that well-being has been worsening for everyone over the past few years, but even more so for refugees in camps. Refugees, on average, have poor food and nutrition security outcomes compared to their hosts. The extent of food insecurity measured by the food insecurity scale is significantly higher for refugees than hosts, both for in-camp and for out-of-camp refugees (Figure 5.9). While the food insecurity Refugee and host communities could differ in multiple dimensions over and above consumption. The Multidimensional Poverty Index (MPI) explores this multiple deprivation, capturing differences across three dimensions of well-being: health, education, and", "output": {"entities": {"named_data": ["Multidimensional Poverty Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 inflow). We then use the composite variable as an instrument to identify the causal impact of refugee presence on livelihood diversification and commercialization in host communities. Figure 2: Household livelihood strategy framework under refugee inflow Source: Adapted from Nielsen et al. (2013) and Walelign and Jiao (2017) 4. Data sources The major data source of the current study is the World Bank ’ s Development Response to Displacement Impacts Project (DRDIP) 12 baseline survey from Ethiopia. The Ethiopia DRDIP survey was administered between September 2017 and August 2018. The survey covers 113 Kebeles (wards) in 16 Woredas (districts) from the top five refugee-hosting regions in Ethiopia. The selection of the sample households follows stratified random sampling with proportion to size (the number of households) using Woredas as a geographic stratum. The sample originally comprised a total of 3, 390 households, who were selected using systematic random sampling within each Woreda. We used data from 3, 375 households, as 15 of them were excluded due to missing location information (GPS). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Ethiopia DRDIP survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "environment. Any issues discovered during these monitoring visits were brought to the attention of service providers and resolved swiftly in conjunction with the project coordination team at MoGD. Eligibility: The EPAG program was targeted to young women who: i) were age 16 to 27, ii) possessed basic literacy and numeracy skills, iii) were not enrolled in school within several months prior of the program initiation, and iv) resided in one of nine target communities in and around Monrovia. 6 These eligibility criteria stemmed from the project's objectives to reach young women at an early enough age to significantly improve the trajectory of their working years, to focus on girls who already had the basic literacy and numeracy skills needed to succeed in the labor market, and to avoid incentivizing applicants to drop out of school.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Similarly, regional representation of the sample was found to be in line with actual regional distribution of the population in the north. 16 The code of the day is the sum of the two figures of the date, i. e. if it ’ s the 25th of August the code of the day is 2 + 5 = 7. The enumerater will chose house number 7 as a starting point. Arab 3 % Tamashek 32 % Songhai 45 % Peulh / Foulbe 7 % Other ethnicities 13 % Figure 2: Ethnic composition of the North, 2009 Census Arab 5 % Tamashek 36 % Songhai 49 % Peulh / Foul be 7 % Other ethnicities 3 % Figure 3: Ethnic composition of sample Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["2009 Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The weight given to each province in constructing the synthetic control is based on pre-treatment outcomes. We use the pre-treatment average of the outcome dimension, Y, the unemployment rate, employment rate and the import and export per capita of the province to determine the degree of similarity between control group provinces and the treated provinces, which in turn determines the weight assigned to control provinces. The unemployment and employment rates are included to control for the general economic performance while trade values are added to control for the degree of ’ openness ’ of the province. 6 The treated unit i = 1 is constructed by taking the mean of the outcome variables in the provinces hosting refugees in 2012 or 2013. 5 Data We use several data sources for the analysis. The IV estimations use data from years 2011 and 2014 while the DD estimations use data from 2009 to 2014. The numbers of refugees up to 2012 are treated as 0. The refugee data for 2012 and 2013 are obtained from UNHCR ’ s oﬃcial weekly statements in December. Data on the number of refugees in 2014 is from Erdo ˘ gan (2014), who uses statements released by the Ministry of the Interior to compile his data. All refugee data we use in the analysis is provided at the level of 81 provinces. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Journal on Education in Emergencies, Inter-agency Network for Education in References 80 References Emergencies, 5(2). https://doi.org/10.33682/f1wr-yk6y. Ravallion, M. (1998). Poverty Lines in Theory and Practice: Living Standards Measurement Study. LSMS Working Paper, Issue 133. https://documents1.worldbank.org/curated/en/916871468766156239/pdf ReDSS. (2018). Local Integration Focus: Refugees in Ethiopia - Gaps and Opportunities for Refugees Who have Lived in Ethiopia for 20 Years or More. ReDSS. Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological monographs: General and applied, 80(1), 1. RRS. (2017). Road Map for Implementing the Federal Democratic Republic of Ethiopia Government Pledges and the Practical Application of the CRRF in Ethiopia. RRS RRS. (2019). Directive to Determine Conditions for Movement and Residence of Refugees Outside Camps, Directive No. 01/2019. RRS RRS. (2019a). Directive to Determine the Procedure for Refugees Right to Work. Directive No. 02/2019. RRS. (2019b). Refugees and Returnees Grievances and Appeals Handling Directive. Directive No. 03/2019. RRS, and UNHCR. (2021). Ethiopia GRF Pledge Progress Report. RRS and UNHCR Shamsuddin, M., Acosta, P.A., Schwengber, R.D., Fix, J., and Pirani, N. (2021). Integration of Venezuelan Refugees and Migrants in Brazil. Policy Research Working Papers 9605. http://hdl.handle.net/10986/35358. Schuettler, K., and Caron, L. (2020). Jobs Interventions for Refugees and Internally", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 though somewhat lower than that of the Turkish at 30- 50 percent. Child labor is also quite prevalent, though there have been extensive efforts made to ensure that refugee children attend school. 19 Publicly available information on refugees comes from an AFAD survey of 2, 700 households in June and July 2013. Figure 2, using data from AFAD (2013), provides an overview of the Syrian governorates from which the refugees to Turkey originated. The refugees primarily come from northwest Syria. The largest source regions are Aleppo (36 percent), Idleb (21 percent) al-Raqqah (11 percent), Lattakia (9 percent), and Hamah (8 percent). Consistent with travel distance being a good predictor of refugee flows to Turkey, 80 percent of respondents report that they chose to flee to Turkey, instead of another country, due to the ease of transportation. The refugees in Turkey, unlike the later 2015 refugee flows to Western Europe, are nearly 50 percent female. Slightly over 50 percent are minors (under the age of 18). These facts reflect that to large extent Syrian families fled to Turkey together. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "6 3. Data Sources and Methods 3. 1. Administrative data on educational outcomes in Italy Two administrative data sources represent the backbone of this paper. These are the administrative data obtained from the Ministry of Education (MoE) for academic years 2021-22, 2022-23 and 2023- 24, and standardized test score data from the Italian National Institute for the Evaluation of the Educational System (INVALSI) for the 2022-2023 academic year. These datasets offer valuable insights into the educational outcomes of students in Italy, including Ukrainian refugees who entered the Italian school system following the invasion in 2022. Deϐinitions. In both datasets, students are categorized into ϐive demographic groups based on their nationality and timing of entry into the Italian educational system. These groups are Italian students, Ukrainian refugee students, non-refugee Ukrainian students, newly arrived foreign students, and other foreign students. Among Ukrainian students, the distinction between refugees and non- refugees is based on their enrollment date in the Italian education system. Ukrainian refugees are deϐined as Ukrainian students who enrolled in Italian schools after February 2022. In this paper, Ukrainian refugees are labeled “ Ukr post-Feb 2022 “, while non-refugee Ukrainians are labeled “ Ukr pre-Feb 2022 “. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["standardized test score data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A growing number of low and middle-income countries have tried to improve the law enforcement response to gender-based violence by training professionals, reorganizing police and courts, and trying to provide a more comprehensive response to survivors. Evidence of effectiveness is relatively limited; most well-evaluated initiatives come from high-income countries, and the lessons learned may not be applicable to developing countries. Evaluations of law enforcement reforms in low and middle income countries have typically been limited to case study approaches drawing from police records (notorious for under-reporting), qualitative perspectives from key informant interviews, intermediate outcomes such as changes in attitudes and knowledge among police and judges, and interviews with small numbers of women who have sought legal redress. Population-based data collection, control groups, or follow-up among more than a handful of survivors are rare. Nonetheless, the following initiatives illustrate the types of efforts that have produced important lessons learned. Training personnel in the police and judiciary and other parts of the justice system Throughout the world, organizations have launched efforts to improve the knowledge, attitudes, and practices of justice sector personnel regarding gender-based violence. Some law enforcement institutions organize training internally, as did South Africa following passage of the 1998 Domestic Violence Act (Usdin et al., 2000). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As expected the positive correlation between refugee flows and 2011 school attendance rates is significant at the one percent significance level (controlling for the gender, age and education composition of a subregion the point estimate is- 0. 17). Even once we instrument for refugee flows the correlation remains significant at the one percent significance level (a point estimate of- 0. 30). However, once we control for the log distance for the Syria border there is no longer a statistically significant relationship, in either the OLS or IV, between school attendance rates in 2011 and subsequent refugee flows. This suggests that, on account of the inclusion of our distance from the border control, we can rule out the 2012 education reform confounding our estimates. 5. 3 Robustness to Varying Sample of Turkish NUTS 2 Subregions Throughout this paper we use all 26 NUTS 2 subregions of Turkey for identification. However, the results are robust to varying the particular sample of subregions. We report results for two alternative samples. First, we drop the Gaziantep subregion from the estimation. Gaziantep has the highest refugee to population ratio among all regions and reportedly towns with a refugee share of over 30 percent. The inclusion of Gaziantep may skew results if there are any non-linearities in the impact of refugees. Second, we follow Ceritoglu et al. (2015) in only considering nine subregions of Turkey. These are the five Syrian border regions of southeastern Anatolia (Hatay, Gaziantep, Sanliurfa, Mardin, and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["school attendance rates"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "also face high turnover of teachers as they take on better-paid jobs (UNHCR, 2017). Secondary education, on the other hand, is provided for refugees in camp-based refugee schools and in government administered public schools, with support from UNHCR’s NGO partner (Development and Inter-Church Aid Commission, DICAC). This relies on qualified national teachers exclusively. UNHCR and partners are working towards progressive transfer of secondary school administration from DICAC to Regional Education Bureaus. The MoE is responsible for managing refugee and national education for higher education. However, enrollment of refugees in higher institutions is low due to low absorption capacity of higher education institutions (UNHCR, 2020a). At the same time, the Ministry of Skills and Labor (MoLS) is responsible for managing Technical and Vocational Education and Training (TVET). OCP refugees receive education at the same levels as nationals through the national education system. Refugee education data is integrated into the MoE’s Education Management Information System (EMIS) (UNHCR, 2020), and a separate chapter on refugee education is included in the annual education statistics report of the MoE. The GoE included expanding primary and secondary education for refugees in the national five-year Education Sector Development Programme VI (ESDP), covering 2020 to 2025, but little", "output": {"entities": {"named_data": ["Education Management Information System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 International migration — the movement of people across national borders — has important economic, social, and political implications. Despite the recent emergence of a dynamic literature, empirical analysis of migration flows and their impact lags behind the policy debate and the theoretical literature. The main reason is the absence of comprehensive and reliable data on international migration patterns and migrant characteristics at either the aggregate or the household level. The objective of this article is to use data from more than one thousand national censuses and population registers to estimate a complete global origin – destination migration matrix for each decade over 1960 – 2000. These 226 * 226 matrices, comprising every country, major territory, and dependency around the world, are divided into periods corresponding to the last five completed census rounds. The gender dimension of international migration over this period is also presented. The primary source of the raw data is the United Nations Population Division ‘ s Global Migration Database, created through the collaboration of the United Nations Population Division, the United Nations Statistics Division, the World Bank, and the University of Sussex (United Nations [2008]). This unique data repository comprises 3, 500 individual census and population register records1 for more than 230 destination countries and territories over the last five decades. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Global Migration Database"], "descriptive_data": [], "vague_data": ["national censuses and population registers"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The report focuses on intent-to-treat (ITT) estimates, measuring the impact of offering volunteering opportunities and soft skills training independently of actual take-up. 9 We estimate the following individual-level intent-to-treat regression: 𝑌 ௜ ௧ ൌ 𝛼 + 𝜇 ௡ ൅ 𝛽𝑇௧ + 𝛾𝐷 ௜ + 𝛿ሺ𝑇 ∗ 𝐷ሻ ௜ ௧ + 𝜀 ௜ ௧ (1) where 𝑌 ௜ ௧ is the outcome of interest for respondent i in period t, 𝑇௧ is a post-treatment year binary variable, 𝐷 ௜ is a binary variable for being assigned to the treatment, and 𝜇 ௡ is a fixed effect for NGOs. 𝛼 represents the baseline average for the outcome of interest for non-selected youth. 𝛽 is the difference in after-and- before intervention in outcomes for non-selected youth. 𝛽 ൅ 𝛿 is the difference in after-and- before intervention in outcomes for selected youth. 𝛾 is the difference in 9 Due to some procurement delays that caused a big time-lag between baseline data collection and actual NGO project implementation, many of the volunteers who belonged to selected NGOs and who were randomly selected to participate in the impact evaluation study dropped out after their baseline data were collected and were replaced by other volunteers. Project monitoring data reveal that 23 percent of volunteers assigned to treatment did not actually end up participating in the NVSP. Given the relatively high number of non-compliance, we are unable to perform Local Average Treatment Effects (LATE) analyses to understand the impact of participating in NVSP on outcomes of interest.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Project monitoring data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 8 of 51 In the 9 cases mentioned, it was possible to identify part-time workers, while temporary workers were only identified in the cases of Argentina, Brazil, Chile, Mexico and El Salvador. 4. 1. 1 Variation of the NSE as a percentage of total employment The prevalence of NSE in the total employment has not shown very significant variations in the countries considered in the last two decades (Figure 1). Indeed, most of the countries analyzed show non-standard employment registers similar to those observed in the mid-1990s. The exceptions where the variation is a little more relevant are Brazil and Uruguay, where there are contractions in the incidence of the NSE of the order of 10 and 5 percentage points respectively and Mexico, where there is an increase of 5 percentage points. Figure 1: Prevalence of NSE among salaried employees. (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the prevalence of NSE by types of occupations, considering the ISCO classification at one digit, we find a quite similar pattern across countries. Indeed, in most of the considered countries, the “ Elementary Occupations ” are the category where the prevalence of NSE is higher.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "www.gasflaretracker.ng](http://www.gasflaretracker.ng/) has the geographic coordinates of each flaring point in Nigeria as well as monthly estimates of the flare volume from each location.\n\nsatellite-detected estimates with the official estimates reported by the Department of Petroleum\n\n\nResources [DPR] (2018). The satellite-detected estimates are broken down by location, while the\n\nThe paper uses the Romanian firm-level data from the Ministry of Finance covering enterprises of all sizes from 2011 to 2020, combined with the new World Bank Businesses of the State dataset, which tracks ownership of state business entities with at least 10 percent stake in Romania.", "output": {"entities": {"named_data": ["gasflaretracker.ng", "World Bank Businesses of the State dataset"], "descriptive_data": ["Romanian firm-level data from the Ministry of Finance"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The household and family dynamics in Liberia, as in other African settings, can be complex, with families often sending their children to live with relatives who are more able to provide for their schooling and basic needs. Young parents, in particular, often leave home to migrate for work, leaving their children back home with relatives until they are able to establish themselves and send for their children. If the economic success of the EPAG participant allowed her to bring non-resident family members into her household (including but not limited to her own children), then overall household size may have been expected to increase as a result of the program. This does not appear to have happened, at least in the short term. The results in Table 8 show that overall household size was not affected by the program. It is possible that the increase in earnings due to EPAG was too small, or too short-lived, to have induced the kinds of migrations described above. Other measures of household well-being, including food security and asset ownership, reflect shorter- term investments that might be influenced by the economic success of EPAG participants. Panel B of Table 8 shows the impact of EPAG on a broad range of food security measures.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We use two main data sources for our estimation of a poverty line for Brazil: the 2017/18 Household Budget Survey (Pesquisa de Orçamentos Familares; POF) and the Brazilian Table of Food Composition (Tabela Brasileira de Composição de Alimentos; TBCA). POF is a nationally representative semiregular survey on income and expenditures in Brazil, conducted every six to nine years.", "output": {"entities": {"named_data": ["Pesquisa de Orçamentos Familares", "POF", "Brazilian Table of Food Composition", "Tabela Brasileira de Composição de Alimentos", "TBCA"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "23 86 % of the IDPs, 91 % of the refugees and 88 % of the returnees are confident or fully confident that a coalition like this would be capable of providing security. Source: Listening to Displaced People Survey, 2014. In an open-ended question on who can be trusted most when it comes to ensuring security in the North, survey results suggest that the majority of refugees in Mauritania (86 %) trust the armed rebel groups as opposed to the army or police. This does not hold for refugees in Niger of whom 75 % trust the army and police. Similar results hold for IDPs and returnees, who put much more confidence in state authorities when it comes to securing the North: most trust is placed in the army and police (72 % of the IDPs and 66 % of the returnees) while little to no trust is placed in armed rebel groups (3 % of IDPs, 1 % of returnees). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Having experienced the work task and therefore able to realistically value the work, we oﬀer individuals in the employment arm an additional [surprise] week of work at a series of wages following the incentivized Becker-DeGroot-Marschak (BDM) method. We inform participants that we have a limited amount of funds remaining and are therefore unable to pay everyone their previous wage. This strategy realistically motivates the reservation wage elicitation exercise and makes clear that there will be no further opportunities for work. We piloted this exercise extensively. To maximize comprehension, we employ a multiple price list strategy, embed repeated conﬁrmations, and conduct a trial run of the exercise for each respondent before the real exercise; this mimics the procedure employed in Burchardi et al. (2021) for which participants in another low-income country ﬁeld context exhibited high comprehension. For those individuals who express willingness to work at a wage of zero, we oﬀer an alternative option of answering a brief survey at the end of the week for a small, randomized fee; we then use the fraction of respondents who are willing to forego this paid option and instead work for free as an estimate of the proportion of respondents who have a negative reservation wage of at least the foregone magnitude. 16 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "11 there is another data source that provides independent information on conflict events. The Uppsala Conflict Data Program, Conflict Encyclopedia Database (UCDP) provides similar information that spans the time period of interest. In contrast, UCDP data is not available for Liberia- so each country draws on similar but distinct data set to incorporate independent information about conflict intensity. The study data set contains one observation for every woman in the DHS individual recode, i. e., the woman level data and variables showing the annual number of ACLED or UCDP events and fatalities beginning in the year the woman was interviewed up to 10 years preceding the survey. Providing conflict for the 10 years preceding the DHS survey is key to the methodology and helps establish temporality in how conflict may affect IPV outcomes. A 10-year period was chosen because this captured hostilities from both Liberian civil conflicts and allowed the data set to reflect the long-standing impact of the conflict in each country. Each conflict data set used is described below. Uppsala Conflict Data Program (UCDP) Data- Colombia The Uppsala Conflict Data Program (UCDP) Georeferenced Event Dataset is an event data set that disaggregates three types of organized violence: state-based conflict, non-state conflict, and one-sided violence. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "under the Out-of-Camp Policy (OCP) constitute 5 and 12 percent of the sample, respectively (Figure 2.1). More than 30 percent of refugees in camps are born in Ethiopia (Figure 2.2). Somali refugees have a higher share of refugees born in Ethiopia (38 percent). According to UNHCR estimates (2023)12, around 1.9 million children were born as refugees between 2018 and 2022 globally. Overall, Ethiopia’s refugee situation is protracted; refugees have been in Ethiopia for an average of about 14 years. Refugees differ by country of origin. For example, Eritrean refugees have been in Ethiopia for average of slightly more than 16 years, Somalis for just under 16 years, and South Sudanese for roughly 15 years. On the other hand, refugees in Addis Ababa arrived nine years ago, on average (Figure 2.3a). Globally, the number of refugees in protracted situations—at least 25,000 refugees from the same country who lived in exile for more than five consecutive years—increased over time, accounting for 40 percent of all refugees in 2021 (World Bank, 2023). In Ethiopia, 95 percent of all refugees live in a protracted situation, according to SESRE data. 11 According to UNHCR mid-2022 statistics, South Sundanese, Somali and Eritrean refugees constitute 46, 29", "output": {"entities": {"named_data": ["UNHCR mid-2022 statistics", "SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "recently introduced the first version of their Global Internal Displacement Database (GIDD) that allows users to explore, filter and sort IDMC ’ s data to produce graphs and tables, and export underlying data. 102 Such platforms need to incorporate safeguards to protect the privacy and confidentiality of individuals ’ data. UNHCR and Statistics Norway are currently leading an initiative to improve forced displacement statistics with the participation of national statistical agencies. This process began with the presentation of the “ Report on Statistics on Refugees and IDPs ” at the 46th session of the UN Statistical Commission in March 2015, 103 followed by an international conference in Turkey in October 2015. 104 The conference set in motion a process for national statistical agencies to collaborate to develop a set of recommendations that both countries and international organizations can use to improve data collection, reporting, data disaggregation, and overall quality, including the preparation of International Recommendations for Refugee Statistics (IRRS). Progress on this agenda was discussed at the 47th session of UNSD held in New York in March 2016, where it was recommended that the expert group should also include IDPs in its scope of work (UNSD 2016). 105 The current initiative is focused on refugees, asylum-seekers and IDPs but would ideally be extended to host communities and returnees. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Global Internal Displacement Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Most papers with few exceptions use standard OLS estimators or some of its variants (Table 2). Two papers use general equilibrium models (Bodvarsson, Van den Berg, and Lewer 2008; Hercowitz and Yashiv 2002) and two papers simply compare means between treated and non-treated groups resulting in simple difference estimations (Card, 1990 and Alix-Garcia and Bartlett, 2015). [Table 2] The unit of observation varies depending on the data at hand. Most studies rely on household survey data where individuals or households are the unit of observations and most studies include some regional dimension (more frequently administrative areas). Where longitudinal or panel data are available time is also included. Other choices for unit of observations include skills or education level, various types of population groups (based on gender, age etc.), and, in a few cases, economic sectors, industry or labor market segments. The use of fixed effects varies. Some papers use the full set of parameters depicting units of observation (for example, household, region and time fixed effects in equations where the unit of observation is constructed using household, region and time). Other papers use subsets of these parameters whereas other papers introduce variables that are not used to identify the unit of observation. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "months” and “last 12 months” as references. Imputed rent for owner-occupied houses is calculated by the Ethiopian Statistical Service (ESS) team and is included in the consumption expenditure data shared with the Bank team. Spatial and temporal price deflators adjust for price variations across time and space. First, nominal consumption is adjusted for price differences across survey domains using spatial deflators calculated using the Household Welfare Statistics (HoWStat 2021) survey data. Second, spatially-deflated consumption levels are expressed in December 2022 prices using the food and non-food Consumer Price Indexes produced and provided by the ESS. Finally, to adjust for variations in household size and composition, the spatially and temporally adjusted consumption expenditure is divided by household size. This is because the poverty rates presented in this chapter are calculated using the international poverty line of USD 2.15 per capita in 2017 PPP. The US$2.15 poverty line was converted to local currency in 2017 using the PPP conversion factor, and then the value was inflated to December 2022 prices using the national CPI. Given that international poverty estimates reported at the global level are based on consumption aggregates not spatially deflated, the poverty reports presented in this report are not strictly", "output": {"entities": {"named_data": ["Household Welfare Statistics (HoWStat 2021) survey"], "descriptive_data": ["food and non-food Consumer Price Indexes"], "vague_data": ["consumption expenditure data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 12: CAT DDO Trigger Conditions and Drawdown Procedure**\n\nThe CAT DDO facility may be drawn down upon declaration of a state of natural disaster by the Council of Ministers, provided that the macroeconomic trigger conditions specified in the Loan Agreement are satisfied. The declaration must be accompanied by a damage and needs assessment prepared by the national disaster management agency and reviewed by a Bank-approved assessment team. Drawdown requests under the CAT DDO are processed within ten business days of receipt of a complete and satisfactory trigger documentation package.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Additionally, local and\nupstream shares of forest cover are measured using satellite data from the European Space Agency.\n\n###### 3. Empirical Strategy\n\nOur econometric specification uses a panel fixed-effects model to link data on droughts to data on\neconomic growth at the level of 0.5-degree grid cells (approximately 56 kilometers x 56 kilometers at the\nequator) between 1991 and 2014, the period for which economic data is available at a granular scale.\n\nIn robustness checks we also make use of the Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano, et al., 2010) that integrates evapotranspiration into the standard precipitation index.\n\nWe use data from the ESA CCI project to determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split the sample based on different shares of cropland ranging from less than 20 percent to more than 75 percent.\n\n2 UNCCD Report \"Drought in Numbers 2022.\"\n3 Defined here as shocks that are at least 2 standard deviations (SDs) below the long-term mean. Note that a 2 SD dry\nshock is a very rare event and includes the driest 2.5 years in a century.\n\nThe magnitudes of the effects are also consistent with the recent 2022 Global Assessment Report on Disaster Risk Reduction which looks at all types of disasters from rapid onset events like typhoons, floods, earthquakes to other events like droughts, saltwater intrusion, air pollution.", "output": {"entities": {"named_data": ["Standardized Precipitation Evapotranspiration Index (SPEI)", "ESA CCI project"], "descriptive_data": ["satellite data from the European Space Agency"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Consequently, they may fear detection by authorities when asked to respond to surveys or participate in public initiatives. 1 Furthermore, collective repre- sentative and longitudinal data on forcibly displaced migrants, a population with high mobility rates, is difficult and costly (Ib ´ a ˜ nez et al. 2024). This complexity is compounded when focusing on children and adolescents, given the need for enumerators to receive specific training to interact with such a vulnerable demographic and for migrant parents to authorize their children ’ s involvement despite prevailing distrust issues. To address this knowledge gap, we launched the Venezuelan Refugee Panel Study for Kids (VenRePs-Kids) in Medell ´ ın, Colombia. VenRePs-Kids is a longitudinal study repre- sentative of forcibly displaced Venezuelan and Colombian children and adolescents aged 5 to 17. To our knowledge, it is the first study to gather panel data specifically on forcibly 1This concern is also prevalent among undocumented migrants in the United States, as highlighted by Amuedo-Dorantes and Lopez (2015). 2", "output": {"entities": {"named_data": ["VenRePs-Kids", "Venezuelan Refugee Panel Study for Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "26 Buller et al. (2016) conducted a mixed methods study in Ecuador, combining secondary analysis from a field experiment on the impact of a transfer program on IPV with in-depth interviews and focus group discussions with male and female beneficiaries. The qualitative component aimed to better understand the mechanisms underlying the quantitative results, which showed substantial reductions in physical and sexual violence among beneficiaries of the cash and in-kind food transfer program. These qualitative interviews revealed a similar finding to Arugay et al. (forthcoming). Specifically, a core feature of women ’ s reported joint decision making was asking their spouse or partner for input into a decision, so that they would not be blamed if something went wrong. Similarly, an analysis of qualitative data from eight projects in Africa and Asia focused on understanding women ’ s empowerment finds that joint decision making can be empowering for women. In particular, in focus groups in Ghana, women stressed the importance of family harmony and in the individual interviews, “ women indicated that they want more input on decisions, but do not want full responsibility for decisions in case they go wrong (Meinzen-Dick et al, 2019, p. 20). The qualitative evidence provides one interpretation of our empirical results. Couples who agree that they jointly make decisions may also be sharing responsibility for these decisions. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This database lists seven categories: refugees, asylum-seekers, returned refugees, internally displaced persons (IDPs), returned IDPs, stateless persons and others of concern. For each group the database provides yearly information about their composition by loca- tion of residence and origin. We exploit only the data on “ refugees ”. 22 In particular, we are interested in the annual stock of refugees for each country of residence, i. e. how many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number of IDPs is extremely high in some instances but cannot be captured with the same level of conﬁdence as refugees generally. 23 Cross-country data about conﬂict is provided by the UCDP / PRIO. As for the index of country-level economic activity, we use again information provided by the Penn World Table and World Bank databases. As mentioned above, our aim is to explore the dynamics of refugees during conﬂicts. In other words, we attempt to answer several questions. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Penn World Table", "World Bank databases"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "refugee situation and to devise policy directions. The SESRE covers three types of groups: (i) refugees in camps; (ii) refugees out-of-camps in Addis Ababa; and (iii) host communities; all of which require a distinct sampling procedure. The sampling frame for refugee camps is based on UNHCR’s proGRES database. SESRE is a representative survey of the refugee population 1 Formerly the Household Consumption and Expenditure Survey and Welfare Monitoring Survey. EXECUTIVE SUMMARY iii of Eritrean, South Sudanese, and Somali origin living in camps in Ethiopia, refugees living in Addis Ababa, and their respective host communities. Host communities are defined as Ethiopian non-displaced households living enumeration areas adjacent to the refugee camps. SESRE data was collected from November 2022 to January 2023, from a nationally representative sample of 3,452. The following represents a summary of findings stemming from the SESRE data and associated statistical regression work using this data. Sociodemographic Ethiopia is a second home for close to one million refugees who predominantly originate from South Sudan, Somalia, and Eritrea. Around 88 percent of refugees live in camps, and the rest reside in urban areas under the Out-of-Camp Policy (OCP) regime. Refugees fled from their country mainly due to conflict and violence.", "output": {"entities": {"named_data": ["UNHCR’s proGRES database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Appendix A Data Appendix A. 1 Linking Ethnic Data from Africa (LEDA) LEDA offers an interface — a language tree — to flexibly link ethnic groups from different databases to each other and calculate the linguistic distances between them. LEDA is currently structured around lists of ethnic groups from 12 original datasets, which are the following:  Afrobarometer Surveys  All Minorities at Risk (AMAR)  Census data from IPUMS  Ethnic Power Relations (EPR) dataset  Ethnologue languages  Political Relevant Ethnic Groups from Posner (2004)  Ethnic groups in Francois, Trebbi & Rainer (2015)  Ethnic groups from Fearon (2003)  GREG Data (based on the Russian Atlas Miradova)  Demographic and Health Surveys  Murdock Atlas  Spatially Interpolated Data on Ethnicity (SIDE) These lists are structured in LEDA ’ s interface by data source, country, year, or in the case of survey data, survey rounds. In our analysis, we use Afrobarometer, EPR, and Murdock Atlas data; therefore, we can use LEDA functions to link the different ethnic groups to each other. LEDA consists of three main linkage types: binary linking based on the relations of sets of language nodes associated with two groups; binary linking based on linguistic distances; and a full computation of dyadic linguistic distances.", "output": {"entities": {"named_data": ["Demographic and Health Surveys", "Ethnic Power Relations", "GREG Data", "Linking Ethnic Data from Africa", "Afrobarometer Surveys", "Afrobarometer", "Murdock Atlas", "Spatially Interpolated Data on Ethnicity"], "descriptive_data": [], "vague_data": ["Census data", "survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Due to the variations in weight and height ratios among children and adolescents according to gender and age, the benchmarks for determining nutritional status are specifically tailored to these factors. We adhere to WHO guidelines to calculate the standardized BMI values for minors. A BMI exceeding one standard deviation (SD) from the mean suggests overweight, while a BMI less than minus one SD indicates underweight. 16 Health status is assessed through a binary variable, assigned a value of one if the caregiver has reported any health issues such as disease or chronic pain, accidents, dental pain, surgical interventions, or preg- 14For the Colombian households the wealth index is measured with contemporaneous data. 15This procedure restricts the sample to the common support of the propensity score for being a forced migrant and weights observations for Colombian kids by a non-parametric function of the propensity score. This procedure has been shown to increase the estimate ’ s efficiency. 16Furthermore, a BMI greater than 2SD is indicative of obesity risk, and less than- 2SD signals a risk of severe thinness. 33 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "which increases signiﬁcantly under both treatment arms. In other words, the psychosocial value to employment appears to be driven largely by the non-pecuniary dimensions of the employment experience. 5. 4 Impacts of employment on reported physical health, cognitive function, and economic decision-making The positive eﬀects of employment extend to other measures beyond psychosocial health. Table 3 presents results on reported physical health, cognitive function, and incentivized measures of risk and time preference. We observe a signiﬁcant increase in the days reported healthy. This eﬀect may be due to ‘ real ’ health improvements from increased exercise (which has also been documented to translate to improved mental health (Herbert et al. (2020))) from the employment task or ‘ perceived ’ health improvements in which improved psychoso- cial well-being translates into feeling less physically ill. Should the channel be exercise, we may expect health improvements to grow over time. Our weekly data on days healthy sug- gests this is not the case: we observe the treatment eﬀect on health from the ﬁrst week of working, and the gap remains steady throughout the following two months (Appendix Figure A3). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["weekly data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "17 significant for both selected and non-selected youth (see the 𝛽 ൅ 𝛿 estimate and the 𝛽 estimate for columns 2 & 3). Lack of results also holds for specification 3, where imputing missing data with upper and lower bound estimates to account for the potential bias introduced by attrition did not alter the lack of impact of the program, as well as for specification 4 (see the 𝛿 estimate). Figures 1, 2, and 3 plot the distribution of soft skills scores at baseline for both selected and non- selected youth. The figures indicate that scores across the three skills are concentrated towards the end of the scale, suggesting that soft skills training offered by NVSP might have been ineffective or too basic for this pool of volunteers. 19 Indeed, as table 1 shows, a high percentage of selected youth (71 percent) and non-selected youth (63 percent) had taken previous training in soft skills prior to NVSP. Results from a process evaluation conducted separately support this explanation. The majority of NVSP volunteers in focus group discussions and interviews mentioned that they would have welcomed more advanced trainings on soft skills, as well as on technical topics and job-relevant skills that can support their employability. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "NGOs. Initiatives such as JIPS, a collaborative project of UN and NGO actors, have also been established to support governments and operational organizations to design and implement data collection processes. There are a variety of data sources for generating statistics on forced displacement, each of which has strengths and weaknesses. Despite the significant challenges, large amounts of data are collected and disseminated every year. The main data sources and methods for the generation of statistics on forcibly displaced populations include: (a) registration of refugees and asylum-seekers; (b) registration of IDPs; (c) profiling of IDPs; (d) population movement tracking systems; (e) national population censuses; (f) sample surveys; (g) border crossings; (h) administrative records and registers; (i) general population registers; and (j) a variety of estimation methods for producing statistics when adequate and reliable data on individuals are unavailable (UNSD 2014). Several of these data sources might be used together to triangulate estimates of stocks and flows for a particular displacement situation. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["general population registers", "sample surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 2. Location of Households in the VenReP-Kids Sample Notes: The figure depicts the exact geographic location of all the households in the VenRePs-Kids study. Green and blue dots depict the location of Colombian and Venezuelan households, respectively. The map in the upper right corner illustrates the location of Medell ´ ın (blue pin) with the department of Antioquia (highlighted in red). survey focuses on strata 1 through 4, intentionally omitting strata 5 and 6 to avoid bias toward higher-income groups which are less likely to include migrants in need of sup- port. 14", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Operators/Assemblers Craf/Related Trade Workers Skilled Agricultural Workers Service/Sales Workers Clerical Support Workers Tech/Associate Professionals Managers/Professionals Percent Figure D.19: Occupation by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Inside the camp Outside the camp Percent Figure D.20: Work location by survey domains Source: World Bank Staff based on SESRE 2023. 0 5 10 15 20 25 30 35 40 45 50 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.21: Hours per week by survey domains Source: World Bank Staff based on SESRE 2023. Annexes 109 0 10 20 30 40 50 60 70 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.22: Hourly earnings by survey domains Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.24: Household owns livestock Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.23: Household owns crops Source: World Bank Staff based on SESRE 2023. 0", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "UNHS. Data were collected over a 12-month period. The sample used in this paper is\n\ncharacteristics also differ between the two groups. Table 1 summarizes relevant socio\neconomic and child anthropometric variables by ownership and per capita consumption\n\n\n8\n\n\n\n<!-- PAGE 11 -->\nexpenditure terciles for the 2005/06 UNHS and the 2009/10 UNPS. [3] Descriptive statistics on\n\nDHS. (2011). Uganda Demographic and Health Surveys, 2011. Data accessed via & Hautvast, J. (1989)\nIncreased Risk of Vitamin B-12 and Iron Deficiency in Infants on Macrobiotic Diets. _The_\n_American Journal of Clinical Nutrition_, 50, 818-824.\n\nPAGE 31 --> Kabubo-Mariara, J., Ndenge, G. & Mwabu, D. (2009). Determinants of Children's Nutritional Status in Kenya: Evidence from Demographic and Health Surveys. _Journal of African_ _Economies_, 18(3), 363-387.", "output": {"entities": {"named_data": ["Uganda Demographic and Health Surveys", "Demographic and Health Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "13 degree of group bias. Thus, should the program reduce bias, 𝜓𝜓7 < 0. 𝑋𝑋is the same 𝑛𝑛 × 𝑘𝑘 matrix of control variables. 𝜓𝜓 is a 𝑘𝑘 × 1 vector of regression coefficients; and 𝜔𝜔 is the idiosyncratic error. We employ two small deviations from these approaches to produce the full set of results. First of all, as we do not have two sets of control variables from outreach to baseline, we run a fixed effects analysis to understand the impact of assignment to treatment status on life and economic optimism. Second, due to a data collection error in the field, indicators of economic scarcity were not collected from all of the control group at baseline. Instead, we seek to approximate the effect of treatment on these indicators by triangulating comparisons in two dimensions. First, we test whether or not these indicators improved for the treatment group from baseline to endline. Second, we test whether or not there are differences between the treatment and control groups at endline. This stops short of causality but still reveals interesting information about the dynamics at play. We produce five outputs for each analysis, with the exception of the economic scarcity indicators. First, we use uncontrolled OLS. Second, we introduce control variables. Third, we remove the controls but add inverse probability weights.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Cross-sectoral programming designed to meet families ’ holistic needs would also increase the odds of maternal and child well-being, including increasing provision of cash transfers through BISP, expanding access to ECE programs, ensuring food availability and affordability for mothers, and implementing emergency-response programs for continuity of basic services in the context of floods and other disruptive disasters to protect vulnerable mothers and young children. Both direct interventions that provide mental health services and indirect interventions that reduce exposure to stressors within communities and households are approaches to alleviate mental health concerns among mothers of young children in the FCV setting of the current study. In addition to individual-level impacts, societal and economic impacts result from reduced maternal depression, anxiety and stress. For example, costing estimates from Pakistan suggest that there is a $ 16. 6 billion cost per cohort of unaddressed maternal mental health concerns (Bauer et al., 2024). Limitations The data collection coincided with a politically sensitive time in Khyber Pakhtunkhwa, inclusive of security concerns for enumerators, ongoing election campaigns, and occasional disruptions to data collection, which could have impacted the accuracy and consistency of data collected. Further, repatriation efforts being implemented during the data collection period led researchers to avoid self-report determination of refugee (documented and undocumented) status and rely on enumerator report using observable indicators as outlined above. This may have therefore impacted the accuracy of the data on identifying refugee households. There are also several measurement constraints to be considered. The tools selected to measure maternal mental health may not cover all relevant dimensions of symptoms and experiences specific to the region of Pakistan studied, even though tools were specifically selected based on previous adaptation, validation and use in the province. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "There are different ways to arrive at a value for housing services consumed by nonrenters, which have different advantages and disadvantages (see Deaton and Zaidi, 2002). In POF, households that own their dwelling (even if still paying the mortgage) or that\n\n_Temporal and spatial price adjustment._ To make the consumption aggregate and average costs per calorie\ncomparable across households, we adjust expenditure values temporally and spatially. POF was collected\nthroughout a full year and its reference periods go back up to 12 months. Figure 2 shows the temporal\nvariation of prices for different Brazilian states over the reference period of the survey. On average, prices\nincreased by 26.8 percent over the reference period - a monthly average of 1 percent. IBGE (2020)\nprovides temporal deflators in the publicly available data, which we use to deflate prices and the\nconsumption aggregate to express all monetary values in January 2018 values.\n\n13 There are several differences between our consumption aggregate and other studies using POF data. In one of the\nmost recent ones (Oliveira et al. 2016), the authors aim to measure and analyze welfare, poverty (using the\nadministrative \"poverty'' line equivalent to half a minimum wage), inequality, and vulnerability.\n\nMidwest-Rural 0.997 0.671 0.933\n_Source:_ Own calculations using POF 2017/18 data.\n_Note:_ Prices in the urban Southeast are the reference prices. While we use household-specific price indices in our estimations,\nfor ease of presentation we calculated regional averages of household-specific indices and divided them by the\nSoutheast-Urban average.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Notes: The table displays the mean and standard deviations of several characteristics of all students\nmatched in the ENILEMS-ENLACE (column 1), students that reported to be in college (column 2),\nout of college (column 3) or employed (column 4). Data: ENILEMS-ENLACE panel.\n\n\n30\n\n\n\n<!-- PAGE 33 -->\nTable 3: OLS - ENLACE test scores and secondary school outcomes: simple correlations\n\nNotes: (1) The table displays the estimation of the effect of early test scores on grade 9 and 12 test scores and enrollment by subject and\ngrade. (2) Specifications for each subject and grade include as independent variables grade 6 test scores of the same subject. (3) Sample:\nTwins (students in the same school in grade 6, with identical last names and birth date. (4) All specifications include twins fixed effects. (5)\nRobust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05,  - p _<_ 0.1.\n\n\n\n<!-- PAGE 46 -->\nTable A.4: OLS       - ENLACE mathematics and Spanish test scores and post-secondary school outcomes\n\nEnlace Score 0.102*** -0.00750 0.0681** -0.000444 (0.0154) (0.0297) (0.0304) (0.0298) Upper Secondary GPA 0.0688*** 0.0412* -0.00579 0.00507 (0.0146) (0.0233) (0.0268) (0.0254) Girl -0.0500* -0.237*** -0.0321 0.00994 (0.0263) (0.0442) (0.0501) (0.0478) Private Upper Secondary 0.113*** -0.0962 0.0606 -0.0842 (0.0320) (0.0649) (0.0736) (0.0653) Urban resident 0.269*** 0.0373 0.0944* 0.137** (0.0375) (0.0472) (0.0535) (0.0566)", "output": {"entities": {"named_data": ["ENILEMS-ENLACE", "ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The age categories are 15 – 19, 20 – 24, 25 – 29, 30 – 34, 35 – 39, 40 – 44, 45 – 49, 50 – 54, 55 – 59, and 60 – 64 years. There are 183 groups since we exclude groups containing less than 40 observations. 24 An additional advantage of the IV approach is that it helps deal with measurement problems. Despite the improved measures of refugee numbers in Turkey by subregion starting in 2014, there is likely considerable measurement error, resulting in attenuation bias in the OLS estimates. For the IV estimates to be consistent, it is only necessary that- conditional on the fixed effects and control variables- the flows of Syrian refugees are uncorrelated with the instrument. 25 Using data from AFAD (2013) we can also weight the aggregate refugee numbers using the Syrian source governorates of refugees in 2012-13 (see Figure 2). Results are qualitatively robust to this alternative instrument and first-stage F-statistics about the same. We prefer the use of the pre-war distribution of population in Syria, Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "6. Robustness Checks 6. 1. Sample attrition Many challenges were encountered during survey implementation, due to the context – the densely populated and impoverished communities in and around Monrovia are difficult settings in which to find and track respondents — as well as the transience of a young study population. Despite vigorous efforts to track and interview each individual in the sample, a certain amount of survey attrition was expected. As the survey response rates in Table 1 show, 1622 (or 80 %) of the individuals in the study sample were successfully interviewed in both the baseline and midline surveys. Another 305 respondents were interviewed at baseline but not at midline and hence are not in the panel used for the analysis in this paper. 27 This survey attrition, while not much higher than other program evaluations in Africa, may cause concern that the results of this evaluation are biased, especially if the loss to follow up is correlated with individual characteristics that might affect the outcomes. To address this concern, Table 10 presents regressions on the likelihood of panel inclusion, that is, the likelihood of being interviewed at both baseline and midline. The first column indicates that treated individuals are significantly more likely than control to have been interviewed twice. This result persists even after controlling for individual characteristics and community dummies. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Firstly, the Hydrosheds variant of SRTM is used, both at 3 and 30 arc second resolutions. A number of additional corrections are applied to the terrain data including a systematic vegetation correction procedure in vegetated areas and an urban correction procedure in urbanized areas. A detailed description of the modeling framework is provided by (Sampson et al. 2015).\n\nFor this study, we developed flood hazard maps representing riverine, flash-flood and coastal flood risk for Vietnam. These flood hazard maps estimate the inundation depth at a grid cell level of 3 arc-seconds, (~ 90m) and provide coastal surge hazard layers, along with pluvial and fluvial layers. The maps provide information on the extent and depth of flood hazard for a specific location. For the coastal component, we explicitly model four return periods - 25, 50, 100, and 200 year events, under current and future climate conditions.\n\nData on natural capital is sourced from a World Bank dataset on national wealth. The dataset disaggregates the three components of national wealth-produced capital, intangible capital and natural capital-and it decomposes the natural wealth stock into renewable and nonrenewable resources.\n\nThe data on income inequality is sourced from the World Bank's \"All-the-Ginis Database,\"\nwhich was last updated in 2014. [8] The database collects Gini indexes from multiple sources\ninto long time series. The data has been standardized for this analysis via the so-called\n\"choice-by-precedence approach,\" which reflects each dataset's reliability, degree of\nvariable standardization, and consistency of geographical coverage. GDP and population\nfigures have been collected from the United Nation's UNCTAD-STAT database. Table 2\npresents the descriptive statistics for per capita GDP and the Gini index by country\nincome group. The Gini index peaks among the upper-middle-income group, falls among\nthe lower-middle-income and low-income groups and is lowest among the high-income\ngroup. These data are consistent with the relationship between inequality and GDP\ndescribed by Kuznets (1955).", "output": {"entities": {"named_data": ["All-the-Ginis Database", "UNCTAD-STAT"], "descriptive_data": ["World Bank dataset on national wealth"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Abstract\nBehavioural surveillance surveys (BSSs), an evolution from the knowledge �attitudes practice surveys (KAPs), are a tool to track trends in HIV/AIDS knowledge, attitudes\nand risk behaviour among populations. The data collected support organizations in\ntargeting specific HIV/AIDS prevention and care activities, monitoring their effectiveness\nand coverage, and allocating scarce resources. The objectives are to evaluate the quality and\nstandardization of BSS-like surveys undertaken in conflict and post-conflict situations, and\nto provide recommendations to humanitarian agencies and governments on how to\nimprove their quality. Survey methodology was classified as reproducible if the populationbased sampling defined a sampling frame using probabilistic sampling. Survey indicators\nwere compared to internationally-accepted HIV indicators. The results showed that 14\n(45.2%) of the 31 BSS-like surveys evaluated between 1998 and 2005 in 14 countries were\nclassified as reproducible. Surveys undertaken by non-governmental organizations\n(NGOs) were significantly less reproducible than those undertaken by non-NGOs (p �/\n0.05). The majority of surveys used at least one identical or similarly worded\ninternationally-accepted HIV indicator for prevention and misperception but not for\npractice and attitudes. Few reported disaggregated indicators according to age or gender. It\nwas concluded that the majority of BSS-like surveys are of insufficient methodological rigor\nto be reproducible. Few surveys reported internationally-accepted HIV indicators by\ngender and age which makes interpretability and comparison difficult. United Nations\nagencies, NGOs, and governments undertaking BSSs in conflict and post-conflict settings\nshould proceed with a BSS survey once the design and plan for execution has been prepared\nby experienced and qualified experts. These experts should then oversee the survey, assure\ndata quality and incorporate training of others in the process. A practical and field userfriendly BSS manual is needed for conflict affected and displaced population situations,\none which is customized to take into account the special circumstances of such populations.", "output": {"entities": {"named_data": ["Abstract\nBehavioural surveillance surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure A4: Participation certiﬁcate to boost ‘ resume ’ CERTIFICATE THIS ACKNOWLEDGES THAT I engaged with Pulse Bangladesh to do data collection Notes: The wording of the certiﬁcate was made such that it could be applied to both arms; cash-only arms participated in weekly surveys along with all other experiment participants, so technically also engaged in data collection for our project. 60 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 14 of 51 Source: Own calculations based on Household surveys From the education perspective, an improvement in the profile of workers employed as NSE can be observed in the countries analyzed, even though there are some exceptions. In fact, in most of the countries of our sample, the prevalence of workers with secondary and tertiary education increases in detriment of workers with a lower educational level. However, there are some differences by type of non-standard employment. In the case of part-time employment (Figure 10), several countries show an obvious rise in the prevalence of workers with secondary and tertiary education (Argentina, Brazil, Peru, and Mexico). There is a second group of countries that presents a decrease in the prevalence of workers at the secondary level, but, this decrease is more than compensated by the greater incidence of tertiary workers, so that, taken together, workers with secondary or higher education increased their share within part-time employees (Uruguay and Chile). Therefore, we can also conclude from this group that part-time employment presents a better educational profile today than two decades ago. In the Dominican Republic and El Salvador, we find a rise in the prevalence of workers with secondary education but a decrease in the share of workers at the tertiary level.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "during the sampling and data collection stages. Moreover, our thanks go to the following UNHCR colleagues for their invaluable comments on the various draft stages of this report: Emily Lugano, Annick-Laure Tchuendem, Jed Fix, Theresa Beltramo, Alessio Baldaccini, Anna Gaunt, Asaad Kadhum, Benoit d’Ansembourg, Berhanu Geneti, Campbell Macknight, Daniel Gebrekidan, Florah Bukania, Florence Nimoh, Johannes Abate, Joyce Wahome, Katie Ogwang, Nada Omeira, Nathalie Bussien, Robert Nyambaka and Yukta Kumar. Our thanks also go to our JDC colleagues Felix Schmieding (Senior Statistician, UNHCR) and Harriet Kasidi Mugera (Senior Data Scientist, World Bank) for their tireless support and advice during the preparation of SESRE. The report benefited from the insights of peer reviewers: Leslie Velez, Mulualem Desta, Takaaki Masaki (Senior Economist, World Bank), and Precious Zikhali (Senior Economist, World Bank). We are indebted to Nistha Sinha (Senior Economist, World Bank) for her constructive comments that significantly enhanced the report. We also recognize Aldo Morri for his excellent editorial support. Finally, we extend our deepest gratitude to the survey respondents for their willingness to share their experiences which has been instrumental in deepening our understanding of the challenges and needs faced by both refugees and host households. ACKNOWLEDGEMENTS ii Introduction E thiopia, with", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Using Afrobarometer ’ s geocoded surveys, we focus on clusters as our unit of observation. 10 Our sample consists of 7, 547 such locations and 76, 518 individuals in 23 countries in Sub-Saharan Africa. “ The sampling universe normally includes all citizens age 18 and older. As a standard practice, they [we] exclude people living in institutionalized settings, such as students in dormitories, patients in hos- pitals, and persons in prisons or nursing homes. ” (Afrobarometer, https: / / afrobarometer. org / surveys − and − methods / sampling − principles) Since the sampling frame is based on recent censuses, with the aim of representing all citizens of voting age in a given country, the Afrobarometer samples are unlikely to include refugees. Note also that “ the sample design is a clustered, stratified, multi-stage, 8We explain the construction of theses indices in Section 4. 2. 9We test the robustness of our results with a smaller (40 km) and a larger (120 km) radius in Section 5. 3. This choice of buffer size assures us that between 75 percent and virtually all refugee camps fall within a cluster buffer. Other studies relying on Afrobarometer data construct buffers ranging from 25 km (e. g., Michaelopoulos and Papaioannou (2011), investigating ethnic-specific pre-colonial institutional structures) to 100 km (e. g., McGuirk and Burke (2020a), analyzing the impact of food-price shocks on conflict). 10Afrobarometer is a pan-African research network conducting public attitude surveys on democracy, governance, the economy, and society in African countries that are repeated on a regular basis (Afrobarometer, 2020). 10 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Second, there are cases where the current study reports data by nationality, but the corresponding figure in the Trends in International Migrant Stock refers to the foreign born. This situation generally arises when a census does not report the number of foreign-born migrants on a bilateral basis. Examples include Austria and Côte d ‘ Ivoire. Third, differences in the years to which the data refer can generate large disparities. For example, this study uses the 1966 data for Australia, whereas Trends in International Migrant Stock reports data for 1970. Overall, however, the fact that the totals are remarkably close in every decade adds credence to the estimates here. IV. THE EVOLUTION OF GLOBAL BILATERAL MIGRATION The greatest strengths of the global migration matrices are their bilateral coverage, the number of decades covered, and the disaggregation by gender. These data are too rich for a full analysis of all movements between all pairs of countries. Instead, this section summarizes the major trends in the evolution of bilateral migrant stocks, based primarily on World Bank regions. 25 Global Trends The migration matrix for the 1960 census round reflects a realigning world in the postcolonial era. Over the 1960-2000 period, the composition of world migration 25 Appendix 1 details the World Bank regions: South Asia, East Asia and Pacific, Sub-Saharan Africa, Latin America and the Caribbean, Europe and Central Asia, and Middle East and North Africa. High-income Middle East and North Africa refers to the predominantly oil producing countries in the Persian Gulf (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) and to Israel. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "### 6.1 Grievance Redress Mechanism\n\nThe project-level GRM will receive and process complaints related to beneficiary selection, service delivery, labor conditions, environmental impacts, and GBV incidents. A dedicated toll-free hotline, an SMS channel, and physical suggestion boxes at community centers will serve as intake points. All complaints will be logged in the MIS within 24 hours of receipt and assigned to the responsible district coordinator for investigation. Complainants will receive a response within 15 working days, with more complex cases referred to the PIU's E&S specialist for resolution.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "natural log of a country ’ s population size and government consumption as a share of GDP; the latter is the most widespread measure of government size (Adsera and Boix, 2002; Alesina and Wacziarg, 1998; Rodrik, 1998). Tax compliance is also related to the government ’ s ability to eﬀectively detect and punish tax avoiders, tax evaders, and tax arrears. Although an imperfect measure of states ’ deterrent capacity, Afrobarometer includes two survey questions on perceptions of government enforcement and monitoring capacities. One question probes respondents on how often ordinary people who break the law go unpunished. The other probes respondents on how often oﬃcials who commit crimes go unpunished. This latter question is also a measure of perceived government fairness- the extent to which a government implements the law evenly across all social groups. 6. 2. 5 Procedural Justice I include two indicators of procedural justice. The ﬁrst probes respondents on how often people are treated unequally under the law. The next taps citizens ’ perceptions of the government ’ s treatment of their ethnic group. Speciﬁcally, respondents were asked how often their ethnic group is treated unfairly by their government. 9 6. 2. 6 Donor and Non-State Actor Provision of Services I include a measure of who citizens believe is responsible for collecting income taxes.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "<!-- PAGE 7 -->\nAs we did not have meteorological data for each and every municipality in Chile, this\ninformation was estimated. Since average annual temperature in any particular location\ndepends principally on distance from the equator and elevation above sea-level, we\nestimated a simple model (see Table 2) using information on average annual temperature,\nlatitude, and altitude for all the Chilean stations for which we could obtain \"normal\"\ntemperature data (from www.worldclimate.org). In order to get sufficient variation in the\nelevation variable, we included 4 stations located close to Chile (Catamarca, Mendoza,\nArequipa, and Oruro).\n\nIn this section we will analyze climate data for Chile from May 1948 to March 2008 to test\nwhether there are any significant trends, and whether these trends differ between regions.\n\n\nWe will use the Monthly Climatic Data for the World database collected by the National\nClimatic Data Center (NCDC) in the US. This project started in May 1948 with 100\nselected stations spread across the World, including five in Chile. Since then, many more\nstations have been included in the data base, but only seven stations in Chile have\ncontributed systematically throughout the period, with only inconsequential gaps. These are\nlisted in Table 5, ordered from north to south.\n\nAccording to the model simulations reported in Christensen et al (2007), temperatures are going to increase faster in the northern part of Chile than in the southern part\n\ntemperature data are extracted from the New et al. (2000) gridded 0.5 degree dataset. Calculation of\n\nindices, such as the Palmer Drought Severity Index which uses precipitation and temperature to measure", "output": {"entities": {"named_data": ["Monthly Climatic Data for the World", "Palmer Drought Severity Index"], "descriptive_data": [], "vague_data": ["gridded 0.5 degree dataset"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Indeed, despite the recent literature rejecting the conflictive impact of refugees in hosting areas (Zhou and Shaver, 2021), the magnitude of our coefficients might be explained by the confounding presence of refugees. Columns (5) and (6) further introduce climatic controls. Column (6) corresponds to Equation 1 and refers to our benchmark specification. Columns (1) and (2) show that without incorporating the changes in ethnic diversity induced by refugees we would not be able to identify a relationship between diversity and violent conflicts. In column (3), the revised refugee fractionalization index has a negative and significant coefficient, while the revised refugee polarization index has a positive and significant effect on the incidence of violent conflicts. In columns (2), (4), and (6), our coefficients of interest are of the same order of magnitude when the number of refugees is controlled for. Our results are not altered by incorporating rainfall and temperature anomalies (columns (5) and (6)), but the estimates become slightly more precise. 21 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "\"Life in Transition Survey, Transition Report 2020-2021: The State Strike Back\", [https://www.ebrd.com/publications/transition-report-](https://www.ebrd.com/publications/transition-report-202021) [202021.](https://www.ebrd.com/publications/transition-report-202021)\n\n\"Strengthening the business environment for productivity convergence,\" in OECD Economic Surveys: Romania 2022, OECD Publishing, Paris, [https://doi.org/10.1787/63318cf5-en.](https://doi.org/10.1787/63318cf5-en)\n\n_Encuesta_ _Dirigida_ _a_ _la_ _Población_ _Venezolana_ _que_ _Reside_ _en_ _El_ _País_ _(ENPOVE)_ is a special ized survey of Venezuelans living in Peru conducted by the National Institute of Statistics (INEI) in December 2018. The sample covers five main urban areas in the country where Venezuelan immigrants were most likely to be present. The survey collects data on the immi grant's origin, migration date, and details on their current employment. Importantly, a full module asks about the immigrant's experiences with locals, which includes questions about discrimination and hostile attitudes towards them. The respondent's current location is iden tified down to the _centro_ _poblado_ level, which roughly corresponds to an urban neighborhood or a rural town.\n\n_Encuesta_ _Nacional_ _de_ _Hogares_ _(ENAHO)_ is the Peruvian version of the Living Standards Measurement Survey, e.g. a nationally representative household survey collected monthly on a continuous basis. For our analysis, we use data from January 2007 to December 2020. The survey covers a wide variety of topics, including basic demographics, educational back ground, labor market conditions, crime victimization, and a module on respondent's percep tions about the main problems in the country and trust on different local and national level institutions. Observations are also spatially identified at the municipality level, but here we focus on variation in the Venezuelan share of the population at the province level, of which there are 196, as these are best representative of local labor markets.\n\n_Latin_ _American_ _Public_ _Opinion_ _Project_ _(LAPOP)_ is a opinion survey conducted bi-annually in all countries in Latin America and designed to be representative of urban populations. This was fielded in Peru in 2010, 2012, 2014, 2017 and 2019 and consists of about 2,000 observations from mostly urban areas. The survey questions are centered around politics,", "output": {"entities": {"named_data": ["Life in Transition Survey", "ENPOVE", "ENAHO", "LAPOP"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "[7] We use the expanded December 2017 vintage of the I2D2   database. Only select members of the research team or individuals in charge of   harmonizing the data can access the database.\n\n**Sample** **Size.** The version of the I2D2 database that we use includes about 1,500   survey/census samples. However, wages are only reported for about two thirds of them.\n\nThe baseline sample that we obtain includes 24,437,020 individuals from 1,073   surveys and 11 censuses in 145 countries from 1990-2016 (median number of samples   7The I2D2 database has been used to study labor markets or returns to education (Montenegro and Patrinos, 2014; de Hoyos et al., 2015; Gindling and Newhouse, 2014; Gindling et al., 2016).    <!-- PAGE 10 --> 7   per country = 6; mean = 7.5; min = 1; max = 44).\n\nAlso, the I2D2 team does not provide details on how the   harmonized variables were created for each survey, and thus the level of consistency   across surveys. However, given the wide use of the data across flagship World Bank   reports, we feel compelled to trust that the I2D2 team did a good enough job that the   results generated in our study do not capture statistical artefacts.\n\nIn addition, we measure _potential_ work   experience as I2D2 does not include direct measures of work experience. Ability (e.g.,   test scores) is also not measured.", "output": {"entities": {"named_data": ["I2D2", "I2D2 database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "internal control over fate, as opposed to a greater sense of chance or the role of other individuals (though in South Sudanese camps, the role of chance and powerful others is more important). LOC increases with higher levels of education for both hosts and refugees. Still, it has little relationship with age or gender, or how much time refugees have spent in Ethiopia, nor their aspirations to go abroad. 0 0.5 1 1.5 2 2.5 3 Internal Control Chance or Fate Powerful Others All Hosts All Refugees Figure 4.4: Locus of control by type of control 1.9 1.95 2 2.05 2.1 2.15 2.2 2.25 2.3 Eritrean Somali South Sudanese Addis All Hosts Refugees Figure 4.3: Locus of control Source: World Bank staff based on SESRE 2023. Note: This index is the unweighted average of 10 questions about feelings of control over one’s fate. The index ranges from 0 to 4, where more positive indicates greater control. The internal control index uses four questions regarding personal control over destiny. The chance index uses five questions regarding the role of chance or determinism. The role of the powerful others index is 1 question on whether others determine fate. Refugees’ Aspirations 42 T", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "impression of the state ’ s role in development and give credit to the state for helping to leverage external resources. Citizens are also likely to give the state credit where mechanisms to voice complaints about non-state actors exist and where bureaucrats are able to eﬀectively respond to complaints. Under these conditions, non-state service provision is likely to strengthen the ﬁscal contract. 5 Data and Methods I explore the relationship between external service provision and deference to government using Afrobarometer survey data from 19 Sub-Saharan African countries (see Table 1). Africa is an especially good place to examine these issues because of the large amount of variation both within and across African countries in the extent to which non-state actors, donors and other states are active in service provision and the extent to which governments are relatively eﬀective and fair. Government responsiveness, corruption and reliance on non-public resources vary considerably among localities with consequences for citizen understanding of and relationship to government (Gibson and Hoﬀman, 2005). This project relies on the fourth round of Afrobarometer data that surveys Africans ’ views towards democracy, economics, and civil society with random, stratiﬁed, nationally representative samples. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Protection Monitoring Tool's data dictionary specifies response codes for 47 variables, covering legal status, documentation, shelter security, livelihoods, and protection incident types. Codes are standardized across all implementing partners using the Protection Monitoring Tool to enable aggregation of responses at national level. When partners introduce context-specific questions not included in the standard Protection Monitoring Tool module, these are flagged as supplementary variables and excluded from inter-agency comparative analyses.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "not work and must rely solely on aid or assistance, the annual cost of basic needs per refugee is approximately US$378. Under the “current” scenario—where refugees can find opportunities to earn money or work—the amount of assistance needed to cover basic needs reduces annual costs by 44 percent to US$210 per capita. The saving can be viewed as an economic-inclusion dividend made possible by Ethiopia’s prevailing refugee policies. Under a hypothetical “full inclusion” scenario— where in-camp refugees have equal opportunities as hosts—the cost of basic needs decreases further to only US$78 per refugee, per year. 0 10 20 30 40 50 60 70 80 Poorest 2 3 4 Richest Percent Figure 5.20: Share of consumption provided in-kind or for free by consumption per capita quintiles among in-camp refugees Source: World Bank Staff based on SESRE 2023. Note: Quintiles are constructed for in-camp refugees only. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Addis Ababa All camp Total Total consumption Pre-assistance income Percent Figure 5.21: Poverty incidence at consumption and pre-assistance consumption levels Source: World Bank Staff based on SESRE 2023. Note: Poverty rates are calculated based on $2.15 in the 2017", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "While we know approximately the number of persons affected by flooding worldwide\n(106 million, on average, between 2000 and 2005 according to the _International_\n_Disaster Database_ ), and by hurricanes (38 million), the total number of people\nthreatened by an eventual increase of this kind of disaster is, however, very difficult to\nestimate (EM-DAT). No climate model is able to predict with accuracy whether or not\nthe affected zones will be densely populated and whether the damage will have tragic\nconsequences.\n\nApart from this difficulty of forecasting, the studies carried out after such events tend\nto relativize their effects in terms of migration in general, and long-term migration in\nparticular. Living mainly in poor countries, the victims have little mobility (Lonergan\n\n5", "output": {"entities": {"named_data": ["EM-DAT"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure A. 2: Injective relations We also isolate many-to-one (bijective) relations. In this case, we have to aggregate the Afro- barometer ethnicities with their unique and more aggregated correspondence in the UNHCR refugee camps data (See Figure A. 3). Figure A. 3: Bijective relations The remaining correspondences are either (i) one-to-many (bijective) but opposite to Figure A. 3 (i. e., many ethnicities from the UNHCR refugee camps data correspond to one ethnicity from the Afrobarometer) or (ii) many-to-many relations. For both cases, we apply a more pragmatic approach: a. In both cases, we disregard ethnicities that do not appear either in the Afrobarometer or in the UNHCR refugee camps data. This means that for the remaining ethnicity that has no counterpart in either the Afrobarometer or the UNHCR refugee camps data, we simply keep the name of the ethnicity as such, i. e., this information is not dropped. b. Then, after ignoring ethnicities that have no occurrence in our datasets, we check whether the one-to-many or the many-to-many relation has not boiled down to a one-to-one resp. many-to- one relation again. If so, we can treat them as above. c.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": ["UNHCR refugee camps data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 11055 The paper examines the early integration of Ukrainian ref­ugee students into Italy ’ s education system following the Russia ’ s invasion of Ukraine in 2022. Using administrative and survey data, the study presents enrollment trends, aca­demic performance, and barriers to educational integration. Findings from the analysis indicate that Ukrainian refugees face lower enrollment rates, higher absenteeism, and lower test scores than other students, particularly in subjects requiring language proficiency. Despite these challenges, teachers often recommend Ukrainian refugee students for advanced educational tracks, thus revealing their optimism about the potential of these students. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["administrative and survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 scored on a Likert scale running from 0 (significantly worse) – 10 (significantly better). The survey questions on optimism were collected at outreach, baseline and endline. Employment status: Due to slight differences in access to labor markets for refugees in Jordan and Lebanon and differences in how we were able to ask about employment status, we tabulate employment status as whether or not an individual is employed. Participants were asked at outreach about their employment status, and, in subsequent rounds, whether or not this had changed. This variable is coded 0 for not currently employed and 1 for employed. Economic scarcity: We collect survey questions on individual perceptions on: ability to meet current needs; ability to meet future needs; expectation that access to jobs is fair; expectation that salaries are fair; and belief that unfair access to labor markets fuels tensions. Ability to meet current and future needs are coded on a Likert scale running from 1 (completely unable) to 5 (fully able). The “ fairness ” indicators are coded: 0 (unfair) or 1 (fair). Whether or not competition around employment contributes to tensions is captured on a 1 (not at all) to 5 (absolutely) Likert scale. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 policy relevance, which is that it could show how differences in camp location and administration affect refugees. This paper aims to analyze how the living arrangement (camp vs. out-of-camp) affects Syrian refugees'QOL by studying the case of Syrian refugees hosted in Jordan. Besides its policy relevance, another reason for looking at Syrian refugees in Jordan is availability of data. The Syrian Refugee and Host Community Survey (SRHCS) implemented in 2015 in Jordan allows comparing living conditions of separate samples of out-of-camp and in-camp refugees, as well as a sample of a host population. While Jordan hosts Syrian refugees in two refugee camps, Zaatari and Al Azraq, most refugees live outside camps in urban, peri-urban, and rural areas of the country. How has the decision to live out-of-camp improved their QOL? Does living out- of-camp reduce deprivations and vulnerability of female-headed refugee households? Moreover, does living in either of the two camps affect the refugees'QOL? This paper investigates these issues. Data are analyzed using the difference-in-difference and propensity score matching methods. Combining these methods helps control selection bias and other unobserved variables that could bias estimates of causal effects. Multidimensional indicators are used to measure QOL to capture deprivations that cannot be measured by income indicators alone. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Between outreach and baseline, one full survey round was collected due to potential survey fatigue and on the understanding that nothing of importance would likely change in such a short period. Basic demographic information, such as age, gender, marital status and employment status were collected at outreach. At baseline, additional indicators were collected, relating to the behavior, attitudes, opinions and personalities of the participants. The only exception to this is data on optimism, which were collected at both outreach and endline. This allowed us to test whether or not the intake decision had effects, even before the training began. Endline data were collected between July 2018 and November 2019 and repeated the combined outreach and baseline surveys and experiments. Variables: We collected a range of survey and experimental indicators in order to assess our key research questions and associated hypotheses: 6 Economic and life optimism: We collected two survey questions about optimism at outreach, baseline and endline. These questions ask individuals to rank their expectation that their life and economic situation will be better in one year than it is now. Answers are 4 In addition, data were collected from Palestinian Refugees in Lebanon (PRL).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["data on optimism"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 we use a cutoff distance of 20 km, we assume there is little economic footprint beyond that distance. Of course, any such distance is arbitrarily chosen, which is why we try different specifications to explore the spatial heterogeneity by varying this distance (using 10 km, 20 km, through 50 km) as well as a spatial lag structure (using 0 to 10 km, 10 to 20 km, through 40 to 50 km distance bins). 4 Second, we collapse the DHS mining data at the district level. 5 The number of districts has changed over time in Ghana, because districts with high population growth have been split into smaller districts. To avoid endogeneity concerns, we use the baseline number of districts that existed at the start of our analysis period, which are 137. Eleven of these districts have industrial mining. Because some mines are close to district boundaries, we additionally test whether there is an effect in neighboring districts. 3. 1 Resource data The Raw Materials Data are from InterraRMG (2013). The data set contains information on past or current industrial mines. All mines have information on annual production volumes, ownership structure, and GPS coordinates on location. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For example, IDPs who subsequently cross international borders may be counted as both IDPs and refugees (e. g. in the case of the Syrian displacement crisis). Table 4: Stocks and Flows Stock Increases Decreases Asylum- seekers  New applications for asylum, separately identifying individuals who were previously IDPs  Positive decisions (convention status, complementary protection status)  Rejected  Otherwise closed Refugees  Spontaneous arrivals (group recognition, temporary protection, individual recognition), separately identifying individuals who were previously IDPs  Resettlement arrivals  Births  Administrative corrections  Repatriation  Resettlement  Cessation  Naturalization  Deaths  Administrative corrections IDPs  New internal displacement  Births  Administrative corrections  Cross border flight, becoming an asylum-seeker or refugee  Return  Settlement elsewhere in the country  Local integration  Administrative corrections Source: UNHCR Global Trends, IDMC Forced Displacement Data Model Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 International migration — the movement of people across national borders — has important economic, social, and political implications. Despite the recent emergence of a dynamic literature, empirical analysis of migration flows and their impact lags behind the policy debate and the theoretical literature. The main reason is the absence of comprehensive and reliable data on international migration patterns and migrant characteristics at either the aggregate or the household level. The objective of this article is to use data from more than one thousand national censuses and population registers to estimate a complete global origin – destination migration matrix for each decade over 1960 – 2000. These 226 * 226 matrices, comprising every country, major territory, and dependency around the world, are divided into periods corresponding to the last five completed census rounds. The gender dimension of international migration over this period is also presented. The primary source of the raw data is the United Nations Population Division ‘ s Global Migration Database, created through the collaboration of the United Nations Population Division, the United Nations Statistics Division, the World Bank, and the University of Sussex (United Nations [2008]). This unique data repository comprises 3, 500 individual census and population register records1 for more than 230 destination countries and territories over the last five decades.", "output": {"entities": {"named_data": ["Global Migration Database"], "descriptive_data": [], "vague_data": ["national censuses and population registers"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Geospatial data overlays comparing the project's planned shelter upgrade sites with post-disaster satellite imagery acquired after the 2022 cyclone revealed that six priority sites had sustained significant damage to existing structures during the storm. The geospatial data analysis was used to update the project's engineering scope and cost estimates to include debris removal and foundation remediation at the affected sites. Updated cost estimates were submitted to the Bank as part of a Level 1 restructuring request that also extended the project's closing date by 14 months.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For example, IDPs who subsequently cross international borders may be counted as both IDPs and refugees (e. g. in the case of the Syrian displacement crisis). Table 4: Stocks and Flows Stock Increases Decreases Asylum- seekers  New applications for asylum, separately identifying individuals who were previously IDPs  Positive decisions (convention status, complementary protection status)  Rejected  Otherwise closed Refugees  Spontaneous arrivals (group recognition, temporary protection, individual recognition), separately identifying individuals who were previously IDPs  Resettlement arrivals  Births  Administrative corrections  Repatriation  Resettlement  Cessation  Naturalization  Deaths  Administrative corrections IDPs  New internal displacement  Births  Administrative corrections  Cross border flight, becoming an asylum-seeker or refugee  Return  Settlement elsewhere in the country  Local integration  Administrative corrections Source: UNHCR Global Trends, IDMC Forced Displacement Data Model Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A survey was carried out in the health sector (health centers) with the support of the World Bank in 2005, but has not been yet validated. In the rural development sector, where the first expenditure tracking survey between the decentralized center and services was to be carried out in 2005 on PEFA funds, the survey is yet to be carried out. It is critical that adequate management measures are gradually put in place to stop the current waste of resources. These measures include, inter alia, the accounting improvement of material, improvement of the inventory and delivery control, and especially a transparent planning of the deliveries, including, the posting of the received deliveries, their comparison with the planned deliveries and their certification by users within each service. It would also be desirable that in each ministry, an action plan is prepared for the implementation of these measures, on the basis of existing ones, and with target indicators as regards improvement of the arrival of the expenditure at intended destination. The social ministries could usefully open the way in this field, on the basis of some projection already carried out. Without a quantitative and qualitative improvement of the arrival of the expenditure at their final recipient, the increase in the budgetary appropriations to the priority sectors will hardly be translated into substantial concrete results on the ground. 29 It is difficult to make accurate forecasts for the implementation of certain programs or developments in economic parameters such as inflation or interest rate. Some immediate needs that were not foreseen during budget execution may appear during budget execution. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["expenditure tracking survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Eritrean refugees26 and 41 percent of Somali refugees have better usage of out-of-camp healthcare services compared to South Sudanese (5 percent). Overall, in- camp refugees with chronic27 illnesses tend to receive treatment in health institutions located outside of camps compared to those with non-chronic diseases (Figure 2.17b). Refugees receive follow-up treatment for tuberculosis and antiretroviral therapy (ART) for HIV/AIDS in camp health facilities and also get treatment for common illnesses such as asthma, diabetes, hypertension, epilepsy, and mental issues. OCP refugees spend more on health compared to hosts. OCP refugees can no longer rely on international aid sources for healthcare, and do not have health insurance. However, OCP refugees who get out-of-camp residency permits due to health problems receive medical services free of charge under the urban assistance program. UNHCR is working with the GoE tow include OCP refugees in the Community Based Health Insurance (CBHI) Scheme. Of the total refugee households living under OCP, 10 percent moved to Addis Ababa to access basic social services, such as education and health. Thus, some refugees from the above may receive medical services free of charge. Based on SESRE data, most OCP refugees (58 percent) rely primarily on private healthcare services, while", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of being available on a yearly basis independently of the quality of local statistical oﬃces and data gathering. While it comes with its own problems it can shed light on local economic activity where gathering of statistical data is incomplete. 7 This makes it a great ﬁt for measuring growth in a context of civil conﬂict. Conﬂict incidence is measured through the number of battle-related deaths from UCDP / PRIO dataset. We run the following regression for country i at time t: git = β × incidenceit + µi + ηt + ϵit (1) where git is economic performance per capita growth of country i in year t, incidenceit is conﬂict incidence, µi and ηt are respectively country and year ﬁxed eﬀects. A cross-country analysis as in equation (1) bears considerable potential for both reverse causality and omitted variable bias. Thus, a priori, a convincing causal link is hard to establish. However, here we expect the resulting bias to be small for two rea- sons.", "output": {"entities": {"named_data": ["PRIO dataset", "UCDP / PRIO dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "such as education and caste. We also construct measures of social proximity between a migrant ’ s place of birth and each possible destination, using detailed available data on ethnicity, caste, language, and religion. We also investigate a number of factors that may inﬂuence the choice of migration destination but have not received much attention in the existing literature. Fafchamps and Shilpi (2009) have shown that the subjective welfare cost of geographical isolation is high. To investigate this issue, we include regressors controlling for population density and for the average distance to various amenities. Fafchamps and Shilpi (2008) have further shown that migrants are concerned with their welfare relative to that of their birth district as well as to that in their destination location. We examine whether relative welfare considerations inﬂuence the choice of migration destination. Additional controls include distance and prices. The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal. The diverse terrain of Nepal along with geographical variation in amenities makes it ideal for our study. The mountainous nature of Nepal means that the country faces daunting challenges in the provision of transport and energy infrastructure. These challenges are unique to Nepal, however. Similar constraints are faced by many developing countries — or regions within such countries.", "output": {"entities": {"named_data": ["2001 population Census data"], "descriptive_data": ["LSMS survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "26 education Turkish to a subregion. In sum, there is some evidence that the inflow of Syrian refugees results in a decrease in the number of Turkish living in a subregion. The evidence, however, is weak and the impact unlikely to be very large. 5. PLACEBO TESTS AND ROBUSTNESS CHECKS 5. 1 Placebo Tests The key threat to the validity of our instrument is that there are subregion specific economic trends that are correlated with the instrument, and not fully controlled for by the inclusion of the log distance of a Turkish subregion from the Syrian border. A priori this seems unlikely since the instrument is also based on travel distances, but we can test for the existence of such trends in a pre-period. Specifically, we run regressions that are analogous to those reported in Tables 5, 6 and 7 using data from the LFS 2009 and 2011. As a placebo test we pretend that the Syrian refugees had arrived between 2009 and 2011, rather than between 2011 and 2014, to see if the instrument is correlated with Turkish outcomes in this pre-period. Table 10a presents the results of our placebo tests. For the overall sample there is no statistically significant trend that is correlated with subsequent (instrumented) refugee flows in formal or informal employment, or in log wages. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from the LFS 2009 and 2011"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Finally, the expenditure shock which we discussed in the theory section (international aid or an increase in public spending associated with the forced displacement crisis) is considered by only a handful of papers. This is a possible confounding factor of the impact of forced displacement on host communities and one that is not easily addressed with the use of fixed effects. This is clearly a shortcoming of this literature that will require increased attention in the future. 4. Meta-analysis of empirical results 4. 1 Data The literature review covers 49 papers spanning over a period of 29 years. We were not able to find published papers prior to the work by Card in 1990, which effectively started this literature, and there is a relatively low interest in this topic between 1990 and 2011 with only one or two papers published per year. With the Syrian crisis starting in 2011 and the EU crisis in 2015 the number of papers per year increased by several fold. Most of the papers and results considered in this review are therefore very recent (Figure 2). We used academic databases and search engines (EconLit, Social Science Research Network, JSTOR, Google Scholar) and searched websites of institutions with relevant working paper series (NBER, IZA, ERF and others). Relevant unpublished papers were included by searching agendas of workshops and conferences organized during the past few years. From the papers reviewed, we selected a total of 762 results summarized in Table 3. The results database was compiled as follows. For each paper we focused on the results that the authors considered the main and Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "40 In camp Addis Ababa Total In camp Addis Ababa Total In camp Addis Ababa Total Stunted Underweight Wasted Hosts Refugees Percent Figure 2.18: Child nutritional indicators Source: World Bank Staff based on SESRE 2023. 0 1 2 3 4 5 6 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total <=18 years In camp Hosts Refugees Hosts Refugees Hosts Refugees 40 30 20 10 0 Percent Addis Ababa Total >18 to 60 years >60 years Percent Figure 2.19: Presence of any disability Source: World Bank Staff based on SESRE 2023. a. Overall b. By age group Sociodemographic Profile 22 housing schemes (such as Kebele housing). OCP refugees thus tend to rent in the private housing market, typically more extensive and better quality, but which increases the cost of renting. High rents are a large challenge for OCP refugees, with rent expenditure taking the highest share of their total non-food expenditure (56 percent), compared to only 37 percent for host households in Addis Ababa. Housing quality varies across camps. Housing quality is measured using three indicators: overcrowding, quality of the wall, and roof construction materials. Both in camps and in Addis Ababa, refugee households live in more overcrowded32", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "in SESRE of 13,898 birr, but still low compared to hosts at 28,324 birr. The level of disaggregation of food items matters. The more disaggregated, the higher the food aggregates. To summarize, food rations received are lower than admin data suggests, regardless of the data source. Possible explanations for lower food quantities include. First, SESRE only asks one aggregate question: “How much on average of your food ration do you sell in the market”? Second, food rations are only received once a month, which may not coincide with the interview date. Yet, SESRE asks about what food they consumed (not even a list of food items), not about food received as aid. Third, refugees might carry over food aid in the future. Annexes 128 Annex F: Comparison of Results from Skills Profile Survey and SESRE Table F.1: Results on common indicators from SPS 2017 and SESRE 2023 Skills Profile Survey 2017 SESRE 2023 (In camp) Hosts Refugees Hosts Refugees Country of origin South Sudanese 23% 53% Somali 24% 30% Eritrean 25% 5% Sudan 28% Demographics Female headed 35% 66% 44% 73% Children (0 to 14) 55% 61% 47% 53% Youth (15 to 24) 13% 17% 19% 21% Adults (25 to", "output": {"entities": {"named_data": ["Skills Profile Survey 2017", "SPS 2017"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This question is examined in Jordan using a unique dataset that links individual data on own schooling and par­ents ’ schooling for adults, from a household survey, with the supply of schools in the subdistrict of birth at the time the individual was of age to enroll, from a school census. The identification strategy exploits the variation in the supply of basic and secondary public schools across cohorts and subdistricts of birth in Jordan, controlling for year and subdistrict-of-birth fixed effects and interactions of gov­ernorate and year-of-birth fixed effects. The findings show that the local availability of basic public schools does, in fact, increase intergenerational mobility in education. For instance, a one standard deviation increase in the supply of basic public schools per 1, 000 people reduces the father- son and mother-son associations of schooling by 18 – 20 percent and the father-daughter and mother-daughter asso­ciations by 33 – 44 percent. However, an increase in the local supply of secondary public schools does not seem to have an effect on the intergenerational mobility in education. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey", "school census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "monthly temperature and rainfall data for the period 1961-2000 (CRU), provided for\n\nWe compute R [2] 's between CRU and each of the eight GCMs for temperature and rainfall\n\nWe require separate benchmarks for the CRU and each of the GCMs. We establish", "output": {"entities": {"named_data": ["CRU"], "descriptive_data": [], "vague_data": ["monthly temperature and rainfall data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Observations are clustered at the DHS cluster level in all regressions to account for design effects arising from the DHS program's stratified multi-stage sampling. The design effect associated with clustering at the DHS cluster level ranges from 1.4 to 2.1 across our main outcome variables, consistent with values reported in prior DHS-based studies for countries with similar levels of within-cluster homogeneity. Failure to cluster at the DHS cluster level would produce standard errors that are approximately 40 percent too small on average.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The raw data has been sourced from Visible Infrared Imaging Radiometer Suite (VIIRS) sensors mounted on US satellites, and has been cleaned to remove irregularities, such as lighting used for the year-round growing of dragon fruit.\n\nIn order to analyze the exposure of industrial activity to natural hazards, a dataset of 372 industrial zones in Vietnam was obtained (World Bank, 2020).\n\nThis analysis circumvents the lack of available data sources by using data from the OpenStreetMap (OSM)\nproject. OSM is an open and free initiative that aims to create and continuously update a map of the world\nthat is created entirely from user-contributed data. Next to detailed street networks, the locations of\nvarious points of interests are published by community volunteers. From this source, a dataset of 5,658\nhotels, hostels, guest houses, and motels was downloaded in early 2019 (OpenStreetMap, 2019). Each\ndatapoint includes an exact point location. Of the hotels, 3,309 are located in coastal provinces and 1,531\nare located within 5 kilometers of the coastline.\n\nInsights into the exposure of health care facilities are based on a dataset provided by a collaboration between the Government of Vietnam, the World Bank, and the World Health Organization. The dataset contains 1,583 geocoded public hospitals and district health centers.\n\nAs no official datasets of school locations could be obtained for this analysis, just as with hotels, volunteer-contributed geolocations of schools were sourced from the OSM project (OpenStreetMap, 2019).", "output": {"entities": {"named_data": ["OpenStreetMap"], "descriptive_data": [], "vague_data": ["OSM project"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Refugees and Host Communities Household Survey expanded the national Household Consumption and Informal Sector Survey to include a representative sample of refugees and host communities, including Sudanese and host communities located in the east of the country. The remainder of this note is organized as follows. Section 2 presents a short discussion of the literature on the economic participation of refugees. Section 3 compares the characteristics of newly arrived refugees from Sudan with previous arrivals for whom survey data is available, to find that both groups are highly comparable. Section 4 uses the existing data to explore how the basic needs refugees are covered from own-income. Sections 5 and 6 dig deeper by exploring econometrically the correlates of higher incomes of refugees. A discussion of the results and their policy implications follows in section 7, after which section 8 concludes. 2. Benefits of economic participation of refugees Whether or not the arrival of Sudanese refugees in Chad contributes to economic growth is of limited immediate relevance as concerns about the safety of fellow humans drive the response. Nor does any decision maker suggest that hosting refugees is a development strategy Chad should pursue. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Refugees and Host Communities Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Therefore, the final sample size consisted of 759 youth, of which 473 treatment and 286 comparison. Detailed baseline data were collected through face-to-face interviews from July to September 2015 prior to implementation. The actual implementation varied between projects and ranged between the second half of August and end of December 2015. Sampled youth from both selected and non- selected NGOs were invited to fill out a questionnaire with detailed information on volunteers ’ socio-economic backgrounds, education levels, interests and attitudes towards volunteering, employment, soft skills, as well as social cohesion values. Follow-up data were collected between November 2016 and March 2017, approximately one year following the start of implementation, through phone and face-to-face interviews. The questionnaire contained the same modules asked and collected at baseline. Despite the high 8 Proposals were ranked based on four main selection criteria: institutional appraisal (25 points), technical appraisal (40 points), project impact (25 points), and financial appraisal (10 points). There was also a fifth criterion related to sustainability of volunteering activities, which was assigned a bonus score (5 points). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "percent for female refugees and 36 percent for female hosts). Boys in camps have higher schooling rates (75 percent relative to 65 percent for hosts), reflecting their higher propensity to stay enrolled in primary or secondary schooling after the typical completion age, especially in South Sudanese camps where 84 percent of boys aged 15- 24 are in school. The relaxed unemployment rate is high among boys in the workforce, as it is for girls (60 percent for male refugees and 24 percent for male hosts). 0 20 40 60 80 100 Addis Hosts Addis Refugees Camp Hosts Camp Refugees Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure 3.27: Youth work status Source: World Bank Staff based on SESRE 2023. Table 3.3: Youth labor force statistics (age 15-24) Camp Hosts Camp Refugees Addis Hosts Addis Refugees Participation (strict) 25% 12% 48% 37% Unemployment (strict) 15% 25% 12% 71% Participation (relaxed) 30% 22% 54% 60% Unemployment (relaxed) 30% 57% 21% 82% Employment-to-population ratio 21% 9% 43% 11% Source: World Bank Staff based on SESRE 2023. Note: Labor force participation ratio is the share of working-age people who are engaged in the labor market, either employed", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "migration, and reason for movement including asylum / refugee protection (or conflict-induced internal migration) (UNHCR 2016), or a specific question to identify IDPs or refugees. However, not all censuses cover refugees and asylum-seekers (if foreigners are considered outside the scope of the census or because they are considered a special category), 71 nor is it common practice for national censuses to include questions related to forced displacement. 72 Nevertheless, there are several examples of national censuses that have included relevant questions on forced displacement. 73, 74 In the case of protracted internal displacement situations, IDPs are likely to be included in national censuses; however, census instruments may be subject to manipulation for political purposes. There are several drawbacks of population censuses including their cost, the significant training required for enumerators to ensure consistent answers to questions on forced displacement, impediments to field operations and data processing (such as weather conditions and technical problems), the relative infrequency with which they are carried out, and the long processing time before data and statistics become available, which have consequences for the timeliness of data. Moreover, often censuses are not conducted in contested territory or conflict zones where many displaced persons reside, and this limits the completeness of the data. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population censuses"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "18 household socioeconomic information. In some cases, socioeconomic information is collected at the individual or household level but more often information is collected at the community level. It is extremely rare to have unit record data sets on IDPs, which explains why we found only 18 studies in the Econpaper repository as already documented. Recognizing the problem of scarcity and availability of information on IDPs, international organizations have set up in several countries coordination mechanisms to count IDPs usually coordinated by IOM, UN ‐ OCHA or the UNHCR. There are also global efforts to centralize this information on the part of organizations such as the UNHCR, UN ‐ OCHA, the international Displacement Monitoring Centre (iDMC) or the Joint IDP Profiling Services (JIPS). These efforts are making good progress on harmonizing counts of IDPs but remain short of establishing proper data collection systems that could deliver in the years to come unit data of quality for research. Hence, research on IDPs remains constrained by lack of data, lack of a blueprint on how to collect data and lack of an organization dedicated to IDP data collection.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**4.3 Climate Risk Screening**\n\nAll infrastructure subprojects were screened against flood hazard maps provided by the national disaster risk management agency and validated by the project's environmental specialist. Subprojects located within the 100-year flood hazard zone were required to incorporate climate-resilient design features, including elevated floor levels, flood-resistant construction materials, and stormwater drainage systems sized for a 50-year return period. Flood hazard maps will be updated at project midterm to incorporate revised hydrological projections under the IPCC SSP2-4.5 scenario.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "interactions between hosts and refugees may help improve hosts’ attitudes (Betts et al., 2023). In some contexts, refugee inflows have been found to harden in-group identification and increase support for ideological extremes. This was the case with refugee inflows in Denmark, for instance, but only in rural areas (Dustmann et al., 2019). On the other hand, refugees hosted in Austrian municipalities for extended periods, as opposed to those who passed through, were found to reduce support for anti-immigrant parties, pointing to the benefits of refugee-host interactions (Steinmayr, 2021). All-in-all, there is little evidence that refugee hosting tends to worsen attitudes toward refugees in the Global South (World Bank, 2023b). 7.1 Attitudes between refugees and hosts SESRE data show that, while some hosts have negative attitudes towards refugees, most attitudes are generally positive. Sixty-five percent of hosts agree that refugees are friendly and good people, and only 20 percent are uncomfortable with having a refugee neighbor. This is an important finding, highlighting the potential for integration policies. Host attitudes are generally most favorable in the Somali region and most negative around South Sudanese camps; the share not comfortable with having a refugee neighbor increases to 37 percent in the South Sudanese", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "6 However, this may reflect differences in rainfall or farming intensity.\n7 We use two poverty maps based on the General Population and Housing Censuses of 1998 and 2009. The poverty\nmap for 1998 was developed by GREAT (Applied and Theoretical Economics Research Group) and combines the\n1998 census and the household survey ELIM (Integrated Light Household Survey) of 2006.", "output": {"entities": {"named_data": ["General Population and Housing Censuses", "1998 census", "household survey ELIM (Integrated Light Household Survey)"], "descriptive_data": ["General Population and Housing Censuses of 1998 and 2009"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "violent conﬂict. Besley and Mueller (2015a) argue that foreign investors seem to know that growth volatility changes with strong executive constraints and therefore react signiﬁcantly to their adoption. In summary, the literature suggests that a lack of constraints on executive power at the country level could play a key role in building inequalities across regions and ethnic groups. In the absence of strong executive constraint, we expect regions populated by ethnic groups that have access to executive power to perform better relative to others due to ethnic favoritism. Conversely, excluded ethnic groups should experience relatively worse economic performance compared to other groups in the absence of such constraints. 46 To test these hypothesis we use data on ethnic groups ’ access to executive power and night light intensity from the GROWup Research Front-End (RFE Release 2. 0) dataset and executive constraint data from the Polity IV dataset. We use night light intensity as a proxy for economic activity at the ethnic group level. 47 Night light data has the beneﬁt of being available on a yearly basis and of being measured at the local level where there is poor availability of statistical data.", "output": {"entities": {"named_data": ["GROWup Research Front-End", "Polity IV dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These data are only for three districts in the province of Punjab, but is very recent, was conducted by an independent team of academics, and is a complete census of all households in the selected villages. Consequently, it yields sufficient madrassa enrollment to examine correlations with household attributes in a meaningful manner (this data source provides information on four times as many children as the PIHS). Table A2 in the appendix shows how these different data sources are used in the paper. Each source asks about madrassa enrollment in a slightly different but comparable way. The population census (1998) asks about the field-of-education (“ What is name ’ s field of education? ”) with options that include (for instance) engineering, medicine, or religious education. This question is also asked of all literate adults irrespective of their current enrollment status, allowing for comparisons in the stock of religious education over time. The PIHS rounds ask, “ What type of school is name currently attending? ” with options that include government school, private school, or deeni-madrassa (religious schooling). Finally, the LEAPS census directly asks, “ Is the child enrolled in a madrassa or an Islamic education school? ” Fortunately these different questions all give rise to similar numbers. This is reassuring since it suggests that any one particular result is not driven by the specific question or definition that was used. 7 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 (www. statpak. gov. pk).", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": ["census of private schools"], "vague_data": ["population census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "animal-drawn carts. Across all domains, refugees have a lower value of productive assets such as farming tools and construction equipment. They are also less likely to own commercial cars, motorcycles, or Bajaj, a cause of a significant portion of the gap in total value of assets between refugees and hosts. With low employment rates, earnings, and value of household income-generating activities, in-camp refugee households rely heavily on aid. Expanding refugee access to agricultural land, livestock, and legal work outside of camps are vital for refugees to maintain their livelihoods without depending on donations. On average, 78 percent of in-camp refugee households report that NGOs or government donations are their primary source of income (Figure 3.20), increasing to 88 percent for South Sudanese households. On the other hand, their host counterparts rely most on employment, and 32 percent rely on agricultural income compared to only 3 percent of refugee households. This contrasts with refugees’ previous livelihoods in their country of birth, where they relied on traditional income sources, especially agriculture and remittances, along with a smaller amount of aid. 0 0.1 0.2 0.3 0.4 0.5 0.6 Camp Hosts Camp Refugees Figure 3.14: Household owns crops Source: World Bank Staff based on", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "General population registers In a small but growing number of countries, information from the central population register is the main source of migration statistics. 80 While population registers may generate statistics on both internal and international migration (if they record changes of residence, and international arrivals and departures) they do not typically record reasons for movement. However, it may be possible to link data from the central population register to those from immigration or border authorities to identify refugees and asylum- 76 Using standard ILO definitions, Labor Force Surveys collect data on work-related issues and provide a basis for measuring employment and unemployment indicators. They are typically conducted monthly in developed countries and quarterly or annually in developing countries. 77 Supported by USAID and implemented by ICF International, the DHS Program has collected, analyzed and disseminated data on population, health, HIV and nutrition through more than 300 surveys in over 90 countries. 78 MICS is an initiative of UNICEF that assists countries in collecting and analyzing health and education data in order to fill data gaps for monitoring the situation of children and women. 79 Many refugee hosting countries issue a form of identification, either specific to refugees or based on national identification documents or those issued to non-national residents. In many cases where such documents are not issued, refugee identity cards are issued in collaboration with UNHCR. 80 A population register provides a mechanism for the continuous recording of selected data on the resident population including a unique identification number, date of birth, sex, marital status, place of birth, place of residence, citizenship and language and possibly also socio-economic data, such as occupation or education.", "output": {"entities": {"named_data": ["Labor Force Surveys", "DHS Program"], "descriptive_data": ["central population register"], "vague_data": ["national identification documents", "population register"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Notably, while we cannot rule out that time outside due to employment may play a role (e. g., fresh air may boost one ’ s mood), our time-use data indicates that the average refugee already spends at least three hours outside per day, with no measurable diﬀerence between employed and cash arms. As we are powered to detect changes of at least twenty minutes for each activity, our results suggest that large substitutions away from unsavory activities are unlikely to be driving the improvements in psychosocial well-being, insofar as the respondent recalls. 1718 We also investigate whether those who were more idle prior to being employed beneﬁt more from employment. We ﬁnd no impact along this margin, suggesting that the elimination of boredom per se is not the driving force behind the psychosocial value of employment (Appendix Table A10). 17Most respondents do not track their day by time, making collection of reliable time use data challenging (though recent literature documents the broader unreliability of such data). We piloted a variety of strategies, and settled on asking respondents how much time they spent on a set of activities in the previous day. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["time-use data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The activity monitoring dashboard provides a consolidated real-time view of implementation progress across the three project components. It is updated daily by the MIS system's automated batch processes and accessed by national and regional coordinators through a password-protected web portal. Dashboard visualizations include cumulative beneficiary counts by gender and age group, disbursement rates by component, and a map of active subproject sites. The dashboard does not constitute an official reporting instrument; official results are reported through the structured Interim Financial Reports and annual progress reports.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The COVID-19 Panel Phone Survey of Households sample is nationally representative, representative of Bamako, and representative of both urban and rural areas.\n\nThe EHCVM sample itself covered 8,390 households across Mali and is nationally representative, representative of Bamako, and representative of both urban and rural areas. The survey relies on a multi-module instrument covering topics including a household's socio-economic characteristics, time use, production activities, and welfare indicators such as consumption expenditure and food security.\n\nuse sampling weights derived from the 2018 EHCVM sampling frame and adjusted for response rates\n\n\nin the COVID-19 Panel Phone Survey of Households. These sampling weights are applied both in our\n\nIn this study, we use the FAO's Food Insecurity Experience Scale (FIES) as primary outcome of interest. The FIES aims to measure food insecurity based on the direct experiences of people relating to food security (Ballard _et_ _al._, 2013; Smith _et_ _al._, 2017).", "output": {"entities": {"named_data": ["COVID-19 Panel Phone Survey of Households", "EHCVM", "Food Insecurity Experience Scale (FIES)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 1. INTRODUCTION Refugees pose a massive moral, political and economic challenge for potential host countries. 1 The scale of the challenge is larger than ever, with 60 million people forcibly displaced by conflicts across the world (UNHCR, 2014). War in Syria has produced more refugees than any other conflict of the past two decades: around 4. 6 million have fled the country, with an additional 7. 6 million internally displaced. 2 About 2. 5 million Syrians have found refuge in Turkey, making it the largest refugee-hosting country worldwide. This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turkish Labour Force Survey to assess the impact on Turkish employment and wages. The large majority (85 percent) of Syrians have left the refugee camps and entered the Turkish labor market. 3 They are overwhelmingly employed informally, since they were not issued work permits. This makes their arrival a well-defined supply shock to informal labor, and a particularly good context in which to test the predictions of basic economic theory. We instrument for refugee flows using travel distance between 13 origin governorates in Syria and 26 Turkish subregions (338 origin-destination pairs). This allows us to also control for distance from the Syrian border, and thus any confounding factors that are correlated with proximity to Syria.", "output": {"entities": {"named_data": ["Turkish Labour Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We also simulate the livestock effects across three AOGCM climate scenarios. The AOGCM scenarios predict a 2-3% increase in the probability of owning livestock by 2020, a 4-7% increase by 2060, and a 0-13% increase by 2100.\n\nWashington W et al., 2000. Parallel Climate Model (PCM): Control and transient scenarios.\n_Climate Dynamics_ 16: 755-774.\n\n<!-- PAGE 28 -->\n**Table 7: AOGCM climate scenarios**\n\nThe empirical analysis is based on a household survey conducted of 11 countries across Africa: Burkina Faso, Cameroon, Egypt, Ethiopia, Kenya, Ghana, Niger, Senegal, South Africa, Zambia and Zimbabwe (for more information about the entire study, see Dinar et al. 2006).\n\nIn this study, we relied on monthly temperature data collected from US Department of Defense satellites (Basist et al. 2001). This set of polar orbiting satellites obtain measurements at a given location on earth at 6am and 6pm every day. The satellites are equipped with sensors that measure surface temperature by detecting microwaves that pass through clouds (Weng & Grody 1998).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey", "monthly temperature data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The GLSS follows a multi-stage stratified cluster design with 580 enumeration areas selected in proportion to population size. Within each GLSS cluster, 20 households are systematically selected using a random start. The GLSS sample is designed to be representative at the regional level and for urban/rural disaggregation within each region. Analytical weights supplied with the GLSS public-use file must be applied to produce nationally representative estimates; unweighted analyses will produce biased results due to differential sampling rates across strata.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This is exactly what we will do in the present paper. Using data from 333 municipalities\n( _comuna_ s) in Chile, we will estimate short-run relationships between climate variables\n\n\n1\nFor example, an extensive drought in Central America might damage its coffee-production and the reduced\nsupply of coffee would increase the world price of coffee, which could benefit coffee-producers in other parts\nof the world.\n\nTwo different types of climate change will be assessed. First, the documented recent\nclimate change in each of the 333 municipalities, as estimated from average monthly\ntemperature series from 1948 to 2008 for all the Chilean meteorological stations that have\ncontributed systematically to the Monthly Climatic Data for the World (MCDW)\npublication of the US National Climatic Data Center. Second, we will use the predictions\nof the Fourth Assessment Report of the Intergovernmental Panel on Climate Change\n(IPCC4) climate models to simulate the likely effects of projected future climate change in\nChile.\n\nThe municipal level cross-section data-base, which is used to estimate the relationship between climate and development in Chile, is constructed using data from different sources. Table 1 lists the variables, their definitions, and the sources of the information.", "output": {"entities": {"named_data": ["Monthly Climatic Data for the World", "US National Climatic Data Center"], "descriptive_data": ["municipal level cross-section data-base"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The database provides information on international bilateral migrant stocks (by citizenship2 or place of birth), sex, and age. There is considerable variation, however, in how destination countries collect, record, and disseminate immigration data. Meaningful comparison of destination country records over time is thus often confounded. In constructing global bilateral migration matrices, several challenges arise. First, destination countries typically classify migrants in different ways — by place of birth, citizenship, duration of stay, or type of visa. Using different criteria for a global dataset generates discrepancies in the data. Second, many geopolitical changes occurred between 1960 and 2000, with many international borders redrawn as new countries emerged and others disappeared. In addition to creating millions of migrants overnight — as when the Soviet Union collapsed — these events complicate the tracking of migrants over time. Third, even when national censuses of destination countries include data on international migrant stocks, the data are presented along aggregate geographic categories rather than by country of origin. Data therefore need to be disaggregated to the country level. Finally, the greatest hurdle is dealing with omitted or missing census data. Very few destination countries — especially developing countries — have conducted rigorous censuses or population registers during every census round over the second half of the twentieth century. Wars, civil strife, lack of funding, and political intransigence are but a few reasons why records may be discontinuous. 1 Of the 3, 500 sources detailed in the overarching UN Global Migration Database, 1, 107 were suitable for analysis, once repeated censuses had been removed or combined. Global Migration Database should not be confused with the Trends in International Migrant Stock Database, which lists aggregate migrant stocks for each destination country in the world at five year intervals (United Nations 2006) 2 The article treats the concepts of nationality and citizenship as analogous and uses the terms interchangeably. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["UN Global Migration Database", "Trends in International Migrant Stock Database"], "descriptive_data": [], "vague_data": ["census data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The model is estimated using data from the Turkish HBS in 2008 and 2009. The HBS is conducted annually by the Turkish Statistical Institute to collect information on household socioeconomic status, living standards, income, and consumption expenditures.\n\nThe survey data are then combined with the actual monthly rate schedule. The Turkish electricity\ntariff data have two advantages that are fairly unique to the study. First, in the retail sector,\nTurkey has applied a price equalization mechanism to maintain a nationwide uniform tariff until\n2011. Under such a system, all residential consumers face a uniform flat-rate price schedule.\n\nation to fish & fishery products     -    GB 14939Fish Hygienic standard for canned fish CAC 70:1995 NEQ 1994    GB 2715Cereal Hygienic standard for grains - 2005    GB 19303Meat 2003   GB/T 22388Milk 2008   GB/T 23376Tea 2009    Hygienic practice of cooked meat and meatproducts factory    Determination of melamine in raw milk and                                          -                                         dairy products   Determination of pesticides residues in tea                                          -                                         GC/MS method    CAC/RCP13 MOD 1976    Vegetabl  es    GB 2714Hygienic standard for preserved vegetables         -         2003    GB 13104- CAC Sugar Hygienic standard for sugars NEQ 2005 212:1999   _Source:_ Authors‟ calculations based on SAC National Standards Query   20     <!-- PAGE 24 -->  **Figure 2.\n\nTo ensure reliability and completeness, the standards have been cross  checked with the \"Chinese Bulletin of Standards\" (SAC 2011a) and the German-Chinese  Standards Portal (DIN and SAC 2011).\n\n(HS 1992) and tariffs are compiled from the TRAINS data base. Consumption variables are\n\ncomputed from the Food and Agriculture Organization of the United Nations‟ statistical\n\ndatabase FAOSTAT. [6] We deflated our data US Bureau of Labor Statistic's HS Import Price", "output": {"entities": {"named_data": ["Turkish HBS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15 4. 1. 2. Empirical strategy In order to make use of the retrospective information on school attendance provided in the dataset, we have constructed what we have called an ‘ ad hoc panel ’, whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset. In this way, we are able to obtain observations for each individual over three academic years. All key education variables are time- variant, while other individuals and households characteristics are time-invariant. Within these three years, we focus our analysis on individuals that were of primary school age (between 7 and 12 years old) in each year. In practice, we keep all children aged at minimum 7 years old in 1998 and at maximum 12 years old in 2000. As a consequence, our panel data contains children aged 8-11 years in 1999, the year of the violence. 11 Since we are interested in looking at different effects across groups of individuals, we have split the sample between boys and girls and between younger children (aged 8-9 in 1999) and older children (aged 10-11 in 1999). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["TLSS 2001 dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The returnee population is relatively more prone to lethal landmine and ERW accidents than the\ncivilian population living in their community of origin. According to data from DMAC and UNMAS,\ntravelers including returnees and IDPs account for 30% of all landmine, ERW and Pressure Plate IED\naccidents. Returnee populations are particularly vulnerable due to their unfamiliarity with the overall\nthreats posed by explosive hazards; lack of information about how to identify them and lack of the\npotentially life-saving behaviour to adopt in response, in addition to their unfamiliarity with their new\nsurroundings, including the history of armed clashes and potential for explosive contamination in\nareas of settlement.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(2009) have shown that, in Nepal, subjective welfare is negatively associated with geographical isolation. Census data on total population and population density in each district are used as proxies for urbanization and geographical proximity: the denser the population, the less geographically isolated individuals are likely to be. We also include data on the average elevation in each district. Nepal being a mountainous country, the higher the average elevation of a district, the more costly it is to build roads, raising transport and delivery costs to the district. Ceteris paribus, we expect migrants to seek out districts with a higher population density and a lower elevation. 4 Econometric results 4. 1 Univariate analysis We now investigate the choice of migration destination. We begin with simple univariate analysis. Variables are of the form ∆ h is = xh s − xh i where i is the district of origin of migrant h and s is each of 74 possible districts of destination. We examine the average value of ∆ h is for the destination district and compare it to the value of ∆ h is for alternative destinations. For instance, let xh s be population density in district s. The average value of ∆ h is for the actual destination of the migrant tells us whether the destination district is more densely populated than the district of origin. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Census data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The imputation of income poverty is done using the Turkish Labor Force Survey (LFS), and with information and modeling parameters determined from the Survey of Income and Living Conditions (SILC). More details and validation of this methodology is discussed throughout this paper. While explicit identification of Syrians in available surveys is not feasible, there is evidence of an increase in the amount of foreign-born individuals that is being captured in the LFS. The arrival year of foreign-born migrants is available in the data which allows for identification of “ Settled Migrants ” and “ Recent Migrants ”. The latter is used as a proxy for Syrian refugees for the purposes of this paper. National official surveys that are conducted under-report the refugee population. Yet, since about 10 percent of Syrian refugees are in camps and the remaining are residing throughout the country, it is not surprising that they are accessible to interviews by the LFS. Despite data limitations, there are strong and significant trends in the poverty rates for the recent foreign- born, especially for those near the Syrian border. In 2013, recent migrants near the Syrian border were the poorest group4 in Turkey. While this statistic in itself is not initially surprising, fluctuating welfare trends of recent migrants over time is noteworthy.", "output": {"entities": {"named_data": ["Survey of Income and Living Conditions", "Turkish Labor Force Survey"], "descriptive_data": [], "vague_data": ["National official surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 19: Trust and Conﬂict in the Cross Section The problem with a lack of trust and strong group identities is that they penetrate and pervert formal institutions. The ethnic politics analyzed by Burgess et al. (2015) is just one example. Shayo and Zussman (2011), for example, use data from Israeli small claims courts to show that Arab and Jewish judges displayed signiﬁcant judicial ingroup bias. Furthermore, this bias is strongly associated with terrorism intensity in the vicinity of the court in the year preceding the ruling. Conﬁdence-building is also a crucial ingredient for the establishment of a fertile investment climate, which in turn is a trigger of economic development post conﬂict. This is the core message of the World Bank Report by Mills and Fan (2006). An important role of increasing trust doubtlessly goes to the media. It has been shown, for example, that hate radio in Rwanda played a critical role in the extent of ethnic violence during the genocide. 59 Other research has shown that media coverage can have strong eﬀects on political preferences more generally. 60 Perhaps the most direct proof of the crucial role played by the media in the post-conﬂict situation comes from DellaVigna et al. (2014). The authors exploit variation in radio reception of na- tionalistic Serbian radio in border regions in Croatia.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from Israeli small claims courts"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "29 evaluations have been conducted (Renton et al., 2000; Shaw, 2000; Shaw, 2002a; Shaw, 2002b; Paine et al., 2002; White, Greene and Murphy, 2003; Interagency working Group, 2003). For example, the Medical Research Council (from the United Kingdom) conducted an external evaluation of program effectiveness in the Gambia using key informant interviews, focus groups, and a KAP (knowledge, attitudes and practices) survey in four villages, including two program sites and two controls. That study found that the Gambia program improved self-reported attitudes and behaviors related to violence against women. Specifically, the program reduced the social acceptability of wife-beating at the community level and appeared to produce a corresponding drop in that behavior. Qualitative findings from other Stepping Stones sites suggest similar benefits. Program H (Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru) is being carried out by four NGOs. It aims to change gender norms and sexual behaviors in Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru (Barker, 2003; White, Green and Murphy, 2003; Guedes, 2004). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "25 less cohesion. The increase in solidarity is less apparent among refugees where the majority (53 %) stated that the crisis had no effect on solidarity. Figure 18: Levels of trust, by group (June) (%) Source: Listening to Displaced People Survey, 2014. Perceptions that different groups have of others are important elements of peace. When asking for the degree to which neighbors, other villagers and people from other ethnic groups can be trusted the survey finds positive outcomes. Although all groups trust people from other ethnic groups slightly less, the general level of trust is high and it remains stable over time. Finally, consider how IDPs, refugees and returnees envision the future of Mali. The majority of refugees in Mauritania vie for an independent or autonomous North, while the majority of IDPs, returnees and refugees in Niger wish to see full government control over the North. 20 20This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Displaced People Survey", "Afrobarometer perception survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "<!-- PAGE 7 -->\ncities in the sample, providing a consistent mid-day activity benchmark for comparing CO2\nconcentration anomalies. [3] OCO-2 has an observation repeat time of 16 days. We have downloaded\ngeoreferenced measures of XCO2 (the column-averaged dry air mole fraction of CO2).\n\nWe compute the daily median XCO2 for each 10-degree latitude band and linearly interpolate the result to each OCO-2 observation with 1-degree resolution.\n\nMistry (2019) has provided global estimates of monthly heating and cooling degree days at 25 km\nresolution. [4] We compute population at 25 km resolution by aggregating data from CIESIN (2021) at\n5 km resolution. Monthly estimates are interpolated from data provided for 2010, 2015, and 2020. We\nuse two sources to construct our georeferenced measure of income per capita. From the G-Econ\ndatabase (Nordhaus et al.\n\nWe merge the results with annual UN estimates of GDP per capita in constant $US 2015 (UN 2021), and use the cell ratios to estimate annual GDP per capita for each cell.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from CIESIN", "UN estimates of GDP per capita"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "MIS data extracted at 30 September showed that cumulative cash transfer disbursements had reached 78 percent of the annual target, with 14,822 households having received at least one payment. The MIS disbursement module flagged 312 records with payment anomalies, of which 287 were resolved through validation against the beneficiary registration database and 25 were referred to the compliance officer for further investigation. The MIS was updated to add a duplicate detection algorithm that screens new registrations against existing records before activation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "23 mega-cities, annual growth rates of peripheral population tend to reach around 10-20 percent compared to central business districts. 4. Implications for public policy Hazard management is a task both for the public sector and for private households and firms. For the public sector in cities, this includes ensuring the safety of municipal buildings and public urban infrastructure, encouraging and supporting private sector hazard risk reduction, and developing first response capacity. A considerable share of hazard risk stems from relatively small but frequent events which cause localized damage and few injuries or deaths (Bull-Kamanga et al. 2003). For instance, an analysis of detailed records of 126 thousand hazard events in Latin America showed that more than 99 percent of reported events caused less than 50 deaths or 500 destroyed houses (ISDR 2009). In aggregate, these accounted for 16. 3 percent of total hazard related mortality and 51. 3 percent of housing damage. The probability of larger events may or may not be predictable. For instance, a city may be in an earthquake risk zone, but the location specific ground shaking probabilities are not known. Individual dwelling unit level mitigation is therefore necessary everywhere in the general area of high earthquake probability. For other hazard types like landslides and floods, potential risk areas can be more easily delineated.", "output": {"entities": {"named_data": [], "descriptive_data": ["detailed records of 126 thousand hazard events in Latin America"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "count of existing contributions and the diﬀering capacities and resources among States. ” It formally “ intends to provide a basis for predictable and equitable burden- and responsibility-sharing among all United Nations Member States, together with other rel- evant stakeholders as appropriate. ” Underpinning the global debate on responsibility- sharing is the assumption that “ the grant of asylum may place unduly heavy burdens on certain countries ” (UN General Assembly 2018), typically countries neighboring a conﬂict area. In this perspective, the number of refugees a country is to host is simply a function of its geography. This paper examines empirically the proposition that the hosting of refugees falls disproportionately on neighboring countries, which in most cases are in the developing world. To do so, we use data on worldwide bilateral refugee stocks compiled by UNHCR to examine the spatial distribution of refugees and its evolution over time. Our period of analysis is 1987-2017. Our main ﬁndings can be summarized as follows. While refugees still remain over- whelmingly in a country neighboring their country of origin, the past decades have seen a trend towards greater geographic diﬀusion. We begin by showing that the global pop- ulation of refugees has been increasingly dispersed across host countries, as captured by a falling Herﬁndahl index of host-country shares. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Historical trends and patterns of forced displacement: Insights from available global data This section provides an overview of the available global data on conflict-induced forced displacement, drawing largely on UNHCR ’ s published data on asylum-seekers, refugees and IDPs. 41 Data are presented visually in a series of figures to highlight the scope and character of the current global forced displacement crisis and identify historical trends and anomalies. These data largely focus on the scale and trends in conflict-induced displacement (i. e. the numbers of forced displaced) with some coverage of other elements such as demographics, location and accommodation. Globally, there has been an unprecedented increase in the numbers of displaced people over the last decade, largely explained by the expansion in the number of reported IDPs. Historical data show a substantial increase in the numbers of forced displaced (see Figure 3), however the expanding geographical scope and quality of displacement monitoring systems are likely to account for much of the increase in forced displacement figures. The numbers of refugees under UNHCR ’ s mandate have recorded a number of variations over time, peaking in the early 1990s (at a level 10 percent over 2015 numbers) with the conflict and displacement associated with the end of the Cold War. The number of Palestinian refugees steadily has increased steadily over time, largely as a result of natural growth. IDP numbers (for which the underlying data are the least robust) have recorded the largest progression as a consequence of: (a) the expanded scope of monitoring efforts (IDPs were not counted before 1989 and methodologies were 41 UNHCR ’ s data only include IDPs protected or assisted by the agency. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["global data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Similarly, regional representation of the sample was found to be in line with actual regional distribution of the population in the north. 16 The code of the day is the sum of the two figures of the date, i. e. if it ’ s the 25th of August the code of the day is 2 + 5 = 7. The enumerater will chose house number 7 as a starting point. Arab 3 % Tamashek 32 % Songhai 45 % Peulh / Foulbe 7 % Other ethnicities 13 % Figure 2: Ethnic composition of the North, 2009 Census Arab 5 % Tamashek 36 % Songhai 49 % Peulh / Foul be 7 % Other ethnicities 3 % Figure 3: Ethnic composition of sample", "output": {"entities": {"named_data": ["2009 Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "When the stable lights data set is examined, most countries display positive growth in _AoL_ and negative\ngrowth in _R_ . There is a weak negative relationship in levels (Figure 7) and a somewhat stronger negative\nrelationship in growth rates (Figure 8). This outcome is not surprising. For all but a handful of rapidly\ngrowing countries, the periphery of an expanding _AoL_ is not as bright as the center. Thus, _R_ must fall\nbecause _R_ = _SoL_ ÷ _AoL_ .\n\nWe use four independent variables. Data for GDP, non-agricultural GDP, and manufacturing value-added\nare all measured in constant 2005 US dollars. These data come from the World Development Indicators\nsupplied by the World Bank. Data for electricity consumption in kilowatt-hours come from the\nInternational Energy Agency via the World Development Indicators. Data for population come from the\nUN Population Division, again via the World Development Indicators. Data for the value of the stock of\nphysical capital, denominated in constant 2005 US dollars, come from the Penn World Tables Version 8.0.", "output": {"entities": {"named_data": ["World Development Indicators", "Penn World Tables Version 8.0"], "descriptive_data": ["stable lights data set", "International Energy Agency via the World Development Indicators"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Further, as Jordan and the international community develop new approaches that respond holistically to the specific needs of refugee and host communities, more evidence is needed about how gender ‐ based constraints and vulnerability affect refugee women ’ s ability to take up economic opportunities and to access the services and resources they need to enable their families to move out of poverty. Our analysis applies a gender lens to a rich set of microdata on Syrian refugees in Jordan collected by UNHCR between 2011 and 2014. As these data do not capture how the changes in policies affect refugees and the constant evolution of their situation since 2014, the analysis is not intended to directly inform current policy choices and decisions. Instead, our aim is to devise an approach that can provide greater insights into gender ‐ specific barriers, based on the premise that the experiences and potential vulnerabilities of women, men, and children are significantly different in refugee settings. We use household ‐ level data to examine the relationship between poverty and gender for Syrian refugees. Our approach is informed by a body of work in the academic literature that has used household survey data to examine the relationship between the gender of the household head and household 2 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 3 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 4 https: / / data2. unhcr. org / en / documents / download / 64568, accessed August 2, 2018. 5 https: / / reliefweb. int / sites / reliefweb. int / files / resources / 64114. pdf, accessed August 2, 2018.", "output": {"entities": {"named_data": [], "descriptive_data": ["microdata on Syrian refugees in Jordan"], "vague_data": ["household survey data", "household ‐ level data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1 We use the generic term HCES to refer to a range of household survey efforts to capture total household\nconsumption expenditures. This can include surveys described as household budget surveys, living standards\nsurveys, or others.\n\nWe explore a unique survey experiment which randomly assigned seven different HCES methods to\n\nof Sub-Saharan environments where, according to the FAO, the proportion of hungry people is highest\n\nsurvey experiment, described below, ensure that any differences in derived hunger numbers are solely", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["HCES", "survey experiment"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "described above, HSP households received not only full rental subsidies for 9 to 18 months but also physical upgrades to the home environment. These proximate outcome measures can be seen as capturing successful program implementation. To measure physical improvements to the shelter, we constructed an index of self-reported ﬂoor, roof, and wall quality; electricity and water access; and crowding. Housing expen- ditures were measured in two ways: First, in the midline phone survey, respondents re- ported their total monthly housing expenditures, including the payments provided by the implementing organization. Second, in the endline in-person survey, once assistance had ended, participants reported their monthly out-of-pocket housing expenditures (excluding the amount paid by the implementer). The latter is our preferred measure of the direct effect of the program on housing expenditures, because it allows us to test whether treated house- holds experienced rental savings. Because the endline measure was collected after assistance ended, any observed expenditure reductions would most likely be an underestimate of the beneﬁts experienced during the program. Recall that HSP also required the landlord to not raise rent for an additional year after the assistance ended, which could help account for any persistent effects on housing expenditures in the treatment group. 2. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "40 Sub ‐ Saharan Africa 8604000 5895000 7055000 5406100 5068000 N. A. MENA 6230000 8000000 6675000 8592900 10892000 N. A. Asia and Pacific 4325000 2405000 3392000 2128800 5490000 N. A. (excl. Australia, Japan, New Zealand) Americas 1126000 1280000 2176000 2900000 3661000 N. A. (excl. North America) Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). As indicated in Figure A1, these data are much lower compared to those provided from 2003 by IDMC but provide a longer time series. UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 scored on a Likert scale running from 0 (significantly worse) – 10 (significantly better). The survey questions on optimism were collected at outreach, baseline and endline. Employment status: Due to slight differences in access to labor markets for refugees in Jordan and Lebanon and differences in how we were able to ask about employment status, we tabulate employment status as whether or not an individual is employed. Participants were asked at outreach about their employment status, and, in subsequent rounds, whether or not this had changed. This variable is coded 0 for not currently employed and 1 for employed. Economic scarcity: We collect survey questions on individual perceptions on: ability to meet current needs; ability to meet future needs; expectation that access to jobs is fair; expectation that salaries are fair; and belief that unfair access to labor markets fuels tensions. Ability to meet current and future needs are coded on a Likert scale running from 1 (completely unable) to 5 (fully able). The “ fairness ” indicators are coded: 0 (unfair) or 1 (fair). Whether or not competition around employment contributes to tensions is captured on a 1 (not at all) to 5 (absolutely) Likert scale.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In order to take account of the building types in Indonesia we use  information from the USGS building inventory for earthquake assessment, which provides  estimates of the fractions of building types observed by country; see Jaiswal & Wald (2008).  The data provides the share of 99 different building types within a country separately for  urban and rural areas, where - due to lack of other information - a homogenous distribution  of buildings is assumed.  Then fragility curves by building type are derived from the curves constructed by the  Global Earthquake Safety Initiative project; see GeoHazards International and United Na tions Centre for Regional Development (2001).\n\nIn order to use these vulnerability curves for Indonesia we first allocated each of the 99  building types given in the USGS building inventory to one of the 9 more aggregate cate gories of the GESI building classification.\n\nof ongoing eruption as a threshold of when to include an eruption in the data set or not.  Second, images containing sulphur dioxide data from the OMI/AURA satellite are used to  model the intensity of the eruptions.\n\nTo construct an inundation map of the affected areas, a  map based on MODIS satellite pictures from Anderson _et_ _al._ (2004) is used with spatial al gorithms to detect the difference in color between inundated and non-inundated areas.", "output": {"entities": {"named_data": ["USGS building inventory"], "descriptive_data": ["MODIS satellite pictures"], "vague_data": ["sulphur dioxide data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "ASPIRE does not cover all countries in the world. To expand the set of countries in this study, we proceed as follows. First, we estimate an econometric model that explains the share of income from transfers, for poor and nonpoor people in each country by the share of GDP spent on social protection at the national level (from the International Labour Organization), an indicator of government effectiveness (from the Worldwide Governance Indicators), and whether the country used to be in the Soviet Union. (Other explanatory variables we tested include World Bank income category of the country, region, and GINI coefficient, but they turn out to have no explanatory power.) This provides an estimate of income from transfers in 26 additional countries (from 98 using only ASPIRE data).\n\nDiversification also comes from financial inclusion. We use the fraction of the population with savings at a financial institution, from the Global Financial Inclusion Database (FINDEX, 2015). The Findex database provides these values for the bottom 40% and for the top 60%, which we use for poor and nonpoor people respectively. We assume that the fraction of income that is diversified increases by 10% for people who have bank accounts.\n\nRather, we proxy the ability of a country to borrow with sovereign credit rating, using the average (long-term) rating of from three agencies: S&P, Moody's and Fitch.\n\nOur study takes advantage of newly available panel data from the Indian Human Development Survey (IHDS), which includes a question asking households to estimate the daily average duration of outages.", "output": {"entities": {"named_data": ["Worldwide Governance Indicators", "Global Financial Inclusion Database", "Indian Human Development Survey (IHDS)", "ASPIRE data", "Findex database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The univariate analysis showed that migrants on average move to destinations where they are on average less likely to ﬁnd people like them. The results presented in Table 4 present a diﬀerent picture. Conditional on the other regressors, the ethnicity and language proximity indices are signiﬁcant with the anticipated sign: social proximity between the migrant and the population of the destination district is higher than in alternative destinations. The religion proximity index is not signiﬁcant. Taken together, these results suggest that, conditional on material beneﬁts from migration, migrants prefer to move to a destination where they integrate more easily — and possibly enjoy network beneﬁts in terms of access to jobs and housing (Munshi 2003, Beaman 2006). 24", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 Flood return periods of 5, 10, 20, 50, 75, 100, 200, 250, 500 and 1000 are included in the Fathom data product.\n\nWe leverage a unique large-scale experiment on consumption measurement in Iraq designed for the Iraq Household and Socio-Economic Survey (IHSES) in 2012.\n\n[1] In recall interviews, households are typically asked to report on food _consumption_ during the specified period and 1The World Bank's Living Standards Measurement Study (LSMS) survey finder includes a list of household consumption and expenditure surveys: of almost 90 surveys from more than 25 countries, more than 75 use recall.\n\n(2017) compare recall questions on food spending from the Canadian Food Expenditure Survey to data from expenditure diaries.\n\nThe availability of repeated diary-recall measurements of food spending or acquisition is not unique to our setting, the Consumer Expenditure Surveys in the United States and the Canadian Food Expenditure Survey being notable examples.", "output": {"entities": {"named_data": ["Fathom data product", "Iraq Household and Socio-Economic Survey", "Living Standards Measurement Study", "Canadian Food Expenditure Survey", "Consumer Expenditure Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We offer less restrictive assumptions and formal tests for these assumptions where data are available, provide more insights into the selection of control variables for model building, and offer simpler variance formulae. Our framework can be generally applied to imputation either from one survey 27 We also experimented with imputation from the HEIS into the DHS. However, one major issue is the latter survey ’ s most recent two rounds are in 2009 and 2012, which do not overlap with the HEIS, thus making it difficult to benchmark the DHS. We tried benchmarking both rounds of the DHS using the HEIS in 2010, and found a qualitatively similar decreasing trend in poverty across these two survey rounds. 28 It is also more demanding to make the distributions of the explanatory variables comparable for smaller population groups (e. g., as disaggregated by regional characteristics or other distributional characteristics such as quintiles) in surveys of different designs. We leave this extension for further research. 33 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "UNICEF (2017) reports that for boys, child labor, school violence, and the high costs of schooling (for transportation and stationery supplies) are the main barriers to their enrollment. For girls, barriers include the distance to the nearest school, the high cost of transportation, the need to help with household chores, health problems, and families refusing to educate their daughters. According to the JD ‐ HV database, the most common reasons parents gave for their children not attending school were financial constraints (35 percent), lack of capacity in schools (29 percent), or that children were required to work to support their family (14 percent). By 2016, the enrolment rate of Syrian refugee school ‐ age children in Jordan was 83 percent, 54 percent in formal education, and 29 percent in nonformal education (World Bank 2017b). 10 Refugee Employment It has been hard for refugees to find work in Jordan. The slowdown in growth in Jordan pre ‐ dates the arrival of Syrian refugees and the economy has been increasingly unable to absorb new labor market entrants. Between 2010 and 2016, labor force inactivity increased, employment decreased, and unemployment increased in Jordan (Malaeb and Wahba, 2018). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["JD ‐ HV database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 The Government of Chad subscribes to this logic as evidenced by the application decree of Chad ’ s Asylum Law signed in 2023, 1 and the National Response Plan to the Impact of the Sudanese Crisis which is under preparation. The Law and the Plan promote the local integration of refugees, aim to avoid settling refugees in permanent camps and promote self-sufficiency. They offer refugees the right to own land, to engage in formal employment and commercial activities, to move freely, and to access to banking services. While participation is the stated policy objective, the reality is that previous arrivals are almost exclusively living in camps, and that the new arrivals live in “ organized sites ” (as humanitarians now call them), presumably to cope with massive arrivals but with little concrete evidence for the onward movement of refugees. This note explores the size of this four-way benefit (for refugees, hosts, the Chadian state and the international community) by estimating how much could be saved on aid for basic needs consumption by enabling refugees from Sudan to realize their economic potential. For its empirical work the note draws primary on data from the ECOSIT4 survey. 2 In 2018 – 19, Chad became one of the first countries in Africa to capture refugees and host communities in its national household survey. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["ECOSIT4 survey"], "descriptive_data": [], "vague_data": ["national household survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Ethiopia has experienced steady economic growth for much of the last two decades, but even before the country’s concurrent crises, economic growth did not transform the labor market structure. Between 2004 and 2020, Ethiopia’s GDP annual growth averaged 10 percent, helping to reduce the poverty by about ten percentage points. But during this period, the distribution of workers across sectors and geographies shifted very little. According to newly released 2021 Labor Force Survey (LFS) data, about 80 percent of Ethiopians live in rural areas, where roughly 75 percent work in agriculture. In urban areas, approximately 70 percent of people work in services. Nationally, self-employment accounts for about half of jobs, with unpaid family work second most common and wage work a distant third. Again, the urban context is different: wage employment is prevalent but often poorly paid. Over the last decade, negative repercussions from the concurrent overlapping crises have threatened this marginal progress. In 2020, the COVID pandemic closed markets, albeit relatively briefly, and this immediately decreased job opportunities. While most reentered the labor market, many changed their work situation, and some permanently exited the workforce or reduced working hours. At the same time, more and more people— rural women", "output": {"entities": {"named_data": ["2021 Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "9 Beans, for instance, which are one of the most important food purchases and items in Brazil, have a raw calorie intake of 306 kcal per 100 g in TBCA (Feijão, carioca), while in the prepared form, TBCA assigns 71 kcal per 100 g, assuming a composition of 50 percent beans and 50 percent water to the meal.\n\nTable 2 shows the top 10 food groups in POF in terms of average calorie intake per 100g. It shows that\n'Meat' is the most expensive food group, followed by 'Breads, Cakes and Pies'. On the other hand, 'Kitchen\nOil' is displayed as the most caloric food group, as expected. It is followed by 'Flour Derivatives' (which\nincludes pasta). The cheapest food group is 'Beans and Legumes', while the least caloric is 'Fruits'.\nTherefore, the data - including our calorie mapping - show some expected patterns of caloric and price\ndistribution across food groups.\n\n_Source:_ Own calculations using POF 2017/18 data and TBCA, based on food items categorizations by IBGE.\n\nFor this, we construct a consumption aggregate using the data from POF. The consumption aggregate is based on household expenditures on goods and services.", "output": {"entities": {"named_data": ["TBCA"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A number of different road indicators are available and we choose road line type to use in the analysis. Road type is defined by the following: The reference category (0) points out squares with dual lane / divided highways, other primary roads, or road connectors within urban areas (types 1 or 8 in the ESRI dataset). The second category include secondary roads (type 2), and the third combines squares with informal or tertiary roads (tracks, trails or footpaths) or no road registered at all (types 3 and 0, respectively, in the ESRI dataset). Figure 4 overlays the types of roads in the original dataset before our recategorization. The shaded area represents the portion of Africa for which we code", "output": {"entities": {"named_data": ["ESRI dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "III THE VENREPS-KIDS STUDY In this section, we offer a detailed overview of the VenRePs-kids Study, covering its de- sign, implementation, the questionnaire utilized, and the primary outcomes that will be employed to evaluate the human development disparities between forcibly displaced children and adolescents and their peers in host communities. III. A Design Location. Our study is conducted in Medell ´ ın, Colombia ’ s second-largest city, following Bogot ´ a. Medell ´ ın was chosen for this study because it hosts the third-largest Venezue- lan migrant population in the country, trailing only Bogot ´ a and C ´ ucuta, as indicated by the 2018 population census data. Additionally, previous research has demonstrated that survey response rates among migrants in Medell ´ ın are notably high. For instance, a na- tionally representative survey of Venezuelan migrants conducted in 2018 — which was representative across Colombia — revealed that Medell ´ ın had the highest response rates among migrants, whereas Bogot ´ a recorded the lowest (Ib ´ a ˜ nez et al. 2022). This finding supports the decision to focus our study exclusively on Medell ´ ın, also considering the challenges and high costs associated with tracking a highly mobile population longitu- dinally in previous research efforts. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["2018 population census data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The data on total gross proﬁts and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key diﬀerence from the Chamber of Commerce data is that the sales and proﬁts data include all businesses including self- proprietorships. 9 Data were provided for the years between 2010 and 2014 and are re- ported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by deﬁnition and they are likely to be less accurate and complete for smaller ﬁrms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and proﬁt ﬁgures. 10 We scale the variables according to province size by dividing sales and proﬁts by the pop- ulation of the provinces. If we use sales and proﬁts in absolute terms, we get qualitatively similar results. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Chamber of Commerce data"], "vague_data": ["administrative taxation data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Data Sources: proGres v4 (PRIMES) hosts the data of refugees and asylum-seekers in 11 countries. In South Africa, the data are managed by the\ngovernment. In Angola, DRC, Zambia and Zimbabwe, some portions of the data are external. For IDP data, the source of DRC’s IDP figure is the OCHA;\nthe sources in Mozambique and Zimbabwe are the Displacement Tracking Matrix (DTM) of the IOM; and the source in the Republic of Congo is the\ngovernment, the Ministry of Social Affairs and Humanitarian Action (MASAH).", "output": {"entities": {"named_data": ["proGres v4 (PRIMES)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "followed the same standards and procedures as the HoWStat. SESRE data was not collected alongside HoWStat due to security concerns at the time of data collection for HoWStat, especially in the refugee areas. The SESRE aimed to solve two problems: (i) gaps in data on the socioeconomic dimensions of refugees, and (ii) gaps in analytical studies presenting the socioeconomic outcomes of refugees and hosts. Lack of up-to-date evidence is a significant obstacle to designing effective policies and support for refugees and host communities. To this end, the availability of the data helps to analyze refugee hosting areas’ social dynamics and longer-term socioeconomic viability by focusing on the: (i) social impact of refugees on host communities, (ii) socioeconomic interaction, (iii) social inclusion, and (iv) social relations among refugees and between refugees and host communities. The data provides valuable information to development partners and governments to inform policies to facilitate refugees’ integration and improve their lives, along with refugee hosting communities. The SESRE covers all current major refugee camps: Eritreans, South Sudanese, and Somalis, as well as the out-of-camp refugees in Addis Ababa. In addition, the survey covers the respective host communities around the camps, including the host communities of Addis Ababa.", "output": {"entities": {"named_data": ["HoWStat", "SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We test the two parts of the redistribution hypothesis using the most recent data from Luxembourg Income Study for 20 OECD countries covering the period 1967-2005 (total number of country/years is 110).\n\n_Economy_ (Milanovic, 2000) and which for the first time used household level data derived from household income surveys to test the hypothesis, and in the process\n\nindividual national identification\", which he argues, based on World Values Survey data, to be stronger among the poor voters, and which reduces their propensity to vote for\n\nseemingly depending on author's preferences, availability of the data (OECD provides the 90-10, 90-50 and 50-10 gross wage ratios), or perhaps contingent on the formulation\n\nis why, in their analysis of eight advanced economies, they use scores of \"intended generosity\" (developed by Scruggs, 2004) of the pension system, unemployment benefits, child benefits and social assistance.", "output": {"entities": {"named_data": ["Luxembourg Income Study", "World Values Survey"], "descriptive_data": [], "vague_data": ["household income surveys", "gross wage ratios"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the intensity of conflicts, and iv) population density in refugee host countries by region. Appendix B presents the list of variables along with description and summary statistics. 5. Empirical strategy To estimate the impact of refugee inflow19 on host community ’ s livelihood strategy choice, we use the following basic econometric model: 𝑌𝑌𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑅𝑅𝑅𝑅𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝜈𝜈 + 𝜀𝜀𝑖𝑖 (1) Where, 𝑖𝑖 indexes a household, 𝑌𝑌𝑖𝑖 is an outcome variable of interest (livelihood diversification or commercialization of agriculture), 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 is the measure of refugee inflow, i. e., the refugee population (average of 2017 and 2018) in the nearest refugee camp weighted by the inverse of distance of the household to the refugee camps, 𝑋𝑋𝑖𝑖 is a set of household controls, 𝜈𝜈 is kebele fixed effects, and 𝜀𝜀𝑖𝑖 is the error term. Several variables, from the DRDIP data set, were used as controls in our model. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["DRDIP data set"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "33 In school: an indicator for whether the respondent currently attends regular education (schooling). This does not preclude also being employed. Retired: an indicator for a respondent who declares that they are not engaged in job search because they are retired. Full / part-time employment: an indicator for whether a person works full or part-time for all people in private sector employment (see above definition of employment). Full-time employment is defined as usual working hours of 30 or more hours per week, part-time employment as usual working hours of less than 30 hours per week. We do not use the indicator provided in the LFS data since there seems to be some confusion in which category 30 hours per week falls (with these evenly divided between full and part-time). Education: we classify people into three education categories. Low education is defined as those with no completed formal education. Medium education is defined as those with at least completed primary education but no high school completion. Higher education is defined as people who have at least completed high school.", "output": {"entities": {"named_data": ["LFS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**6. Conclusions**\n\n\nThis paper has used historical rainfall data to estimate a putative history of payouts on\n\n\nIndian rainfall insurance policies. We have found that indemnities are concentrated in the\n\nAccording to Census data (CNBS, 2001), shares of these crops in five of China's provinces\n\nThe climate data (monthly temperature and precipitation) were gathered from the National   Meteorological Information Center in China.  The data are based on actual measurements in 753   national meteorological stations that are located throughout China. The temperature and   precipitation data were collected from 1951 to 2001.\n\nSocio-economic data come from China's National Bureau of Statistics (CNBS). The data   were collected by a highly trained, professional enumeration staff in 2001 as part of the annual,   nation-wide Household Income and Expenditure Survey (HIES). The data cover 45,700 farm   households in 4365 villages, 533 counties and 31 provinces.\n\naccount for soils, we downloaded a soil map from FAO's website. There are three major soil\n\n\ntypes-clay, sand and loam soils. The final set of variables for our analysis was created by", "output": {"entities": {"named_data": ["Household Income and Expenditure Survey"], "descriptive_data": [], "vague_data": ["historical rainfall data", "Census data", "climate data", "soil map"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 14 of 51 Source: Own calculations based on Household surveys From the education perspective, an improvement in the profile of workers employed as NSE can be observed in the countries analyzed, even though there are some exceptions. In fact, in most of the countries of our sample, the prevalence of workers with secondary and tertiary education increases in detriment of workers with a lower educational level. However, there are some differences by type of non-standard employment. In the case of part-time employment (Figure 10), several countries show an obvious rise in the prevalence of workers with secondary and tertiary education (Argentina, Brazil, Peru, and Mexico). There is a second group of countries that presents a decrease in the prevalence of workers at the secondary level, but, this decrease is more than compensated by the greater incidence of tertiary workers, so that, taken together, workers with secondary or higher education increased their share within part-time employees (Uruguay and Chile). Therefore, we can also conclude from this group that part-time employment presents a better educational profile today than two decades ago. In the Dominican Republic and El Salvador, we find a rise in the prevalence of workers with secondary education but a decrease in the share of workers at the tertiary level. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 five methods, it was not possible to consider non-sampling error. This paper goes a step further by using simulations to describe the sampling error and a field experiment in an IDP camp in South Sudan to measure the total survey error of each design compared to a census, allowing for the disaggregation of the total error into sampling and non-sampling components. In addition, we attempt to separate the components of non-sampling error linked to the sample method from those common across all methods, such as interviewers selecting larger households and other issues in properly implementing the household survey protocols. The next section briefly describes each method and highlights the literature as it relates to the relevant selection methods. Section 3 describes the data set and protocols for each method included in the experiment, followed by Section 4, which discusses implementation issues. Section 5 reports the results of the analysis, and section 6 concludes with further discussion of the overall performance and areas for future research. 2. Description of Methods This paper compares five alternatives of second stage selection (satellite mapping, segmentation, grid squares, “ Qibla ” (or “ walk north ”) method, and random walk) to a human canvassing operation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 3. Refugees as Agents of Their Own Destiny 3. 1 The Composition of Africa ’ s Refugee Population and Its Consequences One of the first elements that catch the eye in Figure 6 is the difference in the composition of the refugee population in Africa compared to the rest of the world. The share of children and women among refugees is higher in Africa than elsewhere, in particular East and West Africa stand out here. This is, at least partly, a consequence of Africa ’ s younger, general population, but other forces could be at work as well, e. g. higher mortality of adult males in Africa or adult males staying behind or being separated from the rest of the household. It does mean however that, relative to other areas, more attention should be going to the needs and capacities of women and children in Africa. This means, for example, adaption of and increased supply of schooling and health services. Figure 6. The composition of refugees by age and gender, 2013 Source: Note: UNHCR statistics (UNHCR 2014). Asia excludes Australia, Japan and New Zealand. Americas exclude Canada and the United States. These percentages have been calculated by country when demographic data are available for at least 30 % of the total. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "to guide and define camp-specific cooking energy options. • In Afar, Gambella, and Melkadida, more than 382,000 refugees and 85,000 hosts have access to alternative cooking fuels and market-based clean electricity from solar-mini grids. • More than 1,739,726 seedlings were planted, 160m3 check dams were built, and 8 km of soil stone band were built to rehabilitate degraded land. Additional GoE GRF Pledges 2023 Climate Action: Protect and restore the environment, manage natural resources, afforestation of degraded lands, and expand renewable energy solutions for the benefit of both refugees and host communities. Recognize vulnerability of women to climate change, and prevent violence against women in all environmental policies and programs, and empower women to have agency and influence in environmental stewardship and adaptation to climate. Human Settlement: Transform selected refugee camps into sustainable urban settlements by enhancing the quality and availability of shelter, infrastructure, and public services, such as roads, electricity, water, sanitation, health, and education by aligning them with adjacent towns’ masterplan, by 2027. Inclusion of refugees into existing national systems: Enhance the capacity of GoE to include 1,000,000 refugees into the national Central Statistics Service (CSS), the national Gender-Based Violence (GBV) prevention and response programs, 814,000 refugees into", "output": {"entities": {"named_data": ["national Central Statistics Service"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Since 2006, Rwanda is divided into 30 districts, which are the main service-delivery units. All\nhousehold surveys are representative at the district level, but there is no information on districtlevel economic growth. Estimating district-level growth based on nightlights is however\ncomplicated by the low intensity of lights: Four districts did not emit any observable lights at all\nduring 2000-2011, and ten districts only have nonzero observations towards the end of the 20002011 period. As a result, we can only estimate nighlights-based GDP growth for 16 districts.\n\nNotes: Dependent variable is the natural log of GDP in constant LCU. All specifications include country and\nyear fixed effects. Robust standard errors clustered by countries in brackets. ***: significant at 1%-level; **:\nsignificant at 5%-level; *: significant at 10%-level. Data source: NGDC (2014) and WDI (2014).\n\nIn Column (3) we replace lights observed from space by data on electrical power consumption (in kilowatt hours, obtained from the World Development Indicators). As data on electricity consumption are only available for 22 countries in SSA, the number of observations drops from 966 to 425 (not all 22 countries have data for all 21 years). We find a strong and statistically significant association between electricity consumption and GDP in the reduced sample (elasticity of _0.37_ ). The coefficient is however considerably smaller than the one estimated between night lights and GDP ( _0.58_ ). This may potentially be explained by the relatively high use", "output": {"entities": {"named_data": ["WDI (2014)", "World Development Indicators"], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Use of state level data on rental restrictions, together with a nationally representative survey from India, suggests that, contrary to original intentions, rental restrictions negatively affect productivity and equity.\n\n1 Using census figures, Appu (1997) estimates that, to avoid having to give rights to tenants, landlords evicted about 30 mn tenants or about one third of the total agriculturally active population.\n\nThe data used in the analysis come from two rounds of NCAER's ARIS/REDS survey conducted in 1982 and 1999, respectively. This survey, the first rounds of which were implemented in 1968-71 to evaluate the impact of an agricultural development program, covers all of India's major states.\n\nof observation 4980 1279 1613 670 1417 7476 1705 2479 1307 1985\n_Source:_ Own computation from 1982 and 1999 ARIS/REDS surveys.\nAll values are in 1982 Rs with 1999 values having been deflated by state level deflators.", "output": {"entities": {"named_data": ["NCAER's ARIS/REDS survey"], "descriptive_data": [], "vague_data": ["state level data on rental restrictions", "nationally representative survey", "census figures"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Integrating refugees into functioning national education systems can improve future outcomes for refugee children and their hosts (UNHCR, 2020; Piper et al., 2020; Abu-Ghaida and Silva, 2020; Crawford et al., 2015; Bilgili et al., 2019). As Chapter 2 highlighted, more than half of all refugees are children under the age of 15. Although over 70 percent of primary school age children attend primary education, they do not make it past primary education. Integrating refugee children into educational programs soon after their arrival in Ethiopia avoids the loss of valuable years of education and human capital accumulation, hindering prospects. It is critical to address the obstacles that hinder children from transitioning to secondary schooling, such as challenges in accessing school records, language barriers, or distance to schools. Supporting Regional Education Bureaus could increase the accessibility of secondary schools Policy Recommendations 74 Policy Recommendations to camp refugees.51 To improve the educational attainment of refugee children, the focus should be on increasing the number of qualified teachers in primary education, increasing the currently low compensation to incentive teachers with similar qualifications as nationals, improving primary-to- secondary transition rates, and reducing classroom overcrowding. Build an inclusive health system. Good health is an essential requirement", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In settings with clearly distinguishable individual structures, these methods can also be reasonably accurate for the purposes of rapid estimation of displaced population (Checchi, et al. 2013). 101 The use of unmanned drones is also becoming more popular as the cost of this technology falls. This technique has been used by UNHCR to update its estimates of IDPs in Somalia ’ s Afgooye corridor (IDMC 2015) and by IOM to monitor disaster-induced displacement in Haiti, including the use of Unmanned Aerial Vehicles (UAVs) in collaboration with UNOSAT. (g) Open data initiatives. There are several initiatives to provide free and open data that enable Internet users to independently mine and analyze data and generate customized summaries, charts and visualizations. For example, the Bank has provided free, open access to its development data since the launch of its Open Data Initiative in 2010, however there is little open data on asylum-seekers, refugees and IDPs. JIPS has developed a web-based platform that allows users to explore, analyze and visualize profiling data online, and IDMC has 100 See http: / / www. flowminder. org /. 101 These methods are not effective in settings with connected structures, a complex pattern of roofs or multi-level buildings, as are prevalent in urban areas. Additionally, cloud cover and dense foliage can also obscure structures. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 1. Introduction The most common sampling approach for cross-sectional household surveys in the developing world is a stratified two-stage design (Grosh and Munoz, 1996). Following stratification based on administrative boundaries, clusters are selected in the first stage with probability proportional to size from a national census-based frame. In the second stage, a canvassing operation is conducted in the selected clusters to compile an updated list from which households are randomly selected. While this methodology is straight forward to implement in the field and reliably produces unbiased estimates, there are several downsides. The first downside is cost. The World Bank ’ s Living Standards Measurement Study team, which provides technical assistance on large-scale household surveys around the world, estimates the field listing operation increases the overall budget for data collection by 25 percent. Due to confidentiality concerns, the data collected during a field listing operation, typically the name of the household head and address or location description of dwellings, does not have any analytical applications beyond as a component of the weight calculations. 2 At a time when typical survey costs are in the USD millions, reducing a significant cost component will increase the financial sustainability of data collection. The second drawback to the traditional design relates to timeliness. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Living Standards Measurement Study"], "descriptive_data": ["national census-based frame"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In order to address these potential sample biases, the CAVR supplemented its documentation with reports produced by Amnesty International and Fokupers (a local NGO). The information contained in these reports was then included into the HRVD database. 9 Note that we do not analyse school completion in 2001 because most children that were of school age in 1999 were still in school in 2001. 10 The questions we used are ― Was [NAME] displaced outside E. Timor in 1999? ‖, and ― Was the [BUILDING] damaged in the violence of 1999? ‖. 14 % of the whole sample surveyed in 2001 report having been displaced, while 26 % report that their house was destroyed. Within our sample of school age children, these figures are 16 % and 25 %, respectively. We have made sure that buildings that are reported to having been destroyed were used for living purposes only.", "output": {"entities": {"named_data": ["HRVD database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The data sources are (1) Romania MoF firm-level data for the period 2011-20, and (2) the World Bank Businesses of the State (BOS) database for Romania, and (3) the taxonomy of sectors developed by Dall'Olio et al.\n\n<!-- PAGE 8 -->\nthe period 2011 to 2020. It is based on financial statements and contains balance sheet information such as\nfirm tax identification number, year of incorporation, operating revenue, average number of employees,\nnumber of employees at the end of the year, labor cost, fixed assets, total assets, amount of subsidies\nreceived from the government, [6] 4-digit NACE industry code, and the county location of the firm.\n\nThe Romania MoF firm-level data did not include a variable that identifies the ownership status of the firm (whether the firm is an SOE), which is the key explanatory variable of interest.\n\n<!-- PAGE 14 -->\n2014 428,618 1,031 0.24 86.8 13.2 25.2 71.2\n2015 440,445 1,092 0.25 87.5 12.5 23.7 72.7\n2016 457,273 1,136 0.25 88.1 11.9 22.9 73.7\n2017 475,757 1,209 0.25 88.9 11.1 21.4 75.4\n2018 491,289 1,277 0.26 89.4 10.6 20.5 76.4\n2019 511,863 1,299 0.25 89.5 10.5 20.1 76.9\nTotal 4,027,783 0.24 87.6 12.4 23.7 72.8\nSource: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. For purposes of providing the\nsummary statistics, the sample is restricted to firms with revenue and employment data. SOEs are defined as firms\nwhere national or subnational governments have ownership stake of at least 10%, while POEs are firms where national\nor subnational governments own 0-9.9%. The percentages for centrally owned or locally owned may not add up to\n100 because there are \"other\" SOEs that are neither owned by the central or local government.", "output": {"entities": {"named_data": ["World Bank Businesses of the State (BOS) database"], "descriptive_data": ["Romania MoF firm-level data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15 causes of death. The Danish Epidemiology Science Centre (1999) found severe malnutrition and high mortality in a survey of 422 refugee children in Guinea ‐ Bissau. They report higher malnutrition and higher mortality for children living in a non ‐ camp setting, compared to children living in a camp. The Goma epidemiology group (1995) found high prevalence of child mortality as well as acute malnutrition among children in refugee camps in Eastern Zaire, especially in female headed households. The magnitude of the difference between ‘ normal ’ mortality in the country under study, in the absence of conflict and the mortality in a refugee camp, depends on several parameters: the health infrastructure in the country as well as in the camp, the food available to camp and non ‐ camp residents, the frequency of visits by nurses or doctors, the intensity of the conflict (e. g. attacks on camps), and so on. Thus, the results are highly dependent on the context. For example, Singh et al (2005) do not find a difference in under 5 mortality among refugee versus non ‐ refugee households in western Uganda and South Sudan, whereas Verwimp and Van Bavel (2005) find higher child mortality and fertility among Rwanda refugees in Congo versus Rwandan women who did not became a refugee. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of 422 refugee children in Guinea ‐ Bissau"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "6. Robustness Checks 6. 1. Sample attrition Many challenges were encountered during survey implementation, due to the context – the densely populated and impoverished communities in and around Monrovia are difficult settings in which to find and track respondents — as well as the transience of a young study population. Despite vigorous efforts to track and interview each individual in the sample, a certain amount of survey attrition was expected. As the survey response rates in Table 1 show, 1622 (or 80 %) of the individuals in the study sample were successfully interviewed in both the baseline and midline surveys. Another 305 respondents were interviewed at baseline but not at midline and hence are not in the panel used for the analysis in this paper. 27 This survey attrition, while not much higher than other program evaluations in Africa, may cause concern that the results of this evaluation are biased, especially if the loss to follow up is correlated with individual characteristics that might affect the outcomes. To address this concern, Table 10 presents regressions on the likelihood of panel inclusion, that is, the likelihood of being interviewed at both baseline and midline. The first column indicates that treated individuals are significantly more likely than control to have been interviewed twice. This result persists even after controlling for individual characteristics and community dummies.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Some data challenges common to refugees and IDPs Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities. Sampling. As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay at relatives ’ places, other live in makeshift shacks and others stay in camps. The information available in the UNHCR registry (the census) can also be quite inaccurate, as already discussed, and the degree of accuracy changes for different groups of refugees. Stratification by urban and rural areas, a typical approach in sampling, may mean little for a population that is mostly in urban areas whether in camps or outside camps. Refugees and IDPs are also mobile and more difficult to track over time than other populations. Several statistical institutes worldwide have developed methodologies to track and measure mobile populations such as herders, nomads or homeless people. However, tracking refugees from other countries has been in the Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR registry"], "descriptive_data": [], "vague_data": ["population census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We developed pseudo-EAs from the list by location (sub-city and Woreda); some EAs covered more than one Woreda, and multiple EAs were in a single Woreda. We selected a sample of EAs and households from each EAs in collaboration with UNHCR. Finding refugees in Addis Ababa was challenging, as they change their location frequently. To minimize the burden of searching for selected refugees, representatives of the selected households were contacted before the survey to ask them to come to a UNHCR center to collect preliminary information, including their current residential address. Since many Eritrean refugees in Addis Ababa had fled from the conflict in Tigray, out- of-camp refugees in Addis Ababa were stratified into two domains: refugees who arrived before the start of the conflict in November 2020, and those who arrived after November 2020. (c) Host Communities Populations around the refugee camps under each domain were meticulously identified in consultation with UNHCR and RRS. We used the ESS EA maps to assess the settlement of communities around camps, ensuring a precise fit to the definition of a “host community”. The assessment highlighted that using the list of EAs obtained from the new cartographic frame57 meets the definition of host", "output": {"entities": {"named_data": ["ESS EA maps"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Appendix G: Comparison of GLSS and DHS Wealth Indices**\n\nThe GLSS wealth index and the DHS wealth index are both constructed using principal component analysis but differ in their input variables. The GLSS wealth index draws on 32 asset and housing variables, while the DHS wealth index uses 40 variables including country-specific items added through DHS national adaptation. The correlation between the two indices, estimated on the subsample of GLSS clusters that were also covered by DHS fieldwork within the same 12-month window, is 0.74, suggesting substantial but not complete overlap in what the two measures capture.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For example, IDPs who subsequently cross international borders may be counted as both IDPs and refugees (e. g. in the case of the Syrian displacement crisis). Table 4: Stocks and Flows Stock Increases Decreases Asylum- seekers  New applications for asylum, separately identifying individuals who were previously IDPs  Positive decisions (convention status, complementary protection status)  Rejected  Otherwise closed Refugees  Spontaneous arrivals (group recognition, temporary protection, individual recognition), separately identifying individuals who were previously IDPs  Resettlement arrivals  Births  Administrative corrections  Repatriation  Resettlement  Cessation  Naturalization  Deaths  Administrative corrections IDPs  New internal displacement  Births  Administrative corrections  Cross border flight, becoming an asylum-seeker or refugee  Return  Settlement elsewhere in the country  Local integration  Administrative corrections Source: UNHCR Global Trends, IDMC Forced Displacement Data Model Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 as self-confidence, responsibility, and respect. Additionally, the curriculum includes workplace readiness skills, such as interviewing and time management. SECTION 2: STUDY TIMELINE & DATA A quasi experimental impact evaluation design was embedded into the NVSP. As mentioned before, the NVSP received 38 applications from eligible NGOs. Per well-developed selection criteria, 8 the highest 22 ranked proposals were selected to receive funding. Each of the 38 proposals included a list of 50 youth (the minimum number of youth set by the NVSP) who would benefit from the project if selected for funding. However, as mentioned before, the 22 selected projects benefited a total of 1, 296 youth, exceeding the set target of 1, 100 volunteers. Of the 50 volunteers included in each of the 38 proposals, 22 youth per proposal were randomly selected to participate in the impact evaluation study. Therefore, the initial sample size of the study comprised a total of 825 youth: 473 youth who served as the treatment group (representing the 22 selected NGOs that received NVSP funding) and 352 youth who served as the comparison group (representing the 16 non-selected NGOs). However, two NGOs refused to participate in the study once informed that their proposals had not been selected for funding. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "6 3 Methodology 3. 1 The A-F method and individual deprivations The Multidimensional Poverty Index (MPI) used in this paper was first presented, with a full methodological discussion, in Admasu et al. (2021). Here we present a general overview of the measure for the individual-level and intrahousehold analyses. The MPI is constructed based on the Alkire-Foster (AF) method of multidimensional poverty measurement (Alkire and Foster 2011). Three key statistics characterize any MPI: incidence or headcount ratio (H), which is the proportion of the population who are multidimensionally poor; intensity (A), which is the average share of weighted indicators in which multidimensionally poor people are deprived; and adjusted headcount ratio (M0 or MPI), which is the product of the incidence and intensity (MPI = H × A). The AF method uses a dual-cutoff counting approach to poverty measurement. Having fixed relative weights across indicators that sum to 100 %, it first identifies who is deprived in each indicator, then sums up the weighted deprivations each person experiences into a deprivation score. A person is identified as poor if their deprivation score meets or exceeds a cross-dimensional poverty cutoff that is greater than 0 and less than or equal to 100 %. It then aggregates this information to compute society-level MPI, incidence, and intensity.", "output": {"entities": {"named_data": ["Multidimensional Poverty Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We find no evidence that Pashtun households are more likely to send their children to Madrassas compared to the rest of the sample, suggesting that geopolitical factors and geographical proximity to Afghanistan matter more than cultural preferences. 16 Similarly there is no evidence for religiosity or household preference-based models of madrassa enrollment. The radical religiosity argument suggests that children are more likely to be sent to madrassas when the family favors a radical brand of Islam. If true, what are we to make of the fact that more than 75 percent of all households with a child in a madrassa also send a child to a public or private school? In a multivariate context we checked whether households identified as “ radically Islamic ” were more likely to send their child to a madrassa. 17 Again, we found no 16 The data from the LEAPS census asked about ethnic and caste identity, and households that classified themselves as “ Pathan ” or “ Afghani ” were used to represent Pashtun households. In line with the usual residential patterns of individuals with Pashtun backgrounds, most of these households are in district Attock in the North of Punjab. 17 In a largely Islamic country it is difficult to find good measures of religiosity. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In addition to the publicly available imagery, NOAA has produced, especially for this paper, monthly frequency composites for the 2004 to 2013 period. These satellite-month datasets\n\nfrom the labor force survey (LFS) ENOE. Specifically, we produce a quarter-year panel of\n\nThe national water commission (CONAGUA) provided us with three datasets: (i) Data on historical rainfall at the day-weather station level, this dataset spans the 1920 to 2015 period,\n\nand contains the universe of weather stations. (ii) The weather station-month level triggers for Fonden eligibility. (iii) The mapping between municipalities and representative weather stations.", "output": {"entities": {"named_data": ["labor force survey (LFS) ENOE"], "descriptive_data": ["Data on historical rainfall"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5 implemented in six areas: Bamako, the regional capitals of Gao, Timbuktu, and Kidal as well as one refugee camp in Mauritania and one in Niger. Bamako was selected because it is home to a large number of IDPs. The refugee camps were selected to obtain a sample of refugees. Returnees were identified in the regional capitals of Timbuktu, Gao and Kidal where the phone network was (still) functional. The approach to selecting respondents differed by location and depended on the availability of pre-existing population information.  Bamako: Listing information of all households with IDPs was obtained from the International Organization for Migration (IOM). Based on this data 10 districts were selected and in each district 10 households were randomly identified.  Gao, Timbuktu and Kidal: No listing data was available and the cities were divided into different sectors. The enumerator was assigned a starting point in a sector, a direction (North, South, East, West) and based on the code of the day 9 the enumerator selected the first household. If the code of the day was 4, the enumerator would choose the 5th house to conduct the first interview. No more than 6 houses were to be interviewed from one starting point. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Religious School Enrollment in Pakistan A Look at the Data Tahir Andrabi1 Pomona College Jishnu Das The World Bank Asim Ijaz Khwaja Harvard University Tristan Zajonc Harvard University Abstract Bold assertions have been made in policy reports and popular articles on the high and increasing enrollment in Pakistani religious schools, commonly known as madrassas. Given the importance placed on the subject by policy makers in Pakistan and those internationally, it is troubling that none of the reports and articles reviewed based their analysis on publicly available data or established statistical methodologies. This paper uses published data sources and a census of schooling choice to show that existing estimates are inflated by an order of magnitude. Madrassas account for less than 1 percent of all enrollment in the country and there is no evidence of a dramatic increase in recent years. The educational landscape in Pakistan has changed substantially in the last decade, but this is due to an explosion of private schools, an important fact that has been left out of the debate on Pakistani education. Moreover, when we look at school choice, we find that no one explanation fits the data. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["census of schooling choice"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Border crossings The registration of people crossing internationals borders is conducted in many countries, and in some cases these data are used to estimate migration flows. Identifying refugees among people crossing borders is a significant challenge, particularly if individuals decide not to apply for asylum or refugee status (UNSD 2014). Additional problems associated with the collection of data on border crossings include: (a) difficulties distinguishing migrants from other people crossing a border, such as tourists, commuters, traders and truck drivers; (b) lack of capacity of many border posts and officials to handle large migration flows; (c) less scrutiny and diligence of emigration flows compared with immigration flow; and (d) lack of tight controls at most borders and the high incidence of undocumented or irregular crossings (UNSD 2014). Administrative records and registers Many countries have administrative records or registers of immigrants that could generate statistics on asylum-seekers and refugees. In particular, data on residence permits issued to refugees or asylum- seekers could be used to generate statistics on both flows and stocks of refugees. 79 For example, Eurostat collects and disseminates data on residence permits granted to those with refugee status and subsidiary protection (UNSD 2014). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data on residence permits"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 MDL contained 4, 539 datasets at the time of this research (December 2022), some of which were also cross-listed in the UNHCR MDL. The UNHCR MDL contains micro-level datasets that are of concern to UNHCR ’ s mission and mandates. These datasets often – if not always – include a sample of refugees, asylum seekers, IDPs, or stateless people. The datasets come from censuses, registration and administrative exercises, and surveys. This MDL contained 546 datasets at the time of our mapping exercise. One of the key advantages of using these databases is that they offer detailed metadata which help us understand the main characteristics of each dataset. The metadata contains a rich set of attributes pertaining to each micro-level dataset, including the country of data collection, the producer (s) of the dataset, brief description or abstract and thematic scope of a dataset, dates of data collection, unit of analysis (e. g., households, individuals), geographic coverage (e. g., national, regions, camps), data type (e. g., sample survey data, census, administrative), questionnaire modules, as well as sampling strategy. A complete list and explanation of each attribute in the metadata can be found in Annex B: Metadata. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The FAO now uses survey data to calculate the CV and coefficient of\nskewness. This revision also took into account the average physical stature of each age-sex group derived from\nDHS data. The FAO updated the CV estimate for 37 countries, and, for the other countries (where they did not\nobtain HCEs), they used the same CV as in the past.\n\nThe FAO method has been widely critiqued by the research community (Svedberg, 1999, de Haen et al.\n\nThe FAO calculates the CV of food availability directly for only for a limited number of countries and", "output": {"entities": {"named_data": ["DHS data"], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Using observations from NASA's OCO-2 platform, we develop the template from the data filtering techniques and econometric analysis employed by Dasgupta, Lall and Wheeler (2022). For a large sample of urban areas, we compare alternative trend estimation models and conclude that the template can be constructed from a simple model that estimates trends directly from OCO-2 data that are prefiltered to isolate local concentration anomalies.\n\nIn an additional exercise, we use our regression model results to compute expected emissions from\nurban areas. The regression residuals identify the directions and relative magnitudes of departures\nfrom expected values for individual areas. We convert the residuals to their emissions equivalents\nusing high-resolution gridded information from the EDGAR global database (Crippa et al. 2020). For\n1,306 urban areas with populations greater than 500,000, we find a rough balance between cities whose\nemissions are higher and lower than their expected values. Converting deviations to percentages of\nexpected values, we find that percent deviations are typically greater in absolute value for cities with\nlower-than-expected emissions.\n\n1 Recent research has used Google Traffic to infer vehicular emissions from high-resolution traffic congestion data for\nsome cities (Heger et al. 2018; Dasgupta, Lall, and Wheeler 2021). However, no currently available technology enables\ndirect estimation of global vehicular emissions at 25 km resolution.\n2 Household air conditioning is powered by fossil-fired home generators in many hot low-income areas where utility-scale\npower is either nonexistent or unreliable. For a detailed assessment, see Lam et al. (2019).\n\nThe design of OCO-2 supports comparative exercises like our analysis. It follows a sun-synchronous\nnear-polar orbit, crossing the equator in ascending mode around 1330 hours local time. This means\nthat the OCO-2 observations for our study are collected between 1200 and 1500 local time for all", "output": {"entities": {"named_data": ["EDGAR global database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "displacees in their communities, the survey asked an additional question in which respondents re- ported whether their communities hosted IDPs (displaced persons from within DRC) or refugees (displaced persons from Burundi, Rwanda, Uganda, South Sudan, or other countries). These re- sponses are used to create a categorical variable that measures whether respondents report hosting IDPs, hosting refugees, or not hosting displaced persons in their communities. Table 3 summarizes the measurement strategies for hosting status. It is possible that respondents may misreport whether they are displaced or whether displaced people are present for a number of reasons. First, they might not know that displaced persons are present, a risk that is especially acute for IDPs. Because the paper is primarily interested in how knowledge of hosting impacts perceptions of social cohesion, this measurement challenge is not as acute a problem as it may at ﬁrst seem. Respondents must know that IDPs or refugees are present in their community for hosting to impact their perceptions of social cohesion. If they are not aware of the presence of IDPs or refugees, their presence is unlikely to systematically impact their perceptions. Second, there might be incentives to either hide or, alternatively, to over-claim the presence of IDPs or refugees. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "11 There may be grounds for skepticism about these estimates for madrassa enrollment. Since the data were collected prior to 2001, geopolitical changes after September 11 could have led to greater madrassa enrollment. In addition, the household-based survey faces the usual problems of accurately estimating a low-probability event — although enrollment is less than 1 percent in these surveys, the sampling error is large (see Bauman, 2001, for a description of similar problems in estimating home-schooling in the United States). Finally, while the census of populations does not face the problem of small samples, it is not that recent (1998) and some may have reservations regarding the quality of government data. 10 The LEAPS census of schooling choice conducted in 2003 provides a rough check on these numbers (see appendix for details). This census was conducted in three districts of Punjab and villages were chosen randomly based on the criterion that each village must have at least one private school. Typically, this means that the villages lies somewhere between fully urban and fully rural populations and are not representative of the districts that they are in. Estimates from the LEAPS census show that as a percentage of enrolled children, the numbers in two of the three districts are slightly higher than those of the population census. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": [], "vague_data": ["census of populations", "household-based survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The study mainly relies on household data from the VHLSS 2010, 2012, and 2014. These surveys are conducted by the General Statistics Office (GSO) with technical support from the World Bank in Vietnam. They are nationally and regionally representative and contain detailed information on individuals, households and communes. In total 9,400 households nationwide are included in each round. Half of these households were also interviewed in the previous round so that the data set includes a short-term panel.\n\n**1)** **Air pollution** is measured by the area-weighted mean of concentration (measured as micrograms per cubic meter) of particulate matter with a diameter of 2.5 micrometers or less (PM2.5) taking the 10 years- average value for 2000-2010. The data is based on satellite imaginary using the total column aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer satellite instruments, which is combined with chemical transport model simulations, and ground measurements from 79 countries to produce a global spatial data set with 0.1° × 0.1° resolution (Brauer et al., 2015). PM2.5 includes dust, dirt, soot, smoke, and liquid droplets, which can lodge deeply into the lungs due to their small size. PM2.5 air pollution has been identified as a leading risk factor for global diseases (Forouzanfar et al., 2015).\n\n**2)** **Tree cover loss** is calculated as the share of the area under tree cover in 2000 that suffered from a tree cover loss between 2000 and 2010. Tree cover is defined as canopy closure for all vegetation taller than 5m in height and is calculated from imagery from the Landsat 4, 5, 7, and 8 satellite data used to produce a global\n\n**3)** **Land degradation** is measured by the share of land area that experienced a significant biomass decline. This loss is calculated based on the inter-annual mean trend of Normalized Difference Vegetation Index based on data from Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA) satellite between 1982 and 2006, which is corrected for climate effects to only measure human-induced degradation (Vu et al., 2014b). Soil fertility, agricultural productivity and ultimately food security of smallholder farmers can be largely compromised on degraded lands (Von Braun et al., 2013).", "output": {"entities": {"named_data": ["VHLSS 2010, 2012, and 2014", "Moderate Resolution Imaging Spectroradiometer (MODIS)", "Landsat 4, 5, 7, and 8 satellite data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of the groups and the distances between them. For instance, (Bazzi et al., 2019) shows that polariza- tion increases ethnic attachment. Others have highlighted the reduction in trust, either interpersonal trust or institutional trust Alesina and Ferrara (2002); Beugelsdijk and Klasing (2016). To assess the importance of alternative explanations, we first replicate our analysis using individual data on violence. In addition to participation in protests, we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault. We then assess the relationship between the revised refugee diversity indices and alternative individual outcomes such as ethnic vs. national identity, generalized trust, trust in neighbors, and institutional trust (trust in government). The questions from the Afrobarometer mentioned below are used as a proxy for these outcomes: 32 1 Attack: Over the past year, how often (if ever) have you or anyone in your family: Been physically attacked? 2 Crime: Over the past year, how often (if ever) have you or anyone in your family: Feared crime in your own home? 3 National identity: Let us suppose that you had to choose between being a [Ghanaian / Kenyan / etc.] and being a [respondent ’ s identity group]. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Afrobarometer survey data"], "descriptive_data": [], "vague_data": ["individual data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In particular we construct ∆ CEIdmt as the change in the conﬂict events index for district d between the quarter immediately prior to month m in year t from the previous quarter. 14 The second term on the right hand side ∆ luminositysmt is analogously constructed using the same lagged time periods. The third term AoCsmt is a series of Area of Control dummy variables, which capture who is in control of sub-district s in month m in year 12Syria is a unitary state, but for administrative purposes it is divided into 14 governorates, which are further divided into 65 districts and 281 sub-districts. 13As a robustness check, we also include month of the year ﬁxed eﬀects to pick up seasonal changes in migration patterns. 14Note that the conﬂict data is only available at the district level. 18 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A panel database tracking outcomes for a cohort of 2,400 households across six districts was established during the baseline survey phase. The panel database will be updated at 18-month intervals through follow-up surveys using the same household roster, allowing the evaluation team to track changes in income, consumption, and access to services at the individual household level. Attrition in the panel database will be documented and analyzed to assess whether survey non-response is systematic and whether inverse probability weighting adjustments are required.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The activity monitoring dashboard was identified as a critical tool for maintaining oversight of the project's rapid cash transfer component, where disbursement targets are time-sensitive and beneficiary eligibility windows are short. The dashboard interface was customized to display daily transfer volumes disaggregated by district, gender of primary recipient, and payment modality (mobile money vs. agent banking). When daily transfer volumes fall below 80% of the planned schedule, an automated notification is sent to the relevant district coordinator and the PIU operations manager.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Afrobarometer finds that the vast majority of Malians want their country to remain a single and unified nation and that the attempt by armed groups to create a breakaway state in Mali ’ s northern territories is decisively rejected. See Afrobarometer Policy Paper 10 (Dec 2013). This difference with the Afrobarometer survey can be explained by the fact that the latter survey only focused on Malians inside the country and did not take the views of refugees into account. 5 93 2 6 86 75 20 2 3 94 Independence of the North Autonomy of the North Establish full government control over the North Figure 19: How do you envision the future of Mali? IDPs Refugees Niger Refguees Mauritania Returnees Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 UNHCR Pakistan Monthly Protection Trends Report, May-Aug 2017, cites similar reasons for return to Afghanistan including the \nfollowing pull factors: reunion with family in Afghanistan, employment / livelihoods, no longer fear of persecution, UNHCR \nassistance, happy to return and the following push factors: strict border entry requirements, uncertainty of POR card extension, no \noverall protection value of POR cards, overall deterioration of security situation in Pakistan, arrest and detentions, denial of access \nto services.  \n4 UNHCR Pakistan Monthly Protection Trends Report, July 2017, reflects similar means for persons to receive information on \nrepatriation, collected during VolRep Exit Interviews and Encatchment Center reports with Afghan returnees, stating they receive \ninformation on Afghanistan from the Afghan community in Pakistan, UNHCR, and visits to Afghanistan.  \n5 Example provided of carpet weaving businesses returning to Afghanistan and thus resulting in a number of carpet weavers out of \nwork in Khyber Pakhtunkhwa.", "output": {"entities": {"named_data": ["UNHCR Pakistan Monthly Protection Trends Report"], "descriptive_data": ["Encatchment Center reports with Afghan returnees"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "this way, profiling of IDP situations aims to underpin advocacy, protection and assistance activities as well as support the achievement of durable solutions by informing joint strategies between government, humanitarian and development actors. Profiling provides an overview of displacement-affected populations through the collection and analysis of minimum core data (number of IDPs, disaggregated by location, age and sex) and where possible additional quantitative and qualitative data (causes of displacement, patterns of displacement, protection concerns, humanitarian needs, vulnerabilities, and aspirations and prospects for durable solutions). Profiling may utilize data collection techniques at individual, household and community levels, often combining population estimation methods, a review of secondary data, focus group discussions, household surveys and key informant interviews targeted specifically at forcibly displaced populations (UNSD 2014). 70 Profiling methods focus on displacement situations, rather than only on displaced populations, and therefore includes comparisons to conditions in the host population. IDMC estimates that humanitarian profiling data forms the basis for 18 of their 60 country estimates and around 63 percent of their annual estimates (IDMC 2015), with the largest volume of data on conflict-induced internal displacement provided by OCHA followed by IOM. There are several practical challenges associated with IDP profiling exercises in displacement situations. Insecurity or terrain may impede access to displaced populations in conflict-affected or hard to reach areas. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["humanitarian profiling data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**pandemic, regional drought, inflation crisis and continuing global economic turbulence.** According to data from\n\nthe Kenya COVID-19 Rapid Response Phone Survey (RRPS), the Shona community experienced significant job\n\nlosses during the pandemic, particularly between July and September 2020, when unemployment increased\n\ndramatically. [[9]] This period of economic disruption caused by the pandemic led to fluctuating employment rates, as\n\nseen in the figure below.\n\n_Figure 3: Labor force participation as a percentage of working age population, between 2019 and 2024_\n\n_Source: 2019 Shona socioeconomic survey,_ [[8]] _Kenya COVID-19 Rapid Response Phone Surveys (RRPS),_ _[[9]]_ _and the authors’ calculation_\n\n_of 2024 survey data._\n\n**While employment has started to improve, it has not yet returned to pre-pandemic levels, especially for women.**\n\nThe employment rate for women decreased from 72 percent in 2019 to 61 percent in 2024, which can largely be\n\nattributed to the job losses incurred during the pandemic and slower recovery in female-dominated sectors that\n\nwere further setback by the food price crisis and global economic headwinds. Meanwhile, male employment\n\nincreased slightly from 74 percent to 76 percent during the same period, reflecting a quicker rebound in male\ndominated occupations.", "output": {"entities": {"named_data": ["Kenya COVID-19 Rapid Response Phone Survey", "2019 Shona socioeconomic survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 The fundamental unit of observation in ACLED is the event. Figure 2 illustrates the ACLED data for Central Africa for the 1980s and the 1990s. Each location of a conflict event is represented by a symbol. In several of these locations, multiple events occured over the periods. Events always involve two actors – a rebel group and a government – and are coded to occur at a specific point location and on a specific day. Most of the events are battles, but the dataset also records other activities. The dataset includes information on and distinguishes between six types of events: battles resulting in no change of territory, battles resulting in a transfer of territory to the rebel actor, battles Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Language barriers, mental health challenges, and uncertain futures are identi­fied as major obstacles to integration. The study highlights the importance of tailored interventions, such as psycho­logical support and more dedicated teaching time, to foster refugee students ’ academic and social inclusion. This paper is a product of the Development Data Group, Development Economics and the Social Protection and Labor Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at michela_carlana @ hks. harvard. edu; pcastaing @ worldbank. org; mtestaverde @ worldbank. org; and mtiberti @ worldbank. org. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "hotspot areas and regularly issues protection of civilians’ advocacy\nbriefs. The Sector also disseminates monthly updates and daily and\nweekly updates in times of crisis.\n\nFunding situation permitted, the UN protection agencies, Protection\nCluster, its AoRs, and their partners implemented activities\nmentioned under funding data.\n\nWhile most of these activities were conducted in response to several\nemergencies in Darfur, South, West Kordofan and Blue Nile states,\nsome activities were also implemented in the areas of protracted\ndisplacement.\n\nThe Protection Sector and its AoRs also actively supported the\ndevelopment of the One UN Protection of Civilians strategy and\nsupport plan for the National Plan for the Protection of Civilians\n(NPPOC). Before the military coup of 25 October 2021, the Sector was\npreparing to organise a joint national and several workshops at the\nstate level to develop further and prioritise proposed activities. The\nProtection Sector and AoRs also actively advocated for establishing\nthe state-level protection of civilians’ committees and working\nrelationships with the National Mechanism on the Protection of\nCivilians.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "26 Source: Listening to Displaced People Survey, 2014. 95 % of the refugees in Mauritania envision an independent or autonomous North and 26 % of the refugees in Mauritania even state the independence of Azawad (= the north) as a main condition for returning home. Stark differences can also be observed with regard to the discussion around a possible federalist solution for the North that was ongoing when the monthly phone interviews were conducted in October. As illustrated in the Figure 20 below, 80 % of the refugees in Mauritania support a federalist solution, while the majority of IDPs, returnees and refugees in Niger are not in favor. Of those who do not support a federalist solution (96 % of the IDPs, 88 % of the refugees in Niger and 95 % of the returnees), the majority of IDPs (61 %) and returnees (70 %) as well as 38 % of the refugees in Niger suggest decentralization as a possible solution to resolve the conflict. 13 % of the refugees in Niger also mention war and 27 % the integration of the North. Nonetheless, 49 % of IDPs, 86 % of returnees and 89 % of refugees believe a stable and sustainable peace accord can be achieved. by 4 % of the population living Timbuktu, 2 % in Gao and nobody in Kidal. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In a mortality study in Iraq, Galway et al. (2012) used GIS and Google earth imagery for household sampling. The method used gridded population data for selection of clusters. The first cluster sampling stage of their study used the ‘ Create Spatially Balanced Points ’ (CSBP) function in the ArcGIS (v10) software. Boo et al. (2020) introduces a sampling design based on gridded population estimates as their sampling frame to implement a PPS design and derive sample size estimates for the number of grid cells. Assuming the grid square method is applied to the area itself rather than a selected PSU, the weights for the grid method are similar to those for segmentation, where the cells are the PSUs, but without the additional step of selecting segments. The weights can therefore be represented as 𝑤𝑤𝑖𝑖 ′ = (𝑁𝑁𝑘𝑘) ൫𝑁𝑁𝑘𝑘𝑘𝑘൯൫𝑁𝑁𝑘𝑘𝑘𝑘𝑘𝑘൯ 𝑘𝑘𝑘𝑘𝑘𝑘. 2. 4. North Method The “ Qibla method ” described in Himelein et al. (2017), or what is called in this paper the “ North method ” method, is an attempt to assign probability weights to random point selection methods. Several random point selection methods can be found in the literature, particularly in relation to epidemiological studies.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["gridded population data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Section 6 presents the differing views of IDPs, refugees and returnees on what happened during the crisis and prospects for peace. Section 7 concludes the paper. 2. The Listening to Displaced People Survey The Listening to Displaced People Survey (LDPS) combines a baseline face-to-face survey with mobile phone follow-up interviews. During the baseline survey respondents were identified and information on household and respondent characteristics was collected. Once the baseline interview was completed, respondents were given a mobile phone and started to receive, at monthly intervals, phone interviews from a call center in Bamako. During these phone interviews structured questions were asked about welfare of the household. Phone interviews are standard practice in developed countries and they are increasingly being used in less developed countries, as the coverage of cell phone networks expands. Not only do these kinds of surveys allow for low cost, high frequency representative data collection (Hoogeveen et", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "on behalf of refugee beneficiaries. III DATA III. A Data for Afghan Refugees in Tajikistan The primary dataset is a detailed census of Afghan refugees in Tajikistan carried out by the World Bank at the end of 2022, after the Taliban regained complete control of Afghanistan in 2021. The questionnaire administered to refugee families was compre- hensive; it collected detailed information on the socio-economic background, migration history, labor market outcomes and current socio-economic situation of Afghan refugee household and their members, and some measures of socio-economic integration. The approximate duration of the interviews was about two hours. The sampling frame for the census is the administrative database of Afghan refugees registered with the United Nations Refugee Agency (UNHCR) in Tajikistan. All households recorded in the registry were interviewed, leading to a total of 1, 958 Afghan refugee households and 9, 763 indi- viduals. Figure 2 displays the dates of arrival of the refugees in the census. As shown in the figure, the vast majority of migrants arrived to Tajikistan in 2021 when the Taliban gained complete control of Afghanistan. We first use these data to describe the main socioeconomic characteristics of the popula- tion of Afghan refugees in Tajikistan and illustrate general patterns, as described in Ap- pendix A. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In certain contexts, there can be significant overlaps in these two groups; however data systems may be maintained separately for conflict-induced displacement and natural disasters (e. g. in Afghanistan) leading to possible gaps or double counting if these categories are combined. 30 The IOM Displacement Tracking Matrix (DTM) is a system to track and monitor displacement and population mobility. It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route. It has been active in over 40 countries since its inception in 2004. See http: / / www. globaldtm. info /. 31 This is typically defined as nomads not having access to their traditional routes, but routes can vary. 32 IDMC has recently adjusted their methodology to facilitate greater comparability across situations and improvements are reflected in IDMC ’ s end-2015 data. 33 This is not necessarily a problem if the purpose of the registration system is to delineate entitlements to assistance rather than to determine status. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["IOM Displacement Tracking Matrix"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "refugees, it is higher compared to their hosts. Moreover, refugees have higher access to improved waste disposal methods38 than hosts. Both refugee and host households have low access to electricity, except for hosts of Eritrean refugees. Hosts around Eritrean refugees have better access to electricity (meter private or shared) for lighting (74 percent). The use of solar energy is common among Eritrean refugees, with 78 percent of Eritrean refugee households get lighting from solar energy. Almost all South Sudanese refugee households have no access to electricity either from meter or solar sources. All refugees in Addis Ababa use electricity for lighting, similar to their hosts. 0 20 40 60 80 100 120 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Improved toilet facility Improved waste disposal method Percent Figure 2.23: Access to toilet facility and waste disposal Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Electricity (meter) Electricity (meter, generator, solar) Percent Figure 2.24: Source of lighting Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In this sense, it is necessary to take into consideration that all indicators of prevalence of NSE and its profile will be limited to a subset of this kind of workers. 4. 1 Latin America and the Caribbean This section focuses on the Latin America and the Caribbean region, where a set of 9 countries, that we consider representing the different realities of the region in an exhaustive way, was analyzed. Specifically, the analysis was conducted for Argentina, Brazil, Bolivia, Chile, El Salvador, Mexico, Peru, Dominican Republic and Uruguay. 5O * NET is the successor of DOT (Dictionary of Occupational Titles) which is no longer updated. O * NET was launched in 1998 on the basis of the BLS Occupational Employment Statistics codes. In 2003, it was changed to SOC which implies that the consistent measures of task content are calculated from 2003. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 areas are listed so that a given number of households can be randomly chosen from the list to be interviewed. The advantage of this approach is that it only requires knowledge of any FDP residing in an enumeration area, so that this person can be included in the sampling frame. The more accurate the number of FDPs in an enumeration area before listing households, the more efficient is the resulting sample. This approach requires appropriate enumeration area maps that can be easily defined through satellite images for camps, but might not be available across the country for FDPs living in host communities. Implementation can be expensive especially with limited knowledge about the number of FDPs in enumeration areas. However, area-based sampling has the big advantage of not requiring FDPs to register. In contrast, list-based sampling uses an existing list of all FDPs and randomly chooses a sample among them. If additional characteristics are available, like location or country of origin, the sample can be stratified. However, the sample will only be representative of registered FDPs. Hence, it is rarely used in the context of IDPs unless a proper registration system is in place. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "based on SESRE 2023. 0 10 20 30 40 50 60 70 80 Eritrean Somali South Sudanese Addis Ababa All Refugees Figure 7.17: Share or refugees involved in a community representative body Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 71 Low refugee social integration is not because they have a low willingness to engage with their communities. Refugees have an extremely high rate of involvement in refugee community representative bodies. This includes participation in refugee central committees, refugee outreach volunteers, refugee community leaders, and leaders in women and youth associations. Sixty percent of refugees participate in an organization like this, the lowest in Eritrean camps at 32 percent. On average, these rates are similar across age and gender groups. Main immediate refugee integration challenges are: (i) to expand involvement outside of refugee communities, and (ii) to better understand the social integration barriers refugees in Ethiopia face. In principle, the positive attitudes among many hosts towards refugees, the high degree of cultural similarity between groups, and the willingness of refugees to be engaged in their community are all promising signs for social integration. Yet, even in the Somali domain, where cultural similarity and host attitudes are greatest,", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "21 Results suggest that 66 % of the returnees trust the Malian police and army most when it comes to providing security in the North. Almost half believe that the Malian army is brave and well trained. The vast majority of returnees believe that the government ’ s policies regarding reconciliation, security and social cohesion are good or very good. They also support the government ’ s approach towards decentralization and providing infrastructure such as access to potable water and electricity. As the next section will illustrate this differs strongly with the opinions of refugees. 6. Prospects for Peace IDPs, refugees and returnees have comparable opinions with regard to the requirements for peace: (i) addressing the ongoing crisis, (ii) improving security and (iii) reconciliation. Although there is agreement on what needs to be done, there is little consensus on what happened during the crisis, who the culprits are and who the main victims. Figure 15: What is the most important problem the Government needs to resolve today? (%) Source: Listening to Displaced People Survey, 2014. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "higher for refugees than hosts, both for in-camp and for out-of-camp refugees (Figure 5.9). While the food insecurity Refugee and host communities could differ in multiple dimensions over and above consumption. The Multidimensional Poverty Index (MPI) explores this multiple deprivation, capturing differences across three dimensions of well-being: health, education, and living standards (Alkire et al., 2021). MPI provides a general picture of the extent of deprivation. In this context, deprivation in education is assessed using school attendance for school-age children and years of schooling among adults. Health is proxied by the presence in the household of a stunted child or death of a child in the last 12 months before the survey. Living standards are assessed by access to electricity, improved water, sanitation, cooking fuel source, housing, and economic assets. The MPI ranges from 0 to 1, with 1 representing a high level of deprivation. It is the product of two partial indices: the headcount ratio (H) and the intensity of poverty (A) i.e. (MPI = H*A). The headcount ratio is the share of poor people in the population, while the intensity shows how much deprivation poor people experience on average. A cut-off point of 0.33 is used for the", "output": {"entities": {"named_data": ["Multidimensional Poverty Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The second source of data we draw on is a short survey on COVID-19 vaccination collected inperson as part of the Ethiopia Socioeconomic Survey (ESS 5), a nationally representative household survey that was implemented between April and June 2022 by the Ethiopia Statistical Service with support from the World Bank's LSMS program. This survey contained a similar module as the phone surveys and collected information on the vaccination status of all household members.\n\nThe source of administrative data for our study is the Our World in Data (OWID) COVID-19 vaccination dataset (Mathieu et al. 2021) that compiles administrative data on COVID-19 vaccine coverage. Amongst others, the dataset contains information on the number of total doses administered, the share of the country population that has received at least one dose, and the share of the population that is fully vaccinated. [3] It covers the period from December 2020 when the first COVID-19 vaccines achieved approval and is regularly updated as new data becomes available on a per-country basis. The data is compiled from country reports (such as government websites, dashboards, or the social media accounts of national authorities) and in some cases third-party aggregators (where national authorities do not publish data in a machine-readable format) and is regularly audited for inconsistencies and technical errors.\n\nWe additionally access a second source of administrative data stemming from the WHO's COVID19 vaccination dashboard (WHO 2020b). The dashboard does not provide longitudinal information for public access but reports the latest available COVID-19 vaccine coverage figures at the time of data access (April 2, 2023, in our case).\n\nLastly, we use data from the World Bank's Statistical Performance Indicators (SPI) available through the World Bank's Open Data library (World Bank n.d.). The SPI is a composite index between 0 - 100 scoring countries' statistical systems across the five pillars of data use, data services, data products, data sources, and data infrastructure (Dang et al. 2023). To capture the performance of administrative data systems in particular, we also use the SPI's indicator of administrative data capacity (Dimension 4.2) that records the availability of Civil Registration and Vital Statistics (CRVS).", "output": {"entities": {"named_data": ["Ethiopia Socioeconomic Survey (ESS 5)", "Our World in Data (OWID) COVID-19 vaccination dataset", "WHO's COVID19 vaccination dashboard", "Statistical Performance Indicators (SPI)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006. 1 A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large. 2 Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010). 3 In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese 1 On the quality of public services indicator, Lebanon ’ s score declined from 2. 5 in 2006 to 0. 8 in 2012 on a 4-point scale. On the territorial coverage indicator, its score went down from 2. 7 in 2006 to 1. 5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Middle East and North Africa Survey Project", "Gallup World Poll", "SWMENA survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 16 of 51 Figure 11: Education profile of Temporary employees. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys However, it should be noted that the improvement of the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in Annex II of the paper. Figure 12 presents the Kernel distribution of the labor income per working hour by country and type of employment. When we analyze what has happened at the salary level and distribution in the period under analysis, two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by these economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases.. 6 The second trend identified as generalized in the countries of study is the increase in the variance of the wage distribution. In fact, in most of the countries considered, non-standard employment wages currently show a significantly greater dispersion than that registered a decade ago. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Women experience particularly pronounced displacement in the informal sector and no formal job gains. In contrast, for men displacement in the informal sector is fully offset by employment growth in formal sector, with no net job losses. Clearly, our main findings do not depend on the particular sample of subregions we analyze, and importantly are robust to restricting the analysis to a more homogenous group of regions. 6. CONCLUSIONS This paper combines newly available data on the 2014 distribution of 1. 6 million Syrian refugees across subregions of Turkey and the Turkish LFS, to assess the impact on Turkish labor market conditions. The Syrian refugees in Turkey are overwhelmingly employed informally, since they were not issued work permits, and so their arrival was a well-defined supply shock to informal labor. Consistent with economic theory our IV estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupational upgrading, there are increases in formal employment for the Turkish. This increase though only occurs among men without completed high school education. The employment patterns of women and the high-skilled mean they are not in a good position to take advantage of lower cost 36 Results are also robust to dropping all subregions with close to no refugees. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Turkish LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the General Social Survey - showed no trend. This paradox acquired quickly popularity across the\n\nlongitudinal data.\n\nof studies, those that rely on one cross-section survey focusing therefore on individuals or\n\nProvided that cross-section and longitudinal data are available, one can of course try to reconcile", "output": {"entities": {"named_data": ["General Social Survey"], "descriptive_data": [], "vague_data": ["longitudinal data", "cross-section survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 9 of 51 Figure 2: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys When these results are analyzed by the type of NSE, we find significant differences between the trends in Part-time and Temporary employment. On the one hand, there is a stable or growing prevalence of part-time employment among the salaried employees, where Uruguay is the only exception, characterized by a decrease in the incidence of this type of employment (Figure 3). In the cases of Peru and Bolivia, we find almost the same prevalence of part-time employment as two decades ago while in the remaining countries (Argentina, Brazil, Chile, Mexico, El Salvador and the Dominican Republic) there is a greater prevalence of part-time employment. Likewise, the prevalence of temporary employment shows a downward trend in most of the analyzed countries in the last 20 years (Figure 4). In fact, three of the five countries in which temporary employment could be identified show a significant drop in the prevalence of this type of employment (Argentina, Brazil, and Chile). El Salvador presents a stable incidence of temporary employment, while Mexico is the only country in our sample for which there is an increase in this type of NSE.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Impacts of Extremism in Refugee ’ s Integration: Evidence from Afghan Refugees in Tajikistan * Laurent Bossavie † Sandra V. Rozo ‡ Mar ´ ıa Jos ´ e Urbina § Keywords: Refugees, Female Education, Extremism JEL Classification: F22, D74, J15, I21, I10 * The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are our own. The team is grateful to the project “ Enhancing Government capacity in hosting temporarily Afghan refugees ”, funded under the Rapid Social Response Program (RSR) multi-donor Trust Fund of the World Bank, for funding data collection and allowing us to use the Afghan refugee survey as one of the multiple sources of data for the paper. The purpose of the project and the data it collected was to identify opportunities for refugee integration in Tajikistan. We acknowledge financial support from the Research Support Budget at the World Bank. We also thank Daniel Garrote for his initial support on instrument design and Merve Demirel for supporting data collection. † World Bank, e-mail: lbossavie @ worldbank. org ‡ World Bank, Dev. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Anderson, J. (2011): “ The Gravity Model, ” Annual Review in Economics, 3 (1), 133 – 160. Angrist and Pischke (2009). Mostly Harmless Econometrics, Princeton University Press. AFAD (Disaster and Emergency Management Presidency of Turkey) (2013). Syrian Refugees in Turkey, 2013: Field Survey Results. Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015a). “ The Impact of Refugee Crisis on Host Labor Markets: The Case of the Syrian Refugee Crisis in Turkey. ” IZA Discussion Paper 8841. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015b). “ The Impact of Refugee Crises on Firm Dynamics and Internal Migration: Evidence from the Syrian Refugee Crisis in Turkey, ” mimeo. Aydemir, Abdurrahman and Murat Kırdar (2013). “ Quasi-Experimental Impact Estimates of Immigrant Labor Supply Shocks: The Role of Treatment and Comparison Group Matching and Relative Skill Composition, ” IZA Discussion Paper 7161. Baez, J. (2011). “ Civil Wars Beyond their Borders: The Human Capital and Health Consequences of Hosting Refugees. ” Journal of Development Economics 96 (2) November: 391 – 408. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Table 8: Flood Risk Classification of Proposed Subproject Sites**\n\n| Site ID | District | Elevation (m) | 50-yr Flood Zone | Design Adjustment |\n|---|---|---|---|---|\n| S-04 | Rambora North | 312 | Outside | None |\n| S-11 | Kalendo East | 287 | Inside | Elevated plinth |\n| S-17 | Mwenda Central | 301 | Boundary | Drainage upgrade |\n| S-22 | Ngozi West | 318 | Outside | None |\n\nFlood hazard maps used for this classification were prepared by the National Flood Risk Management Authority and validated against historical flood records from the Ministry of Home Affairs' disaster registry.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "11 however, since they cannot distinguish between a country where the population is concentrated in one cluster covering 10 % of the territory and one where the population is concentrated in two clusters of 5 % each, but with a considerable geographical distance between them. Population and conflict geography in the Democratic Republic of Congo (DRC) corresponds to these arguments regarding population concentration and dispersion. Concentrations of language-based minorities are evident throughout eastern DRC. Due to the limited access of the government, the close proximity to international borders, and the dense population concentrations, these concentrated minorities have a higher potential of conflict than other, more accessible, sparsely populated areas of DRC. Figure 1 shows the population concentrations in 1990 (CIESIN data) for Central Africa. Heavily populated areas are shaded in deeper tones of red / grey. Civil conflict in DRC has overwhelmingly occurred in the eastern portion of the state, which is the most densely populated area and also geographically peripheral to the capital, Kinshasa. Of the eleven Congolese rebel groups accounted for in the dataset used in this paper, all have operated either exclusively or partially in the eastern and southern areas of DRC. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["CIESIN data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "more recent data. This suggests that ambiguity about index insurance can fall over time, with\n\nloan restructuring after early flooding. The contract would be underwritten against recorded\n\nwater levels at a main river gauge station, using this data as a proxy for flood damage. This\n\n10 This form of cat bond trigger is more analogous to a traditional insurance policy with its loss settlement process.\nOther triggers are on _modeled losses_ or _industry losses_ . For _modeled losses_, instead of dealing with Proactive's\nactual losses, an exposure portfolio is constructed for use with catastrophe modeling software. When there is a\ndisaster, the event parameters are run against the exposure database in the cat model. If the modeled losses are above\na specified threshold, the bond is triggered. For _industry losses_, the cat bond is triggered when an entire industry loss\nfrom a certain peril for the insurance industry doing business in this country reaches a specified threshold.\n\ninformation on renewable policy design was obtained mostly from IEA's renewable policy   database, and cross-checked with government websites, legislative texts and other related\n\ncollect generator-level wind installation data from a global wind farm dataset [20] to match", "output": {"entities": {"named_data": ["IEA's renewable policy   database"], "descriptive_data": [], "vague_data": ["recent data", "exposure database", "global wind farm dataset"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "reason to believe that the provision of services by donors and non-state actors could either strengthen or weaken citizens ’ deference to government. I will identify the conditions under which these two scenarios are likely to occur. On the one hand, citizens may be less likely to support the government with deference to its laws and regulations when they credit non-state actors or donors for service provision. The provision of services by donors and non- state actors is likely to prompt citizens to question why they should pay taxes to a government that is not providing them with anything in exchange. On the other hand, the provision of goods and services by donors and non-state actors might strengthen citizens ’ legitimating beliefs and their willingness to defer to governmental laws and regulations if citizens view their government as essential to leveraging and managing these external resources. I assess these competing hypotheses using multi-level analyses of Afro- barometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows us to analyze associations between donor and non- state actor service provision and the sense of obligation to comply with the tax authorities. Third, I assess the relationship between the provision of ser- vices by donors and non-state actors and citizens ’ willingness to defer to two additional authorities, the police and courts.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afro- barometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The initiative includes four components, namely: a) training professionals to work with young men in the area of health and gender-equity using a set of manuals and videos; b) social marketing of condoms; c) promoting health services; and d) evaluating changes in gender norms. In 2002, PROMUNDO and Horizons began a 2-year evaluation to measure the effectiveness of two different approaches, compared to a control site. Researchers have developed a\"Gender-Equitable Men\"(Leichert) scale with 24 items for measuring attitudes. Methods include pre and post-tests as well as a six-month follow-up community-based survey. In addition, they are gathering qualitative information among men and their female partners. Preliminary results suggest that the program has been successful at increasing gender equitable norms and reducing behavior that puts men at increased risk of HIV / AIDS. ReproSalud (Peru): Manuela Ramos launched ReproSalud in 1995 as a USAID-funded rural reproductive health program. ReproSalud used participatory rural appraisal (PLA) to help women's groups identify women's reproductive health needs and to organize community meetings to design strategies to address those needs. Domestic violence and forced sex within marriage emerged as important problems in those communities. In response, ReproSalud organized workshops for women and men on gender issues, carried out community awareness campaigns and established a microcredit program for women. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["community-based survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "cents. The universe of blocks (“ manzanas ”) was stratified by socioeconomic strata and a representative sample of blocks was selected at random without replacement. To en- sure a sample representative of Colombian children by age group, stratum, and sex, we followed a multi-stage random sampling process. 8 As mentioned earlier, one of the biggest constraints in characterizing the role of forced migration in children ’ s human development within developing countries is the difficulty of finding a representative sample of those migrants. This is specially true in contexts where migrants are not hosted in refugee camps, but are integrated in local communi- ties, which account for 80 % of refugees worldwide (Climate Center 2022). We address these difficulties, leveraging all available information on Venezuelan settlements across the country to construct the largest possible comprehensive listing. The listing included data on Venezuelan settlements from all available sources, such as the 2018 population census, migrant organizations, and settlements identified by iMMAP, a non-profit orga- nization. iMMAP uses multiple sources, including OIM, United Nations, local migrant organizations, and satellite images, to identify Venezuelan settlements geographically. 9 Hence, to create our sampling frame, our field team verified the geographic location of all the Venezuelan settlements in-person and implemented a snowball sampling procedure in all the settlements found. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["2018 population census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 quest for imaginative and convincing instruments for migration (see the review of the migration and poverty literature by McKenzie and Sasin, 2007, and the references therein). An additional hurdle is the need for panel data to study migration and economic mobility. The costs and difficulties in re-surveying migrants mean that attrition may be relatively high for this group and may also result in the loss of some of the most relevant households to study this process (Beegle, 2000; Rosenzweig, 2003). This paper uses unique data from a region in Tanzania to address this key question: What is the impact of physical movement out of the original community on poverty and wealth? Although we do not have experimental data, the nature of our data allows us to limit the potential sources of unobserved heterogeneity considerably. Building on a detailed panel survey conducted in the early 1990s, we re- interviewed individuals in 2004, making a notable effort to track individuals who had moved. The tracking of individuals to new locations proves crucially important for assessing welfare changes among the baseline sample. The average consumption change of individuals who migrated was more than four times higher than that of individuals who did not moved. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turk­ish Labour Force Survey to assess their labor market impact. Syrian refugees are overwhelmingly employed informally, since they were not issued work permits, making their arrival a well-defined supply shock to informal labor. Consistent with economic theory our instrumental variable estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupa­tional upgrading, there are increases in formal employment for the Turkish- though only for men without completed high school education. Women and the high-skilled are not in a good position to take advantage of lower cost informal labor. The low educated and women experience net dis­placement from the labor market and, together with those in the informal sector, declining earning opportunities. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Labour Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Expected location in three years ��������������������������������������������������� iv Figure ES.3: Poverty incidence........................................................................... v Figure ES.4: Food insecurity scale...................................................................... v Figure ES.5: Refugee employment and proximity to resource hubs .................. vi Figure ES.6: Host response to “Refugees are good people” ............................ vii Figure ES.7: Host response to “Would you feel comfortable having a refugee as a neighbor?” ��������������������������������������������������������������� vii Figure 1.1: Refugees and asylum seekers in Ethiopia by country of origin, 1984-2023.................................................................................... 1 Figure 2.1: Refugees by survey domain ���������������������������������������������������������� 10 Figure 2.2: Country of birth............................................................................. 10 Figure 2.3: Refugees arrival in Ethiopia (15 years and above) ..................... 10 Figure 2.4: Age structure................................................................................. 11 Figure 2.5: Gender composition....................................................................... 11 Figure 2.6: Marital status (18 years and above) ��������������������������������������������� 11 Figure 2.7: Education level (18 years and above) ������������������������������������������ 13 Figure 2.8: Youth (15 to 24) education level �������������������������������������������������� 13 Figure 2.9: Refugees’ education outside of Ethiopia (18 years and above) .... 15 Figure 2.10: Children currently attending school ����������������������������������������������� 15 Figure 2.11: Gross Enrollment Rate (GER) �������������������������������������������������������� 16 Figure 2.12: Net Enrollment Rate (NER) ������������������������������������������������������������ 16 Figure 2.13: Share of children and youth above school age in education ....... 16 Figure 2.14: Reasons for not currently attending school ���������������������������������� 17 Figure 2.15:", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Some data challenges common to refugees and IDPs Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities. Sampling. As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay at relatives ’ places, other live in makeshift shacks and others stay in camps. The information available in the UNHCR registry (the census) can also be quite inaccurate, as already discussed, and the degree of accuracy changes for different groups of refugees. Stratification by urban and rural areas, a typical approach in sampling, may mean little for a population that is mostly in urban areas whether in camps or outside camps. Refugees and IDPs are also mobile and more difficult to track over time than other populations. Several statistical institutes worldwide have developed methodologies to track and measure mobile populations such as herders, nomads or homeless people. However, tracking refugees from other countries has been in the Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["UNHCR registry"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The UNHCR data identify principal applicants for each household and our analysis examines differences in household poverty between households with a female rather than male principal applicant. 6 The principal applicant is the person who receives assistance from UNHCR for the family and is self ‐ selected or selected by the family. This definition of female headship has advantages over the way that household headship is commonly identified in household surveys. An often ‐ noted drawback of the headship variable is that female headship may reflect the enumerators ’ perception about who should be considered a family head rather than who has the most responsibility for the family ’ s welfare in practice. 7 Social norms can also affect whether female respondents self ‐ identify as household heads. For example, some Eritrean returnees who would in other cultural settings be regarded as de jure female headed (single mothers, widows, divorcees, separated women) reported being male ‐ headed. Other Eritrean female returnees who would be considered de facto heads reported headship by absent husbands or male relatives (Kibreab, 2003). Our approach is therefore to distinguish between different types of female and male principal applicant households, using a typology that reflects some of the indicators of vulnerability used by UNHCR. We find that distinguishing between different types of female principal applicant households is important in the setting of Syrian refugees in Jordan.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 7.17: Share or refugees involved in a community representative body 70 Figure 7.18: Share or refugees engaged in a community representative body by demographic group ���������������������������������������������������� 70 Figure 7.19: Discrimination and harassment ������������������������������������������������ 71 Figure 7.20: Discrimination and harassment by demographic group ....... 71 Figure D.1: Age group by gender................................................................ 100 Figure D.2: Refugees’ education document ����������������������������������������������� 100 Figure D.3: Share of school-age children in education per household ..... 100 Figure D.4: School-age children currently attending school by gender .... 101 Figure D.5: Reasons for not currently attending school by gender ........... 101 Figure D.6: Average annual household education expenditure (in ETB) ... 101 Figure D.7: Type of health institutions......................................................... 102 Figure D.8: Problems faced in health institutions ��������������������������������������� 102 Figure D.9: Stunting by gender of children ������������������������������������������������� 102 Figure D.10: Childbirth in health institutions (children under five years) 103 Figure D.11: No birth evidence available (children under five years) ......... 103 Figure D.12: Average annual per capita health expenditure ....................... 103 Figure D.13: Types of disability..................................................................... 104 Figure D.14: Rent expenditure (Refugees and hosts in Addis Ababa) ........ 104 Figure D.15: Hand washing facility................................................................. 104 Figure D.16: Top 3 difficulties with being a refugee by survey domains .....", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Beneficiary assessments were conducted in all 24 target communities between February and May using a structured questionnaire administered by trained community development officers. The assessments captured household composition, livelihood strategies, access to basic services, and self-reported protection concerns. Results from the beneficiary assessments informed the design of component-level activities and the weighting of eligibility criteria used in the targeting formula applied during beneficiary registration.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As can be seen from both panel A and panel B, non-violent conflicts seem to occur slightly more than violent conflicts. On average, the likelihood of violent conflict stands at about 48 %, while this figure increases to 52 % in refugee-hosting areas. Conflicts among more structured and large groups, as captured by the UCDP data, appear to be less frequent. UNHCR refugee data. To exploit the variation in ethnic diversity induced by the annual variation in refugees (and also to control for the direct effect of refugees on our outcomes), we use data on refugee camps provided by the UNHCR. The dataset contains detailed time-series information on the locations and sizes of 1, 453 refugee camps across the world and 821 refugee camps in Sub-Saharan Africa over the 2000 – 2016 period. To the best of our knowledge, the UNHCR currently provides the most comprehensive information available on refugees at the subnational level, allowing us to assess the ethnic composition of camps, which is key to our research question. First, we use the country of origin of refugees recorded for each year at the camp level to approximate the ethnic composition each camp. Second, we restrict the data on refugees to those aged 18 and above in order to make it comparable to the Afrobarometer- based individual data. Third, we only use data on refugees hosted within the boundaries of the host country. Merging data on refugee camps with the Afrobarometer, we end up with information on 172 camps 12", "output": {"entities": {"named_data": ["UCDP data"], "descriptive_data": ["data on refugee camps"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Several of the refugees interviewed for this research indicated that no one wanted to give them\nwork because of their status as a refugee in Cameroon. When discussing the treatment they\nreceived from the host population and the ability to find a job, all refugees interviewed explained\nthat it was difficult for them “because I am a stranger”, “from the outside” or because “I am\ndifferent”.\n\nAlthough there are many similarities between the refugee population and the host community,\nthere is also a clear distinction that exists; all refugee interviewed indicated that they experienced\nsome sort of harassment and/or discrimination because they were refugees, and most connected\nthis directly to their ability to find wage-earning work in Yaoundé. However, several refugees\nstated that the discrimination they face is not experienced with all Cameroonians, in some cases\nthey indicated that the locals supported them and treated them well; simply put by one refugee\nfrom CAR, “some Cameroonians are nice, some are not.”\n\nAs discussed previously, identity documentation is a big problem for refugees living in\nCameroon, as many authorities and institutions do not recognise UNHCR identity cards and", "output": {"entities": {"named_data": ["UNHCR identity cards"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the intensity of conflicts, and iv) population density in refugee host countries by region. Appendix B presents the list of variables along with description and summary statistics. 5. Empirical strategy To estimate the impact of refugee inflow19 on host community ’ s livelihood strategy choice, we use the following basic econometric model: 𝑌𝑌𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑅𝑅𝑅𝑅𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝜈𝜈 + 𝜀𝜀𝑖𝑖 (1) Where, 𝑖𝑖 indexes a household, 𝑌𝑌𝑖𝑖 is an outcome variable of interest (livelihood diversification or commercialization of agriculture), 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 is the measure of refugee inflow, i. e., the refugee population (average of 2017 and 2018) in the nearest refugee camp weighted by the inverse of distance of the household to the refugee camps, 𝑋𝑋𝑖𝑖 is a set of household controls, 𝜈𝜈 is kebele fixed effects, and 𝜀𝜀𝑖𝑖 is the error term. Several variables, from the DRDIP data set, were used as controls in our model. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["DRDIP data set"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 3. Refugees as Agents of Their Own Destiny 3. 1 The Composition of Africa ’ s Refugee Population and Its Consequences One of the first elements that catch the eye in Figure 6 is the difference in the composition of the refugee population in Africa compared to the rest of the world. The share of children and women among refugees is higher in Africa than elsewhere, in particular East and West Africa stand out here. This is, at least partly, a consequence of Africa ’ s younger, general population, but other forces could be at work as well, e. g. higher mortality of adult males in Africa or adult males staying behind or being separated from the rest of the household. It does mean however that, relative to other areas, more attention should be going to the needs and capacities of women and children in Africa. This means, for example, adaption of and increased supply of schooling and health services. Figure 6. The composition of refugees by age and gender, 2013 Source: Note: UNHCR statistics (UNHCR 2014). Asia excludes Australia, Japan and New Zealand. Americas exclude Canada and the United States. These percentages have been calculated by country when demographic data are available for at least 30 % of the total. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["demographic data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 The main contributions of this article lie in identifying and overcoming these challenges in order to construct a consistent and complete set of origin – destination matrices of international migrant stocks for 1960 – 2000, disaggregated by gender. The starting point is a master set of 226 origin or destination countries and regions. Despite border changes, all migrants are assigned to this master set so that migrations can be meaningfully tracked over time. These assignments, especially in cases where only aggregate data are available, are made using several alternative propensity measures based either on a destination country ‘ s propensity to accept international migrants or on an origin country ‘ s propensity to send migrants abroad. Cases of omitted data occur when destination countries do not collect or publicly disseminate the information on migrants. When data from census rounds are missing altogether, the approach taken depends on the extent of the omission (see appendices 3 and 4). When sufficient data are available for other decades, interpolation is used. When not enough data are available, propensity measures are used to generate bilateral data. When a gender breakdown is missing, gender splits are calculated based on supplementary statistics or other data in the matrices (see appendix 5). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The novelty is that a new, more spatially and temporally detailed data set for nightlights, the Visible Infrared Imaging Radiometer Suite (VIIRS) product from the National Oceanic and Atmospheric Administration (NOAA), following the work of Elvidge et al. (2017), is employed. In addition, settlement layers have been used to remove nightlight cells with zero population to reduce the impact that \"empty\" cells could have on the analysis.\n\nThis paper utilizes a newer nightlight data set, the VIIRS Day/Night Band (DNB) provided by\nThe Earth Observations Group (EOG) at NOAA/NCEI. The data are produced following the\nmethodology of Elvidge, et al.\n\nThe VIIRS data have a number of advantages over the widely used, but now discontinued,\nDefense Meteorological Satellite Program (DMSP). Firstly, they have a higher resolution,\n450m by 450m compared to 1km by 1km for the DMSP.\n\nThe WorldPop data sets have been used to identify settlement areas in Myanmar and Vietnam\n(WorldPop 2013) (Worldpop 2016). These data sets have a spatial grid cell resolution of\napproximately 100 meters at the equator and estimate the number of persons per square.\n\n\\n<!-- PAGE 13 -->\\nFor Indonesia, the Philippines and Thailand high resolution settlement layers from Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University was used.", "output": {"entities": {"named_data": ["Visible Infrared Imaging Radiometer Suite (VIIRS) product from the National Oceanic and Atmospheric Administration (NOAA)", "Defense Meteorological Satellite Program (DMSP)", "WorldPop"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "by the migration episode. These include the minors age and sex, the parent ’ s and grand- parent ’ s education pre-migration, and a wealth index constructed with retrospective in- formation on the household conditions pre-migration. 14 Grandparent ’ s education is used a proxy for living standards that is unaffected by the migration episode from Venezuela to Colombia and the Venezuelan crisis, which intensified in 2016. ϵij depict the standard errors clustered at the household level to correct for intra-household correlation. For robustness, we will present the estimates of equation 1 with and without controls. As further robustness, we use propensity-score weights (Hirano and Imbens 2001, Hirano, Imbens and Ridder 2003). 15 V. A Physical development: Body Mass Index and health status In our study, we examine disparities in nutritional and health status among Colombian and Venezuelan children aged 5 to 10 years, focusing on standardized body mass index (BMI), instances of overweight and underweight, and overall health status. The BMI serves as an indicator of nutritional status for both adults and children, calculated as an individual ’ s weight in kilograms divided by their height in meters squared, according to World Health Organization (WHO) guidelines.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["wealth index"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel. 19 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["EPR-ER data", "EPR-ER", "Murdock Atlas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "First, most of the studies only consider a subset of occupation or livelihood activities (mainly employee-based) and do not provide a full picture of the livelihood impacts of refugee presence on host communities. We consider an exhaustive set of livelihood activities in which households (individuals) in the impacted communities may engage. 7 Second, prior studies tend to focus on the different livelihood activities separately (i. e., whether the individual adult members or the household engage in each of the livelihood activities). 8 Therefore, they are unable to infer whether households are diversifying or specializing their livelihoods or are engaging more on the commercialization of activities. 9 The current paper goes beyond the allocation of labor to individual (specific) 7 As the data we used does not have a good welfare indicator (e. g., income, consumption, and assets), we could not explore the welfare impact of refugee inflow. 8 We examined households ’ engagement in individual livelihood activities as a mechanism for household livelihood strategies. 9 Generally, households tend to diversify their livelihood when facing negative shocks (e. g., conflicts, droughts) to minimize risk (Ellis 2000a, b). In the case of refugee inflow, households may either diversify or specialize as refugee inflow could be both a negative shock (through increase competition for resources, services, and employment) and a positive shock (through creating opportunities, such as high demand agricultural products, provision of cheap labor). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 Figure 3: Level of education age 25 +; compared with regional average (%) (*) INSTAT refers to census 2009 Source: Listening to Displaced People Survey, 2014. With regards to ownership of consumer durables, IDPs, refugees and returnees were better endowed than the average citizen of the North (see Figure 4). As was the case for education, they are more comparable to the average citizen in Bamako than to the average citizen in the regions of Gao, Timbuktu and Kidal. Figure 4: Asset ownership compared with regional average Source: Listening to Displaced People Survey, 2014 and EMOP 2011 (INSTAT). The main occupation of IDPs, refugees and returnees before the crisis was commerce (Table 5). This held for over half of the IDPs, 37 % of refugees and 34 % of returnees. 18 % of the refugees 51 85 60 47 85 89 87 15 6 18 29 11 8 8 34 9 22 25 5 3 5 IDPs Refugees Returnees Bamako (INSTAT) Gao (INSTAT) Timbuktu (INSTAT) Kidal (INSTAT) Secondary or Higher Primary None 0 100 200 300 400 500 600 IDPs Refugees Returnees Bamako (Instat) Gao (Instat) Timbuktu (Instat) Kidal (Instat) Percentage Mobile Phone Car / Motorized Vehicle Motorbike / scooter Bicycle Refridgerator TV CD Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Displaced People Survey", "Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "cope with the emergencies that arise all too often. 20 To facilitate successful coping and provide a safe place to save, the EPAG program assisted each participant to set up a savings account at a local bank if she did not already have one. Table 5 presents midline survey results that indicate that the treatment group were nearly 50 percentage points more likely to have savings than the control group, and were saving on average LD 2500 (nearly US $ 35) more than the control group. EPAG graduates were also twice as likely as the control group to have outstanding loans (six percent v. three percent), and have loans from formal lenders (five percent v. two percent), 21 although the overall rate of obtaining credit remains extremely low. 4. 3. Empowerment The Adolescent Girls ’ Initiative is based on the hypothesis that livelihood and life skills training for young women will improve their lives in more than just narrowly-defined economic dimensions. In addition, evidence is increasing that these soft skills are also essential for success in employment (Borghans et al. 2008). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 Over the past three or four decades, the Arab world has experienced a massive expansion in educational attainment. According to the Barro and Lee educational attainment dataset, seven out of the top 20 countries in terms of increase in number of years of schooling from 1980 to 2010 were Arab countries (Barro and Lee 2013). 1 Jordan, the subject of this paper, had the seventh highest increase in educational attainment in the world, with an increase of about five years in the average years of schooling over the period. This increase is widely believed to be attributable to a massive public investment in the supply of schooling in the postindependence period in the context of a state-led development model, which virtually guaranteed employment in the public sector for graduates (Assaad 2014; Saleh 2016). The rapid increase in educational attainment has continued unabated despite the fall in returns to education that accompanied the demise in the state-led model and its employment guarantee schemes (Pritchett 2001). A slew of recent literature on the drivers of the Arab Spring protests, some of which occurred in Jordan, has identified the low economic returns to this massive increase in education as the single most important cause of the uprisings (Goldstone 2011; Campante and Chor 2012a, 2012b, 2014; Sanborn and Thyne 2014). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "19 children and 61 % of caregivers prefer to remain in Italy. Adolescents between the ages of 15 and 19 express a greater desire to continue living in Italy compared to children aged between 9 and 14. Another survey conducted across Europe from June to December 2022 shows that only 8 % of Ukrainian refugees planned to settle outside Ukraine (Adema et al., 2024). Compared to other foreign children in Italy, the aspirations of Ukrainian refugees to return to Ukraine seems signiϐicantly higher: indeed, a recent study from ISTAT on children 11 to 19 years old shows that only 11 % of foreign children wish to return to their home country (ISTAT, 2024). The relatively strong desire to return to Ukraine can have negative effects in refugee parents'educational decisions, particularly in encouraging their children to learn the language of the host country and in enrolling in school (Dryden-Peterson et al., 2019; Zengin and Atas-Akdemir, 2020). Figure 6- Aspirations and identity of refugee caregivers and students (Source: World Bank Survey on Ukrainian refugees in Italy) Many students facing uncertain futures try to stay connected to both educational systems. Findings from the World Bank survey indicate that 25 % of children are engaging in online Ukrainian schooling while being enrolled and attending Italian schools.", "output": {"entities": {"named_data": [], "descriptive_data": ["World Bank Survey on Ukrainian refugees in Italy"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "- Advocacy vis-à-vis allied, Government and AGEs with regards to the use of weaponry that may\nleave ERW in heavily populated areas;\n\n- Advocacy on the need to clear battle sites post kinetic engagement, especially in populated\nareas;\n\n- Strengthen resource mobilization efforts to ensure the sector can function at full capacity. The\ntotal funding for mine action requested through the HRP is $3,080,676 (across MRE,\nEOD/survey and coordination), of which 80% ($2,485,695) has been mobilised.\n\n**Child protection**\n\nDuring displacement and emergencies and conflict, violations of child rights occur in multiple forms.\nThe increased insecurity caused by violence and conflict has exacted an increasing toll on children in\nterms of the number of civilian casualties along with serious child protection concerns.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "not the Zone capital or Woreda city, and 17 percent of in-camp refugees live in remote areas far from a Zone capital city, Woreda city, or a border. When looking at accessibility, as defined by a market accessibility index, more than one-third of the refugees are located in areas with low market accessibility (Figure 6.1b). The analysis measures the nearest Zone and Woreda capital cities and the closest international border from refugee camps using straight-line distance in a projected coordinate system (Euclidean distance). The study indexed refugees’ proximity to resource hubs by classifying their presence to a combination of distance to cities and borders. Level one is the presence of refugees within a radius of 20km from a Zone capital city. Level two is 10km away from a Woreda city but not within a radius of 20km from the Zone capital city. Level three is for refugees located 30km from the nearest international border but not within a radius of 20km from the Zone capital city and 10km from Woreda city. We classify level four as “remote”; that is, not located 30km from the nearest international border, not within a radius of 20km from the Zone capital city, and not", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["market accessibility index"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "11 Figure 3: Location of refugee camps in Ethiopia and the Ethiopia Development Response to Displacement Impacts Project (DRDIP) sample households Source: Authors ’ compilation using the database of the Global Administrative Areas (GADM) (https: / / gadm. org / data. html, accessed November 20, 2020) and the Humanitarian Data Exchange (HDX) database for the refugee location (https: / / data. humdata. org / dataset / ethiopia- refugee-camp-locations, accessed November 20, 2020). From the Ethiopia DRDIP data set, we derive two measures of livelihood diversification and two measures of agricultural commercialization (all at household level). The measures of diversification include: (i) the degree of labor diversification in different productive livelihood activities (e. g., farming, wage employment) as a primary activity (occupation), and (ii) the degree of labor diversification in different livelihood activities as a secondary activity (occupation). 13 These two outcomes were constructed using the inverse Simpson diversity index as 1 ∑ 𝑛𝑛 𝑖𝑖 𝑆𝑆𝑖𝑖 2, where 𝑆𝑆𝑖𝑖 is the share of the number of adult labor engages in 𝑖𝑖𝑡𝑡ℎ livelihood activity to total active adult household labor and 𝑖𝑖 ranges from 1 to the number of livelihood activities that a household engages in (Valdivia et al. 1996). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Thirty-eight percent of the sample was already engaged in at least one income-generating activity (IGA) at baseline. This is consistent with the national figures from the 2007 DHS survey, which found labor force participation rates of 34 % for women aged 15-19 and 49 % for women ages 20-24. It is also consistent with the Liberian 2010 Labor Force Survey, which found labor force participation rates of 25 % for women aged 15-19 and 47 % for women aged 20-24. For the purposes of this study, to be consistent with program objectives and the Liberian context, our definition of income-generating activity encompasses the full range of activities through which people earn money, including paid employment, either formal or informal, and self-employment in small business or through petty trade. The most common types of IGAs reported at baseline were petty trade, including 15 The balance tests are run on the same sample as will be used in the impact analysis in Section 4, that is, the subset of individuals for whom we have a panel. Balance tests run on the full sample of baseline survey respondents, regardless of whether they also participated in the midline survey, confirm the same findings. A report summarizing the balance tests on the full sample, including comparisons to nationally representative data, is available upon request from the authors. 9", "output": {"entities": {"named_data": ["2007 DHS survey", "Liberian 2010 Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Following the approach used for the Global Costing of Refugee Inclusion, successful inclusion is defined as earning sufficient income to be no longer poor and to consume more than the (international) poverty line. This definition opens two tracks for investigation: first, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this question is found by identifying the poverty gap for refugees. This opens the second track which explores the factors that determine, or at least that are associated with, the size of the poverty gap. The note is organized as follows. The next section discusses data and presents some key descriptive statistics on refugees and host communities in Uganda. This is followed by a methodological section discussing how own income and aid are complements and how an analysis of poverty gaps informs about the need for assistance. This is followed by two analytical sections. The first identifies refugee poverty gaps, and assistance needs for refugees with distinct characteristics. The following section estimates how much has been saved by including refugees in the economy and explores how more could be saved. Conclusions follow. 2 The poverty numbers in World Bank (2019) are based on the official poverty line adopted in Uganda in 1997. There was a need to update this line as it was too old and producing a very low poverty rate. For example, using this line produced a national poverty rate of about 21 percent in 2019 / 20 compared to more the than 40 percent international poverty rate using the USD 2. 15 2017 PPP daily poverty line. In order to address this criticism, the poverty line was revised by the Uganda Bureau of Statistics in 2021, but it is not available for the 2018 Refugee and Host Communities Household Survey used in this note. Instead, we are using the international poverty line throughout. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["2018 Refugee and Host Communities Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Annual grid-level GDP data between 1990 and 2014 at a 0.5-degree resolution come from Kummu, Taka and\n\nGuillaume (2018a). The data are primarily based on sub-national GDP per capita data constructed by Gennaioli,\n\nIs the relationship between rainfall and GDP explained by agriculture? We use data from the ESA CCI project\n\n\nto determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split\n\ndifferent weights. Population is taken from HYDE 3.2 (Klein, Beusen and Janssen 2010).\n\nThe work compares food prices subnationally and finds that increasing prices are the most significant factor driving recent food insecurity but documents strong spatial heterogeneity in both market price dynamics, as well as relations to food insecurity, and provides examples of markets where prices are more sensitive to localized shocks. 4For instance, the International Finance Statistics (IFS) data base of the IMF reports monthly price data at Consumer Price Index (CPI) component level, but few developing countries report food price data without substantial delays.", "output": {"entities": {"named_data": ["ESA CCI project", "HYDE 3.2", "International Finance Statistics (IFS) data base of the IMF"], "descriptive_data": ["grid-level GDP data", "sub-national GDP per capita data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "scholarship program, and data on school enrollment and attendance from an unannounced school visit.\n\n<!-- PAGE 5 -->\nrandom sample of 6 [th] grade girls in the primary feeder schools to the JFPR secondary schools, or even of\n\nLocal Management Committee (LMC) of the relevant JFPR secondary school. The LMCs were then\n\nThe second source of data for this evaluation is based on an unannounced school visit to each one", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The World Bank's online screening tool was used to determine the project's environmental and social risk classification. Based on the nature and scale of proposed activities, the risk was classified as Substantial, triggering the application of ESS1 (Assessment and Management of Environmental and Social Risks and Impacts), ESS2 (Labor and Working Conditions), ESS4 (Community Health and Safety), and ESS10 (Stakeholder Engagement and Information Disclosure). The findings informed the scope of the Environmental and Social Assessment and the Stakeholder Engagement Plan.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Combining almost 50, 000 responses to 11 cross-sectional surveys between 2017 and 2021, displacement is neg­atively associated with perceptions of social cohesion in aggregate. But at the individual level, those who report hosting displaced populations in their communities often have higher perceptions of social cohesion. These results are strongest among respondents who self-report hosting IDPs as opposed to refugees, but important heterogeneity across indicators, local context, and gender should guide policy meant to promote social cohesion in forced displacement. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at ppham @ hsph. harvard. edu. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["cross-sectional surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Table 6: Flood Hazard Classification and Infrastructure Design Standards**\n\n| Return Period | Design Standard Required | Additional Measures |\n|---|---|---|\n| < 10-year zone | Standard design | None |\n| 10–50 year zone | Elevated foundation | Drainage upgrade |\n| 50–100 year zone | Flood-proofing required | Emergency access route |\n| > 100-year zone | Avoid siting | Relocate |\n\nFlood hazard maps were provided by the national meteorological and hydrological service. All school and health facility sites were verified against the 100-year flood hazard boundary before final design approval.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "41 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Since some adjustments could take place over time, we always use the figures from the last available report. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(2009) have shown that, in Nepal, subjective welfare is negatively associated with geographical isolation. Census data on total population and population density in each district are used as proxies for urbanization and geographical proximity: the denser the population, the less geographically isolated individuals are likely to be. We also include data on the average elevation in each district. Nepal being a mountainous country, the higher the average elevation of a district, the more costly it is to build roads, raising transport and delivery costs to the district. Ceteris paribus, we expect migrants to seek out districts with a higher population density and a lower elevation. 4 Econometric results 4. 1 Univariate analysis We now investigate the choice of migration destination. We begin with simple univariate analysis. Variables are of the form ∆ h is = xh s − xh i where i is the district of origin of migrant h and s is each of 74 possible districts of destination. We examine the average value of ∆ h is for the destination district and compare it to the value of ∆ h is for alternative destinations. For instance, let xh s be population density in district s. The average value of ∆ h is for the actual destination of the migrant tells us whether the destination district is more densely populated than the district of origin.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Census data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5 3. Data The data used in this paper have been collected through the Listening to Displaced People Survey (LDPS). 4 The baseline face-to-face interviews were executed between June and August 2014. The following 12 monthly interviews – from August 2014 until August 2015- were conducted using mobile phones. 5 The original sample comprised 501 respondents (51 % Male, 49 % Female) and was divided between internally displaced people (IDPs) located in the capital city Bamako, 6 refugees living in refugee camps in Mauritania and Niger, as well as returnees living in the regional capitals Gao, Timbuktu and Kidal in Northern Mali. This survey did not collect information on individuals who were never displaced. The attrition rate was very low, always around 1-2 % per wave. We need to stress that the locations were not randomly selected. Bamako was selected because it hosted a large number of IDPs. Furthermore, the main cities in the north of Mali were chosen to obtain a large sample of returnees given the funds available. Finally, a refugee camp was located in Niger since bureaucratic issues did not allow the inclusion of a camp in Burkina Faso. Nevertheless, households were selected randomly within each location.", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The ESMF classifies project activities in five environmental risk tiers. Tier 1 activities — routine administrative operations with no physical footprint — require no screening documentation. Tier 2 activities — minor civil works in existing built-up areas — require completion of the ESMF screening form. Tiers 3 through 5 require progressively more detailed ESMPs. All civil works under Component 2 were classified as Tier 3 or above, and individual ESMPs for each site have been disclosed to affected communities and uploaded to the Bank's Operations Portal.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "and refugees. 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 Eritrean Somali South Sudanese Addis Ababa All Hosts Figure 7.10: Host attitudes Index Source: World Bank Staff based on SESRE 2023. Note: This index is an average of ten questions regarding beliefs about refugees’ character, the rights they should receive, and their impact on the host community. The scale ranges from 1-4, where more positive indicates better attitudes. 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 Male Female Male Female Male Female Male Female Male Female Eritrean Somali South Sudanese Addis Ababa All Hosts Figure 7.11: Host attitudes index by gender Markets and Opportunities 68 framework. This is presented in Annex D, Table D.18. While attitudes are less positive for men, as we have seen, this difference is not statistically significant after controlling for age and education. There is no clear pattern in attitudes by age and education. Based on regression analysis, the most significant predictor of positive host attitudes and trust is whether they think the presence of refugees has improved local infrastructure or services. This hints at the importance of local service delivery in driving host attitudes. On average, controlling for other characteristics and regions, hosts", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 6832 This paper presents findings from the impact evaluation of the Economic Empowerment of Adolescent Girls and Young Women (EPAG) project in Liberia. The EPAG project was launched by the Liberian Ministry of Gender and Development in 2009 with the goal of increasing the employment and income of 2, 500 young Liberian women by providing livelihood and life skills training and facilitating their transition to productive work. The analysis in this paper is based on data collected during two rounds of quantitative surveys in 2010 and 2011, the second of which was conducted six months after the classroom-based phase of the training program ended. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15% 25% 12% 71% Participation (relaxed) 30% 22% 54% 60% Unemployment (relaxed) 30% 57% 21% 82% Employment-to-population ratio 21% 9% 43% 11% Source: World Bank Staff based on SESRE 2023. Note: Labor force participation ratio is the share of working-age people who are engaged in the labor market, either employed or unemployed. Unemployment is the share of people participating in the labor force who are not employed. The “relaxed” definition of labor force participation includes anyone who is available to work. The “strict” definition of labor force participation includes only those who are available to work and also actively searching for work. Employment-to-population ratio is the share of working-age people who are employed. 0 20 40 60 80 100 Camp Hosts Male Camp Refugees Male Addis Hosts Male Addis Refugees Male Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure 3.28: Male youth work status Source: World Bank Staff based on SESRE 2023. Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent 0 20 40 60 80 100 Camp Hosts Female Camp Refugees Female Addis Hosts Female Addis Refugees Female Figure 3.29: Female youth work status Source: World Bank", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "prior to the pandemic, a period characterized by less distinct time trends in the labor market. Since the focus of the paper is on the impact of COVID on labor market stock and dynamics, we also restrict the sample of interest to include only prime-aged working adults (aged 20 to 59). The dataset contains standard variables expected of a labor force survey, including those denoting employment, unemployment, and inactivity. It also contains information on the intensive margin of the labor supply, including hours worked and full-time and part-time status. Information on employment sector, industry, contract status, health insurance coverage, and mode of work (distinguishing between employees and self-employed, for example) is also available, allowing us to construct indicators of formality and to differentiate different modes of employment. Information on occupation is also available, but only at the level of 2-digit ISCO-08 classification. This information is enough to distinguish between white- and blue-collar occupations but it is not enough to observe additional relevant pandemic-related job characteristics such as the degree of contact with the public. 3. 2 Descriptive statistics Figure (1) tracks the evolution of labor market stocks in the West Bank and Gaza respectively over time. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "that are at a maximum distance of 80 km from the 7, 547 clusters. 12 Figure 1 shows the locations of these refugee camps and clusters. Clusters are represented in green, while clusters in the vicinity of a refugee camp are represented in red. Refugee camps are designated with a red + sign. There are some important limitations associated with this data. First, the data only provides information on refugees residing in camps monitored by the UNHCR. In Figure B. 7, we combine the UNHCR refugee camp data on the annual number of refugees and the UNHCR official statistics on refugees (which includes people in refugee-like situations) at the country level. 13 Although the overall trends match, our constructed dataset clearly underestimates the true refugee population in Africa, which is not surprising since our camp-specific data does not contain dispersed refugees or refugees living outside of camps. While our data seem to represent quite fairly the number of refugees in camps, there is significant heterogeneity across countries. Based on the visual inspection of Figure B. 8, the quality of the refugee data appears to be less reliable for the following countries in our sample: Gabon, Mali, Senegal, and Togo.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR official statistics on refugees"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "required to purchase other basic staple foods such as salt and vegetables. Given that the WFP provisions are the only reliable rations that refugees receive, we approximate a cash transfer of 450 taka per week to at least double potential weekly consumption. Relative to the wealth refugees possess, 450 taka per week is likewise sizeable: average baseline savings is 195 taka, with the median refugee reporting zero taka in savings. Average baseline borrowing (typically in the form of store credit) is 1, 600 taka, with a median of 600 taka. Refugees have no economically meaningful assets that may be more common among the rural poor, such as land or cattle, given the unanticipated displacement which forced them from their homes. Relative to other employment opportunities, average reported pay is 300 taka per day for less than three days. The monthly cash transfer is therefore more than double what a refugee might expect from alternative employment if he or she is fortunate enough to secure a job. 4 Data Collection and Survey Instruments Timeline and survey instruments We collected data via a baseline, commencing in November 2019, and endline survey, commencing in February 2020, as well as seven midline surveys conducted prior to payment disbursal each week. These weekly surveys collected a small subset of well-being outcomes. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "occupation. Regression results (see Annex D, Table D.7) also confirm that, for hosts, older and more educated people are, in addition to having higher monthly earnings, more likely to work in a high-skill occupation (managerial or professional occupations). For refugees, older and more educated people are no more likely to work outside the camp. Yet, refugees with higher levels of education are more likely to be in a high-skill occupation when they are able to find work. 0 20 40 60 80 100 Camp Refugees Male Camp Refugees Female Inside the camp Outside the camp Percent Figure 3.9: Refugee work location Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Camp Hosts Female Camp Refugees Female Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure 3.7: Female work type Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Camp Hosts Female Camp Refugees Female Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals Percent Figure 3.8: Female occupations Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 31 0 10 20 30 40 50", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 7253 This paper is a product of the Poverty Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The authors may be contacted at jlendorfer @ worldbank. org and jhoogeveen @ worldbank. org. This paper analyzes the impact of the 2012 crisis in Mali on internally displaced people, refugees and returnees. It uses information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["face-to-face household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "35 Inputs: 1. Household Survey with consumption or income welfare aggregates 2. Project data / Other survey data without welfare aggregates 3. Set of harmonized common variables in both surveys Outputs: 1. Set of imputed welfare variables for project data / other survey for each household in the data 2. Imputed welfare variables can be used for poverty, distributional analysis (quintiles or more), profiling of the poor or group of interest Models: 1. Ordinary Least Squares (OLS) 2. Probit 3. Multiple Imputation (MI) Table 18. Model Specification Variables Demographic Share of children, share of adults, share of adults squared and share of old (omitted) Characteristics of head Age, gender, and level of education Interactions with urban dummy variable Level of education of the head, age of the head Geography Dummies for regions at NUTS 1 level (12 regions) Interactions with Geography Level of education of the head, age of the head interacted with regions at NUTS 1 level (12 regions) and urban-rural division 1. Validation and Robustness Check Figure 7.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household Survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Although much more suited to geographically disaggregated analysis than other datasets, this location dataset has some limititations, It does not record changes over time in the center location and extent of conflicts, and it reports the total extent of the conflict zone without distinguishing between areas that saw repeated and extensive fighting and those that only experienced scattered activities or individual events far from the center of the conflict. 3. 2 Disaggregated Dependent Variable: ACLED The ACLED dataset (Raleigh & Hegre, 2005) deals with these problems. The dataset takes the PRIO / Uppsala Armed Conflicts Dataset as its point of departure. The dataset is limited to events within conflicts that fall within the Uppsala conflict definition; conflicts involving two parties, one of which is a government, and fighting resulting in at least 25 battle deaths. 3 ACLED is designed to parse out both the temporal and spatial actions of rebels and governments within civil wars. 3See the PRIO / Uppsala Armed Conflict Data codebook for more information (Strand, Wilhelmsen & Gleditsch, 2004).", "output": {"entities": {"named_data": ["PRIO / Uppsala Armed Conflicts Dataset", "ACLED dataset"], "descriptive_data": [], "vague_data": ["location dataset"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In comparison, 39 percent of hosts and 55 percent of refugees trust both groups. Once again, hosts’ trust towards refugees is highest in the Somali domain (67 percent) and lowest in the South Sudanese domain (29 percent). On the other hand, refugee trust towards hosts is lowest in the Eritrean camps (38 percent). Results of combining the survey answers into an index echo our prior findings; attitudes toward refugees are better in Somali areas, worse in South Sudan areas, and slightly better among women. The above questions on attitudes and trust towards refugees, the rights refugees should have, and the effects refugees have had on Ethiopia can be combined into an index. To construct the index, we average the response to the ten questions examined above, rescaled to range from 1-4. The index is highest in the Somali domain (.46 standard deviations above the mean) and lowest in the South Sudan domain (.48 standard deviations below the mean). It is higher for female than male hosts in both domains, by .14 standard deviations from average in the Somali domain and .19 standard deviations in the South Sudan domain. The difference in attitudes between male and female hosts is not statistically", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Secondary Figure 3.13: Share employed by age – camp hosts Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 32 When refugees own livestock, the value and flock size of this livestock is low. Somali refugees mostly own sheep, goats, and donkeys, and compared to Somali hosts, they have smaller flock sizes and lag them in terms of cattle ownership. As Figure 3.17 shows, the lower value of livestock in refugee households also reflects lower reported monetary value of equivalent livestock (per Tropical Livestock Unit). For the most part, however, it reflects the lower value of the type and number of livestock refugees own. In Eritrean and Somali camps, refugees and hosts report a similar rate of non-farm business ownership; but the value of productive assets in refugee businesses is low, indicating they are primarily small-scale and low-income. The exception is Somali refugees, partially driven by ownership of animal-drawn carts. Across all domains, refugees have a lower value of productive assets such as farming tools and construction equipment. They are also less likely to own commercial cars, motorcycles, or Bajaj, a cause of a significant portion of the gap in total value of assets between refugees and hosts.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The study: (i) compares the socio- economic profile of refugees with that of the Syrian population before the crisis and with the hosting populations of Jordan and Lebanon; (ii) provides a welfare and vulnerability assessment of Syrian refugees including a poverty profile, the socio-economic characteristics of higher poverty and where pockets of deep poverty are located; (iii) analyzes key drivers of welfare and poverty; and (iv) models monetary and non- monetary vulnerability.  In Lebanon, Jordan and Iraq, the Bank is leading an initiative to evaluate the socio-economic impact of the regional crises on the welfare of Syrian refugees and host communities in neighboring countries [ongoing]. Data on living conditions, access to services and economic opportunities, coping strategies and economic status are to be collected via a specialized household survey and a sub-component of the survey will be carried out on a semi-annual basis to continue to monitor and adapt support as needed.  A recent Bank paper, “ Turkey ’ s Response to the Syrian Refugee Crisis and the Road Ahead ” [completed in 2015] assessed the impact of Syrian refugees on host areas in various sectors. It found that the presence of Syrian refugees is placing a strain on municipal services, housing rental markets, social relations, and education services for Turkish households. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["specialized household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Before this unrest, Mali was recognized as a country with a peaceful democracy. Data gathered for conflict data sets support this reality. The Armed Conflict Location and Events Database (ACLED) shows an average of 16 conflict events reported between 2006 and 2010 compared to 278 events reported in 2011 alone (figure 1). The conflict also led to increased displacement, with over 227, 000 newly forcibly displaced people in 2012 (Internal Displacement Monitoring Center, n. d). High levels of gender inequality pre-date the conflict in Mali. Mali ranked 158 of 162 countries in the most recent Gender Inequality Index (GII), little different from its rank of 143 of 146 countries in 2012 when the conflict began. According to the World Bank Gender Data portal, women ’ s economic participation has declined since 2000 in all industries except services, and women ’ s unemployment has increased. In contrast, women ’ s involvement in politics and decision making has increased since the war began. During the war (2012-2015), the number of seats held by women in parliament decreased. In 2020, political participation of women surpassed pre-war levels (World Bank, n. d). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "natural log of a country ’ s population size and government consumption as a share of GDP; the latter is the most widespread measure of government size (Adsera and Boix, 2002; Alesina and Wacziarg, 1998; Rodrik, 1998). Tax compliance is also related to the government ’ s ability to eﬀectively detect and punish tax avoiders, tax evaders, and tax arrears. Although an imperfect measure of states ’ deterrent capacity, Afrobarometer includes two survey questions on perceptions of government enforcement and monitoring capacities. One question probes respondents on how often ordinary people who break the law go unpunished. The other probes respondents on how often oﬃcials who commit crimes go unpunished. This latter question is also a measure of perceived government fairness- the extent to which a government implements the law evenly across all social groups. 6. 2. 5 Procedural Justice I include two indicators of procedural justice. The ﬁrst probes respondents on how often people are treated unequally under the law. The next taps citizens ’ perceptions of the government ’ s treatment of their ethnic group. Speciﬁcally, respondents were asked how often their ethnic group is treated unfairly by their government. 9 6. 2. 6 Donor and Non-State Actor Provision of Services I include a measure of who citizens believe is responsible for collecting income taxes. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "33 recommendations (Jewkes, 2000). Their efforts contributed to the Employment of Educators Act and new Department of Education guidelines, both of which were introduced in 2000. These regulations mandate dismissal of educators found guilty of sexual or physical assault, or of having a sexual relationship with a student. They also define penalties for failing to report abuse. It remains to be seen whether these measures will have the intended impact. After the act was passed, Human Rights Watch (2001) suggested that the South African government needed to do more to increase awareness of the law among school principals and to strengthen enforcement. Institutional reform Efforts to improve the institutional response to gender-based violence range from sensitization and training of staff, sexual harassment policies, curriculum reform, school-wide anti-violence awareness campaigns, counseling and referrals, and broader efforts to reduce discrimination against girls and improve school safety. Initiatives to increase female enrolment by improving girls ’ safety at and on the way to school As mentioned earlier, parental concerns about girls ’ safety in school appears to lower female school enrolment in settings such as South Asia, Africa and the Middle East. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "T. V., Savadogo, A., and Tanaka, T. (2021). Refugees in Chad: The Road Forward. The World Bank. Norman, T., Borjesson, M., and Anderstig, C. (2017). Labor Market Accessibility and Unemployment. Journal of Transport Economics and Policy. Pape, U. J., Petrini, B., and Iqbal, S. A. (2018). Informing Durable Solutions by Micro-Data: A Skills Survey for Refugees in Ethiopia. Peters, S. and Golden, S. (2019). Assessing Mental Health in Gambella, Ethiopia: A Representative Survey of South Sudanese Refugees in Nguenyyiel Camp. St. Paul, MN: The Center for Victims of Trauma. Pimhidzai, O., Chigumira, E., Tesfaye,W., and Yonis, M. (2022). Ethiopia - Rural Income Diagnostics Study: Leveraging the Transformation in the Agri-Food System and Global Trade to Expand Rural Incomes. Washington, D.C.: World Bank Group. https://documentsinternal.worldbank.org/search/33891468 Piper, B., Dryden-Peterson, S., Chopra, V., Reddick, C., and Oyanga, A. (2020). Are Refugee Children Learning? Early Grade Literacy in a Refugee Camp in Kenya. The Journal on Education in Emergencies, Inter-agency Network for Education in References 80 References Emergencies, 5(2). https://doi.org/10.33682/f1wr-yk6y. Ravallion, M. (1998). Poverty Lines in Theory and Practice: Living Standards Measurement Study. LSMS Working Paper, Issue 133. https://documents1.worldbank.org/curated/en/916871468766156239/pdf ReDSS. (2018). Local Integration Focus: Refugees in Ethiopia - Gaps and Opportunities for Refugees Who", "output": {"entities": {"named_data": ["Ethiopia - Rural Income Diagnostics Study"], "descriptive_data": ["Skills Survey for Refugees in Ethiopia"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "##### **September/ October 2023 report**\n\n###### Venezuela\n\nReproductive Health), reinforcing com\nmunity resilience through livelihoods\n\nand access to adequate food, women's\n\nempowerment, gender equality, and\n\nprovision of assistance and protection\n\nservices (including Gender-Based Vio\nlence (GBV), children and adolescents,\n\nand human trafficking), and supporting\n\nemergency education activities with a\n\nsocio-productive approach.\n\n**XIX. Protection Monitoring Tool**\n\n**(PMT)**\n\n**The outcomes of the Protection Mon-**\n\n**itoring Tool (PMT) were shared with**\n\n**cluster partners. The PMT contin-**\n\n**ues to measure primary protection**\n\n**risks, population needs, access to**\n\n**rights, and humanitarian assistance**\n\n**at the community level.** For the period\n\nof March to August 2023, the PMT\n\nrecorded a total of 674 interviews con\n\nprotection work and the advancements\n\nof their workplan along with areas of\n\nresponsibility (AoRs), and the response\n\nmonitoring for August, showing a total of\n\n385,000 people reached by 95 partner\n\norganizations.\n\nAdditionally, the Service Mapping tool\n\nwas shared with partners, and it was\n\ninformed that together with the UNHCR\n\nInformation Management Unit and the\n\nAoRs, a complete update of the services\n\nregistered in the tool was being made.\n\nOrganizations were invited to always be\n\non the lookout to update services and to\n\nbe able to support the dissemination of", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "`o` Data was collected through phone calls made in November 2021 focusing on the families that received\nassistance during the second semester of 2021.\n\n`o` 15 enumerators supported this exercise (10 women and 5 men).\n\n_[2 UNHCR Global Focus, available at https://reporting.unhcr.org/brazil#toc-populations](https://reporting.unhcr.org/brazil#toc-populations)_\n\n_[3 Full text of this research is available at: https://openknowledge.worldbank.org/bitstream/handle/10986/35358/Integration-of-Venezuelan-Refugees-and-Migrants-in-](https://openknowledge.worldbank.org/bitstream/handle/10986/35358/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf?sequence=1&isAllowed=y)_\n_[Brazil.pdf?sequence=1&isAllowed=y](https://openknowledge.worldbank.org/bitstream/handle/10986/35358/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf?sequence=1&isAllowed=y)_\n\n_4 For more information, see_\n_[https://app.powerbi.com/view?r=eyJrIjoiMmVmNGNkOWEtZjQ2Yi00ZjFlLWExMzQtMjAxNjg2YjMxMzM3IiwidCI6IjE1ODgyNjJkLTIzZmItNDNiNC1iZDZlLWJjZTQ5Y](https://app.powerbi.com/view?r=eyJrIjoiMmVmNGNkOWEtZjQ2Yi00ZjFlLWExMzQtMjAxNjg2YjMxMzM3IiwidCI6IjE1ODgyNjJkLTIzZmItNDNiNC1iZDZlLWJjZTQ5YzhlNjE4NiIsImMiOjh9)_\n_[zhlNjE4NiIsImMiOjh9](https://app.powerbi.com/view?r=eyJrIjoiMmVmNGNkOWEtZjQ2Yi00ZjFlLWExMzQtMjAxNjg2YjMxMzM3IiwidCI6IjE1ODgyNjJkLTIzZmItNDNiNC1iZDZlLWJjZTQ5YzhlNjE4NiIsImMiOjh9)_\n\nwww.unhcr.org 3", "output": {"entities": {"named_data": ["UNHCR Global Focus"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the nearest international border but not within a radius of 20km from the Zone capital city and 10km from Woreda city. We classify level four as “remote”; that is, not located 30km from the nearest international border, not within a radius of 20km from the Zone capital city, and not 10km from Woreda city. The market access indicator in Ethiopia is measured at the Woreda level. Accessibility for a Woreda is estimated as the sum of the travel time of the weighted population to the destination Woredas. With Woreda- to-Woreda origin-destination matrices, we calculate market accessibility by the following equation (Donaldson and Hornbeck, 2016; World Bank, 2019b): where is market access at Woreda “o”, is the trade cost between two Woredas “o” and “d”, is the population of Woreda “d”, and is the trade elasticity. Trade costs between two Woredas, is defined by =exp ( ) with = 0.02 and the optimal travel time between Woredas using the transport network of 2020. The trade elasticity, has a value of 8.28 (Eaton and Kortum, 2002). Box 6.1: Measurement of proximity and market access index in Ethiopia 37 43 19 To Woreda City 1-10km 10-20km >20km 1-20km 20-100km >100km 1-30km 30-50km >50km", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 5: Significant Historical Crises as a Share of Total Forced Displaced 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes IDPs protected or assisted by UNHCR, asylum-seekers and refugees. Excludes IDPs not protected or assisted by UNHCR and Palestinian refugees under UNRWA ’ s mandate. Figure 6: Top 15 Countries of Origin end-2015 Source: IDMC Global Report on Internal Displacement 2016, UNHCR Global Trends 2015, UNRWA A small number of countries carry the burden of hosting the majority of refugees. Historically since 1991, 15 asylum countries, overwhelmingly in the developing world, have hosted more than 50 percent of refugees and asylum-seekers (see Figure 7). 44 By the end of 2015, while almost all countries in the world were hosting refugees, the burden was unevenly shared (see Figure 8). Only seven countries hosted more 44 Major host countries are identified based on the cumulative number of refugees and asylum-seekers over the period 1991-2015. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "##### **September/ October 2023 report**\n\n###### Venezuela\n\nedits to some of the objectives and to the\n\ntimeline stipulated.\n\n**IV. GPC Townhall**\n\n**On October 18th a Townhall was**\n\n**organized by the Global Protection**\n\n**Cluster (GPC) with Field Operations.**\n\nHRP 2024 key messages/instructions\n\nwere discussed and a calendar of\n\nbi-monthly GPC Townhalls with field\n\noperations with planned key topics were\n\npresented. The meeting had the aim to\n\nunderstand the different timelines and\n\nongoing HPC processes in each field\n\noperation as well as provide support to\n\nwhom needed it.\n\n**V. La Carnada Play**\n\n**The Working Group on the Prevention**\n\n**and** **Response** **to** **Trafficking** **in**\n\n**Persons** **(WGTiP)** **organized** **and**\n\nFora for the HNO process are in line with\n\nProtection needs in their area.\n\nThe Protection Cluster assured partic\nipation in the weekly ICCG meetings\n\nwhere HPC ongoing activities have been\n\ndiscussed and planned.\n\nFinally, bilateral meetings have been\n\norganized with PC’s partners NRC and\n\nOHCHR. With both partners their protec\ntion programming has been discuss and\n\npossible coordination in the Cluster.\n\n**III. Accountability for Affected**\n\n**Populations (AAP) WG**\n\n**The PC has participated actively in**\n\n**the monthly meetings conducted by**\n\n**the AAP working group, which aimed**\n\n**at increasing the inputs of affected**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2023c; Peters and Golden, 2019). Over 90 percent of South Sudanese refugees in Ethiopia are in the Gambella region, a multi-ethnic region dominated by two ethnic groups: the agro-pastoralist Anywaa (Anyuak) and pastoralist Nuer (Hagos 2021). While these groups have a long history of peaceful coexistence, they also have a history of conflicts over land and water resources and political representation (Vemuru et al., 2020; Hagos, 2021). The Anywaa were the majority of the population until the mid-1980s, when an influx of South Sudanese refugees shifted the demographic composition towards Nuer (Feyissa, 2015). This trend continued with the influx of more South Sudanese refugees in 2013, creating a sense of marginalization among the Anywaa in terms of changes in demographic composition, widening educational disparities, and increasing insecurity (Vemuru et al., 2020). The struggle between these two ethnic groups in the Gambella region has created socio-political tensions and influenced South Sudanese refugees’ social integration (ReDSS, 2018). Box 7.1: Socio-political tensions in the Gambella Region Figure 7.3: Host response to “Refugees are good people” by gender Source: World Bank Staff based on SESRE 2023. Figure 7.4: Host response to “Would you feel comfortable having a refugee as a neighbor?” by gender Source:", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These 25 were included in the surveys but have been dropped from the analysis because they were not assigned randomly. 10 The reasons given for not entering the training included: 1) they were back in school, 2) they had moved to a distant location, 3) they were seriously ill, 4) they had found full-time work, 5) they were not interested or able to make such a big time commitment, or 6) they could not be located despite numerous efforts. 11 This includes 1155 of the original 1273 assigned to treatment, plus 36 out of the 39\"replacements\"- young women from the control group who were offered a chance to be reassigned to round 1. 12 Note that a previously released baseline report for this evaluation was based on 2008 observations. However, after cleaning, 4 were found to be duplicate observations and 15 were not found in the program data and hence were dropped from the midline analysis. This leaves 1989 baseline observations that are included in the midline analysis. 13 These are cases in which the adolescent girl was interviewed but the household head interview was not conducted because the head was not available, could not be found, or declined to take part. 14 This suggests that the loss of the 118 young women who were selected but declined to take part, and the 60 who started but did not complete the program, does not bias the results. 8", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Afrobarometer finds that the vast majority of Malians want their country to remain a single and unified nation and that the attempt by armed groups to create a breakaway state in Mali ’ s northern territories is decisively rejected. See Afrobarometer Policy Paper 10 (Dec 2013). This difference with the Afrobarometer survey can be explained by the fact that the latter survey only focused on Malians inside the country and did not take the views of refugees into account. 5 93 2 6 86 75 20 2 3 94 Independence of the North Autonomy of the North Establish full government control over the North Figure 19: How do you envision the future of Mali? IDPs Refugees Niger Refguees Mauritania Returnees", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Colombian and Venezuelan migrant children and adolescents. VII Discussion In this study, we analyze novel and unique data on forcibly displaced migrants and hosts, focusing on children and adolescents, to highlight the disparities in human development between them. The structure our analysis in two parts. In the first part, we characterize the main trends in the data. We show that forcibly displaced households have a wealth distribution skewed towards lower values relative to Colombian households. This is likely explained by the assets ownership loss that forcibly displaced households expe- rienced after the migration episode. We also identify meaningful lags in human capital accumulation between Colombian and Venezuelan children and adolescents of approxi- mately 1 year. We further note that the Colombian government ’ s supportive policies for Venezuelan forced migrants are evident through high levels of service access and pro- gram participation for migrants. Nevertheless, it remains surprising that participation is not higher, suggesting significant potential for improvement in increasing sisb ´ en and health insurance enrollments. In a second part of our analysis, we document sizeable lags in physical and cognitive de- velopment of Venezuelan children and adolescents, relative to their Colombian counter- parts. However, we were not able to identify any gaps in the socioemotional and mental health between the two groups. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Due to our research design and the complexity of introducing a third grouping who would play outgroup in the behavioral experiments regardless of partner identity, we exclude PRL from the main analyses presented here. 5 Data were also collected six months after the end of the training but this was heavily disrupted due to the outbreak of the COVID-19 pandemic. In Lebanon, this resulted in a change to the method of data collection (from in-person to telephone) and in Jordan, an end to data collection entirely. In Jordan, this had a more pronounced effect on the control group, due to the scheduling of data collection and implementation of restrictions in Jordan. Given these complexities, we do not present results from these analyses. 6 In addition, we attempted to collect information on the extent of social and economic interactions between hosts and refugees. At baseline, almost 95 % of respondents in both the treatment and control group reported such interactions. For this reason, we do not include this information in these analyses. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "19 As property prices in the worst affected areas reduced the most, low income households responded by moving into low-rent housing being offered in these locations. On the other hand, middle income households moved away to avoid risk, and the wealthy, for whom insurance and self-protection was the most affordable, did not change where they lived. Poor people “ sort ” into low rent locations – which are often at higher risk to natural hazards. The problem is particularly acute in developing countries where there is a divide between the formal and informal markets for land. While formal developments may respect land use regulations, informal settlements are often located in hazard prone locations, such as on hill slopes, close to river banks, or near open drains and sewers. In Dhaka for example, informal settlements are developing across the metropolitan area, with many residents lacking basic public services and in locations at risk from flooding. In fact, most informal settlements do not have access to a public toilet within 100 meters, and 7, 600 households in 44 slums live within 50m of the river (World Bank 2005, Dhaka Urban Poverty Assessment). For the city of Bogotá, we use the same database discussed earlier to examine if poor people are at greater risk from natural hazards – particularly earthquakes. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The JLMPS sample is restricted, after matching it to the 2010 school census, to individuals born in Jordan who are aged 25 to 70 in 2010 and who have nonmissing information on age, subdistrict of birth, years of schooling, father ’ s schooling, mother ’ s schooling, and local supply of schools in subdistrict of birth. 9 These exclusions resulted in a sample of 4, 139 males and 4, 131 females, which are referred to as the male and female full samples, respectively. 7. Because of the absence of annual estimates of subdistrict populations, the population used to normalize the supply of schooling at the subdistrict level is the 2004 population of the subdistrict. There are 86 subdistricts in Jordan. If subdistrict populations are growing at different rates, this could introduce some measurement error of the true supply of schooling available to different cohorts. 8. Secondary schools include both general and vocational secondary schools. Public schools include schools under the jurisdiction of: (i) Ministry of Education, (ii) Ministry of Higher Education, (iii) Ministry of Defense, (iv) Ministry of Social Development, (v) Ministry of Religious Endowments (Awqaf), and (vi) UNRWA. 9. The original sample size of all individuals who are aged 25 to 70 years in 2010 and are born in Jordan is 8, 312 observations. The sample restrictions on the missing values result in the exclusion of 34 observations (missing age), 1 observation (missing father ’ s schooling), and 7 observations (missing mother ’ s schooling). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["JLMPS", "2010 school census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The FAO estimates of the hunger rate combine aggregate food balance sheets for every country with\n\nrepresentative household consumption expenditure surveys (HCES) available for 129 developing\n\ncountries. [1] These HCES are already being used to monitor global poverty trends (Chen and Ravallion", "output": {"entities": {"named_data": ["HCES"], "descriptive_data": [], "vague_data": ["food balance sheets"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Keywords: HIV, AIDS, behavioural surveillance survey, humanitarian emergency,\nconflict, post-conflict, refugee, IDP, methodology and quality\n\nCorrespondence: Paul B. Spiegel, MD, MPH, UNHCR, Case Postale 2500, Geneva 1211,\nSwitzerland. Tel: 41 22 739 8289. Fax: 41227397366. E-mail: spiegel@unhcr.org\n\nISSN 1744-1692 print/ISSN 1744-1706 online # 2006 Taylor & Francis\nDOI: 10.1080/17441690600679764", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["behavioural surveillance survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "from UNHCR items valued using SESRE prices Food expenditure (UNHCR in-kind + cash): aggregate food expenditure from UNHCR items valued using SESRE prices plus the cash equivalent transfers Annexes 126 Food aid data received from WFP Food aid information received information from the WFP includes five food items and their quantities distributed to refugees: cereal (mainly wheat but in some camps rice), pulses (mostly yellow split peas), CSB+, vegetable oil, and salt (Table E.5). The per person per month aid (in-kind) are converted into annual values and mapped them to closest food item in SESRE (this was not straightforward as the items are different). We assumed a 50 percent ration until November 2022 (for our Oct/Nov sample) and an 84 percent after Dec 2022 (Dec, Jan, and Feb sample). The WFP data are converted to annual values using household size and prices from SESRE. The data source is the “Revised cash transfer value from Oct 2022_refugee camps” file received from the WFP document that helps to get information regarding the changes in cereal cash equivalent – data on cereal cash equivalent for cash camps which is used to calculate cereals provided in those camps and cash transfer value per year.", "output": {"entities": {"named_data": ["Revised cash transfer value from Oct 2022_refugee camps"], "descriptive_data": ["cereal cash equivalent for cash camps"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 Background Information 2. 1 Characteristics of West Bank and Gaza ’ s labor markets The labor markets of the West Bank and Gaza exhibit features typical of the broader Middle East and North Africa (MENA) region, but also have attributes that are highly unique. Additionally, important differences exist between the West Bank and Gaza. This section provides an overview of these characteristics. We use data from the Labor Force Surveys (LFS) of the West Bank and Gaza and we focus on 20-59 years old men. In Section 3. 1, we provide more information about the data sources and sample selection. We divide each labor market into five mutually exclusive and jointly exhaustive states: public sector employment, private formal sector employment, private informal sector employment, unemployment, and out of labor force. 1 We focus our discussion exclusively on men, as women ’ s labor force participation in both the West Bank and Gaza is very low, never reaching values above 25 %. This low participation rate is common in MENA countries and makes the role of the pandemic on women ’ s labor market outcomes relatively less important than other, more relevant structural factors. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Labor Force Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This database lists seven categories: refugees, asylum-seekers, returned refugees, internally displaced persons (IDPs), returned IDPs, stateless persons and others of concern. For each group the database provides yearly information about their composition by loca- tion of residence and origin. We exploit only the data on “ refugees ”. 22 In particular, we are interested in the annual stock of refugees for each country of residence, i. e. how many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number of IDPs is extremely high in some instances but cannot be captured with the same level of conﬁdence as refugees generally. 23 Cross-country data about conﬂict is provided by the UCDP / PRIO. As for the index of country-level economic activity, we use again information provided by the Penn World Table and World Bank databases. As mentioned above, our aim is to explore the dynamics of refugees during conﬂicts. In other words, we attempt to answer several questions. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["World Bank databases", "Penn World Table"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 15 20 25 30 35 40 45 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Refugees Hosts In camp Addis Ababa Total Sanitation problem Long waiting time Shortage of health professionals Too expensive Shortage/unavailability of medicines Unavailability of laboratory Shortage of medical equipment Lack of cooperativeness of the staﬀ Percent Percent Figure D.8: Problems faced in health institutions Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Government/public Private Mission/Religious/NGO Other Percent Figure D.7: Type of health institutions Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Female Male Percent Figure D.9: Stunting by gender of children Source: World Bank Staff based on SESRE 2023. a. Faced any problem b. Types of problems faced Annexes 103 - 500 1,000 1,500 2,000 2,500 3,000 3,500 Hosts Refugees Hosts Refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "include the rice price — which appears with the wrong sign but is only marginally signiﬁcant — and elevation and population density — which are no longer signiﬁcant. Comparing Tables 7 and 5, we ﬁnd that in the smaller NLSS 2002 / 3 dataset none of the anticipated consumption variables is statistically signiﬁcant. Other results are as before. 4. 4 Magnitude To assess the relative magnitude of our results, we multiply coeﬃcients estimated in Tables 4 and 5 by the standard deviation of their respective regressors. We then average over the various regressions reported in Tables 4 and 5. Calculations are summarized in Table 8. The larger the value, the more inﬂuence the regressor has on the choice of a destination district. We see that the most important regressors in terms of magnitude are travel time to the near- est road, elevation, language similarity, and the price of rice. Consumption variables have an eﬀect on migration destination that is smaller in magnitude: a one standard deviation increase in anticipated relative consumption, for instance, has an eﬀect on destination that corresponds to a third of the eﬀect of a one standard deviation in elevation — and one-sixth of a one stan- dard deviation in distance from the nearest road. Income variables have a negligible eﬀect on migration decisions.", "output": {"entities": {"named_data": ["NLSS 2002 / 3 dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "No data on religiosity was collected as part of the census and a more recent and detailed household survey that includes information on time-use elicits little variation — everyone reports high mosque attendance and regular prayers. An alternative, suggested by David Evans at Harvard University, which we pursue here, is to use recent developments in the use of “ names. ” Research by Fryer and Leavitt (2004) demonstrates the increasing use of names to define race identity in the United States. We postulate that households who named (at least) one child “ Osama ” (also spelt Usamah, Usamma or Usama) are more likely to favor a radical brand of Islam. The use of the name Osama was minimal until 1998, and then peaks in 1998 and 2001, following disruptive events. Of course, the naming of the child may reflect name recognition rather Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "34 Any statistics on the imputed welfare will based on the set of imputed welfares for each household. The estimator takes the form, with R denotes the number of simulation: ܪ ෡ ൌ 1 ܴ ෍ ݄ ሺݕ ෤ ௥ ሻ ோ ௥ ୀ ଵ where ݄ ሺݕሻ is a function that converts the vector y with (log) incomes for all households into a poverty measure (such as the head-count rate or bottom 40 %), and where ݕ ෤ ௥ denotes the r-th simulated imputed welfare. Figure 6. Survey-to-Survey Imputation Methodology, an illustration For the case of Turkey, we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey. Income is used instead of consumption for this paper ’ s analysis. The model included variables related to: household demographics (age, gender, age composition, etc.), household characteristics (education, labor activity, etc.), household head ’ s characteristics (age, gender, labor, education, marital status, etc.) and household assets holding (both livestock and durables). Based on that model the simulated values of consumption (at household level) were imputed for the households in the corruption survey. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 9 of 51 Figure 2: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys When these results are analyzed by the type of NSE, we find significant differences between the trends in Part-time and Temporary employment. On the one hand, there is a stable or growing prevalence of part-time employment among the salaried employees, where Uruguay is the only exception, characterized by a decrease in the incidence of this type of employment (Figure 3). In the cases of Peru and Bolivia, we find almost the same prevalence of part-time employment as two decades ago while in the remaining countries (Argentina, Brazil, Chile, Mexico, El Salvador and the Dominican Republic) there is a greater prevalence of part-time employment. Likewise, the prevalence of temporary employment shows a downward trend in most of the analyzed countries in the last 20 years (Figure 4). In fact, three of the five countries in which temporary employment could be identified show a significant drop in the prevalence of this type of employment (Argentina, Brazil, and Chile). El Salvador presents a stable incidence of temporary employment, while Mexico is the only country in our sample for which there is an increase in this type of NSE. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "On average, the tax-to-GDP ratio in Sub-Saharan Africa is around 21 percent, compared with the OECD average of about 32 percent. In Tanzania and Uganda, the total tax share drops to about 10 percent. Historical data suggests that the tax share of many European countries did not reach 15 percent of GDP until World War II when incomes were substantially higher than they are in many African countries (Fjeldstad and Rakner, 2003, 3). The types and amount of taxes citizens pay varies both within and be- tween countries. We do know there are taxes on agricultural crops, but the rates and processes of collection vary within countries (Kasara, 2007). User fees from electricity, water, sanitation, and other services comprise the major- ity of local revenue in South Africa (Hoﬀman, 2007). In Tanzania, Fjeldstad and Semboja (2001) count ten major categories of taxes, eighteen major categories of licenses, forty groups of charges and fees, and seventeen items listed as other revenue sources. In some countries including Kenya, Malawi, 11 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Chew et al. (2018) use a baseline convolutional neural network model on a gridded population sampling frame to select a sample of households in Nigeria and Guatemala. The authors found this technique to be on par with human canvassing in terms of accuracy, and to outperform other machine learning models based on crowdsource or remote sensing data. Grais et al (2007) compared an unweighted random point selection methodology to a random walk in their study of vaccination rates in urban Niger. The authors do not find statistically significant differences between the methods, though the sample size was limited and both methods were non-probabilistic. 3. Design and Field Protocols 3. 1. Experiment Design This paper makes use of a dataset from the purposefully designed methodology experiment conducted in one section of the Protection of Civilians site 1 (PoC1, Figure 1), one of the largest IDP camps in Juba, South Sudan. To generate a gold standard as the basis of comparison, a household census was conducted between August and September 2017. During this exercise, 2, 655 households were interviewed using a questionnaire designed to collect demographic information, dwelling characteristics, household consumption, and perception data. At the end of each census interview, households received a unique barcode that could be used to identify them later in the experiment. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Wages: the earnings measure we use is the response to the question “ how much did you earn from your main job activity during the last month? ” In the LFS 2011 there is further information on how much of that income was irregular, for example a bonus payment, but the LFS 2014 no longer provides that breakdown. There is also a measure of the “ number of hours per week worked in the main job ” (both usual and total hours), which can be used to construct hourly wages. Since the hours worked measure does not correspond exactly to the earnings measure and introduces additional measurement error, our preferred wage measure is the monthly wage. We exclude wage observations were respondents report having usual working hours of less than 14 or more than 84 hours per week.", "output": {"entities": {"named_data": ["LFS 2014", "LFS 2011"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 10: Social Assessment Summary**\n\nBeneficiary assessments, stakeholder consultations, and a desk review of secondary sources were conducted between September and November of the preparation year to inform the social assessment. Key findings from the beneficiary assessments include: (i) 61 percent of households in the target area report income below the national poverty line; (ii) land tenure insecurity affects approximately 35 percent of households, particularly those without formal documentation; and (iii) access to financial services is limited to approximately 18 percent of households, with mobile money adoption exceeding bank account ownership.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This study aims to assess the consequences of forced migration on ethnic diversity and conflict in Sub-Saharan Africa. We combine a unique dataset on refugee camps with individual data from the Afrobarometer Surveys across 23 African countries for the 2005 — 2016 period. We construct two standard measures of ethnic diversity: indices of ethnic fractionalization (EF) and ethnic polarization (EP). Ethnic fractionalization measures the probability that two individuals drawn from the society at random will belong to two different ethnic groups and thus increases with the number of ethnic groups present. Ethnic polarization captures antagonism between individuals and is maximized when the society is divided into two equally sized and distant ethnic groups. Although these indices have been widely used, little variation over time has been found, making causal inference difficult. The innovative aspect of our analysis is that we use data on the precise locations of refugee camps, their yearly size, and — most importantly — their annual composition in terms of countries of origin. Combined with the Ethnic Power Relations- Ethnicity of Refugees 2019 dataset, we are able to predict changes in ethnic diversity induced by refugee inflows. We then assess the relationship between refugee diversity and the likelihood of conflict. In an additional analysis, we also assess how refugee-induced changes in diversity affect the incidence of theft and violence, participation in protests, and perceptions of ethnic attachment, inter-personal trust, and institutional trust. Other studies have investigated the links between displacement and social conflict or social co- 3 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Afrobarometer Surveys", "Ethnic Power Relations- Ethnicity of Refugees 2019"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "20 capacity to offer these services. Many may be at risk of violence or trafficking. These are very important aspects from the perspective of welfare economists interested in measuring well ‐ being but these measurements are complex and not usually included in multidimensional indicators of deprivation or poverty. The dimensions of deprivations to consider are more numerous and more complex to measure. Again, there is very little research in welfare economics dedicated to the special needs of these populations. Risks and vulnerabilities. The analysis of risk and vulnerability is also much more complex in the context of the forcibly displaced. Welfare economics has only approached these topics recently, in the past decade or so. Essentially, the idea is to measure the risk of being poor or falling poor in the future using cross ‐ section or panel data studying spells of poverty over time. This is work that requires accurate and complex data sets that would be rarely available in a refugee or IDP context. More importantly, the nature of the problem changes. Refugees and IDPs are by definition more at risk and more vulnerable than regular populations and these vulnerabilities are not only linked to skills and efforts but to legal status, discrimination, limited mobility and other factors that are unique or much more acute with refugees and IDPs. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["cross ‐ section or panel data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "most recent year in our CRU dataset). We begin by computing average annual rainfall\n\nand temperature for the nine datasets (CRU; 8 GCMs). Then we use a least-squares fit\n\ngenerates nine benchmark annual datasets - CRU and eight GCMs - for each of the 372", "output": {"entities": {"named_data": ["CRU dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 11: GIS-Supported Targeting Methodology**\n\nBeneficiary targeting in rural subcomponents relied on GIS to overlay poverty incidence maps, displacement flow data, and existing service facility locations. Spatial clustering algorithms were applied to identify communities with the highest unmet need scores within each target district. The GIS analysis was conducted by the Ministry of Planning's geospatial unit using ArcGIS Pro and the outputs validated against field-verification data collected by community mobilizers. GIS-derived targeting lists are subject to a 10% random audit by the Bank's task team during each implementation support mission.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A UNESCO report written during the height of Liberia ’ s second civil war reported that Liberia had one of the lowest gender parity scores globally, and girls were disproportionately represented in the number of out-of-school children in Liberia. This disruption in education presaged lifelong consequences for this cohort of women (Kirk, 2003). In 2007, the literacy rate for adult men was 55 % and women was 41 %-- the number lowering to 26 % for rural women (OECD et al., 2009). In 2001 an education law was enacted making primary education free and compulsory, however, as of 2006, primary school enrollment in urban areas was 63. 7 % for girls and 33. 1 % for girls in urban areas (Woodon, 2012). Liberia also scores in the bottom eight countries that reported women having fewer than half of the years of education as men (Klugman, 2010). Five years after the war ended, women ’ s labor force participant was high, with women accounting for 54 % of the labor force. However, women were disproportionally represented in the informal sector (CWIQ, 2007). According to data from the 2017 Women, Peace, and Security Index, Liberia still has a number of gaps to fill particularly related to education, financial inclusion, legal discrimination and intimate partner violence (WPS, 2017). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of Stunting among under-five Children in Ethiopia: A Multilevel Mixed-effects Analysis of 2016 Ethiopian Demographic and Health Survey Data. In BMC Pediatrics 19 (1). BioMed Central Ltd. .org/10.1186/s12887-019-1545-0 Ginn, T., Resstack, R., Dempster, H., Arnold-Femandez, E., Miller, S., Guerrero, M., and Kanyamanza, B. (2022). 2022 Global Refugee Work Rights Report. Center for Global Development, Refugees International, and Asylum Access. Ginn, T. (2023). Labor Market Access and Outcomes for Refugees. World Bank – UNHCR Joint Data Center. Quarterly Digest on Forced Displacement, Seventh Issue. Washington, D.C.: World Bank Group. Giordano, N., Ercolano, F., and Makhoul, M. (2021). Skills and Labour Market Transitions for Refugees and Host Communities: Case Studies and Country Practices on Including Refugees in Technical and Vocational Education and Training (TVET) and Employment. UNHCR Gobillon, L. and Selod, H. (2014). Spatial Mismatch, Poverty, and Vulnerable Populations. In: Fischer, M.M., Nijkamp, P. (eds) Handbook of Regional Science. Springer, pp.93-107. https://doi.org/10.1007/978-3-642-23430-9 Hagos, S. Z. (2021). Refugees and Local Power Dynamics: The Case of The Gambella Region of Ethiopia. Hahn, E., Richter, D., Schupp, J., and Back, M. D. (2019). Predictors of Refugee Adjustment: The Importance of Cognitive Skills and Personality. Collabra: Psychology, 5(1), 23. Hainmueller, J., and Hopkins, D. J. (2014).", "output": {"entities": {"named_data": ["2016 Ethiopian Demographic and Health Survey Data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Wages: the earnings measure we use is the response to the question “ how much did you earn from your main job activity during the last month? ” In the LFS 2011 there is further information on how much of that income was irregular, for example a bonus payment, but the LFS 2014 no longer provides that breakdown. There is also a measure of the “ number of hours per week worked in the main job ” (both usual and total hours), which can be used to construct hourly wages. Since the hours worked measure does not correspond exactly to the earnings measure and introduces additional measurement error, our preferred wage measure is the monthly wage. We exclude wage observations were respondents report having usual working hours of less than 14 or more than 84 hours per week. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The UNCRPD defines a person with disability “ _as those who have long-term physical, psychosocial, intellectual or sensory_\n_impairments which, in interaction with various barriers, may hinder their full and effective participation in society on an equal_\n_basis with others_ ”. However, obtaining accurate data on persons with disabilities in South Sudan remains a challenge. The\nlatest reliable data originates from the 2008 census, reflecting a disability prevalence of 5.1%. In the absence of current data,\nthe World Health Organization (WHO) approximates that 16% of any population has a disability. Nonetheless, collaborative\ninsights from various humanitarian and development organisations suggest this estimate might even be higher, given the\ninfluence of additional factors such as poverty, conflict, climate shocks, and inadequate healthcare facilities. An\nanthropological study conducted in Pibor in 2022 by Humanity and Inclusion (HI) underscores the interplay of poverty,\nprolonged conflict, and societal barriers leading to exclusion and marginalization. [vi]\n\n**BARRIERS TO AN EFFECTIVE SYSTEM TO ENSURE THE RIGHTS OF PERSONS WITH DISABILITY AND OLDER PERSONS**", "output": {"entities": {"named_data": ["2008 census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "GIS layers depicting soil type, slope gradient, and land cover were used by the project's environmental specialist to conduct a preliminary erosion risk assessment for all proposed earthworks sites. Sites classified as having a high erosion risk under the GIS analysis were required to submit an erosion and sediment control plan developed by a qualified engineer before earth-moving works could begin. The GIS-based erosion risk layer was created by combining existing national datasets using weighted overlay analysis in QGIS.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Refugees and Host Communities Household Survey expanded the national Household Consumption and Informal Sector Survey to include a representative sample of refugees and host communities, including Sudanese and host communities located in the east of the country. The remainder of this note is organized as follows. Section 2 presents a short discussion of the literature on the economic participation of refugees. Section 3 compares the characteristics of newly arrived refugees from Sudan with previous arrivals for whom survey data is available, to find that both groups are highly comparable. Section 4 uses the existing data to explore how the basic needs refugees are covered from own-income. Sections 5 and 6 dig deeper by exploring econometrically the correlates of higher incomes of refugees. A discussion of the results and their policy implications follows in section 7, after which section 8 concludes. 2. Benefits of economic participation of refugees Whether or not the arrival of Sudanese refugees in Chad contributes to economic growth is of limited immediate relevance as concerns about the safety of fellow humans drive the response. Nor does any decision maker suggest that hosting refugees is a development strategy Chad should pursue. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Refugees and Host Communities Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10044 Most refugee hosting communities are characterized by high levels of poverty with precarious livelihood conditions, low access to public services, and underdeveloped infra­structure. While the unexpected inflow of refugees might bring both constraints and opportunities for improving and maintaining local livelihoods in these communities, the understanding of these effects remains limited. Using a household level micro data set from a 2018 baseline survey of the Ethiopia Development Response to Displacement Impacts Project, this paper assesses the impact of refugee inflow on the livelihood strategies of host communities with respect to diversification and agricultural commer­cialization. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "“relaxed” definition of labor force participation includes anyone who is available to work. The “strict” definition of labor force participation includes only those who are available to work and also actively searching for work. Employment-to- population ratio is the share of working-age people who are employed. 41 In Ethiopia, in-camp refugees can work as incentive workers, with standardized pay scales according to their skills, in different organizations, including RRS, a government entity. Thus in-camp refugees who indicated that they work in the public sector were assumed to be incentive workers under RRS. 0 10 20 30 40 50 60 70 80 90 100 Camp Hosts Camp Refugees Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure 3.3: Work type Source: World Bank Staff based on SESRE 2023. Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals 0 10 20 30 40 50 60 70 80 90 100 Camp Hosts Camp Refugees Percent Figure 3.4: Occupation Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 29 Female refugees have high employment rates relative to male refugees of all ages and make a critical contribution to refugee household incomes. Figure 3.5", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Papaioannou, 2016; Berman et al., 2017; Harari and Ferrara, 2018; Eberle et al., 2020; McGuirk and Burke, 2020b). As a further robustness check, we also use data on conflict incidence and intensity from the UCDP, which uses a more conservative definition of conflict. The UCDP dataset is manually curated and compiled with automated computer assistance (Sundberg and Melander, 2013). The UCDP defines an armed conflict event as “ an incident where armed force was used by an organized actor against another organized actor, or against civilians, resulting in at least one direct death at a specific location and a specific date ” (Pettersson et al., 2020). We extract daily event observations from the UCDP dataset if the location of the actual event is exactly known, the event location is within a radius of less than 25 km around a known point, or at least the administrative district where the event happened is known. As pointed out by Eberle et al. (2020), the UCDP events are more likely to capture violence between large-scale and more structured groups. Table B. 2 shows that on average, conflict events seem to occur more in refugee-hosting areas. This is of course not a causal interpretation but a simple correlation. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["UCDP dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 people may also gain from conflict in terms of income and wealth and this may explain why some people do not move. The defining attributes of the alternative choices are very different from any other model and the task of economics is to understand what these defining attributes should be. In terms of independent variables, “ push ” factors become more important than “ pull ” factors in forced displacement models. The intensity of a conflict may be more important than the income opportunities in potential destination areas. In addition to the classic socioeconomic variables, risk aversion, stress, anxiety, other traits of personality and behavioral factors in general have to be well understood and measured. Hence, one could think of four essential blocks of independent variables including individual or household socioeconomic characteristics, “ push ” factors, “ pull ” factors and behavioral factors. Also, access to and dissemination of information related to the conflict in the place of origin but also in the potential places of destination may be crucial for people to make choices. This is where social psychology, behavioral economics and neuroeconomics may offer insights into such choices. Forced displacement data are also unusual in their form. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Forced displacement data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Table 2: Ethnic composition of IDPs, refugees, returnees in the North Ethnicity IDPs in Bamako (%) Refugees Niger (%) Refugees Mauritania (%) Returnees (%) Total I + R + R (%) Ethnic composition of the North (%) Songhai 75 21- 71 43 45 Kel Tamasheq 12 56 69 12 38 32 Arab 3- 28 4 11 3 Peulh 4 21- 6 4 7 Other 6 11 3 7 4 12 Total (%) 100 100 100 100 100 100 Total (n) 100 81 100 220 501 1, 268, 009 Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census. The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94 % of returnees were displaced within Mali. Only 6 % returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5 % of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2 % lived in Gao town and 1 % in Kidal town. The remaining 92 % lived in 27 different towns and villages in northern Mali, locations not covered by the survey.", "output": {"entities": {"named_data": ["Displaced People Survey", "Housing Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The data on total gross proﬁts and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key diﬀerence from the Chamber of Commerce data is that the sales and proﬁts data include all businesses including self- proprietorships. 9 Data were provided for the years between 2010 and 2014 and are re- ported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by deﬁnition and they are likely to be less accurate and complete for smaller ﬁrms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and proﬁt ﬁgures. 10 We scale the variables according to province size by dividing sales and proﬁts by the pop- ulation of the provinces. If we use sales and proﬁts in absolute terms, we get qualitatively similar results. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Chamber of Commerce data"], "vague_data": ["administrative taxation data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of the groups and the distances between them. For instance, (Bazzi et al., 2019) shows that polariza- tion increases ethnic attachment. Others have highlighted the reduction in trust, either interpersonal trust or institutional trust Alesina and Ferrara (2002); Beugelsdijk and Klasing (2016). To assess the importance of alternative explanations, we first replicate our analysis using individual data on violence. In addition to participation in protests, we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault. We then assess the relationship between the revised refugee diversity indices and alternative individual outcomes such as ethnic vs. national identity, generalized trust, trust in neighbors, and institutional trust (trust in government). The questions from the Afrobarometer mentioned below are used as a proxy for these outcomes: 32 1 Attack: Over the past year, how often (if ever) have you or anyone in your family: Been physically attacked? 2 Crime: Over the past year, how often (if ever) have you or anyone in your family: Feared crime in your own home? 3 National identity: Let us suppose that you had to choose between being a [Ghanaian / Kenyan / etc.] and being a [respondent ’ s identity group]. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Afrobarometer survey data"], "descriptive_data": [], "vague_data": ["individual data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "60 80 100 Camp Hosts Female Camp Refugees Female Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals Percent Figure 3.8: Female occupations Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 31 0 10 20 30 40 50 60 Camp Hosts Male Camp Refugees Male Camp Hosts Female Camp Refugees Female Figure 3.11: Hourly earnings 0 5 10 15 20 25 30 35 40 45 Camp Hosts Male Camp Hosts Female Camp Refugees Male Camp Refugees Female Figure 3.10: Hours per week Source: World Bank Staff based on SESRE 2023. Note: Hourly earnings are past-month earnings in the main occupation in Birr divided by the typical hours worked in a month over the past year. Only refugees working outside of camps start to see earnings improve with schooling. Working outside the camp is associated with a 42 percent increase in earnings. Most importantly, the relationship between completing secondary and post-secondary schooling and wage earnings becomes significant only when the refugee sample is restricted to workers outside the camps (see Annex D, Table D.7). This indicates that refugees only benefit from education and are incentivized to invest in education", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The 355 municipalities are grouped into 25 GGD areas, each covering a population of approximately 600,000 inhabitants. The GGD borders are visible in figure 5 which visualizes the hospital admissions. The data is combined with demographic statistics (2019) obtained from the Dutch Central Bureau of Statistics.\n\nA number of surveyed health\nstatistics (2016) have been obtained as well from the RIVM (maps can be viewed in the\nsource link). [11] . The data is based on a survey of 457,000 people and includes the share of\npopulation in each district with a documented long-term illness (illnesses over 6 months), the\nprevalence of overweight and obesity, alcohol abuse, smoking and noise due to traffic.\n\nFor the main analysis, annual average particulate matter concentrations from the RIVM are used to capture long-term exposure (2017, published September 2019).\n\nhe temporal lag in the pollution data also ensures that there is no endogeneity due to feedback between case incidence and changes in pollution levels that follow lock-down policies. To test whether the main findings of the analysis generalize to other pollution data sets, a second analysis presented in the appendix uses the coarser grids from the global PM2 _._ 5 data set of van Donkelaar et al. (2016).\n\nCases are reported to the RIVM by the Municipal Health Service (GGD).The GGD is organized as collaboration between municipalities to provide base level public health service", "output": {"entities": {"named_data": [], "descriptive_data": ["annual average particulate matter concentrations from the RIVM"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In doing so, this paper also relates to recent research using firm-level data to document effects of foreign acquisitions on firm performance, including Arnold and Javorcik (2009), Bloom et al.\n\nIn order to estimate equations (1), (2) and (3), we use data from the Orbis database\nof Bureau Van Dijk. The Orbis dataset provides retrospective information on\ncompany ownership and reports the country of origin of each foreign shareholder,\nalthough the share value is often unknown.\n\nThe data covers about 58% of Polish firms as reported by Eurostat and coverage varies significantly across regions (Farole et al., 2017). [5] Figure A.4 shows the evolution of the percentage of companies with at least one foreign shareholder from either Germany, French, the United States or the United Kingdom.", "output": {"entities": {"named_data": ["Orbis database"], "descriptive_data": [], "vague_data": ["firm-level data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5 interviewed (i. e., on the premises when the enumerators began their interviews) were included in this survey. Enumerators selected households within the catchment area randomly. Among households with an age-eligible child present (i. e., 0-35 months or 36-72 months), enumerators explained the purpose of the study and invited the target child ’ s primary caregiver to be interviewed. Respondents were supported to withdraw their consent anytime during or after the interview and such cases were dropped from analyses. For children 0-35 months of age, assuming a minimum power level of. 8 and an alpha of. 05, the study design predetermined that the minimum desired detectable difference in average ECD Z- score measured by the Caregiver Reported Early Development Instrument (CREDI) was a 0. 2 standard deviation (SD) difference, which has been shown to be the average difference between urban and rural Pakistani children assessed using the CREDI (Hentschel et al., 2024). For children between 36 and 72 months of age, assuming a minimum power level of. 8 and an alpha of. 05, the study design predetermined that the minimum detectable difference in average ECD score (measured by the AIM-ECD score) is a 0. 23-point difference (the difference between males and females in Hentschel et al., 2024). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 households in each. To select a starting point the enumerator used a code of the day16 and chose every second house in rural areas and every fifth house in urban areas. The selection of individuals within the household to answer the questionnaire was conducted as follows: the head of household (male or female) was selected to answer the first part of the questionnaire dealing with general questions about the households. Using the roster of household members which was compiled during the first part of the interview, another member of the household aged 18 or above was selected randomly to answer the second part of the questionnaire in which perception questions were asked. Alternation between male and female was ensured. The survey thus generated data that are reflective of the opinions of those aged 18 and above in northern Mali. To assess the representativeness of the data, which were collected under rather challenging circumstances, the ethnic composition of the sample was compared with the ethnic composition in the North as reported by the 2009 Census. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["2009 Census"], "descriptive_data": ["roster of household members"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In contrast, the self-assessed entrepreneurial score is based on questions asking how well the respondent believes she could perform a series of six tasks related to starting or running a business, and can be considered a task-oriented measure of self-efficacy. The aggregate measure of entrepreneurial ability increased by roughly nine percentage points among EPAG beneficiaries relative to those in the control group, equivalent to a quarter of a standard deviation. Enhancing participants ’ self-confidence to perform these tasks was one of the main immediate objectives of the BDS training program. Table 6B summarizes the results on a series of questions on attitudes and self-confidence that were added during the midline survey only (hence no panel analysis is possible). EPAG graduates report a more positive attitude: they feel more in control and more comfortable, and they have greater confidence in their own business abilities as well as in their personal and social lives. They are also more confident than the control group in their personal relationships with spouses and partners, consistent with the findings in Table 6A. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Nevertheless, we believe that this analysis provides suggestive   13As mentioned earlier, this is per capita income for the three months prior to the ENIGH survey.\n\nCADENA now offers weather index insurance   for a variety of perils (e.g., drought, flood, hail), as well as area-based yield index insurance, which   2Subsidies from the federal government depend on the marginality index of the insured municipality, as computed by CONAPO.\n\nNote. The data used to created this table comes from the 2010 census. % Illiterate is defined as the number of\nilliterate individuals over the age of 15 divide by the total population over 15.\n\n**Karlan,** **Dean,** **Robert** **Osei,** **Isaac** **Osei-Akoto,** **and** **Christopher** **Udry**, \"Agricultural\n\n\nDecisions after Relaxing Credit and Risk Constraints,\" _Quarterly_ _Journal_ _of_ _Economics_, 2014,\n\nIn addition, this study computes environmental risks at the commune level [2] to relate it to household information based on the Vietnam Household Living Standard Surveys (VHLSS) for 2010, 2012, and 2014. Benefiting from the panel", "output": {"entities": {"named_data": ["ENIGH survey", "2010 census", "Vietnam Household Living Standard Surveys (VHLSS)"], "descriptive_data": [], "vague_data": ["marginality index"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For a detailed definition see section 4 Household size Number of people included in the case records of each PA in Individual ProGress dataset Wage Income 1 if the household receives income from employment and / or daily or irregular work Income from remittances 1 if the household receives income from remittances Income per capita Raw sum of household income from all sources; work, pension, assets in Syria transfers, donations, other organizations'humanitarian aid, and other divided by household size Male Adults Number of males above 18 (inclusive) in the household Marital Status Categorical variable. The classification includes married PAs with spouse in the household, married PAs without spouse in the household, widowed, single or engaged, and divorced or separated. Proportion of female Number of female divided by the household size Location Categorical variable for 11 Governorates / cities. Ajloun City, Aqaba, Balqa, Irbid Jerash, Karak, Maan, Madaba, Mafraq, Tafilah, Zarqa. In Camp 1 if the household is located in a refugee camp Poverty before UNHCR and WFP assistance 1 if household expenditure before UNHCR plus WFP assistance is below the poverty line (JD50) Poverty before UNHCR assistance 1 if household expenditure after WFP assistance but before UNHCR assistanc is below the poverty line (JD50) Source: Authors ’ elaboration.", "output": {"entities": {"named_data": ["Individual ProGress dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "obtained from the multilevel logistic model. Thus, I am conﬁdent that I did not lose substantial statistical information by dichotomizing this variable. 6. 2 Independent Variables 6. 2. 1 Socio-Demographic Variables I control for standard socio-demographic variables that can aﬀect citizens ’ acceptance of government ’ s right to make people pay taxes. A question prob- ing respondents on their household income was not included in the fourth round of Afrobarometer surveys. Asking respondents to quantify their in- come can be problematic in the context of developing economies, where in- dividuals are often embedded in barter or commodity exchange, rather than, market economies. There are, however, reasonably good proxies including whether respondents own a television, radio, car, and mobile phone, and use the internet. Age, education, employment, and urban or rural residence are demographic factors that also aﬀect household resources. 6. 2. 2 Experience with Paying Taxes or Fees It is diﬃcult to assess just how ubiquitous taxes are in ordinary Africans ’ lives. There has not been any systematic eﬀort to take stock of the types and amount of taxes citizens pay across Africa. Similar to pre-modern European states, African states ’ revenue raising capacity is generally low.", "output": {"entities": {"named_data": ["Afrobarometer surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These include topics like labor, finance (e. g., credit, debt, banking), agriculture / livestock / fishery, and education whereas FDP datasets are relatively rich in health, food insecurity, and water and sanitation in addition to coping strategies and protection. Furthermore, a large majority of questionnaires for these existing micro-level datasets do not include a core set of questions needed for proper identification of displacement status. This calls for further efforts 2 See https: / / www. unhcr. org / news / stories / unhcr-s-grandi-110-million-displaced-indictment-our-world. 3 The UNHCR MDL is available at https: / / microdata. unhcr. org / index. php / about. 4 The WB MDL is available at https: / / microdata. worldbank. org / index. php / about. 5 We focus on low-income and middle-income countries because they account for roughly 95 percent of FDP (refugees + IDPs) in the world based on data from UNHCR Refugee Finder and IDMC. 6 A representative sample means that when analyzed, the observed characteristics of the sample reflect the true characteristics in the target population that is being researched (Baal and Ronkainen, 2017). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The weight given to each province in constructing the synthetic control is based on pre-treatment outcomes. We use the pre-treatment average of the outcome dimension, Y, the unemployment rate, employment rate and the import and export per capita of the province to determine the degree of similarity between control group provinces and the treated provinces, which in turn determines the weight assigned to control provinces. The unemployment and employment rates are included to control for the general economic performance while trade values are added to control for the degree of ’ openness ’ of the province. 6 The treated unit i = 1 is constructed by taking the mean of the outcome variables in the provinces hosting refugees in 2012 or 2013. 5 Data We use several data sources for the analysis. The IV estimations use data from years 2011 and 2014 while the DD estimations use data from 2009 to 2014. The numbers of refugees up to 2012 are treated as 0. The refugee data for 2012 and 2013 are obtained from UNHCR ’ s oﬃcial weekly statements in December. Data on the number of refugees in 2014 is from Erdo ˘ gan (2014), who uses statements released by the Ministry of the Interior to compile his data. All refugee data we use in the analysis is provided at the level of 81 provinces.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "_replications. Contemporaneous cross-unit correlations are generated by computing the cross-correlations of residuals from_ _AR(I) regression of the first difference of Gross State Product for 20 U.S. states from 1963 to 1986 and of Gross Domestic_ _Product for 24 O.E. C.D. economies from 1960 to 1991._\n\nareas, of which 72 percent depends on agriculture (Census of India, 2011). Agriculture accounts for 17 percent of GDP and employs 51 percent of the total labor force (World Bank, 2010). [2]\n\n_Source_ : Author's analysis based on the 1982 and 2006 rounds of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS)   from the National Council of Applied Economic   Research.", "output": {"entities": {"named_data": ["Census of India", "ARIS-REDS", "Additional Rural Incomes Survey and Rural Economic & Demographic Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "<!-- PAGE 10 -->\nILRI, and the World Bank (as part of the LDIA and LSMS-ISA projects [2] ) to start the survey\n\nvalidation work that is documented in this paper.\n\n\n2.2 _Milk production recall methods_\n\nUsing data from two microenterprise surveys in Sri Lanka, De Mel et al. (2009) find that\n\nthe same survey experiment in Tanzania to obtain evidence on the nature of measurement\n\nScott and Amenuvegbe (1990) conduct an experimental study on 135 households in Ghana.\n\nDantlait survey. The fieldwork was managed by two experienced enumerators, and a", "output": {"entities": {"named_data": ["LSMS-ISA projects", "Dantlait survey"], "descriptive_data": ["microenterprise surveys in Sri Lanka"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "market outcomes (Kotsadam and Tolonen 2016). Mining is also associated with more economic\n\n\nactivity measured by nightlights (Benshaul-Tolonen, 2019; Mamo et al, 2019).\n\n\nKotsadam and Tolonen (2016) use DHS data from Africa, and find that mine openings cause\n\nand GLSS with production data for 17 large-scale gold mines in Ghana. We find that a new\n\n\nlarge-scale gold mine changes economic outcomes, such as access to employment and cash\n\nincrease in total production that rose from 541,147 oz in 1990 to 3,119,823 oz in 2009 according\n\n\nofficial Ghana statistics (Bloch and Owusu, 2012). This production increase led to an increased\n\nSouth Africa\nTeberebie 1990 2005 Anglogold Ashanti South Africa\nWassa 1999 active Golden Star Resources USA\n_Source:_ InterraRMG 2013.\n_Note:_ Active is production status as of December 2012, the last available data point.", "output": {"entities": {"named_data": ["DHS data"], "descriptive_data": [], "vague_data": ["production data", "official Ghana statistics"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "displaced due to armed conflict, situations of generalized violence and violations of human rights. 28 Data on IDPs monitored by IDMC are disaggregated and currently published separately for conflict-induced displacement and disaster-induced displacement. 29 At the country level the IOM ’ s Displacement Tracking Matrix (DTM) 30 provides data on IDPs in both conflict and natural disaster settings (activated in all major natural disaster contexts in recent years). Global data on conflict-induced internal displacement reflect variations in how IDPs are defined across situations. There is no consensus on how far a person must flee in order to be considered internally displaced. The definition of internal displacement for nomadic populations, which account for a significant share of IDPs in the Horn of Africa and increasingly in the Sahel, is open to controversy. 31 Moreover, while some countries register IDP children born in displacement (e. g. Azerbaijan, Cyprus and Georgia), other countries do not (IDMC 2015). The crafting of a definition for IDPs and its application in a particular context may be heavily influenced by local and national politics in conflict and post-conflict countries, as well as the direct link between estimates of displaced populations and humanitarian assistance, which can lead to both over- and under-reporting.", "output": {"entities": {"named_data": ["Displacement Tracking Matrix"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 crop products (e. g., wheat, potatoes) and the value of five livestock products (i. e., milk, egg, butter, hides, and honey) sold in the market. 14 Several other data sources were utilized. First, the Ethiopian refugee camps location data set from the Humanitarian Data Exchange (HDX) 15 and the total number of refugees by camps from the United Nations High Commissioner for Refugees (UNHCR), Addis Ababa office. We use data from 26 official UNCHR refugee camps in Ethiopia that were operational in 2018 (see Figure 1; 3). Second, we use administrative data sets for Ethiopia and refugee source countries from the database of Global Administrative Areas (GADM). 16 We also use the conflict data set from the Armed Conflict Location and Event Data Project (ACLED) 17 and the population data from the Gridded Population of the World (GPW) data set. 18 On the basis of these data sets and the location of sample households from Ethiopia DRDIP data set, we generated the following variables: i) distance of sample households to the nearest refugee camp, the nearest region (administration level 1 in GADM) to the refugee camps, ii) distance of the refugee camps to the nearest border of the refugee source country, iii)", "output": {"entities": {"named_data": ["database of Global Administrative Areas", "Gridded Population of the World (GPW) data set", "Humanitarian Data Exchange (HDX)", "Ethiopia DRDIP data set"], "descriptive_data": ["Ethiopian refugee camps location data set"], "vague_data": ["administrative data sets"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, should ASM respond to large-scale activities, either by increasing or decreasing activity in the close geographic area, we will end up estimating the impact of these sectors jointly. In a later stage, should the opportunity arise, we encourage researchers to try to disentangle the effects of small-scale and large-scale mining. 3 Data To conduct this analysis, we combine different data sources using spatial analysis. The main mining data is a dataset from InterraRMG covering all large-scale mines in Ghana, explained in more detail in section 3. 1. This dataset is linked to survey data from the DHS and GLSS, using spatial information. Geographical coordinates of enumeration areas in GLSS are from Ghana Statistical Services (GSS). 2 Point coordinates (global positioning system [GPS]) for the surveyed DHS clusters3 allow us to match all individuals to one or several mineral mines. We do this in two ways. First, we calculate distance spans from an exact mine location given by its GPS coordinates, and match surveyed individuals to mines. These are concentric circles with radiuses of 10, 20, and 30 kilometers (km), and so on, up to 100 km and beyond. In the baseline analysis where 2 The data was shared by Aragón and Rud (2013) 3 Both the DHS and GLSS enumeration area coordinates have a 1-5 km offset. The DHS clusters have up to 10km displacement in 1 % of the cases. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The authors argue that Croats listened to Serbian radio for its consumption value but reacted negatively to national- istic messages intended for Serbian ears. Election results and street surveys are used to elicit preference for extremist nationalist parties among Croats who are able to listen to Serbian radio and those that do not. The authors ﬁnd that 3 to 4 percent of those 59See Yanagizawa-Drott (2014). 60See, for example, Enikolopov et al. (2011) and DellaVigna and Kaplan (2007) who ﬁnd large eﬀects on voting shares. 70", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["street surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 employment status and education level also significant at 10 %. Marital status and education are important predictors of attrition. As we might expect, these imbalances suggest some threats that, if left uncorrected, could undermine the parallel trends assumption of difference-in-difference estimators. That said, we see no sign of differences between treatment and control, or attritors and non-attritors, over the key GRIT personality features. This suggests that members of the treatment group are not, for example, more motivated to succeed than members of the control group. To account for these biases, we generate a series of inverse probability weights to balance the data. These weights define the probability of an individual with particular characteristics (e. g. host or refugee status) being in each of the treatment and control groups at baseline and endline and are used to rebalance the data in order to closer support the parallel trends assumption. Results are shown in Column 3 of Table 4. Following weighting, data balances on all key factors, including nationality. This suggests that the parallel trends assumption is more reasonable under the weighted dataset than in the raw treatment / control data. 11 Based on these analyses, we conclude that it is safe to use weighted OLS-based approaches. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "not better positioned to work in the formal private or public sector, their participation in trade and service relates to economic 47 This analysis uses the relaxed definition to measure the current employment status of the host community and refugees. 48 As the previous chapter highlighted, most of refugees engaged in agriculture activity are livestock holders (see Chapter 3). 44 37 42 55 LFPR Nearest to Zone Nearest to Woreda but not Zone Nearest to border but not Zone & Woreda Remote 40 40 49 LFPR High accessibility Medium accessibility Low accessibility Figure 6.2: Labor force participation rate by proximity to resource hub, market accessibility Source: World Bank Staff based on SESRE 2023. Note: High, medium, and low accessibility refers to the level of market access, with >0, [-0.5, 0], and <-0.5 standard deviations from the average, respectively. 60 40 56 44 68 32 46 54 Employment rate Unemployment rate Nearest to Zone Nearest to Woreda but not Zone Nearest to border but not Zone & Woreda Remote 64 36 60 40 50 50 Employment rate Unemployment rate High accessibility Medium accessibility Low accessibility Figure 6.3: Refugees’ labor market outcomes Source: World Bank Staff based on SESRE 2023. Note: High,", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Since the poverty headcount only reflects the proportion of poor people, it is usually complemented by a measure of the depth of deprivation, the poverty gap, which estimates the average gap between poor individuals and the poverty line. All poverty measures constructed in such a way are based on an underlying welfare metric. In most cases, the metric is defined at the household level and needs to be transformed into an individual measure. The transformation either divides household welfare by household size, providing a per capita measure or, in a more sophisticated way, takes into account differences in household composition (usually age and gender), leading to a per-adult equivalent measure. To measure household welfare, different metrics have been proposed and can generally be classified either into monetary or non-monetary metrics. Monetary metrics equate levels of well-being to a monetary indicator of utility (Samuelson, 1974), usually income, consumption or expenditure. Non-monetary metrics define normatively dimensions of deprivations, e. g., access to education and clean water, and aggregate them either by using dimension-specific thresholds or an aggregated threshold. The most commonly used approach in this class is the Multidimensional Poverty Index by UNDP and Oxford University (MPI; OPHI, 2018). While it is not uncommon to find experts with a strong preference for one measure over the other, both classes of poverty measures are in most cases complementing each other. While the monetary metric has the advantage of its theory-grounded definition without normative choices, it is complex to measure Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Addis Ababa Eritrean Somali South Sudanese Addis Ababa Eritrean Somali South Sudanese Addis Ababa Has hand washing place/item Water available Detergent available Hosts Refugees Percent Figure D.15: Hand washing facility Source: World Bank Staff based on SESRE 2023. Annexes 105 Table D.5: Labor force statistics by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Participation (strict) 57% 38% 44% 35% 55% 28% Employment (strict) 89% 72% 94% 77% 96% 82% Unemployment (strict) 11% 28% 6% 23% 4% 18% Participation (relaxed) 62% 60% 51% 49% 57% 38% Employment (relaxed) 81% 45% 81% 55% 91% 60% Unemployment (relaxed) 19% 55% 19% 45% 9% 40% Source: World Bank Staff based on SESRE 2023. Table D.6: Determinants of refugee-host earnings gap (1) (2) (3) (4) Earnings Earnings Earnings Earnings Refugee (% difference from hosts) -69.8*** -63.7*** -62.1*** -40.7*** (0.077) (0.123) (0.122) (0.126) Control: Region Yes Yes Yes Yes Control: Demographics Yes Yes Yes Control: Occupation/Sector Yes Yes Sample: Working outside camp Yes Sample Size 743 742 742 572 Source: World Bank Staff based on SESRE 2023. Note: Monthly earnings are collected for employees only (including work for government, NGOs, and private households). Log earnings are winsorized at the 1st and", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Each respondent was presented with three vignettes, where key aspects of the sce- narios were randomly varied across respondents. These three vignettes were designed to probe the impact of diﬀerent pull and push factors on the refugees ’ return deci- sion, allowing us to go beyond the data limitations of the above analysis. That is, the vignettes not only explore the impact of security on return decisions, but also of employment prospects in both the country of asylum and Syria, the status of property in the home community, and the availability of ﬁnancial assistance. In particular, the ﬁrst vignette probes three questions: ﬁrst, whether the ability to 11According to this classiﬁcation, Jebel Saman, Deir-ez-Zor, Homs, Al Ma ’ ra, and Duma are high- conﬂict districts. 16 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "used in the Skills Profile Survey (SPS) and SESRE, we find similar patterns in some of the indicators common in both surveys among in camp refugees such as demographic composition, primary and secondary net enrollments, housing condition, access to basic infrastructures, employment and attitude of hosts toward refugees. Moreover, both surveys find that in camp refugees in Ethiopia are much poorer than host community households and poverty rates are heterogenous across refugee groups: South Sudanese refugees have the highest incidence of poverty, while Eritrean refugees have the lowest poverty incidence amongst the refugees. However, different poverty estimation methodologies and different poverty lines are used. Likewise, both surveys indicate that refugees are more food insecure than the host community. Annex F, Table F.1 shows a summary of these findings. Source: Pape, U. J., Petrini, B., and Iqbal, S. A. (2018). Informing Durable Solutions by Micro-Data: A Skills Survey for Refugees in Ethiopia. Box 1.1: Comparison of SPS 2017 and SESRE 2023 Sociodemographic Profile 9 2. Sociodemographic Profile T his chapter presents results on sociodemographic outcomes of refugees and hosts. It provides the context for refugees and their hosts, covering demographic characteristics, human capital, living conditions, and displacement experience, which are crucial", "output": {"entities": {"named_data": ["SESRE", "Skills Profile Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 permits and subsequently for the right to work. In practice, this is a long and cumbersome process and by late 2015 at most several thousand had been issued. 14 The economic impact of Syrian refugees in Turkey extends beyond changes in the potential labor supply of informal workers in important ways. There has been extensive humanitarian aid provided to the refugees, overwhelmingly by the Turkish government. Reportedly, by early 2015 the Turkish state had spent $ 6 billion (with total outside contributions $ 300 million). 15 Much of these funds have been spent on food, various services, non-food items such as medicines, clothing, shelter, and housing-related goods. In particular, there are 20 accommodation centers (camps) in 10 cities in Turkey. 2. 2 Data Sources We use the Turkish Household Labor Force Survey (LFS) micro-level data sets compiled and published by the Turkish Statistical Institute. The data contains a rich set of labor market variables along with individual-level characteristics and the region of residence. We primarily rely on two years of LFS data: 2011 (just before the arrival of the refugees) and 2014 (the last year available). 16 By design the LFS does not contain any information on Syrian refugees. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Turkish Household Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "### Financial Management Risk and Mitigation\n\nThe overall FM risk is rated **Substantial** due to the multiplicity of implementing agencies and the limited capacity of district-level accountants. Key mitigation measures include: (i) appointment of a dedicated FM specialist at the central PIU; (ii) mandatory use of the SIGAF government accounting system for all transactions above USD 5,000; (iii) quarterly internal audits by the Ministry's internal audit department; and (iv) annual external audits by a firm acceptable to the Bank, with reports submitted within six months of fiscal year-end.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The upcoming section provides an overview of the situation facing Venezuelan migrants in Colombia, setting the stage for understanding the context within which our study is situated. Section three offers a comprehensive description of the VenRePS-Kids study, covering aspects such as the sampling frame, the instrument used for data collection, the representativeness of the study, its implementation process, and an overview of descriptive statistics. Section four delves into the human development disparities observed among forcibly displaced chil- dren and adolescents, providing detailed insights into the nature of these gaps. In section 9", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This assistance is crucial and has been reported to support a meal a day, a better roof, and dignity for Syrians who have fled to Jordan. 8 The JD ‐ HV database has detailed information on expenditure, sources of income, and indicators of household ‐ level welfare, for example, as reflected by recourse to coping mechanisms, standard of accommodation, or access to water, sanitation, and hygiene (WASH). JD ‐ HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need. Verme et al. (2016) draw attention to the precarious circumstances of Syrian refugees in Jordan and Lebanon. Around 55 percent of refugees in Jordan are vulnerable to monetary poverty and more than half are vulnerable to food shocks. Family size increases the probability of being poor, with the poverty rate almost doubling if the size of the family goes from one to two members and increasing by 17 percent when the number of children increases from one to two.", "output": {"entities": {"named_data": ["JD ‐ HV database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "An alternative version of the database that has been mapped to the United Nations (2006, 2009) Trends in International Migrant Stock database is available from the authors. These data are standardized over time in terms of the years to which they refer. { Table 3 here} Calculating Missing Gender Splits Although common in the underlying data, bilateral migration data disaggregated by gender are sparser than aggregate migrant totals (see table 1). An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits; they are described in appendix 5. Combining Migrant Definitions Only a single definition of a migrant (foreign born or foreign citizen) can be applied to each destination country in the final matrices. Switching definitions over time 17 The subregions used for the disaggregations are the 21 UN regions (see http: / / unstats. un. org / unsd / methods / m49 / m49regin. htm, with the countries of Oceania aggregated into a single subregion. They do not match the large World Bank regions used in the analysis in section IV. 18 While this propensity measure is clearly inappropriate, less than 1 percent of all migrants and observations are assigned on this basis. This method is included so that every migrant in the underlying data is accounted for. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock", "Global Migration Database"], "descriptive_data": [], "vague_data": ["bilateral migration data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "III THE VENREPS-KIDS STUDY In this section, we offer a detailed overview of the VenRePs-kids Study, covering its de- sign, implementation, the questionnaire utilized, and the primary outcomes that will be employed to evaluate the human development disparities between forcibly displaced children and adolescents and their peers in host communities. III. A Design Location. Our study is conducted in Medell ´ ın, Colombia ’ s second-largest city, following Bogot ´ a. Medell ´ ın was chosen for this study because it hosts the third-largest Venezue- lan migrant population in the country, trailing only Bogot ´ a and C ´ ucuta, as indicated by the 2018 population census data. Additionally, previous research has demonstrated that survey response rates among migrants in Medell ´ ın are notably high. For instance, a na- tionally representative survey of Venezuelan migrants conducted in 2018 — which was representative across Colombia — revealed that Medell ´ ın had the highest response rates among migrants, whereas Bogot ´ a recorded the lowest (Ib ´ a ˜ nez et al. 2022). This finding supports the decision to focus our study exclusively on Medell ´ ın, also considering the challenges and high costs associated with tracking a highly mobile population longitu- dinally in previous research efforts. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["2018 population census data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Anderson, J. (2011): “ The Gravity Model, ” Annual Review in Economics, 3 (1), 133 – 160. Angrist and Pischke (2009). Mostly Harmless Econometrics, Princeton University Press. AFAD (Disaster and Emergency Management Presidency of Turkey) (2013). Syrian Refugees in Turkey, 2013: Field Survey Results. Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015a). “ The Impact of Refugee Crisis on Host Labor Markets: The Case of the Syrian Refugee Crisis in Turkey. ” IZA Discussion Paper 8841. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015b). “ The Impact of Refugee Crises on Firm Dynamics and Internal Migration: Evidence from the Syrian Refugee Crisis in Turkey, ” mimeo. Aydemir, Abdurrahman and Murat Kırdar (2013). “ Quasi-Experimental Impact Estimates of Immigrant Labor Supply Shocks: The Role of Treatment and Comparison Group Matching and Relative Skill Composition, ” IZA Discussion Paper 7161. Baez, J. (2011). “ Civil Wars Beyond their Borders: The Human Capital and Health Consequences of Hosting Refugees. ” Journal of Development Economics 96 (2) November: 391 – 408.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Formula (5) can be decomposed into two parts: eδs, which measures the average income level in district s, and eηszh ≡ eβs (Eh s − Es) + eχs (Hh s − Hs) which captures individual-speciﬁc variation in income. Migration models predict that, other things being equal, the choice of migration destination should depend on g E [yhs | zh]. This means that if we regress the choice of destination separately on eδs and eηszh, they should have the same coeﬃcient. The same methodology is used to construct other variables that may aﬀect the choice of 9The literature has often emphasized that migrations often serve an important role in household formation. For migrants, the prospect of forming a large, successful household is likely to be one of the purposes of migration. 10The 1995 / 96 NLSS survey adopted the following sampling strategy. Within each district a small number of wards were selected at random. Within each ward, 12 randomly selected households were interviewed. Because the wards diﬀer widely in terms of population, applying sampling weights is essential in order to obtain consistent estimates of δs. 16 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The resulting migration matrices should be viewed as work in progress, but they are an important step in an ongoing global effort to improve migration data. The matrices can be readily updated as additional or superior information surfaces, and they can easily be extended to include future census rounds. Bilateral datasets of international migration are rare. Attempts to create them have focused almost exclusively on industrialized countries as destinations because these countries have more accurate and more frequently produced data. Harrison and others (2003) calculate bilateral remittances for the countries of the Organisation for Economic Co-operation and Development (OECD) together with the 27 largest nonmembers. These estimates are based on international bilateral migrant stock data that the authors also provide, although many of the data are derived from the Trends in International Migration (OECD 2002). This report, published annually since 1973, was arguably the most comprehensive guide to international migration for many years and has been the basis for many studies (see, for example, Mayda 2007). More recently, the OECD has developed a database that provides a comprehensive overview of migration to OECD countries in 2000 (OECD 2008). These data are disaggregated by a number of covariates including age, gender, educational attainment, and place of birth. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Trends in International Migration"], "descriptive_data": ["international bilateral migrant stock data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "UNHCR has adapted a policy of structured provision of health services for different\nnationalities in order to maintain affordable access to secondary and tertiary referral\nservices. Essential secondary and tertiary referrals are available to eligible refugees of all\nnationalities based on a pre-defined set of criteria at governmental hospitals and other\nprivate affiliated hospitals (though UNHCR’s implementing partner Jordan Health Aid\nSociety; JHAS).\n\nIn order to facilitate referrals, UNHCR has established two levels of authority with the\nimplementing partner in order to facilitate and control the referral process. If the estimated\ntreatment cost is less than JODs 750 per person per year then the UNHCR partner will\nmanage the referral directly, while if the referral cost is more than JODs 750 per person per\nyear, the case has to be approved by the UNHCR health unit (for emergency cases) and/or\nExceptional Care Committee (ECC) for non-emergency cases before the referral takes place.\n\n**Data Collection and Analysis**\n\nReferral care is considered an essential part of access to comprehensive health services, thus\nUNHCR since 2014 has maintained a medical referral database in order to monitor trends in\nurban and camp settings in Jordan.", "output": {"entities": {"named_data": ["medical referral database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Despite notable disparities in the characteristics of our sample compared to the VenRePS and RAMV samples, understanding the interaction of these attributes within a frame- work of self-selection among migrants into Medellin is challenging. Moreover, it is cru- cial to note two primary distinctions between our survey and the VenRePS and RAMV surveys. Firstly, the inherent differences in the sampling frames of each survey stem from their distinct measurement objectives. Second, both surveys were conducted at different times compared to our survey. The RAMV survey was undertaken in 2018 in response 10See Ib ´ a ˜ nez et al. (2022) for specific survey and sampling details. 11Refer to Ib ´ a ˜ nez et al. (2022) for further details. 12Since the VenRePS and RAMV surveys lack information regarding children and adolescents within households, our analysis concentrates only on the household and household head characteristics that are available in all three surveys. 20 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["RAMV survey", "VenRePS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Afrobarometer data reveal that technical capacity and not corruption or lack of accountability may be the reason local governments are not successful in transforming revenues into better public goods and services for better welfare outcomes.\n\nEmployment estimates in the artisanal gold-mining sector vary considerably-from 6,000\naccording to a Sustainable Development Observatory (ODHD) survey (2011) to 1 million in the\nthree active mining regions, according to the Chamber of mines (2013), and 200,000 according to\na survey by Central Bank of West African States (BCEAO 2013). The latest population census\nestimates the number of people involved in artisanal gold mining at 25,000. The national labor\nsurvey (the Permanent Household Survey, EPAM) of 2010 estimates that 28,000 people are\ninvolved primarily in extractives. In addition, 14,000 people work in extractives as a secondary\nactivity, with farming being the primary activity of 76 percent of these people. The difficulty in\nestimating the number of people employed in artisanal gold mining arises from the fact that the\nactivity is sometimes practiced in a \"rush\" manner, which may not coincide with a census or survey", "output": {"entities": {"named_data": ["Afrobarometer data", "Permanent Household Survey, EPAM"], "descriptive_data": ["survey by Central Bank of West African States"], "vague_data": ["population census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Since the set of household members at baseline have subsequently moved, and usually not as a unit, the 2004 round results in more than 2, 700 household interviews (from the baseline sample of 912 households). Although the KHDS is a panel of respondents and the concept of a ‘ household ’ after 10-13 years is a vague notion, it is common in panel surveys to consider re-contact rates in terms of households. Excluding households in which all previous members are deceased (17 households with 27 people), the field team managed to re-contact 93 % of the baseline households. This is an excellent rate of recontact compared to panel surveys in low-income countries and high-income countries. The KHDS panel has an attrition rate that is much lower than that of other well-known panel survey summarized in Alderman et al. (2001) in which the rates ranged from 17. 5 % attrition per year to the lowest rate of 1. 5 % per year. Most of these surveys in Alderman et al. (2001) covered considerably shorter time periods (two to five years). Figure 1 charts the evolution of households from baseline to 2004. One-half of all households interviewed were tracking cases, meaning they did not reside in the baseline communities. Of those households tracked, only 38 % were located nearby the baseline community. Overall, 32 % of all households were not located in or relatively nearby the baseline communities. While tracking is costly, it is an important exercise because migration and dissolution of households are often hypothesized to be important", "output": {"entities": {"named_data": ["KHDS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "-- PAGE 12 --> For the decomposition analysis of emissions intensity, we obtain annual country-level total passenger and freight transport traveled by road and rail from the World Road Statistics and the International Transport Forum databases.\n\nThe level of urbanization, and the value added from agriculture, manufacturing, and service sectors are all obtained from the World Bank World Development Indicators database.\n\nData on country-level BRT systems are obtained from global BRT database.\n\nDiesel and gasoline prices are compiled from fuel price documentation from the Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ).\n\nFarm data were collected by conducting a face-to-face survey among a", "output": {"entities": {"named_data": ["World Road Statistics", "World Bank World Development Indicators database", "global BRT database"], "descriptive_data": ["fuel price documentation from the Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Our research complements and extends the findings of Demirci, Foster and Kirdar (2022), who investigated health and nutrition disparities between native children and Syrian refugee children aged 0 to 5 years in T ¨ urkiye, using data from the Demographic and Health Survey. The authors document find no significant differences in infant or child mortality rates between refugee children born in T ¨ urkiye and their native counterparts, it did reveal that refugee infants have lower birth weights and age-adjusted weights and heights compared to native infants. Our work broadens the scope of analysis beyond anthropometric indicators to encompass a holistic assessment of child development. By incorporating measures of physical, cognitive, socio-emotional, and mental health devel- opment, along with factors such as food security, time use, risky behaviors, and social integration, we offer a more comprehensive understanding of the developmental chal- lenges faced by displaced minors. Additionally, our study includes a wider age range, 5Chiovelli et al. (2021) examine the effects of forced displacement on separated sibling in the long-term. 8", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 The only publicly available sources of data to document patterns of enrollment and available educational options for Pakistani families are household-based surveys. These are the official 1998 Census of Population (Government of Pakistan) 3, the 1991, 1998, and 2001 rounds of the Pakistan Integrated Household Survey4, and a 2003 census of schooling choice conducted by our research team. The fact that three sources use different definitions of madrassa enrollment, and were collected at different times by individuals with very different institutional affiliations provides independent verification of enrollment estimates and allows us to determine the sensitivity of our results. The household data tell us whether a child is enrolled full-time in a madrassa, but not whether a child goes for an hour on any given day to study the Quran. Therefore this data does not confound full-time with part-time attendees — a child who attends a public school during the day and a madrassa in the evening is recorded as enrolled in a public school. This is an important distinction since parents might use a modicum of madrassa or mosque based education to teach their children about religion. Consequently, if we contrast these household-based numbers with numbers from establishment-based reports, discrepancies can arise. From virtually any policy perspective, including evening quran classes in enrollment figures seems misguided.", "output": {"entities": {"named_data": ["Pakistan Integrated Household Survey", "1998 Census of Population"], "descriptive_data": ["2003 census of schooling choice"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "cents. The universe of blocks (“ manzanas ”) was stratified by socioeconomic strata and a representative sample of blocks was selected at random without replacement. To en- sure a sample representative of Colombian children by age group, stratum, and sex, we followed a multi-stage random sampling process. 8 As mentioned earlier, one of the biggest constraints in characterizing the role of forced migration in children ’ s human development within developing countries is the difficulty of finding a representative sample of those migrants. This is specially true in contexts where migrants are not hosted in refugee camps, but are integrated in local communi- ties, which account for 80 % of refugees worldwide (Climate Center 2022). We address these difficulties, leveraging all available information on Venezuelan settlements across the country to construct the largest possible comprehensive listing. The listing included data on Venezuelan settlements from all available sources, such as the 2018 population census, migrant organizations, and settlements identified by iMMAP, a non-profit orga- nization. iMMAP uses multiple sources, including OIM, United Nations, local migrant organizations, and satellite images, to identify Venezuelan settlements geographically. 9 Hence, to create our sampling frame, our field team verified the geographic location of all the Venezuelan settlements in-person and implemented a snowball sampling procedure in all the settlements found.", "output": {"entities": {"named_data": ["2018 population census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, permit-holding status is neither sufficient for, nor necessary to, working in Israel. As of 2019Q4, just under a fifth of West Bank residents had the right to work in Israel and the occupied territories, but almost a quarter among them were not commuting across the border for work. The vast majority of such individuals are holders of Israeli or Jerusalem IDs. Conversely, among those who do commute to Israel and occupied territories, 17 % do not hold valid permits or IDs. Likewise, permit-holding status does not logically affect the formality status of the commuter. The frequent border crossings between the West 2This is derived from authors ’ own calculations using the 2016 Jordan Labor Market Panel Survey. 3It is worth noting that the LFS is representative of the residents of the West Bank and Gaza, whose work may not lie in the country. 6 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 3. ANALYSIS Has the increase in Syrian refugees impacted the welfare and socioeconomic conditions of the host community? Summary statistics in the previous section showed clear trends of increasing poverty among recent migrants throughout the country, both near the Syrian border and across rest of the country. While the poverty rates of recent migrant households spiked in 2013, poverty of host community households maintained a relatively constant level in the whole country. From these trends, it appears that there at least has not been an increasing trend in poverty among the host community over the latest years. The empirical model is shown in Equation 1. Regressions are estimated at the NUTS2-year level and using data from only the years 2011 and 2013. The dependent variable of interest is the host community poverty rate by region and year, where the poverty rate is based on spatially deflated imputed household income. Unlike the computation of the poverty rates, “ recent migrant ” information is not used for the analysis. Only the host community poverty rates are calculated using the LFS and the number of Syrians are taken from government sources. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "17 Revealingly, in Betts et al. ’ s (2013) survey of refugees in Uganda, 96 % of all interviewed households in the capital and 70 % outside the capital said they owned and used a mobile phone. They use this mobile phone to communicate with customers and suppliers, to get market information and to transfer money. Half of the urban refugees and 11 % of rural refugees also have access to the Internet. 4. Refugees As a Burden? As pointed in Section 2, most refugees in SSA are hosted in neighboring countries. Most of these hosting countries are likely among the least developed countries. It has been argued that these refugees may constitute an additional burden in terms of economic development in hosting countries (Mabiso et al. 2014). UNHCR (2014: 17) implicitly recognizes that potential burden by suggesting that the ratio of the size of the country ’ s hosted refugee population to its average income level can provide a proxy measure of the burden of hosting refugees.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of refugees in Uganda"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The paper extends the GTAP 8 database by separating Lebanon, Jordan, Syria, Iraq, and West Bank and Gaza from the rest of the Western Asia aggregate and Algeria and Libya from the rest of North Africa. Kuwait, Qatar, Bahrain, Saudi Arabia, UAE, and Oman are aggregated into a GCC composite group. In addition, the 57 sectors in the GTAP 8 database are aggregated into 22 sectors based on their importance for the countries in the MENA region (Table 1). The resulting MENA-specific database contains 26 countries, among which are the six Levant economies of interest in this paper (Turkey, Lebanon, Syria, Iraq, Jordan, and Egypt) and the rest of the developing MENA countries (Table 1). The procedure used to construct the individual country information employs data from several sources. The UN Statistics Division data for 2007 is the source for the six components of GDP – agriculture, hunting, forestry, and fishing (ISIC A-B); mining, manufacturing, and utilities (ISIC C-E); construction (ISIC-F); transport, storage, and communication (ISIC I); wholesale, retail trade, restaurants and hotels (ISIC G-H); and other activities (ISIC J-P). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["GTAP 8 database"], "descriptive_data": ["MENA-specific database"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). As the Sudanese sample surveys IDPs living in the Abu Shouk and El Salam camps, we must understand these deprivations with the background that these settlements were created as emergency and crisis responses rather than durable, long-term solutions (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019). Although Sudan does have a national electric grid that supplies electricity to the urban and peri-urban areas of the nearby city El Fasher, IDP communities living in the camps report limited connection to the city ’ s electricity supply, reflected in the high deprivations in the electricity and cooking fuel indicators. The ad-hoc construction of dwellings in the two camps explains why 71 % of the IDP households in Abu Shouk and 65 % in El Salam live in tukuls or other permanent mud or wood structures (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019: p. 50), both of which register as unimproved housing types. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "21 Table 6: Percentage of displaced individuals deprived in selected indicators by gender Ethiopia South Sudan Sudan Male Female Male Female Male Female Years of schooling 55 78 * * * 36 63 * * * 32 46 * * * School attendance 16 19 * * 21 29 23 23 Early marriage 3 13 * * * 8 75 * * * 6 50 * * * Unemployment 7 5 * * * 2 0 * 3 3 Legal id 45 46 48 74 * * * 10 10 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Asterisks indicate statistical significance of mean differences between male and female at 1 % * * *, 5 % * * and 10 % * levels. Returning to patterns of household headship, Figure 5 breaks down the variation in censored headcount ratios among refugee households in Ethiopia, depending on the gender of the household head. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 12 of 51 Figure 6: Temporary employment by age group. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys. In the case of temporary employment, there is evidence of increasing female participation in the five countries analyzed, with large variations in several cases. Indeed, in the five countries for which we identified temporary workers, while temporary employment was predominantly male in the 1990s, today women show a participation higher than 50 % in all cases. Figure 7: Temporary employment by age group. (Mid-1990s / Mid-2010s) employment by gender (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Brazil Chile Mexico El Salvador > 64 years old 50 ‐ 64 40 ‐ 49 25 ‐ 39 15 ‐ 24 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Uruguay Brazil Chile Peru Bolivia Dominican Republic Mexico El Salvador Female Male", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15 resulting in government forces recapturing rebel held territory, establishment of a rebel base or headquarters, rebel activity that is not battle related (e. g. presence or the killing of civilians), and territorial transfers. The dataset consists of 4, 145 battle events for the 1960 – 2004 period. In the present analysis, we use 2, 530 of these. The remaining events were dropped as they either were in countries not included in the analysis, or because information was missing for one of the key variables. Each conflict event is associated with geographic coordinates and a date of occurrence. This information allows for spatial and temporal modeling of conflict events. The dataset used in this article covers 14 countries in Central Africa. 6 of them had a conflict in the 1960 – 2004 period according to the Uppsala / PRIO Armed Conflict Dataset (Gleditsch et al., 2002): Angola, Burundi, Republic of Congo (Brazzaville), Democratic Republic of Congo (Zaire), Rwanda, and Uganda. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Uppsala / PRIO Armed Conflict Dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "NASA EOSDIS Land Processes DAAC. World Bank, 2018. Somalia drought impact and needs assessment: synthesis report (English). World Bank Group, Washington, D.C.\n\nI consider 10 years of daily data for US Treasury zero rates (provided by Bloomberg):\n\nIn addition to the return matrix, some model versions presented here augment the returns data with cross-sectional data. These models are referred to in this document as 'mixed' models. The basic data used in these cases are presented in table 3.\n\n**Keywords:** CO2 emissions, OCO-2, Urban pollution, Emissions tracking\n\nHigh-resolution observations of atmospheric GHG concentrations are now available from several platforms, including NASA's OCO-2 and OCO-3 instruments, the European Space Agency's METOP-A and TROPOMI (Sentinel-5P) platforms, China's TANSAT and the Japan Space Exploration Agency's GOSAT and GOSAT-2.", "output": {"entities": {"named_data": [], "descriptive_data": ["US Treasury zero rates"], "vague_data": ["cross-sectional data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Notes: The graph plots local means of post-secondary school outcomes by ENLACE test\nscore ventile in grade 12. The solid line shows a linear fit estimated using the grouped data.\nPanel (a) reports the probability of college enrollment; (b) reports the probability of being\nemployed conditional on not being enrolled in college; and (c) and (d) report, respectively,\nthe logarithm of the hourly wage and the probability of working in a formal firm conditional\nin both cases on being employed. Outcomes are measured in the ENILEMS survey at ages\n18 to 20 in the third quarter of 2010. ENLACE test scores come from the years 2008, 2009\nand 2010. Data: ENILEMS-ENLACE panel.\n\nTable A.1: ENLACE Twins and Non-twins: Means and Standard Deviations\n\n(1) (2) T-test\n0 1 Difference\nVariable N Mean/SE N Mean/SE (1)-(2)\nEnlace Score Spanish Grade 6 1942306 512.842 20982 515.377 -2.536***\n(0.075) (0.719)\n\n\nEnlace Score Math Grade 6 1942246 514.237 20982 516.474 -2.238***\n(0.079) (0.762)\n\nEnlace Score Math Grade 6 1942246 514.237 20982 516.474 -2.238***\n(0.079) (0.762)\n\n\nEnlace taker Grade 9 1943583 0.702 20995 0.742 -0.040***\n(0.000) (0.003)", "output": {"entities": {"named_data": ["ENILEMS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Using data from the Armenia Land Tenure and Area study - a study designed specifically for this analysis - we compare the implications of the use of a proxy respondent versus the recommended self-respondent approach and the use of aggregated land data versus parcel-level land data (recommended).\n\nThe ALTA study was implemented with the technical guidance of the lead author by the International Centre for Agribusiness Research and Education (ICARE) in partnership with the Statistical Committee of the Republic of Armenia (ArmStat) and with the financial support of the 50x2030 Initiative.\n\nThe paper is organized as follows: section II describes the land tenure system in Armenia, providing context for the results that follow; Section III describes the ALTA study design and data; Section IV discusses SDG indicator computation and methods; Section V presents the results; and Section VI concludes.\n\nThe sample of the ALTA study consists of 1,200 households, scientifically selected from 100 urban and rural enumeration areas (EAs) covering both agricultural and non-agricultural households.\n\nIn addition to the module on land tenure, the ALTA questionnaire included a brief set of modules capturing\nindividual socio-demographic characteristics, educational attainment, employment status, and agricultural activity. [6]\nAn additional focus of the ALTA study was the validation of measurement approaches to land area estimation. To\nthis end, both Arms 1 and 3 included a module on land area measurement that incorporated GPS and satellite\nimagery-based parcel-level area measurement. A series of cognitive interviews were conducted to ensure that the\nland tenure questions were translated as intended from English to Armenian, as well as to understand how the\ninterpretation of questions varied across individuals, with a view to understanding male versus female", "output": {"entities": {"named_data": ["Armenia Land Tenure and Area study"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "20 Future prospects There are now a wide variety of survey instruments that are being designed for or adapted to measure income or consumption among FDP populations. Home visits initially designed by the UNHCR to question FDPs for protection purposes are being revised to ask questions on income and consumption, becoming in this way viable instruments for poverty measurement. Large home visit exercises are conducted every year in countries such as Jordan and Lebanon and these data have been used to conduct poverty assessments of Syrian refugees (Verme et al., 2016). The WFP conducts vulnerability assessments that have been used by the WFP, UNHCR and World Bank to make gross poverty estimates using the consumption modules of these surveys, even if these modules are typically very short, with few items. The UNHCR conducts Multi- sector Needs Assessment such as the one conducted in Cox ’ s Bazar, Bangladesh, in 2018, Socio-economic assessments such as the one conducted in Zimbabwe in 2017, or nutrition surveys such as the one conducted in Tanzania in 2017. 12 All these surveys contain some information on income, consumption, or expenditure that is being used to assess the well-being of FDPs. The World Bank has conducted welfare assessments of Venezuelans in various Latin American countries including Colombia, Peru and Ecuador, with a forthcoming study expected for Chile. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "reason to believe that the provision of services by donors and non-state actors could either strengthen or weaken citizens ’ deference to government. I will identify the conditions under which these two scenarios are likely to occur. On the one hand, citizens may be less likely to support the government with deference to its laws and regulations when they credit non-state actors or donors for service provision. The provision of services by donors and non- state actors is likely to prompt citizens to question why they should pay taxes to a government that is not providing them with anything in exchange. On the other hand, the provision of goods and services by donors and non-state actors might strengthen citizens ’ legitimating beliefs and their willingness to defer to governmental laws and regulations if citizens view their government as essential to leveraging and managing these external resources. I assess these competing hypotheses using multi-level analyses of Afro- barometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows us to analyze associations between donor and non- state actor service provision and the sense of obligation to comply with the tax authorities. Third, I assess the relationship between the provision of ser- vices by donors and non-state actors and citizens ’ willingness to defer to two additional authorities, the police and courts. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afro- barometer survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1 POST-CONFLICT TRANSITIONS WORKING PAPER NO. 16 Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis * Håvard Hegre Centre for the Study of Civil War, PRIO (CSCW) Clionadh Raleigh CSCW, PRIO & University of Colorado at Boulder Abstract Why do larger countries have more armed conflict? This paper surveys three sets of hypotheses forwarded in the conflict literature regarding the relationship between the size and location of population groups: Hypotheses based on pure population mass, on distances, on population concentrations, and some residual state-level characteristics. The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events. The analysis covers 14 countries in Central Africa. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper develops a statistical method to analyze this type of data. The analysis confirms several of the hypotheses. World Bank Policy Research Working Paper 4243, June 2007 The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post- Conflict Transitions Project can be found at http: / / econ. worldbank. org / programs / conflict). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Armed Conflict Location and Events Dataset"], "descriptive_data": [], "vague_data": ["conflict event data", "geographically disaggregated data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "They have also been piloted ahead of data collection to test their validity and reliability. Results in table 4 indicate that assignment to NVSP has no impact on the three indicators for soft skills among selected youth for specifications 1 & 2 (see the 𝛿 estimate for columns 1, 2, & 3). The leadership skills measure appears to have worsened for both selected and non-selected youth over time, which is somewhat puzzling given that both groups are active volunteers and members in their communities (see the 𝛽 ൅ 𝛿 estimate and the 𝛽 estimate for column 1). Any changes for the communication and confidence scores one year following NVSP were not statistically 15 The selection of the indicator for this study was based on its extensive utilization (to maximize the chance for the scale to be reliable when calculating Cronbach ’ s Alpha with the data of the pilot), on the availability of detailed information regarding how the indicator was designed, and of how the scales should be interpreted once data have been collected. 16 This scale had been tested with youth aged 12-18 showing high levels of internal consistency. Additionally, it was a relatively simple scale with no need for special training to administer it or to analyze the results of the scale. 17 These skills include: awareness of one ’ s own styles of communication; understanding and valuing different styles of communication; practicing empathy; adjusting one ’ s own styles of communication to match others'styles. (communicative adaptability); and communication of essential information; Interaction management. 18 This scale has been used extensively in the psycho-social / soft skills literature, ensuring possible comparability with other studies of the soft skills literature.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "29 evaluations have been conducted (Renton et al., 2000; Shaw, 2000; Shaw, 2002a; Shaw, 2002b; Paine et al., 2002; White, Greene and Murphy, 2003; Interagency working Group, 2003). For example, the Medical Research Council (from the United Kingdom) conducted an external evaluation of program effectiveness in the Gambia using key informant interviews, focus groups, and a KAP (knowledge, attitudes and practices) survey in four villages, including two program sites and two controls. That study found that the Gambia program improved self-reported attitudes and behaviors related to violence against women. Specifically, the program reduced the social acceptability of wife-beating at the community level and appeared to produce a corresponding drop in that behavior. Qualitative findings from other Stepping Stones sites suggest similar benefits. Program H (Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru) is being carried out by four NGOs. It aims to change gender norms and sexual behaviors in Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru (Barker, 2003; White, Green and Murphy, 2003; Guedes, 2004).", "output": {"entities": {"named_data": ["KAP (knowledge, attitudes and practices) survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The most recent labor force survey from Liberia mirrors these statistics: there are roughly 1. 1 million people in the workforce, of whom 195, 000 (about 18 percent) are engaged in wage employment; the remaining 900, 000-plus workers (82 percent) are considered in vulnerable employment, working for themselves or working unpaid for their own households (LISGIS 2010). Among young women (15-24) in Liberia, the unemployment rate is 8 percent, double the rate among young men (LISGIS 2010). Most of these gaps can be explained by differences across individuals, especially in educational attainment, skills training, and years of experience. But segregation, market segmentation, and discrimination do play a role in determining these individual characteristics. Women have fewer opportunities for education or training, less access to credit, a larger share of domestic responsibilities, and less independence and control over their own lives. In Liberia, women comprise half of the employed, but only about one-quarter of paid employment (LISGIS 2011). Fourteen years of civil war in Liberia devastated the country ’ s infrastructure and institutions, and left a generation of young people with very low levels of education and training. Girls were particularly disadvantaged.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "No data on religiosity was collected as part of the census and a more recent and detailed household survey that includes information on time-use elicits little variation — everyone reports high mosque attendance and regular prayers. An alternative, suggested by David Evans at Harvard University, which we pursue here, is to use recent developments in the use of “ names. ” Research by Fryer and Leavitt (2004) demonstrates the increasing use of names to define race identity in the United States. We postulate that households who named (at least) one child “ Osama ” (also spelt Usamah, Usamma or Usama) are more likely to favor a radical brand of Islam. The use of the name Osama was minimal until 1998, and then peaks in 1998 and 2001, following disruptive events. Of course, the naming of the child may reflect name recognition rather Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 Finally, we divide the communities based on land scarcity before the 1993 conflict in order to explore the possible role of posterior rules regarding land provision to returnees. Respondents in communities that had more and less pre-war land available have broadly similar attitudes towards return. 4. Research design 4. 1 The survey We collected the data for this project during January to March 2015 as part of a nationwide survey on issues related to migration for the Labour Market Impacts of Forced Migration (LAMFOR) project. The survey had two components. First, a household survey in which 15 households were interviewed in 100 communities (i. e. sous-collines) across the 17 provinces of the country. Second, a community survey in which a local leader was interviewed in each of the 100 communities. The number of communities selected in each province was based on information from the 2008 Census. Figure 4 indicates the location of the communities surveyed. Figure 4 – Location of communities surveyed in Burundi Note: Geolocation of the 100 communities (i. e. sous-collines) sampled in the survey. Each community corresponds to a dot. Fifteen households and a local leader were interviewed in each community. The number of communities selected in each province was based on information from the 2008 Census. In the analysis below we focus on rural areas.", "output": {"entities": {"named_data": ["2008 Census"], "descriptive_data": [], "vague_data": ["household survey", "community survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This allowed for consistent ranking the households into welfare quintiles and cross- tabulation of welfare status with household characteristics and indicators derived from the survey data. The imputation was carried out using s2sc algorithm in STATA.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "in December 2022 values. 61% 58% 59% 68% 56% 65% In Camp Addis Ababa Total Hosts Refugees Figure 5.4: Shares of food expenditure Refugees’ Aspirations 45 Expenditures for in-camp refugees is almost half that of hosts, despite the sizeable food aid and cash transfers (in selected camps) the WFP and UNHCR provide. The significantly lower expenditures (food and non-food) among refugees compared to host populations led to higher poverty rates. The team cross-checked the food aid received by in-camp refugees based on administrative data from the UNHCR and WFP and food consumption data from SESRE. The information received from UNHCR on food aid provided to refugees in each camp includes quantities per food item per month and cash transfers per person per month for each camp and period. The food items include cereal, wheat, maize, rice, sorghum, CSB/famex (CSB+), pulse, biscuit, date biscuit, dates, oil, vegetable oil, salt, and cash (see Annex E for detailed information). Food aid information received from WFP includes five food items and their quantities distributed to refugees: cereal (mainly wheat but in some camps rice), pulses (mostly yellow split peas), CSB+, vegetable oil, and salt. We have computed the per person, per month in-kind aid", "output": {"entities": {"named_data": [], "descriptive_data": ["administrative data from the UNHCR", "food consumption data from SESRE"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These tariff rate modifications are essential for this analysis as suggested by the substantial differences between the tariff rates available in the GTAP 8 database, especially those implied for Jordan, Iraq, Lebanon, and Syria (Figure 1), and the updated tariff rates, presented by country, product, and source in Appendix Tables B1-B6. Since the GTAP tariffs attributed to Jordan, Iraq, Lebanon, and Syria are composite rates, they do not correspond to the actual trade profile of these countries. Therefore, the new tariff rates differ from the GTAP ones both because of differences in the tariff lines and trade composition. By contrast, the tariff information on Egypt and Turkey in the GTAP 8 database represents relatively accurately existing preferences (Figure 1). 3. Simulation design The pre-war efforts for deeper trade integration in the Levant are reflected in the pre-simulation analysis. Starting from the newly constructed database, the pre-simulation analysis implements the deep trade initiatives discussed by the Levant countries prior to the onset of the Syrian war in 2011. The context for these reforms and the shocks associated with each of these reforms are presented in section 3. 1. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["GTAP 8 database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "by the migration episode. These include the minors age and sex, the parent ’ s and grand- parent ’ s education pre-migration, and a wealth index constructed with retrospective in- formation on the household conditions pre-migration. 14 Grandparent ’ s education is used a proxy for living standards that is unaffected by the migration episode from Venezuela to Colombia and the Venezuelan crisis, which intensified in 2016. ϵij depict the standard errors clustered at the household level to correct for intra-household correlation. For robustness, we will present the estimates of equation 1 with and without controls. As further robustness, we use propensity-score weights (Hirano and Imbens 2001, Hirano, Imbens and Ridder 2003). 15 V. A Physical development: Body Mass Index and health status In our study, we examine disparities in nutritional and health status among Colombian and Venezuelan children aged 5 to 10 years, focusing on standardized body mass index (BMI), instances of overweight and underweight, and overall health status. The BMI serves as an indicator of nutritional status for both adults and children, calculated as an individual ’ s weight in kilograms divided by their height in meters squared, according to World Health Organization (WHO) guidelines. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["wealth index"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Language barriers, mental health challenges, and uncertain futures are identi­fied as major obstacles to integration. The study highlights the importance of tailored interventions, such as psycho­logical support and more dedicated teaching time, to foster refugee students ’ academic and social inclusion. This paper is a product of the Development Data Group, Development Economics and the Social Protection and Labor Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at michela_carlana @ hks. harvard. edu; pcastaing @ worldbank. org; mtestaverde @ worldbank. org; and mtiberti @ worldbank. org.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Macro International, Demographic and Health Surveys, Beltsville, MD.\n\n<!-- PAGE 33 -->\n8. **Comparison with Monitoring Results for India**\n\n\nA recent monitoring study for Indian households (World Bank, 2002; Balakrishnan,\n\n\net al., 2002; Parikh, et al., 2001) has provided useful comparative information about\n\nsurvey suggests that progress has been quite limited. Of 686 biofuel-using households in\n\n\nour 7-region survey (including Dhaka), only 9 (1.3%) report using an improved stove: 4\n\nIn this paper, we investigate individuals' exposure to indoor air pollution (IAP). Using new survey data from Bangladesh, we analyze exposure at two levels: differences within households attributable\n\nIn this paper, we use our survey data to estimate the incidence of IAP exposure for family", "output": {"entities": {"named_data": ["Demographic and Health Surveys"], "descriptive_data": ["monitoring study for Indian households", "7-region survey", "new survey data from Bangladesh"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 5. 2 Composition of poverty Unpacking the headline numbers further, important patterns emerge about the composition of multidimensional poverty among forcibly displaced and host communities in these countries. Overall, the censored headcount ratios (proportion of people who are poor and deprived in a given indicator) are lower among non-displaced communities than among refugees and IDPs, but there are large differences in which indicators are the most salient in different countries. The indicators with the largest difference between the two populations are bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria. These findings reinforce the need for policies and programming that take into account the measured experiences of IDPs and refugees. In this way, the MPI can function both as tool to monitor, track, and bear witness to the lived experiences of forcibly displaced communities, as well as advise on evidence-based interventions that address the needs of the local population. Figure 1 shows the censored headcounts of each indicator in Sudan ’ s MPI, with large differences appearing by displacement.", "output": {"entities": {"named_data": [], "descriptive_data": ["High Frequency Surveys"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\nhumanitarian \r mine \r action \r programme \r with \r local \r and \r international \r humanitarian\nagencies. \r Despite \r these \r positive \r developments \r there \r are \r still \r no \r activities \r related \r to\nsurvey \r and \r clearance, \r marking, \r or \r fencing \r being \r undertaken. \r Mine \r risk \r education\nprogrammes \r and \r assistance \r to \r mine \r victims \r remain \r still \r limited \r in \r scope.\n\nLand \r registration \r documents \r are \r held \r by \r township \r authorities \r in \r Myanmar. \r Land \r tenure\ndocuments \r and \r deeds \r are \r not \r always \r recorded \r or \r respected \r and \r there \r are \r frequent\nreports \r of \r land \r expropriation \r (or \r “land \r grabbing”) \r by \r the \r Government, \r the \r Myanmar\nArmy, \r non-­‐state \r armed \r groups, \r and \r private \r companies, \r often \r resulting \r in \r internal\ndisplacement \r without \r appropriate \r guarantees \r of \r compensation. \r Although \r the \r reforms\nintroduced \r by \r the \r Government \r in \r 2008 \r provide \r some \r additional \r security \r of \r land \r tenure,\nthey \r still \r fail \r to \r adequately \r recognise \r widely \r used \r customary \r rights.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Land \r registration \r documents"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Many detainees are facing ill ‐ treatment. Females are subjected to sexual harassment by security officers. According to UNHCR, there are 2. 7 million people of concern in Sudan (includes refugees, asylum ‐ seekers, IDPs, returned refugees, returned IDPs, stateless persons, and other concern), a lower number than 2015. About 37, 000 refugees returned to Sudan in 2016. At the same time, there were over 2 million IDPs, over 420, 000 refugees and over 16, 000 asylum ‐ seekers in other countries. Economic Opportunity Oil output in Sudan has been low due to civil war, poor infrastructure, and low productivity. Compared to South Sudan, Sudan also has fewer oil resources; nevertheless, it still has abundant resources72. Sudan ranks 186th of 190 states in the World Bank ’ s Doing Business 67 CIA Factbook, South Sudan, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / od. html 68 UNHCR, South Sudan http: / / reporting. unhcr. org / node / 2553 69 CIA Factbook, South Sudan, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / od. html 70 UNHCR, Sudan, http: / / reporting. unhcr. org / node / 2535 71 Human Rights Watch, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / sudan 72 EIU Country Outlook, Sudan, June 19th 2017 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "##### **September/ October 2023 report**\n\n###### Venezuela\n\n**trafficking networks.** After bilateral\n\nmeeting between the Protection Cluster,\n\nthe WGTiP and the FSL Cluster, a focal\n\ngroup was organized on October 10th.\n\nA total of 7 participants from UN Agen\ncies, AoRs, and local NGOs participated.\n\nWG members actively participated by\n\nsharing field experiences and proposing\n\nrecommendations to address challenges\n\nraised during the discussion.\n\n**VIII. Monthly Meetings of the**\n\n**WGTiP**\n\n**The WGTiP convenes monthly to**\n\n**review its working plan and provide**\n\n**a platform for members to discuss**\n\n**trends and activities related to TiP. In**\n\n**the last meeting, the WG collectively**\n\n**decided to establish a Task Force to**\n\n**draft a guide on security for orga-**\n\n**nizations working with Victims of**\n\n**Trafficking (VoT).** Additionally, members\n\nhave developed standardized training\n\nand sensitization activities/workshops to\n\nimplement in communities on the topic of\n\nTiP. These trainings will be beneficial to\n\nother organizations addressing human\n\ntrafficking issues. In October’s monthly\n\nmeeting, the WG hosted the office of UN\n\nSpecial Rapporteur for Trafficking in Per\nsons. The meeting involved discussions\n\non potential collaboration with WG mem\nbers and how the UN Rapporteur's office\n\ncan support advocacy efforts.\n\nMember organizations also shared infor\nmation on available services for victims", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Due to the variations in weight and height ratios among children and adolescents according to gender and age, the benchmarks for determining nutritional status are specifically tailored to these factors. We adhere to WHO guidelines to calculate the standardized BMI values for minors. A BMI exceeding one standard deviation (SD) from the mean suggests overweight, while a BMI less than minus one SD indicates underweight. 16 Health status is assessed through a binary variable, assigned a value of one if the caregiver has reported any health issues such as disease or chronic pain, accidents, dental pain, surgical interventions, or preg- 14For the Colombian households the wealth index is measured with contemporaneous data. 15This procedure restricts the sample to the common support of the propensity score for being a forced migrant and weights observations for Colombian kids by a non-parametric function of the propensity score. This procedure has been shown to increase the estimate ’ s efficiency. 16Furthermore, a BMI greater than 2SD is indicative of obesity risk, and less than- 2SD signals a risk of severe thinness. 33 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Some data sources (such as registration systems and population censuses) are more appropriate for estimating stocks of asylum-seekers, refugees and IDPs at a particular point in time, while other data sources (such as population tracking systems and border crossings) are more appropriate for estimating flows over a specific period. In general, there is a lack of comprehensive and up-to-date data available on all stocks and flows for a particular displacement situation (see Table 4). Consequently, data on flows might be used to estimate stocks, for example in the absence of government data, the stock of refugees in many industrialized countries is estimated by UNHCR based on 10 years of individual asylum-seeker recognition. And, especially in the case of IDPs, changes in the total population combined with some contextual analysis, may be used to deduce estimates of new internal displacement or returns. However, these approximations are flawed unless data on all other flows (births, deaths, repatriation etc.) are also available, which is not usually the case. Even a static figure for the stock of IDPs in a particular location might obscure substantial flows including new displacement and returns. Moreover, there are no common definitions of the various stocks and flows, and therefore the risk of double counting or gaps cannot be discounted. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population censuses", "population tracking systems"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "### 4.1 Operational Risk: Financial Management\n\nThe CAT DDO facility will be the sole financing instrument for this operation. Disbursement is contingent on the government declaring a state of emergency following a qualifying natural hazard event and on the existence of an adequate crisis preparedness program. The CAT DDO does not require activity-level reporting; instead, the borrower is required to maintain a satisfactory macroeconomic policy framework assessed through the annual IMF Article IV consultation process. A program document update will trigger a new three-year drawdown period.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As we theorize, the positive externalities for local communities (in the form of improved service delivery), generated by Uganda ’ s integrative approach to hosting refugees, generally balance out these fears, which do not generate a backlash against refugees and inclusive migration policies. Improving basic services is of the utmost importance to host communities in developing contexts regardless of who is providing them (Sacks, 2012). All the results reported here use our main speciﬁcation of the Nearest + 20km exposure measure (which takes into account not only the nearest settlements, but also all settlements within 20km) and the subset of parishes that are within 150km of a settlement. In SI Section S4, we present the regression tables for all of the results shown in Figures 5 and 6, along with their robustness speciﬁcations. These tables display the eﬀects across all three measures of exposure (Nearest, Nearest + 20km, Nearest + 50km) and across the various radius cutoﬀs for parishes (within 100km, 150km, 200km, and all parishes). These results demonstrate that our main results are robust across speciﬁcations. In additional robustness checks, we also conduct formal sensitivity analyses and address concerns about multiple hypothesis testing by adjusting for the false discovery rate and showing Benjamini-Hochberg-adjusted p-values in SI Sections S5 and S6. An alternative explanation is that our results are driven by a change in the composition of host citizens. It is possible that the positive eﬀects we observe are due to the internal migration of Ugandans: those who move to refugee settlement areas may be positively disposed toward refugees, while those who leave are more likely to be anti-migrants. While there is no data to test 23 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "26 percent of refugees and their host children are too short for their age. Children in Addis Ababa have lower shares of stunting, but stunting rates are still high for OCP refugees (27 percent) and their hosts (24 percent) (Annex D, Table D.3). Overall, stunting rates are higher for boys than girls among both refugees and hosts (Annex D, Figure D.9), which is also confirmed by studies using the demographic and health survey in Ethiopia (Tasic et al., 2020; Gebreegziabher and Regassa, 2019; Gebru et al., 2019). Being underweight is another large challenge related to nutrition among children under age five in Ethiopia. In-camp refugees and hosts have a higher percentage of underweight children (25 and 29 percent) compared to OCP refugees and hosts (2 and 11 percent). Across refugees, the proportion of underweight children is higher among Eritrean and Somali refugees compared to their hosts. At the same time, it is lower among South Sudanese and OCP refugees compared to hosts. In-camp refugee and host children also suffer from wasting. The percentage of wasted in-camp refugee children (14 percent) is higher compared to OCP refugee children (6 percent). Somali and OCP refugees have a higher proportion of wasted children", "output": {"entities": {"named_data": [], "descriptive_data": ["demographic and health survey in Ethiopia"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The cooperative medical scheme's claims database was accessed under a data-sharing agreement with the Ministry of Health to assess hospitalization rates and out-of-pocket expenditure patterns for the target population before the project intervention. These administrative records provided a cost baseline used in the economic analysis. The analysis found that average annual out-of-pocket health expenditure per enrolled household under the cooperative medical scheme was 22 percent lower than among non-enrolled households in comparable income deciles, justifying the enrollment subsidy approach.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "20 between 1948 and 1965, with increases in the percentage of adults with religious education in the cohorts born after this date. There is also wide geographical dispersion in the prevalence of madrassa education in Pakistan. Although all districts report that less than 2. 5 percent of children in the relevant age group (children between the ages of 5 and 19) are going to madrassas, the Pashto speaking belt that borders Afghanistan stands out in terms of the popularity of madrassas as an educational choice. The notion that the madrassa movement coincided with resistance to the Soviet invasion of Afghanistan is supported by the 1998 data from the population census. The increase in the stock of religiously educated individuals starts with the cohort that came of age in 1979 (the year of the Soviet invasion of Afghanistan) and the largest increase is for the cohort co-terminus with the rise of the Taliban. Combined with the fact that the largest enrollment percentage in Pakistan is in the Pashtun belt bordering Afghanistan, this suggests events in neighboring Afghanistan influence madrassa enrollment. Is there something intrinsic about Pashtun sensibility or tribal culture that leads to higher madrassa enrollment? The differentiation of the Pashtun and non-Pashtun districts does not extend to Pashtun and non-Pashtun households in the LEAPS data.", "output": {"entities": {"named_data": ["LEAPS data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "lending operations database in each fiscal year.\n\n\nIn sections 2 and 3 all operations that are IDA financed are considered. This includes development policy\n\nof outcome ratings provided by the World Bank's Independent Evaluation Group (IEG). The portfolio review\n\n\nexamines how the IDA portfolio has evolved since 2001, in terms of the number of projects, size of the portfolio,\n\nRegression analysis comparing IEG outcome ratings of projects in FCS and non-FCS countries shows that\n\ncome rating from IEG (see Table 2). There are also no differential time series trends for this measure of outcome\n\nstated project development objectives, as well as macro level factors, such as GDP growth and country policy and institutional assessment (CPIA) ratings.", "output": {"entities": {"named_data": [], "descriptive_data": ["IEG outcome ratings"], "vague_data": ["lending operations database"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "sanitation including a lack of latrines, and fragile livelihood strategies; others reside in shared and\novercrowded rental accommodation, or with relatives.\n\n**Returnees** : In 2016, more than 600,000 documented and undocumented Afghans returned from\nPakistan and Iran including 372,577 registered refugees who returned under UNHCR’s facilitated\nreturn program and were provided with UNHCR cash grants as part of their repatriation assistance\npackage. The majority of returnees have indicated Kabul, Nangarhar, Kandahar, Herat, Balkh, Ghazni,\nBaghlan and Kunduz provinces as their intended destination for return, including areas subject to\nattacks by armed groups. Returnees report a lack of land and adequate shelter, insufficient livelihoods,\ninsecurity, and poor access to services as obstacles to sustainable return and reintegration. These and\nother factors have forced many returnees to undertake secondary movement to locations, particularly\nin urban centers, other than their place of origin.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "due to armed conﬂict but for more idiosyncratic reasons. We check robustness of this approach in two ways: (i) using all of the refugee stock observations available in the data set, and (ii) computing refugee ﬂows as the positive time diﬀerences in refugee stocks from year to year (setting negative time diﬀerences to zero). The results are robust to these two alternatives. 5 10 15 20 Number of refugees (million) 1987 1992 1997 2002 2007 2012 2017 Year Figure 1: Global refugee population, 1987-2017 Note: This ﬁgure plots the global stock of refugees. The data on bilateral distance and contiguity come from CEPII. The distance vari- able refers to the great circle distance between the most populated cities of each country in the pair. The contiguity indicator is equal to one if the two countries share a land border. Figure 1 charts the global refugee population over time. The sharp increase in the number of refugees over the past decade is evident. Such refugee movements can have signiﬁcant impacts on the destination countries. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "such as education and caste. We also construct measures of social proximity between a migrant ’ s place of birth and each possible destination, using detailed available data on ethnicity, caste, language, and religion. We also investigate a number of factors that may inﬂuence the choice of migration destination but have not received much attention in the existing literature. Fafchamps and Shilpi (2009) have shown that the subjective welfare cost of geographical isolation is high. To investigate this issue, we include regressors controlling for population density and for the average distance to various amenities. Fafchamps and Shilpi (2008) have further shown that migrants are concerned with their welfare relative to that of their birth district as well as to that in their destination location. We examine whether relative welfare considerations inﬂuence the choice of migration destination. Additional controls include distance and prices. The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal. The diverse terrain of Nepal along with geographical variation in amenities makes it ideal for our study. The mountainous nature of Nepal means that the country faces daunting challenges in the provision of transport and energy infrastructure. These challenges are unique to Nepal, however. Similar constraints are faced by many developing countries — or regions within such countries. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["2001 population Census data"], "descriptive_data": [], "vague_data": ["LSMS survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This study aims to assess the consequences of forced migration on ethnic diversity and conflict in Sub-Saharan Africa. We combine a unique dataset on refugee camps with individual data from the Afrobarometer Surveys across 23 African countries for the 2005 — 2016 period. We construct two standard measures of ethnic diversity: indices of ethnic fractionalization (EF) and ethnic polarization (EP). Ethnic fractionalization measures the probability that two individuals drawn from the society at random will belong to two different ethnic groups and thus increases with the number of ethnic groups present. Ethnic polarization captures antagonism between individuals and is maximized when the society is divided into two equally sized and distant ethnic groups. Although these indices have been widely used, little variation over time has been found, making causal inference difficult. The innovative aspect of our analysis is that we use data on the precise locations of refugee camps, their yearly size, and — most importantly — their annual composition in terms of countries of origin. Combined with the Ethnic Power Relations- Ethnicity of Refugees 2019 dataset, we are able to predict changes in ethnic diversity induced by refugee inflows. We then assess the relationship between refugee diversity and the likelihood of conflict. In an additional analysis, we also assess how refugee-induced changes in diversity affect the incidence of theft and violence, participation in protests, and perceptions of ethnic attachment, inter-personal trust, and institutional trust. Other studies have investigated the links between displacement and social conflict or social co- 3 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Afrobarometer Surveys", "Ethnic Power Relations- Ethnicity of Refugees 2019"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "community sampling were listed, and 12 households were randomly chosen and surveyed per EA. SESRE also selected 12 refugee and host households per EA, treating EAs as the Primary Sampling Unit and households as the Secondary Sampling Unit. (i) The distinct sampling designs and objectives of the two surveys render challenges in comparing findings from the two surveys. Moreover, the two surveys are not comparable in other ways, including: (ii) Surveyed population: SESRE includes out-of-camp refugees living in Addis Ababa, while SPS does not. (iii) Survey scope: The methodology to sample and definitions of host communities varied between the two surveys. (iv) Survey design: SESRE’s questionnaire aimed at comparability with the national poverty survey, while SPS aimed at comparability across countries. This rendered differences in the contents of the surveys, where the SESRE employed the same survey instrument as the national poverty survey, while the SPS used an instrument specific to the survey. (v) Differences in consumption: SESRE includes a full consumption module while SPS relied on the Rapid Consumption methodology. Moreover, there are differences in the recall period for food consumption data collection. While SPS used the past 7 days recall period, SESRE collects food consumption data through two", "output": {"entities": {"named_data": [], "descriptive_data": ["national poverty survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 Internal Conﬂicts and Refugees A particularly serious aspect of internal conﬂicts is the human suﬀering they generate. This is not only those who are killed or injured in conﬂict but the large number of people who are forced to leave their homes. The issue of refugees has received particular attention in Western media in recent years as refugee ﬂows from Northern Africa, the Middle East and Afghanistan are increasingly reaching Europe. These refugee streams are linked to a severe humanitarian crisis with considerable funding needs for international donors and heavy strains on host countries. 21 The current refugee crisis, however, is in no way unique. Civil war has always been closely linked to humanitarian crisis and refugee streams are one way to capture this. In this section we provide a cross-country analysis aimed at investigating how the stock of refugees evolves when a civil conﬂict hits a country. In the analysis we will focus entirely on showing changes in the stock of refugees across time to illustrate the dimensions involved. We will base our later analysis on these population movements. We exploit country-level data gathered from several sources. Data about refugees is provided by the UNHCR Population Statistics Database. The database provides in- formation about UNHCR ’ s populations of concern from the year 1951 up to 2014.", "output": {"entities": {"named_data": ["UNHCR Population Statistics Database"], "descriptive_data": [], "vague_data": ["country-level data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "problems. Previous literature has shown that immigrants are more likely to move to locations where they have a network of peers from the same country. We show below that this is true among Venezuelans in Peru as well. Our main interest is on β1, which represent the impact of the labor market conditions in centro poblado j on the discrimination experienced by Venezuelans. Venezuelans who arrive to the country clearly evaluate where to settle based on the labor market opportunities (among other reasons), and therefore to causally identify β1 we need a source of exogenous variation for the labor market at the local level. We use an instrumental variable strategy that exploits variation in the share of workers employed in diﬀerent industries in 2007, along with national level shocks to trade in speciﬁc industries between Oct 2016 and Oct 2017 when the census was collected. More precisely, the ﬁrst stage regression is given by: lnEmpj = α + νSharejk (2007) × ∆ lnExportk + ηXij + νZj + αo + ϵj (2) where Sharejk (t − 1) is the share of workers in centro poblado j employed in industry k in 2007, and ∆ Exportk represents the log change in national level exports in industry k between 2016 and 2017. The remaining control variables are similar to those in Equation 1. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Maps of soil salinity and waterlogging risk were produced by the national agricultural research institute using a combination of soil profile data from the national soil database and satellite-derived vegetation stress indicators. These maps were reviewed during site selection to identify agricultural plots within the project area that were unsuitable for the proposed irrigation investments due to soil degradation risks. Maps were updated with ground-truth observations collected by agronomists during field missions conducted in January and February.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "seekers. 81 There are several challenges associated with using central population registers to estimate refugee and asylum-seeker populations, including: consistency of the definition of refugees with the definition in the 1951 Convention and 1967 Protocol; difficulty and cost of establishing and maintaining a population register (UNSD 2014); and confidentiality safeguards. Compilation of statistics on forcibly displaced populations Several international organizations are involved in the compilation, analysis and dissemination of statistics on forced displacement including UNHCR, 82 Eurostat, IDMC, OCHA, International Committee of the Red Cross (ICRC), 83 WFP84 and IOM. Each of these actors has their own thematic focus and specific objectives, and applies their own methodologies. Asylum-seekers and refugees UNHCR is the principal organization responsible for the compilation, analysis and dissemination of data on asylum-seekers and refugees. UNHCR maintains a publicly available statistical online database85 with data for the period 1951-2014 on refugees (including people in refugee-like situations), asylum-seekers (pending cases), returned refugees, IDPs protected or assisted by UNHCR, returned IDPs previously protected or assisted by UNHCR, stateless persons and others of concern to UNHCR, disaggregated by country of origin and asylum. 86 Data are also provided on demographics, location, asylum-seekers (refugee status determination and monthly data) and resettlement. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["central population registers"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The employment arm also signiﬁcantly improves cognitive function as measured through an index of memory and basic arithmetic tests, a ﬁnding consistent with a large psychol- ogy literature documenting the relationship between cognitive processes and depression (Semkovska et al., 2019). As with physical health, improvements to cognitive function are unlikely to be a direct product of the employment task itself, which was speciﬁcally de- signed to require no literacy or mathematical skill. Rather, these results are suggestive of a downstream impact to reducing depression through the experience of employment. Finally, we ﬁnd no change in time preferences: treated individuals are no more or less likely to discount the future relative to control counterparts, although results may have diﬀered had we engaged participants in an eﬀort or consumption-based time preference game rather than a ﬁnancial one. However, we ﬁnd a substantial increase in risk tolerance among the employed. A greater preference for risk-taking may be indicative of employment serving as a form of psychological ‘ insurance ’ that allows participants the mental bandwidth to exercise greater risk. This is consistent with the positive impacts of employment on stability as well as with a key motive underlying universal basic income (UBI) in the developing world (Banerjee, Niehaus, and Suri, 2019). Interestingly, however, we document no parallel increase in risk tolerance in the cash transfer arm. Our result on risk preference also echoes a potential consequence of depression and anxiety described in Ridley et al. (2020), although empirical 15 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "countries covered in this study. We next turn to analyzing the factors that helped or hindered the return of Syrian refugees until early 2018. 3 Data and Empirical Strategy Return migration decisions are potentially inﬂuenced by expected payoﬀs in both the country of origin and country of asylum, as well as the individual characteristics of refugees. To analyze these factors, we need a comprehensive data set, which is often not available especially in active conﬂict situations. In what follows, we describe the strategy we followed in exploiting the available information. 3. 1 Data With an active conﬂict situation in Syria, a complete longitudinal data set for conditions in countries of asylum and origin was not available. Thus, we adopt a pragmatic approach that combines diﬀerent sources and types of data. Refugee attributes: We use the Proﬁle Global Registration System (ProGres) database, which is compiled by UNHCR to record each person of concern who ap- proaches it. 7 Our version comprised about 2. 16 million Syrian refugees in the Middle East and North Africa region (Turkey and European countries are not included), with a cutoﬀdate of March 2018. The ProGres database is a limited administrative database, which functions like a civil register. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Programs designed to enhance employment among young people can focus on the supply side of the labor market, including skills training programs for both wage employment and entrepreneurship training; or programs to augment demand, such as wage subsidies, public works, and community service programs; or programs to help the labor market clear, such as job search assistance and placement services. In addition, it may be that the constraints facing young people are not in the labor market itself, but in other markets, such as for credit. The vast majority of jobs programs have focused on the supply side: training programs make up about 79 percent of over 600 cases included in the World Bank ’ s Youth Employment Inventory database. 3 Although rigorous and general evidence of success is limited, it seems that successful skills training programs share a few key features: they are responsive to local market conditions, they provide more than just technical skills in a specific area (including, for example, “ life skills ”), and they include ancillary services that alleviate other constraints preventing successful labor market integration (e. g., access to credit). Among the most celebrated are the Jovenes programs that provide demand-driven technical training, plus social skills that help in the labor market, plus internships.", "output": {"entities": {"named_data": ["Youth Employment Inventory database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Table 2 reports average daily hours in the three locations for a representative sample of 4,612 individuals drawn from 600 households in rural, peri-urban and urban areas of seven Bangladeshi regions\n\nA previous paper by the authors (Dasgupta, et al., 2004) has documented extensive indoor air-quality monitoring in the peri-urban area of Narshingdi (Dhaka region).\n\nDrawing on information from continuous, 24-hour monitoring of PM10 in 27 households in Narshingdi, Figure 2 displays a typical daily pollution cycle as\n\ncloser to cooking-area concentrations, and our 24-hour monitoring data indicate that daily pollution", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["monitoring of PM10", "24-hour monitoring data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 displacement. 55 % of the returnees reported to have been employed before the crisis and 36 % in June 2014. Over time the employment situation among the displaced has improved steadily and by December 2014 more people reported being employed than prior to the crisis. All the returnees were able to regain employment after returning. The employment situation of IDPs, returnees, and refugees in Niger is steadily improving; only for refugees in Mauritania does one notice a steady decrease, with 100 % reporting no employment during January and February. Source: Listening to Displaced People Survey, 2014 and 2015. The ownership of livestock and consumer durables was reduced significantly as a consequence of the crisis. Table 7 demonstrates this by showing the Tropical Livestock Units (TLU) 12 owned prior to the crisis and in June 2014 as well as the percentage of ‘ yes ’ responses on a question whether a given asset was owned by the household. 13 The loss on livestock has been enormous particularly amongst IDPs and refugees who lost respectively more than 90 % and 75 % of their animals. 12 TLU is a common unit to describe livestock numbers of various species as a single figure that expresses the total amount of livestock present – irrespective of the specific composition. 13 This was a ‘ yes / no ’ question meaning that if 56 % of the Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "WDI data on electricity consumption only encompass output from power plants and hence may underestimate the intensity and volume of night lights in countries with a strong reliance on private generators.\n\nWhat about the economy of Somalia? National accounts data for Somalia have been non-existing since 1991, when the country sunk into chaos following the toppling of the Badre regime.", "output": {"entities": {"named_data": ["WDI data"], "descriptive_data": [], "vague_data": ["National accounts data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "9 this oversubscribed list. Selection into the training group was based on a “ vulnerability score ” that gave priority to younger, female and unemployed individuals. Despite this approach, intake was “ fuzzy ”- participants were ordered by their vulnerability score, with the most vulnerable entering up until capacity. In some intakes, individuals with comparatively high scores were not taken into the program. In others, individuals with comparatively low scores were included. We construct our treatment and control groups from these intake decisions. Data were collected from members of the host and refugee communities in each country. 4 In both Jordan and Lebanon, the intervention was implemented on a rolling basis. As soon as one training cycle was completed, another would begin. Data were collected in three waves during each training cycle. First, during an “ outreach ” phase, where data were collected in order to assign treatment status. Second, at “ baseline ”, which occurred before the training had begun but after treatment assignment was known. Third, data were collected at “ endline ”, immediately following the end of the training. Data collection for those assigned to the treatment and control followed the same pattern. 5 Outreach and baseline data collection took place less than a week apart and were collected between July 2018 and September 2019. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "6. Robustness Checks 6. 1. Sample attrition Many challenges were encountered during survey implementation, due to the context – the densely populated and impoverished communities in and around Monrovia are difficult settings in which to find and track respondents — as well as the transience of a young study population. Despite vigorous efforts to track and interview each individual in the sample, a certain amount of survey attrition was expected. As the survey response rates in Table 1 show, 1622 (or 80 %) of the individuals in the study sample were successfully interviewed in both the baseline and midline surveys. Another 305 respondents were interviewed at baseline but not at midline and hence are not in the panel used for the analysis in this paper. 27 This survey attrition, while not much higher than other program evaluations in Africa, may cause concern that the results of this evaluation are biased, especially if the loss to follow up is correlated with individual characteristics that might affect the outcomes. To address this concern, Table 10 presents regressions on the likelihood of panel inclusion, that is, the likelihood of being interviewed at both baseline and midline. The first column indicates that treated individuals are significantly more likely than control to have been interviewed twice. This result persists even after controlling for individual characteristics and community dummies. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For the remaining one-to-many relations, we keep these ethnicities in the Afrobarometer as such and consider them as a single ethnic group. Some manual treatment can even further improve 4", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Do people run away from their home country when a conﬂict breaks out? Does the seriousness of the conﬂict matter in this decision? In which phase of the conﬂict do they leave? When do refugees come back to their home country? In order to answer these questions, we look at the impact of conﬂict incidence on 21We will return to these issues in the policy section. 22According to the UNHCR deﬁnition, this category includes “ individuals recognized under the 1951 Convention relating to the Status of Refugees; its 1967 Protocol; the 1969 OAU Convention Governing the Speciﬁc Aspects of Refugee Problems in Africa; those recognized in accordance with the UNHCR Statute; individuals granted complementary forms of protection; or those enjoying temporary protection; and people in a refugee-like situation ”. 23The UNHCR Global Trends Report 2014 provides evidence that conﬁrms this hypothesis. About 59. 5 million people were forcibly displaced worldwide by the end of year 2014. Among them, 19. 5 million were refugees and 38. 2 million were IDPs. 28", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "education. In addition, information on their registration status is recorded, including refugee status, arrival and, where applicable, return date, and sub-district-level location information for last residence in Syria and current residence in the country of asylum. It also identiﬁes kinship of individuals within each “ case\"(e. g., familial relationships of everyone within a case to the principal applicant, ranging from members of the nuclear family to extended family, such as in-laws and aunts). Following the initial registration, entries are updated in subsequent contacts. Up- date frequencies vary from one operation to another, with at least 5 percent of all observations being updated in a given month. 8 Therefore, although information on single-shot events like arrival and return dates is ﬁxed, other information like occupa- tion, education and marital status may change over time. In the case of education and marital status, these changes are largely driven by the aging of the refugee. However, the occupation variable is more problematic, since it could refer to current employ- ment or past employment (including in Syria) depending on when it was last updated. Therefore, while we are able to use all of the demographic and registration information of the ProGres database, we exclude the occupation variable from the main analysis. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Table 5: Coverage of Published Data on Location, Accommodation and Demographics 2015 Population end- 2015 (millions) Urban or Rural Location Accommodatio n Age Sex Refugees and people in refugee-like situations 16. 1 85 % 83 % 58 % 68 % Other people of concern to UNHCR 47. 8 (including 37. 5 IDPs) 70 % 47 % 25 % 39 % UNHCR ’ s total population of concern 63. 994 73 % 56 % 33 % 46 % IDPs monitored by IDMC but not included in UNHCR's data 3. 3 0 % 0 % 0 % 0 % Source: UNHCR Global Trends 2015 Note: Other people of concern to UNHCR include asylum-seekers, IDPs and people in IDP-like situations protected or assisted by UNHCR, stateless persons, and ‘ other ’. Overall robustness of current data The robustness of data is difficult to estimate. A review of data collection and compilation methodologies shows broad variations in terms of the accuracy and reliability of the global estimates of forced displacement that are widely used. Headline figures on forced displacement are significant in shaping public opinion and are critical for sound decision making, both to inform the allocation of resources and to design effective humanitarian and development responses. However, the available estimates are potentially misleading and should not be referred to without appropriate caveats and qualifiers. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The observations of positive events contain more information than the non-event observations We therefore sample asymmetrically: We sample all of the transition events and 1. 0 % of the non-transition events. 3. 4 Disaggregated Independent Variables Local level data on land, population, and elevation is available in the geospatial format of raster files with a resolution of 1km. Using Geographic Information systems (GIS), attributes from raster and point data are associated with the grid square in which they lie. In this way, spatial data is georeferenced to a location that is defined by the grid cell. This process results in a data structure in which each row has within it combined information on a square defined by the grid, the national level information in which is it located, and () () () ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ ∑ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ = = ∉ = t X t X t d d j j p j R i d j j p j w w t β β 1 1 exp exp at out breaks war a | square a in war Pr", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Existing research shows that due to the number of factors involved, no climatic or\nenvironmental hazards inevitably result in migrations. Many authors note that even if\ndisasters become more frequent in the future, political efforts and measures of\nprotection will be able to lessen the need to emigrate provided that the necessary\nfinancial means are made available. Even rising sea levels could be partially\ncounteracted by the erection of dykes or the filling in of threatened zones. The Stern\nreport is clear in this respect and states that \"the exact number who will actually be\ndisplaced or forced to migrate will depend on the level of investment, planning and\nresources\" (112), before estimating the cost of mitigation to be several billion dollars.\n\nThe overview we have carried out also shows that the very concept of climate or\nenvironmental refugees, because of its connotations of urgency and unavoidability, is\nto be handled with care. It actually evokes fantasies of uncontrollable waves of\nmigration that run the risk of stoking xenophobic reactions or serving as justification\nfor generalized policies of restriction for people seeking asylum.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "17 Figure 10: Children aged 7-12 attending school (%) Source: Listening to Displaced People Survey, 2014 and 2015. 5. Challenges Faced by Returnees The results suggest that returnees were less affected by the crisis than IDPs and refugees. This is reflected in data on asset and livestock ownership, but also in the information pertaining to exposure to violence. Returnees reported fewer victims; fewer returnees reported to have lost income as a consequence of the crisis; more of their children were able to continue schooling; and relative to IDPs and refugees, fewer perceived being poorer in June 2014 than before the crisis. Returnees are also the group that feels most secure, that has high levels of trust in the Malian army and police and that has a positive attitude towards most government policies. 88 88 97 79 76 78 99 88 92 55 74 72 91 98 92 96 100 85 90 91 96 90 86 87 87 95 89 76 75 90 94 92 97 86 73 98 Bamako Gao Timbuktu Kidal Niger Mauritania IDPs Returnees Refugees 14-Aug 14-Oct 14-Nov 14-Dec 15-Jan 15-Feb Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "UNHCR (2021a) projects that the number of returnees will reach 141, 000 in 2021, up from 41, 000 in 2020. There are no datasets such as the one used in this study to explore the impact of post-2015 returnees on social cohesion and it is not possible to determine the degree to which our findings our applicable to this new context. However, it is possible to explore similarities and differences between the two contexts. Looking at a UNHCR report about the latest wave of returnees, it states that “ almost all returnee households rely on food obtained from their own gardens (93 %) and / or fields- households struggle to get food during the period they do not produce. 81 % of households declared that they are not satisfied with their level of food security because of the low dietary diversity ” (UNHCR 2021a). The report also suggests that “ 88 % of returnee heads of households are subsistence farmers, but most of them declared not having the adequate resources to produce their land. ” This high level of dependence on agriculture and prevalence of food insecurity are similar to the ones in our dataset for returnees and suggests that tensions related to access to agricultural land could also be present for post-2015 returnees. There are also signs of potential differences between current dynamics and the pre-2015 period. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Survey instruments enable detailed questions to be asked about the characteristics and situations of households, and if they identify displaced populations based on self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status. Additionally, more innovative tools and technologies for data collection, analysis and compilation should be explored and leveraged. For example, new methodologies (such as high resolution satellite imagery and unmanned drones) may expand the coverage of data collection efforts in insecure or inaccessible areas. Additionally, new techniques could be explored to improve the collection of robust data on flows of refugees and IDPs. Organizations such as the World Bank, UNHCR, IOM and IDMC are already exploring and in some cases are beginning to use more innovative data collection tools. These techniques include: 99 Several standardized international sample surveys have been designed for special purposes including Living Standards Measurement Studies, Labor Force Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys. The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner", "output": {"entities": {"named_data": ["Labor Force Surveys", "Multiple Indicator Cluster Surveys", "Demographic and Health Surveys", "Living Standards Measurement Studies"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "List of acronyms CAR Central African Republic DHS Demographic and Health Surveys DRC Democratic Republic of Congo DTM Displacement Tracking Matrix FCS Fragile and Conflict-affected Situations GIDD Global Internal Displacement Database GIS Geographic Information Systems IASC Inter-Agency Standing Committee ICRC International Committee of the Red Cross IDMC Internal Displacement Monitoring Centre IDPs Internally Displaced Persons ILO IOM International Labour Organization International Organization for Migration IRRS International Recommendations for Refugee Statistics JIPs Joint IDP Profiling Service LSMS Living Standards Measurement Study MICS Multiple Indicator Cluster Surveys NGOs Non-Governmental Organizations NRC Norwegian Refugee Council OCHA Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat OAU Organization of African Unity ODA Official Development Assistance OECD Organisation for Economic Co-operation and Development SDG Sustainable Development Goal SKOPE Somalia Knowledge for Operations and Political Economy SuTPs Syrians under Temporary Protection UAV Unmanned Aerial Vehicle UNDP United Nations Development Programme UNHCR United Nations High Commissioner for Refugees UNITAR United Nations Institute for Training and Research UNOSAT UNITAR ’ s Operational Satellite Applications Programme UNRWA United Nations Relief and Works Agency for Palestine Refugees in the Near East UNSD United Nations Statistical Commission WFP World Food Programme Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research. 1989-2019. 50 (4). Taylor, J., M. Filipski, and M. Alloush (2016). Economic impact of refugees. Proceedings of the National Academy of Sciences 113 (27), 7449 – 53. United Nations High Commissioner for Refugees (2020). Global Trends: Forced Displacement in 2019. Geneva. Verme, P. and K. Schuettler (2021). The impact of forced displacement on host communities a review of the empirical literature in economics. Journal of Development Economics 102606. Verwimp, P. and J. Maystadt (2015, December). Forced Displacement and Refugees in Sub-Saharan Africa. An Economic Inquiry. World Bank Policy Research Working paper 7517. Vogt, Manuel, N.- C. B. S. R. L.- E. C. P. H. and L. Girardin (2015). Integrating Data on Ethnicity, Geography, and Conflict: The Ethnic Power Relations Data Set Family. 51 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Ethnic Power Relations Data Set Family", "UCDP Georeferenced Event Dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "assignment process was conducted to assign the trainees to the first or second round of training. 9 Of those entered into the random selection, 1273 young women were assigned to the first round of training, with the remaining 808 to serve as a control group (the control group would participate in the second round of training starting in July 2011). Of the 1273 assigned to treatment, 118 women were not found or chose not to participate after they were selected. 10 In order to fill at least some of these slots, 39 young women from the control group were randomly issued as replacements, resulting in a modified control group of 769 individuals. In the end, 1191 young women entered the first round of training. 11 The assignment process and all post-randomization modifications are summarized in Figure 2. Table 1 reports the baseline and midline survey response rates leading to the sample used for the analysis in this paper. The target sample for both the baseline and midline survey consisted of the original 2106 EPAG recruits, of which 1989 were successfully interviewed during the baseline survey. 12 At midline, 1736 were interviewed, including 56 who were not interviewed at baseline. For our analysis, we drop individuals who were excluded from the randomization or who were manually re-assigned from control to treatment as replacements after the randomization. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["baseline survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "27 Source: Listening to Displaced People Survey, 2014. 7. Conclusion The 2012 crisis in northern Mali led to widespread displacement. The welfare of the displaced – and those who returned – is monitored by combining a baseline survey with structured monthly follow-up interviews carried out by mobile phone. This innovative approach allows tracking changes in welfare with high frequency – even for those who returned to areas that are insecure and inaccessible to enumerators. After 6 rounds of follow-up interviews attrition rates are very low (more than 99 % response rate), demonstrating that it is possible to collect robust and representative data from hard-to-reach, conflict-affected populations. The results show that those who fled were better educated, better off and less affected by violence than the average population in the North. Those who fled lost significant amounts of durable goods (20-60 %) and livestock (50-90 %); many of their children ended up being taken out of school and their welfare (measured subjectively and by the number of meals consumed) declined considerably. Over time, the impact of the crisis on welfare has lessened and by February 2015 the majority of eligible children of the displaced were going to school and levels of employment and number of meals consumed were at pre-crisis levels.", "output": {"entities": {"named_data": ["Listening to Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 In this study, we apply their techniques to understand gender differences among adults as well. 2. 2 Intrahousehold analyses of multidimensional poverty The literature on multidimensional poverty measurement and intrahousehold analysis is limited. Espinoza-Delgado and Klasen (2018) propose an individual-based multidimensional poverty measure for Nicaragua and estimate gender gaps in headline statistics. Klasen and Lahoti (2016) question the neglect of intrahousehold inequality in multidimensional poverty indices by comparing a standard household-level MPI and an individual-level MPI to the MPIs proposed by Alkire and Santos (2014) and UNDP (2014), finding that females recorded a far higher poverty rate when using the individual measure and that age differentials in poverty were also larger. We follow their work of investigating poverty in the indicators for which individual data is available and compare the achievements of men and women and boys and girls living together. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Appendix Table A3: LFS Sample Composition by Quarter and Region**\n\n| Region | Q1 2019 | Q2 2019 | Q3 2019 | Q4 2019 |\n|---|---|---|---|---|\n| Northern | 2,418 | 2,391 | 2,340 | 2,306 |\n| Central | 3,102 | 3,088 | 3,074 | 3,051 |\n| Southern | 4,211 | 4,187 | 4,190 | 4,168 |\n| Coastal | 1,890 | 1,878 | 1,855 | 1,843 |\n\nAttrition across LFS quarters is minimal (less than 2 percent per quarter). The LFS uses an imputed replacement procedure for household members absent during a wave; imputed observations are weighted at 0.5 in the regression analysis.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "18 Figure 11: Timing of Return (%) Source: Listening to Displaced People Survey, 2014. 94 % of the returnees were displaced inside Mali and 6 % outside the country. 67 % went to Bamako, 11 % in Koulikoro, 9 % to Ségou, 5 % to Mopti and 3 % went elsewhere (Kidal, Gao and Sikasso). The majority returned between June and October 2013 a period that followed the signing of a peace deal between the interim government and rebel factions to allow presidential elections to be held in July (first round) and August (second round) 2013. In October security in the North worsened again and ever since the number of people returning has been very limited. The main challenges reported by returnees in June 2014 were (i) poverty and food insecurity; (ii) lack of infrastructure (including lack of safe drinking water) and (iii) unemployment. 11 % of the returnees stated not to be facing any challenges (Figure 12). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer.", "output": {"entities": {"named_data": ["Linking Ethnic Data from Africa", "Afrobarometer", "EPR-ER dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "ethnic group e at time t. It can also be expressed as one minus the Herfindahl index (Alesina et al., 2016). The EP index gives more weight to intergroup differences at the expense of within group homo- geneity. It can be defined as (Esteban and Ray, 1994, 1999; Montalvo and Reynal-Querol, 2005) 14 EPjt = Nrt X e = 1 (g2 et) (1 − get). (3) We compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity. In order to construct the revised refugee diversity indices according to ethnicity e, we first combine information about the country of origin of refugees hosted in refugee camps c in year t with the data from the EPR-ER 2019 dataset. The EPR-ER records the ethnic composition of refugee stocks originating from neighboring countries and countries in proximity to each other (maximal distance between country borders ≤ 950 km) with at least 2, 000 refugees and provides the ethnic composition of refugees (Vogt and Girardin, 2015). More specifically, the EPR-ER dataset gives us the share of refugees from ethnic group e moving from country o to country d at year t. The EPR-ER data gives us the three main ethnic groups.", "output": {"entities": {"named_data": ["EPR-ER 2019 dataset", "Afrobarometer survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Another ex- planation of these results could be the vulnerability of the Colombian population who in many cases has also a long history of internal forced displacement and violence. V. D Social Cohesion We also delve into the differences in secondary outcomes among Colombian and Venezue- lan adolescents concerning social cohesion. We focus on assessing altruism, trust, iden- tity towards specific domains, networks, and experiences of discrimination. To measure altruism and trust, we employ the questions from the Global Preference Survey, a tool developed by Falk et al. (2022) to elicit risk, time, and social preferences. Specifically, to measure altruism we ask the adolescents how much of a fictional endowment would they be willing to donate to a good cause. To measure trust, we include the 7-itme ques- 38 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Global Preference Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Data on community characteristics were not collected. Due to confidentiality policies we only know the province in which a respondent currently resides, but not the administrative unit below the province level. This prevents us from merging the RDHS data with secondary data on geographical conflict intensity. Moreover, no information on income or consumption expenditure is recorded and households ’ physical asset endowments, such as owning a radio or the quality of roofing materials, is the only implicit measure available on household wealth. Instead, we construct a wealth index based on recorded household assets. Components of the index include durables, such as radio and bicycle, source of drinking water, characteristics of floor materials, and type of toilet facility. While the 2000 and 2005 surveys record a larger number of assets than the 1992 5 This description of the sample design refers to the 2005 RDHS, with slightly different designs used in the two previous RDHS waves. The 1992 RDHS builds on the 1991 Census as a sampling frame. At the time of the 1992 survey collection, a civil war was ongoing, with most actions of warfare taking place along the Ugandan-Rwandan border. Due to security concerns, 44 rural sectors in the provinces of Byumba and Ruhengeri in northern Rwanda were excluded from the sample frame at the outset. The 2000 RDHS builds on the listing of enumeration areas outlined for another household survey, the Enquête Intégrale sur les Conditions de Vie des Ménages (EICV) collected in 2000, as no other population records were available at the time. The sampling frame of the EICV itself is based on the pre-genocide Census of 1991. Three strata were used – Kigali, other urban areas, rural areas – and rural areas were further stratified into provinces, resulting in 13 strata (Ministère des Finances et la Planification Economique 2003). The sample design of the 2000 RDHS is only representative of rural areas of each province and Kigali City. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In the ALTA study, perception of tenure security is asked on a scale from 1 to 5, ranging from \"not at all likely [to involuntarily lose land]\" to 'extremely likely [to involuntarily lose land]\", in line with the joint module by FAO, World Bank, and UN-Habitat (2019).\n\nIn the ALTA study, the non-response rates at the individual level for Arms 1 and 2 are 13% and 12%, respectively, with the difference not statistically different from zero.\n\nThis paper uses data from a methodological experiment in Armenia to assess the implications of survey design-namely, respondent strategy and the level of disaggregation of land data-on the measurement of individual land rights and SDG indicator monitoring.\n\nEvidence from the Uganda Methodological Survey Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA), which fed into the development of the UN EDGE guidelines, illustrates the asymmetric impacts of respondent approach by gender.\n\n3 For reports on the LSMS+ surveys in Tanzania, Malawi, Ethiopia, and Cambodia, see Hasanbasri et al. (2021a) and\nHasanbasri et al. (2021b).", "output": {"entities": {"named_data": ["Uganda Methodological Survey"], "descriptive_data": ["LSMS+ surveys in Tanzania, Malawi, Ethiopia, and Cambodia"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1) _Food consumption._ We include the value of food consumed from all possible sources, as measured in POF. This includes food acquired for consumption at home, which is collected in the household questionnaire of frequent acquisitions (POF 3). Food consumed at home can be acquired via monetary means, obtained through own production, or received in kind or via other means that do not involve monetary payment.\n\nThe value of consumption of FAFH is collected in the individual-level expenditure questionnaire on nonhousehold goods and services (POF 4). Again, this component of food consumption might have been acquired via monetary as well as nonmonetary means.\n\nFinally, POF also collects information on food consumption outside the home - in particular, meals consumed in settings like at school or on vacation.\n\nPOF collects information on a wide range of nonfood items, but several are typically not included in the construction of a consumption aggregate used for welfare analysis.\n\nFor this, a detailed inventory of durable goods is needed, including information on their value (either original purchase date and value, or current replacement value). POF, however, collects information only on quantity, mode of acquisition, year, and state (new or used) in its durable goods inventory, but not on values.", "output": {"entities": {"named_data": ["POF"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "than 500, 000 refugees, together accounting for half of all refugees and people in refugee-like situations (excluding Palestine refugees). Major refugee hosting countries are typically the neighbors of countries of origin. For example, Syria ’ s neighbors (Turkey, Lebanon, and Jordan) together accounted for 27 percent of total refugee numbers; Afghanistan ’ s neighbors (Pakistan and the Islamic Republic of Iran) together accounted for 16 percent; and Somalia ’ s and South Sudan ’ s neighbors (Ethiopia, Kenya and Uganda) together accounted for 11 percent. Some countries (Lebanon, Jordan and Turkey) are hosting a particularly large share of refugees relative to their population (see Figure 10). 45 However, in all other countries, the number of refugees as a percentage of the population is 3 percent or lower, and most often below 1 percent. Figure 7: Top 15 Host Countries as a Share of Total Refugees and Asylum-Seekers 1991 – 2015 Source: UNHCR Statistical Online Population Database Note: Includes refugees, people in refugee-like situations and asylum-seekers. Excludes Palestinian refugees under UNRWA ’ s mandate. 45 Nauru is a special case since the Australian government funds the offshore processing center where refugees and asylum-seekers intercepted at sea are detained pending determination of their status. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "19 As property prices in the worst affected areas reduced the most, low income households responded by moving into low-rent housing being offered in these locations. On the other hand, middle income households moved away to avoid risk, and the wealthy, for whom insurance and self-protection was the most affordable, did not change where they lived. Poor people “ sort ” into low rent locations – which are often at higher risk to natural hazards. The problem is particularly acute in developing countries where there is a divide between the formal and informal markets for land. While formal developments may respect land use regulations, informal settlements are often located in hazard prone locations, such as on hill slopes, close to river banks, or near open drains and sewers. In Dhaka for example, informal settlements are developing across the metropolitan area, with many residents lacking basic public services and in locations at risk from flooding. In fact, most informal settlements do not have access to a public toilet within 100 meters, and 7, 600 households in 44 slums live within 50m of the river (World Bank 2005, Dhaka Urban Poverty Assessment). For the city of Bogotá, we use the same database discussed earlier to examine if poor people are at greater risk from natural hazards – particularly earthquakes. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 1: Data and Descriptive Statistics: Clusters, Refugee Camps, and Conflicts Revised refugee diversity indices. We first use Afrobarometer data to construct standard indices of diversity, namely the EF and the EP indices (Bazzi et al., 2019; Esteban and Ray, 1994). The EF index describes the probability that two randomly selected individuals from a given location belong to two different ethnic groups (Alesina et al., 2003, 2016; Gomes, 2020b). The EF index can be defined as EFjt = Njt X e = 1 get (1 − get), (2) where Nj is the number of ethnic groups in cluster j at time t and get is the population share of 14", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "humanitarian emergencies and protracted refugee status. Forced displacement is a pressing issue in the country, a result of conflict, drought, flood, economic instability, and political instability in neighboring countries (Martin, 2010; UNHCR, 2020d; IPCC, 2019). As of year-end 2023, more than 922,000 refugees and asylum seekers 1. Introduction 0 200,000 400,000 600,000 800,000 1,000,000 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Population Years Sudan Somalia South Sudan Eritrea Kenya Other countries Figure 1.1: Refugees and asylum seekers in Ethiopia by country of origin, 1984-2023 Source: UNHCR Refugee Data Finder 2024. Introduction 2 were seeking refuge in Ethiopia, with the majority originating from South Sudan (420,000), Somalia (280,000), Eritrea (170,000), and Sudan (49,000). Ethiopia is a signatory to the 1951 UN Convention on the Status of Refugees and its 1967 Protocol, with an obligation to protect refugees and asylum seekers. Most refugees (92 percent) are living in approximately 30 camps and sites located in Afar, Amhara, Benishangul-Gumuz, Gambella, Somali, and Tigray regions, with an increasing number of refugees", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "13 Proposition 7 State-Level Effects of Population Size: The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects. 3 Research Design 3. 1 Unit of Analysis To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8. 6 km x 8. 6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope'and `location'variables in the Uppsala / PRIO dataset.", "output": {"entities": {"named_data": ["ACLED dataset", "Uppsala / PRIO dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**4)** **Slope** is measured by the area weighted average of slope categories. This variable is calculated based on data from the Harmonized World Soil Database version 1.2 with eight slope classes: 1 for least steep (elevation of 0-0.5 percent) and 8 for most steep slope (elevation greater than 45 percent).\n\n**5)** **Rainfall variability** is defined as the 1981-2010 standard deviation of monthly rainfall levels. The variable is constructed from the global CRU TS3.21 dataset from the University of East Anglia, containing a long-term time series of monthly rainfall levels at 0.5x0.5 grid resolution, which was produced using statistical interpolation based on data from 4,000 weather stations (Harris et al., 2014).\n\nThe VHLSS provide detailed information to estimate consumption expenditure, which can be used to estimate poverty rates. This study uses district-level poverty maps based on estimates from the VHLSS 2010 combined\n\n<!-- PAGE 6 --> with the 15-percent sample of the 2009 Population and Housing Census as calculated by Lanjouw et al. (2013). In addition, household consumption is calculated from the VHLSS 2010, 2012, and 2014 based on detailed expenditure data in line with the methodology for determining the GSO-World Bank poverty line. All consumption values are expressed in 2011 Purchasing Power Parity (PPP) values using data on the Consumer Price Index from the World Development Indicators.", "output": {"entities": {"named_data": ["Harmonized World Soil Database", "CRU TS3.21 dataset", "2009 Population and Housing Census"], "descriptive_data": ["district-level poverty maps", "Consumer Price Index from the World Development Indicators"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, it also highlights the APC’s limited capacity to collect protection data and build up a more\nrobust and comprehensive protection analysis. The main challenges have been identified as limited\nhumanitarian access, sensitivity related to some protection thematic, a lack of information sharing\namong different agencies, limited capacity and knowledge to operationalize the protection risks\nequation, a lack of conflict sensitivity analysis and limited protection assessments. The available\nprotection analysis is usually limited to the overall chronic issues in the country, while conflict and\ndisplacement related risks and threats remain less explored and assessed.\n\nThose limitations can be overcome should the APC members prioritize the resources to fill those gaps\nand increase their commitment to the coordination system for the protection sector.\n\nA workshop with APC members will be organized in the course of August 2017 which will provide an\nopportunity to produce a joint protection analysis that would support a prioritization exercise.\n\nIntegrated protection response plans are also under development at regional level and will provide a\nregional perspective of the protection context and priorities.\n\nPage **10** of **12**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "is largest in the Somali domain (where it increases to 89 compared 76 percent for males). A similar pattern is observed regarding being comfortable with having a refugee neighbor. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Strongly agree Agree Disagree Strongly disagree Percent Figure 7.1: Host response to “Refugees are good people” Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Comfortable Neutral Not comfortable Percent Figure 7.2: Host response to “Would you feel comfortable having a refugee as a neighbor?” Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 65 0 10 20 30 40 50 60 70 80 90 100 Male Female Male Female Male Female Male Female Eritrean Somali South Sudanese Addis Ababa Comfortable Neutral Not comfortable Percent Most Ethiopian hosts want refugees to have access to free primary education and healthcare and the right to work, and to live where they choose. Eighty-seven percent of hosts believe that refugees should have the right to free primary education and healthcare, increasing to 95 percent in", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "11 the region. Third, again due to cultural closeness, the nature of these variations is known across the region. 9 Additional measures: We collected usual socio-economic and demographic information, including: age, gender, marital status and education. In addition, we collected data on self- reported risk preference and a short-form personality survey to ascertain GRIT among participants. Noting that a small number of participants did not answer all survey questions, we undertake a regression-based data interpolation process to complete the dataset. 10 Table 1: Partner Assignment and Sample Sizes by Treatment and Community Status Host Refugee Ingroup Outgroup Ingroup Outgroup T C T C T C T C Outreach / Baseline 219 48 203 48 147 72 147 49 Endline 179 34 222 37 148 45 133 51 We present summary statistics of demographic data and other covariates for the baseline (Top) and endline (Bottom) for Jordan in Table 2 and for Lebanon in Table 3. [TABLES 2 AND 3 ABOUT HERE] Identification: The “ fuzzy ”, treatment intake is not random. As can be seen in Table 1, there are some elements of attrition from the sample. The sample decreases by about 10 % from baseline to endline.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "capital, and concentration in low wage jobs. Furthermore, most of these jobs are in the service sector which potentially could have freed up time, especially for Peruvian women to be more engaged in the labor market, as well as lowered the costs for these types of goods and services. Venezuelans also might have expanded opportunities by increasing the demand for certain goods and services. One widespread claim mentioned in some media reports is that Venezuelan migration led to an increase in crime (Freier et al., 2021). We test whether this claim is supported by the data in Table 6, where we use administrative information on the number of non-violent and violent crimes reported in each municipality, the personal security index from Gallup, and reports on whether crime is perceived as a major problem in ENAHO. The structure of this table is the same as the previous with Panel C our preferred speciﬁcation. Consistent with the idea that Venezuelan inﬂow lead to labor market conditions improving, we observe that locations that received a larger number of immigrants have lower number of reported non-violent crimes (columns 2). This eﬀect is large with a double of Venezuelans in a province leading to a 42 % decline in reported non-violent crimes. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Beneficiary assessments confirmed that the most significant barrier to cooperative membership for women was the requirement to present land title documentation as proof of agricultural practice. Since women in the project area rarely hold formal land title independently of their husbands, this requirement effectively excluded female farmers from cooperative membership. The project's operational manual was revised to accept a sworn declaration of land use from the village council as an alternative to formal land title for female applicants.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of difficulty, ranging from those suitable for children as young as 2 and a half years old to more complex items for individuals over 18 years old. 19 The Peabody Vocabulary Test is calibrated with a mean standard score of 100 and a standard deviation of 15, placing scores between 85 and 115 within the average range. This test, in its Spanish edition, has been validated for use in Colombia. Additionally, to ensure relevance and accuracy for Venezuelan nationals, we conducted a preliminary validation by administering the test to a sample of Venezuelan mothers in our study. This step confirmed that the words used in the test held consistent meanings for participants from Venezuela. Figure B. 1 depicts the distribution of PVTS scores for Venezuelan and Colombian chil- dren and adolescents in our sample. This visualization indicates that Venezuelan minors consistently score lower on the PVTS compared to their Colombian peers across the en- tire score distribution. In Table 6, we present the average disparities in percentile rank on the Peabody scale, revealing that Venezuelan children and adolescents, who are forcibly displaced, score approximately 12 p. p. lower than their Colombian counterparts. The difference is meaningful and in turn translated into Venezuelan minors falling into the a higher likelihood of having extremely low, moderately low, and low score categories. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel. 19", "output": {"entities": {"named_data": ["EPR-ER data", "Murdock Atlas", "EPR-ER"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Second, the Turkish LFS asks respondents whether they had previously lived in a different province (one of Turkey ’ s 81 NUTS 3 regions), and if so in what year they moved to their current province. We estimate the impact of refugees on the probability a native moved to a subregion in the past year. Table 9 reports OLS and IV estimates of the impact of refugee on net population growth in subregion (Columns 1 and 2) and gross population inflows (Columns 3 and 4). Net population growth is estimated at the level of NUTS 2 subregions. Population inflows to a subregion are estimated at the individual level (and standard errors clustered by subregion- year). All regressions include subregion and year fixed effects and a year-specific control for log distance from the Syrian border. The first column presents the estimates for the whole sample, subsequent columns for different sub-samples by gender, age and education. For the full sample the net population growth in a subregion is positively correlated with refugee flows, while the IV point estimate is negative (though neither estimate is statistically significant). The probability of a Turkish person migrating to a subregion is negatively correlated with refugee flows (the OLS estimate is highly statistically significant). The IV estimate is of a similar magnitude, but no longer statistically significant. This same pattern broadly holds for both women and men. The only other statistically significant IV estimates are a decrease in the population aged 15 – 24, an age group that is likely more mobile, and of those with medium educational attainment. There is also a decrease in the inflow of low Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Turkish LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "that are at a maximum distance of 80 km from the 7, 547 clusters. 12 Figure 1 shows the locations of these refugee camps and clusters. Clusters are represented in green, while clusters in the vicinity of a refugee camp are represented in red. Refugee camps are designated with a red + sign. There are some important limitations associated with this data. First, the data only provides information on refugees residing in camps monitored by the UNHCR. In Figure B. 7, we combine the UNHCR refugee camp data on the annual number of refugees and the UNHCR official statistics on refugees (which includes people in refugee-like situations) at the country level. 13 Although the overall trends match, our constructed dataset clearly underestimates the true refugee population in Africa, which is not surprising since our camp-specific data does not contain dispersed refugees or refugees living outside of camps. While our data seem to represent quite fairly the number of refugees in camps, there is significant heterogeneity across countries. Based on the visual inspection of Figure B. 8, the quality of the refugee data appears to be less reliable for the following countries in our sample: Gabon, Mali, Senegal, and Togo. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR official statistics on refugees"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "and girls. Maternal and reproductive health services remain underdeveloped in some areas, struggling\nto be fully effective due to a lack of material and financial support.\n\n**5.2** **Social inclusion**\n\nThe most significant differences or restrictions in terms of socio-economic development affecting refugees\nwith particular characteristics are:\n\n**a.** **Access to biometric identity cards and unique identifier number:** The continued lack of access to\n\nlegally recognized identity cards for refugees issued by the competent authority in Chad, ANATS, and\nnational identification numbers, challenges their inclusion into national system and limits their socioeconomic integration in the country.\n\n**b.** **Access to civil registry civil status documents:** The low percentage of registered births, due to\n\nsignificant deficiencies in the Civil Registration and Vital Statistics (CRVS) system in Chad, especially in\nrural areas, exposes refugees born in Chad to the risk of statelessness.\n\nR E F U G E E P O L I C Y R E V I E W F R A M E W O R K > **R E P U B L I C O F C H A D** 13", "output": {"entities": {"named_data": ["Civil Registration and Vital Statistics"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The SEIS was designed as a nationally representative survey of households with at least one member holding refugee or asylum-seeker status, and the sampling frame was constructed from UNHCR's biometric registration database. SEIS enumeration areas were selected with probability proportional to size, stratified by settlement type (camp, urban, rural dispersed). Questionnaire modules cover household composition, labor market participation, housing conditions, access to education and health services, and subjective well-being. The SEIS sampling methodology ensures that estimates are representative at the national level and by settlement type.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Third, the EPAG program was designed around the girls'needs: service providers held both morning and afternoon sessions, to accommodate the participants'busy schedules; trainings were held in the communities where the girls reside; and every site offered free childcare. Fourth, frequent and unannounced monitoring visits by MoGD staff ensured that the service providers created and maintained a high-quality learning 4 There are: National Adult Education Association of Liberia (NAEAL), Community Empowerment Sustainable Program (CESP), EduCare, and Children ’ s Assistance Program (CAP). 5 A total of 19 training venues were used during the first round of training. They were chosen with the following considerations in mind: 1. Girls ’ safety, so that the buildings are not so isolated or otherwise dangerous, raising security concerns for girls. 2. Conducive atmosphere for learning, spacious and sanitary with access to water and latrine facilities. Reasonably outside community noise concentration. 3. Proximity to community center and to security posts such as police depots. 4. Accessible to girls from various parts of the community. 5 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "By 2002, ReproSalud had reached over 123, 000 women and 66, 000 men. Qualitative and quantitative evaluation data suggest that the community-based PLA approach had a positive impact on attitudes and behaviors related to gender based violence (Rogow and Bruce 2000; Ferrando, Serrano, and Pure, 2002, cited in Boender et al., 2004). The quantitative evaluation (using community based surveys) was complicated by the fact that the project coincided with a period of strong investment by the Ministry of Health, which made it difficult to isolate the project ’ s impact. Gender-equitable attitudes and practices increased significantly in both intervention and control communities, though improvements in intervention sites were slightly higher. The qualitative data suggested a much greater difference in intervention and control sites and gathered evidence of dramatic changes in social relations and men's behavior. Respondents spoke at length about decreased alcohol consumption, domestic violence, and forced sex in all intervention villages studied. In the words of one 35 year-old woman,\"Before, they brutally forced sex. They hit, especially when they were drunk. Now, no more\"(Rogow and Bruce, 2000, page 20). Individual behavior change strategies Many other programs have attempted to produce individual (rather than community-level) behavior change by working with individual men and boys. White, Greene and Murphy (2003) reviewed the literature on such programs aimed at men. That review suggests that less information is available on the effectiveness of individual behavior change strategies compared to community-level approaches. Some Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative evaluation data", "community based surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 The article employs a unique data source from Jordan, the 2010 Jordan Labor Market Panel Survey (JLMPS 2010), which includes information on parents ’ schooling for every adult in the sample, along with the 2010 School Census produced by the Jordanian Ministry of Education (Hashemite Kingdom of Jordan, 2010). The school census provides the subdistrict, type, and date of establishment of every school in Jordan, allowing us to measure the local supply of each type of schools in each subdistrict in every year (under the presumption that there were no significant school closures or changes in type over time, which is likely the case). The exposure of an individual in the JLMPS 2010 sample to the supply of public schooling is then determined by the number of sex-appropriate basic (or secondary) public schools (per 1, 000 individuals) that were available to them in their subdistrict of birth at the time they were of age to enroll in that school level (six years of age for basic and 15 years for secondary). The richness of the data set makes it the first in the Middle East to allow such a study. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["2010 School Census", "2010 Jordan Labor Market Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Finally, we show using the IPUMS dataset that children born in the United States to men born in China are more likely to be boys, but this finding does not hold for children born to women from China.\"\n\nA very clear pattern emerges from the third of a million births in the one percent sample of the\n1990 Chinese census. Note that this pertains to births during 1989-90, which is close in time to\nthat for which Oster made her calculations on the proportion of the female deficit in China\nattributable to HBV (Oster 2005, Table 11).\n\nUsing data on expected average annual loss (AAL) and estimates of AAL reductions resulting\n\nSurvey results indicated that only 55 percent of respondents reported understanding the insurance\n\naversion may influence adoption of index insurance (Bryan, 2010), in the Gujarati data the", "output": {"entities": {"named_data": ["IPUMS dataset", "1990 Chinese census"], "descriptive_data": ["data on expected average annual loss (AAL)", "Gujarati data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 3. ANALYSIS Has the increase in Syrian refugees impacted the welfare and socioeconomic conditions of the host community? Summary statistics in the previous section showed clear trends of increasing poverty among recent migrants throughout the country, both near the Syrian border and across rest of the country. While the poverty rates of recent migrant households spiked in 2013, poverty of host community households maintained a relatively constant level in the whole country. From these trends, it appears that there at least has not been an increasing trend in poverty among the host community over the latest years. The empirical model is shown in Equation 1. Regressions are estimated at the NUTS2-year level and using data from only the years 2011 and 2013. The dependent variable of interest is the host community poverty rate by region and year, where the poverty rate is based on spatially deflated imputed household income. Unlike the computation of the poverty rates, “ recent migrant ” information is not used for the analysis. Only the host community poverty rates are calculated using the LFS and the number of Syrians are taken from government sources. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 In this study, we apply their techniques to understand gender differences among adults as well. 2. 2 Intrahousehold analyses of multidimensional poverty The literature on multidimensional poverty measurement and intrahousehold analysis is limited. Espinoza-Delgado and Klasen (2018) propose an individual-based multidimensional poverty measure for Nicaragua and estimate gender gaps in headline statistics. Klasen and Lahoti (2016) question the neglect of intrahousehold inequality in multidimensional poverty indices by comparing a standard household-level MPI and an individual-level MPI to the MPIs proposed by Alkire and Santos (2014) and UNDP (2014), finding that females recorded a far higher poverty rate when using the individual measure and that age differentials in poverty were also larger. We follow their work of investigating poverty in the indicators for which individual data is available and compare the achievements of men and women and boys and girls living together.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "REGIONAL BUREAU FOR SOUTHERN AFRICA\n\n###### **POPULATION OF CONCERN IN SOUTHERN AFRICA REGION**\n\n30 September 2022\n\n**PoCs IN SOUTHERN** **AFRICA REGION***\n\n**521,512** **REF**\n\n**41,283** **REF**\n\n**13,762** **ASY**\n\n**1,291** **ASY**\n\n**5,526,022** **IDP**\n\n**REF**\n\n**ASY**\n\n**OOC**\n\n**RET**\n\n**KEY FIGURES**\n\n## **8,572,919**\n\nTotal Population of concern\n\n###### **1,099,585**\n\nRefugees, asylum-seekers, other\nof concern & returnees**\n\n**785,119**\n\n**278,090**\n\n**36,165**\n\n**211**\n\n###### **7,473,334**\n\nConflict induced and Natural Disaster IDPs\n\nNatural Disaster IDPs\n\n**528,466**\n\n**7%**\n\n**6,419,356**\n\n**86%**\n\nIDPs RET\n\n**525,512**\n\n**7.0%**\n\ndo not imply official endorsement or acceptance by the United Nations\n\n**Author: UNHCR DIMA - RSA** Contact : rsarbdima@unhcr.org **Source:** UNHCR Primes, Government, IOM, OCHA, UNHCR\n\n**Author: UNHCR DIMA - RSA** Contact : rsarbdima@unhcr.org **Source: REF, ASY, OOC, RET** (UNHCR PRIMES, Government); **IDP DRC** (OCHA); IDP Zimbabwe & Mozambique (IOM); **IDP ROC** (Government, Ministry of Social Affairs and Humanitarian Action (MASAH).\n\n*PoCs = Persons of Concern   ** REF = Refugee; ASY = Asylum-seeker; OOC = Other person of concern; RET = Returnee. DRC = Democratic Republic of the Congo  ROC = Republic of the Congo Date of creation : 30 September\n2022\n\nFor more information visit: UNHCR Data Portal", "output": {"entities": {"named_data": ["UNHCR Primes"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3. 5 Forecasts of Program Impacts The forecasters were mainly researchers working on topics in migration and development eco- nomics, or those with expertise in humanitarian program implementation, and were asked to predict effects on the program ’ s primary outcomes. For round 1, a total of 61 individuals, comprising 37 researchers and 23 non-researchers, responded to surveys about the program ’ s effects on refugee well-being. For round 2, 63 respondents, including 53 researchers and 10 non-researchers, pro- vided predictions on the effects on neighbors ’ social cohesion responses. Gathering forecast data allows us to assess whether estimated impacts were in line with the prior beliefs of research and policy experts. Table 2 presents the mean predicted effects, as well as the interval from the 10th to 90th percentiles of predictions. Generally, forecasters anticipated modest improvements in refugee well-being, particularly regarding housing outcomes, while predicting no average impact on the social cohesion measures collected among neighbors. 4 Empirical Strategy 4. 1 First stage compliance with program assignment Table 3, Panel B presents the ﬁrst stage analysis and the HSP take-up rate of 33 %. While sta- tistically signiﬁcant, the compliance rate is lower than expected, especially given the substantial funding offered by the program. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the standard ethnic diversity indices to include the annual variation in refugee ethnicities. 8 We then construct a measure of proximity between the clusters in the host country and refugees in surrounding camps by defining an 80-km buffer around each cluster. 9 To control for unobserved heterogeneity and changes within a given cluster, we introduce cluster and year fixed effects, αj and δt. To minimize the risk of confounding the refugee-induced changes in diversity with the annual changes in refugee numbers, we also control for the presence of refugees based on the same buffer as the one used to construct the refugee-induced change in diversity. More specifically, the variable Refugeesjt − 1 counts the number of refugees present in cluster j at year t − 1 within the predefined buffer. The variable is also transformed into an inverse hyperbolic sine to ease interpretation. Finally, Qjt controls for yearly shocks at the cluster level, such as weather shocks. In particular, we control for rain and temperature anomalies. Standard errors are clustered at the Afrobarometer cluster level. 4. 2 Data and descriptive statistics Our analysis combines various sources of data: Afrobarometer, UNHCR refugee camp data, Armed Conflict Location and Event Data (ACLED), Uppsala Conflict Data (UCDP), and the Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Armed Conflict Location and Event Data", "Uppsala Conflict Data", "Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset"], "descriptive_data": ["UNHCR refugee camp data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "month placement and support phase in which the trainees were supported in their transition to self or wage employment. Upon recruitment, the participants are assigned to a\"Job Skills (JS)\"track or a\"Business Development Services (BDS)\"track. When possible, the participant's track preference was honored; however, the demand for the Job Skills track greatly exceeded the supply, so the remaining trainees were placed into the BDS track. In the first round of training, the proportion of Job Skills track places was limited to 35 % of the total training places available given the expectation that few wage jobs will be available in the Liberian job market. The Job Skills track provided training in six areas: 1) hospitality, 2) professional cleaning / waste management, 3) office / computer skills, 4) professional house / office painting, 5) security guard services, and 6) professional driving. These areas were determined based on independent labor market assessments, a review of the available market data, and input from EPAG ’ s private sector partners. All Job Skills trainees received training in entrepreneurship skills as well. The BDS training taught young women how to identify micro-enterprise opportunities based on an assessment of market needs, and how to grow and manage any existing businesses they already had.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["available market data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Notably, while we cannot rule out that time outside due to employment may play a role (e. g., fresh air may boost one ’ s mood), our time-use data indicates that the average refugee already spends at least three hours outside per day, with no measurable diﬀerence between employed and cash arms. As we are powered to detect changes of at least twenty minutes for each activity, our results suggest that large substitutions away from unsavory activities are unlikely to be driving the improvements in psychosocial well-being, insofar as the respondent recalls. 1718 We also investigate whether those who were more idle prior to being employed beneﬁt more from employment. We ﬁnd no impact along this margin, suggesting that the elimination of boredom per se is not the driving force behind the psychosocial value of employment (Appendix Table A10). 17Most respondents do not track their day by time, making collection of reliable time use data challenging (though recent literature documents the broader unreliability of such data). We piloted a variety of strategies, and settled on asking respondents how much time they spent on a set of activities in the previous day. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "this marginal progress. In 2020, the COVID pandemic closed markets, albeit relatively briefly, and this immediately decreased job opportunities. While most reentered the labor market, many changed their work situation, and some permanently exited the workforce or reduced working hours. At the same time, more and more people— rural women in particular—are unemployed or out of the labor market altogether. Nationally, unemployment doubled between 2013 and 2021, and it tripled in rural areas. Women and youth both saw particularly sharp increases in unemployment. There was also a decrease in the labor force participation rate (LFPR), from 86 percent in 2013 to 74 percent in 2021, following a long period of steady LFPR in the two previous LFS surveys. Like for unemployment, LFPR was much more affected in the rural labor market, and these trends are particularly striking for women. 3. Jobs and Livelihoods Jobs and Livelihoods 25 Within this challenging context, vulnerable populations—including refugees—face unique barriers to accessing quality work. On average, rural women, urban youth, people with disabilities, and rural-urban migrants are more likely to be inactive and less likely to have improved their livelihoods over the last two decades. In this context, it is unsurprising that refugees cite", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["LFS surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "If many people migrate to a speciﬁc location, such as the capital city, this is likely to aﬀect wages, incomes, and access to amenities in that location. 7 This would generate a potential endogeneity bias due to the fact that incomes and amenities in that location result in part from the decision of many migrants to locate there. To eliminate this bias, we use past data to estimate the income regression. More precisely, let T be the period for which we have income information and T + t the period at which we 5The dropped observation corresponds to the location of origin M h ii which, as explained earlier, we do not include in the analysis since including M h ii would mean de facto including the decision of whether to migrate or not. 6McFadden (1974) has shown that, in multiple choice problems of the kind studied here, the application of logit estimation is justiﬁed if (1) the errors in each latent choice equation follow the extreme value distribution and (2) errors are independent across choices. See Train (2003), Chapter 3 for a detailed discussion. The estimation of models with correlated errors across choices requires either multiple integration or the use of Bayesian estimation techniques relying on Gibbs sampling. With a choice of over 70 possible destinations, multiple integration is out of the question. Gibbs sampling remains a possibility but would require extensive programming. We choose instead to keep the logit approach but to correct the standard errors for possible correlation in errors across choices. In our case the possible eﬃciency gain achieved by Bayesian methods does not appear to justify the programming cost. 7The eﬀect could be negative — e. g., congestion — or positive — e. g., agglomeration externalities. 10 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["past data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "- This report includes all referrals data for the period January to December 2016\n\n- Data was collected from 11 sites; 6 Urban (Amman, Zarqa, Mafraq, Irbid, Ramtha,\nand south Mobile Medical Unit), 4 camps (Zaatri, Azraq, Cyber City and King\nAbdullah Park), and Ruwaishid.\n\n- Data was captured on-site daily then compiled and shared on a monthly basis with\nJHAS referral hub, where the initial data compilation and cleaning was done.\nCompiled and cleaned data was then shared with the UNHCR Public Health Unit,\nwhere secondary data cleaning and analysis was carried out.\n\n- Descriptive analysis carried out using Microsoft Excel 2013.\n\n**3 |** P a g e", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "7 UNHCR Pakistan PA 2016 participants reported regularly experienced arrest, detention, threats of deportation and extortion \n(Punjab: 24% female / 17% male; KP: 4% female / 5% male; Sindh: female 7% /male 10%; Baluchistan: 7% female /  4% male) \n8 Locations that reported Police Harassment to be the most common safety and security concern, include Peshawar in KP, Attock \nand Rawalpindi in Punjab, and Chiltan Town (Tehsil) in Baluchistan. However, male respondents from Chiltan Town Tehsil (Killi Landi \nKuchlak, Qadri Abad, Ghous Abad) reported a significant improvement in the overall security situation compared to last year.", "output": {"entities": {"named_data": ["UNHCR Pakistan PA 2016"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Humanitarian organizations such as UNHCR and OCHA as well as international organizations such as IOM are also involved in the collection of data on IDPs, often involving international and local 59 OCHA is the part of the United Nations Secretariat responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. See http: / / www. unocha. org /. 60 This section draws heavily on the “ Report of Statistics Norway and the Office of the United Nations High Commissioner for Refugees on statistics on refugees and IDPs ” presented at the UNSD in March 2015. 61 The number of countries where UNHCR exclusively collects data on refugees declined from 76 in 2010 to 72 in 2014, while the proportion of countries where refugee data were exclusively provided by governments gradually increased over the same period from 33 to 38 percent. In 2014, the proportion of countries where data were provided through collection conducted jointly by governments and UNHCR was 15 percent, while in the remaining proportion (13 percent), refugee data were provided exclusively by NGOs and other organizations. In 2014, more than 173 countries and territories provided data on refugees. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The age categories are 15 – 19, 20 – 24, 25 – 29, 30 – 34, 35 – 39, 40 – 44, 45 – 49, 50 – 54, 55 – 59, and 60 – 64 years. There are 183 groups since we exclude groups containing less than 40 observations. 24 An additional advantage of the IV approach is that it helps deal with measurement problems. Despite the improved measures of refugee numbers in Turkey by subregion starting in 2014, there is likely considerable measurement error, resulting in attenuation bias in the OLS estimates. For the IV estimates to be consistent, it is only necessary that- conditional on the fixed effects and control variables- the flows of Syrian refugees are uncorrelated with the instrument. 25 Using data from AFAD (2013) we can also weight the aggregate refugee numbers using the Syrian source governorates of refugees in 2012-13 (see Figure 2). Results are qualitatively robust to this alternative instrument and first-stage F-statistics about the same. We prefer the use of the pre-war distribution of population in Syria,", "output": {"entities": {"named_data": [], "descriptive_data": ["data from AFAD"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 Bearing in mind that this data is then translated to semi-annual data, i. e. the specific month is not used, among those who reported the starting month of their different jobs, the probability of starting a job in a given half-year was approximately 50 percent. We, therefore, think that a random assignment that gives equal weight to all months (and to each of the two half years) is a sensible choice. We construct a semi-annual synthetic panel. In doing so, we assume that each individual in the sample is observed over the period from the first half year of 2016 to the second half year of 2020. Individuals who entered the job market later than the first half of 2016 are considered non- employed until entry. Using the starting and ending dates for all the jobs an individual has engaged in, we can identify the individual ’ s job finding and job separation events. In particular, we convert the starting and ending month of a job into the half year they belong to. For example, a person who started a job in May of 2017 is coded as found a job in the first half year of 2017. In Table 2, we present the transitions by half-year, nationality of individual, their camp residency if they are Syrian and the imputation status. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The most recent labor force survey from Liberia mirrors these statistics: there are roughly 1. 1 million people in the workforce, of whom 195, 000 (about 18 percent) are engaged in wage employment; the remaining 900, 000-plus workers (82 percent) are considered in vulnerable employment, working for themselves or working unpaid for their own households (LISGIS 2010). Among young women (15-24) in Liberia, the unemployment rate is 8 percent, double the rate among young men (LISGIS 2010). Most of these gaps can be explained by differences across individuals, especially in educational attainment, skills training, and years of experience. But segregation, market segmentation, and discrimination do play a role in determining these individual characteristics. Women have fewer opportunities for education or training, less access to credit, a larger share of domestic responsibilities, and less independence and control over their own lives. In Liberia, women comprise half of the employed, but only about one-quarter of paid employment (LISGIS 2011). Fourteen years of civil war in Liberia devastated the country ’ s infrastructure and institutions, and left a generation of young people with very low levels of education and training. Girls were particularly disadvantaged. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant ’ s for these households remain after humanitarian assistance is received. To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV). The ProGres database for Jordan includes information on refugees ’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘ proof of registration ’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016).", "output": {"entities": {"named_data": ["Jordan Home Visits round 3", "Profile Global Registration System (ProGres)", "Profile Global Registration System", "asylum seeker certificate"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 8: SIGAF Configuration and Reporting Requirements**\n\nThe SIGAF installation at the PIU uses Version 4.2 of the government's integrated financial management system. Key configuration parameters include: project budget structure mapped to the loan disbursement categories; reporting hierarchy linking district accounts to the central project account; and automated monthly statements that consolidate transactions across all spending units. SIGAF user accounts are managed by the Ministry of Finance's system administrator and must comply with the government's password security policy updated in 2022.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Restrictive norms perpetuated by men continue to severely restrict women ’ s access to the formal labor market. Fortunately, the Jordanian government has worked in recent years to address this problem. For example, in the amendment made to the Jordanian labor law in 2019, the provision of childcare services was made mandatory in every business that has 15 or more employees, male or female, with children under 5 (World Bank, 2019). However, implementation is not consistent and low-income households report less access to childcare services (Weldali, 2022). Home-based businesses can also provide important access to economic opportunities for women, albeit within a limited number of sectors and the Government of Jordan ’ s 2017 amendments of the regulations governing the licensing of home-based businesses is particularly important for female entrepreneurs in both host and refugee communities (Slimane et al, 2020; Turner, 2019). 3. Data and descriptive statistics We use two rounds of household-level data collected by UNHCR in 2013-14 and 2017-18. 6 These data come from two sources, namely, the Profile Global Registration System (ProGres) and Jordan Home 6 For simplicity, from here on we refer to the first wave as the 2013 wave, and to the second wave as the 2018 wave. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Women ’ s Empowerment in Agriculture Index (WEAI) (Alkire et al. 2012) uses individual-level data, and the linked Gender Parity Index reflects inequalities across women and men ’ s deprivation scores within the same household. Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer. Espinoza-Delgado and Klasen (2018) create an individual-level MPI to understand differences in poverty between women and men in Nicaragua, finding similar overall incidence, but much higher intensity of poverty among women. Bessell (2015) and Pogge and Wisor (2016) explore deeply contextual gendered poverty measures and elucidate the ways that participatory consultations can inform the design and uses of gendered measures. Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa. Alkire, Ul Haq, and Alim (2019) use individual-level data alongside MPI data to expose gendered and intrahousehold differences among MPI poor and non-poor children.", "output": {"entities": {"named_data": ["EU-SILC data sets", "global MPI", "Women ’ s Empowerment in Agriculture Index", "Gender Parity Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 3: Flood Hazard Maps — Data Sources and Update Schedule**\n\nFlood hazard maps were derived from three hydrological datasets: (i) daily streamflow records from 18 gauge stations maintained by the national water authority for the period 1990–2022; (ii) a 12.5-meter resolution digital elevation model produced from ALOS PALSAR radar data; and (iii) land cover classifications from the national land use inventory. Flood hazard maps will be updated at project midterm using a revised hydrological model that incorporates non-stationarity in flood frequency distributions attributable to observed trends in extreme precipitation.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). As the Sudanese sample surveys IDPs living in the Abu Shouk and El Salam camps, we must understand these deprivations with the background that these settlements were created as emergency and crisis responses rather than durable, long-term solutions (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019). Although Sudan does have a national electric grid that supplies electricity to the urban and peri-urban areas of the nearby city El Fasher, IDP communities living in the camps report limited connection to the city ’ s electricity supply, reflected in the high deprivations in the electricity and cooking fuel indicators. The ad-hoc construction of dwellings in the two camps explains why 71 % of the IDP households in Abu Shouk and 65 % in El Salam live in tukuls or other permanent mud or wood structures (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019: p. 50), both of which register as unimproved housing types.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Using a survey of 4,500 manufacturing firms for the year 2010-11, this paper estimates the impact of electricity shortages on firm productivity in Pakistan.\n\nWe match firm-level data from the Census of Manufacturing Industry conducted by the Pakistan Bureau of Statistics with district-level power shortage data reported by distribution utilities.\n\nThe Census of Manufacturing Industries provides a thorough annual overview of firmlevel activities, including detailed information on a variety of input costs, including labor, capital and electricity, as well as revenue data. The 2010-2011 census mainly covers firms in Punjab province.\n\nThe census covers 4,499 firms in 23 sectors at the 2-digit level of Pakistan Standard\nIndustrial Classification (PSIC). [ 4] The distribution of firms by the 2-digit classification are\nshown in Table 1. Of the 23 divisions, number 13 (Manufacturing of Textiles) covers\nroughly 28 percent of our sample, division 10 (Manufacturing of Food Products)\naccounts for 15 percent, and division 32 (Other Manufacturing) is the third-largest with\n11 percent of the sample. The 23 sectors can be further broken down into 236\nsubcategories at the 5-digit level of PSIC.", "output": {"entities": {"named_data": ["Census of Manufacturing Industry", "Census of Manufacturing Industries"], "descriptive_data": ["survey of 4,500 manufacturing firms"], "vague_data": ["power shortage data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "violent conﬂict. Besley and Mueller (2015a) argue that foreign investors seem to know that growth volatility changes with strong executive constraints and therefore react signiﬁcantly to their adoption. In summary, the literature suggests that a lack of constraints on executive power at the country level could play a key role in building inequalities across regions and ethnic groups. In the absence of strong executive constraint, we expect regions populated by ethnic groups that have access to executive power to perform better relative to others due to ethnic favoritism. Conversely, excluded ethnic groups should experience relatively worse economic performance compared to other groups in the absence of such constraints. 46 To test these hypothesis we use data on ethnic groups ’ access to executive power and night light intensity from the GROWup Research Front-End (RFE Release 2. 0) dataset and executive constraint data from the Polity IV dataset. We use night light intensity as a proxy for economic activity at the ethnic group level. 47 Night light data has the beneﬁt of being available on a yearly basis and of being measured at the local level where there is poor availability of statistical data. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["GROWup Research Front-End", "Polity IV dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The household head interviews also provided an opportunity to gather useful household-level information (including assets, housing characteristics, and household head characteristics) to be used as control variables in our analysis. The baseline survey was conducted before participants were informed of the outcome of the randomization, and the midline survey was conducted one year after the baseline survey, one month after completion of the first round of training and before the start of the second round. The results in this paper are based on a panel data set that includes data from the baseline and midline surveys as well as administrative and monitoring data from the program implementation team. Note that although the midline data collection occurred just one month after the completion of the intervention, the intervention itself includes 6 months of post-training follow-up; hence the midline survey captures outcomes of the first cohort 7 months after they exited the classroom-based training. This timing was necessary to prevent too long of a delay between cohorts; the second cohort of trainees started immediately after the midline survey was completed. Future analysis using endline data will examine the trends in outcomes of both groups after Round 1 completion, but will not involve comparison against a pure control group of non-participants. 3. 2. Sample and attrition Following the communications and outreach campaigns in each of the nine target communities, during which 2, 106 young women were originally recruited to be a part of the EPAG program, 8 a randomized 8 The original recruitment effort fell short of the target of enrolling 2500 participants. Hence, before the second round of training, another recruitment effort was launched and an additional 617 girls were enrolled. These girls are not included in the impact evaluation since they were not subject to the initial random assignment. 7 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel data set"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the West Bank and Gaza. Out of all the estimated coefficients for each period for both outcomes (job loss and job gain), we only find a negative effect on job gain in 2019Q4, which is very small in magnitude (1 percentage point). Taken altogether, the results presented in this section bolster our confidence that we are correctly identifying the effects of the pandemic shock on labor market outcomes. Figure 12: Placebo effect on labor market flows Notes: The figure shows the output of a placebo test with a set-up analogous to Figures 6 and 9. We perform the same regression as specified in Equation (2). Our sample includes data from 2018Q2 to 2020Q1 and assumes that the pandemic started in 2019Q2. Therefore, the post-pandemic period refers to the quarters between 2019Q2 to 2020Q1. The analysis is restricted to men aged 20-59. 7 Conclusion This paper examines the effect of the pandemic on labor markets in the West Bank and Gaza using quarterly labor market data provided by national labor force surveys. With a focus on men ’ s labor market outcomes, this paper sheds light on how labor markets in the West Bank and Gaza adjusted to the COVID-19 shock examining adjustments at the extensive (employment) and intensive (hours of work) margins.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national labor force surveys", "quarterly labor market data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The poverty map for 2009 is the result of collaboration between the World Bank and INSTAT (National Statistics Institute) and uses data from the 2009 census and the 2010 ELIM. One major methodological difference between the two maps is that the map of 1998 is based on a consumption model that considers urban and rural areas inside each administrative region with a total number of 17 models, whereas the map of 2009 uses only 8 regional models.\n\n|Figure 16a Own revenues (CFA francs/capita),<br>2005-08|Figure 16b Capital investment and Current<br>expenditures (CFA francs/capita), 2000-08|\n|---|---|\n|<br> <br>|<br>|\n|**Figure 16c Capital investment by sector (CFA**<br>**francs/capita, 2000-08)**|**Figure 16d Components of current expenditures**<br>**(CFA francs/capita, 2006-08)**|\n|||\n|_Source:_ Authors' calculations from ODHD (Sustainable Development Observatory) data 2003, 2006, 2008.|_Source:_ Authors' calculations from ODHD (Sustainable Development Observatory) data 2003, 2006, 2008.|\n\nA clean econometric identification of the impact of mining activities on local government budget\noutcomes would require data on budgets for both mining and non-mining _communes_ during preand post-mining. However, we have annual budget data available for only 2006 to 2008, during\nwhich most of the mines had already opened. Industrial gold production started with the first site\nin Sadiola in 1996, just two years before the second population census of Mali. Fortunately, we\nare able to use the 1998 population census to fully control for some initial differences across\ncommunes, such as poverty and education levels prior to the mining boom. We also test whether\nlarger mines have bigger impacts on budget outcomes by using the cumulative level of production\nmeasured in metric tons as an alternative measure of mining activity.", "output": {"entities": {"named_data": ["2009 census", "2010 ELIM", "ODHD (Sustainable Development Observatory) data"], "descriptive_data": [], "vague_data": ["annual budget data", "population census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Power shortage data are reported annually by distribution companies (DISCO) to NEPRA and are published in the DISCO Performance Evaluation Report. The latest evaluation report includes shortage data at the DISCO level from year 2010-2011 to year 2014-2015.\n\nSource: DISCO Performance Evaluation Report 2014-2015 by NEPRA.\nNotes: SAIFI is the average number of interruptions that a customer experiences in a year.\nSpecifically, SPAIFI is calculated as the total annual number of consumer supply\ninterruptions divided by the total number of consumers that the distribution company\nserves in any a given year. For the purposes of illustration, the index has been\ntransformed using the inverse hyperbolic sine.\n\nThe second measure, the System Average Interruption Duration Index (SAIDI), captures the outage duration (in minutes) that an average customer experiences in a year.\n\n- (2016). Enterprise Surveys. Available http://www.enterprisesurveys.org,\nAccessed June 10, 2017.\n\n\n13\n\n\n\n<!-- PAGE 16 -->\n### **Figures**\n\n**Figure 1 Power outages and their impact**\n\nSource: World Bank Enterprise Survey for Pakistan (2013)", "output": {"entities": {"named_data": ["System Average Interruption Duration Index (SAIDI)", "Enterprise Surveys", "World Bank Enterprise Survey for Pakistan"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "E Nigeria Somalia Sudan Host community Refugees Host community IDP Host community IDP Host community IDP No earners 40 % 51 % 62 % 50 % 32 % 64 % 7 % 45 % Remittance recipients only 7 % 8 % 76 % 69 % 16 % 37 %-- Female single earner 12 % 52 % 23 % 37 % 35 % 70 % 24 % 55 % Male single earner 8 % 23 % 4 % 14 % 24 % 50 % 8 % 39 % Majority female earners 3 % 57 % 9 % 28 % 9 % 28 % 15 % 45 % Equal contribution 10 % 24 % 17 % 16 % 23 % 63 % 8 % 41 % Majority male earners 5 % 16 % 15 % 19 % 20 % 52 % 9 % 39 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Overall, the results show that besides gender, displacement status and the number of household contributors plays a key role in the identification and level of poverty. In comparison with female-headed non-displaced households, more female-headed displaced households are classified as multidimensionally poor. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Some data sources (such as registration systems and population censuses) are more appropriate for estimating stocks of asylum-seekers, refugees and IDPs at a particular point in time, while other data sources (such as population tracking systems and border crossings) are more appropriate for estimating flows over a specific period. In general, there is a lack of comprehensive and up-to-date data available on all stocks and flows for a particular displacement situation (see Table 4). Consequently, data on flows might be used to estimate stocks, for example in the absence of government data, the stock of refugees in many industrialized countries is estimated by UNHCR based on 10 years of individual asylum-seeker recognition. And, especially in the case of IDPs, changes in the total population combined with some contextual analysis, may be used to deduce estimates of new internal displacement or returns. However, these approximations are flawed unless data on all other flows (births, deaths, repatriation etc.) are also available, which is not usually the case. Even a static figure for the stock of IDPs in a particular location might obscure substantial flows including new displacement and returns. Moreover, there are no common definitions of the various stocks and flows, and therefore the risk of double counting or gaps cannot be discounted. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population censuses", "population tracking systems"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Beneficiary assessments conducted in the southern provinces highlighted that women-headed households faced compounded barriers to accessing project services due to mobility constraints related to caregiving responsibilities and social norms discouraging women from attending mixed-gender community meetings. In response, the project's operational manual specifies that beneficiary assessments in subsequent years must include separate consultation sessions for women, held at times and locations identified as accessible by female community members themselves.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Markandya and Pedroso-Galinato (2007) dealt with cross-sectional data of 208 countries for 2000. The underlying production function was assumed to take a nested CES form. The results indicated a relatively high elasticity of substitutability between different types of capital; for example, loss of natural capital could be made up relatively easily by increases in human and physical capital. In addition, the paper also showed that the efficiency of all capital is significantly influenced by changes in economic indicators (trade openness and private sector investment).\n\nData will be gathered for \"household final consumption expenditure (current US$)\" as an alternative.|World Bank- World Development Indicators (WDI) database| |Wages (10-45 years old)|Wages affect schooling outcomes but the direction of influence is uncertain.\n\nData for the following will be gathered: `o` Public spending on education, total (% of GDP) `o` Public spending on education, total (% of government expenditure)\n\n|Determinants|Description|References|Notes|Data Source| |---|---|---|---|---| |Income from agriculture|This variable captures the importance of agriculture to the economy.|Turner, et al. (1993); Lopez and Galinato (2005a,b); Irwin (2006)|Agriculture, value added (% of GDP)|WDI| |Demographic factors|Major determinants of land use include demographic factors such as population size and density.|Major determinants of land use include demographic factors such as population size and density.|Data needed: `o` Total population `o` Population density (people per sq.\n\nWe already have data for the following indicators of governance: `o` Government effectiveness - perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies.", "output": {"entities": {"named_data": ["World Development Indicators (WDI) database", "WDI"], "descriptive_data": ["cross-sectional data of 208 countries"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "market access Level two 0.2065*** (0.0607) Level three 0.2358** (0.0788) Level four 0.3559*** (0.0769) Distance to Zone city (Km) 0.0022*** (0.0006) Market accessibility indicator -0.0792* (0.0330) Observations 742 737 742 Chi-square test 0.0000 0.0000 0.0000 R2 0.2517 0.2435 0.2128 Source: World Bank Staff based on SESRE 2023. Standard errors in parentheses. All estimates are controlled for regions. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001 Annexes 120 Table D.17: Proximity and market accessibility effects on engagement in service sector: logit model Variables Proximity Market access Model I Model II Individual feature Male -0.2151*** -0.2199*** -0.2153*** (0.0309) (0.0304) (0.0312) Age 0.0368*** 0.0331*** 0.0340*** (0.0089) (0.0089) (0.0096) Age squared -0.0005*** -0.0004*** -0.0004*** (0.0001) (0.0001) (0.0001) Some primary 0.1561*** 0.1636*** 0.1683*** (0.0333) (0.0334) (0.0344) Household feature HH size: member age [15,29] 0.0217+ 0.0226+ 0.0248* (0.0121) (0.0117) (0.0119) HH size: member age (30,44] -0.0664*** -0.0657*** -0.0553** (0.0197) (0.0195) (0.0205) HH size: member age (45,64] -0.0146 -0.0232 -0.0248 (0.0246) (0.0258) (0.0269) HH access electricity -0.0637 -0.0571 -0.1051+ (0.0489) (0.0499) (0.0559) Proximity and market access Level two -0.3000*** (0.0713) Level three -0.3988*** (0.0821) Level four -0.4862*** (0.0832) Distance to Zone city (Km) -0.002* (0.0008) Market accessibility indicator 0.0897** (0.0307) Observations 787 782 787 Chi-square test 0.0000 0.0000", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "UNHCR regularly publishes statistical reports, including “ Global Trends ”, “ Mid-Year Trends ”, “ Asylum Trends ” and “ Statistical Yearbook ”. Additionally, UNHCR hosts interagency information sharing portals for significant emergencies. 87 These portals provide data on populations of concern at regional and country levels, including time series data, demographics, location and accommodation information. 88 Eurostat compiles and publishes data on asylum (applications and decisions) and managed migration in European Union member countries. Countries and national and international NGOs also publish these statistics, based on sources of various completeness, quality and timeliness (UNSD 2014). There are sometimes substantial inconsistencies between the numbers published by different organizations for the same country, including high-income countries with good statistical systems, usually due to differences in definitions, times and statistical methods, including the mixing of data on flows and stocks (UNSD 2014). There are several challenges associated with the compilation of data on asylum-seekers and refugees. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 18: Significant Refugee Returns by Country of Origin 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Countries selected based on their cumulative returns of refugees over the period 1991-2015. Return does not necessarily lead to the full reintegration of a person into their home country or area of origin. In the absence of global data on the success of reintegration following return, data on returns appear to be taken as indication of sustainable return. In reality, many returnees face impediments to reintegration and continue to have specific economic and social vulnerabilities linked to their displacement. They may not be able to reclaim land, access sufficient financial resources (e. g. accumulated during their displacement) or reestablish social networks in areas of origin, which are critical factors for successful reintegration (World Bank 2015). Sustainable refugee return is therefore not a one-off event but a process that provides returnees with adequate safety, housing, livelihoods and services that address their specific vulnerabilities and reduce the likelihood of secondary displacement (World Bank 2015). Figure 19: Voluntary Returns of Refugees 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of refugees and people in refugee- like situations protected or assisted by UNHCR. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of trade with government. Because the dataset used for this study is cross- sectional rather than longitudinal, I am unable to examine whether citizens adjust their beliefs and behavior to relative changes in service delivery. I was only able to test whether there are associations between the absolute service quality across neighborhoods, countries and ethnic groups and deference to the tax department, police and courts. Further, citizens may not be attributing goods and services to the gov- ernment. Rather, citizens may be attributing goods and services, such as roads, electricity grids, sewage systems, health care and education to vari- ous non-state actors including the following: the private sector; NGOs and community-based groups; churches, mosques and other religious institutions; traditional leaders; and, bilateral and multilateral donors. Survey questions on the Afrobarometer only indicate the presence or absence of services and infrastructure, and the quality of these services, but these questions do not probe respondents on who they believe are providing these services. Each of the indicators of perceptions of government performance is sig- niﬁcant at the p < 0. 05 level. Food security is positively associated with a willingness to defer to the tax department. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This paper analyzes data from the initial wave of VenRePS-Kids, conducted from Oc- tober to December 2022, to outline key demographics and stylized facts about forcibly displaced children and adolescents. Initially, we examine the human development dis- parities of forcibly displaced Venezuelan children and adolescents in comparison to their Colombian counterparts. Our approach to human development is broad, covering physi- cal, cognitive, socio-emotional, and mental health aspects. Additionally, we complement our analysis by exploring differences in food security, social cohesion, and the economic status of parents. Although our analysis is descriptive, it represents a crucial initial step 2Venezuelan households are defined as those where both parents and their children have a Venezuelan nationality. Colombian households are composed of Colombian citizens only. 3 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["VenRePS-Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(24 percent) with higher livestock ownership relative to other domains. Somalis are also more likely to work for private households, including household services, construction, and agricultural work. 0 10 20 30 40 50 60 70 80 90 100 Camp Hosts Camp Refugees Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure 3.2: Work status Source: World Bank Staff based on SESRE 2023. Table 3.1: Labor force statistics Camp- Hosts Camp- Refugees Labor force participation rate (strict) 52% 31% Unemployment rate (strict) 7% 21% Labor force participation rate (relaxed) 57% 43% Unemployment (relaxed) 15% 43% Employment-to-population ratio 48% 25% Source: World Bank Staff based on SESRE 2023. Note: Labor force participation ratio is the share of working-age people who are engaged in the labor market, either employed or unemployed. Unemployment is the share of people participating in the labor force who are not employed. The “relaxed” definition of labor force participation includes anyone who is available to work. The “strict” definition of labor force participation includes only those who are available to work and also actively searching for work. Employment-to- population ratio is the share of working-age people who are employed. 41 In Ethiopia, in-camp", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The assumption of perfect implementation, however, is quite strong as interviewers have shown a preference for selecting respondents willing to participate in the survey (Alt, 1991), and a number of other studies found that data collected with random walk designs exhibit differences from known population statistics on gender, age, education, household size, and marital status (Bien et al. 1997, Hoffmeyer ‐ Zlotnick 2003, Blohm 2006, Eckman & Koch 2016). Probabilities of selection inherently cannot be calculated in a random walk sample design as no information is collected on how many structures are in the camp, or how likely it was that a given structure was the xth structure along any path. Random walk must then assume all structures have the same selection probability, implying constant sampling weights. Therefore, the only component of the weights for the random walk is the sub-sampling of households within a selected structure: 𝑤𝑤𝑖𝑖 ′ = 𝑁𝑁𝑗𝑗𝑗𝑗 𝑖𝑖. 2. 6. Comparison of Methods As mentioned above, stratified cluster samples with the canvassing of selected clusters is the most common sample design used to collect official socioeconomic statistics in the developing world, but in Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 1: Data and Descriptive Statistics: Clusters, Refugee Camps, and Conflicts Revised refugee diversity indices. We first use Afrobarometer data to construct standard indices of diversity, namely the EF and the EP indices (Bazzi et al., 2019; Esteban and Ray, 1994). The EF index describes the probability that two randomly selected individuals from a given location belong to two different ethnic groups (Alesina et al., 2003, 2016; Gomes, 2020b). The EF index can be defined as EFjt = Njt X e = 1 get (1 − get), (2) where Nj is the number of ethnic groups in cluster j at time t and get is the population share of 14 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["Afrobarometer data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Component 2 Update: Infrastructure Works**\n\nCivil works under 12 of the 14 contracted sites are progressing on schedule. ESMF compliance audits conducted in March found that all sites met the LMP requirements for worker accommodation and wage payment, but two sites required corrective action on dust and noise management during construction. GIS-based photographic tracking of construction progress — using geotagged photos uploaded by site engineers — has been added to the activity monitoring dashboard, providing real-time visual verification of works completion to supplement the progress certificates issued by the supervising engineer.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of trade with government. Because the dataset used for this study is cross- sectional rather than longitudinal, I am unable to examine whether citizens adjust their beliefs and behavior to relative changes in service delivery. I was only able to test whether there are associations between the absolute service quality across neighborhoods, countries and ethnic groups and deference to the tax department, police and courts. Further, citizens may not be attributing goods and services to the gov- ernment. Rather, citizens may be attributing goods and services, such as roads, electricity grids, sewage systems, health care and education to vari- ous non-state actors including the following: the private sector; NGOs and community-based groups; churches, mosques and other religious institutions; traditional leaders; and, bilateral and multilateral donors. Survey questions on the Afrobarometer only indicate the presence or absence of services and infrastructure, and the quality of these services, but these questions do not probe respondents on who they believe are providing these services. Each of the indicators of perceptions of government performance is sig- niﬁcant at the p < 0. 05 level. Food security is positively associated with a willingness to defer to the tax department.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This study aims to assess the consequences of forced migration on ethnic diversity and conflict in Sub-Saharan Africa. We combine a unique dataset on refugee camps with individual data from the Afrobarometer Surveys across 23 African countries for the 2005 — 2016 period. We construct two standard measures of ethnic diversity: indices of ethnic fractionalization (EF) and ethnic polarization (EP). Ethnic fractionalization measures the probability that two individuals drawn from the society at random will belong to two different ethnic groups and thus increases with the number of ethnic groups present. Ethnic polarization captures antagonism between individuals and is maximized when the society is divided into two equally sized and distant ethnic groups. Although these indices have been widely used, little variation over time has been found, making causal inference difficult. The innovative aspect of our analysis is that we use data on the precise locations of refugee camps, their yearly size, and — most importantly — their annual composition in terms of countries of origin. Combined with the Ethnic Power Relations- Ethnicity of Refugees 2019 dataset, we are able to predict changes in ethnic diversity induced by refugee inflows. We then assess the relationship between refugee diversity and the likelihood of conflict. In an additional analysis, we also assess how refugee-induced changes in diversity affect the incidence of theft and violence, participation in protests, and perceptions of ethnic attachment, inter-personal trust, and institutional trust. Other studies have investigated the links between displacement and social conflict or social co- 3", "output": {"entities": {"named_data": ["Afrobarometer Surveys", "Ethnic Power Relations- Ethnicity of Refugees 2019"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 One could think of Jordan ’ s growth of local supply of public basic and secondary schools as a progressive public investment in human capital that increases the human capital production for children of marginal parents in terms of income and educational attainment. Those are parents who would have chosen higher investment in the human capital of their offspring, but were constrained by the limited supply of schools in their subdistricts of residence and could not afford to send their children to more distant schools outside their jurisdiction or to provide them with homeschooling. However, the increase in public schools is expected to have less of an effect on richer or more educated parents, who are expected to provide education to their children regardless of the availability of schools in their subdistricts either by sending their children to distant schools or through homeschooling. On average, however, the increase in the local supply of public schools is expected to reduce the intergenerational correlation of educational attainment or enhance intergenerational educational mobility. III. DATA Two new and unique data sources are employed in the empirical analysis. First, the Jordan Labor Market Panel Survey of 2010, carried out by the Economic Research Forum in cooperation with the Jordanian Department of Statistics, is a rich source of information on all aspects of the Jordanian labor market (JLMPS 2010). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Jordan Labor Market Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "16 { Tables 4 and 5 here} Among the first six categories that are based on raw census data, three categories (raw scaled, R & R not scaled, and R & R scaled) are constructed through the summation of bilateral raw numbers and disaggregations of some aggregate categories in the original censuses. Since these categories together constitute around 45 percent of migrants in each census round, the original bilateral portion of each cell was compared with the final number assigned to them after the various calculations as a check on accuracy. For each decade, therefore, the overall percentage contribution of the raw bilateral data to the total is calculated (table 6). 23 In each census round, at least 92 percent of all those categories are derived from the raw data. { Table 6 here} Simulating Missing Data Finally, to examine the reliability of the estimated missing census data and test the methodologies, several scenarios are assumed. All bilateral observations for a single year for four countries (Australia, United States, Switzerland, and Chile) in different parts of the world are deleted and the missing cells are filled using one of five methods. 24 The first simulation assumes that all bilateral data for 2000 are missing but that the total number of migrants is available. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["raw census data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "26 value of consumption flow of durable goods. 22 While monetary poverty can measure temporal resource holdings, multidimensional poverty, as a more comprehensive measure, includes chronic and exacerbating sources of poverty. This difference explains the existence of mismatches between individuals identified as monetary versus MPI poor, which are often more prominent in poorer countries (Evans et al 2020). This section examines these differences in the contexts of displacement. Table 9. Percentage of the sample in each poverty category: Rows sum to 100 % Non-poor by both measures Only Monetary Poor Only Multidimensional Poor Monetary and multidimensional poor Ethiopia 38 % 23 % 12 % 27 % N. E Nigeria 13 % 69 % 4 % 15 % Somalia 20 % 32 % 14 % 34 % Sudan 33 % 47 % 4 % 17 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). This table presents the distribution of households in each of the categories in the columns. Thus, each row adds up to 100 %. South Sudan is excluded from this analysis as monetary data is not available for the country. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "assemble annual observations as follows for each grid square: the first 40 from the CRU\n\nscores). We do this for both future periods, as well as for the historical CRU data (here\n\nof rainfall and temperature for the past (from the benchmark CRU series that we have\n\ntable presents average R [2] scores for the bivariate relationships between the CRU", "output": {"entities": {"named_data": ["CRU\n\nscores"], "descriptive_data": [], "vague_data": ["CRU data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In settings with clearly distinguishable individual structures, these methods can also be reasonably accurate for the purposes of rapid estimation of displaced population (Checchi, et al. 2013). 101 The use of unmanned drones is also becoming more popular as the cost of this technology falls. This technique has been used by UNHCR to update its estimates of IDPs in Somalia ’ s Afgooye corridor (IDMC 2015) and by IOM to monitor disaster-induced displacement in Haiti, including the use of Unmanned Aerial Vehicles (UAVs) in collaboration with UNOSAT. (g) Open data initiatives. There are several initiatives to provide free and open data that enable Internet users to independently mine and analyze data and generate customized summaries, charts and visualizations. For example, the Bank has provided free, open access to its development data since the launch of its Open Data Initiative in 2010, however there is little open data on asylum-seekers, refugees and IDPs. JIPS has developed a web-based platform that allows users to explore, analyze and visualize profiling data online, and IDMC has 100 See http: / / www. flowminder. org /. 101 These methods are not effective in settings with connected structures, a complex pattern of roofs or multi-level buildings, as are prevalent in urban areas. Additionally, cloud cover and dense foliage can also obscure structures. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "13 Commission for Reception, Truth and Reconciliation (CAVR). 7 This information has been collected from deponents to the Commission ‘ s statement-taking process. 8 We make use of data on the number of killings that occurred during the war in order to derive patterns and variation of violence in Timor Leste over time and across space. We use this data to identify districts and years that experienced high and low violence-intensity, both at the start of the occupation and following the withdrawal of Indonesian troops in 1999. This allows us to estimate both the impact of the first years of the conflict and the impact of the last wave of violence in 1999. 4. 1. Identification strategy: The impact of violence on school attendance in 2001 We first investigate the short-term impact of the 1999 violence. The empirical questions being addressed are: (i) whether the violence in 1999 imperiled school attendance9 and school grade deficit, and (ii) whether different channels of exposure to conflict – displacement and house destruction – affected boys and girls and different age groups differently. 4. 1. 1. Primary school attendance and grade deficit rates in 2001 We make use of information in TLSS 2001 collected at the individual and household levels on displacement and house destruction to identify conflict-affected individuals. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["TLSS 2001"], "descriptive_data": ["data on the number of killings that occurred during the war"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "9 3. 1 Data Sources We use three different types of data to verify our estimates and determine how sensitive they are to changes in definition and the year of the survey. Two sources are nationally representative, but date from 2001 or before, the third is data from a census of households carried out by the authors in 2003 as part of a project on educational choice. The first source is the “ long ” form of the population census in 1998, which is a large sample-based survey with information on enrollment. This survey is representative at the level of the district and region (rural or urban) and provides comprehensive coverage of the entire country. 7 We use this data to examine enrollment patterns across districts. The second type of data, based on household surveys, are different rounds of the Pakistan Integrated Household Survey (PIHS) carried out in 1991, 1998 and 2001. While the data is not as extensive as the census, it contains detailed household information on schooling and income, and has been used extensively by researchers both in Pakistan and the United States. Finally, we use the census of schooling choice among households that our research team conducted in August 2003 (referred to as the project on “ Learning and Educational Achievement in Punjab Schools ”, or LEAPS). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Pakistan Integrated Household Survey"], "descriptive_data": ["census of schooling choice among households"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Appendix C: Robustness to DHS Cluster Geocoding Uncertainty**\n\nThe standard DHS jitter algorithm displaces cluster centroids by up to 5 km (urban) or 10 km (rural). To assess sensitivity of our results to this measurement error, we implement a simulation in which each DHS cluster centroid is displaced 1,000 times by a random draw from the DHS jitter distribution, and the treatment indicator is recomputed after each displacement. We report the median coefficient and the 5th and 95th percentile bounds across simulations. The simulated bounds are narrow relative to the estimated confidence intervals, indicating that DHS cluster geocoding uncertainty does not substantially bias our estimates.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 to 32 percent (see Table A1 in appendix). The total number of internally displaced people (IDP) outnumbers the stock of refugees in SSA and in the world but overall has followed a similar trend compared to the number of refugees (by country of origin). 1 Major civil wars in Central Africa mainly explained the peak in 1993 and 1994 and the increase at the end of the 1990s. Figure 1. Refugee population by origin, 1990 ‐ 2013 Note: Authors ’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014. Data from 2007 to 2013 include people in refugee ‐ like situations. Persons in refugee ‐ like situations include “ groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained ” (UNHCR 2014: 39). Refugees in Africa seem to have mainly remained in Africa. Although SSA also hosts refugees from other regions, the closeness of the ‘ blue ’ and ‘ red ’ lines in Figure 2 ‐ representing the number of refugees originating from and hosted in SSA ‐ is an indication that most refugees cross borders within Africa. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "0 10 20 30 40 50 60 70 80 90 100 Percent Worse Same Better Figure 5.8: Perceived changes in household living standards Source: World Bank Staff based on SESRE 2023. Note: The survey asks how the household living standard has changed compared to last year and the last five years. a. Last 5 years b. Last 1 year Refugees’ Aspirations 48 scale44 gap between refugees and hosts is higher for in-camp refugees, the gap is relatively narrower for Addis Ababa refugees. The average food insecurity scale for in-camp refugees is “8” and for their hosts it is “4” out of 10; that is, in- camp refugee households experienced about eight food insecurity events while host households experienced about 4 in the past year. Consistent with other welfare indicators discussed, food insecurity tends to be more severe among in-camp refugees than their hosts or OCP refugees. In-camp refugees have less diverse diets and poor food consumption status compared to their hosts. The average household dietary diversity score—the number of food groups consumed out of twelve—is 7.5 for hosts and 6.5 for refugees (Figure 5.10a). Overall, the average dietary diversity score is also lower for in-camp refugees than their hosts. The", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The model includes ϐixed effects for grade, school, and language spoken. 14 The results are presented in Table 3. We then narrow our focus to foreign students who joined Italian schools after February 2022, speciϐically comparing Ukrainian refugees to other newly arrived foreign students. This approach allows us to examine how Ukrainian refugees compare to other foreign students who entered the education system around the same time. By restricting the sample to these two categories of students, we estimate the following regression: 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 + 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 + 𝑓𝑓𝑔𝑔 + 𝑓𝑓𝑠𝑠 + 𝑓𝑓𝑙𝑙 + 𝜖𝜖𝑖𝑖𝑖𝑖𝑖𝑖 (2), with variables as deϐined in (1), and results presented in Table 4. Our analysis also aims to explore potential mechanisms that could explain results derived from equations (1) and (2). Using the administrative data, we investigate whether being placed in a smaller class inϐluences school achievement in the sample of Ukrainian refugees. The results are presented in Table 5. We then draw on ϐindings from the survey data to unpack and analyze how Ukrainian refugees feel in Italy, the challenges they face, and their aspirations. 4. Results 4. 1. Integration challenges faced by Ukrainian refugees in Italy Low enrollment and substantial dropout rates At the end of the 2021-2022 school year, the enrollment rate of Ukrainian refugee children in Italian schools was low. In the months following Russia ’ s full-scale invasion of Ukraine in 2022, 3, 320 Ukrainian refugees were enrolled into Italian secondary schools. This ϐigure constitutes 24 % of the 14, 106 Ukrainian refugees aged between 11 and 18 years who sought temporary protection as of 14 This variable is included to account for the potentially greater ease of learning experienced by students who speak languages that are considered closer to Italian. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "approximated by wealth measured by the EMDHS asset index; and e)mothers' education. Note that the alternative\n\nThe asterisks indicate the significance level: *** p<0.01, ** p<0.05, * p<0.1. Standard errors in parentheses. Regressions take sample\ndesign and household weights into account by using Stata's svy command. Data: EMDHS 2014.\n\n2007 Census. The 2007 census has two formats - a long and a short format. The long format is richer in terms of\n\nThe Ethiopian census has both a short and a long form, and to increase model fit, the models use the long\n\nobserved in Ethiopia before, based on other data sets [30]. SAE results in addition to these variables are highly", "output": {"entities": {"named_data": ["EMDHS", "EMDHS 2014", "2007 Census", "Ethiopian census"], "descriptive_data": [], "vague_data": ["other data sets"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Typically, registers have evolved over time (from parish records, for example). They were never developed specifically to record international migration information, and they vary considerably across countries. For example, the laws under which individuals are classified as migrants and the conditions under which they are inscribed or deregistered differ greatly (Bilsborrow and others 1997). The Raw Data The Global Migration Database is a vast collection of destination country data sources detailing migrant stocks from numerous origin countries and regions (United Nations [2008]). Compiling and maintaining the underlying primary sources require herculean efforts to scour the key census collections of the world and enter the data manually. In total, the database comprises records from some 3, 500 separate censuses from more than 230 migrant destination countries and territories, by sex and age. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Global Migration Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The LMP appended to the ESMF specifies that all contractors must submit workforce management plans prior to mobilization, detailing recruitment procedures, wage payment schedules, and the mechanism through which workers may raise concerns without fear of retaliation. Contractors employing more than fifty workers are required to establish a worker accommodation standard compliant with IFC Performance Standard 2. Compliance with the LMP will be verified through quarterly site inspections conducted jointly by the PIU's E&S officer and a representative of the national labor inspectorate.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Management Information System (EMIS) (UNHCR, 2020), and a separate chapter on refugee education is included in the annual education statistics report of the MoE. The GoE included expanding primary and secondary education for refugees in the national five-year Education Sector Development Programme VI (ESDP), covering 2020 to 2025, but little progress has been made. Box 2.1: Education system for refugees in Ethiopia 0 20 40 60 Percent 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total No education Incomplete primary Complete primary Complete secondary Complete post-secondary Figure 2.7: Education level (18 years and above) Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total No education Incomplete primary Complete primary Complete secondary Complete post-secondary Percent Figure 2.8: Youth (15 to 24) education level Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 14 OCP refugees attended education outside of Ethiopia and have relatively better educational attainment. One reason why OCP refugees have higher educational attainment compared to other refugees (especially other Eritreans) could be related to being relatively better off in their countries of origin (qualification for OCP requires having", "output": {"entities": {"named_data": ["SESRE 2023", "Management Information System"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the intensity of conflicts, and iv) population density in refugee host countries by region. Appendix B presents the list of variables along with description and summary statistics. 5. Empirical strategy To estimate the impact of refugee inflow19 on host community ’ s livelihood strategy choice, we use the following basic econometric model: 𝑌𝑌𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑅𝑅𝑅𝑅𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝜈𝜈 + 𝜀𝜀𝑖𝑖 (1) Where, 𝑖𝑖 indexes a household, 𝑌𝑌𝑖𝑖 is an outcome variable of interest (livelihood diversification or commercialization of agriculture), 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 is the measure of refugee inflow, i. e., the refugee population (average of 2017 and 2018) in the nearest refugee camp weighted by the inverse of distance of the household to the refugee camps, 𝑋𝑋𝑖𝑖 is a set of household controls, 𝜈𝜈 is kebele fixed effects, and 𝜀𝜀𝑖𝑖 is the error term. Several variables, from the DRDIP data set, were used as controls in our model.", "output": {"entities": {"named_data": ["DRDIP data set"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Protection monitoring data from the last four cycles indicate that freedom of movement restrictions remain the most frequently reported protection incident, accounting for 34 percent of cases. The Protection Monitoring Tool was updated in cycle 5 to include an additional question distinguishing between formal government-imposed restrictions and informal social barriers to movement, following a recommendation from the protection analysis technical advisor. This change improved the actionability of protection monitoring findings for both advocacy and programmatic response teams.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the World Bank's Poverty and Inequality Platform (PIP). PIP is managed jointly by the Data and\n\nResearch Groups in the World Bank's Development Economics Division. It reports both poverty\n\nmeasures and inequality measures, including the Gini Index for 168 countries from 1968 to 2022. The\n\nGini index is based on primary household survey data obtained from government statistical agencies\n\nalternative dataset that provides inequality measures. Specifically, we use the Standardized World\n\nIncome Inequality Database (SWIID). [5] The SWIID maximizes the comparability of available income\n\n<!-- PAGE 10 -->\nIncome Study database.\n\nData on natural disasters is from the EM-DAT database maintained by the Centre for Research", "output": {"entities": {"named_data": ["Poverty and Inequality Platform", "Gini Index", "Income Study database", "EM-DAT database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Individual characteristics Age, sex, education, language skill, years in exile SESRE Household characteristics Gender of household head, household size, head education level, access to electricity, productive asset ownership, food insecurity experience SESRE Community characteristics Community economic development status Predominant land cover in the community SESRE Local Factors Remoteness Distance to towns and cities, distance to the nearest international border Ethiopian shapefile and Refugee geospatial from ESS Local labor market LFPR, unemployment rate, the share of wage employment, the share of employment by economic sector LMS, 2021 Market access Market accessibility index Ethiopia transport network layer, 2020 (ERA) & gridded population (GPWv4) Notes: For logistic regression, we assume local factors are exogenous in the model as refugees do not select their location. Since refugees’ residential location is not self-selected, they do not choose their respective camps to maximize their utility. Instead, they come across the border and are either assigned to camps close to where they crossed or a new camp is established. Our model compares refugee employment status by local factors in the hosting Zones and community: where subscripts denote : individual and : camp. refers to local factors; represents personal and presents household characteristics; refers to the characteristics of", "output": {"entities": {"named_data": ["Ethiopian shapefile", "gridded population (GPWv4)"], "descriptive_data": ["Refugee geospatial from ESS"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These may include skills on how to write a CV, prepare for job interviews, business and entrepreneurial skills to start a business, etc. In this regard, the mechanism for improving social cohesion values appears to have come from inter-community volunteering activities, rather than improvements in soft skills. (c) IMPACTS ON LABOR MARKET OUTCOMES While the NVSP was designed primarily to improve social cohesion values among participating Lebanese youth, it was also hoped that engaging them in volunteering activities, coupled with soft skills training, would enhance their employability and thus increase their chances of employment. At baseline, half of the selected and non-selected volunteers were active and searching for a job. Among them, 49 percent reported being unemployed, 31 percent wage employed, 13 percent employed in unpaid jobs, and 7 percent self-employed (see table 1). Those active volunteers were older in age than the rest of volunteers who reported being inactive in the study ’ s sample (with an average age of 21 and closer to labor market insertion). One year later, it appears that many of 19 Our interpretation that offered soft skills are likely too basic for this pool of volunteers is provided given the scale that we used in the questionnaire to test their knowledge on soft skills. We cannot rule out the possibility that had we used a different scale, we might have found an impact, either negative or positive. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "17 Revealingly, in Betts et al. ’ s (2013) survey of refugees in Uganda, 96 % of all interviewed households in the capital and 70 % outside the capital said they owned and used a mobile phone. They use this mobile phone to communicate with customers and suppliers, to get market information and to transfer money. Half of the urban refugees and 11 % of rural refugees also have access to the Internet. 4. Refugees As a Burden? As pointed in Section 2, most refugees in SSA are hosted in neighboring countries. Most of these hosting countries are likely among the least developed countries. It has been argued that these refugees may constitute an additional burden in terms of economic development in hosting countries (Mabiso et al. 2014). UNHCR (2014: 17) implicitly recognizes that potential burden by suggesting that the ratio of the size of the country ’ s hosted refugee population to its average income level can provide a proxy measure of the burden of hosting refugees. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of refugees in Uganda"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location. Area stratifi- cation reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analyzed. ” Afrobarometer provides geocoded data for 6 rounds, which correspond to the 1991 – 2016 period, with the information on an individual ’ s ethnicity available from round 3 (corresponding to 2005 – 2006). We therefore restrict our analysis to the 2005 – 2016 period. The selection of countries is driven by data availability. Among the 33 countries with available Afrobarometer data, we exclude Botswana, Cape Verde, Lesotho, Madagascar, Mauritius, Sao Tome and Principe, South Africa, and Swaziland, for which no data is available on refugee camps or from the EPR-ER. We also exclude Sudan since the question on individual ethnicity is not asked in this country ’ s survey. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["EPR-ER"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "decreased satisfaction. For example, using the German Socio-Economic Panel Survey (SOEP),\n\nTwo studies that use the German SOEP data (D'Ambrosio and Frick, 2007 and 2012) find that\n\nusing a set of European panel surveys concludes that absolute income is more important than\n\nCurrent global counts rely on combining each country's total food balance with information on distribution patterns from household consumption expenditure surveys. Recent research has advocated for calculating hunger numbers directly from these same surveys.\n\ndetails about how household surveys are designed and\nhow these data are then used. Using a survey experiment\nin Tanzania, this study finds great fragility in hunger\ncounts stemming from alternative survey designs. As a\nconsequence, comparable and valid hunger numbers will\nbe lacking until more effort is made to either harmonize\nsurvey designs or better understand the consequences of\nsurvey design variation.", "output": {"entities": {"named_data": ["German Socio-Economic Panel Survey (SOEP)", "German SOEP data"], "descriptive_data": ["household consumption expenditure surveys"], "vague_data": ["European panel surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Another way to view the connection between low return on education and youth frustrations is that the rapid intergenerational mobility in education has failed to yield similar mobility in either income or social status. This delinking between educational and occupational mobility has been documented for Egypt by Binzel and Carvalho (2013). While there is no similar work on Jordan, this article contributes to this agenda by documenting the first step in this process, which is the link between public investment in schooling and the educational mobility across generations. 1. This is based on version 2. 0 of the Barro-Lee dataset for educational attainment among the total population 15 and older. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Barro-Lee dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In principle, individual registration of IDPs is not used to determine the ‘ status ’ of an IDP, since IDPs have the same rights and entitlements as other citizens and do not need to apply or be granted a special legal status. 66 Rather, registration of IDPs can provide a basis for: (a) establishing the number, location, and key demographic characteristics of displaced populations; (b) providing protection and assistance; (c) keeping track of family relationships; (d) preventing fraudulent access to scarce humanitarian assistance; (e) facilitating the issuance of temporary identity cards to replace lost personal documentation (Brookings 2008); and (f) providing social security benefits. 67 Full IDP registration by international organizations is not 62 By the end of 2014, individual refugee registration was the source of about 77 percent of the data on refugees; estimation accounted for 13 percent of data, combined estimation and registration for 5 percent and other sources for 5 percent (UNHCR 2016). 63 UNHCR may undertake registration activities when national governments do not have the capacity to do so. 64 Additional data can also be recorded such as education and occupation. 65 Insufficient budgetary resources, staff, training or materials. 66 Countries with national legislation that provides a legal status for IDPs are an exception to this international standard. 67 The scope of data collected depends on the objectives of the registration exercise, for example in Kenya, registration of individuals displaced by the 2007 and 2008 post-election violence excluded ‘ integrated ’ IDPs, i. e. those who had Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Nassar, R., J. Mastrogiacomo, W. Bateman-Hemphill et al. 2021. Advances in quantifying\npower plant CO2 emissions with OCO-2. Remote Sensing of Environment. 264: 112579.\n\nNordhaus, W., A. Azam et al. 2006. The G-Econ Database on Gridded Output: Methods and\nData. Yale University.\n\nYe, X., T. Lauvaux, E. Kort, T. Oda, S. Feng, J. Lin, E. Yang and D. Wu. 2020. Constraining\nfossil fuel CO2 emissions from urban area using OCO-2 observations of total column CO2. JGR\nAtmospheres, 125(8).\n\n<!-- PAGE 41 -->\n**Appendix: Mobilizing OCO-2 Data for the Stakeholder Community**\n\nIn this paper, we have shown that a relatively simple tracking model can provide useful information\nabout changes in local CO2 concentration anomalies for areas of interest. However, we recognize that\nmost stakeholders do not have the requisite hardware and software for mobilizing the OCO-2 data\ndirectly. Accordingly, the World Bank's Development Economics Vice Presidency (DEC) has\nestablished an open web facility with the following features. We believe that it will contribute to the\nglobal effort to reduce CO2 emissions.", "output": {"entities": {"named_data": ["G-Econ Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 3. Refugees as Agents of Their Own Destiny 3. 1 The Composition of Africa ’ s Refugee Population and Its Consequences One of the first elements that catch the eye in Figure 6 is the difference in the composition of the refugee population in Africa compared to the rest of the world. The share of children and women among refugees is higher in Africa than elsewhere, in particular East and West Africa stand out here. This is, at least partly, a consequence of Africa ’ s younger, general population, but other forces could be at work as well, e. g. higher mortality of adult males in Africa or adult males staying behind or being separated from the rest of the household. It does mean however that, relative to other areas, more attention should be going to the needs and capacities of women and children in Africa. This means, for example, adaption of and increased supply of schooling and health services. Figure 6. The composition of refugees by age and gender, 2013 Source: Note: UNHCR statistics (UNHCR 2014). Asia excludes Australia, Japan and New Zealand. Americas exclude Canada and the United States. These percentages have been calculated by country when demographic data are available for at least 30 % of the total.", "output": {"entities": {"named_data": ["UNHCR statistics"], "descriptive_data": [], "vague_data": ["demographic data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For a detailed definition see section 4 Household size Number of people included in the case records of each PA in Individual ProGress dataset Wage Income 1 if the household receives income from employment and / or daily or irregular work Income from remittances 1 if the household receives income from remittances Income per capita Raw sum of household income from all sources; work, pension, assets in Syria transfers, donations, other organizations'humanitarian aid, and other divided by household size Male Adults Number of males above 18 (inclusive) in the household Marital Status Categorical variable. The classification includes married PAs with spouse in the household, married PAs without spouse in the household, widowed, single or engaged, and divorced or separated. Proportion of female Number of female divided by the household size Location Categorical variable for 11 Governorates / cities. Ajloun City, Aqaba, Balqa, Irbid Jerash, Karak, Maan, Madaba, Mafraq, Tafilah, Zarqa. In Camp 1 if the household is located in a refugee camp Poverty before UNHCR and WFP assistance 1 if household expenditure before UNHCR plus WFP assistance is below the poverty line (JD50) Poverty before UNHCR assistance 1 if household expenditure after WFP assistance but before UNHCR assistanc is below the poverty line (JD50) Source: Authors ’ elaboration. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Individual ProGress dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**4.1 Identification Strategy**\n\nOur identification relies on variation in distance from each DHS cluster to the nearest gold mine, interacted with an indicator for whether the mine was actively extracting gold in the year the DHS cluster was surveyed. The key assumption is that, conditional on district fixed effects, year fixed effects, and a set of community-level controls, mine-induced variation in local economic activity is uncorrelated with unobserved determinants of household welfare. We test this assumption by examining whether DHS cluster characteristics predict mine proximity after controlling for the fixed effects.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "with Disabilities in Addis Ababa. Briefing Note. UNICEF. (2019). Birth Registration for Every Child by 2030: Are WE on Track? New York. UNICEF. (2021). Education in South Sudan. Briefing Note. Vemuru, V., Sarkar, A., and Woodhouse, A.F. (2020). Impact of Refugees on Hosting Communities in Ethiopia: A Social Analysis. World Bank Group. Verme, P., and Schuettler, K. (2021). The Impact of Forced Displacement on Host Communities: A Review of the Empirical Literature in Economics. Journal of Development Economics, 150, 102606. Walelign, S. Z., Wang Sonne, S. E., and Seshan, G. (2022). Livelihood Impacts of Refugees on Host Communities. Wieser, C., Dampha,N.K., Ambel, A.A., Tsegay, A.H., Mugera, H.K., Tanner, J. (2020). Monitoring COVID-19 Impact on Refugees in Ethiopia : Results from a High-Frequency Phone Survey of Refugees (English). Monitoring COVID-19 Impact on Refugees in Ethiopia Washington, D.C.: World Bank Group. Whitaker B.E. (2023). Border Proximity and Attitudes Toward Free Movement in Africa. The Afro Barometer Working Papers No. 200. Woldehanna, T., Hoddinott, J., and Dercon, S. (2008). Poverty and Inequality in Ethiopia:1995/96 – 2004/05. May. World Bank (2017). Forcibly Displaced: Toward a Development Approach Supporting Refugees, the Internally Displaced, and Their Hosts. Washington, D.C: World Bank. https://doi.org/10.1596/978-1-4648-0938-5. World Bank. (2019). Informing", "output": {"entities": {"named_data": ["High-Frequency Phone Survey of Refugees"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Migration and Economic Mobility in Tanzania: Evidence from a Tracking Survey Kathleen Beegle The World Bank Joachim De Weerdt EDI, Tanzania Stefan Dercon Oxford University, UK We thank Karen Macours, David McKenzie, and seminar participants at the Massachusetts Avenue Development Seminar, Oxford University and the World Bank for very useful comments. All views are those of the authors and do not reflect the views of the World Bank or its member countries. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Acknowledging the need for change, the South Sudanese Government signed the UNCRPD in February 2023. However,\nsubstantial steps remain, including ratifying the optional protocol, establishing a national monitoring mechanism, and\neffectively realising and implementing the principles of the UNCRPD.\n\nMoreover, the African Union Policy Framework and the Plan of Action on Ageing has emphasised the need to recognize the\nrights of older persons and to eliminate all forms of age discrimination to ensure that those rights are protected under\nappropriate legislation. However, many African countries, including South Sudan still need to take up such initiatives to cater\nfor the protection of older persons.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The sample households are located within varying distance from the nearest refugee camp (approx. 67 to 76, 665 meters) (see Figure 3). 12DRDIP aims to improve access to basic social services, expand economic opportunities, and enhance environmental management for communities hosting refugees through providing funding for community driven projects. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "REFodet = αod + γe + τt + β1Conflictot − 1 + β2Conflictet − 1 + β3Distanceed + ϵodet, (5) where REFodet is the stock of refugees of ethnic group e from country o in country d at year t. As we have data on yearly refugee stocks and would like to estimate the changes in these stocks over time using a gravity model, we include origin – destination fixed effects αod so that identification is based only on changes in stock over time (Zylkin, 2019). 20 We also include time τt and ethnic group fixed effects γe. Here we obtain data on the ethnicity of refugees from Murdock ’ s Atlas, which provides a map of ethnographic regions for Africa and the historical homelands of refugees (Murdock, 1967). To match ethnic groups across datasets, we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset and, later, with data from Afrobarometer.", "output": {"entities": {"named_data": ["Afrobarometer", "LEDA21", "EPR-ER dataset", "Murdock ’ s Atlas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Deciding on whether to flee or not to flee a conflict (the migration choice) can be an individual or household choice and risk coping strategies may include temporary migration, shuttling between places, migration of only selected members of the households or migration of the whole household. This implies that individuals may stay put throughout the period observed, join or leave the household during the period, or have several episodes of out and immigration. Households may decide to leave and come back several times. In econometric terms, this means that longitudinal data may be left and right censored and have spells within. They are therefore the most complex set of panel data possible and require particular treatment of data and modeling. Survival or duration models can usually accommodate many of these complexities but it is very rare to find similar data sets used in published articles. Collecting such type of data is also not obvious, particularly if conflict is intense and survey areas cannot be reached. This is an issue where empirical economics could provide a real contribution by defining the optimal data format and adapting panel models to this format. Macro models Macroeconomics has attempted to model forced migration using models borrowed from the trade and economic migration literature such as the gravitational model (Echevarria and Gardeazabal, 2016) or used other macro models to test the impact of refugees on trade (White and Tadess, 2010). A more recent body of work is adapting trade models to take into account stochastic shocks in a dynamic framework (Cameron et al., 2007; Artuc et al., 2008). These are rational expectations models that are able to model the unpredictability of shocks, and recent work has tried to adapt these models to the context of violent Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["longitudinal data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "21 Table 6: Percentage of displaced individuals deprived in selected indicators by gender Ethiopia South Sudan Sudan Male Female Male Female Male Female Years of schooling 55 78 * * * 36 63 * * * 32 46 * * * School attendance 16 19 * * 21 29 23 23 Early marriage 3 13 * * * 8 75 * * * 6 50 * * * Unemployment 7 5 * * * 2 0 * 3 3 Legal id 45 46 48 74 * * * 10 10 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Asterisks indicate statistical significance of mean differences between male and female at 1 % * * *, 5 % * * and 10 % * levels. Returning to patterns of household headship, Figure 5 breaks down the variation in censored headcount ratios among refugee households in Ethiopia, depending on the gender of the household head. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Both treatment and control group respondents report equally high confidence in their ability to return to school “ should [she] decide to do so. ” These findings from the quantitative impact evaluation complement the results from a set of qualitative focus group discussions that were held with participants at the end of the 6 months of classroom training. Twenty-five percent of the trainees from Round 1 participated in a total of 34 focus group discussions that covered a variety of topics including their satisfaction with the program and their empowerment in both social and economic realms. The trainees overwhelmingly voiced a high degree of satisfaction with the training, and trainers commented on how the motivation or “ seriousness ” of the participants grew over the 6 month period. The trainees credited the transport allowance and free childcare in particular as features that facilitated their full participation; as one trainee commented, 22 Questions adapted from the Adolescent Self-Regulation Inventory, developed and validated for youth in the United States by Moilanen, 2006. The questions were revised and translated into an 11-item for the Liberian context. In the future, we plan to conduct basic testing on this scale on internal consistency and reliability. 17 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The ESMF screening form requires the subproject proposer to answer a series of yes/no questions covering potential impacts on biodiversity, cultural heritage, land acquisition, labor rights, community health, and flood risk. Responses trigger a risk classification that determines the level of environmental and social documentation required. ESMF screening is completed electronically through the MIS's subproject pipeline module, and all completed screening forms are reviewed by the PIU E&S officer within five working days of submission.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "street vending (48 % of those with at least 1 IGA), food processing for sale, including baking, cooking, and drying (16 %), and home production of crops, livestock, and fish (11 %). It is important to note that the EPAG program was not targeted toward the most vulnerable segments of Liberian society, but rather toward young women with enough education to be able to benefit from a training program of this nature. Based on comparisons from the 2007 CWIQ (Core Welfare Indicators Questionnaire) survey, the average EPAG participant is more educated, more literate, more likely to be engaged in an income-generating activity, and owns more assets than the average Liberian women of the same age group: mobile phone ownership was high (63 %), as was the proportion reporting that they had some money of their own (79 %). Even compared to other similar residents of Monrovia, the EPAG participants are better educated and have higher income. A strong sense of female empowerment at baseline emerges from the sections of the survey instrument having to do with self-confidence and agency. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Core Welfare Indicators Questionnaire"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Protection monitoring in settlement areas revealed that the most common protection incidents relate to intra-community disputes over land boundaries and natural resource access, followed by documentation denial and cases of gender-based violence. Protection monitoring findings are disaggregated by sex, age, and legal status of the affected individual and shared with the national protection cluster monthly. Patterns emerging from three consecutive protection monitoring cycles are escalated to national authorities through formal dialogue letters drafted by the protection cluster coordinator.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "<!-- PAGE 5 -->\ntracks ownership of state business entities with at least 10 percent stake in over 90 countries, including\nRomania. The first part of the paper examines whether the degree of ownership and control levels of SOEs\nmatter for differences in performances-in terms of employment, average wages, assets per worker,\ninvestment, and (labor) productivity-between SOEs and POEs over the 2011-2019 period.\n\n**that justifies an SOE presence** . The taxonomy was developed by Dall'Olio et al. (2022b) in conjunction\nwith the BOS database. It classifies 4-digit NACE industries into three broad sectors (competitive, partially\ncontestable, and natural monopoly) based on the economic rationale-the intrinsic features and associated\nmarket failures-that justifies SOE presence in an industry (see Table 2). Other NACE codes are excluded\nfrom the sector classification of SOEs because firms in those industries provide public goods (e.g., public\nadministration and defense and activities of extraterritorial organizations). In contrast, others are\ncharacterized by externalities (e.g., education and human health activities).\n\n3.2 World Bank Global BOS Database\n**The World Bank BOS database maps the footprint of the state within the corporate sector and across**\n**economic activities based on a uniform definition** . The BOS dataset tracks all corporations where\nnational or subnational governments have an ownership stake of at least 10%, either directly or indirectly\n(Dall'Olio et al. (2022a)). In this dataset, corporations are business entities that are (a) capable of generating\na profit or other financial gain for their owners, (b) recognized by law as legal entities separate from their\nowners and with limited liability, and (c) set up for purposes of engaging in market production.", "output": {"entities": {"named_data": ["BOS database", "World Bank BOS database", "BOS dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "GIS-enabled monitoring of road rehabilitation subprojects allowed the PIU to track physical progress at the segment level using georeferenced inspection records uploaded by field engineers. Each road segment was assigned a unique GIS identifier linked to the MIS contract record, allowing financial and physical progress to be reconciled in a single dashboard view. The GIS monitoring layer was shared with the national road authority and the Ministry of Public Works to support their own infrastructure maintenance planning.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "29 evaluations have been conducted (Renton et al., 2000; Shaw, 2000; Shaw, 2002a; Shaw, 2002b; Paine et al., 2002; White, Greene and Murphy, 2003; Interagency working Group, 2003). For example, the Medical Research Council (from the United Kingdom) conducted an external evaluation of program effectiveness in the Gambia using key informant interviews, focus groups, and a KAP (knowledge, attitudes and practices) survey in four villages, including two program sites and two controls. That study found that the Gambia program improved self-reported attitudes and behaviors related to violence against women. Specifically, the program reduced the social acceptability of wife-beating at the community level and appeared to produce a corresponding drop in that behavior. Qualitative findings from other Stepping Stones sites suggest similar benefits. Program H (Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru) is being carried out by four NGOs. It aims to change gender norms and sexual behaviors in Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru (Barker, 2003; White, Green and Murphy, 2003; Guedes, 2004). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["KAP (knowledge, attitudes and practices) survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The MHPSS technical working group convened three times during the reporting period to review the integration of psychosocial support into primary health care delivery. Discussions focused on staff caseload thresholds, supervision ratios for community-based MHPSS workers, and referral criteria for cases requiring clinical psychiatric intervention. The working group endorsed revised MHPSS training modules and recommended that implementation partners adopt standardized intake forms to enable consistent tracking of service utilization across sites.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The information was provided to the family either by a resident of the village or from family members who remained in their village in Syria. As part of this survey, we also collected information on the vulnerability of the sur- veyed refugees using the subset of common questions from the VAF and VASyr surveys. Since the data for this survey was collected through a third party unaﬃliated with the UNHCR- and, thus, any decision to allocate assistance- we would expect answers on income, food security, and poverty coping strategies to be more truthful. These data provide important contextual information on the correlations between income, food security and employment status. Overall, our ability to put together a comprehensive data set with key dimensions 17 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "There is one limit to our approximation in Equation 4. The ethnic composition of refugees in each year t for a given origin – destination pair of countries obtained from the EPR-ER database is assumed to be homogeneous across camps of the same origin – destination pair of countries for the refugees at year t. This may seem to be a strong assumption; however, the risk of misallocating refugees is reduced as the annual variation in the EPR-ER is generated by just a few dominant groups for a given origin – destination pair and the geographical distribution of refugees by country of origin is highly influenced by the proximity to their countries of origin. 16 As can be seen from panel A of Table B. 2, in refugee-hosting areas, on average, both EF and the EP seem to increase quite significantly when they are revised by incorporating the number of refugees in an 80-km buffer: the mean value of the standard EF index is 25. 58 %, while the mean value of the revised refugee EF index is 37. 90 %. The mean value of the standard EP index is 10. 11 %, while the mean value of the revised refugee EP index is 14. 07 %. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["EPR-ER database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "set that satisfies this requirement is the World Value Survey (WVS) spanning the years from   1990 to 2011, that is, a period covering about 20 years.\n\nin the WVS regarding immigrants are quite different from those of the ESS. Moreover, they are   not available for all years, so we end up with only two countries from the Europe area: Spain", "output": {"entities": {"named_data": ["World Value Survey (WVS)", "WVS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These tariff rate modifications are essential for this analysis as suggested by the substantial differences between the tariff rates available in the GTAP 8 database, especially those implied for Jordan, Iraq, Lebanon, and Syria (Figure 1), and the updated tariff rates, presented by country, product, and source in Appendix Tables B1-B6. Since the GTAP tariffs attributed to Jordan, Iraq, Lebanon, and Syria are composite rates, they do not correspond to the actual trade profile of these countries. Therefore, the new tariff rates differ from the GTAP ones both because of differences in the tariff lines and trade composition. By contrast, the tariff information on Egypt and Turkey in the GTAP 8 database represents relatively accurately existing preferences (Figure 1). 3. Simulation design The pre-war efforts for deeper trade integration in the Levant are reflected in the pre-simulation analysis. Starting from the newly constructed database, the pre-simulation analysis implements the deep trade initiatives discussed by the Levant countries prior to the onset of the Syrian war in 2011. The context for these reforms and the shocks associated with each of these reforms are presented in section 3. 1.", "output": {"entities": {"named_data": ["GTAP 8 database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In aggregate, we expect to observe a negative relationship between the percentage of the population that reports displacement, but at the individual level we expect that experience with hosting may, in certain circumstances, be positively associated with perceptions of social cohesion, regarding perceptions of relationships and solidarity. 4 Displacement and Social Cohesion: Survey Evidence To empirically evaluate the relationships between hosting displaced populations and social cohe- sion, this paper analyzes a series of surveys of civilian adults conducted in eastern DRC. 8 Each survey uses a multi-stage cluster sampling strategy capturing all territoires9 in North Kivu, South Kivu and Ituri provinces. The ﬁnal sampling units are randomly selected adults above the age of 18 to avoid bias toward men and / or heads of households. Multiple attempts are made over the course of one day to contact selected respondents and if necessary, appointments are made for in- terview. Surveys are enumerated by Congolese college students or professionals and interviews are conducted by members of the same gender and ethnicity as respondents to minimize enumerator- induced response bias. Further methodological details have been published (Vinck, Pham, Bindu, Bedford & Nilles 2019) elsewhere and additional details and sample size calculation are detailed in Appendix. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["surveys of civilian adults"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the degree to which the global response should include a development element. We ﬁnd that the average stood at around 10. 3 years at the end of 2015, with a median duration of 4 years, and signiﬁcant sensitivity to a few situations. Such numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete protracted situations. To produce these numbers, we rely on the Population Statistics Database compiled and main- tained by UNHCR. The database records the number of “ persons of interest ” to UNHCR in each year since 1951 and for each situation, where a situation consists of a pair host-origin countries. The calculation of duration of exile is obtained under a no-turnover assumption, whereby a de- crease in the number of refugees for any given situation is fully attributed to exits from refugee status, while increases are assumed to be fully accounted for by new cases. Although such ap- proach tends to over-estimate the true duration of exile, the lack of individual-level data on regis- tration precludes reﬁning the estimate further. Attempts to estimate similar statistics have been limited. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "They can be viewed and downloaded at [https://doi.org/10.48529/2ZH0-JF55](https://doi.org/10.48529/2ZH0-JF55) or, currently, [https://microdata.worldbank.org/index.php/catalog/4218.](https://microdata.worldbank.org/index.php/catalog/4218) The citation is Andr´ee (2021). 6There are also country-specific data gathered by FSNAU (Somalia) and CLiMIS (South Sudan). More recently, the International Food Policy Research Institute has piloted gathering high frequency prices in several countries. 7For instance, estimates have also been developed for Papua New Guinea in collaboration with IFPRI in a separate pilot project.\n\nFor example, maize, sorghum, millet, wheat, vegetable oil, to name a few common food items, are also tracked in the World Bank Commodities Price Data (The Pink Sheet) that is used to construct the World Bank Food Price Index used to track international food price developments.\n\nUsing detailed data on expenditures from a 2017/18 household budget survey and caloric information from the Brazilian Table of Food Composition, calorie intake is assigned to more than 1,400 items to estimate the cost per calorie for a representative group of the population.", "output": {"entities": {"named_data": ["World Bank Food Price Index", "Brazilian Table of Food Composition"], "descriptive_data": ["country-specific data gathered by FSNAU", "household budget survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "17 Figure 10: Children aged 7-12 attending school (%) Source: Listening to Displaced People Survey, 2014 and 2015. 5. Challenges Faced by Returnees The results suggest that returnees were less affected by the crisis than IDPs and refugees. This is reflected in data on asset and livestock ownership, but also in the information pertaining to exposure to violence. Returnees reported fewer victims; fewer returnees reported to have lost income as a consequence of the crisis; more of their children were able to continue schooling; and relative to IDPs and refugees, fewer perceived being poorer in June 2014 than before the crisis. Returnees are also the group that feels most secure, that has high levels of trust in the Malian army and police and that has a positive attitude towards most government policies. 88 88 97 79 76 78 99 88 92 55 74 72 91 98 92 96 100 85 90 91 96 90 86 87 87 95 89 76 75 90 94 92 97 86 73 98 Bamako Gao Timbuktu Kidal Niger Mauritania IDPs Returnees Refugees 14-Aug 14-Oct 14-Nov 14-Dec 15-Jan 15-Feb", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "An alternative version of the database that has been mapped to the United Nations (2006, 2009) Trends in International Migrant Stock database is available from the authors. These data are standardized over time in terms of the years to which they refer. { Table 3 here} Calculating Missing Gender Splits Although common in the underlying data, bilateral migration data disaggregated by gender are sparser than aggregate migrant totals (see table 1). An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits; they are described in appendix 5. Combining Migrant Definitions Only a single definition of a migrant (foreign born or foreign citizen) can be applied to each destination country in the final matrices. Switching definitions over time 17 The subregions used for the disaggregations are the 21 UN regions (see http: / / unstats. un. org / unsd / methods / m49 / m49regin. htm, with the countries of Oceania aggregated into a single subregion. They do not match the large World Bank regions used in the analysis in section IV. 18 While this propensity measure is clearly inappropriate, less than 1 percent of all migrants and observations are assigned on this basis. This method is included so that every migrant in the underlying data is accounted for.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock", "Global Migration Database"], "descriptive_data": [], "vague_data": ["bilateral migration data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "80 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure 3.23: Work type by gender Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals Percent Figure 3.24: Occupation Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals Percent Figure 3.25: Occupation among completed secondary or more Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 37 These results indicate that the OCP model is not ideal for refugees’ labor market inclusion. Few refugees primarily enroll through an existing formal employer; thus, the OCP is mainly open to refugees with networks that can support them with remittances, and this makes these households less likely to work", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 28 of 51 Figure 20: Part-time employment by age groups. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Figure 21: Temporary employment by age groups. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys A less clear picture is observed in the case of temporary employment. Indeed, in the four countries where temporary employees are identified, we observe different dynamics in the age profile. On the one hand, Kyrgyzstan and Turkey do not evidence significant changes in the age profile of temporary workers in the last 10 / 15 years. On the other hand, Georgia and Armenia present changes in the age profile but in opposite directions. Armenia shows today a higher share of the older groups within temporary employees while Georgia evidences a younger profile of temporary workers. Finally, analyzing the composition of non-standard employment by gender, we have a heterogeneous picture by types of non-standard employment in the levels but with a similar trend (Figure 22 and 23).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In 42 % of interviewed families, two children were attending school in displacement,\nfollowed by families with three kids attending schools (35 %) and four kids attending school\n(29 %). 16 % of families have one child at school, while in 10 % of families five children\nattend the school in displacement.\n\nFive families have one elderly in their families, and eight families have two. 11 families were\nhaving at the time of interview a pregnant or lactating women present in their family.\n\nMost of the interviewed IDPs currently live in Peshawar district (38 %), whole breakdown of\nlocations is provided below.\n\n_Graph 1: Current location of IDPs originating from Shalozan Tangi_\n\n**IV.** **Main findings of the Return Intention Survey**\n\n**A.** **Displacement timing and trends**\n\nOnly two of the interviewed persons came from areas of origin in less than 18 months,\nothers were in displacement for a longer period. For most of the interviewed persons (62 %)\nthe **reason for displacement** was sectarian violence, followed by reason of military\noperations (29 %) and lack of livelihood opportunities due to the conflict (3 %).\n\n4", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Coordination and advocacy.** In response to the significant conflict-driven internal\ndisplacement that has occurred, UNHCR has begun scaling up protection coordination in\nareas where new IDP responses are now underway. UNHCR and partners are also taking\npragmatic approaches to address the mixed nature of displacement flows, i.e. refugees\nand IDPs. Protection Sector coordination platforms have been established in White Nile,\nKassala, Gedaref, Madani, and Wadi Halfa. Additionally, in areas where protection sector\ncoordination platforms were already present, outreach to protection partners planning to\ninitiate responses in new areas affected by conflict and displacement is underway. In\naddition, since the outbreak of the conflict on 15 April, UNHCR-led Protection Sector has\nissued nine flash updates titled ‘At a Glance: Protection Impacts of the Conflict’. These\nweekly updates, primarily based on desk review of secondary data, highlight the severity\nof the protection impacts experienced by the civilian population as a result of the conflict.\nThe specific protection concerns around the intensity of the violence and its intercommunal\ndimension in Darfur were underscored in dedicated Darfur Protection of Civilians Advocacy\nNotes. These have included two regional-level advocacy notes and one focused\nspecifically on West Darfur. Flash updates were also issued in relation to the attacks on\nKutum and Tawila in North Darfur. Finally, key advocacy messages related to urgent\nprotection of civilians’ priorities were drafted in preparation for the high-level pledging event\nto support the humanitarian response in Sudan on 15 June, in collaboration with the Global\nProtection Cluster.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1 POST-CONFLICT TRANSITIONS WORKING PAPER NO. 16 Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis * Håvard Hegre Centre for the Study of Civil War, PRIO (CSCW) Clionadh Raleigh CSCW, PRIO & University of Colorado at Boulder Abstract Why do larger countries have more armed conflict? This paper surveys three sets of hypotheses forwarded in the conflict literature regarding the relationship between the size and location of population groups: Hypotheses based on pure population mass, on distances, on population concentrations, and some residual state-level characteristics. The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events. The analysis covers 14 countries in Central Africa. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper develops a statistical method to analyze this type of data. The analysis confirms several of the hypotheses. World Bank Policy Research Working Paper 4243, June 2007 The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post- Conflict Transitions Project can be found at http: / / econ. worldbank. org / programs / conflict). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 5. 2 Composition of poverty Unpacking the headline numbers further, important patterns emerge about the composition of multidimensional poverty among forcibly displaced and host communities in these countries. Overall, the censored headcount ratios (proportion of people who are poor and deprived in a given indicator) are lower among non-displaced communities than among refugees and IDPs, but there are large differences in which indicators are the most salient in different countries. The indicators with the largest difference between the two populations are bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria. These findings reinforce the need for policies and programming that take into account the measured experiences of IDPs and refugees. In this way, the MPI can function both as tool to monitor, track, and bear witness to the lived experiences of forcibly displaced communities, as well as advise on evidence-based interventions that address the needs of the local population. Figure 1 shows the censored headcounts of each indicator in Sudan ’ s MPI, with large differences appearing by displacement. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Using Afrobarometer ’ s geocoded surveys, we focus on clusters as our unit of observation. 10 Our sample consists of 7, 547 such locations and 76, 518 individuals in 23 countries in Sub-Saharan Africa. “ The sampling universe normally includes all citizens age 18 and older. As a standard practice, they [we] exclude people living in institutionalized settings, such as students in dormitories, patients in hos- pitals, and persons in prisons or nursing homes. ” (Afrobarometer, https: / / afrobarometer. org / surveys − and − methods / sampling − principles) Since the sampling frame is based on recent censuses, with the aim of representing all citizens of voting age in a given country, the Afrobarometer samples are unlikely to include refugees. Note also that “ the sample design is a clustered, stratified, multi-stage, 8We explain the construction of theses indices in Section 4. 2. 9We test the robustness of our results with a smaller (40 km) and a larger (120 km) radius in Section 5. 3. This choice of buffer size assures us that between 75 percent and virtually all refugee camps fall within a cluster buffer. Other studies relying on Afrobarometer data construct buffers ranging from 25 km (e. g., Michaelopoulos and Papaioannou (2011), investigating ethnic-specific pre-colonial institutional structures) to 100 km (e. g., McGuirk and Burke (2020a), analyzing the impact of food-price shocks on conflict). 10Afrobarometer is a pan-African research network conducting public attitude surveys on democracy, governance, the economy, and society in African countries that are repeated on a regular basis (Afrobarometer, 2020). 10 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "services or institutional support Lack of freedom or mobility Lack of community/family networks Insecurity or discrimination Figure D.16: Top 3 difficulties with being a refugee by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure D.18: Type of work by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure D.17: Work status by survey domains Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Elementary Occupations Machine Operators/Assemblers Craf/Related Trade Workers Skilled Agricultural Workers Service/Sales Workers Clerical Support Workers Tech/Associate Professionals Managers/Professionals Percent Figure D.19: Occupation by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Inside the camp Outside the", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "C Details on outcome measures Outcome Variable Descriptions Psychological Well-being PHQ9 The standardized total score of 9 questions from the Patient Health Questionnaire-9 (PHQ9) Life Satisfaction Index A standardized average of survey responses to four questions from Di- ener ’ s standardized scale, responses made along a seven-point Likert scale. Stress Index The standardized total score from three elements of adapted from the Cohen Stress scale. “ How many of the last 7 days have you [been able to fall asleep peacefully / felt nervous / felt frustrated]? ” Sociability (Total) The total number of conversations in the past day with adults. Sociability (Positive) The total number of conversations in the past day with adults that the respondent felt were positive. Self-Worth Index The standardized total score from the responses on a scale from 1 to 10 to two questions: “ Think of a person you know who you most respect and who brings greatest value to your [family / community]. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Patient Health Questionnaire-9"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 3. ANALYSIS Has the increase in Syrian refugees impacted the welfare and socioeconomic conditions of the host community? Summary statistics in the previous section showed clear trends of increasing poverty among recent migrants throughout the country, both near the Syrian border and across rest of the country. While the poverty rates of recent migrant households spiked in 2013, poverty of host community households maintained a relatively constant level in the whole country. From these trends, it appears that there at least has not been an increasing trend in poverty among the host community over the latest years. The empirical model is shown in Equation 1. Regressions are estimated at the NUTS2-year level and using data from only the years 2011 and 2013. The dependent variable of interest is the host community poverty rate by region and year, where the poverty rate is based on spatially deflated imputed household income. Unlike the computation of the poverty rates, “ recent migrant ” information is not used for the analysis. Only the host community poverty rates are calculated using the LFS and the number of Syrians are taken from government sources.", "output": {"entities": {"named_data": ["LFS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10100 This paper explores the impact of refugee return on social cohesion using data from Burundi, a country that expe­rienced high levels of repatriation during the 2000s. It uses a nationwide survey conducted in 2015 and relies on geographic features of the communities for identification purposes. The results suggest varying impacts of refugee return on different aspects of social cohesion. The stronger effects, suggest that refugee return has a negative impact on the feeling that community members help each other, could borrow money for emergencies from non-household members and feeling that the community is peaceful. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["nationwide survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Papaioannou, 2016; Berman et al., 2017; Harari and Ferrara, 2018; Eberle et al., 2020; McGuirk and Burke, 2020b). As a further robustness check, we also use data on conflict incidence and intensity from the UCDP, which uses a more conservative definition of conflict. The UCDP dataset is manually curated and compiled with automated computer assistance (Sundberg and Melander, 2013). The UCDP defines an armed conflict event as “ an incident where armed force was used by an organized actor against another organized actor, or against civilians, resulting in at least one direct death at a specific location and a specific date ” (Pettersson et al., 2020). We extract daily event observations from the UCDP dataset if the location of the actual event is exactly known, the event location is within a radius of less than 25 km around a known point, or at least the administrative district where the event happened is known. As pointed out by Eberle et al. (2020), the UCDP events are more likely to capture violence between large-scale and more structured groups. Table B. 2 shows that on average, conflict events seem to occur more in refugee-hosting areas. This is of course not a causal interpretation but a simple correlation. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["UCDP dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Specifically, a 1 percent increase of the refugees ’ presence5 leads to a 2. 7 and 15. 9 percent increase in the diversification of livelihood activities as a secondary occupation and value of livestock product sale, respectively. It should be noted that this analysis is taking place during a period where refugees in Ethiopia were prohibited by law from seeking work outside designated camps. This has changed after 2019 because of the revised Ethiopian Refugee Law. These effects tend to be heterogeneous across regions and to a limited extent, vary depending on the gender of the household head. The negative effects tend to be concentrated in Gambella, a region that hosts most of the refugee population in Ethiopia and where the refugee population is as large as the population of the region. Overall, compared to women-headed households, households with a male head seem to benefit through increased diversification of activities as a secondary 4 Region refers to the administration level 1 from the Database of Global Administrative Areas (GADM). The nearest region to the refugee camp is identified as the one that has the shortest straight distance to the refugee camp among all neighboring regions in the major refugee source countries. 5 As explained above, refugee presence is the number of refugees (population) in the nearest refugee camp to the household location weighted by the household ’ s inverted distance to the camp.", "output": {"entities": {"named_data": ["Database of Global Administrative Areas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "random variation, seasonal variation, and measurement error. Furthermore the FAO forces any CV to lie\n\nmotivation for this second approach, which is purely illustrative, is that the FAO derives this mean from\n\nThe third and final component needed to replicate the FAO calculations is to use the sample average of\n\ndaily energy requirement (2068 kcal per person per day). Using these estimates, the FAO approach\n\n11 This is the last FAO report on which we have a detailed description of the exact mechanisms used. The\nmethodology used has been modified slightly since then, as explained Annex 2 of FAO (2012) and footnote 4.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10,000 15,000 20,000 25,000 30,000 35,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.28: Value of productive assets in households with business Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 Percent 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Figure D.30: Youth work status by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees-Current Refugees-COB Hosts Refugees-Current Refugees-COB Hosts Refugees-Current Refugees-COB Eritrean Somali South Sudanese Salary (employment/casual labor) Crops/livestock Donations(NGO/gov) Remittances (local/international) Other (rental income, PSNP, pension) Percent Figure D.29: Primary source of income pre-post migration by survey domains Source: World Bank Staff based on SESRE 2023. Annexes 111 Results on Refugees’ Aspirations Table D.10: Refugee intention to migrate abroad (1) (2) Camp-Based Refugees OCP Refugees Male 0.022 -0.001 (0.019) (0.008) Age Under 30 - - Age 30-44 -0.000 -0.007 (0.020) (0.008) Age 45-64 -0.082*** -0.088** (0.027) (0.036) Education: Primary incomplete - - Education: Completed primary -0.008 0.004 (0.036) (0.009) Education: Completed secondary 0.016 0.006 (0.064)", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "than 500, 000 refugees, together accounting for half of all refugees and people in refugee-like situations (excluding Palestine refugees). Major refugee hosting countries are typically the neighbors of countries of origin. For example, Syria ’ s neighbors (Turkey, Lebanon, and Jordan) together accounted for 27 percent of total refugee numbers; Afghanistan ’ s neighbors (Pakistan and the Islamic Republic of Iran) together accounted for 16 percent; and Somalia ’ s and South Sudan ’ s neighbors (Ethiopia, Kenya and Uganda) together accounted for 11 percent. Some countries (Lebanon, Jordan and Turkey) are hosting a particularly large share of refugees relative to their population (see Figure 10). 45 However, in all other countries, the number of refugees as a percentage of the population is 3 percent or lower, and most often below 1 percent. Figure 7: Top 15 Host Countries as a Share of Total Refugees and Asylum-Seekers 1991 – 2015 Source: UNHCR Statistical Online Population Database Note: Includes refugees, people in refugee-like situations and asylum-seekers. Excludes Palestinian refugees under UNRWA ’ s mandate. 45 Nauru is a special case since the Australian government funds the offshore processing center where refugees and asylum-seekers intercepted at sea are detained pending determination of their status. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Surveys of employers consistently find that more than hard, technical skills, employers value these harder-to-quantify skills of honesty and integrity, problem-solving ability, work ethic, communication skills, the ability to work productively with others, responsibility and dependability (Blom and Hobbs 2007). The AGI program has focused on the development and measurement of these softer attributes that matter for employment as well as those that matter more to the individual, such as self-confidence and empowerment. Despite the challenges of measuring such subjective outcomes, the survey instruments included panels of questions designed to elucidate a nuanced picture of the personality and psychosocial characteristics that are most relevant for labor market success. Table 6A presents results on empowerment and decision-making. The first series of questions have to do with control over resources, spending decisions and earnings. Respondents were asked how much control they had over how to spend their own earnings; also, whether they had money of their own for basic uses that they alone could decide how to use, without having to ask for permission. The EPAG baseline survey found that respondents reported a high degree of control over resources even before the program started.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Surveys of employers"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In aggregate, we expect to observe a negative relationship between the percentage of the population that reports displacement, but at the individual level we expect that experience with hosting may, in certain circumstances, be positively associated with perceptions of social cohesion, regarding perceptions of relationships and solidarity. 4 Displacement and Social Cohesion: Survey Evidence To empirically evaluate the relationships between hosting displaced populations and social cohe- sion, this paper analyzes a series of surveys of civilian adults conducted in eastern DRC. 8 Each survey uses a multi-stage cluster sampling strategy capturing all territoires9 in North Kivu, South Kivu and Ituri provinces. The ﬁnal sampling units are randomly selected adults above the age of 18 to avoid bias toward men and / or heads of households. Multiple attempts are made over the course of one day to contact selected respondents and if necessary, appointments are made for in- terview. Surveys are enumerated by Congolese college students or professionals and interviews are conducted by members of the same gender and ethnicity as respondents to minimize enumerator- induced response bias. Further methodological details have been published (Vinck, Pham, Bindu, Bedford & Nilles 2019) elsewhere and additional details and sample size calculation are detailed in Appendix. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["surveys of civilian adults"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 Last, specific adjustments are made in the case of Germany and the Republic of Korea. For Germany, bilateral data are available only by nationality. However, these data fail to take adequate account of the large number of ethnic Germans who arrived from other countries between 1944 and 1950 (mainly expellees) and those who arrived after1950 (mainly resettlers). Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3). In the case of Korea, data by nationality are readily available for each census round. However, these data fail to account for the large numbers of migrants from the People ‘ s Democratic Republic of Korea living in the Republic of Korea. Since the United Nations Trends in International Migrant Stock details the total migrant stock in the Republic of Korea by the country of birth definition and because citizenship is rarely granted to people from outside, it is simply assumed that the nationality data were comparable to the foreign-born definition. The nationality total was then subtracted from the UN total and the remaining migrants were assigned to the People ‘ s Democratic Republic of Korea.", "output": {"entities": {"named_data": ["United Nations Trends in International Migrant Stock", "German 2005 micro-census"], "descriptive_data": [], "vague_data": ["bilateral data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In a mortality study in Iraq, Galway et al. (2012) used GIS and Google earth imagery for household sampling. The method used gridded population data for selection of clusters. The first cluster sampling stage of their study used the ‘ Create Spatially Balanced Points ’ (CSBP) function in the ArcGIS (v10) software. Boo et al. (2020) introduces a sampling design based on gridded population estimates as their sampling frame to implement a PPS design and derive sample size estimates for the number of grid cells. Assuming the grid square method is applied to the area itself rather than a selected PSU, the weights for the grid method are similar to those for segmentation, where the cells are the PSUs, but without the additional step of selecting segments. The weights can therefore be represented as 𝑤𝑤𝑖𝑖 ′ = (𝑁𝑁𝑘𝑘) ൫𝑁𝑁𝑘𝑘𝑘𝑘൯൫𝑁𝑁𝑘𝑘𝑘𝑘𝑘𝑘൯ 𝑘𝑘𝑘𝑘𝑘𝑘. 2. 4. North Method The “ Qibla method ” described in Himelein et al. (2017), or what is called in this paper the “ North method ” method, is an attempt to assign probability weights to random point selection methods. Several random point selection methods can be found in the literature, particularly in relation to epidemiological studies. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["gridded population data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "33 recommendations (Jewkes, 2000). Their efforts contributed to the Employment of Educators Act and new Department of Education guidelines, both of which were introduced in 2000. These regulations mandate dismissal of educators found guilty of sexual or physical assault, or of having a sexual relationship with a student. They also define penalties for failing to report abuse. It remains to be seen whether these measures will have the intended impact. After the act was passed, Human Rights Watch (2001) suggested that the South African government needed to do more to increase awareness of the law among school principals and to strengthen enforcement. Institutional reform Efforts to improve the institutional response to gender-based violence range from sensitization and training of staff, sexual harassment policies, curriculum reform, school-wide anti-violence awareness campaigns, counseling and referrals, and broader efforts to reduce discrimination against girls and improve school safety. Initiatives to increase female enrolment by improving girls ’ safety at and on the way to school As mentioned earlier, parental concerns about girls ’ safety in school appears to lower female school enrolment in settings such as South Asia, Africa and the Middle East.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 1. Introduction The most common sampling approach for cross-sectional household surveys in the developing world is a stratified two-stage design (Grosh and Munoz, 1996). Following stratification based on administrative boundaries, clusters are selected in the first stage with probability proportional to size from a national census-based frame. In the second stage, a canvassing operation is conducted in the selected clusters to compile an updated list from which households are randomly selected. While this methodology is straight forward to implement in the field and reliably produces unbiased estimates, there are several downsides. The first downside is cost. The World Bank ’ s Living Standards Measurement Study team, which provides technical assistance on large-scale household surveys around the world, estimates the field listing operation increases the overall budget for data collection by 25 percent. Due to confidentiality concerns, the data collected during a field listing operation, typically the name of the household head and address or location description of dwellings, does not have any analytical applications beyond as a component of the weight calculations. 2 At a time when typical survey costs are in the USD millions, reducing a significant cost component will increase the financial sustainability of data collection. The second drawback to the traditional design relates to timeliness. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Living Standards Measurement Study"], "descriptive_data": ["national census-based frame"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "vices. Our findings reveal significant delays in the physical and cognitive development of Venezuelan minors compared to their Colombian peers. Specifically, we observe a 0. 3 standard deviation difference in Body Mass Index (BMI), indicative of nutritional status, and a 12-percentage point difference in the Peabody Vocabulary Test scores, which as- sesses receptive vocabulary and verbal ability. Surprisingly, our analysis does not identify any disparities in socio-emotional and mental health between the two groups. This out- come is unexpected, given the high incidence of socio-emotional and mental health chal- lenges among forcibly displaced populations. The absence of discernible gaps in these areas could be attributed to the non-exposure of Venezuelan migrants to warfare, or it may reflect the vulnerabilities of the Colombian population, which has its own extensive history of internal forced displacement and violence. When examining the role of time of settlement, regularization status, and service access on the developmental disparities between Venezuelan and Colombian minors, we un- cover two significant facts. On the one hand, the gaps in both cognitive and physical development are diminishing over time. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "stipend money, and they were formed into small groups or\"EPAG teams\", each with a coach or mentor, to foster support networks and boost attendance. 3. Methodology 3. 1. Research design The impact evaluation of the EPAG project uses a randomized controlled trial, in which eligible applicants to the program were randomly assigned to participate in one of two cohorts (or “ rounds ”) of training. The treatment group is defined as those who were offered a space in the first round of training and the control group comprises those assigned to the second round. Selection into the training rounds was performed on a computer (using Excel) and was stratified by the track choice of the applicant (job skills versus business development skills), community, and service provider. Data were collected using three quantitative household surveys (baseline, midline, and endline) and two sets of qualitative focus group discussions (one after each round of training). A timeline of the impact evaluation is depicted in Figure 1. During both the baseline and midline surveys, the head of the household in which the EPAG participant was residing was also interviewed, in order to examine potential spillover effects of the program on non-treated household members. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Nevertheless, censuses are the only nation-wide source of population data for potentially providing estimates of displaced populations, especially those in non-camp settings, and often provide a basis for sampling frames for survey instruments (UNHCR 2016). Capturing displacement situations in official statistics also increases their visibility. Sample surveys Sample surveys can potentially provide a rich source of data on forcibly displaced populations. Compared with censuses and registers, sample surveys allow more detailed questions to be asked about the characteristics and situations of households. If survey instruments identify displaced populations based on individuals ’ and households ’ self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status (UNSD 2014). There are opportunities to mainstream forced displacement into international survey instruments, but this has only been done in a handful of cases. Several standardized international sample surveys have been designed for special purposes including the Living Standards Measurement Study (LSMS), 75 Labor Force 71 The UN census recommendations for the 2010 World Population and Housing Census Programme stipulate that refugees and IDPs living in camps should be counted and their numbers disaggregated in population statistics, however there is no requirement to separately distinguish displaced people living outside of camps (UNHCR 2016).", "output": {"entities": {"named_data": ["World Population and Housing Census Programme"], "descriptive_data": [], "vague_data": ["population statistics"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5. 5 Ethnic diversity at different levels Despite the use of sampling weights in the construction of the diversity indices, we have no guarantee that our diversity indices are representative at the local level. Although similar ethnic diversity indices have been used at the local level (Nunn and Wantchekon, 2011; Rohner et al., 2013; Robinson, 2017; Desmet et al., 2020; Gomes, 2020b, a; Hodler et al., 2020), we cannot exclude the possibility that a lack of representativeness at the local level introduces some noise into our estimates. Ideally, we would have liked to construct our local diversity indices based on census data. However, such data are not available on an annual basis and only a minority of African countries include ethnicity questions on their censuses (Robinson, 2017). Robinson (2017) highlights other benefits but also warns against the risk of using non-random samples or of the size of samples introducing significant errors.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["census data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In 2008, trained enumerators conducted face-to-face interviews in local languages with 26, 513 respondents across 19 countries. 5 The sample is designed as a representative cross-section of all citizens of voting age in a given country. The dataset used for this paper has a multilevel structure; individuals are nested within primary sampling units (PSUs), which are nested within countries. The PSUs are the smallest, well-deﬁned geographic units for which reliable population data are available and they tend to be socially homoge- nous, thereby producing highly clustered data. In most countries, these will be Census Enumeration Areas (Afrobarometer, 2005, 37-38). Although re- spondents were not sampled based on their ethnic aﬃliation, there is likely to be a high level of clustering in the dataset around ethnicity. In other work, I discuss the advantages of multilevel modeling (Levi and Sacks, 2009). Treating the dependent variable as a binary outcome and taking into account the multilevel nature of our data, I estimate random intercepts for 5I excluded Zimbabwe from the analysis because of missing data on key variables. 9", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "their earnings on household expenses than men. Most of these studies have focused on adult women, specifically married women with children. It is not known whether the same holds true for young women, who may have other spending priorities, have less experience in managing households, and have younger children. Given the large increases in employment and earnings documented above, the EPAG program serves as a good setting to examine these types of spillovers. The evaluation included detailed interviews with the heads of the household in which EPAG participants were residing. The purpose of the household questionnaires was precisely to examine the hypothesis that investing in young girls would benefit her household. A secondary hypothesis was that EPAG participation may change gender-related attitudes in the participants ’ households. Household data was collected for 1601 out of the 1622 individuals who were interviewed at both baseline and midline; this same sample of 1601 individuals serves as the basis for both the individual and household level analysis in this paper. The estimated impact of the program on a broad range of household outcomes is summarized in Tables 8 and 9. Panel A of Table 8 examines the household size.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Chew et al. (2018) use a baseline convolutional neural network model on a gridded population sampling frame to select a sample of households in Nigeria and Guatemala. The authors found this technique to be on par with human canvassing in terms of accuracy, and to outperform other machine learning models based on crowdsource or remote sensing data. Grais et al (2007) compared an unweighted random point selection methodology to a random walk in their study of vaccination rates in urban Niger. The authors do not find statistically significant differences between the methods, though the sample size was limited and both methods were non-probabilistic. 3. Design and Field Protocols 3. 1. Experiment Design This paper makes use of a dataset from the purposefully designed methodology experiment conducted in one section of the Protection of Civilians site 1 (PoC1, Figure 1), one of the largest IDP camps in Juba, South Sudan. To generate a gold standard as the basis of comparison, a household census was conducted between August and September 2017. During this exercise, 2, 655 households were interviewed using a questionnaire designed to collect demographic information, dwelling characteristics, household consumption, and perception data. At the end of each census interview, households received a unique barcode that could be used to identify them later in the experiment. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 quest for imaginative and convincing instruments for migration (see the review of the migration and poverty literature by McKenzie and Sasin, 2007, and the references therein). An additional hurdle is the need for panel data to study migration and economic mobility. The costs and difficulties in re-surveying migrants mean that attrition may be relatively high for this group and may also result in the loss of some of the most relevant households to study this process (Beegle, 2000; Rosenzweig, 2003). This paper uses unique data from a region in Tanzania to address this key question: What is the impact of physical movement out of the original community on poverty and wealth? Although we do not have experimental data, the nature of our data allows us to limit the potential sources of unobserved heterogeneity considerably. Building on a detailed panel survey conducted in the early 1990s, we re- interviewed individuals in 2004, making a notable effort to track individuals who had moved. The tracking of individuals to new locations proves crucially important for assessing welfare changes among the baseline sample. The average consumption change of individuals who migrated was more than four times higher than that of individuals who did not moved. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "150 P. B. Spiegel & P. V. Le\n\nsituations and lack of epidemiological expertise in many humanitarian agencies,\nthe data provided are often unreliable (Boss et al. 1994, Spiegel et al. 2004). To\nassess the quality and standardization of BSSs, and its predecessor, the KAP\nsurvey, undertaken in conflict and post-conflict situations, we evaluated the\nmethodological quality and use of internationally-accepted indicators of these\nsurveys conducted among refugee, IDP, host community, returnee, conflict and\npost-conflict populations. Recommendations were then provided to humanitarian\nagencies and governments on how to improve the quality and standardization of\nBSSs among conflict and post-conflict populations.\n\nMethods", "output": {"entities": {"named_data": ["KAP\nsurvey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 19 of 51 Source: Own calculations based on Household surveys Finally, we analyze the trends in the task content performed by non-standard workers following the task methodology following the methodology proposed by Acemoglu and Autor (2011). Figure 13 presents the variation in the task content index by non-standard workers vis-a-vis standard workers. A first general view suggests that non-routine cognitive task content of jobs (both analytical and interpersonal) increased in both NSE and SE even though we have some exceptions. Indeed, the only countries where non-standard employment shows a less intense profile in non-routine cognitive analytical tasks are Peru and the Dominican Republic. Additionally, Chile and El Salvador show a virtually null change in the intensity of this kind of tasks. In the case of standard employment, the change in the profile towards non-routine cognitive analytical tasks is even more obvious (the Dominican Republic is the only exception). A similar scenario is recorded in the case of the intensity of non-routine cognitive interpersonal tasks, even though in this case the trend is more pronounced in both, standard and non-standard employment. Additionally, the trends in SE and NSE are more correlated for this type of tasks. The evolution of the intensity in the routine cognitive tasks in the last two decades in NSE presents a much more heterogeneous picture. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This paper analyzes data from the initial wave of VenRePS-Kids, conducted from Oc- tober to December 2022, to outline key demographics and stylized facts about forcibly displaced children and adolescents. Initially, we examine the human development dis- parities of forcibly displaced Venezuelan children and adolescents in comparison to their Colombian counterparts. Our approach to human development is broad, covering physi- cal, cognitive, socio-emotional, and mental health aspects. Additionally, we complement our analysis by exploring differences in food security, social cohesion, and the economic status of parents. Although our analysis is descriptive, it represents a crucial initial step 2Venezuelan households are defined as those where both parents and their children have a Venezuelan nationality. Colombian households are composed of Colombian citizens only. 3", "output": {"entities": {"named_data": ["VenRePS-Kids"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "representative sample of undocumented migrants in Colombia ’ s major cities as of 2020. 10 This survey encompasses information on the socioeconomic status, health, well-being, access to services, and labor market outcomes of adult undocumented migrants. Second, we compare our sample with the Administrative Venezuelan Migrant Registry (RAMV), a nationwide census of undocumented Venezuelan migrants conducted by the Colom- bian government in 2018. 11 This census surveyed Venezuelan households regarding their socioeconomic conditions and the labor market characteristics of the household head. We compare the household characteristics and the labor market outcomes of the house- hold heads in our sample with those in VenRePS and RAMV surveys in Table A. 3. 12 We observe that households in the VenRePS-Kids survey are smaller on average and have a greater number of children living in the household. The latter is anticipated since one of the eligibility criteria to participate in our survey is the presence of at least one child in the household. Furthermore, the household heads in our sample are disproportionately female and more likely to be married, aligning with the family structure targeted in our sampling frame. Regarding labor outcomes, household heads in our sample are more likely to be employed and engaged in the informal sector compared to those surveyed in VenRePS and the RAMV census. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["VenRePS", "Administrative Venezuelan Migrant Registry", "VenRePS-Kids survey", "RAMV surveys", "Administrative Venezuelan Migrant Registry (RAMV)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9533 Refugee camps are believed to represent safe havens for forcibly displaced persons, but studies looking at refugees ’ quality of life in camps are few. This paper explores how Syrian refugees ’ quality of life in camps in Jordan differs from that of Syrian refugees residing outside camps. Using data from the Syrian Refugee and Host Community Survey, the study measures life quality through indicators of subjective life experience and material living conditions. Data are analyzed using advanced statistical methods (difference-in-difference and propensity score matching) to control for selection bias that could skew estimates of causal effects. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This distinction is also applied to foreign students, with those entering the system after February 2022 referred to as “ Migrant post-Feb 2022 ” and others referred as “ Migrant pre-Feb 2022 ”. 7 Administrative data from MoE. The Ministry data includes information for all students who enrolled at any point during the academic year. 8 This information covers academic year, grade, gender, birth date, birthplace, citizenship. They also include school-speciϐic information such as the name and identifying code of the institution where the student is enrolled. Furthermore, the dataset includes a variety of school outcome variables, including grades in English, Italian, Mathematics, overall GPA calculated as the average across all subjects, behavior scores from grade 9 to grade 12, guidance council evaluations from lower secondary school, records of absences, late entries, and early exits. Given the timing of this study, data for academic year 2022-23 are the most complete. For academic year 2022-2023, school enrollment data at the provincial level was provided for 4, 269, 348 enrolled students across the 8 years of Italian lower and upper secondary school, encompassing both public and private institutions. The dataset includes nearly all students in the country irrespective of their citizenship. 9 Table 1 shows the distribution of the different groups of students by grade. In the 7 For ease of reference, we refer to non-Italian and non-Ukrainian students as migrants. However, we acknowledge that some of these students may be refugees or displaced students. 8 At the time of writing this paper, both MoE and INVALSI data were not fully available and, as such, only the information on enrollment was used for the academic year 2023-24. 9These numbers do not include students enrolled in Provincial centers for adult education (CPIA). See footnote 5. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["INVALSI data"], "descriptive_data": [], "vague_data": ["school enrollment data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 Background Information 2. 1 Characteristics of West Bank and Gaza ’ s labor markets The labor markets of the West Bank and Gaza exhibit features typical of the broader Middle East and North Africa (MENA) region, but also have attributes that are highly unique. Additionally, important differences exist between the West Bank and Gaza. This section provides an overview of these characteristics. We use data from the Labor Force Surveys (LFS) of the West Bank and Gaza and we focus on 20-59 years old men. In Section 3. 1, we provide more information about the data sources and sample selection. We divide each labor market into five mutually exclusive and jointly exhaustive states: public sector employment, private formal sector employment, private informal sector employment, unemployment, and out of labor force. 1 We focus our discussion exclusively on men, as women ’ s labor force participation in both the West Bank and Gaza is very low, never reaching values above 25 %. This low participation rate is common in MENA countries and makes the role of the pandemic on women ’ s labor market outcomes relatively less important than other, more relevant structural factors.", "output": {"entities": {"named_data": ["Labor Force Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The third proxy for economic activity is given by per capita growth of night light, computed using satellite data from the National Oceanic and Atmospheric Administration (NOAA). 6 Night light data has the beneﬁt 6Satellite data is available for a shorter time period, 1992-2013. 9 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["satellite data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "- Promote meaningful participation of persons with disabilities in humanitarian action, seek advice and collaborate with\nnational and local organizations of persons with disabilities in coordination structures and build their capacities.\n\n- Prioritize the integration of disability inclusion and Mental Health and Psychosocial Support (MHPSS) services into the\noverarching humanitarian response framework. This involves incorporating MHPSS elements into needs assessments and\nprogram planning.\n\n- Encourage the recruitment of persons with disabilities as staff at all levels of humanitarian organizations, including as\nfront-line workers and community mobilizers.\n\n- Enhance age, gender and disability disaggregated data collection and analysis to develop risk mitigating measures and\nappropriate indicators and use them to monitor the inclusion of persons with disabilities in all phases of humanitarian\naction. When possible, collect data and information on the risks, barriers and needs of persons with disabilities,\nparticularly in remote regions that are difficult to access.\n\n- Address the attitudinal and other barriers within the humanitarian community and perceptions of disability as an\n‘additional complexity’ in an already complex context, and include it from the onset as part of human diversity.\n\n#### RISK 2 Gender-based violence\n\n**PROTECTION SECTOR AND PARTNERS**", "output": {"entities": {"named_data": [], "descriptive_data": ["age, gender and disability disaggregated data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The endogeneity of refugee inflow is addressed by exploiting differences in factors that influence refugee arrival in the host communities. Specifically, the analysis uses potential refugee inflow as an instrument, which is the product of population density and intensity of con­flicts (number of fatalities per event) in the closest region of the origin country to the refugee camp weighted by the distance of the refugee camp to the closest region. The paper also constructs an aggregate index to proxy house­holds ’ livelihood diversification strategies. The findings show that refugee inflow brings substantial benefits to host communities by creating significant jobs, in which people engage as secondary occupations, and triggers an increasing demand for livestock products. Specifically, while no effect was found on diversification of activities such as a primary occupation and crop product sales, a 1 percent increase in refugee inflow leads to a 2. 7 percent rise in diversifica­tion of livelihood activities as a secondary occupation and a 15. 9 percent increase in the value of livestock product sales. These effects tend to be heterogeneous across refu­gee hosting regions and the gender of the household head: negative effects were mainly observed in Gambella region, which hosts the largest refugee population in the country, and male-headed households were more likely to benefit from the refugee presence for the whole sample.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Eritrean Somali South Sudanese In camp Addis Ababa Total Female Male Percent Figure D.9: Stunting by gender of children Source: World Bank Staff based on SESRE 2023. a. Faced any problem b. Types of problems faced Annexes 103 - 500 1,000 1,500 2,000 2,500 3,000 3,500 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Average household annual expenditure on health Average per capita annual expenditure on health Figure D.12: Average annual per capita health expenditure Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 Eritrean Somali South Sudanese In camp Addis Ababa Total Hosts Refugees Percent Figure D.11: No birth evidence available (children under five years) Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Health institutions (health center/hospital) Home Other Percent Figure D.10: Childbirth in health institutions (children under five years) Source: World Bank Staff based on SESRE 2023. Annexes 104 Hearing Walking or climbing steps Percent Refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 MPI, constructed in Admasu et al. (2021), to capture the deprivations of forcibly displaced individuals and their gendered lives. The paper proceeds as follows. Section 2 reviews the existing literature to provide the background and motivation for the analysis, including a summary of the different country contexts covered by the case studies in this paper. Section 3 outlines the measurement strategy for deconstructing the MPI used for analysis and its limitations, followed by Section 4, which introduces the data. Section 5 presents the findings, first for deprivation results at the individual level and then results evaluating intrahousehold inequalities. Concluding remarks are discussed in Section 6. 2 Background and Literature Review 2. 1 Individual-level measures of gender and multidimensional poverty Individual-level analyses of multidimensional poverty have mostly centered around children, with various studies analyzing the relevance of indicators for children (aged 0- 17 years), 2 as well as other age ranges. The MPI has also been used to better understand gender issues, for example, Batana (2008) implemented a women ’ s MPI in Sub-Saharan Africa. Bhutan ’ s Gross National Happiness measures (2010, 2015), Vijaya et al. (2014), and Klasen and Lahoti (2016) are implemented at the individual level. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "there are approximately 30 refugee camps in Ethiopia, this report refers to the 24 camps included in SESRE. vii Hosts tend to support refugees’ right to work and move to locations with better economic opportunities. Hosts support increasing refugees’ economic opportunities in Ethiopia, but some perceive that refugees increase insecurity and are taking their land. Hosts’ perceptions of adverse effects from refugees are low, but they indicate the impact on economic competition, price increases, deforestation, and security issues. Trust between refugees and hosts is similar, with refugees being more trusting. Refugees are more likely to trust a host if they are culturally similar. Cultural proximity and positive perceptions of economic benefits improve the co-existence of refugees and hosts. Still, additional effort is required to improve the social integration of refugees for enhanced economic integration. Policy Recommendations Addressing challenges refugees face in Ethiopia requires a concerted effort to promote their self- reliance, economic integration, and access to education and health. By leveraging data from initiatives like SESRE and adopting a comprehensive approach that considers the needs of both refugees and host communities, Ethiopia can maximize the benefits from hosting refugees while minimizing associated costs. The Government of Ethiopia has committed to", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 though somewhat lower than that of the Turkish at 30- 50 percent. Child labor is also quite prevalent, though there have been extensive efforts made to ensure that refugee children attend school. 19 Publicly available information on refugees comes from an AFAD survey of 2, 700 households in June and July 2013. Figure 2, using data from AFAD (2013), provides an overview of the Syrian governorates from which the refugees to Turkey originated. The refugees primarily come from northwest Syria. The largest source regions are Aleppo (36 percent), Idleb (21 percent) al-Raqqah (11 percent), Lattakia (9 percent), and Hamah (8 percent). Consistent with travel distance being a good predictor of refugee flows to Turkey, 80 percent of respondents report that they chose to flee to Turkey, instead of another country, due to the ease of transportation. The refugees in Turkey, unlike the later 2015 refugee flows to Western Europe, are nearly 50 percent female. Slightly over 50 percent are minors (under the age of 18). These facts reflect that to large extent Syrian families fled to Turkey together. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["AFAD survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "21 Table 6: Percentage of displaced individuals deprived in selected indicators by gender Ethiopia South Sudan Sudan Male Female Male Female Male Female Years of schooling 55 78 * * * 36 63 * * * 32 46 * * * School attendance 16 19 * * 21 29 23 23 Early marriage 3 13 * * * 8 75 * * * 6 50 * * * Unemployment 7 5 * * * 2 0 * 3 3 Legal id 45 46 48 74 * * * 10 10 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Asterisks indicate statistical significance of mean differences between male and female at 1 % * * *, 5 % * * and 10 % * levels. Returning to patterns of household headship, Figure 5 breaks down the variation in censored headcount ratios among refugee households in Ethiopia, depending on the gender of the household head.", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of Return Migration, Human Capital Accumulation, and Wage Assimilation. The Review of Economic Studies, 89(6), 2841-2871. Alix-Garcia, J., Walker, S., Bartlett, A., Onder, H., and Sanghi, A. (2018). Do Refugee Camps Help or Hurt Hosts? The Case of Kakuma, Kenya. Journal of Development Economics, 130, 66-83. Alkire, S., Kanagaratman, U., and Suppa, N. (2021). The Global Multidimensional Poverty Index (MPI) 2021, OPHI MPI Methodological Note 51. Oxford Poverty and Human Development Initiative, University of Oxford. Altindag, O., Bakls, O., and Rozo, M. S. (2020). Blessing or Burden? Impacts of Refugees on Businesses and the Informal Economy. Journal of Development Economics, 146. https://doi.org/10.1016/j.jdeveco.2020.102490 Andersen, H.L., Osland, L. and Zhang, M.L. (2023). Labour Market Integration of Refugees and The Importance of the Neighborhood: Norwegian Quasi-experimental Evidence. Journal of Labour Market Research 57, 16. https://doi.org/10.1186/ s12651-023-00341-y Aracl, D., Demirci, M., and Klrdar, M. (2022). Development Level of Hosting Areas and the Impact of Refugees on Natives’ Labor Market Outcomes in Turkey. European Economic Review, 145. https://doi.org/10.1016/j.euroecorev.2022.104132 Atamanov, A., Hoogeveen, J., and Reese, B. (2023). The Costs Come Before the Benefits Follow. Why Should Donors Invest More in Refugee Autonomy in Uganda? 1–11. Azlor, L., Damm, A. P., and Schultz-Nielsen, M. L. (2020). Local", "output": {"entities": {"named_data": ["Global Multidimensional Poverty Index (MPI) 2021"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Data sources: UNHCR PRIMES.  For more information or to contribute, please contact UNHCR RBSA DIMA (rsarbdima@unhcr.org)\n\nThe boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations", "output": {"entities": {"named_data": ["UNHCR PRIMES"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 Survey design and administration Designing a socio-economic survey including consumption measures for non-displaced households is challenging as the interview time is limited and one has to be very selective to restrict the number of questions in surveys. For FDPs, the challenge is exacerbated given the need to understand not only their present socio-economic well-being, but also their displacement trajectory as well as their aid dependency. In this context, spending 90 or more minutes on an elaborate consumption module might not be a priority. Instead, detailed information on displacement and aid is crucial and something that is not properly assessed in non FDPs surveys. Fatigue from over-surveying is often cited as an anecdotal challenge in the context of FDPs, leading to survey non-response. Even though FDPs are often subject to intensive surveying by multiple agencies, survey non-response is not as high as for regular populations and the likelihood for a household to be interviewed multiple times is limited. Only censuses interview everyone in the population, they are often prohibitively expensive, and suffer from low data quality outstripping their size advantage. Thus, only few censuses – with very short questionnaires – are necessary for verification exercises. Hence, the chance of multiple interviews for the same households in a short period of time is low. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Looking also at the impact of IDPs in Colombia on wages, Morales (2017) uses a labor force survey, census data and registry data to study short and long-term effects as follows: 𝑆ℎ𝑜𝑟𝑡 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ௧ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ௧ ି ଵ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ௧ ൅ 𝜆 ௠ 𝑋 ௠ ௧ ൅ 𝛾௧ ൅ 𝛿 ௠ ൅ 𝛿 ௠ 𝑇 ൅ 𝜀 ௜ ௠ ௧ 𝐿𝑜𝑛𝑔 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ൅ 𝜆 ௠ 𝑋 ௠ ൅ 𝛿 ஽ ௠ ൅ 𝜀 ௜ ௠ where y is the log of wages, i, m, and i are individuals, municipalities and time respectively, 𝑋 ௜ ௠ ௧ are individual controls, 𝑋 ௠ ௧ is the log of total population or other municipality controls, 𝛾௧ and 𝛿 ௠ are time and municipality fixed effects, 𝛿 ௠ 𝑇 are municipality time trends, 𝛿 ஽ ௠ are department fixed effects and d is the inflow of IDPs defined as 𝑑 ௠ ௧ ൌ 100 𝑝𝑜𝑝 ௠ ௧ 𝑓 ௠ ௧ where 𝑓 ௠ ௧ is the total number of IDPs arriving in municipality m at time t. The same variable without the t subscript is used for the long-run effects equation. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["labor force survey", "census data", "registry data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "A recent global hazard analysis generated a comprehensive database of hazard events during 1975- 2007 from observed data and of event probabilities from geo-physical models (ISDR 2009). We combined this hazard information with city-specific population projections 2 It should be noted that well-documented evidence for such externalities is quite scarce. Their importance is usually taken as given: “ A building collapse may create externalities in the form of economic dislocations and other social costs in addition to the economic loss suffered by the owner. The owners may not have taken these consequences into account when evaluating specific mitigation measures. Consider the following example. A building toppling off its foundation after an earthquake could break a pipeline and cause a major fire, which would damage other homes that had not been affected by the earthquake in the first place. “ Kuenreuther and Roth (1998). See also www. quakesmart. org / index. php? option = com_content & view = article & id = 92 & Itemid = 209. But some experiences have been documented: “ As shown by research on the Great Hanshin-Awaji Earthquake, including that conducted by the Architectural Institute of Japan, Architectural Institute of Japan (1997), houses with inferior earthquake-resistant quality triggered large negative externalities in the neighborhood. For example, broken fragile houses blocked transportation networks, thereby preventing effective fire fighting and, by severing lifelines, they made recovery more difficult. ” (Nakagawaa et al. 2007). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "They indicate that a 10% increase in Germany's export share is associated with a 8% increase in German ownership (0.04 percentage points over the average ownership of 0.5%). We do not find evidence of border regions receiving more FDI from Germany (Figure A.8 of the Appendix). As a falsification experiment, we employ the same specification on similar data for Romanian firms, also from the Orbis database. Since Romania acceded the EU in 2007, we would not expect to observe a significant effect in this case.\n\nIt is worth noting that the sharp increase in foreign acquisitions was not likely driven by the lift of restrictions on FDI. OECD data on FDI restrictiveness for Poland show that screening and legal restrictions on FDI in manufacturing sectors had already been greatly removed by the time of accession.\n\nTable 2 presents summary statistics from the pooled 2013 and 2018 Demographic Health Surveys for our child health outcomes, parental and household characteristics in the oil producing states\n\nOur child health data come from the 2013 and 2018 Nigerian Demographic Health Survey (DHS). These nationally representative cross-sectional surveys have demographic and health details for women aged (15-49) and for children aged (0-5).\n\nof flare volumes. These come from the Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the", "output": {"entities": {"named_data": ["Orbis database", "Nigerian Demographic Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 Table 1 provides on overview of the definition and data source of all conflict exposure proxies discussed in the following. a) Child and sibling mortality A first proxy that captures replacement effects is whether or not a woman lost a child during the genocide (CHILDDEATH). The RDHS questionnaires record child mortality in great detail and even ask for the month of death. This allows us to precisely code CHILDDEATH to take the value one if a woman lost one or more children between April and July 1994. Moreover, we differentiate child death by gender in two further conflict proxies: SONDEATH indicates the death of at least one son during the genocide; DAUGHTERDEATH the death of at least one daughter. Fig. 2 displays the occurrence of child deaths over time as calculated from the 2000 and 2005 RDHS. Child deaths peak during the 1994 genocide, although child mortality remains relatively high in the immediate post-war period. Given that many mothers would have been killed in the genocide at the same time as their (young) children, this proxy somewhat underestimates the effects of genocide on child mortality. We interpret this proxy as capturing also the negative effects of conflict on health, sanitation and nutrition, leading to (even) higher child mortality. A second proxy indicating replacement effects uses sibling mortality during the genocide (SIBLINGDEATH). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "|Strategic<br>priorities<br>Modalities|Protection of civilians|Displacement|Access to services|\n|---|---|---|---|\n|<br>**Advocacy**| Monitoring and reporting<br>(civilians casualties, MRM)<br> Engagement with parties<br>to the conflict<br> Support to the NUG to<br>publish and implement its<br>National Policy on Civilian<br>Casualty Prevention and<br>Mitigation (Cf. PoC key<br>advocacy areas)| Support to DiREC<br> Information products<br> Briefing notes to<br>HC/HCT, GPC, Donors<br> Adherence to refugee<br>law especially with<br>regard to refoulement| Access to civil<br>documentation and<br>policies for the affected<br>population to be able to<br>access justice, education,<br>finance and inheritance<br>rights in the interim<br> Linkage with Citizen<br>Charter CDCs|\n|<br>**Advocacy**|Protection monitoring|Protection monitoring|Protection monitoring|\n|<br>**Advocacy**|HCT Protection strategy<br><br><br>|HCT Protection strategy<br><br><br>|HCT Protection strategy<br><br><br>|\n|<br>**Access**| Conflict and stakeholder<br>analysis| Protection integration<br>in Health, FS,<br>Shelter/NFI,WASH,<br>cash based<br>programming| Communication and<br>information strategy|\n|<br>**Access**|HAG - Negotiated access strategies of international actors|HAG - Negotiated access strategies of international actors|HAG - Negotiated access strategies of international actors|\n|<br>**Access**|Partnership and capacity building of local actors|Partnership and capacity building of local actors|Partnership and capacity building of local actors|\n|<br>**Access**|Establishment of Community Network|Establishment of Community Network|Establishment of Community Network|\n|<br>**Access**|Mobile outreach and remote monitoring<br><br><br>|Mobile outreach and remote monitoring<br><br><br>|Mobile outreach and remote monitoring<br><br><br>|\n|<br>**Protection **| Mine action| Protection assessment<br>and analysis<br> Alert system<br> Gender analysis<br> Preparedness| PSN Network<br>(Identification and<br>referral)<br> linkage with AIHRC to<br>address areas of human<br>trafficking, lack of access<br>to essential services and<br>justice through a human<br>rights lens|\n|<br>**Protection **|Community Based<br>Measures<br>|Protection Mainstreaming in Health, FS, Shelter/NFI,<br>WASH, cash based programming<br><br>|Protection Mainstreaming in Health, FS, Shelter/NFI,<br>WASH, cash based programming<br><br>|\n|<br>**Protection **| UNAMA PoC<br> Local advocacy| Assistance and<br>Mediation to issues<br>related to HLP| Child Protection (support<br>to CPANs, CFS, packet of<br>services)<br>  GBV capacity<br>(Identification and<br>referral),legal assistance<br> Access to tazkera|\n|<br>**Protection **|Call center|Call center|Call center|", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "except biscuits. Refugee households still report lower quantities, even when valuing the food ration quantities indicated as sold in markets. Possible explanations for lower food quantities are that food rations are only received once a month, which may not coincide with the interview date. Moreover, SESRE asks what food people consumed (not based on a pre-set list of food items), not food received as aid. Refugees may sell more than indicated. Valuing quantities of food aid with prices from SESRE suggests that if UNHCR food aid quantities were received/reported by refugees, refugees’ food expenditures would be much more comparable to those of hosts. Using WFP food aid information, we found a picture similar to UNHCR’s. Quantities consumed in SESRE are lower than food aid, as reported by WFP, except for CSB+ and salt (See Annex E for details of the disparity in food aid between the admin data disparity SESRE report). Box 5.2: Disparity between refugee ration aid and reported consumption quantities 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Own production Market purchase Transfers/gifs Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total", "output": {"entities": {"named_data": [], "descriptive_data": ["WFP food aid information"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "status Source: World Bank Staff based on SESRE 2023. Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent 0 20 40 60 80 100 Camp Hosts Female Camp Refugees Female Addis Hosts Female Addis Refugees Female Figure 3.29: Female youth work status Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 39 T his chapter looks at how low sociodemographic and labor market outcomes shape how refugees perceive their future prospects and aspirations. While “resettlement” to a high-income country is an attractive solution, the share of refugees resettled globally is marginal. Resettlement is considered one of the three “durable solutions” for refugee protection under the 1951 Refugee Convention, alongside naturalization and return. Yet, the share of refugees resettled globally—including private sponsorship and other complementary pathways of refugee admission to third countries outside of UNHCR processes—was below 2 percent over the past twenty years (World Bank, 2023). According to government statistics, there has also been a downward global trend in the number of resettlement opportunities, fluctuating from 99,000 in 2010 to just 34,000 in 2020, even as the number of forcibly displaced persons increases globally. In Ethiopia, resettlement numbers are similarly low; in 2022,", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 statuses in February 2020, right before the pandemic. We deemed the SYPJ data to be more suited to our question since it allows to look at the trend of employment outcomes before the pandemic as well as right after the shock in the summer of 2020 and therefore allows investigating the effect of the pandemic on the two populations. The CMM data would be better suited to capture the recovery from the pandemic (Krafft et al., Forthcoming). The overlapping portion of the CMM data with the SYPJ data (February 2020 to February 2021) did not allow for a comparable analysis to the one we did in this paper. The overlap is not perfect. CMM data was collected a few months after the bulk of the SYPJ data only allowing for a comparison between the first half of 2021 (CMM) to the second half of 2020 rates (SYPJ). This is not a reasonable comparison especially in a period with rapidly changing events like the pandemic time. In addition, the time intervals are different as CMM data allows for examining annual change (February 2020 to February 2021) as opposed to semi-annual, and that is only one period which does not allow to compare the trend. In our analysis we focus on young males aged 15 and above who ever worked. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "high informality. Some of these studies also ﬁnd that women and young workers were disproportionately aﬀected. More recently, researchers have shifted the focus to other dimensions of the eﬀects of Syrian refugee inﬂows into Turkey. Altindag et al. (2020) argue that refugee inﬂows had a positive impact on ﬁrm production, both in terms of the volume of production and the introduction of new product varieties. Akgunduz and Torun (2020) examine other channels by which Turkish workers and ﬁrms accommodated the inﬂows of Syrian refugees. The authors ﬁnd that skilled native workers increased their specialization in complex tasks, moving away from manual tasks, and domestic companies took advantage of the increased abundance of labor by reducing capital intensity. As shown in earlier studies, both mechanisms contribute to mitigate the eﬀects of immigration on the wages of the receiving country (Lewis (2005), Peri and Sparber (2009), Gonzalez and Ortega (2011) and Dustmann and Glitz (2015)). In the last few years, some researchers have begun to analyze the economic eﬀects of the exodus of Venezuelans on the surrounding countries but progress has been slow due to the diﬃculty of analyzing Venezuelan migrants equipped solely with government- provided data or the standard labor force surveys. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "First, I include a measure of whether citizens believe that a large portion of tax administrators is corrupt. Second, I in- clude a variable indicating whether citizens approve of how well their local government is handling the collection of license fees on bicycles, carts and barrows. 8 Third, both the size of a country and the size of the government may aﬀect a government ’ s ability to detect and punish evaders. I include the 7I also include a country-level indicator of government performance, the World Bank Governance indicator of government eﬀectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implemen- tation, and the credibility of the government ’ s commitment to such policies (Kaufmann, Kraay and Mastruzzi, 2006, 4). This variable is not signiﬁcant at the p <. 05 level. 8I included two additional measures in the model neither of which were signiﬁcant at the p < 0. 05 level. One is a measure of citizens ’ approval of how well their local government council provides citizens with the information about the councils budget (i. e. revenues and expenditures). The other, the World Bank governance indicator, control of corruption, measures the extent to which public power is exercised for private gain, as well as capture of the state by elites and private interest (Kaufmann, Kraay and Mastruzzi, 2006, 4). 13 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["World Bank Governance indicator"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "thirty years of autocratic rule that followed the Congolese independence from Belgium colonial rule paved the way to a violent transition, culminating in two internationalized wars from 1996 to 1997, in the aftermath of the Rwandan genocide, and from 1998 to 2003. Since the end of the Second Congo War in 2003, eastern Congo has remained unstable and violent, with many domestic and foreign-backed armed groups – including elements of the state military, FARDC – using violence against civilians and each other (Autesserre 2010). The violence and instability have resulted in poor living conditions and regular forced displacement for Congolese civilians. This project focuses on three provinces of eastern DRC that are especially impacted by forced displacement and political violence: North Kivu, South Kivu, and Ituri. 4 These three provinces account for 4. 5 million out of an estimated 5. 268 million total (85 %) IDPs in DRC 2020 (UNHCR Operational Data Portal: Democratic Republic of Congo 2021). Other provinces not included in this study but hosting IDPs include southern and central provinces such as Kasai, Kasai-Central, Kasai-Oriental, Lomani, Sankuru, and Tanganyika. The analysis in this paper focuses exclusively on dynamics in eastern Congo where the authors collected data. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Health institutions (health center/hospital) Home Other Percent Figure D.10: Childbirth in health institutions (children under five years) Source: World Bank Staff based on SESRE 2023. Annexes 104 Hearing Walking or climbing steps Percent Refugees Eritrean Somali South Sudanese In Camp Addis Ababa Total Hosts Seeing Diﬀiculty with selfcare Communicating Remembering or concentrating Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts 50 40 30 20 90 100 80 70 60 10 0 Figure D.13: Types of disability Source: World Bank Staff based on SESRE 2023. - 10,000 20,000 30,000 40,000 50,000 60,000 Hosts Refugees Addis Ababa Total annual rent expenditure Per adult equivalent rent expenditure Figure D.14: Rent expenditure (Refugees and hosts in Addis Ababa) Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Eritrean Somali South Sudanese Addis Ababa Eritrean Somali South Sudanese Addis Ababa Eritrean Somali South Sudanese Addis Ababa Has hand washing place/item Water available Detergent available Hosts Refugees Percent Figure D.15: Hand washing facility Source: World Bank Staff based on SESRE 2023. Annexes 105 Table D.5: Labor force statistics by survey domains Eritrean Somali", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 1 ‐ Number of Forcibly Displaced Persons (1951 ‐ 2015) Source: Constructed from UNHCR population data (http: / / popstats. unhcr. org / en / time_series). Note: 2015 data are mid ‐ year and lower than end of year data. 5 http: / / www. unhcr. org / en ‐ us / figures ‐ at ‐ a ‐ glance. html. 0 10000000 20000000 30000000 40000000 50000000 60000000 70000000 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR population data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 18: Significant Refugee Returns by Country of Origin 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Countries selected based on their cumulative returns of refugees over the period 1991-2015. Return does not necessarily lead to the full reintegration of a person into their home country or area of origin. In the absence of global data on the success of reintegration following return, data on returns appear to be taken as indication of sustainable return. In reality, many returnees face impediments to reintegration and continue to have specific economic and social vulnerabilities linked to their displacement. They may not be able to reclaim land, access sufficient financial resources (e. g. accumulated during their displacement) or reestablish social networks in areas of origin, which are critical factors for successful reintegration (World Bank 2015). Sustainable refugee return is therefore not a one-off event but a process that provides returnees with adequate safety, housing, livelihoods and services that address their specific vulnerabilities and reduce the likelihood of secondary displacement (World Bank 2015). Figure 19: Voluntary Returns of Refugees 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of refugees and people in refugee- like situations protected or assisted by UNHCR. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The United Nations Economic Commission for Europe ’ s (UNECE) guidelines include a question on reason for migration, population with a refugee-like background and IDPs as non-core topics / questions (UNHCR 2016). 72 While most countries include questions on country of birth and citizenship, only about 40 percent include a question on year of migration, less than a quarter include a question on reason for international migration, and about a fifth include a question on the reason for internal migration (UNHCR 2016). 73 E. g. Kyrgyz Republic 1999 (refugee status), West Bank and Gaza 2007 (refugee status), Zambia 2000 and 2010 (purpose of stay), Germany 1970 (federal refugee identity card), Greece 2001 (reason for settling in Greece), Sudan and South Sudan 2008 (type of household including IDP and refugee), Liberia 1990 (ever displaced by war since 1990), Uganda 2014 (refugees). 74 UNHCR is collaborating with the Statistics Norway on systematically embedding forcibly displaced peoples in national statistics exercises and collaborates with national authorities and with UNFPA in various countries on the design of census exercises that include refugees, IDPs, returnees and stateless persons. 75 LSMS is a household survey program housed in the Bank's Development Research Group that provides technical assistance to national statistical offices in the design and implementation of multi-topic household surveys covering household behavior, welfare and interactions with government policies. All data gathered through the LSMS is published online in the Bank ’ s Central Microdata Catalog. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["LSMS", "Central Microdata Catalog"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Excluding respondents who have relocated would omit those with the higher rates of income growth and poverty reduction. Table 5 reports confidence intervals for the incremental samples (which are not mutually exclusive); it gives a more detailed picture of how inference on consumption growth and poverty reduction would have changed if we had not tracked movers. It is apparent that inference from a ‘ simple ’ panel survey of respondents continuing to reside within the original communities would have produced underestimates of actual consumption growth and poverty reduction in this population. These conclusions are robust across the distribution of consumption, as well as at the mean and poverty line. Panel A in Figure 2 depicts the cumulative density function for consumption per capita for those people who remained living in the same community. Panels B, C and D make the same graph for respondents found residing in neighboring communities, elswehere in Kagera Region and outside Kagera Region. As respondents were located further from their location in 1991, so the difference between the 1991 and 2004 graphs becomes more pronounced. Note how, for people who remained in the baseline community, the 1991 and 2004 distributions lie close to each other under the poverty line and diverge above it, while for other mobility categories there is more divergence at the bottom of the graph. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The comparison between ∆ h is for actual and hypothetical destinations tells us whether the actual district of destination is more densely populated than alternative destinations. Results are presented in Table 3 for all variables used in the analysis. We begin with district log income eδs. We have two estimates of eδs, one obtained using reported income data, and the other based on reported consumption data. Given that most respondents to the NLSS survey are self-employed, measurement error is typically larger for income than for consumption. We see that our estimates of log income and consumption eδs are on average 20 % and 8 % higher in 20 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["NLSS survey"], "descriptive_data": [], "vague_data": ["reported consumption data", "reported income data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 3 also shows that there is considerable variation in both indexes within our sample when averaging these indexes at the regional level over the period of investigation. 16It is possible that our approximation is noisy and could potentially induce non-random measurement errors. In Section 4. 3, we propose an instrumental variable approach and estimate Shareodet from the EPR-ER data using a gravity model. Our findings concerning the number of ethnic groups across time for a given origin – destination pair are in line with the EPR-ER data. It seems that refugees of a given origin – destination pair mainly belong to two major ethnic groups. This also means that the variation in diversity in refugee-hosting areas is coming from the refugee composition at the camp level. Figure B. 5 shows the movements of refugees from origin to destination countries under scrutiny. Somalia, the Democratic Republic of Congo, Liberia, South Sudan, and Sudan are major source countries for refugees, while Kenya, Tanzania, Uganda, Zambia, and Ghana appear to be countries hosting most refugees. Representing refugees in camps per ethnic group for the top 5 asylum countries over the sample period, Figure B. 9 shows that there is considerable variation in ethnic composition across camps. 17 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["EPR-ER data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "_UNHCR \r Discussion \r Paper \r –15 \r June \r 2013_\n\nfrom \r the \r temporary \r shelters \r in \r Thailand \r to \r their \r place \r of \r origin, \r the \r majority \r of \r whom\nare \r single \r males \r who \r stated \r that \r they \r had \r returned \r to \r assess \r the \r security \r situation \r and\nstart \r re-­‐establishing \r their \r livelihood \r before \r the \r return \r of \r other \r family \r members. \r The\ndimensions \r of \r this \r spontaneous \r return \r movement \r have \r been, \r so \r far, \r negligible.\n\nWhile \r the \r dimensions \r of \r IDP \r return \r movements \r remain \r extremely \r difficult \r to \r assess,\nsome \r 37,000 \r IDPs \r are \r estimated \r by \r the \r The \r Border \r Consortium \r (TBC) \r to \r have \r returned\nhome \r or \r resettled \r in \r surrounding \r areas \r between \r August \r 2011 \r and \r July \r 2012 [3] .\n\nIf \r the \r current \r trend \r of \r political \r and \r socio-­‐economic \r reforms \r continues \r and \r as \r larger\npolitical \r events \r draw \r closer, \r such \r as \r the \r ASEAN/AEC \r agenda \r with \r Myanmar \r as \r Chair \r in\n2014, \r a \r national \r census \r in \r 2014, \r and \r national \r elections \r in \r 2015, \r then \r the \r momentum \r to\ntranslate \r cease-­‐fire \r negotiations \r into \r peace \r agreements \r may \r increase. \r This \r may \r lead \r to\nan \r increase \r in \r the \r number \r of \r spontaneous \r returns \r and \r the \r possibility \r of \r sudden\ndemands \r upon \r UNHCR \r to \r facilitate \r the \r voluntary \r repatriation \r of \r refugees.\n\n**2.2. \r Protection \r environment**", "output": {"entities": {"named_data": [], "descriptive_data": ["national \r census"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "First, I include a measure of whether citizens believe that a large portion of tax administrators is corrupt. Second, I in- clude a variable indicating whether citizens approve of how well their local government is handling the collection of license fees on bicycles, carts and barrows. 8 Third, both the size of a country and the size of the government may aﬀect a government ’ s ability to detect and punish evaders. I include the 7I also include a country-level indicator of government performance, the World Bank Governance indicator of government eﬀectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implemen- tation, and the credibility of the government ’ s commitment to such policies (Kaufmann, Kraay and Mastruzzi, 2006, 4). This variable is not signiﬁcant at the p <. 05 level. 8I included two additional measures in the model neither of which were signiﬁcant at the p < 0. 05 level. One is a measure of citizens ’ approval of how well their local government council provides citizens with the information about the councils budget (i. e. revenues and expenditures). The other, the World Bank governance indicator, control of corruption, measures the extent to which public power is exercised for private gain, as well as capture of the state by elites and private interest (Kaufmann, Kraay and Mastruzzi, 2006, 4). 13 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["World Bank Governance indicator"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "race or ethnic group as the majority (ESS). _Education_ is the average number of years of full-time education completed (ESS).   _Age_ is the median age of all respondents (ESS). All summary statistics use design and population size weights.\n\nThe estimates are small area estimations\nbased on the 2014 Demographic and Health Survey and\n\n\n\nthe latest population census. It is shown that small area\nestimations are powerful predictors of undernutrition, even\ncontrolling for household characteristics, such as wealth and\neducation, and hence a valuable targeting metric.\n\n**Keywords:** Child malnutrition, Small Area Estimation, Demographic and Health Survey, Targeting, Ethiopia", "output": {"entities": {"named_data": ["2014 Demographic and Health Survey", "Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Total Consumption (percent, Production) 66 66 66 73 81 78 77 1.2 _Source:_ Publications of and contacts with Public Services Regulatory Commission, Republic of Armenia\n\n_Source:_ International Energy Agency, _Energy Statistics, 2007_\n\nThe 2008 ILCS data is used in the simulation of the impact of gasp price hike on April 1, 2010.\n\n3 For detailed description of the 2008 ILCS, please refer to NSS (2009).\n\nThe ILCS collects data from nearly 8,000 Armenian households surveyed year round. It is based", "output": {"entities": {"named_data": ["2008 ILCS", "ILCS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "36 7. Conclusions To make progress in promoting equality and improving the living standards of the population in Uganda, it is necessary to understand the effect of Uganda ’ s fiscal policy on inequality and poverty. This report uses the internationally comparable CEQ methodology to assess the individual and combined effects of taxes and public social spending, based on the microdata from the UNHS 2016 / 17 household survey. The main objective is to provide considerations for policy makers in terms of which fiscal instruments or, more specifically, which mix of fiscal instruments can contribute to reduce poverty and inequality. Overall, our results show that Uganda ’ s fiscal system is modestly equalizing. As a whole, taxes and transfers in 2016 / 17 reduced inequality by approximately 3. 23 Gini points. This result is comparable to Ghana, moderately lower than in Kenya and Tanzania, and much lower than the result observed for South Africa. The largest contributive factor to this equalizing effect is direct taxation (personal income tax or PAYE), followed by the education in-kind transfers (net transfers). This is not surprising, given the size of these fiscal interventions as a proportion of pre-fiscal income and their progressiveness. Direct transfers and social protection programs more generally contribute to the redistributive effect and are simulated as highly concentrated among the poorest households. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "If many people migrate to a speciﬁc location, such as the capital city, this is likely to aﬀect wages, incomes, and access to amenities in that location. 7 This would generate a potential endogeneity bias due to the fact that incomes and amenities in that location result in part from the decision of many migrants to locate there. To eliminate this bias, we use past data to estimate the income regression. More precisely, let T be the period for which we have income information and T + t the period at which we 5The dropped observation corresponds to the location of origin M h ii which, as explained earlier, we do not include in the analysis since including M h ii would mean de facto including the decision of whether to migrate or not. 6McFadden (1974) has shown that, in multiple choice problems of the kind studied here, the application of logit estimation is justiﬁed if (1) the errors in each latent choice equation follow the extreme value distribution and (2) errors are independent across choices. See Train (2003), Chapter 3 for a detailed discussion. The estimation of models with correlated errors across choices requires either multiple integration or the use of Bayesian estimation techniques relying on Gibbs sampling. With a choice of over 70 possible destinations, multiple integration is out of the question. Gibbs sampling remains a possibility but would require extensive programming. We choose instead to keep the logit approach but to correct the standard errors for possible correlation in errors across choices. In our case the possible eﬃciency gain achieved by Bayesian methods does not appear to justify the programming cost. 7The eﬀect could be negative — e. g., congestion — or positive — e. g., agglomeration externalities. 10", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["past data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "are combined. The surveys follow a repeated cross-sectional design and are not panels (i. e. the same administrative units, not the same people, are re-sampled), so responses are aggregated to the groupement level, the lowest level at which the project consistently collect representative data. Table 2 provides a summary of the dates, sizes, and percent of respondents who report being displaced within each survey wave. This aggregated temporal analysis can show, associations between ﬂuctuations in displacement and perceptions of social cohesion over space and time at the groupement level. Question coverage varies across survey waves, but a battery of core questions enables consistent observation of how many individual respondents self-report being displaced at the time of the survey and being involuntarily moved within the past year. Poll Date N % Currently Displaced % Displaced Last Yr % Hosting Displacees # 11 July 2017 5834 4. 35 7. 42 – # 12 September-October 2017 4013 1. 62 2. 62 – # 13 December 2017 4883 3. 50 7. 97 – # 14 March-April 2018 1933 4. 97 8. 85 31. 35 # 15 June-July 2018 5951 3. 70 8. 35 30. 33 # 16 October 2018 1112 6. 47 4. 68 – # 17 December 2018 5918 5. 86 11. 20 – # 19 July-August 2019 5961 5", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Some data sources (such as registration systems and population censuses) are more appropriate for estimating stocks of asylum-seekers, refugees and IDPs at a particular point in time, while other data sources (such as population tracking systems and border crossings) are more appropriate for estimating flows over a specific period. In general, there is a lack of comprehensive and up-to-date data available on all stocks and flows for a particular displacement situation (see Table 4). Consequently, data on flows might be used to estimate stocks, for example in the absence of government data, the stock of refugees in many industrialized countries is estimated by UNHCR based on 10 years of individual asylum-seeker recognition. And, especially in the case of IDPs, changes in the total population combined with some contextual analysis, may be used to deduce estimates of new internal displacement or returns. However, these approximations are flawed unless data on all other flows (births, deaths, repatriation etc.) are also available, which is not usually the case. Even a static figure for the stock of IDPs in a particular location might obscure substantial flows including new displacement and returns. Moreover, there are no common definitions of the various stocks and flows, and therefore the risk of double counting or gaps cannot be discounted.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population censuses", "population tracking systems"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The 2024 survey data revealed that 62 percent of enrolled cooperatives had adopted at least one new post-harvest management practice introduced during the training-of-trainers workshops conducted in Year 1. Among cooperatives that adopted improved storage techniques, average post-harvest losses declined from an estimated 28 percent of harvested output to 14 percent, representing an annualized value retention gain of approximately USD 340 per metric ton of stored produce. These findings will be used to calibrate the economic rate of return estimates in the project's completion report.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "16 Investors actively trade-off disaster risk with gains from economic density. In addition to the city or location specific analysis, we examine how investors value risk from natural disasters. Data from a recently compiled dataset of a sample of global cities provides some insights. Gomez-Ibañez and Ruiz Nuñez (2006) constructed a dataset of central business district office rents for 155 cities around the world in 2005 to identify cities where rents seem elevated or depressed by poor land use or infrastructure policies. Their dataset also includes information on many factors that determine the supply and demand for central office space such as construction wage rates, steel and cement prices, geographic constraints, metropolitan populations and incomes. We link this information to the natural disasters hotspot dataset (Dilley et al. 2005), and examine if city demand – as reflected in office rents, is sensitive to risk from natural disasters. Gomez-Ibañez and Ruiz Nuñez (2006) focus on offices in the primary business district, which they define as the district having the highest density of employment; a very large, if not the largest, concentration of offices; and the highest rents in the metropolitan area. As we are interested in the tradeoff between economic density and disaster risk, using the central business district works well for our analysis. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["natural disasters hotspot dataset", "dataset of central business district office rents for 155 cities around the world"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "IV. D Validity of the local continuity assumption Table 1 examines whether migrants who migrated just before and after the RAMV cutoff date were similar across a range of individual and household characteristics. For this pur- pose, a sharp RDD model was estimated with a set of pre-migration and pre-RAMV controls used in the RDD as the outcome variables. Only one out of 22 estimated coefﬁcients is sta- tistically signiﬁcant for the robust RDD estimator. The conventional, bias-corrected, and robust estimators, illustrated in Figure G. 2, further conﬁrm the validity of the local continu- ity assumption. Moreover, Tables B. 2 – B. 3 report the same exercise but restrict the sample of non-RAMV migrants obtained through referrals or refugee organizations. The data in both tables conﬁrms that the local continuity assumption holds regardless of the sample of non-RAMV migrants. Finally, we present robust evidence that the socioeconomic characteristics of migrants are uncorrelated with their arrival date during our period of analysis. For this purpose, we ﬁrst regress the arrival date on a rich set of baseline socioeconomic characteristics before the pro- gram onset (and the RAMV registration). The results show that the covariates are not jointly statistically signiﬁcant (Table C. 1). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Table of Contents:**\n\n**Background** ....................................................................................................................................... 3\n\n**Overview of UNHCR’s Referral Guidelines** .......................................................................... 3\n\n**Data Collection and Analysis** ...................................................................................................... 3\n\n**Summary of Findings** .................................................................................................................... 4\n\n**i.** **Demographic Characteristics of Medical Referrals in January – December**\n\n**2016 (n= 34,571)** ............................................................................................................................... 5\n\nFigure 1. Medical referrals by admission category and final outcome on discharge .......... 5\n\nFigure 2. Medical referrals per month; January – December 2016 (n= 34,571) ................. 5\n\nFigure 3. Frequency of referrals per unique patient; January – December 2016 (n=\n23,296) ........................................................................................................................................... 6\n\nFigure 4. Proportion of referrals by gender and age group (n= 34,571) ............................... 6\n\nFigure 5. Proportion of referrals by nationality (n= 34,571) .................................................. 7\n\nFigure 6. Proportion of referrals by referral hospital (n= 34,571) ......................................... 7\n\nFigure 7. Proportion of referrals by referring clinic (n= 34,571) ........................................... 8\n\nFigure 8. Number of JHAS Madina clinic referrals by nationality (n= 9,488) ................... 8\n\nFigure 9. Zaatri and Azraq referrals per month; January – December 2016 (n= 19,473) . 9\n\nFigure 10. Proportion of referrals by diagnosis at discharge ............................................... 10\n\n**ii.** **Mortality** ................................................................................................................................... 11\n\nFigure 11. Number and proportion of mortalities by age group (n= 94) ............................ 11", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "representative sample of undocumented migrants in Colombia ’ s major cities as of 2020. 10 This survey encompasses information on the socioeconomic status, health, well-being, access to services, and labor market outcomes of adult undocumented migrants. Second, we compare our sample with the Administrative Venezuelan Migrant Registry (RAMV), a nationwide census of undocumented Venezuelan migrants conducted by the Colom- bian government in 2018. 11 This census surveyed Venezuelan households regarding their socioeconomic conditions and the labor market characteristics of the household head. We compare the household characteristics and the labor market outcomes of the house- hold heads in our sample with those in VenRePS and RAMV surveys in Table A. 3. 12 We observe that households in the VenRePS-Kids survey are smaller on average and have a greater number of children living in the household. The latter is anticipated since one of the eligibility criteria to participate in our survey is the presence of at least one child in the household. Furthermore, the household heads in our sample are disproportionately female and more likely to be married, aligning with the family structure targeted in our sampling frame. Regarding labor outcomes, household heads in our sample are more likely to be employed and engaged in the informal sector compared to those surveyed in VenRePS and the RAMV census.", "output": {"entities": {"named_data": ["VenRePS", "Administrative Venezuelan Migrant Registry", "VenRePS-Kids survey", "RAMV surveys", "Administrative Venezuelan Migrant Registry (RAMV)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "There is one limit to our approximation in Equation 4. The ethnic composition of refugees in each year t for a given origin – destination pair of countries obtained from the EPR-ER database is assumed to be homogeneous across camps of the same origin – destination pair of countries for the refugees at year t. This may seem to be a strong assumption; however, the risk of misallocating refugees is reduced as the annual variation in the EPR-ER is generated by just a few dominant groups for a given origin – destination pair and the geographical distribution of refugees by country of origin is highly influenced by the proximity to their countries of origin. 16 As can be seen from panel A of Table B. 2, in refugee-hosting areas, on average, both EF and the EP seem to increase quite significantly when they are revised by incorporating the number of refugees in an 80-km buffer: the mean value of the standard EF index is 25. 58 %, while the mean value of the revised refugee EF index is 37. 90 %. The mean value of the standard EP index is 10. 11 %, while the mean value of the revised refugee EP index is 14. 07 %.", "output": {"entities": {"named_data": ["EPR-ER database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Combining almost 50, 000 responses to 11 cross-sectional surveys between 2017 and 2021, displacement is neg­atively associated with perceptions of social cohesion in aggregate. But at the individual level, those who report hosting displaced populations in their communities often have higher perceptions of social cohesion. These results are strongest among respondents who self-report hosting IDPs as opposed to refugees, but important heterogeneity across indicators, local context, and gender should guide policy meant to promote social cohesion in forced displacement. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at ppham @ hsph. harvard. edu. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["cross-sectional surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "28% 49% 54% 73% Secondary school (19 to 24 years) 34% 11% 30% 32% 32% 38% Enrollment rates Primary NER 81% 65% 65% 62% 82% 80% Secondary NER 47% 14% 44% 32% 28% 17% Primary GER 101% 86% 79% 84% 114% 116% Secondary GER 54% 14% 47% 36% 31% 18% Source: World Bank Staff based on SESRE 2023. Annexes 99 Table D.3: Health outcomes by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Faced any health problem 21% 20% 6% 7% 29% 25% Received medical assistance 86% 85% 85% 85% 85% 93% Child Nutrition Stunted 43% 52% 37% 47% 26% 26% Underweight 28% 37% 31% 38% 26% 19% Wasted 10% 10% 14% 19% 17% 12% Disability Seeing 2% 3% 1% 2% 3% 3% Hearing 1% 3% 1% 1% 2% 2% Walking or climbing steps 2% 2% 1% 2% 3% 2% Remembering or concentrating 1% 2% 1% 1% 1% 2% Difficulty with self-care 1% 1% 1% 1% 1% 2% Communicating 0% 1% 1% 1% 1% 1% Any disability 5% 8% 3% 4% 6% 5% Source: World Bank Staff based on SESRE 2023. Table D.4: Living conditions by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": " In Somalia, the Mogadishu Household Survey covered both residential areas and IDP camps, collecting data on expenditures, demographics and living conditions [survey completed; analysis forthcoming]. As part of the Somalia Knowledge for Operations and Political Economy (SKOPE) initiative, the Puntland Household Survey will also cover both residential and IDP populations [ongoing].  An IDP study in South Sudan [ongoing] aims to assess the economic needs of IDPs and host communities in urban areas, covering livelihoods, water and sanitation, infrastructure as well as intentions and conditions to return. Basic information about education, employment and general health variables will also be collected.  The Iraq Crisis Response Study [ongoing] will assess the impact of the Islamic State and oil price-related crises on IDPs and households left behind in IS controlled areas. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Iraq Crisis Response Study", "Mogadishu Household Survey", "Puntland Household Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Table 3 shows summary statistics for a select group of baseline characteristics, as well as a\n\n<!-- PAGE 5 -->\nland owners in all countries in sub-Saharan Africa for which survey data are available\n\ngenerally lasts 35-40 days. During this time, rainfall data is collected daily at a designated weather\n\nThe data set consists of the entire set of BASIX's purchasers of rainfall index insurance from 2005-2007,\n\nthat season. The BASIX data covers 42 weather stations, and includes a total of 19,882 customers from", "output": {"entities": {"named_data": ["BASIX data"], "descriptive_data": ["entire set of BASIX's purchasers of rainfall index insurance"], "vague_data": ["survey data", "rainfall data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "own/parents’ COB 0.05 (0.04) HH received humanitarian food aid in past 12 months -0.00 (0.04) Distance to woreda capital (log) -0.10*** (0.01) Distance to border (log) -0.03 (0.02) Medium market accessibility -0.02 (0.06) High market accessibility 0.29*** (0.05) Constant 11.10*** (0.34) Survey domain Yes Survey time Yes Observations 1,266 Source: World Bank Staff based on SESRE 2023. Note: Dependent variable is the log of total per capita consumption expenditure. All regressions include the controls in Table D.12. This table provides the results for the additional independent variables of interest. Standard errors in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 115 38% 60% 2% Drought prone, lowland, pastoralist Humid moisture reliable, lowland Moisture reliable, highland-Cereal Figure D.31: In-camp refugee locations by ecological Zone Source: World Bank Staff based on SESRE 2023 and Ethiopia Ecological Zone Classification from ESS. 0 10 20 30 40 50 60 70 80 90 100 Employment rate Unemployment rate LFP Share of individuals (%) 1-20km 20 -100km >100km 0 10 20 30 40 50 60 70 80 90 100 Share of individuals (%) 1-30km 30 -50km >50km Employment rate Unemployment rate LFP Figure D.32: Refugee’s labor market performance Source: World", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). As the Sudanese sample surveys IDPs living in the Abu Shouk and El Salam camps, we must understand these deprivations with the background that these settlements were created as emergency and crisis responses rather than durable, long-term solutions (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019). Although Sudan does have a national electric grid that supplies electricity to the urban and peri-urban areas of the nearby city El Fasher, IDP communities living in the camps report limited connection to the city ’ s electricity supply, reflected in the high deprivations in the electricity and cooking fuel indicators. The ad-hoc construction of dwellings in the two camps explains why 71 % of the IDP households in Abu Shouk and 65 % in El Salam live in tukuls or other permanent mud or wood structures (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019: p. 50), both of which register as unimproved housing types. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017)"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5 narrative around the regressions and aims to explain why not more people migrate when benefits of doing so are so high. 2. The Setting: Tanzania and Kagera, 1994-2004 In the last decade, Tanzania has experienced a period of relatively rapid growth, attributed to liberalization, a renewed trade orientation, a stable political context, and a relatively positive business climate to boost economic performance. Real GDP growth was of the order of 4. 2 % per year between 1994 and 2004, while annual population growth was around 3. 2 % in the same period (URT, 2004). There is also evidence that growth had accelerated in the last few years compared to the 1990s. However, this growth has not been sufficiently broad-based to result in rapid poverty reduction. On the basis of the available evidence, poverty rates have declined only slightly and most of the poverty reduction progress has been made in urban areas. According to the Household Budget Survey (HBS), between 1991 and 2000 / 01, poverty declined from 39 percent to 36 percent in mainland Tanzania. The decline in poverty was steep in Dar es Salaam (from 28 % to 18 %) but minimal in rural Tanzania (from 41 % to 39 %). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Household Budget Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 30 of 51 Figure 23: Temporary employment by gender. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys In summary, the evidence does not suggest a clear trend across countries regarding the prevalence of non-standard employment but rather identifies a heterogeneous panorama by countries and types of non-standard employment. 4. 2. 2 Profile of Non-Standard Employment In this section, we investigate possible changes in the profile of non-standard employment, usually associated with lower productivity and greater vulnerability. Like in the case of the LAC countries, the analysis of the employment profile was made based on the education, wages and the content of tasks performed. 9 From the educational point of view, a general tendency can be observed in the countries analyzed to improve the profile of workers linked to non-standard work contracts. In fact, in most of the countries in our sample, it is observed that taken together, the prevalence of workers with secondary and tertiary educational levels increases to the detriment of workers with a lower educational level.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15 resulting in government forces recapturing rebel held territory, establishment of a rebel base or headquarters, rebel activity that is not battle related (e. g. presence or the killing of civilians), and territorial transfers. The dataset consists of 4, 145 battle events for the 1960 – 2004 period. In the present analysis, we use 2, 530 of these. The remaining events were dropped as they either were in countries not included in the analysis, or because information was missing for one of the key variables. Each conflict event is associated with geographic coordinates and a date of occurrence. This information allows for spatial and temporal modeling of conflict events. The dataset used in this article covers 14 countries in Central Africa. 6 of them had a conflict in the 1960 – 2004 period according to the Uppsala / PRIO Armed Conflict Dataset (Gleditsch et al., 2002): Angola, Burundi, Republic of Congo (Brazzaville), Democratic Republic of Congo (Zaire), Rwanda, and Uganda. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Uppsala / PRIO Armed Conflict Dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As a result, imputed poverty is measured as income poverty. Another advantage of using the LFS is the availability of CPI at the NUTS2 level in Turkey which allows for spatial deflation of different price levels across the country. Table 1. Survey Comparison and Data Availability Years Available Migration Variables Income or Consumption Geographic Identifier Spatial Deflation HICES 2003-2012 No Consumption, income National, urban / rural No SILC 2009-2012 No Income NUTS1 No LFS 2009-2013 Yes Imputed Income NUTS2 Yes However, there are other issues for consideration when using the LFS. Principally, there is a low number of sample points that are migrant households. Moreover, the study cannot identify migrant households and individuals that are specifically Syrian refugees. Foreign migrants are defined as those who were born abroad and have lived abroad for at least more than 12 months. Some Turkish-born households have also lived abroad for over a year, and these individuals are not considered to be migrants. Amongst foreign-born individuals, only the ones who have been in the country for more than 12 months are included in the sample which underrepresents the actual number of foreign migrants in the region. In addition, no specific procedure is adopted by the enumerators if the household does not speak Turkish. Given that a majority of Syrian refugees do not speak Turkish, the language barrier might result in the removal of Syrian households from the sample. Finally, refugee camps are not included in the sample frame, which limits the study to only examining recent migrants who do not live in refugee camps. 9 Wage income is only available for regular and casual employees in the LFS which accounts for around 60 % of total employment. There is no other monetary income value for the rest of the working population. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["LFS", "HICES", "SILC"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Second, there are cases where the current study reports data by nationality, but the corresponding figure in the Trends in International Migrant Stock refers to the foreign born. This situation generally arises when a census does not report the number of foreign-born migrants on a bilateral basis. Examples include Austria and Côte d ‘ Ivoire. Third, differences in the years to which the data refer can generate large disparities. For example, this study uses the 1966 data for Australia, whereas Trends in International Migrant Stock reports data for 1970. Overall, however, the fact that the totals are remarkably close in every decade adds credence to the estimates here. IV. THE EVOLUTION OF GLOBAL BILATERAL MIGRATION The greatest strengths of the global migration matrices are their bilateral coverage, the number of decades covered, and the disaggregation by gender. These data are too rich for a full analysis of all movements between all pairs of countries. Instead, this section summarizes the major trends in the evolution of bilateral migrant stocks, based primarily on World Bank regions. 25 Global Trends The migration matrix for the 1960 census round reflects a realigning world in the postcolonial era. Over the 1960-2000 period, the composition of world migration 25 Appendix 1 details the World Bank regions: South Asia, East Asia and Pacific, Sub-Saharan Africa, Latin America and the Caribbean, Europe and Central Asia, and Middle East and North Africa. High-income Middle East and North Africa refers to the predominantly oil producing countries in the Persian Gulf (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) and to Israel.", "output": {"entities": {"named_data": ["Trends in International Migrant Stock"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Having returned (early) to Northern Mali did not always lead to a stable condition either: the transition probabilities from returnee to IDP or to refugee are 1. 1 % and 0. 2 % respectively. As we can see from Figure 1, the majority of the sample is Songhai and Kel Tamasheq (almost everybody identified themselves as Muslim). There are clear differences in migration decisions between ethnic groups. The reaction of most of Arab and Kel Tamasheq origin was to leave the country, while most Songhai people preferred to go south, to Bamako, or, by the time of our survey, had already returned to Northern Mali. In fact, as pointed out in (Etang-Ndip et al., 2015), IDPs and returnees have a similar ethnic composition because 94 % of returnees in our sample were IDPs. Far fewer returnees in the sampled cities of Gao, Tombouctou and Kidal returned from refugee camps in the neighboring countries for the simple reason that most refugees used to live in towns and villages outside the regional capitals of Northern Mali. Displaced Refugee Returnee Total Tamasheq Arab Songhai Peulh Bella Other Analytic weights used Source: LDPS 2014-15 Figure 1: Ethnic composition Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["LDPS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Methods Research Design and Data Collection Strategy This household survey was commissioned by The World Bank and carried out by the Center for Evaluation and Development between December 2023 and February 2024 in Khyber Pakhtunkhwa, Pakistan. Households were selected based on being located in the catchment area of one of 200 public schools across Khyber Pakhtunkhwa (excluding newly merged districts). 2 In most cases, the boundaries of a catchment area represented a maximum of a 30-minute walk from the proximate school. Households living in a given catchment area who had at least one child under the age of 72 months (for either of the 0-35-month or 36-72-month age groups) available to be 2 The sample of 200 public schools was drawn for a different World Bank survey in 2022. These 200 schools are a representative sample of public schools across all districts of Khyber Pakhtunkhwa (excluding newly merged districts). The households in question are thus representative of those in the catchment area of a representative sample of schools. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure A. 2: Injective relations We also isolate many-to-one (bijective) relations. In this case, we have to aggregate the Afro- barometer ethnicities with their unique and more aggregated correspondence in the UNHCR refugee camps data (See Figure A. 3). Figure A. 3: Bijective relations The remaining correspondences are either (i) one-to-many (bijective) but opposite to Figure A. 3 (i. e., many ethnicities from the UNHCR refugee camps data correspond to one ethnicity from the Afrobarometer) or (ii) many-to-many relations. For both cases, we apply a more pragmatic approach: a. In both cases, we disregard ethnicities that do not appear either in the Afrobarometer or in the UNHCR refugee camps data. This means that for the remaining ethnicity that has no counterpart in either the Afrobarometer or the UNHCR refugee camps data, we simply keep the name of the ethnicity as such, i. e., this information is not dropped. b. Then, after ignoring ethnicities that have no occurrence in our datasets, we check whether the one-to-many or the many-to-many relation has not boiled down to a one-to-one resp. many-to- one relation again. If so, we can treat them as above. c. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "formal labor market in the same way as nationals—Burkina Faso, Cameroon, Democratic Republic of the Congo, Djibouti, Mauritania, Niger, and Rwanda— often restrict access in practice by requiring certain identification documents, the country employers are reluctant to hire refugees (World Bank Group, 2021), or restrictions exist, such as wait periods, limited mobility, owning property, or accessing finance. In other countries—Burundi, Chad, Uganda, and Ethiopia—access to the labor market is limited by regulations, such as requiring work permits, caping the percentage of foreign workers, or restricting work to certain sectors of employment (World Bank Group, 2021). In addition to wage employment, self-employment can be an important avenue, but in many countries, access to self-employment is restricted for refugees, including in Ethiopia where refugees require business licenses. Overall, while the GoE has made progress in creating a legal framework for refugees to obtain work permits, refugees still face significant challenges accessing employment, contributing to their overall vulnerability and lack of self-reliance. As this report shows, few refugees in Ethiopia work, and those who do work mostly inside camps. Research indicates that extended periods of forced unemployment negatively affect refugees’ longer- 8 As granted under Article 26 of the 1951 Geneva Refugee Convention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "20 Note: Poverty is defined as the percent of the population living with less than $ 2 a day (World Development Indicators database). The annual number of refugees in each country is given by the Center for Systematic Peace (http: / / www. systemicpeace. org /). 4. 2. Lessons from Case Studies in Kenya, Tanzania, and Uganda Given the limits of cross ‐ country comparisons, we present below three short case studies on the impact of protracted refugee situations on hosting communities. These case studies were not chosen based on a systematic review but they are sufficiently close to each other to allow for comparative learning. These case studies are also those emerging from a growing literature on the quantitative assessment of the impact of refugees on hosting communities (Mabiso et al. 2014). Case Study # 1: The protracted refugee situations in Tanzania Tanzania has been known as a refugee ‐ hosting country for long due to its peaceful history and its location surrounded by conflict ‐ affected countries (Burundi, Rwanda, Uganda, Mozambique). The first president of Tanzania, Julius Nyerere, welcomed most of refugees as a sign of pan ‐ African solidarity in the post ‐ independence periods from many African nations.", "output": {"entities": {"named_data": ["World Development Indicators"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 International migration — the movement of people across national borders — has important economic, social, and political implications. Despite the recent emergence of a dynamic literature, empirical analysis of migration flows and their impact lags behind the policy debate and the theoretical literature. The main reason is the absence of comprehensive and reliable data on international migration patterns and migrant characteristics at either the aggregate or the household level. The objective of this article is to use data from more than one thousand national censuses and population registers to estimate a complete global origin – destination migration matrix for each decade over 1960 – 2000. These 226 * 226 matrices, comprising every country, major territory, and dependency around the world, are divided into periods corresponding to the last five completed census rounds. The gender dimension of international migration over this period is also presented. The primary source of the raw data is the United Nations Population Division ‘ s Global Migration Database, created through the collaboration of the United Nations Population Division, the United Nations Statistics Division, the World Bank, and the University of Sussex (United Nations [2008]). This unique data repository comprises 3, 500 individual census and population register records1 for more than 230 destination countries and territories over the last five decades. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Global Migration Database"], "descriptive_data": [], "vague_data": ["national censuses and population registers"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The surveys were conducted\neach year between May and November of the survey year, with expenditures referring to the past\n12 months.\n\n\nReal total per capita expenditure less per capita expenses on health care is taken as the\ndependent variable.\n\nIn particular, using the historical daily rainfall records over 1980-2006 in 166 geo-referenced weather stations from across Vietnam (see Figure 1) [9], monthly rainfall grids of 0.1 degree resolution are constructed [10] using both inverse distance weighting (IDW) and inverse elevation difference weighting (IEW).\n\nTo also capture riverine floods, which may occur downstream following heavy rainfall in the mountains or as a result of storm surges in coastal areas, riverine flood indicators will be constructed based on the satellite based flood data from the Dartmouth Flood Observatory (DFO) (2008). The 1980-2006 geo- referenced UNEP/GRID-Europe storm track dataset is used to separately explore the effects from high winds and gusts from cyclones. As the damage from heavy rainfall associated with cyclones will already be captured by the localized and riverine flood indicators, these measures will capture the wind damage associated with cyclones. [5]\n\nTropical storms approach Vietnam from the east. They typically arrive during the Southeast and Northeast monsoon (Christiaensen et al., 2009), with 90 percent of them occurring between June and November, and almost half of them coming ashore in the northern part of the country. To generate annual cyclone maps, a GIS dataset of areas affected by hurricane force winds was developed from the UNEP/GRID-Europe (2007a, 2007b) tropical cyclones databases. In particular, the storm track and wind speed data were used to create symmetric polygons about each path, based on the algorithm by Klotzbach and Gray (no date). [21]\n\n4 The frequently used CRU TS2.1 monthly rainfall database (Mitchell and Jones, 2005) only has a half degree resolution, which is around 55 kilometres at the equator. 5 See Thomas (2009) for a detailed description of the different data sources and a more elaborate description of the construction of the different natural hazard maps. 6 The nearest neighbour method assigns the value of the weather station nearest to the grid cell centre. 7 Radial basis functions are functions of distance used for exact interpolation of point data.", "output": {"entities": {"named_data": ["satellite based flood data from the Dartmouth Flood Observatory (DFO)", "UNEP/GRID-Europe storm track dataset", "CRU TS2.1"], "descriptive_data": [], "vague_data": ["daily rainfall records"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In our employment arm, we oﬀer gainful employment in the form of a surveying assignment for an average of three days per week for two months. 1 The surveying task requires workers to walk through their blocks four times per day tallying the various activities their neighbors are engaged with and consumes approximately 2. 5 hours per workday, resulting in a form of part-time employment. The job is designed to embody the key features inherent to ‘ work ’. Drawing from the economics literature, workers must exert real eﬀort and their task occupies a meaningful portion of their work day. Drawing from the sociology literature, the work involves some degree of sociability and purpose in the completion of a productive task. Employment lasts for eight weeks, a long duration given the scarce daily labor opportunities that arise in our setting. Relative to this employment arm, our control arm receives no work and a small fee for weekly survey participation. A comparison of the control to the employment arm therefore yields the psychosocial beneﬁts of the employment intervention. In order to estimate the non-pecuniary psychosocial value of employment, we include a cash treatment arm, in which no work is oﬀered, but a large fee (equivalent to that received by those in the employment arm) for weekly survey participation is provided. We work in the Rohingya refugee camps, situated upon the southern tip of Bangladesh. 1We obtained formal permissions from camp administration to engage our study participants in this manner through our NGO partner, Pulse Bangladesh. 1 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Finally, the expenditure shock which we discussed in the theory section (international aid or an increase in public spending associated with the forced displacement crisis) is considered by only a handful of papers. This is a possible confounding factor of the impact of forced displacement on host communities and one that is not easily addressed with the use of fixed effects. This is clearly a shortcoming of this literature that will require increased attention in the future. 4. Meta-analysis of empirical results 4. 1 Data The literature review covers 49 papers spanning over a period of 29 years. We were not able to find published papers prior to the work by Card in 1990, which effectively started this literature, and there is a relatively low interest in this topic between 1990 and 2011 with only one or two papers published per year. With the Syrian crisis starting in 2011 and the EU crisis in 2015 the number of papers per year increased by several fold. Most of the papers and results considered in this review are therefore very recent (Figure 2). We used academic databases and search engines (EconLit, Social Science Research Network, JSTOR, Google Scholar) and searched websites of institutions with relevant working paper series (NBER, IZA, ERF and others). Relevant unpublished papers were included by searching agendas of workshops and conferences organized during the past few years. From the papers reviewed, we selected a total of 762 results summarized in Table 3. The results database was compiled as follows. For each paper we focused on the results that the authors considered the main and Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 people may also gain from conflict in terms of income and wealth and this may explain why some people do not move. The defining attributes of the alternative choices are very different from any other model and the task of economics is to understand what these defining attributes should be. In terms of independent variables, “ push ” factors become more important than “ pull ” factors in forced displacement models. The intensity of a conflict may be more important than the income opportunities in potential destination areas. In addition to the classic socioeconomic variables, risk aversion, stress, anxiety, other traits of personality and behavioral factors in general have to be well understood and measured. Hence, one could think of four essential blocks of independent variables including individual or household socioeconomic characteristics, “ push ” factors, “ pull ” factors and behavioral factors. Also, access to and dissemination of information related to the conflict in the place of origin but also in the potential places of destination may be crucial for people to make choices. This is where social psychology, behavioral economics and neuroeconomics may offer insights into such choices. Forced displacement data are also unusual in their form. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The comparison between ∆ h is for actual and hypothetical destinations tells us whether the actual district of destination is more densely populated than alternative destinations. Results are presented in Table 3 for all variables used in the analysis. We begin with district log income eδs. We have two estimates of eδs, one obtained using reported income data, and the other based on reported consumption data. Given that most respondents to the NLSS survey are self-employed, measurement error is typically larger for income than for consumption. We see that our estimates of log income and consumption eδs are on average 20 % and 8 % higher in 20 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["NLSS survey"], "descriptive_data": [], "vague_data": ["reported consumption data", "reported income data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In order to understand the role of vulnerability, we turn to international data. The international regressions measure the relationship between damage and fatalities and population and income (using international data EMDAT 2009).\n\n<!-- PAGE 17 -->\nto follow projections made by demographers (United Nations 2004). GDP is assumed to\n\nDartmouth Flood Observatory's (DFO) Global Archive of Large Flood Events, which is\n\n\nhoused at the University of Colorado (floodobservatory.colorado.edu). The DFO is funded\n\nEmergency Events Database (Cavallo and Noy 2010), which is affiliated with the World\n\nmaps from the Gridded Population of the World v3 (CIESIN-CIAT 2005) to obtain", "output": {"entities": {"named_data": ["EMDAT 2009", "Dartmouth Flood Observatory's (DFO) Global Archive of Large Flood Events", "Emergency Events Database", "Gridded Population of the World v3"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "26 education Turkish to a subregion. In sum, there is some evidence that the inflow of Syrian refugees results in a decrease in the number of Turkish living in a subregion. The evidence, however, is weak and the impact unlikely to be very large. 5. PLACEBO TESTS AND ROBUSTNESS CHECKS 5. 1 Placebo Tests The key threat to the validity of our instrument is that there are subregion specific economic trends that are correlated with the instrument, and not fully controlled for by the inclusion of the log distance of a Turkish subregion from the Syrian border. A priori this seems unlikely since the instrument is also based on travel distances, but we can test for the existence of such trends in a pre-period. Specifically, we run regressions that are analogous to those reported in Tables 5, 6 and 7 using data from the LFS 2009 and 2011. As a placebo test we pretend that the Syrian refugees had arrived between 2009 and 2011, rather than between 2011 and 2014, to see if the instrument is correlated with Turkish outcomes in this pre-period. Table 10a presents the results of our placebo tests. For the overall sample there is no statistically significant trend that is correlated with subsequent (instrumented) refugee flows in formal or informal employment, or in log wages.", "output": {"entities": {"named_data": [], "descriptive_data": ["data from the LFS 2009 and 2011"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1 2 3 4 5 6 7 8 9 10\nDecile\n\n\n\nJFPR applicants: Enrollment at any school, by\nscholarship status and decile\n\n<!-- PAGE 20 -->\n**Figure 2: Proportion of girls who complete grade 7 among those who completed grade 5,**\n**by economic status decile, DHS data**\n\nOutcomes** Enrolled in JFPR school 0.87 0.65 0.22 0.00 Attending on the day of school visit 0.80 0.58 0.22 0.00 Enrolled in any school 0.90 0.77 0.13 0.00\n\n**form)** **variables)**\n\n**Enrolled at JFPR school** 0.222*** 0.292*** 0.303*** 0.413*** 0.065* 0.302*\n(0.018) (0.021) (0.022) (0.056) (0.036) (0.166)\n\n**Enrolled at JFPR school** 0.222*** 0.292*** 0.303*** 0.413*** 0.065* 0.302*\n(0.018) (0.021) (0.022) (0.056) (0.036) (0.166)\n**Attending JFPR school on day of visit** 0.223*** 0.299*** 0.313*** 0.426*** 0.094** 0.436**\n(0.018) (0.022) (0.023) (0.056) (0.040) (0.188)", "output": {"entities": {"named_data": ["DHS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "in secondary schools (Figure 2.13). Reasons for not attending school differ among children of primary and secondary school age. Most children who do not attend primary school do so because their families think they are too young or unwilling to send them to school. This is similar for hosts and refugees. For secondary school-age children, the reasons for not attending school differ between refugees and hosts and across refugee domains. Reasons related to need to work is higher among hosts (47 percent) compared to refugees (17 percent), whereas family unwillingness is higher for refugees (33 percent) than hosts (16 percent). Being unable to attend school due to need to work is higher among host boys than girls, while 24 Grade 12 national examinations for refugees and host communities are administered in nearby government public universities—a long distance for refugees based in remote locations—impacting the performance of the refugee students. 0 20 40 60 80 100 120 In camp Addis Ababa Total In camp Addis Ababa Total Primary GER Secondary GER Hosts Refugees Percent Figure 2.11: Gross Enrollment Rate (GER) Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 120 In camp Addis Ababa Total In", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "REGIONAL BUREAU FOR SOUTHERN AFRICA\n\n**DEMOCRATIC REPUBLIC OF THE CONGO REFUGEES SITUATION**\n\nAs of 3 0 September 2022\n\n**Author: UNHCR DIMA - RSA** Contact : rsarbdima@unhcr.org **Source:** UNHCR Primes, Government, UNHCR", "output": {"entities": {"named_data": ["UNHCR Primes"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "a significant challenge for both hosts and refugees. The nutritional status of children under age five is based on anthropometry measures; that is, stunting, underweight, and wasting. A child is identified as 26 Driven by Eritrean refugees in Alemwach camp who do not have a health facility inside the refugee site and get medical services from government health facilities outside the camp. 27 Chronic illness: tuberculosis, hepatitis-B, asthma, uric acid, blood pressure, diabetes, HIV/AIDS, kidney problem, epilepsy, cancer, mental illness 0 10 20 30 40 50 60 70 Eritrean Somali South Sudanese All in camp Eritrean Somali South Sudanese All in camp 0 10 20 30 40 50 60 70 Chronic illness Non-chronic illness Percent Percent Figure 2.17: Use of the national healthcare system when faced with health problems Source: World Bank Staff based on SESRE 2023. a. Overall b. By type of illness Sociodemographic Profile 20 “stunted”, “underweight”, or “wasted” if height- for-age, weight-for-age, and weight-for-height “z-scores28” are more than two standard deviations below the 2006 World Health Organization (WHO) Child Growth Standard medians for these measures. Child stunting is a major child health problem for both hosts and refugees, but stunting rates are largest for refugee children. Stunting", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "CIESIN (Center for International Earth Science Information Network), Columbia University; IFPRI (International Food Policy Research Institute); The World Bank; and CIAT (Centro Internacional de Agricultura Tropical). 2004a. \"Global Rural-Urban Mapping Project (GRUMP), Alpha Version: Settlement Points\". Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available at http://sedac.ciesin.columbia.edu/gpw. Downloaded March 17, 2005.\n\nFor rivers and lakes, the CIA World Data Bank II (CIA 1972) was used. The population centers are from the GRUMP settlement points dataset (CIESIN et al. 2004a) and the World Gazetteer database (Helders 2005). Urban boundaries are from the GRUMP urban extents database (CIESIN et al. 2004b). The international boundaries are from the World Bank mapping office. 35 To maintain confidentiality, the geographic coordinates of each household in the VHLSS are not made publicly available, only the shapefiles for the different communes surveyed.\n\n30 Communes (the primary sampling units) were selected in the first stage with a probability proportionate to population size based on the 1999 Population census.\n\nThe average current global damage from tropical cyclones is currently $26\n\n\nbillion/year (EMDAT 2009). Several authors have relied on the general result by", "output": {"entities": {"named_data": ["Global Rural-Urban Mapping Project (GRUMP)", "CIA World Data Bank II", "World Gazetteer database", "1999 Population census", "EMDAT 2009"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Our data is unique in at least four dimensions: i) it leverages the high cell phone penetration and coverage throughout the country, including the refugee communities, to reach households during lockdowns when face-to-face interviews are impossible to conduct; 5 ii) its longitudinal nature allows not only to assess the first order impact of the COVID-19 shock but also its longer-term implications for recovery; iii) interviews cover refugees and nationals over the same period and are 2 Khamis et al. (2021) estimate that the work-stoppage rate in Kenya reached up to 62 percent compared to before the pandemic. 3 Results from socioeconomic surveys carried out by UNCHR and the World Bank in Kalobeyei settlement in 2018 and in Kakuma camp in 2019 show that 65 percent of Kalobeyei refugees and 68 percent of Kakuma refugees are poor, while at least 7 in 10 of them are highly food insecure (UNHCR & World Bank, 2020; UNHCR & World Bank, 2020). 4Kenya hosted around 530, 000 refugees in August 2021 which makes it the second largest hosting country in Africa after Ethiopia (UNHCR Kenya, 2021). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "13 baseline that were found to be uncorrelated with the treatment in Table 1. 12 Observations are then weighted by the inverse of their probability of having data observed. Therefore, those who had a small chance of being observed are given increased weight, to compensate for those similar observations who are missing. The pseudo R-squared from the probit model suggests that those baseline covariates explain about 8 percent of the probability of data being observed. A Wald test confirmed that those variables are jointly statistically different from zero (the P-value is 0. 000). However, this still leaves a large percentage of attrition (around 92 percent) unexplained. 13 Therefore, we note that the results in the following section should be interpreted with caution. We present results in the next section for four specifications. Specification 1 presents OLS estimates from equation 1. Specification 2 presents results that control for individual fixed effects from equation 3. Specification 3 presents OLS estimates for the full sample by imputing missing observations for attritors at follow-up using lower and upper bound estimates. Specification 4 presents OLS estimates with the estimated constructed weights. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of difficulty, ranging from those suitable for children as young as 2 and a half years old to more complex items for individuals over 18 years old. 19 The Peabody Vocabulary Test is calibrated with a mean standard score of 100 and a standard deviation of 15, placing scores between 85 and 115 within the average range. This test, in its Spanish edition, has been validated for use in Colombia. Additionally, to ensure relevance and accuracy for Venezuelan nationals, we conducted a preliminary validation by administering the test to a sample of Venezuelan mothers in our study. This step confirmed that the words used in the test held consistent meanings for participants from Venezuela. Figure B. 1 depicts the distribution of PVTS scores for Venezuelan and Colombian chil- dren and adolescents in our sample. This visualization indicates that Venezuelan minors consistently score lower on the PVTS compared to their Colombian peers across the en- tire score distribution. In Table 6, we present the average disparities in percentile rank on the Peabody scale, revealing that Venezuelan children and adolescents, who are forcibly displaced, score approximately 12 p. p. lower than their Colombian counterparts. The difference is meaningful and in turn translated into Venezuelan minors falling into the a higher likelihood of having extremely low, moderately low, and low score categories.", "output": {"entities": {"named_data": ["Peabody Vocabulary Test"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The GBV Action Plan was finalized in consultation with the Ministry of Gender, UN Women, and civil society organizations specializing in survivor support. It prescribes mandatory training for all project workers on the prevention of sexual exploitation, abuse, and harassment (SEAH), establishes a confidential reporting hotline, and defines the referral pathway from grievance intake to survivor-centered case management. The GBV Action Plan will be reviewed and updated at 18-month intervals to incorporate lessons from case management data and survivor feedback mechanisms.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In contrast, districts of origin are distributed widely across the country. This reﬂects the fact that much work migration is from remote rural areas to towns and cities. The main characteristics of work migrants are reported in Table 1, together with those of non- migrant adult males. We see that work migrants are on average younger and better educated. The census contains detailed information about ethnicity, language, and religion. In the Nepal census, the term ‘ ethnicity ’ is used to capture a hodgepodge of caste and tribal distinctions. The census distinguishes up to 103 ethnic categories. Most of these categories only account for a tiny proportion of the total population. In terms of the total adult population, the most common ethnic categories are Chhetri, Brahmin, and Newar who, together, account for 35 % of 13 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Nepal census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Internally displaced women are extremely vulnerable to rape by armed men, “ including government soldiers and militia. ” Protection for them basically does not exist58. According to UNHCR, Somalia has over 1. 4 million internally displaced people. In the year 2016, some 24, 500 refugees and asylum ‐ seekers were registered in Somalia. Refugee return from Kenya began in 2014. Voluntary repatriation number is close to 40, 000 Somali nationals from 2014 to December 201659. Economic Opportunity According to Berlin ‐ based Transparency International, Somalia is one of the world ’ s most corrupt countries. Improved governance could enable Somalia ’ s economy to grow on the basis of its oil and gas reserves. Ongoing droughts continue to drive hungry and thirsty refugees to surrounding countries, and large parts of the population are in need of humanitarian aid. The agriculture sector contributes to over two ‐ thirds of its GDP while industry only makes up for 7 % in 201360. According to the IMF, Somalia has a very high youth 57 EIU Syria economy: Quick View ‐ Wheat harvest set to fall short of government forecast, July 2017 58 Human Rights Watch, Somalia Events of 2016, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / somalia 59 UNHCR, Somalia, http: / / reporting. unhcr. org / node / 2550? y = 2016 # year 60 CIA The World FactBook, https: / / www. cia. gov / library / publications / the ‐ world ‐ factbook / geos / so. html Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the power calculations performed to determine the sample size. 3. 3 Randomized Assignment and Job Applications Each week we randomly selected 300 job ads from the sample of ads meeting our inclusion criteria. Among these 300 ads, each job ad was randomly assigned to a single applicant profile. Job ads are stratified to ensure that our treatment and comparison units are balanced on key variables. We use two variables for our strata: company size and company location. Company size is a dummy variable that takes the value of 1 if the company has up to 50 employees, and takes the value of 0 if companies have 51 or more employees. Company location is a dummy variable that takes the value of 1 if the company is located in greater Kuala Lumpur, the capital and largest metropolitan area in Malaysia, and 0 otherwise. 6 Our stratified randomization procedure guarantees balance in the assignment of job profiles to specific characteristics of companies. The application process was carried out manually from May 17 to July 28, 2023. At the beginning of each week, a research assistant was given a list of randomly assigned jobs for each applicant profile. Applications were completed on Mondays, Wednesdays, and Fridays of every week (with day of the week randomly assigned).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Appendix Figure A3 replicates the analysis using all refugee stocks available in the data, without constraining the sample to refugee events. The results are very similar to the baseline. Taking another approach, Appendix Figure A4 instead uses refugee ﬂows. As argued above, without individual-level data, ﬂows cannot be computed precisely. We build ﬂows by taking annual time diﬀerences in stocks by source-destination pair. In some instances, stocks fall over time. Since we do not have conﬁdence that a reduction in stocks represents a return to the home country – as opposed to transition to another host country – we set ﬂows to zero whenever the diﬀerence in stocks is negative. As evidenced in the ﬁgure, the point estimates of the time eﬀects and their statistical signiﬁcance are quite similar for ﬂows to the baseline. Fourth, it may be that the destination-speciﬁc conditions (such as the global ﬁnan- cial crisis) also aﬀect the distance traveled by refugees, or the probability of not going to a contiguous country. To account for this possibility, we net out the time variation in the destination country conditions as follows. In step 1, we project the refugee stocks at the 16 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 10 of 51 Source: Own calculations based on Household surveys Figure 4: Prevalence of Temporary employment among salaried employees (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the evolution of non-standard employment according to their age profile, we found a slight increase in the share of the older groups (Figure 5 and 6). This slight aging in the profile of non- standard workers is observed in both part-time and temporary employment. This finding is striking since, in principle, it was expected that the non-standard modalities of employment would show an increasing participation of the younger groups of the population. However, this change in the age composition of NSE is consistent with the age profile observed in total employment. In fact, the 0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % Argentina Brazil Peru Dominican Republic El Salvador Starting point 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % Argentina Brazil Chile Mexico El Salvador Starting Point Ending Point", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "ethnic group e at time t. It can also be expressed as one minus the Herfindahl index (Alesina et al., 2016). The EP index gives more weight to intergroup differences at the expense of within group homo- geneity. It can be defined as (Esteban and Ray, 1994, 1999; Montalvo and Reynal-Querol, 2005) 14 EPjt = Nrt X e = 1 (g2 et) (1 − get). (3) We compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity. In order to construct the revised refugee diversity indices according to ethnicity e, we first combine information about the country of origin of refugees hosted in refugee camps c in year t with the data from the EPR-ER 2019 dataset. The EPR-ER records the ethnic composition of refugee stocks originating from neighboring countries and countries in proximity to each other (maximal distance between country borders ≤ 950 km) with at least 2, 000 refugees and provides the ethnic composition of refugees (Vogt and Girardin, 2015). More specifically, the EPR-ER dataset gives us the share of refugees from ethnic group e moving from country o to country d at year t. The EPR-ER data gives us the three main ethnic groups. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Afrobarometer survey", "EPR-ER 2019 dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "148 P. B. Spiegel & P. V. Le\n\nIntroduction\n\nThe human immunodeficiency virus (HIV) behavioural surveillance surveys\n(BSSs), an evolution from the knowledge-attitudes-practice surveys (KAPs), are\nan assessment, monitoring and evaluation tool designed to track trends in HIV/\nAIDS knowledge, attitudes and risk behaviour among populations. When used\ntogether with qualitative and quantitative research and proper measurement of\nappropriate programme indicators, the data collected from BSSs can assist\norganizations in targeting specific HIV/AIDS prevention and care activities,\nallocating scarce resources, and monitoring and evaluating the interventions’\neffectiveness and coverage. BSSs are useful because they alert policy makers and\nprogramme managers to emerging or changing risks in existing behaviour, reveal\ngaps in knowledge and attitudes, help to identify vulnerable segments of\npopulations, contribute to improved programme content, provide data on specific\ntarget groups and ensure compatibility and standardization of data collection\n(Family Health International 2000).\nThe core BSS indicators have been evolving over time (Table I). Until the\nUnited Nations General Assembly Special Session on HIV/AIDS (UNGASS)\nindicators were developed in 2002, there were no internationally-accepted HIV\nindicators. The UNGASS indicators were followed by the development of the\nMillennium Development Goal (MDG) indicators in 2003 and, subsequently,\nthe US President’s Emergency Preparedness Fund on AIDS Relief (PEPFAR)\nindicators in 2004. Although all of these indicators are similar to one another,\nthere are minor differences. Thus, it is difficult for persons implementing BSSs to\nchoose which indicators to use and complicated for others to compare studies\nwhich use different indicators. Furthermore, there are numerous other indicators\nthat can be used in BSSs depending upon the target groups and objectives of the\nsurvey.\nConflict, displacement, food insecurity and poverty have the potential to make\naffected populations more vulnerable to HIV transmission. The UNGASS\nDeclaration of Commitment on HIV/AIDS, states that ‘populations destabilised\nby armed conflict . . . including refugees, internally displaced persons, and in\nparticular women and children, are at increased risk of exposure to HIV infection’\n(United Nations General Assembly 2001). However, the common assumption\nthat this vulnerability necessarily translates into increased HIV infections and\nconsequently fuels the epidemic is not supported by data (Spiegel 2004). In the\nrecent past, HIV/AIDS interventions were generally not included by humanitarian organizations as part of their immediate response to conflict; HIV/AIDS was\nconsidered more of a developmental issue and not an immediate life threatening\ndisease such as malaria or cholera. However, thinking has evolved and it is now\ngenerally accepted that HIV/AIDS programmes must begin at the onset of a\nhumanitarian emergency, be multisectoral, and continue at every stage thereafter\n(Inter-Agency Standing Committee 2003). Furthermore, for refugees and\ninternally displaced persons (IDPs), HIV/AIDS programmes should be integrated", "output": {"entities": {"named_data": ["knowledge-attitudes-practice surveys (KAPs)", "Millennium Development Goal (MDG) indicators"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The 2024 survey data will be supplemented by qualitative case studies in six purposively selected communities to capture implementation dynamics and beneficiary experiences that structured questionnaire data cannot adequately document. Case study fieldwork will involve key informant interviews with community leaders, implementing agency staff, and local government officials, as well as focus group discussions with male and female beneficiary subgroups. Findings from the case studies will be presented alongside quantitative results in the project's midterm review report.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 For a disaster to be listed in the EM-DAT database, at least one of the following criteria should be met: (i) 10 or more people are reported killed; (ii) 100 people are reported affected; (iii) a state of emergency is declared; (iv) a call for international assistance is issued. 4 The study was carried out by a consortium including the OECD, Risk Management Solutions, CIRED, Météo-France, NATCOM PMC, and the Indian Institute for Technology Bombay at Mumbai, and published in Ranger et al. (2011).\n\nThen, the population and assets exposed to flood risks is assessed, using data on population and assets collected by Risk Management Solutions from an insurance database developed for the assessment of earthquake risks.\n\nIn the absence of vulnerability curves for the buildings that can be found in Mumbai, the analysis uses \"average damage ratio\". It is assumed that when a property is flooded, a constant share of its value is lost, regardless of the water level and the detailed characteristics of buildings. Using three different techniques (based on published loss estimates for the 2005 floods, insurance data for the 2005 floods, and simple", "output": {"entities": {"named_data": ["EM-DAT database"], "descriptive_data": ["data on population and assets", "insurance data for the 2005 floods"], "vague_data": ["insurance database"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Sources of information This work includes a descriptive analysis that allows us to identify how the prevalence of non- standard employment (NSE) has evolved in the last two decades in different regions of the planet, as well as the evolution of the profile of workers in those roles, in terms of their educational level, salary per hour, and type of tasks performed. This analysis was based mainly on periodic surveys of households that included information regarding the employment and educational situation of individuals. Although the denomination of this type of surveys varies from country to country, in all the cases analyzed there is usually a survey of annual or higher frequency that includes information required to identify the labor status of the individuals as well as to analyze the salary profile and education of the employed. However, it should be noted that the identification of the type of work relationship (standard or non- standard) is frequently limited in these data sources. Indeed, it is only possible to identify part-time employment and temporary employment (not in all cases) within the non-standard forms of employment mentioned in the previous section. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["periodic surveys of households"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "to the Venezuelan migratory crisis, whereas the VenRepPS survey was conducted during the pandemic in 2020. These temporal inconsistencies result in varying sample composi- tions across surveys, diverging from the landscape we observe in 2022. Notably, forced migrants in the VenReps Kids survey migrated during the crisis but have since remained in the country for several years, potentially leading to disparities in household integration outcomes. IV GENERAL DESCRIPTIVE STATISTICS IV. A Key characteristics of adults Table 2 provides descriptive statistics for the adults in our study, encompassing the pri- mary caregiver, mother and father (if residing with the child), and the individual finan- cially responsible for the child (should they be different from the aforementioned per- sons). Typically, the roles of primary caregiver and financial provider are fulfilled by either the mother or the father. The table is organized into three panels for clarity: Panel A details key individual characteristics, Panel B outlines adults ’ access to services, and Panel C focuses on labor market characteristics. Within the table, columns (1) and (2) present average values for adults from Colombia and Venezuela, respectively, while the final column displays the results of mean difference tests between these two groups, with standard errors noted in brackets. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["VenRepPS survey", "VenReps Kids survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 1**\n\n**Demographics information.**\n\n This report is based on the information provided by 2176\nyoung Syrians, between 18 – 23 ages, living in 34\ndifferent sub-districts within the governorates of Aleppo\nand Idleb in Northwest Syria, covering 225 communities,\nand 106 IDPs camp sites. 47% identified as IDPs and 53%\nas part of the host community.\n\n**Are you from the Host community or**\n\n**IDPs?**\n\n**Internally**\n\n**displaced**\n\n**person …**\n\n**Host**\n\n**Community**\n\n**53%**\n\n In terms of marital status, the results showed a close split\n\nbetween married (48%) and single (49%).\n With a representative number of respondents (at least 200) as widows and same amount for divorced.\nAdding a layer of complexity and needs probably related to management of loss, livelihoods, and social\nstigma.\n\n**Marital status of interviewee**\n\n**48%** **49%**\n\nMarried Single Widow Divorced Seperated\n\n**16**", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These data are only for three districts in the province of Punjab, but is very recent, was conducted by an independent team of academics, and is a complete census of all households in the selected villages. Consequently, it yields sufficient madrassa enrollment to examine correlations with household attributes in a meaningful manner (this data source provides information on four times as many children as the PIHS). Table A2 in the appendix shows how these different data sources are used in the paper. Each source asks about madrassa enrollment in a slightly different but comparable way. The population census (1998) asks about the field-of-education (“ What is name ’ s field of education? ”) with options that include (for instance) engineering, medicine, or religious education. This question is also asked of all literate adults irrespective of their current enrollment status, allowing for comparisons in the stock of religious education over time. The PIHS rounds ask, “ What type of school is name currently attending? ” with options that include government school, private school, or deeni-madrassa (religious schooling). Finally, the LEAPS census directly asks, “ Is the child enrolled in a madrassa or an Islamic education school? ” Fortunately these different questions all give rise to similar numbers. This is reassuring since it suggests that any one particular result is not driven by the specific question or definition that was used. 7 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 (www. statpak. gov. pk). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": ["census of private schools"], "vague_data": ["population census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Because of this, the program impacts on control over resources were small, albeit statistically significant: 80 percent of respondents at baseline said they controlled their own resources, with a seven percentage point increase for the treatment group (relative to control). Similarly, 80 percent of those engaged in income-generating activities said that they controlled the money they earned. The midline results indicated that among the treatment group, this had increased by roughly eight percentage points. As shown in the second panel, EPAG graduates report that they worry less than those in the control group. They are less likely to worry about their jobs or incomes or that they won ’ t be able to pay for basic necessities, and those with partners are less worried about their relationships breaking up. The impact on subjective well-being, as measured by a series of questions about the respondent ’ s satisfaction with various dimensions on her life, indicate that EPAG was most 20 See for example Ashraf et. al. (2010); Morcos and Sebstad (2010); and Austrian and Ghati (2010). 21 “ Formal ” loans are those from banks, credit groups, susu, or money lenders; “ informal loans ” are those from parents, friends, relatives, or business partners. 16 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In 2003, almost 60 % of young girls and 40 % of young boys had no formal schooling (ILO / UNICEF 2005). Data from the Demographic and Health survey shows that more than 40 percent of adult women have no education, compared to fewer than 20 percent of men, while 23 percent of women and 44 percent of men have some secondary schooling (DHS 2007). Happily, access to education is rising rapidly, especially for girls: according to the Liberian labor force survey from 2010, the ratio of girls to boys enrolled in primary school has risen from 72 in 2000 to 90 in 2009. Enrollment levels and sex ratios are lower among older children and youth, as they become increasingly engaged in 1 Both are unweighted averages; Barro-Lee comprises 32 countries with data from 2010; Edstats comprises 43 countries with data from 2007-2011. 2 Defined as without work, available for work, and actively looking for work (LISGIS 2010). 2", "output": {"entities": {"named_data": ["Barro-Lee", "Demographic and Health survey", "Liberian labor force survey", "Edstats"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Disaggregated data on IDPs who are not protected or assisted by UNHCR are collected by other agencies, including IOM, but data are not comprehensive and therefore not published in IDMC ’ s global reports. 93 The difficulties of collecting disaggregated data on locations of IDPs are compounded by the fluidity of IDP movements — IDPs might suffer multiple displacements or they might resort to changing locations as a coping strategy (e. g. moving between their homes and place of displacement or testing different locations before deciding where to stay) (Brookings 2011). In recent years, efforts have been made to improve data collection for IDPs living outside of camps by employing a range of techniques including: (a) profiling; (b) household surveys; (c) collecting information on IDPs who come to camps to visit family members or collect relief items; and (d) community outreach programs (Brookings 2013). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Disaggregated data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "World Bank team undertook field supervision, providing on-time and on-the-spot guidance for the field teams whenever and wherever they encountered a challenge. (e) Challenges faced and lessons learned The SESRE served as a learning experience for including refugees in future rounds of the official household survey (HoWStat). Given the unique feature of refugees compared to Ethiopians, the sampling methodology for the SESRE is unique for sampling refugees. Therefore, SESRE successfully tested the feasibility of sampling refugees and their hosts. To ensure the successful implementation of the sampling procedures, the ESS implemented a pilot sampling methodology before data collection started to ensure that all systems and processes were functioning. The ESS and World Bank teams conducted field visits for this pre-test in Afar and Addis Ababa. The field visits included discussions with camp community leaders, including the refugee community leaders, about the upcoming survey to understand better any sensitivities that may arise. The field visits helped to understand the camp administrative structure and environment of the teams facilitating the camp and to test the accessibility of sampled refugee households inside the camp, in Addis Ababa, and the identification of the host. ESS provided detailed feedback on the fieldwork procedures and adjustments", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["official household survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "There is one limit to our approximation in Equation 4. The ethnic composition of refugees in each year t for a given origin – destination pair of countries obtained from the EPR-ER database is assumed to be homogeneous across camps of the same origin – destination pair of countries for the refugees at year t. This may seem to be a strong assumption; however, the risk of misallocating refugees is reduced as the annual variation in the EPR-ER is generated by just a few dominant groups for a given origin – destination pair and the geographical distribution of refugees by country of origin is highly influenced by the proximity to their countries of origin. 16 As can be seen from panel A of Table B. 2, in refugee-hosting areas, on average, both EF and the EP seem to increase quite significantly when they are revised by incorporating the number of refugees in an 80-km buffer: the mean value of the standard EF index is 25. 58 %, while the mean value of the revised refugee EF index is 37. 90 %. The mean value of the standard EP index is 10. 11 %, while the mean value of the revised refugee EP index is 14. 07 %. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["EPR-ER database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "16 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 6. Gender differences in multidimensional poverty Next we examine differences in multidimensional poverty outcomes by the gender of the household head. Existing literature points out the limitation of household level MPI analysis in masking the intrahousehold distribution of deprivations, and thus being less sensitive to gender based differences in individual outcomes within the family unit, which might lead to underestimation of inequality and gender gaps (Espinoza-Delgado and Klasen 2018; Franco 2017; Klasen and Lahoti 2020, Rodriguez, 2016). However, as the MPI identifies poverty at the household level, our initial analysis focuses on disaggregated results by the gender of the household head. 19 We acknowledge that this approach has several limitations since most women reside in male-headed households, and the composition of households can change after displacement due to separation of family members, and widowhood. Regardless, the analysis at the household level remains relevant given the high prevalence of female-headed households that emerge after displacement, with the analysis showing large differences across countries between households based on the gender of the head. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "income has not led to more happiness using Eurobarometer and US GSS data. Variables used\n\nor panel data) but a few use variables transformations of income and relative income that would\n\nproblem one would need panel data, good instruments for personality traits or variables that", "output": {"entities": {"named_data": ["Eurobarometer", "US GSS data"], "descriptive_data": [], "vague_data": ["panel data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Chew et al. (2018) use a baseline convolutional neural network model on a gridded population sampling frame to select a sample of households in Nigeria and Guatemala. The authors found this technique to be on par with human canvassing in terms of accuracy, and to outperform other machine learning models based on crowdsource or remote sensing data. Grais et al (2007) compared an unweighted random point selection methodology to a random walk in their study of vaccination rates in urban Niger. The authors do not find statistically significant differences between the methods, though the sample size was limited and both methods were non-probabilistic. 3. Design and Field Protocols 3. 1. Experiment Design This paper makes use of a dataset from the purposefully designed methodology experiment conducted in one section of the Protection of Civilians site 1 (PoC1, Figure 1), one of the largest IDP camps in Juba, South Sudan. To generate a gold standard as the basis of comparison, a household census was conducted between August and September 2017. During this exercise, 2, 655 households were interviewed using a questionnaire designed to collect demographic information, dwelling characteristics, household consumption, and perception data. At the end of each census interview, households received a unique barcode that could be used to identify them later in the experiment.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The panel database was constructed by merging administrative enrollment records with survey data collected at baseline for a representative sample of 1,800 households. Individual household identifiers were assigned at enrollment and used to link administrative and survey records throughout the panel. The panel database is maintained by the evaluation firm and stored on encrypted servers with access restricted to the principal investigator and designated data analysts. De-identified extracts from the panel database will be provided to the Bank's task team for independent verification of reported outcome changes.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The relation- ships between displacement dynamics and perceptions of each manifestation of social cohesion are analyzed separately by running 12 models for each independent variable (6 regressions for each sample). The regressions are correlations and should not be interpreted causally. Hosting status and displacement ﬂows are likely related to perceptions of social cohesion in indirect ways and the structure of the survey data limit the ability to specify the channels through which these relation- ships run. Each regression controls for characteristics that may inﬂuence respondents ’ perceptions of social cohesion outside of the presence of IDPs or refugees in the local community such as province, gender, age, marital status, level of education, employment, and exposure to violence. 11Poll numbers correspond to the number wave in our larger project, as described and shown in Table 2. 28", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "HIV behavioural surveillance surveys 149\n\nTable I. Internationally accepted key BSS indicators.\n\nKnowledge International standard\n\nPrevention Percentage of young women and men\naged 15 �24 years who, in response to\nprompted questions, say that:\n1) people can protect themselves from\ncontracting HIV by having sex with only one\nfaithful, uninfected partner. [a]\n\nUNGASS, MDG, PEPFAR\n\n2) people can protect themselves from UNGASS, MDG, PEPFAR\ncontracting HIV by using condoms. [a]\n\nMisconceptions Percentage of young women and men\naged 15 �24 years who, in response to\nprompted questions, correctly reject that:\n1) A person can get HIV from mosquito UNGASS, MDG, PEPFAR\nbites. [a]\n\n2) A person can get HIV from sharing a UNGASS, MDG, PEPFAR\nmeal with someone who is infected. [a]\n\nGeneral Percentage of young women and men\naged 15 �24 who, in response to\nprompted questions, know that:\n1) A healthy-looking person can have UNGASS, MDG, PEPFAR\nHIV. [ab]\n\nAttitudes\n\nCare and support The number of respondents who report\nan accepting or supportive attitude of:\n1) Would be willing to care for a family\nmember who became sick with the AIDS\nvirus.\n\nPEPFAR\n\n2) Would buy fresh vegetables from a vendor PEPFAR\nwhom they knew was HIV�/.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["HIV behavioural surveillance surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "twins) variation on 6th grade ENLACE, identifying, therefore, the relationship between\n\nas captured by ENLACE, on future outcomes and the (within-family) individual-level\n\ncharacteristics. In the ENLACE panel, this last specification uses the sample of students\n\nwho answered the ENLACE context questionnaire to control for differences in household\n\nENILEMS-ENLACE panel regressions, _Xi_ _[′]_ [includes] [upper] [secondary] [school] [grade] [point]", "output": {"entities": {"named_data": ["ENLACE", "ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "<!-- PAGE 3 -->\n**Estimating a Poverty Line for Brazil Based on the 2017/18 Household Budget Survey** **[+]**\n\n**Keywords:** poverty lines; food poverty line; basic needs; household budget survey; Brazil", "output": {"entities": {"named_data": ["2017/18 Household Budget Survey", "Household Budget Survey"], "descriptive_data": [], "vague_data": ["Household Budget Survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "FAO (2012) being the latest. More recently, the FAO indicator is used to track progress toward the first\n\ncountry. [2] Combining this with population data allows the FAO to estimate the total kilo calories available\n\nnumber of HCES. [3] For most countries the CV was kept constant across years and only the mean was\n\nrevised. [4] Finally, the FAO estimates the required energy of a population by determining age-sex specific", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**3.3** **Open Street Map (OSM) Road Network Data Set**\n\nIn addition to the five public transportation networks, we also added the drive as well as the pedestrian network leveraging the Open Street Map data set to account for commuters who walk or drive to work.\n\n**3.4** **Fathom Flood Maps**\n\n<!-- PAGE 10 --> Pluvial and fluvial flood maps from the Fathom global flood model were first clipped to the bounding box [9] of the Kinshasa city and then mosaiced together using the maximum operator so that the maximum flood depth from both fluvial and pluvial flood estimates were preserved.\n\nFlood models are an integral tool for understanding and managing flood risks on transportation networks. In\nthe past decades, increased computing power and precision of remote sensing data sets have led to the\ndevelopment of multiple global flood models (Bernhofen et al., 2018; Wood et al., 2011). Among them, the\nlastest flood map products from Fathom (A. Smith et al., 2015) are selected to provide flood depth and extent\nestimates in Kinshasa for the following reasons: 1) the map products from Fathom have relatively high\nspatial resolution (90 meters) which is sufficient for this study given the geographic extent of the Kinshasa\ncity; 2) flood extent and depth estimates for both pluvial and fluvial floods under 10 return periods [8] are\nincluded in this data product; 3) it uses 2D hydrologic flood model which is more advanced in mapping flood\nplain and modeling dynamic water flows.", "output": {"entities": {"named_data": ["Open Street Map (OSM) Road Network Data Set", "Open Street Map data set", "Fathom Flood Maps", "Fathom global flood model"], "descriptive_data": ["flood map products from Fathom"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In practice, most of those approved for the OCP have family and friends in Ethiopia who support them with remittances—fewer than 1,500 OCP work permits were issued by 2022. The permit allows refugees to freely move and establish residence in all areas of the country except restricted areas. OCP refugees are systematically different from in-camp refugees, as seen in their livelihood strategies. Therefore, the remainder of this chapter divides the analysis between in-camp refugees and OCP refugees. 3.1 Labor market outcomes of in-camp refugees and their hosts In-camp refugees have high inactivity rates (not working or unemployed) and low labor force participation. Table 3.1 shows that only 31 percent of all in-camp refugees aged 15-64 “participated” in the workforce (in the week before the survey), meaning they were employed or available to work and actively searching (strict unemployment). This compares to 52 percent for hosts. This figure increases to 43 percent for refugees and 57 percent for hosts if you include all available to work regardless of whether they are searching (relaxed unemployment). The remaining 57 percent of refugees are inactive, and just over half are currently in school, leaving 23 percent of refugees neither working nor studying, compared to", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Impacts on the labor market are unclear; initial research suggests that there has been a supply shock to informal labor markets. This has had a large-scale impact on the employment of natives in the informal sector. At the same time, research suggests there has been a boost to formal employment for the Turks, but this has been uneven: the low educated and women experience net displacement from the labor market and, together with those in the informal sector, declining earning opportunities.  The “ Socio-economic Assessment of the Impact of Syrians under Temporary Protection (SuTPs) on Turkish Hosting Communities ” [ongoing], to be undertaken in partnership with the Government of Turkey, will include a nationally representative household survey with SuTP and local Turkish households including camp and non-camp environments. The questionnaire will cover welfare (assets, income, expenditure), municipal services, labor and employment, education, social networks and quality of life. F. Options to improve forced displacement statistics Significant efforts are needed to enhance the reliability, comparability, quality and scope of the global data on forced displacement. In particular, more robust estimates are needed of the scale (stocks, flows and locations) and typology (demographics, location and accommodation) of forced displacement crises. This requires substantial improvements in the rigor of data collection and compilation methodologies including: (a) Harmonization of definitions and methodologies used in the collection and analysis of statistical data on forced displacement — covering stocks and flows of refugees, asylum- seekers and IDPs — to ensure comparability across regions and countries;", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "camp refugee households with improved bathing facilities36 is low, especially among Somali refugees and their hosts. In addition, few refugee and host households have a place or item designated for hand washing in their dwellings. Availability of water or detergent for hand washing is low, especially among refugees (Annex D, Figure D.15). Regarding rented homes, OCP refugee households live in houses with better bathing facilities (82 percent) than hosts (66 percent). Also, around 80 percent of refugee and host households in Addis Ababa have a place for hand washing, and more than half of refugees and hosts have water or soap. 32 Overcrowding occurs when if more than three people live per room (UN-Habitat). 33 Improved wall is made of stone & cement, blocks-plastered with cement or bricks. 34 Improved roof is made of corrugated iron sheet or concrete/cement. 35 Improved sources of drinking water are piped, bottled, sachet, or tanker water. 36 Improved bathing refers private or shared bathtub, shower, separate room for bathing. 0 20 40 60 80 100 120 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Overcrowded Improved wall Improved roof Percent Figure 2.21: Housing quality Source: World Bank Staff based on SESRE", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "23 86 % of the IDPs, 91 % of the refugees and 88 % of the returnees are confident or fully confident that a coalition like this would be capable of providing security. Source: Listening to Displaced People Survey, 2014. In an open-ended question on who can be trusted most when it comes to ensuring security in the North, survey results suggest that the majority of refugees in Mauritania (86 %) trust the armed rebel groups as opposed to the army or police. This does not hold for refugees in Niger of whom 75 % trust the army and police. Similar results hold for IDPs and returnees, who put much more confidence in state authorities when it comes to securing the North: most trust is placed in the army and police (72 % of the IDPs and 66 % of the returnees) while little to no trust is placed in armed rebel groups (3 % of IDPs, 1 % of returnees). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Most papers with few exceptions use standard OLS estimators or some of its variants (Table 2). Two papers use general equilibrium models (Bodvarsson, Van den Berg, and Lewer 2008; Hercowitz and Yashiv 2002) and two papers simply compare means between treated and non-treated groups resulting in simple difference estimations (Card, 1990 and Alix-Garcia and Bartlett, 2015). [Table 2] The unit of observation varies depending on the data at hand. Most studies rely on household survey data where individuals or households are the unit of observations and most studies include some regional dimension (more frequently administrative areas). Where longitudinal or panel data are available time is also included. Other choices for unit of observations include skills or education level, various types of population groups (based on gender, age etc.), and, in a few cases, economic sectors, industry or labor market segments. The use of fixed effects varies. Some papers use the full set of parameters depicting units of observation (for example, household, region and time fixed effects in equations where the unit of observation is constructed using household, region and time). Other papers use subsets of these parameters whereas other papers introduce variables that are not used to identify the unit of observation. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["household survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "16 5. 3 Desire to return (Y / N) Keeping our attention on refugees and IDPs, we wanted to deepen our understanding about their migration plans. In particular, we would like to discover which characteristics are associated with the desire to return to Northern Mali. To this end, we estimated a probit model using the same regressors as in the previous sections. The dependent variable was set equal to one when the respondent was considering the possibility to eventually go back to the North, zero otherwise. The estimated marginal effects have been reported in Table 3 for all respondents (Column 1-2), as well as for only the household heads or their spouses (Column 3-4). The strongest predictor of a planned future return was refugee status: individuals living abroad in refugee camps were up to 25 percentage points more willing to go back than IDPs. Joining this result with those on unemployment presented in the descriptive statistics, we may wonder whether this desire to go back home may have resulted from a more general malaise experienced by these respondents forced to migrate and halted in a limbo not fully integrated with the local community and labor market.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["descriptive statistics"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10631 This paper aims to understand the existing gaps in micro- level data on forcibly displaced people — refugees and internally displaced persons. The paper undertakes a com­prehensive review of all existing micro-level data sets in the United Nations High Commissioner for Refugees Micro­data Library and the World Bank Microdata Library. It first identifies a corpus of micro-level data sets that are designed to have a representative sample of refugees and / or internally displaced persons and assesses gaps in geographical and the­matic coverage. The paper then evaluates whether the data sets contain a core set of questions that are essential for the proper identification of refugees and internally displaced persons. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "They exclude other categories of displaced people who were not forced to move because of conflict or violence, such as economic migrants and victims of natural or environmental disasters. 4 Of course, many people cannot be simply categorized in these groups and this makes statistics on FDPs gross estimates, but the growth and relevance of these numbers are undisputed. The growth in the number of FDPs poses a challenge to the measurement of global and national poverty. Those who are forcibly displaced and in need of international protection tend to be persons who have lost their assets, financial resources, and social networks. They are typically very poor with no obvious path out of poverty. For refugees, their number vanishes from poverty statistics of their own country because they are no longer counted in the place of origin. Both IDPs and refugees are also not properly accounted for in the country in which they reside. Their numbers – even though high in absolute terms – are often low relative to the non-displaced population (with some exceptions like Lebanon and South Sudan). Hence, they do not explicitly show up in official statistics. Even if – as in some but not all countries – their locations are appropriately included in the sampling frame, they are unlikely to be sampled due to their small proportion relative to the population and high clustering in specific locations. 3 https: / / www. unhcr. org / refugee-statistics / 4 Note that the IASC definition of IDPs explicitly includes those fleeing from disasters. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "assignment process was conducted to assign the trainees to the first or second round of training. 9 Of those entered into the random selection, 1273 young women were assigned to the first round of training, with the remaining 808 to serve as a control group (the control group would participate in the second round of training starting in July 2011). Of the 1273 assigned to treatment, 118 women were not found or chose not to participate after they were selected. 10 In order to fill at least some of these slots, 39 young women from the control group were randomly issued as replacements, resulting in a modified control group of 769 individuals. In the end, 1191 young women entered the first round of training. 11 The assignment process and all post-randomization modifications are summarized in Figure 2. Table 1 reports the baseline and midline survey response rates leading to the sample used for the analysis in this paper. The target sample for both the baseline and midline survey consisted of the original 2106 EPAG recruits, of which 1989 were successfully interviewed during the baseline survey. 12 At midline, 1736 were interviewed, including 56 who were not interviewed at baseline. For our analysis, we drop individuals who were excluded from the randomization or who were manually re-assigned from control to treatment as replacements after the randomization.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["baseline survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "milk and milk products, oils and fats, sugar/honey, and others. Food consumption status is determined based on food consumption score. a. Dietary diversity score (out of 12 groups) b. Food consumption status Refugees’ Aspirations 49 Moreover, insecurity and displacement-related shocks are common for Eritrean refugees, with 14 percent having experienced a recent displacement event. This result is driven by refugees in the Alemwach refugee hosting site, all of whom moved to the refugee site within a few months before the survey as a result in the conflict in Tigray, and would have reported a recent displacement event. Both refugees and host communities use “consumption-smoothing” to cope with the various shocks they face. Households utilize a mix of coping strategies to mitigate harm to their welfare that shocks cause. “Consumption smoothing”, among the major risk coping strategies, mainly involves relying less on preferred food and more on less expensive food (diet changes) and reducing the number of meals eaten daily (negative food intake). Borrowing food or cash from friends and relatives and purchasing food on credit second represent the second and third most common coping strategies. Refugees in camps and in Addis Ababa are more likely to rely on these coping", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These people are then expected to return to their place of origin once the conflict is over and governments are typically over optimistic about the duration of civil conflicts and about return of IDPs. In some cases, governments also have an interest in denying the very existence of IDPs for political purposes. Therefore, little time is spent in surveying IDPs or trying to find durable solutions in the place where they migrated. Moreover, national censuses are usually conducted every ten years and statistical agencies have little incentives to revise censuses, master samples and sample survey structure for situations that are perceived as short ‐ term. In most cases, new surveys are suspended or carried out under the pre ‐ crisis frameworks and, in either case, information on IDPs is not collected or poorly collected. This leaves specialized government agencies or international organizations in charge of IDP statistics (and care). However, unlike refugees, the IDPs do not benefit from a specialized international agency such as the UNHCR. IDP assistance is currently provided by a multitude of organizations including ministries of interior, specialized government agencies, the UNHCR, the International Organization for Migration (IOM), the UN Office for Humanitarian Affairs (UN ‐ OCHA), specialized NGOs and others. Some of these organizations collect information on IDPs and make this information public while others collect information that is not published and others do not collect information and focus on providing assistance. Most data collected are for the simple purpose of counting IDPs and do not include individual or Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national censuses"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "ESS translated the survey instrument into different main languages and undertook an in- depth training of supervisors and enumerators for enumerators to understand better the concepts of the questions related to the refugee context. Moreover, a close follow-up and coordination in the field helped to get better quality data and provided timely responses to challenges faced during data collection. During the survey implementation period, the main challenge was tracking sampled refugee households in all refugee domains. One of the Eritrean camps, Alemwach Camp, was newly established at time of data collection. Tracing the originally sampled and backup households initially took a lot of work. The issue of missing households in Asayita camp was severe during the second data collection phase. Moreover, some camps were very large; for example, there were more than 100,000 refugees in one camp, creating challenges for field workers in tracing the sampled households. Refugees in Addis Ababa live in rented houses; the team faced challenges in tracing some refugees due to changes in their residential locations. Challenges related to identifying the eligible sample households were also observed due to outdated names of the household heads in UNHCR lists, and UNHCR’s registration of names which is not", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Road data quality in the southern provinces is lower than in other regions due to the absence of electronic traffic count equipment on rural roads and the reliance on visual condition assessments conducted by untrained district public works staff. The project will support a road condition data quality improvement program under Component 3 that trains district engineers in standardized pavement condition index methodology and equips them with ruggedized tablets for electronic data entry. Improved road data quality is expected to reduce the cost of prioritization errors in future maintenance planning.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The SEIS was fielded over a 14-week period by a nationally certified survey firm using CAPI on Android tablets. Interviewers received five days of training covering the survey instrument, tablet operation, and protocols for interviewing households with protection concerns. The SEIS questionnaire was translated into four languages in addition to the national language to accommodate the linguistic diversity of the target population. Response rates exceeded 94 percent in all settlement types, and back-checks were completed for 12 percent of interviews, revealing no systematic interviewer effects.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 The only publicly available sources of data to document patterns of enrollment and available educational options for Pakistani families are household-based surveys. These are the official 1998 Census of Population (Government of Pakistan) 3, the 1991, 1998, and 2001 rounds of the Pakistan Integrated Household Survey4, and a 2003 census of schooling choice conducted by our research team. The fact that three sources use different definitions of madrassa enrollment, and were collected at different times by individuals with very different institutional affiliations provides independent verification of enrollment estimates and allows us to determine the sensitivity of our results. The household data tell us whether a child is enrolled full-time in a madrassa, but not whether a child goes for an hour on any given day to study the Quran. Therefore this data does not confound full-time with part-time attendees — a child who attends a public school during the day and a madrassa in the evening is recorded as enrolled in a public school. This is an important distinction since parents might use a modicum of madrassa or mosque based education to teach their children about religion. Consequently, if we contrast these household-based numbers with numbers from establishment-based reports, discrepancies can arise. From virtually any policy perspective, including evening quran classes in enrollment figures seems misguided. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["1998 Census of Population", "Pakistan Integrated Household Survey"], "descriptive_data": ["2003 census of schooling choice"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "come ratios relative to extractives,<br>2008-13|\n|---|---|\n||Kayes<br>Sikasso<br>Other<br>regions<br>Total<br>Agriculture<br>6%<br>37%<br>60%<br>40%<br>**Extractives**<br>**100%**<br>**100%**<br>**100%**<br>**100%**<br>Industry<br>111%<br>50%<br>110%<br>85%<br>Construction<br>14%<br>75%<br>413%<br>214%<br>Services (tradable)<br>129%<br>68%<br>107%<br>84%<br>Services (non tradable)<br>76%<br>73%<br>183%<br>126%|\n|_Source:_ CPS/SME (Mining and Energy Sector Planning and<br>Statistics Unit), 2013.|_Source:_ EPAM (Permanent Household Survey), 2010.|", "output": {"entities": {"named_data": ["EPAM (Permanent Household Survey)"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Ideally, we would conduct a comparative analysis of the characteristics of migrants residing in Medellin versus those in other parts of the coun- try to discern the extent of these differences. However, the lack of comprehensive data regarding the living conditions of this population makes such analysis unfeasible. To explore how this population compares with other migrant groups in the country, we turn to the only two available data sources on migrants. First, we use the Venezuelan Refugees Panel Survey (VenRePS), conducted by Ib ´ a ˜ nez et al. (2022), which captures a 19 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Venezuelan Refugees Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "composite measure for human capital. We extract real GDP and human capital data from Penn World\n\nTable 10 (Feenstra et al., 2015), using the \"rgdpna\" series, which measures real GDP in constant 2017\n\nschooling and returns to education (Inklaar and Timmer, 2013). We extract the population from World\n\nDevelopment Indicators (WDI).\n\n#### **3.3 Sample Construction**\n\nThe baseline populations served by the East Road (within the buffer zones) were established by utilizing\nWorldPop open source data. To estimate the number of people affected by rain event, however, a series\nof models were run to simulate the locus of impacts for each rain event (3-, 10-, and 30-year events) and\ntheir subsequent effects on access. Repeated events might affect the same people, and so, the cumulative\nimpacts over the lifetime of the road can be thought of as 'person-disruptions' - i.e., the sum of individual\ndisruptions. If, for example, an individual living on the East Road was cut off from access to hospitals three\ntimes over the thirty-year period, this experience would account for three person-disruptions.\n\nUsing Global Positioning System (GPS) tracking records for the Malaita East Road from Atori to Dala, 5,856\nroad segments with gradient details were delineated and mapped using geographic information system\n(GIS) mapping software. These segments were mapped for the current road and also used to model surface\nconditions defined by the proposed upgrading projects.\n\nCost data for similar road projects also informed assumptions about per-unit (km) repair costs\nfor each type of road surface subject to various damage levels.", "output": {"entities": {"named_data": ["WorldPop open source data"], "descriptive_data": ["Global Positioning System (GPS) tracking records"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Poverty remains widespread and social indicators are well below the average for Sub-Saharan Africa. Chad is ranked 173 among the 177 countries surveyed in the 2006 UNDP Human Development Report. The incidence of poverty (defined as the proportion of households with annual spending below what is necessary to meet minimal needs) is estimated at 55 percent according to a 2003 household survey; an estimated four-fifths of the population of about 8. 8 million is living on less than a dollar a day3. Of the population over 15 years old, more than 73 percent (and 76 percent of women) are illiterate. Access to potable water has improved over past years, but is still limited to one out of three people in 2005. Less than two percent of the population has access to electricity and only 1021 kilometers of roads has been paved on a surface area of over 1. 2 million square kilometers. As already mentioned, Chad has recently become oil producing country; however, the economy remains largely agricultural and pastoral. About 80 percent of the country ’ s population lives in rural areas and continue to make their living4 from agriculture and livestock. Cotton is the principal cash crop, employing about 300, 000 families. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["2003 household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "a relatively higher percentage of married individuals. In Addis Ababa, 60 percent of refugees are unmarried. 16.2 15.7 14.9 8.8 14.1 Eritrean Somali South Sudanese Addis refugees All 0 20 40 Percent 60 80 100 Eritrean Somali South Sudanese Addis refugees Conflict and violence Natural/man-made disaster Personal threat and political persecution Social and economic reasons Hope to go to a Western country Other reasons Figure 2.3: Refugees arrival in Ethiopia (15 years and above) Source: World Bank Staff based on SESRE 2023. a. Years since arrival b. Reasons for leaving the country of birth 13 For details on OCP refugees, please see Box 2.2. Sociodemographic Profile 11 Refugees have larger households, younger heads, and a higher proportion of female heads than hosts. Household size is higher among in-camp refugees compared to hosts. Across in-camp refugees, South Sudanese refugees have the highest number of household members, averaging roughly seven members per household. The dependency ratio— the ratio of dependents of those under age 15 and above age 64—to working members in a household, is also higher for in-camp refugees relative to hosts and highest among South Sudanese refugees. OCP refugees have the smallest average household size and the lowest dependency ratio.", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The ESMF prepared for this project builds on environmental screening templates developed under the predecessor operation and incorporates updated guidelines on asbestos management and e-waste disposal issued by the Ministry of Environment in 2023. Subproject screening will be conducted using the ESMF's standardized checklist, and outputs will be retained in the project's environmental and social documentation register. Where screening identifies potential impacts on natural habitats or physical cultural resources, a qualified environmental specialist will prepare an ESMP prior to works commencement.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Kreibaum (2016) corroborate the ﬁnding that host communities near refugee settlements in Uganda have relatively higher consumption levels using data on three south-western districts, though the eﬀect is small in magnitude. d ’ Errico et al. (2021) ﬁnd that the presence of refugees has only modest eﬀects on local households ’ consumption levels. The authors attribute this ﬁnding not to an increase in demand for local produce, but to greater participation by host households in paid employment in aid agencies, and to the resulting increase in wage incomes. However, the eﬀects they observe are small, and concentrated in areas very near refugee settlements. 11 Most relevant to our study, Kreibaum (2016) reports that access to private primary schools (but not public schools or health services) has increased at a greater rate as a function of refugee presence. They measure refugee presence at the district level (refugee share of the district popu- lation) and focus on the south-western districts between 2002 and 2010. Thus, by including all of Uganda at the parish-year level as well as the post-2014 inﬂux, we extend their analysis. In sum, our study is the ﬁrst to measure the eﬀects of refugee presence on service delivery outcomes in Ugandan host communities. 10See Gianvenuti, Jalal and Kirule (2020) for more details. 11Note that both Zhu et al. (2016) and d ’ Errico et al. (2021) use original cross-sectional surveys. Without pre-treatment data, it is harder for them to make causal claims. 12 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "recently introduced the first version of their Global Internal Displacement Database (GIDD) that allows users to explore, filter and sort IDMC ’ s data to produce graphs and tables, and export underlying data. 102 Such platforms need to incorporate safeguards to protect the privacy and confidentiality of individuals ’ data. UNHCR and Statistics Norway are currently leading an initiative to improve forced displacement statistics with the participation of national statistical agencies. This process began with the presentation of the “ Report on Statistics on Refugees and IDPs ” at the 46th session of the UN Statistical Commission in March 2015, 103 followed by an international conference in Turkey in October 2015. 104 The conference set in motion a process for national statistical agencies to collaborate to develop a set of recommendations that both countries and international organizations can use to improve data collection, reporting, data disaggregation, and overall quality, including the preparation of International Recommendations for Refugee Statistics (IRRS). Progress on this agenda was discussed at the 47th session of UNSD held in New York in March 2016, where it was recommended that the expert group should also include IDPs in its scope of work (UNSD 2016). 105 The current initiative is focused on refugees, asylum-seekers and IDPs but would ideally be extended to host communities and returnees. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Global Internal Displacement Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Although 86 % of the refugee sample was surveyed at least once, there was some differential attrition between the treatment and control refugee sam- ples, and therefore we estimate bounded effects following Lee (2009) (and the text highlights the results for which both estimated bounds take the same sign unless otherwise noted). These data are analyzed largely following the econometric models and primary outcomes speciﬁed in an AEA pre-registration (AEARCT # 0006141) and associated pre-analysis plan, while making note of ad- ditional and exploratory results. Another core contribution of this study is to examine the host community reaction to refugee assistance, utilizing a detailed survey of the attitudes and experiences collected among a repre- sentative sample of the Jordanian neighbors (N = 2, 146) of both treatment and control households. To our knowledge, this is among the ﬁrst studies to experimentally examine how humanitarian assistance to refugees affects the views of the local communities who do not directly beneﬁt. Speciﬁcally, we examine whether refugee-targeted transfers — in this case via rental payments 1Statistic calculated directly from data on the universe of Syrian refugees in Jordan registered with UNHCR. 2 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 do selected volunteers increase their teamwork / leadership and their communication skills? Do they increase their self-esteem / self-satisfaction? iii) As a result of their assignment to NVSP volunteering experience, are selected youth more likely to find a job than non-selected ones? (a) IMPACTS ON SOCIAL COHESION VALUES The two main indicators that measure improvements in social cohesion values are tolerance and a sense of belonging to the Lebanese community. Measuring social cohesion values in large-scale surveys is challenging. We are unable to use extensive measures, but rely instead on brief measures adapted from Harb (2010). The tolerance measure relies on a series of 12 questions, each of which is ranked on a four-point scale, which makes the total possible score range between 12 and 48 points. The sense of belonging to the Lebanese community measure consists of 18 questions, each of which is ranked on a seven-point scale, which makes the total possible score range between 18 and 126. Thus, higher scale values indicate higher tolerance values and a stronger sense of belonging to the Lebanese community. Both values are internally standardized so that they have a mean of 0 and a standard deviation (S. D.) of 1 in the comparison group at baseline. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 Figure 3 Sample Distribution for Refugee Children Compared to Global Distribution (n = 92) Figure 4 Sample Distribution for Children Living in Rural Areas Compared to Global Distribution (n = 902) AIM-ECD direct assessment scores were created by calculating the percent correct out of the 77 direct assessment tasks and questions asked. There were four early literacy tasks (listening comprehension, letter identification, initial sound discrimination and name writing), six numeracy Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Gender may inﬂuence how members of the host community experience and perceive their role as hosts. Since each of the samples are gender-balanced, Figure 6 re-reruns the logistic regres- sions above but after sub-setting the data by gender. Hosting IDPs is associated with improved perceptions of social cohesion among men for all sub dimensions other than access to basic needs in the general sample, but women ’ s perceptions of social cohesion are only positively associated with contact with other ethnic groups (OR: 1. 26, CI: 1. 01 – 1. 56) and access to services (OR: 1. 44, CI: 1. 15 – 1. 80). In cities, female respondents were more likely to report negative perceptions of in-group (OR: 0. 59, CI: 0. 41 – 0. 84) and out-group relationships (OR: 0. 63, CI: 0. 44 – 0. 90) when hosting IDPs. Women were less likely to participate with other ethnic groups (OR: 0. 57, CI: 0. 40 – 0. 82) if they had IDPs in their communities. But men had positive associations for IDPs with relationships and solidarity in cities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The 2023 survey of transport infrastructure conditions found that 68 percent of rural access roads in the project's five target provinces were in poor or very poor condition, with pavement failure attributed primarily to inadequate drainage maintenance and axle-load violations by commercial freight vehicles. The 2023 survey was administered by provincial public works directorates and compiled by the national road authority using standardized pavement condition index scoring. Results were shared with the Ministry of Finance to support the project's economic justification.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The weight given to each province in constructing the synthetic control is based on pre-treatment outcomes. We use the pre-treatment average of the outcome dimension, Y, the unemployment rate, employment rate and the import and export per capita of the province to determine the degree of similarity between control group provinces and the treated provinces, which in turn determines the weight assigned to control provinces. The unemployment and employment rates are included to control for the general economic performance while trade values are added to control for the degree of ’ openness ’ of the province. 6 The treated unit i = 1 is constructed by taking the mean of the outcome variables in the provinces hosting refugees in 2012 or 2013. 5 Data We use several data sources for the analysis. The IV estimations use data from years 2011 and 2014 while the DD estimations use data from 2009 to 2014. The numbers of refugees up to 2012 are treated as 0. The refugee data for 2012 and 2013 are obtained from UNHCR ’ s oﬃcial weekly statements in December. Data on the number of refugees in 2014 is from Erdo ˘ gan (2014), who uses statements released by the Ministry of the Interior to compile his data. All refugee data we use in the analysis is provided at the level of 81 provinces. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["refugee data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 4. 1 Variation across Districts Pakistan is divided administratively into four provinces with 102 districts — Punjab, Balochistan, North-West Frontier Provinces (NWFP), and Sindh — plus the federal capital Islamabad, the Federally Administered Tribal Areas (FATA), the federally administered Northern Areas and Azad Jammu and Kashmir (AJK). The four provinces or Punjab, Balochistan, Sindh and NWFP, together with Islamabad, account for more than 97 percent of the population. Geographically, parts of Balochistan, the NWFP and FATA border Afghanistan. Sindh and Balochistan are sparsely populated provinces, with the exception of Karachi in Sindh, which is the single biggest metropolis in the country with a population approaching 10 million. We use data from the population census, 1998, as well as the census of private schooling, 2000, to provide estimates of madrassa, private, and government school enrollment in each district except for those in the province of FATA. The geographical dispersion of madrassa enrollment depends on how we define madrassa prevalence. There are three alternatives. We could present a geographical breakdown of the total number of children enrolled in madrassas. This number is related to the total population of the district, and may thus reflect only the size of the district relative to others. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["population census", "census of private schooling, 2000"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "stipend money, and they were formed into small groups or\"EPAG teams\", each with a coach or mentor, to foster support networks and boost attendance. 3. Methodology 3. 1. Research design The impact evaluation of the EPAG project uses a randomized controlled trial, in which eligible applicants to the program were randomly assigned to participate in one of two cohorts (or “ rounds ”) of training. The treatment group is defined as those who were offered a space in the first round of training and the control group comprises those assigned to the second round. Selection into the training rounds was performed on a computer (using Excel) and was stratified by the track choice of the applicant (job skills versus business development skills), community, and service provider. Data were collected using three quantitative household surveys (baseline, midline, and endline) and two sets of qualitative focus group discussions (one after each round of training). A timeline of the impact evaluation is depicted in Figure 1. During both the baseline and midline surveys, the head of the household in which the EPAG participant was residing was also interviewed, in order to examine potential spillover effects of the program on non-treated household members.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["quantitative household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Research on long-term migration has established positive effects on host countries and shown that these effects are larger or faster if migrants are permitted to integrate eco- nomically (Abramitzky and Boustan 2022). The features of PEP facilitate causal identiﬁcation of its effects. First, the program was in- troduced unexpectedly, thereby isolating anticipatory decisions or ex-ante behavioral re- sponses. Unknown to both migrants and government ofﬁcials, ex-post eligibility for the program was based solely on prior registration in a nationwide census of irregular forced mi- grants, the Registro Administrativo de Migrantes Venezolanos (RAMV for its Spanish acronym), that was administered between April and June of 2018. According to the government of- ﬁcials who designed RAMV, the census was implemented to count the number of irregu- lar Venezuelan forced migrants in Colombia and was not intended to precede or lead to a regularization program. However, in August 2018, Colombia ’ s president unexpectedly 3 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": ". 12 10. 45 – # 20 December 2019 5752 4. 71 8. 14 – # 21 November 2020 2627 4. 19 5. 14 – # 22 February-March 2021 5847 6. 86 9. 30 – Overall July 2017-March 2021 49831 4. 64 8. 19 30. 58 Table 2: Details on Surveys and Displacement Trends Second, the paper conducts an individual-level analysis of two cross-sectional surveys of 1, 933 and 5, 951 individuals conducted in March-April 2018 and June- July 2018, respectively, to probe the relationship between hosting displacees and social cohesion in more detail. This survey wave 22", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["cross-sectional surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Climate data on temperature and precipitation were taken from Bitan and Rubin (2000). Average annual temperature calculations are based on data collected in 38\n\nTable 3 presents the results of the two models. In the first model, linking farm profits to\n\n\nfarm exogenous variables, the irrigation water quota was omitted. The second model in\n\nand 2 percent for flowers and other garden plants (Israeli Central Bureau of Statistics, 2005). Almost all the crops excluding field crops are irrigated. Field crops are grown on\n\n_Source: World Bank calculations based on Ministry of Energy and Mineral Resources 2007 data._\n\n(2007), reports that from a survey of 51 countries, nearly half had an ad hoc pricing mechanism where the government adjusted the price level irregularly, 14 percent an automatic adjusting mechanism that holds the margin between world and local prices constant while allowing domestic prices to adjust, and 37 percent enjoyed a liberalized pricing system.", "output": {"entities": {"named_data": [], "descriptive_data": ["Ministry of Energy and Mineral Resources 2007 data", "survey of 51 countries"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The relation- ships between displacement dynamics and perceptions of each manifestation of social cohesion are analyzed separately by running 12 models for each independent variable (6 regressions for each sample). The regressions are correlations and should not be interpreted causally. Hosting status and displacement ﬂows are likely related to perceptions of social cohesion in indirect ways and the structure of the survey data limit the ability to specify the channels through which these relation- ships run. Each regression controls for characteristics that may inﬂuence respondents ’ perceptions of social cohesion outside of the presence of IDPs or refugees in the local community such as province, gender, age, marital status, level of education, employment, and exposure to violence. 11Poll numbers correspond to the number wave in our larger project, as described and shown in Table 2. 28 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "and improve their lives, along with refugee hosting communities. The SESRE covers all current major refugee camps: Eritreans, South Sudanese, and Somalis, as well as the out-of-camp refugees in Addis Ababa. In addition, the survey covers the respective host communities around the camps, including the host communities of Addis Ababa. Due to the conflict in the Tigray region of Ethiopia between 2020 and 2022, Eritrean refugees living in camps in Tigray could not be included in this survey. To avoid exclusion of Eritrean refugees in Ethiopia, we included Eritrean refugees living in camps in the Afar region and the newly established refugee hosting zone Alemwach. Eritrean refugees who were in the Tigray region prior to the conflict are included in this survey in two ways: we sampled (i) refugees from Alemwach, where most of the refugees previously located in Tigray moved after conflict broke out and (ii) from Addis Ababa, namely those refugees who arrived in Addis Ababa after November 2020. Data collection took place between November 2022 and January 2023. Sample population The SESRE covers three types of groups, all of which require a distinct sampling procedure:55 (i) refugees in camps; (ii) refugees out-of-camps; and (iii) host communities. This", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Notes: (1) The table displays the estimation of the probability of being enrolled in college, employed, or employed in a formal firm given ENLACE grade 12\nscores and a set of socioconomic variables. (2) Sample: ENILEMS-ENLACE panel. (3) All specifications include school fixed effects\n\n45\n\n\n\n<!-- PAGE 48 -->\nTable A.6: Probit         - ENLACE test scores and post-secondary school outcomes\n\nEnlace Score 0.301*** -0.0474 -0.0474 0.114*** -0.0152 -0.0175 (0.0499) (0.0870) (0.0811) (0.0186) (0.0280) (0.0298) Upper secondary GPA 0.223*** 0.128* 0.0667 0.0846*** 0.0409* 0.0246 (0.0427) (0.0671) (0.0661) (0.0162) (0.0216) (0.0245) Girl -0.194** -0.661*** -0.0615 -0.0736** -0.212*** -0.0227 (0.0792) (0.132) (0.127) (0.0291) (0.0339) (0.0473) Private upper secondary 0.275*** -0.277 -0.271 0.104*** -0.0886 -0.100 (0.102) (0.186) (0.173) (0.0392) (0.0585) (0.0646) Urban resident 0.244*** -0.0709 0", "output": {"entities": {"named_data": ["ENLACE panel", "ENILEMS-ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Maps provided by the humanitarian coordination body delineated displacement hotspots, areas of refugee concentration, and border crossing points used in the preparation of the targeting methodology. These maps were produced using UNHCR's population of concern data combined with host government administrative records. The maps were last updated in October and reflect registration data through September 30. Any changes in population distribution identified through subsequent biannual registration updates will trigger a review of the targeting lists by the PIU and the relevant district authorities.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Thirty-eight percent of the sample was already engaged in at least one income-generating activity (IGA) at baseline. This is consistent with the national figures from the 2007 DHS survey, which found labor force participation rates of 34 % for women aged 15-19 and 49 % for women ages 20-24. It is also consistent with the Liberian 2010 Labor Force Survey, which found labor force participation rates of 25 % for women aged 15-19 and 47 % for women aged 20-24. For the purposes of this study, to be consistent with program objectives and the Liberian context, our definition of income-generating activity encompasses the full range of activities through which people earn money, including paid employment, either formal or informal, and self-employment in small business or through petty trade. The most common types of IGAs reported at baseline were petty trade, including 15 The balance tests are run on the same sample as will be used in the impact analysis in Section 4, that is, the subset of individuals for whom we have a panel. Balance tests run on the full sample of baseline survey respondents, regardless of whether they also participated in the midline survey, confirm the same findings. A report summarizing the balance tests on the full sample, including comparisons to nationally representative data, is available upon request from the authors. 9 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["2007 DHS survey", "Liberian 2010 Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Geographic Information Systems (GIS) and geospatial analysis can be used to map, monitor and analyze data on forced displacement. Triangulation of this information with socio-economic and other indicators can provide a rich source of data and enable insights into underlying patterns and trends over time. (e) Use of big data (mobile phone data, news scraping, social media). IDMC is pursuing big data approaches to capturing displacement data in real time in order to report on displacement situations as they are happening and to provide updates on how they are evolving (IDMC 2015). These data are not necessarily representative but can be used in conjunction with other methods to triangulate trends. For example, the Swedish NGO, Flowminder, has pioneered the use of de-identified data from mobile operators to track population displacement caused by natural disasters such as earthquakes in Haiti in 2010 and Nepal in 2015, and these techniques may also have applications in conflict-induced displacement crises. 100 (f) High-resolution satellite imagery and unmanned drones. High resolutions satellite imagery can be used to map physical structures in refugee and IDP camps including changes to the number and type of these over time, support the remote detection of displaced populations in hard to reach or insecure settings; and conduct rapid assessments during or immediately after a mass displacement (Harvard Humanitarian Initiative 2014).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["de-identified data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "daily changes in the amount of time spent at different places within a given geographic area. These data\n\n\ncapture daily percent changes from February 15 through July 15 relative to a baseline representing the\n\nFigure 2 displays the Google Community Mobility data for both the entire country of Mali and\n\n\nfor Bamako specifically. Google aggregates locations into six types of places: Grocery and Pharmacy,\n\nFinally, we use information from the COVID-19 phone panel survey, our third source of information used to investigate the intensity of pandemic-related disruptions within Mali.\n\nas suggested by the Google Mobility Data, price effects could influence behavior on the intensive margin.\n\n\nMore research is needed to fully understand these dynamics.\n\n\nFigure 4 shows self-reported estimates of the impact of the coronavirus pandemic on economic out", "output": {"entities": {"named_data": ["Google Community Mobility data", "COVID-19 phone panel survey", "Google Mobility Data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Estimation Results Using Proposition 3 (i), we start first with transforming some positive variables including household size and age in the HEIS and LFS to normality using the Box-Cox method, then standardizing the variables in the LFS according to the distributions of the corresponding variables in the HEIS respectively for 2008 and 2010. As a result, t-tests (not shown) indicate that the distributions of the standardized LFS variables are not statistically different from those in the HEIS, which satisfies Assumption 1. To ensure that Assumption 2 is satisfied, we use the closest version of Model 6 in all the following estimation, where the income variable is in a categorical format as earlier discussed. 26 We implement this test by pooling data from the two surveys, setting the data to incorporate the complex sampling design, and running a (complex survey adjusted) regression of the variable of interest on a dummy variable indicating the survey round. 31 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The other source of micro data for Jordan during the pandemic is the COVID-19 MENA Monitor (CMM) data (OAMDI, 2021). This data was collected over three waves in February, June and August 2021 through a phone survey. It also has retrospective data on individuals ’ employment 6 Defence order number 6 stipulated, early April, that institutions subject to the labor law must allow their employees who were dismissed or whose services were terminated since the beginning of the pandemic to return to their work < https: / / www. jordantimes. com / news / local / pm-issues-defence-order-no-6-stipulating-labour-rights- under-defence-law >. 7 Additional data was collected between February and March 2021 from refugee camps. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "consultation with UNHCR and RRS. We used the ESS EA maps to assess the settlement of communities around camps, ensuring a precise fit to the definition of a “host community”. The assessment highlighted that using the list of EAs obtained from the new cartographic frame57 meets the definition of host community In the SESRE, host community members are defined as those who live adjacent to a refugee camp but within a radius of 5km. We use the updated Ethiopian Statistics Service 2018 cartographic database of enumeration areas (EAs) to define them. An EA is a defined area where 100-150 households live in rural areas, while in urban areas, it is an area where 150- 200 households live. The first stage of sampling for the host community involved using simple random sampling to select EAs—the primary sampling unit— from the list of EAs that are adjacent but within a radius of 5km. Following EA selection, a fresh list of households was prepared at the beginning of this survey, which was used as a frame to choose sampled households from each sample EA. In Addis Ababa, a separate host domain was developed as refugees spatially concentrate in a few sub-cities and Woredas.", "output": {"entities": {"named_data": ["ESS EA maps"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In households with more than two children, one child from each age group was chosen through a random selection process to participate, ensuring a broad representation of experiences within the study ’ s scope. Sampling frame. We use the 2018 population census data as a sampling frame for the Colombian sample. It allowed the identification of residential blocks and households with children in the desired age range. With this source of information, it was possible to identify the number of households and residential blocks with children and adoles- 7Although Colombia only grants nationality to children of Colombian nationals, it follows a jus sanguini principle, the Colombian government has introduced reforms, such as the the program Primero la Ni ˜ nez to give Colombian nationality to children of Venezuelan parents born at times when diplomatic relations between Colombia and Venezuela were cut and hence, it was not possible to apply for a Venezuelan nation- ality for this minors in Colombia. 15 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["2018 population census data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Regrettably, until now almost all enrollment numbers cited have been based on establishment surveys which do just that. These data sources show that around 200, 000 children were enrolled full-time in madrassas before 2001. Since 2001, our school census suggests that these numbers may have increased somewhat, although the experience varies across districts. To put this number in context, total primary enrollment (grades 1-5) in public and private schools stood at 17. 4 million in 2003 (Government of Pakistan, Ministry of Finance, 2003). The choice of madrassa schooling viewed as either the percentage of eligible children or the percentage of enrolled children, is statistically insignificant for the average Pakistani household. Enrollment in madrassas accounts for approximately 0. 3 percent of all children between the ages of 5 and 19. Given that the overall enrollment rate for this age group is roughly 42 percent, this represents less than 0. 7 percent of all enrolled children, an order of magnitude less than the 33 percent cited by the International Crisis Group report (2002). 3 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 to provide statistics for private versus public enrollment. 4The PIHS is the equivalent of the widely used Living Standard Measurement Surveys (LSMS) implemented in various countries. See http: / / www. worldbank. org / lsms for extensive notes on the 1991 PIHS. See also www. statpak. gov. pk for information on the census and the Federal Bureau of Statistics data.", "output": {"entities": {"named_data": ["PIHS", "Living Standard Measurement Surveys"], "descriptive_data": ["census of private schools"], "vague_data": ["school census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15 causes of death. The Danish Epidemiology Science Centre (1999) found severe malnutrition and high mortality in a survey of 422 refugee children in Guinea ‐ Bissau. They report higher malnutrition and higher mortality for children living in a non ‐ camp setting, compared to children living in a camp. The Goma epidemiology group (1995) found high prevalence of child mortality as well as acute malnutrition among children in refugee camps in Eastern Zaire, especially in female headed households. The magnitude of the difference between ‘ normal ’ mortality in the country under study, in the absence of conflict and the mortality in a refugee camp, depends on several parameters: the health infrastructure in the country as well as in the camp, the food available to camp and non ‐ camp residents, the frequency of visits by nurses or doctors, the intensity of the conflict (e. g. attacks on camps), and so on. Thus, the results are highly dependent on the context. For example, Singh et al (2005) do not find a difference in under 5 mortality among refugee versus non ‐ refugee households in western Uganda and South Sudan, whereas Verwimp and Van Bavel (2005) find higher child mortality and fertility among Rwanda refugees in Congo versus Rwandan women who did not became a refugee.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of 422 refugee children in Guinea ‐ Bissau"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Our research complements and extends the findings of Demirci, Foster and Kirdar (2022), who investigated health and nutrition disparities between native children and Syrian refugee children aged 0 to 5 years in T ¨ urkiye, using data from the Demographic and Health Survey. The authors document find no significant differences in infant or child mortality rates between refugee children born in T ¨ urkiye and their native counterparts, it did reveal that refugee infants have lower birth weights and age-adjusted weights and heights compared to native infants. Our work broadens the scope of analysis beyond anthropometric indicators to encompass a holistic assessment of child development. By incorporating measures of physical, cognitive, socio-emotional, and mental health devel- opment, along with factors such as food security, time use, risky behaviors, and social integration, we offer a more comprehensive understanding of the developmental chal- lenges faced by displaced minors. Additionally, our study includes a wider age range, 5Chiovelli et al. (2021) examine the effects of forced displacement on separated sibling in the long-term. 8 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The ZAR dataset includes all foreigners residing in Switzerland and contains similar information than AUPER. We are combining these two datasets with the yearly population census data that includes ZAR and AUPER starting from 2010. By combining these three datasets we can follow refugees and migrants over 1997-2018 and natives over 2010-2018. We define as refugees, all foreign-born individuals that went through an asylum process, and as migrants all other foreign-born individuals. Natives are defined as individuals born in Switzerland. In the final sample, we are drawing a random sample of 6 % of the native population. For the main outcome variables used in the descriptive analysis, we are first adding Swiss social security data provided by the Federal Compensation Office. This data collects information about every Swiss resident that contributed to old age provision (i. e., the old-age and survivor ’ s insurance OASI or AVS in French). We know the size and nature of the contribution made by individuals (from paid work, independent work, voluntary contribution or other kinds). This data is available from 1998-2018. Our main outcome variables measuring economic integration include employment, earnings and self- employment, and are constructed from the social security data. An individual is defined as employed if he or she contributed to old age provision from salaried or independent work. Earnings are defined as the sum of all positive contributions made from salaried and independent work in a year. Lastly, we 10 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The severe escalation of the conflict has led to significant destruction of UNHCR property.\nDuring the month of June, both UNHCR offices in Khartoum were destroyed, the UNHCR\nwarehouse in Al Obeid was looted, and other UNHCR warehouses remain inaccessible or\nhave recently been looted such as in South Darfur. In locations accessible to humanitarian\nworkers, UNHCR and partners continue to scale up humanitarian delivery despite capacity\nlimitations. UNHCR has established a small operational presence in Wadi Halfa while\nscaling up its footprint in Wad Madani, Kosti, Gedaref, Kassala and Port Sudan. Further in\nNorth Darfur, in collaboration with the sectors, UNHCR has been able to continue delivering\nNFIs to IDP sites in El Fasher as well as conduct protection monitoring of new arrivals from\nKutum and Tawilla.\n\nUNHCR 3", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "29 should be entered immediately into the accounts; and (c) Payment must be recorded as soon as they are made. Budget implementation should be reviewed periodically to ensure that programs are implemented effectively and to identify any financial or policy derailment. The review of budget execution should cover financial, physical and other performance indicators. Development budgets are often beset by implementation problems because of insufficient implementation capacities and other factors such as delays in mobilizing external financing, overoptimistic implementation schedules or difficulties in importing supplies. It is thus important to have in place mechanisms for reviewing the most significant or problematic projects. These could consist of a regular monthly or quarterly review of projects within the line ministries and a midyear review involving line ministries and central agencies29. The government has taken steps to improve the tracking of budget expenditure until the intended destination, particularly investments spending, for which a tracking survey was entrusted in 2005 with the Ministry of infrastructures. In addition, the ministries took themselves certain internal initiatives, in particular in health and education sectors, but the action plans of these ministries were not updated as envisaged in 2005, and there are neither reliable benchmark, nor quantitative targets as regards improvement of the arrival of the expenditure at intended destination in these sectors. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["tracking survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "- The XCO2 database (https://datacatalog.worldbank.org/search/dataset/0062760), an OCO2 panel database for a 25 km grid (G25), in Stata format, beginning in September 2014 and\nupdated regularly. The database includes G25 grid cell ID numbers, cell centroid coordinates,\nmonthly means of measured CO2 concentrations, and monthly means of Hakkarainen prefiltered CO2 concentration anomalies. The JPL/NASA database publishes OCO-2 data with\na lag of approximately two months.\n\n\n  - For functional urban areas (FUAs) with sufficient data, annually-updated change parameter\nestimates for models (6) and (7), with statistical significance categories [p>.05, ≤ .05, ≤ .01,\n≤ .001].\n\nUsing a rich dataset consisting of geocoded household data combined with detailed information on gold mining activities, the authors conduct two types of difference-in-differences estimations that provide complementary evidence.\n\nWe use two complementary geocoded household data sets to analyze outcomes in Ghana: the Demographic and Health Survey (DHS) and the Ghana Living Standard Survey (GLSS), which provide information on a wide range of welfare outcomes.", "output": {"entities": {"named_data": ["XCO2 database", "OCO-2", "Demographic and Health Survey (DHS)", "Ghana Living Standard Survey (GLSS)"], "descriptive_data": ["geocoded household data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Border crossings The registration of people crossing internationals borders is conducted in many countries, and in some cases these data are used to estimate migration flows. Identifying refugees among people crossing borders is a significant challenge, particularly if individuals decide not to apply for asylum or refugee status (UNSD 2014). Additional problems associated with the collection of data on border crossings include: (a) difficulties distinguishing migrants from other people crossing a border, such as tourists, commuters, traders and truck drivers; (b) lack of capacity of many border posts and officials to handle large migration flows; (c) less scrutiny and diligence of emigration flows compared with immigration flow; and (d) lack of tight controls at most borders and the high incidence of undocumented or irregular crossings (UNSD 2014). Administrative records and registers Many countries have administrative records or registers of immigrants that could generate statistics on asylum-seekers and refugees. In particular, data on residence permits issued to refugees or asylum- seekers could be used to generate statistics on both flows and stocks of refugees. 79 For example, Eurostat collects and disseminates data on residence permits granted to those with refugee status and subsidiary protection (UNSD 2014).", "output": {"entities": {"named_data": [], "descriptive_data": ["data on residence permits"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "select EAs and 12 households per EA, whereby the EAs were considered a Primary Sampling Unit and the households as the Secondary Sampling Unit. The SESRE is designed to estimate demographic, socioeconomic, welfare, and refugee- specific indicators of the eight domains. 57 The cartographic map (frame) was prepared in 2018 for the upcoming Population and Housing Census. 58 See Annex C for sample size estimation. Annexes 94 Sample size estimation (a) First Stage Sampling In the first stage sampling, each domain is considered an explicit sampling domain. We used the list of all EAs as a sampling frame and their estimated population as a Measure of Size (MoS). A sample is selected with Probability Proportional to Size (PPS). The sample size is evaluated regarding the expected precision of the key indicator for the SESRE, the national household survey to measure poverty as the percentage is 0.235 (2016 Household Consumption and Expenditure). In the calculation, values for the measuring poverty rate (P) and design factor (deft) 1.5, the expected Relative Standard Error (RSE) of 4.63%, and finally, an adjusted Response Rate of 99% at a 95% Confidence level used to represent the expected precision is acceptable at the domain level. To", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national household survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Primary well-being outcomes: This category includes three main outcomes that seek to cap- ture refugee household well-being, including household consumption, mental health (mea- sured through the Center for Epidemiological Studies Depression Scale, CES-D), and an index summarizing the 25-item child Strengths and Difﬁculties Questionnaire (SDQ) that measures emotional and conduct problems, inattention, peer relations, and prosocial behav- iors. Both the CES-D and SDQ are validated tools that have been utilized in a variety of international contexts (Park and Yu 2021, Woerner et al. 2004). In each of the three survey rounds, the primary respondent completed the CES-D depression screening. The SDQ was completed by an adult respondent regarding a randomly selected child aged 3 to 8 years old in the endline survey, as well as for the same child in the one and a half year follow-up. Total household consumption was measured only at endline (which was collected in person). 12 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Colombian and Venezuelan migrant children and adolescents. VII Discussion In this study, we analyze novel and unique data on forcibly displaced migrants and hosts, focusing on children and adolescents, to highlight the disparities in human development between them. The structure our analysis in two parts. In the first part, we characterize the main trends in the data. We show that forcibly displaced households have a wealth distribution skewed towards lower values relative to Colombian households. This is likely explained by the assets ownership loss that forcibly displaced households expe- rienced after the migration episode. We also identify meaningful lags in human capital accumulation between Colombian and Venezuelan children and adolescents of approxi- mately 1 year. We further note that the Colombian government ’ s supportive policies for Venezuelan forced migrants are evident through high levels of service access and pro- gram participation for migrants. Nevertheless, it remains surprising that participation is not higher, suggesting significant potential for improvement in increasing sisb ´ en and health insurance enrollments. In a second part of our analysis, we document sizeable lags in physical and cognitive de- velopment of Venezuelan children and adolescents, relative to their Colombian counter- parts. However, we were not able to identify any gaps in the socioemotional and mental health between the two groups. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We complete this data with exact geographic location data from MineAtlas (2013), where satellite imagery shows the actual mine boundaries, which allows us to identify and update the center point of each mine.\n\n1 In the 2010 Ghana population census average district size is 112,000\n\nWe combine the respondents from all four DHS standard surveys in Ghana for which there are geographic identifiers. The total data set includes 19,705 women (of which 12,392 live within 100 km of a mine) aged 15-49 from 137 districts. They were surveyed in 1993, 1998, 2003, and 2008,\n\nWe complement the analysis with household data from the GLSS collected in the years-1998-\n\n\n99, 2004-05, and 2012-13. These data are a good complement to the DHS data, because they", "output": {"entities": {"named_data": ["MineAtlas (2013)", "2010 Ghana population census", "DHS standard surveys"], "descriptive_data": ["household data from the GLSS"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "percentage of government debt ...nanced by means of concessional resources, e.g. from\n\nmultilateral and bilateral donors. We assume that the higher the concessional share\n\nof government debt the lower the incentive for the government to adopt a strategy\n\nThe  standard deviation of exchange rate is computed using the exchange rate series from   18     <!-- PAGE 24 -->  the Penn World Tables (Heston et al, 2006).\n\nstandard deviation, creates a genuine dilemma for country authorities of whether to\n\n\n21We have carried out the estimation using LOGIT and LP models as well to check on any\nmispeci...cation problems. We did not detect any. The estimation results are available from the\nauthor.\n\n[7] Heston, A., Summers, R., Aten, B. (2006). Penn World Table Version 6.2. Center\n\nSince price stability is the basic objective and goal of monetary policy we ...nd the\n\nstandard deviation of in‡ation indicative of the quality of internal macroeconomic", "output": {"entities": {"named_data": ["Penn World Tables"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The MIS platform was procured through a competitive process and is hosted on servers maintained by the Ministry of Finance's Information Technology Department. The MIS uses role-based access control, with read-only access granted to Bank task team members to facilitate real-time supervision. Data backup procedures run automatically every four hours, and full system backups are stored off-site in compliance with government data security regulations. User access logs are retained for three years and are available for review during audit exercises.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "REFodet = αod + γe + τt + β1Conflictot − 1 + β2Conflictet − 1 + β3Distanceed + ϵodet, (5) where REFodet is the stock of refugees of ethnic group e from country o in country d at year t. As we have data on yearly refugee stocks and would like to estimate the changes in these stocks over time using a gravity model, we include origin – destination fixed effects αod so that identification is based only on changes in stock over time (Zylkin, 2019). 20 We also include time τt and ethnic group fixed effects γe. Here we obtain data on the ethnicity of refugees from Murdock ’ s Atlas, which provides a map of ethnographic regions for Africa and the historical homelands of refugees (Murdock, 1967). To match ethnic groups across datasets, we again use LEDA21 to link data on ethnicity from Murdock ’ s Atlas with data on ethnicity from the EPR-ER dataset and, later, with data from Afrobarometer. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Afrobarometer", "EPR-ER dataset", "Murdock ’ s Atlas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "20 of intense conflict. We examine also the overall impact of the 25 years of conflict on educational outcomes. This analysis allows us to consider the full long-term impact of the conflict on educational outcomes of different generations of children in Timor Leste. 4. 2. 1. The educational impact of the 1999 wave of violence We exploit variation in the number of killings over time and across districts to identify conflict affected individuals. Our intention here is to analyze whether individuals exposed to the violence during their primary school age show different primary school completion rates eight years after the end of the war, relative to those not affected by the conflict. The outcome variable in which we are interested is whether individuals completed primary school in 2007. Figure 7 shows average primary school attainment for all individuals in our sample. The graph shows an increasing trend in primary school completion across cohorts and a progressive reduction of the gender gap. The gap among the younger cohort (those born after 1987) is almost zero. The drop in the curve for the younger cohort confirms the presence of significant delays in school attendance. For the purpose of this analysis, we use the TLSS 2007 dataset and the HRVD dataset contained in the CAVR data publication. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["TLSS 2007 dataset", "HRVD dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "proxied by observing student \"i\" sitting for ENLACE in Grades 9 and 12, respectively.\n\nWe also use the ENLACE panel to examine the relationship between Grade 6 test scores\n\nThe ENILEMS-ENLACE panel is used to examine the relationship between Grade 12\n\nLearning achievement is measured using an aggregated ENLACE test score, the simple\n\nderstand how much of ENLACE's predictive power is related to the skills it captures as", "output": {"entities": {"named_data": ["ENLACE panel"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Geospatial data from the national forest inventory, covering forest type classifications, canopy density estimates, and deforestation risk scores at 100-meter resolution, were reviewed during the environmental baseline assessment. The geospatial data confirmed that three proposed subproject sites fall within degraded secondary forest zones classified as eligible for restoration activities under the national REDD+ strategy. Restoration activities at these sites will be included as co-benefits in the project's greenhouse gas accounting methodology.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The ESMF consultation process documented 14 community meetings across the project area, attended by a total of 1,847 participants including women (52 percent), youth representatives, persons with disabilities, and representatives of indigenous communities. Meeting summaries, attendance lists, and photographic evidence are appended to the ESMF in Volume II. All written feedback received during the ESMF public comment period has been reviewed, and responses are documented in the ESMF Consultation Report finalized in November.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 3 also shows that there is considerable variation in both indexes within our sample when averaging these indexes at the regional level over the period of investigation. 16It is possible that our approximation is noisy and could potentially induce non-random measurement errors. In Section 4. 3, we propose an instrumental variable approach and estimate Shareodet from the EPR-ER data using a gravity model. Our findings concerning the number of ethnic groups across time for a given origin – destination pair are in line with the EPR-ER data. It seems that refugees of a given origin – destination pair mainly belong to two major ethnic groups. This also means that the variation in diversity in refugee-hosting areas is coming from the refugee composition at the camp level. Figure B. 5 shows the movements of refugees from origin to destination countries under scrutiny. Somalia, the Democratic Republic of Congo, Liberia, South Sudan, and Sudan are major source countries for refugees, while Kenya, Tanzania, Uganda, Zambia, and Ghana appear to be countries hosting most refugees. Representing refugees in camps per ethnic group for the top 5 asylum countries over the sample period, Figure B. 9 shows that there is considerable variation in ethnic composition across camps. 17", "output": {"entities": {"named_data": ["EPR-ER data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This multiresolution decomposition is performed using Maximum Overlap Discrete Wavelet Transformation (MODWT) on first difference of the annual series from Penn World Tables 7.0.\n\nNext, we consider actual data. We use annual series of income, consumption, and investment data from Penn World Tables 7.0, all in 2005 international dollars per capita terms. The income series are provided by the Purchasing Power Parity (PPP) converted GDP per capita (chain series) data, whereas consumption and investment series are computed by using the share of actual consumption and investment in the PPP converted GDP series at 2005 prices.\n\nFigure 9: Wavelet cross-correlation between the monthly Industrial production index\nchanges of USA (a) TUR, (b) BRA, (c) IND, and (d) PAK.\n\n\nof industrial production series between Turkey and United States at 2 month frequency\n\nThe selection of the countries roughly reflects a cross-section of the geographic spread, as well as per capita income levels as defined by the World Bank classification using 2011 Gross National Incomes.\n\ninconsistency between longitudinal and cross-section data, a paradox that would eventually lead", "output": {"entities": {"named_data": ["Penn World Tables 7.0", "Industrial production index"], "descriptive_data": [], "vague_data": ["cross-section data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The vulnerability of the Internally Displaced People in SSA has certainly been overlooked for too long, but the increased support provided by UNHCR is an encouraging but challenging sign in that respect. Figure 3. Refugees and Internally Displaced People in SSA, 2003 ‐ 2013 Source: Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). The annual number of IDPs is collected from the IDMC (2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2012, 2013, 2014) annual reviews. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "natural log of a country ’ s population size and government consumption as a share of GDP; the latter is the most widespread measure of government size (Adsera and Boix, 2002; Alesina and Wacziarg, 1998; Rodrik, 1998). Tax compliance is also related to the government ’ s ability to eﬀectively detect and punish tax avoiders, tax evaders, and tax arrears. Although an imperfect measure of states ’ deterrent capacity, Afrobarometer includes two survey questions on perceptions of government enforcement and monitoring capacities. One question probes respondents on how often ordinary people who break the law go unpunished. The other probes respondents on how often oﬃcials who commit crimes go unpunished. This latter question is also a measure of perceived government fairness- the extent to which a government implements the law evenly across all social groups. 6. 2. 5 Procedural Justice I include two indicators of procedural justice. The ﬁrst probes respondents on how often people are treated unequally under the law. The next taps citizens ’ perceptions of the government ’ s treatment of their ethnic group. Speciﬁcally, respondents were asked how often their ethnic group is treated unfairly by their government. 9 6. 2. 6 Donor and Non-State Actor Provision of Services I include a measure of who citizens believe is responsible for collecting income taxes. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": ["Afrobarometer"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "##### **September/ October 2023 report**\n\n### **MONITORING THE PROTECTION RESPONSE**\n\n###### Venezuela\n\n### **PERFORMANCE AND FUNDING**\n\nThe PC continues to reinforce the impor\ntance of reporting, which allows it to\n\ntrack funding and conduct advocacy.\n\nThroughout the last years the number of\n\npartners that regularly report 5Ws (cur\nrenly 345ws), including the implementing\n\npartners of the lead agencies of the Clus\nter and AoRs, increased notoriously.\n\nThe activity monitoring dashboard co\nvering latest data collection in October\n\n2023 and other information products\n\n[can be accessed in the protection clus-](https://app.powerbi.com/view?r=eyJrIjoiYzg3NGFkM2EtYjZhMS00YWNkLWE1NjItMjg0OThlMTAzMDM1IiwidCI6ImU1YzM3OTgxLTY2NjQtNDEzNC04YTBjLTY1NDNkMmFmODBiZSIsImMiOjh9)\n\n[ter website, and currently 105 partners](https://app.powerbi.com/view?r=eyJrIjoiYzg3NGFkM2EtYjZhMS00YWNkLWE1NjItMjg0OThlMTAzMDM1IiwidCI6ImU1YzM3OTgxLTY2NjQtNDEzNC04YTBjLTY1NDNkMmFmODBiZSIsImMiOjh9)\n\nare supporting the 345W reports. Please\n\nnote the list of Specific Objectives in\n\nFigure 1 are aligned to the HRP 2022.\n\nThe percentage (%) in each chart (see\n\nfigure 1) represent the number of benefi\nciaries (individuals) reached against the\n\ntarget set in the HRP 2022 - 2023.\n\n**14**\n\n353, 037 million USD were received\n\nby the Venezuelan Republic so far in\n\n2023 through the HRP plan, accord\ning to the Financial Tracking Service\n\n(FTS). Requested funding for the Protec\ntion Cluster was 101.2 million USD and\n\n33,385,8929 million USD were granted.\n\nAccording to FTS, the total coverage has", "output": {"entities": {"named_data": [], "descriptive_data": ["activity monitoring dashboard"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "18 the local data on physical geography and population from the raster data. These data can then be imported into statistical programs for analysis. We aggregate all data up to a grid of 8. 6x8. 6km squares. Each grid square is assigned attributes of the country it is in along with information from data disaggregated to the level of the individual squares. Figure 3 illustrates this grid as a fictive country somewhat smaller than the average size in our dataset (50x50 squares, or 430x430 km) with a fairly representative but stylized population distribution. The country has three major cities, one of which is the capital, and two smaller ones. A rebel group has its headquarters at the Eastern border. The ACLED data for the Central African conflicts were aggregated up to the 8. 6x8. 6km squares and merged with information on other explanatory variables aggregated to the same level. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["ACLED data"], "descriptive_data": [], "vague_data": ["raster data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "It consists of seven questionnaires that collect information on demographics, work and income, quality-of-life perceptions, expenditures, and food consumption (at the household and individual level). For the estimation of the poverty line, we use data from the three expenditure questionnaires (POF 2-4), in addition to key demographic data collected in the general household questionnaire (POF 1). The expenditure questionnaires collect information on monetary consumption expenses as well as the value of nonmonetary consumption.\n\nThis information can be obtained from the TBCA. It has nutritional values per 100 grams, including calorie intake, for an extensive list of meals and food items typically consumed in Brazil.\n\n[7 The TBCA can be accessed at http://www.tbca.net.br/base-dados/composicao_alimentos.php.](http://www.tbca.net.br/base-dados/composicao_alimentos.php)\n\n_Estimating the cost per calorie._ To estimate the cost per calorie, we use information on food purchases (expenditures and quantity consumed) by food item from POF. POF collects information on food purchases for consumption at home as well as food consumed away from home (FAFH). Food purchases for consumption at home are collected in the household expenditure diary, which collects expenditures on frequent purchases over a reference period of one week (POF 3). It collects expenditures and quantities purchased by food item, containing 4,549 different food items.\n\n8 We assessed the possibility of addressing this shortcoming by using data from another questionnaire (POF 7) that\nregisters quantities of items consumed, location, time, and its calorie intake. However, it was not possible to match\nthe data from POF 7 (quantities) and POF 4 (expenditures) in a reliable way. First, item specifications differ across\nthe two questionnaires. Food items in POF 4 are mostly vague. The item with highest expenditure is \"takeaway meal\n(lunch/dinner).\" In contrast, POF 7 has items as detailed as white rice, beans, eggs, potatoes, and so on consumed\nduring lunch or dinnertime.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Ideally, we would conduct a comparative analysis of the characteristics of migrants residing in Medellin versus those in other parts of the coun- try to discern the extent of these differences. However, the lack of comprehensive data regarding the living conditions of this population makes such analysis unfeasible. To explore how this population compares with other migrant groups in the country, we turn to the only two available data sources on migrants. First, we use the Venezuelan Refugees Panel Survey (VenRePS), conducted by Ib ´ a ˜ nez et al. (2022), which captures a 19", "output": {"entities": {"named_data": ["Venezuelan Refugees Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The objective of the survey was to analyze perceptions and priorities with regard to the Peace Accord, to analyze perceptions of security, to determine access to basic infrastructure and school attendance, to understand nutrition levels, and to measure household asset ownership. In addition to this baseline survey, the World Bank was sponsoring a mobile phone survey among IDPs in Bamako, returnees in Gao, Kidal and Timbuktu town and refugees in camps in Niger and Mauritania. This survey interviewed 500 respondents on a monthly basis. In the August 2015 round of this survey, questions about perceptions and priorities with regard to the Peace Accord were included. This paper also makes use of a subset of the responses obtained from that survey, particularly those from refugees in Niger (n = 80) and Mauritania (n = 100) as these sub-populations who live outside Mali ’ s borders are important stakeholders in the peace process whose opinions risk being ignored. 15 To select a household in a village or neighborhood for the baseline survey, random selection was used: the enumerator divided the locality into two parts and selected five 15 For a more elaborate description of this mobile phone survey, see: Etang Ndip, A., J. Hoogeveen and J. Lendorfer (2016). Socioeconomic Impact of the Crisis in Mali on Displaced People. Journal of Refugee Studies.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["mobile phone survey", "baseline survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Gender Perceptions- Violence (IPV) The standardized total score of ﬁve questions regarding norms for inti- mate partner violence (IPV) from the Demographic and Health Survey (DHS) (The important decisions in the family should be made only by the men of the family. How often would you agree? The wife has the right to express her opinion even when she disagrees with what her husband is saying. How often would you agree? A wife should tolerate being beaten by her husband in order to keep the family together. How often would you agree? A husband has the right to beat his wife. How often would you agree? It is more important to send a son to school than it is to send a daughter. How often would you agree?). Financial Well-being Savings Response to the question “ How much money do you currently have in savings? ” During the collection surveys (midlines) this question instead asked “ How much money did you save in the past week? ” Borrowing Total amount of money the household has borrowed. Economic Decision Making Risk Preference Measured using incentivized responses to the multiple price list deci- sions adapted from Holt-Laury and Sprenger (2002). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Demographic and Health Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Formula (5) can be decomposed into two parts: eδs, which measures the average income level in district s, and eηszh ≡ eβs (Eh s − Es) + eχs (Hh s − Hs) which captures individual-speciﬁc variation in income. Migration models predict that, other things being equal, the choice of migration destination should depend on g E [yhs | zh]. This means that if we regress the choice of destination separately on eδs and eηszh, they should have the same coeﬃcient. The same methodology is used to construct other variables that may aﬀect the choice of 9The literature has often emphasized that migrations often serve an important role in household formation. For migrants, the prospect of forming a large, successful household is likely to be one of the purposes of migration. 10The 1995 / 96 NLSS survey adopted the following sampling strategy. Within each district a small number of wards were selected at random. Within each ward, 12 randomly selected households were interviewed. Because the wards diﬀer widely in terms of population, applying sampling weights is essential in order to obtain consistent estimates of δs. 16 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 3 also shows that there is considerable variation in both indexes within our sample when averaging these indexes at the regional level over the period of investigation. 16It is possible that our approximation is noisy and could potentially induce non-random measurement errors. In Section 4. 3, we propose an instrumental variable approach and estimate Shareodet from the EPR-ER data using a gravity model. Our findings concerning the number of ethnic groups across time for a given origin – destination pair are in line with the EPR-ER data. It seems that refugees of a given origin – destination pair mainly belong to two major ethnic groups. This also means that the variation in diversity in refugee-hosting areas is coming from the refugee composition at the camp level. Figure B. 5 shows the movements of refugees from origin to destination countries under scrutiny. Somalia, the Democratic Republic of Congo, Liberia, South Sudan, and Sudan are major source countries for refugees, while Kenya, Tanzania, Uganda, Zambia, and Ghana appear to be countries hosting most refugees. Representing refugees in camps per ethnic group for the top 5 asylum countries over the sample period, Figure B. 9 shows that there is considerable variation in ethnic composition across camps. 17 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Survey data\nare available for the period 1995-2013 and contain information on a set of firm\n\n\n5On the other hand, when comparing the distribution of firms across sectors in Orbis and in\nthe survey of firms provided by the Central Statistical Office of Poland (Figure A.3), the two\ndistributions are broadly aligned suggesting that coverage is likely to be similar across sectors.\n6In our empirical analysis of firm performance, we exclude the year 1999 due to anomalies in\nthe data.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of firms", "survey of firms provided by the Central Statistical Office of Poland"], "vague_data": ["Survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Refugees Hosts Refugees Eritrean Somali South Sudanese In Camp Addis Ababa Total Average household expenditure on education (children in school) Average household expenditure on education per child (school age (4 to 18 years)) - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18.000 Figure D.6: Average annual household education expenditure (in ETB) Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Boys Girls Boys Girls 8 to 14 years 15 to 18 years Need to work Unable to cover education expenses (fee and materials) School too far Too young Marriage or pregnancy Family not willing Sickness/injury or natural or human calamites Negative perception towards the benefit of education Other Percent Figure D.5: Reasons for not currently attending school by gender Source: World Bank Staff based on SESRE 2023. Annexes 102 0 5 10 15 20 25 30 35 40 45 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Refugees Hosts In camp Addis Ababa Total Sanitation problem Long waiting time Shortage of health professionals", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 7: Environmental Baseline — Flood Risk Profile**\n\nThe project area encompasses portions of three river catchments classified as high-risk under the national flood hazard maps maintained by the Directorate of Water Resources. Flood hazard maps were last updated in 2019 using a hydrological model calibrated against gauge station records from 1980 to 2018. The maps delineate inundation extents for 10-, 25-, 50-, and 100-year return period events at 30-meter spatial resolution. All proposed community infrastructure sites were checked against the 50-year inundation boundary, and six sites were relocated to avoid high-hazard zones.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 6: Procurement Plan — Year 1**\n\nProcurement of goods, works, and non-consulting services will follow the World Bank Procurement Regulations for IPF Borrowers (2016, revised 2020). The Procurement Strategy for Development (PDSD) was agreed during preparation and disclosed on the Bank's website. All contracts above the prior review threshold will be submitted to the Bank's procurement team for no-objection before award. The PIU has been granted access to the STEP system, through which procurement plans and contract awards are recorded and reported. The SIGAF module for procurement will be configured to interface with STEP to avoid duplication of reporting.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Data for these measures of German influence are obtained from Eurostat, OECD and UNIDO. Finally, we use data on exports from COMTRADE to construct a measure of Germany's revealed comparative advantage (RCA) relative to Poland and to the world.\n\nResults in this section draw on the Orbis data set, and for this reason refer only to\nemployment as the outcome variable. Unfortunately, the variable turnover presents\nan excessive amount of missing values, which makes it unreliable.", "output": {"entities": {"named_data": ["Eurostat", "OECD", "COMTRADE", "Orbis data set"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**(1)**\n**Enrollment at a JFPR**\n**school**\n\nUsing local-level survey data collected for this purpose, we test whether the evidence supports the standard economic argument that there will be little or no impact on rural roads rehabilitated, given fungibility.\n\nindependent administrative data reports that an average of 4.6 kilometers per commune\n\nRoads in Vietnam\" (SIRRV) is a panel data set of pre-project baseline and post-project\n\n6 Least cost techniques refer to the minimum-cost engineering solution that ensures a minimum level of\nmotorized passability.\n7 For a more detailed description of the SIRRV see van de Walle (2007).", "output": {"entities": {"named_data": ["SIRRV"], "descriptive_data": [], "vague_data": ["local-level survey data", "independent administrative data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Annual surveys under the project are designed to be nationally representative and use the same sampling frame as the national statistics office's multi-purpose household survey, enabling comparison of project-area trends with national trends. The first annual survey field work was completed in March, covering 4,800 households across the six project provinces. Preliminary review of the annual survey data found data quality to be high, with item non-response rates below 2 percent on all key variables. Cleaned data will be available to the evaluation team by end of April.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "6 3. Data Sources and Methods 3. 1. Administrative data on educational outcomes in Italy Two administrative data sources represent the backbone of this paper. These are the administrative data obtained from the Ministry of Education (MoE) for academic years 2021-22, 2022-23 and 2023- 24, and standardized test score data from the Italian National Institute for the Evaluation of the Educational System (INVALSI) for the 2022-2023 academic year. These datasets offer valuable insights into the educational outcomes of students in Italy, including Ukrainian refugees who entered the Italian school system following the invasion in 2022. Deϐinitions. In both datasets, students are categorized into ϐive demographic groups based on their nationality and timing of entry into the Italian educational system. These groups are Italian students, Ukrainian refugee students, non-refugee Ukrainian students, newly arrived foreign students, and other foreign students. Among Ukrainian students, the distinction between refugees and non- refugees is based on their enrollment date in the Italian education system. Ukrainian refugees are deϐined as Ukrainian students who enrolled in Italian schools after February 2022. In this paper, Ukrainian refugees are labeled “ Ukr post-Feb 2022 “, while non-refugee Ukrainians are labeled “ Ukr pre-Feb 2022 “. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "destination. Building on a growing literature documenting the relationship between subjective welfare and relative income, Fafchamps and Shilpi (2008) show that Nepalese households care about their consumption level relative to that of others in the same location. If this is the case, it is conceivable that migrants choose their destination not so much for the absolute gain in income it may provide but for the gain in relative status that would ensue. For instance, if returns to education and ability are higher in an urban setting, an educated individual may improve his relative position in society by moving from a rural to an urban setting. To investigate this possibility, we estimate equation (4) using the log of relative income (or relative consumption) as dependent variable and construct a predicted relative income measure using the same formula (5). These are shown in the second panel of Table 1. Theories of work migration predict that individuals move to increase their utility or welfare. The 1995 / 96 NLSS asked respondents a number of questions regarding their subjective satisfac- tion level with various dimensions of consumption — namely, food, clothing, housing, health care, and child schooling. They were also asked their subjective satisfaction with their level of total income. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["NLSS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5. Time Use and Child Labor: The fifth module examined how children spend their leisure time, their involvement in child labor, and interactions with peers. 6. Pro-social Preferences and Migration Outlook: The sixth module concentrated on adolescents ’ pro-social behaviors, such as altruism and trust, and explored their expectations and intentions regarding migration. 7. Socio-emotional and Mental Health: The final module involved the administration of various scales to assess socio-emotional well-being and mental health, includ- ing trauma, behavioral problems, anxiety, and depression. The scales include the Trauma Symptom Checklist for Young Children (TSCYC), Strengths and Difficulties Questionnaire (SDQ), General Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). All these scales and the corresponding outcomes that we evaluated are described in the next subsection. The survey also employed the Peabody vocabulary test to evaluate the cognitive devel- opment of all participating children and adolescents. A summary of the survey modules is depicted in Table A. 1. III. C Sample comparability While Medell ´ ın ranks as the third city with the highest migration in Colombia, it is crucial to recognize the degree to which migrants arriving in the city differ from those migrating to other regions in Colombia. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Strengths and Difficulties Questionnaire", "Patient Health Questionnaire", "General Anxiety Disorder Scale"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Those who had moved out of the Kagera Region by 2004 experienced consumption growth that was 10 times higher compared to those who remained in their original community. These averages translate into very different poverty dynamics patterns for the physically mobile and immobile. For those who stayed in the community, poverty rates drop by about 4 percentage points over these 13 years. For those who moved elsewhere within the region, poverty rates drop by about 12 percentage points, and for those who moved out of the region, they drop by 23 percentage points. Had we not tracked and interviewed people who moved out of the community – a practice found in many panel surveys – we would have seriously underestimated the extent to which poverty has gone down over the past 13 years in the Kagera Region; we would have reported poverty reduction at about half of its true value. Clemens and Pritchett (2007) raise similar concerns in the context of income growth and international migration. In addition, the data would omit the part of the population with a high information content on pathways out of poverty. Still, these statistics are not evidence that moving out of the community leads to higher income growth. As noted above, we cannot observe the counterfactual: What would income growth have been for migrants had they not migrated? We exploit some unique features of these data to address concerns about unobserved heterogeneity. First, individual fixed effects regressions for movers and stayers produce a difference-in-difference estimation of the impact of physical movement, controlling for any fixed individual factors that affect consumption. Second, we can control for initial household fixed effects in the growth rate of consumption since we observe baseline households in which some individuals migrate", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["panel surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As a result, migration may entail negative eﬀects on the productivity and wages of migrants, often reﬂected in terms of occupa- tional downgrading (Jasso et al. (2000)). For instance, our survey indicates that 72 % of the Venezuelans who migrated to Ecuador report that their skills were used more productively in their jobs back in Venezuela. On the other hand, international migration typically entails moving from low to high productivity countries, which can lead to increases in wages and productivity (Clemens (2011)). Migration driven by natural or man-made disasters, such as the Venezuelan exodus, is much more likely to be of the South-South type and, as a result, this type of productivity gain may be less relevant. An important feature of the EPEC survey is that it contains retrospective information on the last job held by migrants prior to leaving Venezuela. The goal in the remainder of this section is to compare the changes in occupation experienced by Venezuelan migrants 14 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "UNHCR \r ProGres \r data \r on \r the \r 83,044 \r registered \r refugees \r in \r Thailand \r indicates \r an\nestimated \r 84 \r per \r cent \r are \r ethnic \r Karen \r and \r 12 \r per \r cent \r are \r ethnic \r Karenni. \r The\nremaining \r 4 \r per \r cent \r are \r of \r Burman, \r Shan, \r and \r Mon \r descent, \r and \r other \r groups. \r The\nmajority \r of \r registered \r refugees \r come \r from \r Kayin \r State \r (65.3 \r per \r cent), \r followed \r by\nKayah \r (14.6 \r per \r cent), \r Tanintharyi \r (7.3 \r per \r cent), \r Bago \r (5.2 \r per \r cent) \r and \r Mon \r (5 \r per\ncent). \r (Annex: \r Myanmar \r Thailand \r Border \r – \r Refugee \r Overview, \r as \r of \r end \r of \r March \r 2013)", "output": {"entities": {"named_data": ["UNHCR \r ProGres \r data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "source-destination-year level on source-time, destination-time, and source-destination ﬁxed eﬀects in a gravity-like speciﬁcation: Refugeessdt = δst + δdt + δsd + εsdt. We estimate this equation by Poisson Pseudo-Maximum Likelihood (Eaton, Kortum and Sotelo 2012), pooling countries and years (and thus including observations with zero bilateral stocks). We then construct a destination-adjusted refugee stock by subtracting the destination-time eﬀect from the actual stock: AdjustedRefugeessdt = Refugeessdt − δdt. Then, we compute the average distance traveled, share of refugees going to a contiguous country, the Herﬁndahl index of destinations, and share in wealthy OECD countries using this adjusted refugee data set instead of the actual data. Appendix Figure A5 re- ports the results. Netting out destination-time eﬀects prior to carrying out the analysis leaves the main results virtually unchanged. 4 Conclusion Our analysis suggests that the assumption underpinning the debate on responsibility- sharing may need to be partly revisited. Countries neighboring a conﬂict do host a majority of refugees and are hence bearing a disproportionate portion of the respon- sibility for providing asylum to those who are ﬂeeing from violence and oppression. Yet, the share of refugees who move to further-away destinations, including OECD countries, has been growing over time. In other words, responsibilities are increasingly shared across countries. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "this way, profiling of IDP situations aims to underpin advocacy, protection and assistance activities as well as support the achievement of durable solutions by informing joint strategies between government, humanitarian and development actors. Profiling provides an overview of displacement-affected populations through the collection and analysis of minimum core data (number of IDPs, disaggregated by location, age and sex) and where possible additional quantitative and qualitative data (causes of displacement, patterns of displacement, protection concerns, humanitarian needs, vulnerabilities, and aspirations and prospects for durable solutions). Profiling may utilize data collection techniques at individual, household and community levels, often combining population estimation methods, a review of secondary data, focus group discussions, household surveys and key informant interviews targeted specifically at forcibly displaced populations (UNSD 2014). 70 Profiling methods focus on displacement situations, rather than only on displaced populations, and therefore includes comparisons to conditions in the host population. IDMC estimates that humanitarian profiling data forms the basis for 18 of their 60 country estimates and around 63 percent of their annual estimates (IDMC 2015), with the largest volume of data on conflict-induced internal displacement provided by OCHA followed by IOM. There are several practical challenges associated with IDP profiling exercises in displacement situations. Insecurity or terrain may impede access to displaced populations in conflict-affected or hard to reach areas. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["humanitarian profiling data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The ODHD survey (2011) found that about 7 in 10 gold washers practice\nagriculture in parallel, and about 1 in 10 of them practice a trade. The higher estimates of 200,000\nto 1 million consider the possibility of artisanal gold mining being practiced as a secondary or\ntemporary occupation.\n\nOn the positive side, mining could create a mini-boom in the local economy-that is, higher\nemployment and higher wages leading to an increase in local aggregate demand for food crops.\nHowever, due to lack of geocoded agricultural modules in censuses and household budget surveys,\n\n_**Governance**_ **Afrobarometer Question** **Coding**\n\nimagery has inspired many researchers to investigate the\nuse of earth observation data for monitoring economic\nactivity around the world. One of the most popular earth\nobservation data sets is the so-called nighttime lights from\nthe Defense Meteorological Satellite Program. Researchers have found positive correlations between nighttime\nlights and several economic variables.\n\nThe study finds that the Defense Meteorological Satellite Program data are quite noisy and therefore the resulting growth elasticities of Defense Meteorological Satellite Program nighttime lights with respect to most of these socioeconomic variables are low, unstable over time, and generate little explanatory power.", "output": {"entities": {"named_data": ["ODHD survey", "Defense Meteorological Satellite Program"], "descriptive_data": [], "vague_data": ["household budget surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 3 also shows that there is considerable variation in both indexes within our sample when averaging these indexes at the regional level over the period of investigation. 16It is possible that our approximation is noisy and could potentially induce non-random measurement errors. In Section 4. 3, we propose an instrumental variable approach and estimate Shareodet from the EPR-ER data using a gravity model. Our findings concerning the number of ethnic groups across time for a given origin – destination pair are in line with the EPR-ER data. It seems that refugees of a given origin – destination pair mainly belong to two major ethnic groups. This also means that the variation in diversity in refugee-hosting areas is coming from the refugee composition at the camp level. Figure B. 5 shows the movements of refugees from origin to destination countries under scrutiny. Somalia, the Democratic Republic of Congo, Liberia, South Sudan, and Sudan are major source countries for refugees, while Kenya, Tanzania, Uganda, Zambia, and Ghana appear to be countries hosting most refugees. Representing refugees in camps per ethnic group for the top 5 asylum countries over the sample period, Figure B. 9 shows that there is considerable variation in ethnic composition across camps. 17 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["EPR-ER data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Domestic violence experiences and attitudes were measured based on self- report using three questions: (a) In your opinion, is a husband justified in hitting or beating his wife? (b) Did your husband ever hit or beat you? And (c) Are (were) you afraid of your husband: most of the time, sometimes, or never? These questions have been used in the Government of Pakistan Demographic and Health Survey 2017-2018. For the regression analyses, fear of husband was controlled for as it was the only variable that was correlated with all mental health outcomes and child development. Fear of husband was categorized from 1 to 3, with 1 indicating never afraid, 2 indicating sometimes afraid, and 3 indicating afraid most of the time. Community Safety. Perceptions of and experiences of safety in the community (or lack thereof) were determined by exposure to crime (i. e., if anyone has taken or tried taking something from you by using force or threatening to use force in the last 12 months) and perceptions of safety in the community — specifically, feeling safe while home alone at night or while walking through the neighborhood alone after dark. Discrimination. Respondents were asked if, in the past 12 months, they felt discriminated against or harassed on the basis of ethnic origin, immigration or refugee status, sex, sexual orientation, Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These data are only for three districts in the province of Punjab, but is very recent, was conducted by an independent team of academics, and is a complete census of all households in the selected villages. Consequently, it yields sufficient madrassa enrollment to examine correlations with household attributes in a meaningful manner (this data source provides information on four times as many children as the PIHS). Table A2 in the appendix shows how these different data sources are used in the paper. Each source asks about madrassa enrollment in a slightly different but comparable way. The population census (1998) asks about the field-of-education (“ What is name ’ s field of education? ”) with options that include (for instance) engineering, medicine, or religious education. This question is also asked of all literate adults irrespective of their current enrollment status, allowing for comparisons in the stock of religious education over time. The PIHS rounds ask, “ What type of school is name currently attending? ” with options that include government school, private school, or deeni-madrassa (religious schooling). Finally, the LEAPS census directly asks, “ Is the child enrolled in a madrassa or an Islamic education school? ” Fortunately these different questions all give rise to similar numbers. This is reassuring since it suggests that any one particular result is not driven by the specific question or definition that was used. 7 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 (www. statpak. gov. pk). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["LEAPS census"], "descriptive_data": ["census of private schools"], "vague_data": ["population census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2, about 97 percent of refugee and 39 percent of host households in Addis Ababa live in rented houses. The data show that Addis Ababa refugees pay higher rents (ETB 31,600 per year, per adult equivalent) than hosts (ETB 18,700 per year, per adult equivalent). Moreover, rent expenditures make up 56 percent of refugees’ non-food expenditure. In-camp refugees in Ethiopia are much poorer than their hosts, but because everyone suffers from similarly low expenditures, inequality is also low for refugees. As measured by the Gini 43 For details on the OCP policy, see Box 2.2. 32% 18% 25% 84% 7% 75% In camp Addis Ababa Total Poverty headcount rate (%) Hosts Refugees Figure 5.1: Poverty incidence Source: World Bank Staff based on SESRE 2023. 0 5 10 15 20 25 30 35 40 45 In Camp Addis Ababa Total Hosts Refugees Figure 5.2: Income inequality, Gini index Source: World Bank Staff based on SESRE 2023. Most of the analysis presented in this chapter is based on detailed consumption data from the Socioeconomic Survey of Refugees in Ethiopia (SESRE) conducted between October 2022 and February 2023. All consumption of food and non-food items is included, regardless of whether these items are", "output": {"entities": {"named_data": ["Socioeconomic Survey of Refugees in Ethiopia"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "List of acronyms CAR Central African Republic DHS Demographic and Health Surveys DRC Democratic Republic of Congo DTM Displacement Tracking Matrix FCS Fragile and Conflict-affected Situations GIDD Global Internal Displacement Database GIS Geographic Information Systems IASC Inter-Agency Standing Committee ICRC International Committee of the Red Cross IDMC Internal Displacement Monitoring Centre IDPs Internally Displaced Persons ILO IOM International Labour Organization International Organization for Migration IRRS International Recommendations for Refugee Statistics JIPs Joint IDP Profiling Service LSMS Living Standards Measurement Study MICS Multiple Indicator Cluster Surveys NGOs Non-Governmental Organizations NRC Norwegian Refugee Council OCHA Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat OAU Organization of African Unity ODA Official Development Assistance OECD Organisation for Economic Co-operation and Development SDG Sustainable Development Goal SKOPE Somalia Knowledge for Operations and Political Economy SuTPs Syrians under Temporary Protection UAV Unmanned Aerial Vehicle UNDP United Nations Development Programme UNHCR United Nations High Commissioner for Refugees UNITAR United Nations Institute for Training and Research UNOSAT UNITAR ’ s Operational Satellite Applications Programme UNRWA United Nations Relief and Works Agency for Palestine Refugees in the Near East UNSD United Nations Statistical Commission WFP World Food Programme", "output": {"entities": {"named_data": ["Multiple Indicator Cluster Surveys", "International Recommendations for Refugee Statistics", "Demographic and Health Surveys", "Global Internal Displacement Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "There is also qualitative evidence that low take-up was at least in part due to some landlords ’ reluctance to make signed legal commitments to refugees for the lease, and with the implementing partner for the necessary construction. This is a setting in which most rental contracts are informal and landlords have ample discretion over their terms and a largely free hand to evict tenants. Despite the appeal of guaranteed rent for a year plus funding for housing upgrades, some landlords preferred not to “ bind ” themselves to the program and the particular recipient refugee household currently residing in their property. 3 One of the study ’ s main empirical ﬁndings is that we detect no signiﬁcant positive impacts of the housing assistance program for refugees along a range of pre-speciﬁed primary outcomes, with the exception of housing expenditures where there is the expected (and somewhat mechanical) drop in spending. Beyond housing expenditures, the other primary pre-speciﬁed outcomes in- 2We do this by surveyed neighbors but excluding landlords, who were often involved in the program. 3The data underlying these ﬁndings is from our implementing partner ’ s Integrated Assessment Shelter Analysis. 3 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The SEIS questionnaire includes a dedicated section on labor market status that mirrors the ILO reference week definition used in the national LFS. This alignment allows direct comparison of employment rates between the refugee population captured in SEIS and the national working-age population captured in the LFS, controlling for differences in age structure and educational attainment. Preliminary comparisons show that SEIS-measured employment rates for working-age refugees are 14 percentage points below LFS-measured national rates, net of compositional differences.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "9 Figure 10. Selected characteristics across Ugandan and refugee households, % Source: RHCS 2018, WB staff calculations. A simple comparison between refugees and Ugandan households demonstrates that refugees lag with regards to selected characteristics found to narrow the poverty gap. For example, refugees are less likely to have access to land than Ugandans. If refugees have access to land, the majority do not own it, but have user rights. The size of land also differs a lot among Ugandan and refugee households. Most Ugandans have at least 0. 05 hectare per capita, while the majority of refugees have less than 0. 05 hectare per capita. Refugee heads of household are also less likely to work and less likely to be literate compared to their Ugandan counterparts. Refugees have higher shares of children and elderly in household size compared to Ugandans. For example, among almost 60 percent of refugee households, more than half of the household members are children and elderly compared to 42 percent of households among Ugandans. Economic inclusion dividend When a development approach to hosting refugees is followed and refugees earn incomes, there are two key beneficiaries. Refugees themselves, who gain dignity, financial autonomy and pathways to self-reliance. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["RHCS"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "23 mega-cities, annual growth rates of peripheral population tend to reach around 10-20 percent compared to central business districts. 4. Implications for public policy Hazard management is a task both for the public sector and for private households and firms. For the public sector in cities, this includes ensuring the safety of municipal buildings and public urban infrastructure, encouraging and supporting private sector hazard risk reduction, and developing first response capacity. A considerable share of hazard risk stems from relatively small but frequent events which cause localized damage and few injuries or deaths (Bull-Kamanga et al. 2003). For instance, an analysis of detailed records of 126 thousand hazard events in Latin America showed that more than 99 percent of reported events caused less than 50 deaths or 500 destroyed houses (ISDR 2009). In aggregate, these accounted for 16. 3 percent of total hazard related mortality and 51. 3 percent of housing damage. The probability of larger events may or may not be predictable. For instance, a city may be in an earthquake risk zone, but the location specific ground shaking probabilities are not known. Individual dwelling unit level mitigation is therefore necessary everywhere in the general area of high earthquake probability. For other hazard types like landslides and floods, potential risk areas can be more easily delineated. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["detailed records of 126 thousand hazard events in Latin America"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure A4: Participation certiﬁcate to boost ‘ resume ’ CERTIFICATE THIS ACKNOWLEDGES THAT I engaged with Pulse Bangladesh to do data collection Notes: The wording of the certiﬁcate was made such that it could be applied to both arms; cash-only arms participated in weekly surveys along with all other experiment participants, so technically also engaged in data collection for our project. 60 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Surveys of employers consistently find that more than hard, technical skills, employers value these harder-to-quantify skills of honesty and integrity, problem-solving ability, work ethic, communication skills, the ability to work productively with others, responsibility and dependability (Blom and Hobbs 2007). The AGI program has focused on the development and measurement of these softer attributes that matter for employment as well as those that matter more to the individual, such as self-confidence and empowerment. Despite the challenges of measuring such subjective outcomes, the survey instruments included panels of questions designed to elucidate a nuanced picture of the personality and psychosocial characteristics that are most relevant for labor market success. Table 6A presents results on empowerment and decision-making. The first series of questions have to do with control over resources, spending decisions and earnings. Respondents were asked how much control they had over how to spend their own earnings; also, whether they had money of their own for basic uses that they alone could decide how to use, without having to ask for permission. The EPAG baseline survey found that respondents reported a high degree of control over resources even before the program started. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Martin et al. (2018) propose a measure of business relationships' stickiness based on the duration of firm-to-firm trade and use firm level data in France to measure a \"stickiness\" index for more than 4,000 HS6 products.\n\nWe use data consolidated banking statistics from the Bank for International Settlement to construct an index of financial proximity. [12] We use the total bilateral cross-border claims (including bank and non-bank sectors for all maturities)\n\n**Foreign** **Direct** **Investments.** Data from the UNCTAD's Bilateral FDI Statistics provides up-to-date and systematic FDI data for 206 economies around the world, covering inflows, outflows, inward stock and outward stock by region and economy.\n\nan index of \" _proximity_ _in_ _sectoral_ _composition_ \" based on the World Development Indicators. We use the share in value added of main sectors: service and agricultural sectors and we decompose manufacturing sectors into 7 main sub-sectors.", "output": {"entities": {"named_data": ["UNCTAD's Bilateral FDI Statistics", "World Development Indicators"], "descriptive_data": ["consolidated banking statistics"], "vague_data": ["firm level data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In the third (Rahim Yar Khan) there is a large difference, with the census reporting that 1 percent of all school-going children attended madrassas, and the LEAPS showing that the fraction is closer to 3. 7 percent (Table II). There are three potential explanations for this difference. First, the LEAPS data is not representative of the district and could be off the mark for districts with wide variation in madrassa enrollment across rural and urban samples. Second, the experience of the last five years could have varied dramatically across districts — in some, the enrollment fractions did not change and in others it increased substantially. Third, the data could point to systematic problems with the census estimates from certain districts, or the statistical problems that arise when we try to estimate low-probability events. 3. 3 Explaining the Differences A number of reasons could account for differences between the estimates presented here and those in the popular press. 1. Differences in the sampling unit. Our estimates are all based on household surveys — an interviewer goes to a household and asks about the enrollment status of every child. Some census estimates of home rather than religious schooling in the United States — the former ranges from 1 to 2 percent (Bauman 2001) while the latter is closer to 8 percent (National Center for Education Statistics, 2001). 10 In our own analysis, we find the quality of the data generated by the Federal Bureau of Statistics in Pakistan to be consistently high. We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["FBS Census of Private Schools", "LEAPS"], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "19 As property prices in the worst affected areas reduced the most, low income households responded by moving into low-rent housing being offered in these locations. On the other hand, middle income households moved away to avoid risk, and the wealthy, for whom insurance and self-protection was the most affordable, did not change where they lived. Poor people “ sort ” into low rent locations – which are often at higher risk to natural hazards. The problem is particularly acute in developing countries where there is a divide between the formal and informal markets for land. While formal developments may respect land use regulations, informal settlements are often located in hazard prone locations, such as on hill slopes, close to river banks, or near open drains and sewers. In Dhaka for example, informal settlements are developing across the metropolitan area, with many residents lacking basic public services and in locations at risk from flooding. In fact, most informal settlements do not have access to a public toilet within 100 meters, and 7, 600 households in 44 slums live within 50m of the river (World Bank 2005, Dhaka Urban Poverty Assessment). For the city of Bogotá, we use the same database discussed earlier to examine if poor people are at greater risk from natural hazards – particularly earthquakes.", "output": {"entities": {"named_data": ["Dhaka Urban Poverty Assessment"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The wealth index used in this analysis is computed from GLSS household asset data following the principal component analysis method developed by Filmer and Pritchett (2001). The GLSS wealth index is based on ownership of durable assets (television, refrigerator, mobile phone, bicycle, motor vehicle), housing characteristics (wall and roof materials, number of rooms per capita), and access to utilities (electricity, type of toilet, drinking water source). This index has been validated against consumption-based welfare measures in prior applications of GLSS data to poverty analysis.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "of being available on a yearly basis independently of the quality of local statistical oﬃces and data gathering. While it comes with its own problems it can shed light on local economic activity where gathering of statistical data is incomplete. 7 This makes it a great ﬁt for measuring growth in a context of civil conﬂict. Conﬂict incidence is measured through the number of battle-related deaths from UCDP / PRIO dataset. We run the following regression for country i at time t: git = β × incidenceit + µi + ηt + ϵit (1) where git is economic performance per capita growth of country i in year t, incidenceit is conﬂict incidence, µi and ηt are respectively country and year ﬁxed eﬀects. A cross-country analysis as in equation (1) bears considerable potential for both reverse causality and omitted variable bias. Thus, a priori, a convincing causal link is hard to establish. However, here we expect the resulting bias to be small for two rea- sons. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["UCDP / PRIO dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "different social norms and customs, as well as ethnic prejudice. Card et al (2012) use the ESS to\n\ncountries. In order to identify age patterns using repeated cross-section data, Deaton and\n\ndegree of pro-immigration attitudes, we estimate the models using stacked micro-data from   the cross-section surveys instead of averaging over time-invariant characteristics.\n\nthe cross-section surveys instead of averaging over time-invariant characteristics. As a\n\nTo identify the effect of age on attitudes toward migration, we append household surveys from", "output": {"entities": {"named_data": ["ESS"], "descriptive_data": [], "vague_data": ["repeated cross-section data", "stacked micro-data", "cross-section surveys", "household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 8 of 51 In the 9 cases mentioned, it was possible to identify part-time workers, while temporary workers were only identified in the cases of Argentina, Brazil, Chile, Mexico and El Salvador. 4. 1. 1 Variation of the NSE as a percentage of total employment The prevalence of NSE in the total employment has not shown very significant variations in the countries considered in the last two decades (Figure 1). Indeed, most of the countries analyzed show non-standard employment registers similar to those observed in the mid-1990s. The exceptions where the variation is a little more relevant are Brazil and Uruguay, where there are contractions in the incidence of the NSE of the order of 10 and 5 percentage points respectively and Mexico, where there is an increase of 5 percentage points. Figure 1: Prevalence of NSE among salaried employees. (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the prevalence of NSE by types of occupations, considering the ISCO classification at one digit, we find a quite similar pattern across countries. Indeed, in most of the considered countries, the “ Elementary Occupations ” are the category where the prevalence of NSE is higher. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1 Introduction Company owners and managers make two decisions with important implications in the labor market: what skills are demanded and who to hire. On the demanded skills, a driver of trends in employment is the changing demand for soft skills (Heckman and Kautz (2012), Weidmann and Deming (2021)). However, we know relatively little about what kinds of soft skills employers value in modern entry- level jobs (Heller and Kessler, 2022). On the decision of who to hire, it is in the best interest of companies to hire based on workers productivity. However, several studies have documented the existence of labor market discrimination in a wide range of contexts (Bertrand and Duflo (2017), Neumark (2018)) and it remains unclear how discrimination operates throughout the hiring process and how the existent empirical evidence on discrimination is linked to economic theory (Bertrand and Duflo, 2017). We conducted a correspondence study in 2023 using a large online job platform to assess demand for soft skills in the context of hiring discrimination in Malaysia. Malaysia is a particularly interesting setting because it is an upper-middle-income economy, home to multiple ethnicities representing large shares of the population, and previously documented gender gaps in labor force participation and wages. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["large online job platform"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Medium ‐ intensity, on the other hand refers to “ regular armed clashes between governments, government forces and insurgents. ” (IISS 2017) Within the first category, according to the Armed Conflict Survey in 2016 are Afghanistan, Syria, Somalia, South Sudan, and Iraq, while Myanmar, Central African Republic, Democratic Republic of the Congo, and Sudan are in medium ‐ intensity conflict. (Eritrea is not included in the Armed Conflict Survey in 2017, discussed in Annex). Minorities in Albania, Kosovo and Serbia are often the objects of discrimination but countries are not in conflict. A capsule summary of the security and social situation in each of the other countries included in this survey can be found in Annex 2. The survey illustrates how impractical return is today in the countries in conflict, such as Syria, Afghanistan, and Iraq, and voluntary return in large 36 Best practice and new methods in return policy, July 2017, http: / / www. bamf. de / SharedDocs / Meldungen / EN / 2017 / EMN / 20170504 ‐ emnjahrestagung ‐ rueckkehr. html Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Armed Conflict Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "IDMC reports that of the 52 countries it monitored in 2015, it was only able to obtain data on new displacements in 20 countries, 92 on returns in 20 countries, on integration in one country, on resettlement in two countries, on children born in displacement in two countries and on deaths in one country; no data was obtained for any county on cross-border flight in 2015 (IDMC 2016). Moreover, existing systems for collecting data on refugees and asylum-seekers make it difficult to know how many were formerly IDPs, and it is possible for some people to be simultaneously counted in both categories, e. g. in the case of the Syrian displacement crisis (IDMC 2016). If no data on returns are available, IDMC risks overstating the number of IDPs (IDMC 2015).", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "among refugees and their best-preferred pulse. CSB is a corn soya blend with added essential micronutrients and vitamins called super cereal. Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than WFP food aid admin data for every item except for CSB+ and salt (Table E.6). Even when correcting for shares indicated as sold, refugee households still reported lower quantities. Using the WFP food aid information, we found a picture similar to UNHCR’s. Annexes 127 Table E.5: Food quantity and expenditure comparisons Items Quantity (per capita/year) Expenditure (per capita/year) SESRE WFP SESRE WFP Nonzero All Net of sold ration* Cereals 82.0 77.9 2,179 Wheat 60.5 1,427 Pulse 17.1 15.6 1,366 Peas 5.7 137 Vegetable oil 3.7 5.4 5.0 627 967 CSB+ 19.3 11.1 10.1 36 394 Salt 1.6 1.8 1.7 57 63 All cereals 76.9 - - 2,994 All pulses 8.3 - - 385 Aggregate ration/month 46.7 - Source: WFP and World Bank Staff based on SESRE 2023. Note: *Net of sold ration = quantity*share of ration sold (we asked the share of ration sold in SESRE) Table", "output": {"entities": {"named_data": [], "descriptive_data": ["WFP food aid information", "WFP food aid admin data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "34 Any statistics on the imputed welfare will based on the set of imputed welfares for each household. The estimator takes the form, with R denotes the number of simulation: ܪ ෡ ൌ 1 ܴ ෍ ݄ ሺݕ ෤ ௥ ሻ ோ ௥ ୀ ଵ where ݄ ሺݕሻ is a function that converts the vector y with (log) incomes for all households into a poverty measure (such as the head-count rate or bottom 40 %), and where ݕ ෤ ௥ denotes the r-th simulated imputed welfare. Figure 6. Survey-to-Survey Imputation Methodology, an illustration For the case of Turkey, we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey. Income is used instead of consumption for this paper ’ s analysis. The model included variables related to: household demographics (age, gender, age composition, etc.), household characteristics (education, labor activity, etc.), household head ’ s characteristics (age, gender, labor, education, marital status, etc.) and household assets holding (both livestock and durables). Based on that model the simulated values of consumption (at household level) were imputed for the households in the corruption survey. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Survey on Income and Living Conditions survey", "Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Hosting new neighbors: Perspectives of host communities on social cohesion in eastern DRC * Phuong Pham, † Thomas O ’ Mealia, ‡ Carol Wei, § Kennedy Kihangi Bindu, ¶ Anupah Makoond, | | & Patrick Vinck * * * Pham and O ’ Mealia are co-ﬁrst authors. Acquisition of the data used in this manuscript was supported by the United Nations Development Programme (UNDP). The funder played no role in the analysis, inter- pretation or writing of the results and decision to submit the manuscript. This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “ Preventing Social Conﬂict and Promoting Social Cohesion in Forced Displacement Contexts. ” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. This work is part of the program “ Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership ”. The program is funded by UK aid from the United Kingdom ’ s Foreign, Commonwealth and Development Ofﬁce (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the original variables used to predict fragility as controls without changing results. It is only in the post-war period that exclusion and refugees become a factor that inﬂuences foreign investment ﬂows. Finally, the results we ﬁnd are robust across all datasets of foreign investment we use. These results make it at least plausible that political exclusion and refugees matter because they predict a relapse to more intense violence. As ﬁnal piece of evidence for this idea we use data on political short and mid-term credit risk from the Belgian insurer Delcredere Ducroire (ONDD). We collected data on political risk evaluations from ONDD who, according to their annual report, insured transactions worth about 7 billion EUR in 2011. The variable we use measures the risk of a credit default for rea- sons beyond the control of the debtor, i. e. due to political or ﬁnancial macroeconomic events. We choose this variable because it provides the most consistent time-series in the ONDD data. ONDD measures both short- and mid-term risk on a scale from 1 (low risk) to 7 (high risk). Table 12, columns (1) and (4) show that risk ratings are decreasing in peacetime. Note that, as before, we control for country ﬁxed eﬀects which implies that we look at changes within country. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["ONDD data"], "descriptive_data": ["data on political short and mid-term credit risk"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The lack of certainty means children are trying to prepare for further studies (e. g., in universities) in two separate systems with varying requirements. The survey results indicate that students enrolled in both systems spend as much time in Italian schools as those attending only Italian schools, averaging 31 hours per week. However, students participating in both systems spend an additional 8 hours per week on online Ukrainian classes. This puts an extra burden on these children. Connectedness to Italy is correlated with demographic characteristics and social environment of refugee children. Additionally, Table 6 shows that making new friends in the country of destination and speaking Italian are strongly associated with higher connection to Italy. The mental distress resulting from displacement is a key barrier to educational integration for many Ukrainian refugees in Italy. The link between poor mental health and low school attendance and performance is widely acknowledged in the literature (see Fiining et al., 2019 for a systematic review). In the World Bank survey data, children and caregivers reported signs of mental distress, with 16 % of children and 24 % of refugee caregivers reported experiencing psychological distress 61 % 35 % 31 % 68 % 50 % 36 % 23 % 68 % 59 % 26 % 26 % 59 % Would like to continue living in Italy Would like to move back to Ukraine Feel strongly connected to Italy Feel strongly connected to Ukraine Caregivers (N = 283) Children between 9 and 14 years old (N = 141) Children between 15 and 20 years old (N = 96)", "output": {"entities": {"named_data": [], "descriptive_data": ["World Bank survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 1 ‐ Number of Forcibly Displaced Persons (1951 ‐ 2015) Source: Constructed from UNHCR population data (http: / / popstats. unhcr. org / en / time_series). Note: 2015 data are mid ‐ year and lower than end of year data. 5 http: / / www. unhcr. org / en ‐ us / figures ‐ at ‐ a ‐ glance. html. 0 10000000 20000000 30000000 40000000 50000000 60000000 70000000 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR population data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Similarly, regional representation of the sample was found to be in line with actual regional distribution of the population in the north. 16 The code of the day is the sum of the two figures of the date, i. e. if it ’ s the 25th of August the code of the day is 2 + 5 = 7. The enumerater will chose house number 7 as a starting point. Arab 3 % Tamashek 32 % Songhai 45 % Peulh / Foulbe 7 % Other ethnicities 13 % Figure 2: Ethnic composition of the North, 2009 Census Arab 5 % Tamashek 36 % Songhai 49 % Peulh / Foul be 7 % Other ethnicities 3 % Figure 3: Ethnic composition of sample Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["2009 Census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "But, given the presence of thousands of refugees in the country, the constrained fiscal space the authorities face, and the likelihood that international assistance for refugees will taper in the future, an imminent policy question is how to ensure that refugees can be hosted in a sustainable manner, without becoming a fiscal burden in the future. The fear that refugees are (or might become) a fiscal burden is driven by a broadly held perspective about forcibly displaced persons in general, and refugees in particular, namely that they are humanitarian subjects, vulnerable and worthy of public assistance (Betts and Collier 2017). This perspective is not universal, however. The economic contributions of refugees have been extolled for years, from posters 1 https: / / www. ecoi. net / en / file / local / 2091861 / 645b938a4. pdf. 2 Enquête sur la Consommation des ménages et le Secteur Informel au Tchad. The survey was carried out jointly with the National Statistics Office (Institut national de la statistique, des études économiques et démographiques, INSEED) and the UNHCR in Chad.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "poverty and well-being. With increasing income, more affluent households are more likely to spend a greater share of their budget on high-value food items such as animal- origin diets, processed food, and food away from home, as well as on non-food items. Except for Addis Ababa, food consumption patterns, as indicated by expenditure shares, differ by food groups (Figure 5.6). Overall, refugees expenditures are higher on cereals and less on animal-origin food items associated with the types of food aid provided. This could be because refugees receive assistance for cereals/grains, not animal-origin food items. Food away from home is lower for refugees than hosts, except in Addis Ababa. - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Food Non-food non-durables Durables Rent Figure 5.3: Expenditure components (in birr) Source: World Bank staff based on SESRE 2023. Note: The expenditures are in December 2022 values. 61% 58% 59% 68% 56% 65% In Camp Addis Ababa Total Hosts Refugees Figure 5.4: Shares of food expenditure Refugees’ Aspirations 45 Expenditures for in-camp refugees is almost half that of hosts, despite the sizeable food aid and cash transfers (in selected camps) the WFP and", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Beneficiary assessments revealed significant differences in protection concerns between newly arrived and long-staying refugees: new arrivals cited documentation and freedom of movement as primary concerns, while long-stayers reported livelihood insecurity and access to education as more pressing. These findings from the beneficiary assessments were used to differentiate the intensity and content of case management services offered to households by time-since-arrival cohort. Cohort-specific program designs are reflected in the operational manual's guidance for intake officers.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Some data challenges common to refugees and IDPs Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities. Sampling. As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay at relatives ’ places, other live in makeshift shacks and others stay in camps. The information available in the UNHCR registry (the census) can also be quite inaccurate, as already discussed, and the degree of accuracy changes for different groups of refugees. Stratification by urban and rural areas, a typical approach in sampling, may mean little for a population that is mostly in urban areas whether in camps or outside camps. Refugees and IDPs are also mobile and more difficult to track over time than other populations. Several statistical institutes worldwide have developed methodologies to track and measure mobile populations such as herders, nomads or homeless people. However, tracking refugees from other countries has been in the", "output": {"entities": {"named_data": ["UNHCR registry"], "descriptive_data": [], "vague_data": ["population census"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Table 2 presents summary statistics for the ENILEMS-ENLACE panel dataset. Columns\n\n12 ENLACE scores than individuals out of college (by around 0.38 SD), but they are also\n\nWe are interested in the predictive power of ENLACE test scores over future schooling\n\nindividual's ENLACE test score in Grade 6 as a predictor of future education outcomes or\n\nWe use the ENLACE panel to study the relationship between Grade 6 test scores", "output": {"entities": {"named_data": ["ENILEMS-ENLACE panel dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Several studies have examined the benefits of rural electrification in India. For example, van de Walle et al. (2015) estimate the long-run effects of electrification on household consumption in rural India based on data for 1981-98; Burlig and Preonas (2016) investigate the effect of India's national rural electrification program on labor force participation, living standards, and other village-wide outcomes using census data for 2001 and 2011; and Khandker et al. (2014) estimate the benefits of electrification projects in rural India based on cross-sectional data for 2005.\n\nFor example, using night lights data, Min and Gaba (2016) show that many villages in India that were officially classified as electrified under the RGGVY program remained in the dark for years after the completion of electrification projects.\n\nThis paper estimates the welfare impact of rural electrifi cation in India using nationally representative household panel survey data for 2005 and 2012.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["census data", "cross-sectional data", "data for 1981-98", "night lights data", "nationally representative household panel survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "labor markets shape the employment trajectories of refugees. Restrictions on land access for refugees restrict their access to rural labor markets, primarily shaped by agricultural activities. Livelihood activities in cities or work similar to that found in urban areas are most promising for refugees to utilize their labor and skills. Yet, many refugee camps are in more agrarian locations, and local labor market characteristics and connectivity drive refugees’ labor market outcomes (Hedberg and Tammaru, 2013; Kalter and Kogan, 2014; Kogan and Kalter, 2020; Schuettler and Caron, 2020; Dorian and Burmann, 2023). The GoE vision to create sustainable livelihood opportunities and build refugees’ self-reliance and resilience has yet to be fully implemented; roughly 88 percent of refugees in Ethiopia remain in camps based on SESRE data. Globally, approximately one- quarter of all refugees live in camps, a proportion that varies widely by country income status. Roughly half of refugees hosted in low-income countries live in camps (UNHCR, 2022b), but this share is much higher in Ethiopia (88 percent). Long-term encampment policies leave refugees isolated with limited or no economic rights, a situation that wastes their human capital and capacity for work (World Bank, 2017; Ibáñez et al., 2022). Although it may", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "cash transfer value from Oct 2022_refugee camps” file received from the WFP document that helps to get information regarding the changes in cereal cash equivalent – data on cereal cash equivalent for cash camps which is used to calculate cereals provided in those camps and cash transfer value per year. Based on this information, we compare how the list shared with us on what refugees should have received to what they reported regarding food consumption. Table E.4: Food aid data/information received from WFP Item Remark Assumptions Cereal Not clear Mapped to wheat* Pulse Not clear Mapped to peas* Vegetable oil Mapped to edible oil CSB/famex (CSB+) Not in SESRE Average of other cereals/pulses Salt Matched Cash - - Source: UNHCR Note: Rice was distributed for some months in Afar and Somali Dollo area camps, though wheat remained the main cereal distributed. YSP (Yellow Split Pea) was the main pulse distributed among refugees and their best-preferred pulse. CSB is a corn soya blend with added essential micronutrients and vitamins called super cereal. Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported", "output": {"entities": {"named_data": [], "descriptive_data": ["cereal cash equivalent for cash camps"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "9 where a government might be a real or perceived contributor to displacement or is harboring an overt or covert policy to reduce the number of refugees in the country. With limited results in including FDPs in national sampling frames, alternative approaches remain necessary. The UNHCR, the World Bank and numerous scholars and practitioners worldwide are experimenting with satellite images and phone surveys to try detecting refugee and IDP populations that may escape the UNHCR and national registers with some initial encouraging results. In Lebanon, Jordan and the Kurdistan region of Iraq, Aguilera et al. (2020) designed sampling strategies for Syrian refugees with known ex-ante selection probabilities. They used a variety of data sources, including data collected by humanitarian agencies, and also employed geospatial segmenting to create enumeration areas where they did not exist. Systematic field experiments are also underway to test different sampling approaches for IDPs living in camps. For example, Himelein, Pape and Wild (forthcoming) compare the performance of five alternative sampling approaches (satellite mapping, segmentation, grid squares, “ Qibla method, ” and random walk). Different indicators are assessed including household size, consumption, poverty and ownership of assets. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**5.2 Geospatial Data Integration in M&E**\n\nGeospatial data from the national statistics office's 2020 census cartographic database have been integrated into the MIS to enable automatic generation of geographic coverage reports. When a community is registered in the MIS, its geographic code is linked to the corresponding census enumeration area boundary in the geospatial data layer, allowing the M&E team to produce maps of beneficiary coverage against the total population of each target district without additional data processing.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 17: Peace and Foreign Inﬂow Across Cut-oﬀs and Data Sources The basic results are in Table 8 which shows results for equation (9) using the threshold of 0. 008 battle-related deaths per 1000 population. According to this the investment from OECD countries was almost 70 percent larger in peacetime than during conﬂict. The ﬂows from other data sources shows an increase of between 35 and 50 percent. The consistency of this result across very diﬀerent datasets is striking. Note also that the average change in inﬂows implied by these rates is very large. In 2012, average inﬂows in the World Bank dataset were over 9. 5 billion USD and over 3 billion USD in the OECD data. Our estimates therefore imply a gain of between 2 billion and 4 billion USD in yearly inﬂows for countries which emerge from conﬂict. In order to understand the dynamics of recovery it is useful to understand the dynamics of this change around the end of conﬂict. For this purpose we add a set of dummies to the equation above. We construct a dummy that indicates the start of recovery and add three forward and lag dummies to trace average investment around this date. As before we always lag the explanatory variables by one year. Results for the OECD data are shown in Figure 18. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["World Bank dataset"], "descriptive_data": [], "vague_data": ["OECD data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Institutional arrangements would need to delineate data collector and data compiler roles, as well as reflect the following principles to ensure sustainability: (a) Pursue additional activities within the overall framework of existing initiatives to ensure coherence with the activities of other actors. (b) Primary responsibility for data collection rests with national statistical agencies (with adequate arrangements in the case of IDPs to mitigate political risks). (c) Definitions and methodologies should be harmonized across countries, through a process managed under the auspices of the UN Statistical Commission. (d) Agencies such as UNHCR and IDMC can play a leading role in ensuring quality, providing technical assistance as may be needed, and aggregating data for global analyses. 102 See http: / / www. internal-displacement. org / database. 103 See http: / / unstats. un. org / unsd / statcom / doc15 / 2015-9-RefugeeStats-E. pdf. 104 See conference documentation at http: / / www. efta. int / seminars / refugee. 105 See http: / / unstats. un. org / unsd / statcom / 47th-session / documents / 2016-14-Refugee-statistics-E. pdf. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "3 trust in the government and its institutions and perspectives on conflict resolution. By analyzing the impact of the crisis on welfare, the consequences of returning home versus remaining in displacement and by comparing immediate with longer term impacts, this paper contributes to the literature on refugee, IDP and returnee populations. The paper combines data from a face-to-face baseline survey with information collected via mobile phone interviews from respondents identified during the baseline. This innovative approach to data collection makes it possible to collect welfare data with high frequency (monthly) – important in a volatile crisis situation – and allows measuring changes over time. It also permits following displaced and refugee households once they return, even if they return to areas that are inaccessible to enumerators. The remainder of this paper is organized as follows. Section 2 provides a brief overview of the methodology, the sample and sample selection. Section 3 discusses the characteristics of the displaced and returnees, looking specifically at ethnic composition, place of origin, household size, education, asset ownership and employment status. Section 4 considers how the crisis affected food consumption, employment, assets and school attendance. Section 5 is devoted to the specificities of returnees who turn out to be, on aggregate, less affected by the crisis and better off than IDPs or refugees.", "output": {"entities": {"named_data": [], "descriptive_data": ["face-to-face baseline survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Expenditure). In the calculation, values for the measuring poverty rate (P) and design factor (deft) 1.5, the expected Relative Standard Error (RSE) of 4.63%, and finally, an adjusted Response Rate of 99% at a 95% Confidence level used to represent the expected precision is acceptable at the domain level. To select a representative sample from this population, first, the initial sample size was determined by using the following scientific formula: where the deft is the design factor defined as the ratio between the square root of standard error using the given sample design and the standard error resulting from a simple random sample used. Based on the above scenario, total sample size =3,456 Households, and EAs = 288 . An equal allocation method was used to ensure that the survey precision was comparable across domains, where 36 EAs were selected from each domain. Based on a fixed sample take of 12 households per cluster, Equal Allocation formula Where: = total number of sample households and = Number of sample households allocated to stratum Table C.1: The distribution of sampled and surveyed households by domains EA HH Sampled Covered Sampled Covered Eritrean refugee domain 36 36 432 432 Somalian refugee domain", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15 We followed Anginer, Demirgüç-Kunt, and Zhu's (2013) methodology and computed the index by aggregating the answers to 14 selected questions on supervisory powers that were collected in the 2003, 2007 and 2011 surveys conducted by Barth, Caprio, and Levine (2008). 16 The methodology for assessing central banks' FSRs was introduced by Čihák (2006) in the first worldwide survey of FSRs. 17 Alternatively, one could use as the control the availability or even better the actual implementation of macroprudential tools prior to the 2008 crisis.\n\nIt derives from the database developed by Melecky and   Podpiera (2013) and the Bank Regulation and Supervision Surveys carried out by the World Bank in\n\nempirical link between a simple publication of FSRs and financial stability. The data on FSR publication\n\nare available for only 78 countries, covering 22 of the 25 crisis countries. The data on the quality of FSRs", "output": {"entities": {"named_data": ["Bank Regulation and Supervision Surveys"], "descriptive_data": ["2003, 2007 and 2011 surveys", "database developed by Melecky and   Podpiera", "data on FSR publication"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Sarvimaki (2011) uses the elements of the government ’ s placement policy as instruments (i. e. the proportion of a municipality ’ s population speaking Swedish and the hectares of potential agricultural land). Other authors focus instead on the counterfactual group testing alternative designs of the control group, sometimes including placebo groups and other times recurring to matching methods. The choice of matching methods varies from ordinary methods such as nearest neighbor to more recent advances such as Synthetic Control Methods (Abadie and Gardeazabal, 2003). The inclusion of fixed effects is common to almost all papers although the choice of fixed effects can be very different, as described above. Only one paper uses Fixed Effects (FE) and Random Effects (RE) formal models in conjunction and tests for differences (Esen and Binatli 2017). Cross-section econometrics is, by far, the method of choice even if time is included into the equations but we also found three papers employing time-series models (Carrington and de Lima 1996, Makela 2017, Fakih and Ibrahim 2015). Only few papers are able to exploit panel data (Foged and Peri 2015, Depetris-Chauvin and Santos 2017) and several of them use the same data set (Maystadt and Duranton 2018, Maystadt and Verwimp 2014; Ruiz and Vargas-Silva 2015, 2016, 2017). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "21 Results suggest that 66 % of the returnees trust the Malian police and army most when it comes to providing security in the North. Almost half believe that the Malian army is brave and well trained. The vast majority of returnees believe that the government ’ s policies regarding reconciliation, security and social cohesion are good or very good. They also support the government ’ s approach towards decentralization and providing infrastructure such as access to potable water and electricity. As the next section will illustrate this differs strongly with the opinions of refugees. 6. Prospects for Peace IDPs, refugees and returnees have comparable opinions with regard to the requirements for peace: (i) addressing the ongoing crisis, (ii) improving security and (iii) reconciliation. Although there is agreement on what needs to be done, there is little consensus on what happened during the crisis, who the culprits are and who the main victims. Figure 15: What is the most important problem the Government needs to resolve today? (%) Source: Listening to Displaced People Survey, 2014.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 9: Implementation Support Plan**\n\nWorld Bank supervision missions will be conducted biannually and will focus on: (i) review of implementation progress against the results framework; (ii) fiduciary compliance, including review of IFRs and audit reports; (iii) environmental and social risk management, including field visits to at least two active subproject sites; and (iv) assessment of data quality for MIS-generated indicators. Between missions, task team members will maintain monthly contact with PIU coordinators via video conference and review outputs from the activity monitoring dashboard.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure A2: Refugees a burden for SSA? Panel A: Not weighted by economic capacity Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "hinders future prospects. Globally in 2019, almost half of all refugee children were out of school (UNHCR, 2020d). Of those attending school, most do not make it past basic education; gross enrolment in primary education stood at 77 percent in 2019. Yet, the contrast between primary and secondary school enrolment remains stark, and only 31 percent of refugee children were enrolled in secondary school, much below the global average of secondary school enrolment. A recent study of refugee children in Kakuma refugee camp in Kenya, for example, found that literacy and learning outcomes for refugee children were significantly lower than in immediate host community or the rest of Kenya (Piper et al., 2020). This report shows that education outcomes for children in Ethiopia are low across all population groups and ages but particularly for refugee children. COVID-19 exacerbated this situation for many refugee children (Wieser, 2020). 1.2 How does Socio-Economic Survey of Refugees in Ethiopia (SESRE) contribute to the debate on policies? The Socio-Economic Survey of Refugees in Ethiopia (SESRE) is a representative survey of the refugee population in Ethiopia and their host communities, the first of its kind.9 Ethiopia made significant progress over the past few years in articulating", "output": {"entities": {"named_data": ["Socio-Economic Survey of Refugees in Ethiopia"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "One of the main contributions of this paper is the use of panel data which allows us to examine the effect of the pandemic on labor market transitions — job loss and job gain rates — in addition to the effect on labor market stocks. Studying both stocks and flows provides a comprehensive framework to analyze the impact of the pandemic on labor markets and allows for a better understanding of the underlying mechanisms 30 Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "13 The 2007 Liberia DHS asks respondents “ During the war, did you leave your house? ”- a question intended to explore whether respondents moved to a camp, lived in the bush or faced another form of displacement because of conflict. Those respondents who answered “ yes ” to this question were classified as forcibly displaced. It is noteworthy that this question was followed by a question about where the respondent was displaced. Answers included: stayed with relatives or friends inside Liberia; went to a camp; living in the bush; went outside Liberia. However, respondents could choose multiple of these options. For this reason, the first question was chosen as the most simple and accurate measure of displacement. Conflict at the District-Level Previous studies have used number of conflict fatalities as an effective proxy for levels of political instability (Kelly, 2018), since fatalities are the most definitive and violent measure of armed conflict. In addition, since the UCDP and ACLED data sets code conflict events differently, fatality measures were the most comparable between the data sets. For both UCDP and ACLED, the conflict was coded as 1 if women lived in a district with conflict fatalities and 0 if she did not. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Ghana is experiencing its third gold rush, and this paper sheds light on the socioeconomic impacts of this rapid expansion in industrial production. Using a rich data­set consisting of geocoded household data combined with detailed information on gold mining activities, the authors conduct two types of difference-in-differences estimations that provide complementary evidence. The first is a local-level analysis that identifies an economic footprint area very close to a mine, and the second is a district-level analysis that captures the fiscal channel. The results indicate that men are more likely to benefit from direct employment as miners compared to men further away, and that women in mining communities may more likely gain from indirect employment opportunities and earn cash for work. Authors also find that infant mortal­ity rates decrease significantly in mining communities, compared to the evolution in communities further away. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "As explained in section 4, the HRVD dataset contains data on the number of human rights violations occurred since the start of the conflict in 1975 until its end in 1999 for each district. The types of violations recorded are killings, deaths due to deprivation and disappearances. We use only the number of killings to identify years and districts affected by the conflict. We exclude the deaths due to deprivation because the districts in which this occurred may very likely not be those where the conflict was most intense, but were simply places were the victims were hiding as a consequence of escaping from the troops, and died for starvation. In addition, since killings are less likely to affect entire families than deaths due to deprivation, there is a lower underreporting bias attached to the former measure relative to the latter one (Silva and Ball 2006). We also exclude disappearances as, according to HRVD data, they do not show enough time and geographical variation in order to identify individuals more or less exposed to the conflict. We believe that the number of killings proxies quite well the intensity of the conflict across time and space as their occurrence largely tracked the movements of the Indonesian military operations. The other two types of violations do not seem to show the same pattern (Silva and Ball 2006). For the same reason, we believe that it proxies quite well the destruction of houses and infrastructure and the displacement of people given the way in which the last wave of violence occurred (i. e. the scorch-earth technique employed by Indonesian troops as they moved towards West Timor). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["HRVD dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "16 { Tables 4 and 5 here} Among the first six categories that are based on raw census data, three categories (raw scaled, R & R not scaled, and R & R scaled) are constructed through the summation of bilateral raw numbers and disaggregations of some aggregate categories in the original censuses. Since these categories together constitute around 45 percent of migrants in each census round, the original bilateral portion of each cell was compared with the final number assigned to them after the various calculations as a check on accuracy. For each decade, therefore, the overall percentage contribution of the raw bilateral data to the total is calculated (table 6). 23 In each census round, at least 92 percent of all those categories are derived from the raw data. { Table 6 here} Simulating Missing Data Finally, to examine the reliability of the estimated missing census data and test the methodologies, several scenarios are assumed. All bilateral observations for a single year for four countries (Australia, United States, Switzerland, and Chile) in different parts of the world are deleted and the missing cells are filled using one of five methods. 24 The first simulation assumes that all bilateral data for 2000 are missing but that the total number of migrants is available.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["raw census data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Refugee Policies on Self-Reliance and Resilience. Journal of Refugee Studies, 33(1), 22-41. Kvittingen A., Valenta M., Tabbara H., Baslan D., and Berg B. (2018). The Conditions and Migratory Aspirations of Syrian and Iraqi Refugees in Jordan. Journal of Refugee Studies 32(1), 106-124. https://doi.org/10.1093/jrs/fey015. Lebow, J. (2023). Immigration and Occupational Downgrading in Colombia. Journal of Development Economics, Forthcoming Levenson, H. (1981). Differentiating among Internality, Powerful Others, and Chance. Research with the Locus of Control Construct, 1, 15-63. Liu, D. and Kwan, M. (2020). Measuring Spatial Mismatch and Job Access Inequity Based on Transit-Based Job Accessibility for Poor Job Seekers. Travel Behaviour and Society, 19, 184-193. https://doi.org/10.1016/j.tbs.2020.01.005 ESS and World Bank. (2023). Ethiopia Socioeconomic Panel Survey Report – Wave 2, 2021/22. Marbach, M., Hainmueller, J., Hangartner, D. (2018). The Long-Term Impact of Employment Bans on the Economic Integration of Refugees. Science Advances, 4. https://doi.org/10.1126/sciadv.aap9519. Martin, S. F. (2010). The Causes and Consequences of Forced Migration. Paper presented at the Annual Meeting of the Population Association of America, Dallas, TX. Maystadt, J. F., and Verwimp, P. (2014). Winners and Losers among a Refugee-Hosting Population. Economic Development and Cultural Change, 62(4), 769-809. MoE. (2022). Education Statistics Annual Abstract (ESAA). MoE MoLSA. (2019). Revised Directives", "output": {"entities": {"named_data": ["Ethiopia Socioeconomic Panel Survey Report"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "street vending (48 % of those with at least 1 IGA), food processing for sale, including baking, cooking, and drying (16 %), and home production of crops, livestock, and fish (11 %). It is important to note that the EPAG program was not targeted toward the most vulnerable segments of Liberian society, but rather toward young women with enough education to be able to benefit from a training program of this nature. Based on comparisons from the 2007 CWIQ (Core Welfare Indicators Questionnaire) survey, the average EPAG participant is more educated, more literate, more likely to be engaged in an income-generating activity, and owns more assets than the average Liberian women of the same age group: mobile phone ownership was high (63 %), as was the proportion reporting that they had some money of their own (79 %). Even compared to other similar residents of Monrovia, the EPAG participants are better educated and have higher income. A strong sense of female empowerment at baseline emerges from the sections of the survey instrument having to do with self-confidence and agency. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Core Welfare Indicators Questionnaire"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "35 Inputs: 1. Household Survey with consumption or income welfare aggregates 2. Project data / Other survey data without welfare aggregates 3. Set of harmonized common variables in both surveys Outputs: 1. Set of imputed welfare variables for project data / other survey for each household in the data 2. Imputed welfare variables can be used for poverty, distributional analysis (quintiles or more), profiling of the poor or group of interest Models: 1. Ordinary Least Squares (OLS) 2. Probit 3. Multiple Imputation (MI) Table 18. Model Specification Variables Demographic Share of children, share of adults, share of adults squared and share of old (omitted) Characteristics of head Age, gender, and level of education Interactions with urban dummy variable Level of education of the head, age of the head Geography Dummies for regions at NUTS 1 level (12 regions) Interactions with Geography Level of education of the head, age of the head interacted with regions at NUTS 1 level (12 regions) and urban-rural division 1. Validation and Robustness Check Figure 7. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household Survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "21 Lebanon. It was the obvious step forward in overcoming these problems and the answer to the increasing demand for evidence around the Syrian crisis. The study benefitted from a unique and unprecedented set of data. The UNHCR registry data in Jordan and Lebanon were among the better quality registry data available worldwide and the UNHCR also conducted home visits in Jordan that, at the time of the study, covered over a third of all refugees. There were also sample surveys in both Jordan and Lebanon that were small in size but representative of the population present in the registry. The home visits and the surveys included questions on income and expenditure that could be used for the welfare assessment. Using these data, the study addressed ten questions defined as follows: 1) Who are the refugees?; 2) How different are refugees from “ regular ” populations?; 3) How poor are refugees?; 4) What are the main predictors of refugees ’ welfare and poverty?; 5) How vulnerable are refugees from a monetary and non ‐ monetary perspective?; 6) Do poverty and vulnerability statuses overlap?; 7) How effective are refugee assistance programs?; 8) What is the potential for alternative policies?; 9) How does welfare compare across countries and data sets?; 10) How transferable are Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The activity monitoring dashboard displays three traffic-light indicators for each of the four project components: disbursement rate against plan, beneficiary registration progress, and grievance resolution rate. Components rated yellow have fallen 10–20 percent below target; red indicates more than 20 percent below target and triggers an escalation report to the Bank within five business days. At the end of Q3, Component 1 was rated green on all three indicators, Component 2 yellow on disbursement, and Components 3 and 4 green.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "this way, profiling of IDP situations aims to underpin advocacy, protection and assistance activities as well as support the achievement of durable solutions by informing joint strategies between government, humanitarian and development actors. Profiling provides an overview of displacement-affected populations through the collection and analysis of minimum core data (number of IDPs, disaggregated by location, age and sex) and where possible additional quantitative and qualitative data (causes of displacement, patterns of displacement, protection concerns, humanitarian needs, vulnerabilities, and aspirations and prospects for durable solutions). Profiling may utilize data collection techniques at individual, household and community levels, often combining population estimation methods, a review of secondary data, focus group discussions, household surveys and key informant interviews targeted specifically at forcibly displaced populations (UNSD 2014). 70 Profiling methods focus on displacement situations, rather than only on displaced populations, and therefore includes comparisons to conditions in the host population. IDMC estimates that humanitarian profiling data forms the basis for 18 of their 60 country estimates and around 63 percent of their annual estimates (IDMC 2015), with the largest volume of data on conflict-induced internal displacement provided by OCHA followed by IOM. There are several practical challenges associated with IDP profiling exercises in displacement situations. Insecurity or terrain may impede access to displaced populations in conflict-affected or hard to reach areas. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "_Source_ : EM-DAT (2008) _Notes_ : Figures greater than 1,000 such as the total number affected by storm in 1995 (  1,145 affected per 1,000 people) are possible due to instances where there are multiple occurrences of a particular disaster in one country in a given year.\n\nThis paper presents a set of estimates of the impacts of natural disasters on different forms of capital (physical, human, natural and energy), and thereby on real GDP per capita. The capital database is compiled by the World Bank for three periods - 1995, 2000 and 2005 and for 210 countries. This was combined with data on four types of natural disasters - droughts earthquakes, floods, and hurricanes/storms - for 196 countries, taken from the Emergency Events Database (EM-DAT). The disasters database lists a total of 55 events of drought, 82 events of earthquake, 447 events of flood and 303 events of storm that started during the three years of 1995, 2000 and 2005.\n\nEmergency Events Database (EM-DAT). 2008. _Data on Natural Disasters by type_ . Centre for Research on the Epidemiology of Disasters (CRED), School of Public Health of the Université Catholique de Louvain, Brussels, Belgium.", "output": {"entities": {"named_data": ["EM-DAT", "Emergency Events Database (EM-DAT)"], "descriptive_data": [], "vague_data": ["Data on Natural Disasters", "capital database"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In a mortality study in Iraq, Galway et al. (2012) used GIS and Google earth imagery for household sampling. The method used gridded population data for selection of clusters. The first cluster sampling stage of their study used the ‘ Create Spatially Balanced Points ’ (CSBP) function in the ArcGIS (v10) software. Boo et al. (2020) introduces a sampling design based on gridded population estimates as their sampling frame to implement a PPS design and derive sample size estimates for the number of grid cells. Assuming the grid square method is applied to the area itself rather than a selected PSU, the weights for the grid method are similar to those for segmentation, where the cells are the PSUs, but without the additional step of selecting segments. The weights can therefore be represented as 𝑤𝑤𝑖𝑖 ′ = (𝑁𝑁𝑘𝑘) ൫𝑁𝑁𝑘𝑘𝑘𝑘൯൫𝑁𝑁𝑘𝑘𝑘𝑘𝑘𝑘൯ 𝑘𝑘𝑘𝑘𝑘𝑘. 2. 4. North Method The “ Qibla method ” described in Himelein et al. (2017), or what is called in this paper the “ North method ” method, is an attempt to assign probability weights to random point selection methods. Several random point selection methods can be found in the literature, particularly in relation to epidemiological studies. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["gridded population data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "rents to GDP from WDI), we can implicitly compute 𝛾.\n\nSource:_\n_Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._\n\n\nFigure 2 plots the sources of growth for the Sub-Saharan Africa region as well as the different groups according\n\nSource: Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._\n\nFrankel, J. A., & Romer, D. H. (1999). \"Does trade cause growth?\" American Economic Review 89(3): 379-399.\n\nreported in the Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015). [15]", "output": {"entities": {"named_data": ["Penn World Tables 9.0", "Penn World Tables"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 of the most fragile countries where due to security concerns, physical access to reach them is restricted. Furthermore, many IDPs do not live in camps but rather mix with other population groups particularly in urban areas, thereby making it even more difficult to identify them (Baal and Ronkainen 2017). Figure 4: Number of IDP datasets vis-à-vis size of IDPs hosted by country A) Number of IDP datasets and number of IDP (ln) B) Number of IDP datasets by country Notes: Panel A shows the bivariate relationship between the number of IDP datasets and number of IDPs (log-transformed). Panel B shows the ranking of countries in terms of the number of IDP datasets available in the MDLs. Note that some survey datasets are stored as separate entries in the UNHCR and WB MDLs even though they are indeed part of the same survey (e. g., entries by camp or by wave). Those independent entries are counted as one dataset when they are part of the same survey, thus resulting in a total number of 22 IDP datasets excluding those datasets that are project specific or collected before 2010. Topic coverage In terms of the coverage of topics, FDP datasets are relatively richer in topics such as coping mechanisms, protection, water and sanitation, food insecurity, and health. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The GLSS poverty module follows the Foster-Greer-Thorbecke poverty measurement framework, using the national absolute poverty line established by the Ghana Statistical Service. The GLSS consumption aggregate includes food and non-food expenditure, imputed rental values for owner-occupied housing, and the estimated value of in-kind transfers. The GLSS poverty line is set at the 2005/06 prices and updated using a spatial price deflator that accounts for urban-rural and regional price differences. All welfare estimates in this paper use GLSS analytical weights and are adjusted for adult equivalence.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "13 Figure 5. Share of refugees hosted in camps, 2013 Source: Authors ’ presentation based on UNHCR Global Trends 2013 (UNHCR 2014). In summary, investigating the recent trends in forced displacement in Sub ‐ Saharan Africa points to the regional nature of this displacement, emphasizing the unfortunate increase in refugee movements in Eastern Africa over the most recent years. Such regional emphasis also takes some distance from the widespread view that refugees are mainly moving to Europe or other developed countries. In 2013, about 3. 7 million refugees originated from SSA but about 5. 6 million were hosted there. Most refugees from SSA remain in Africa. Refugees are mainly hosted in camps in peripheral and poor areas. The next sections will explore how refugees and hosting communities are affected by such forced displacement.", "output": {"entities": {"named_data": ["UNHCR Global Trends 2013"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Between outreach and baseline, one full survey round was collected due to potential survey fatigue and on the understanding that nothing of importance would likely change in such a short period. Basic demographic information, such as age, gender, marital status and employment status were collected at outreach. At baseline, additional indicators were collected, relating to the behavior, attitudes, opinions and personalities of the participants. The only exception to this is data on optimism, which were collected at both outreach and endline. This allowed us to test whether or not the intake decision had effects, even before the training began. Endline data were collected between July 2018 and November 2019 and repeated the combined outreach and baseline surveys and experiments. Variables: We collected a range of survey and experimental indicators in order to assess our key research questions and associated hypotheses: 6 Economic and life optimism: We collected two survey questions about optimism at outreach, baseline and endline. These questions ask individuals to rank their expectation that their life and economic situation will be better in one year than it is now. Answers are 4 In addition, data were collected from Palestinian Refugees in Lebanon (PRL). Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["data on optimism"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Between outreach and baseline, one full survey round was collected due to potential survey fatigue and on the understanding that nothing of importance would likely change in such a short period. Basic demographic information, such as age, gender, marital status and employment status were collected at outreach. At baseline, additional indicators were collected, relating to the behavior, attitudes, opinions and personalities of the participants. The only exception to this is data on optimism, which were collected at both outreach and endline. This allowed us to test whether or not the intake decision had effects, even before the training began. Endline data were collected between July 2018 and November 2019 and repeated the combined outreach and baseline surveys and experiments. Variables: We collected a range of survey and experimental indicators in order to assess our key research questions and associated hypotheses: 6 Economic and life optimism: We collected two survey questions about optimism at outreach, baseline and endline. These questions ask individuals to rank their expectation that their life and economic situation will be better in one year than it is now. Answers are 4 In addition, data were collected from Palestinian Refugees in Lebanon (PRL). Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["data on optimism"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Firstly, we do not capture all items in Uganda ’ s public spending or revenue collections, such as the large investments in infrastructure, expenditure on national security (on the spending side) or the corporate income tax (on the revenue side), despite that these fiscal instruments provide indirect benefits (or detriments) for households. 7 Secondly, as Inchauste and Lustig (2017) point out, the approach does not provide information on the trade-off between long-term human capital spending- which creates higher future levels of human capital (for example, a better educated and healthier, more productive citizenry)- and short-term spending on programs that bring immediate poverty relief (such as conditional cash transfers). 5 The corresponding publications for these analyses are: Hill et al. (2017) for Ethiopia, Younger et al. (2017) for Ghana, Inchauste et al. (2017) for South Africa, Younger et al. (2016) for Tanzania, and World Bank (2018a) for Kenya. 6 http: / / www. oecd. org / tax / tax-policy / revenue-statistics-in-africa-2617653x. htm 7 Identifying the individual beneficiaries of general infrastructure spending as well as the individuals who might bear the burden of a corporate income tax is difficult with only a standard household budget survey. In addition, investment in infrastructure in the current fiscal year will also provide benefits in the future that are impossible to allocate to the households identified in the household survey. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "12 though somewhat lower than that of the Turkish at 30- 50 percent. Child labor is also quite prevalent, though there have been extensive efforts made to ensure that refugee children attend school. 19 Publicly available information on refugees comes from an AFAD survey of 2, 700 households in June and July 2013. Figure 2, using data from AFAD (2013), provides an overview of the Syrian governorates from which the refugees to Turkey originated. The refugees primarily come from northwest Syria. The largest source regions are Aleppo (36 percent), Idleb (21 percent) al-Raqqah (11 percent), Lattakia (9 percent), and Hamah (8 percent). Consistent with travel distance being a good predictor of refugee flows to Turkey, 80 percent of respondents report that they chose to flee to Turkey, instead of another country, due to the ease of transportation. The refugees in Turkey, unlike the later 2015 refugee flows to Western Europe, are nearly 50 percent female. Slightly over 50 percent are minors (under the age of 18). These facts reflect that to large extent Syrian families fled to Turkey together.", "output": {"entities": {"named_data": ["AFAD survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Border crossings The registration of people crossing internationals borders is conducted in many countries, and in some cases these data are used to estimate migration flows. Identifying refugees among people crossing borders is a significant challenge, particularly if individuals decide not to apply for asylum or refugee status (UNSD 2014). Additional problems associated with the collection of data on border crossings include: (a) difficulties distinguishing migrants from other people crossing a border, such as tourists, commuters, traders and truck drivers; (b) lack of capacity of many border posts and officials to handle large migration flows; (c) less scrutiny and diligence of emigration flows compared with immigration flow; and (d) lack of tight controls at most borders and the high incidence of undocumented or irregular crossings (UNSD 2014). Administrative records and registers Many countries have administrative records or registers of immigrants that could generate statistics on asylum-seekers and refugees. In particular, data on residence permits issued to refugees or asylum- seekers could be used to generate statistics on both flows and stocks of refugees. 79 For example, Eurostat collects and disseminates data on residence permits granted to those with refugee status and subsidiary protection (UNSD 2014). Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "When national or international actors provide assistance, there may be an incentive for people to register in camps even if they are staying elsewhere, or to register in multiple locations (Brookings 2011). 69 Furthermore, registration data provide only a snapshot of the stock of IDPs at a particular point in time and may be out of date if registers are not maintained regularly. Registration methodologies can vary across displacement situations. For example, families may be registered rather than individuals and the population estimated based on an assumption of average family size, which can differ among organizations (UNSD 2014). IDPs may be required to present documentation, meet specific criteria or re-register periodically to maintain their benefits, which affects aggregate numbers (IDMC 2015). For example, in Ukraine, pre-requisites for IDP registration (including valid documentation, arrival from a recognized conflict zone and permanent residence registration in recognized conflict zone) means that people displaced within a non-government controlled area, people displaced from a non- recognized conflict zone in a government controlled area, unaccompanied children or people without current / valid identification are not counted as IDPs (IDMC 2015). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["registration data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In aggregate, we expect to observe a negative relationship between the percentage of the population that reports displacement, but at the individual level we expect that experience with hosting may, in certain circumstances, be positively associated with perceptions of social cohesion, regarding perceptions of relationships and solidarity. 4 Displacement and Social Cohesion: Survey Evidence To empirically evaluate the relationships between hosting displaced populations and social cohe- sion, this paper analyzes a series of surveys of civilian adults conducted in eastern DRC. 8 Each survey uses a multi-stage cluster sampling strategy capturing all territoires9 in North Kivu, South Kivu and Ituri provinces. The ﬁnal sampling units are randomly selected adults above the age of 18 to avoid bias toward men and / or heads of households. Multiple attempts are made over the course of one day to contact selected respondents and if necessary, appointments are made for in- terview. Surveys are enumerated by Congolese college students or professionals and interviews are conducted by members of the same gender and ethnicity as respondents to minimize enumerator- induced response bias. Further methodological details have been published (Vinck, Pham, Bindu, Bedford & Nilles 2019) elsewhere and additional details and sample size calculation are detailed in Appendix.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["surveys of civilian adults"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "11 Figure 3: Location of refugee camps in Ethiopia and the Ethiopia Development Response to Displacement Impacts Project (DRDIP) sample households Source: Authors ’ compilation using the database of the Global Administrative Areas (GADM) (https: / / gadm. org / data. html, accessed November 20, 2020) and the Humanitarian Data Exchange (HDX) database for the refugee location (https: / / data. humdata. org / dataset / ethiopia- refugee-camp-locations, accessed November 20, 2020). From the Ethiopia DRDIP data set, we derive two measures of livelihood diversification and two measures of agricultural commercialization (all at household level). The measures of diversification include: (i) the degree of labor diversification in different productive livelihood activities (e. g., farming, wage employment) as a primary activity (occupation), and (ii) the degree of labor diversification in different livelihood activities as a secondary activity (occupation). 13 These two outcomes were constructed using the inverse Simpson diversity index as 1 ∑ 𝑛𝑛 𝑖𝑖 𝑆𝑆𝑖𝑖 2, where 𝑆𝑆𝑖𝑖 is the share of the number of adult labor engages in 𝑖𝑖𝑡𝑡ℎ livelihood activity to total active adult household labor and 𝑖𝑖 ranges from 1 to the number of livelihood activities that a household engages in (Valdivia et al. 1996).", "output": {"entities": {"named_data": ["database of the Global Administrative Areas", "Ethiopia DRDIP data set", "Humanitarian Data Exchange"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "13 Where 𝑌𝑌𝑖𝑖 represents one of the indicators of social cohesion explained above, 𝛿𝛿𝑗𝑗 is the province indicator, 𝑅𝑅𝑐𝑐 is the share of returnees in the community, 𝐻𝐻𝑖𝑖 indicates a series of household level controls and 𝐶𝐶𝑐𝑐 are a series of community level of controls. In the main regressions we estimate the share of returnees in the community, using the information from the survey (i. e. share who are returnees), but in the robustness section we show that results are robust to the use of an alternative indicator in which the information is provided by a community leader. The Appendix (Table A2) includes the descriptive statistics for the control variables. We present results for the full sample and divided by communities with lower / higher ethnic diversity, less / more pre-1993 war land availability and better / worse attitudes towards return. In the robustness checks we also present the results if we limit the analysis to stayees only. Limiting the sample in this way does not affect the main results of the paper. 4. 4 Identification As mentioned above, Tanzania mandated the return of all Burundian refugees from the 1993 conflict. Returnees also had a very strong incentive to return to their communities of origin as this was the place in which they were entitled to land, a very scarce resource in the country.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "17 Revealingly, in Betts et al. ’ s (2013) survey of refugees in Uganda, 96 % of all interviewed households in the capital and 70 % outside the capital said they owned and used a mobile phone. They use this mobile phone to communicate with customers and suppliers, to get market information and to transfer money. Half of the urban refugees and 11 % of rural refugees also have access to the Internet. 4. Refugees As a Burden? As pointed in Section 2, most refugees in SSA are hosted in neighboring countries. Most of these hosting countries are likely among the least developed countries. It has been argued that these refugees may constitute an additional burden in terms of economic development in hosting countries (Mabiso et al. 2014). UNHCR (2014: 17) implicitly recognizes that potential burden by suggesting that the ratio of the size of the country ’ s hosted refugee population to its average income level can provide a proxy measure of the burden of hosting refugees. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["survey of refugees in Uganda"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "ethnic group e at time t. It can also be expressed as one minus the Herfindahl index (Alesina et al., 2016). The EP index gives more weight to intergroup differences at the expense of within group homo- geneity. It can be defined as (Esteban and Ray, 1994, 1999; Montalvo and Reynal-Querol, 2005) 14 EPjt = Nrt X e = 1 (g2 et) (1 − get). (3) We compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity. In order to construct the revised refugee diversity indices according to ethnicity e, we first combine information about the country of origin of refugees hosted in refugee camps c in year t with the data from the EPR-ER 2019 dataset. The EPR-ER records the ethnic composition of refugee stocks originating from neighboring countries and countries in proximity to each other (maximal distance between country borders ≤ 950 km) with at least 2, 000 refugees and provides the ethnic composition of refugees (Vogt and Girardin, 2015). More specifically, the EPR-ER dataset gives us the share of refugees from ethnic group e moving from country o to country d at year t. The EPR-ER data gives us the three main ethnic groups. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["EPR-ER 2019 dataset", "Afrobarometer survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "itative exercise empowered focus group participants to guide the research and conceptualization. The results from these exercises then dictating the deﬁnition and measurement strategy for social cohesion in the quantitative assessment. The participatory research strategy was based mainly on structured focus group discussions. The project conducted consultations in seven territoires with the objective to develop localized understandings of what elements were important to social cohesion in eastern DRC. Participants were selected from civil society and the public sector. 96 individuals participated in these exercises (Table 1), 55 % of whom were men and 45 % of whom were women. 6 Location (Groupement) Date Number Participants Goma City 06 Oct 2017 11 Bukavu City 13 Oct 2017 13 Nyabibwe (Kalehe) 12 Oct 2017 14 Ishungu & Lughendo (Kabare) 12 Oct 2017 15 Kamisimbi (Walungu) 16 Oct 2017 18 Wassa (Walikale) 20 Oct 2017 12 Biiri (Masisi) 03 Nov 2017 13 Table 1: Descriptive Information on Focus Groups The focus group discussions began with an open discussion on social cohesion designed to ascertain participants familiarity with the concept. The facilitator further asked participants to write down words or concepts participants related to social cohesion. These words were written on individual post-its, which were then posted on a wall. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5 if they are built to code. 2 These spillovers or externalities are absent in more sparsely populated rural areas where damages to smaller sized and dispersed dwellings will cause less or no collateral damage. Exposure The main reason why urban risk is large and increasing is the rise in exposure. Although urbanization statistics suffer from a lack of standard definitions of what should be considered ‘ urban ’, the assumption of half the world ’ s population living in cities seems realistic. Urban populations are growing in practically all developing countries. About 40-60 percent of this growth can be attributed to natural growth, i. e., fertility of urban dwellers (Montgomery 2009). The remaining growth is due to urban expansion and migration, reducing the share of rural residents except where rural fertility is vastly larger. The latest UN urban population estimates suggest that, globally, urban population exceeded rural population for the first time in 2008 (UN 2008). In less developed regions, this threshold is expected to be reached by 2019. This continuing urbanization process will lead to an increase of exposure of people and economic activity in hazard prone urban areas. Although we can only speculate about the global distribution of disaster damage in cities today and in the future, newly available geographically referenced data yield some estimates of urban exposure to natural hazards. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["geographically referenced data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1. Introduction Sub-Saharan Africa is the youngest region in the world, and the world ’ s fastest-growing. Between 2010 and 2025, the number of people between 15 and 24 will grow to 250 million – a net increase of nearly 50 percent. Over the next decade, roughly one million young people will enter the labor market each month in Sub-Saharan Africa. However, these young people often enter the labor market too early and unprepared. Although access to education is growing, illiteracy remains high and schooling low: among the 32 Sub-Saharan African countries in the Barro-Lee (2010) data set, nearly 40 percent of women aged 15 and above have received no education at all; and the most recent (2007-2011) statistics in the World Bank ’ s Edstats data reveal that female literacy is less than 60 percent, on average. 1 This lack of preparedness contributes to a growing problem of youth unemployment. Quantifying the level of unemployment is bedeviled by lack of data and measurement issues. Household and labor force surveys throughout Africa usually record unemployment rates of less than 10 percent, 2 but those figures belie the extent of underemployment and vulnerability. Those same surveys indicate that the vast majority of working adults have insecure work in the informal sector, on the family farm, or in less- productive or unremunerated labor.", "output": {"entities": {"named_data": ["Barro-Lee", "Edstats data"], "descriptive_data": [], "vague_data": ["Household and labor force surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Refugees and Host Communities Household Survey expanded the national Household Consumption and Informal Sector Survey to include a representative sample of refugees and host communities, including Sudanese and host communities located in the east of the country. The remainder of this note is organized as follows. Section 2 presents a short discussion of the literature on the economic participation of refugees. Section 3 compares the characteristics of newly arrived refugees from Sudan with previous arrivals for whom survey data is available, to find that both groups are highly comparable. Section 4 uses the existing data to explore how the basic needs refugees are covered from own-income. Sections 5 and 6 dig deeper by exploring econometrically the correlates of higher incomes of refugees. A discussion of the results and their policy implications follows in section 7, after which section 8 concludes. 2. Benefits of economic participation of refugees Whether or not the arrival of Sudanese refugees in Chad contributes to economic growth is of limited immediate relevance as concerns about the safety of fellow humans drive the response. Nor does any decision maker suggest that hosting refugees is a development strategy Chad should pursue. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Overall, the results remain unchanged from those in table 4. A third concern is that the samples of males and females that are employed in the analysis may include adult siblings who belong to the same household. This could introduce intra- household serial correlation among these observations. Thus, as a robustness check, the sample is restricted to males and females who are household heads or their spouses, hence excluding adult 10. This results in excluding 381 “ movers ” among males and 371 “ movers ” among females, or roughly 9 percent of the original samples. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We only found reliable approximations from the International Displacement Monitoring Center (IDMC) between 2003 and 2013. According to IDMC, there were about 12. 5 million internally displaced people in SSA at the end of 2013 (IDMC 2014), more than one third of the total number of IDPs and more than tripling the number of refugees in SSA. Although the number of IDPs in SSA is the highest since 2007, the share of IDPs in SSA has been decreasing from 53 % in 2003.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Formula (5) can be decomposed into two parts: eδs, which measures the average income level in district s, and eηszh ≡ eβs (Eh s − Es) + eχs (Hh s − Hs) which captures individual-speciﬁc variation in income. Migration models predict that, other things being equal, the choice of migration destination should depend on g E [yhs | zh]. This means that if we regress the choice of destination separately on eδs and eηszh, they should have the same coeﬃcient. The same methodology is used to construct other variables that may aﬀect the choice of 9The literature has often emphasized that migrations often serve an important role in household formation. For migrants, the prospect of forming a large, successful household is likely to be one of the purposes of migration. 10The 1995 / 96 NLSS survey adopted the following sampling strategy. Within each district a small number of wards were selected at random. Within each ward, 12 randomly selected households were interviewed. Because the wards diﬀer widely in terms of population, applying sampling weights is essential in order to obtain consistent estimates of δs. 16", "output": {"entities": {"named_data": ["NLSS survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We then use international data (EMDAT 2009) to estimate the relationship between storm damages and national income and population density (vulnerability).\n\nThe analysis relies on the A1B SRES emissions scenario generated by the Intergovernmental Panel on Climate Change (IPCC 2000). The scenario assumes that mitigation is tightened gradually over time so that greenhouse gas concentrations finally peak and stabilize at 720 ppm.", "output": {"entities": {"named_data": ["EMDAT 2009"], "descriptive_data": [], "vague_data": ["international data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Cash assistance is provided after a thorough assessment process. Following a family’s registration in UNHCR’s\nregistration and case management system (ProGres V4), partner organizations carry out an evaluation to assess\nthe applicants’ socio-economic situation and prioritize assistance requests based on pre-set vulnerability criteria,\nincluding persons at risk, with disabilities or serious medical conditions.\n\nAssistance is provided for up to 3 months, which can be extended for an additional three months after supplementary\nevaluations. The MPG value depends on family composition, and ranges from BRL 839 for one person and BRL\n1,284 for a family of 6 or more. The amount of assistance provided is standardized based on socio-economic publicly\navailable data through an annual costing survey (minimum expenditure basket). Beneficiaries receive the cash\ntransfer immediately after it is approved by UNHCR. Prepaid cards are distributed by partners and cash is transferred\nto the cards directly by UNHCR. During the COVID-19 emergency, cash assistance has been delivered following\nsecurity and preventive measures, reducing personal interactions with remote evaluations and registration.\n\n### 2.PDM METHODOLOGY\n\n- Details about the PDM:", "output": {"entities": {"named_data": ["UNHCR’s\nregistration and case management system"], "descriptive_data": [], "vague_data": ["annual costing survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 to 32 percent (see Table A1 in appendix). The total number of internally displaced people (IDP) outnumbers the stock of refugees in SSA and in the world but overall has followed a similar trend compared to the number of refugees (by country of origin). 1 Major civil wars in Central Africa mainly explained the peak in 1993 and 1994 and the increase at the end of the 1990s. Figure 1. Refugee population by origin, 1990 ‐ 2013 Note: Authors ’ aggregation based on UNHCR statistical population online dataset, accessed in September 2014. Data from 2007 to 2013 include people in refugee ‐ like situations. Persons in refugee ‐ like situations include “ groups of persons who are outside their country or territory of origin and who face protection risks similar to refugees but for whom refugee status has, for practical or other reasons, not been ascertained ” (UNHCR 2014: 39). Refugees in Africa seem to have mainly remained in Africa. Although SSA also hosts refugees from other regions, the closeness of the ‘ blue ’ and ‘ red ’ lines in Figure 2 ‐ representing the number of refugees originating from and hosted in SSA ‐ is an indication that most refugees cross borders within Africa. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["UNHCR statistical population online dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15 resulting in government forces recapturing rebel held territory, establishment of a rebel base or headquarters, rebel activity that is not battle related (e. g. presence or the killing of civilians), and territorial transfers. The dataset consists of 4, 145 battle events for the 1960 – 2004 period. In the present analysis, we use 2, 530 of these. The remaining events were dropped as they either were in countries not included in the analysis, or because information was missing for one of the key variables. Each conflict event is associated with geographic coordinates and a date of occurrence. This information allows for spatial and temporal modeling of conflict events. The dataset used in this article covers 14 countries in Central Africa. 6 of them had a conflict in the 1960 – 2004 period according to the Uppsala / PRIO Armed Conflict Dataset (Gleditsch et al., 2002): Angola, Burundi, Republic of Congo (Brazzaville), Democratic Republic of Congo (Zaire), Rwanda, and Uganda.", "output": {"entities": {"named_data": ["Uppsala / PRIO Armed Conflict Dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Concerns raised during consultations about the GBV risk associated with construction-phase migrant labor were addressed through a site-specific GBV annex appended to each contractor's ESMP. The annex requires contractors to conduct community sensitization sessions on the project's SEAH prohibitions before works mobilization, to post information on the GBV Action Plan reporting hotline at all worksites, and to participate in quarterly coordination meetings with the local SGBV response service providers.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**SOUTH SUDAN** | October 2023\n\n#### **PROTECTION RISKS**\n\nPersons with disabilities in South Sudan share a history of exclusion and are generally considered to be non-equal members of\nsociety. Today, persons with disabilities still have few opportunities to improve their socioeconomic status, integrate into\nsociety, or demand rights and recognition by society and the government, despite recognition of their rights under the\nTransitional Constitution of South Sudan, 2011, as amended in Article 30. They continue to face significant social and political\nexclusion and are among the most marginalized in society. Awareness of disability issues among key decision-makers and the\npublic is low, negative social attitudes and structural discrimination prevail, and persons with disabilities have limited access\nto essential services and employment. [ix] According to a survey conducted by Ministry of Gender, Child, and Social Welfare in\n2011, in Central Equatoria, Eastern Equatoria and Jonglei states, 89% of persons with disabilities are unemployed. [x]", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4 al. 2014), mobile phone surveys also turn out to be remarkably flexible and adaptive. New questions can be introduced on a needs basis and in-depth qualitative interviews can be carried out at a moment ’ s notice. These qualities make mobile phone surveys well suited for monitoring welfare in volatile environments: they have been used for welfare monitoring during the ebola crisis in Liberia (Himelein 2014) and for welfare monitoring in conflict-affected areas such as South Sudan (Demombynes et al. 2013). Unique about using a mobile phone survey with a displaced, mobile population is that it allows tracking welfare during displacement, and upon return. 8 Three target populations were identified for the purpose of this survey: Internally Displaced Persons (IDPs) living in Bamako, refugees in refugee camps in Mauritania and Niger, and returnees in Gao, Timbuktu and Kidal, the capitals of regions that bear their names. The sample does not include those who were not displaced by the crisis nor those who returned to places other than the three regional capitals in the North. While the sub-sample of IDPs includes exclusively IDPs in Bamako, and the refugee sub-sample only refugees in Niger and Mauritania, the returnee group includes people who were displaced elsewhere (33 %). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "10 Our sample consists of married couples in 12 Sub-Saharan African countries covered by the DHS for which both husband and wife answered the question “ who usually makes decisions about making major household purchases? ” The question is asked about large purchases overall and is not obtained by aggregating different survey questions on decision making over individual asset purchases. It was introduced in the questionnaire administered to husbands in 2004. The last two phases of the DHS thus include a consistent question on decision making for both women and men in married couples over making large household purchases. Response options were (a) respondent, (b) husband / wife, (c) respondent and husband / wife jointly, (d) someone else, (e) other. In these 12 countries, women were also asked questions about their experience with different forms of violence through the Domestic Violence Module. This module asks eligible women whether they ever experienced emotional, physical, or sexual violence perpetrated by their husband. 1 The answer options are (a) never, (b) often (c) sometimes (d) yes, but not in the last 12 months or (e) yes, but currently widowed / divorced / separated. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For example, profiling exercises in Pakistan do not cover all IDPs or areas affected by displacement due to insecurity, and in Afghanistan profiling of IDPs by UNHCR underestimates the scale of the initial displacement as IDPs are only interviewed once displacement sites are accessible, if they are profiled at all (IDMC 2015). Additional challenges include unwillingness of IDPs to participate due to fear of persecution, and mobile populations (IDMC 2008). There may also be political pressures to inflate or reduce numbers. Population movement tracking systems In situations where the movement of displaced populations is fluid or continuous, a movement tracking system can be a useful tool for providing rough estimates of population flows, including recurrent displacements. Movement tracking systems are useful for monitoring fluid population movements (including spontaneous and organized, internal and cross-border, and returns and resettlement) in remote or inaccessible routes and locations (including displacement sites, places of origin, and places of return and resettlement). UNHCR, IOM and other organizations have developed methods for tracking and monitoring movements of IDPs in over 30 countries, particularly in cases of disaster-induced displacement, but also in some cases of conflict-induced displacement (UNSD 2014). Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the ENILEMS-ENLACE panel. The first panel is formed by a cohort of students' test\n\npanel merges a special module of the Mexican labor survey, _Encuesta Nacional de Empleo_\n\n_y_ _Ocupaciones_, ENOE, applied to individuals aged 18, 19 and 20 years during the third\n\nquarter of 2010, with students that sat for the ENLACE test in Grade 12 in 2008, 2009,", "output": {"entities": {"named_data": ["ENILEMS-ENLACE panel", "Encuesta Nacional de Empleo", "ENOE"], "descriptive_data": ["Mexican labor survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 as self-confidence, responsibility, and respect. Additionally, the curriculum includes workplace readiness skills, such as interviewing and time management. SECTION 2: STUDY TIMELINE & DATA A quasi experimental impact evaluation design was embedded into the NVSP. As mentioned before, the NVSP received 38 applications from eligible NGOs. Per well-developed selection criteria, 8 the highest 22 ranked proposals were selected to receive funding. Each of the 38 proposals included a list of 50 youth (the minimum number of youth set by the NVSP) who would benefit from the project if selected for funding. However, as mentioned before, the 22 selected projects benefited a total of 1, 296 youth, exceeding the set target of 1, 100 volunteers. Of the 50 volunteers included in each of the 38 proposals, 22 youth per proposal were randomly selected to participate in the impact evaluation study. Therefore, the initial sample size of the study comprised a total of 825 youth: 473 youth who served as the treatment group (representing the 22 selected NGOs that received NVSP funding) and 352 youth who served as the comparison group (representing the 16 non-selected NGOs). However, two NGOs refused to participate in the study once informed that their proposals had not been selected for funding. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The welfare of Afghan refugees has now been studied in their main host country, Pakistan, and in Afghanistan upon return, providing elements to compare the living conditions of this population in the two locations. Again, all these surveys and studies are either very recent or were not used before for poverty / welfare analyses. Whereas individual and household data on refugees are now more systematically collected, data collection for IDPs remains extremely scarce when compared to refugees, mostly limited to head counting. Refugees and IDPs are very different in that they have a different legal status, which leads to different access to public services, labor markets, government, and international assistance. Surveys for IDPs are often more difficult to conduct because the host country might be linked to the cause of displacement, and a registration system like UNHCR ’ s proGres is absent, making it more challenging to obtain a representative sample. Given the large number of IDPs (58 percent of all FDPs), the real challenge will be to collect microdata on IDPs systematically in all those countries that are home to large numbers of IDPs. So far, the only country that collects data on IDPs systematically is Colombia and this country has shown that, when quality microdata are available, research on IDPs flourishes. While many of the discussed issues need more attention and research, some processes are in place that should lead to the establishment of guidelines that address some of these questions. A recent process 12 All these surveys can be found in the World Bank microdata library at: https: / / microdata. worldbank. org / index. php / catalog? sort_by = rank & sort_order = desc & sk = refugees. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Page 12 of 51 Figure 6: Temporary employment by age group. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys. In the case of temporary employment, there is evidence of increasing female participation in the five countries analyzed, with large variations in several cases. Indeed, in the five countries for which we identified temporary workers, while temporary employment was predominantly male in the 1990s, today women show a participation higher than 50 % in all cases. Figure 7: Temporary employment by age group. (Mid-1990s / Mid-2010s) employment by gender (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Brazil Chile Mexico El Salvador > 64 years old 50 ‐ 64 40 ‐ 49 25 ‐ 39 15 ‐ 24 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 % 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s 1990s 2010s Argentina Uruguay Brazil Chile Peru Bolivia Dominican Republic Mexico El Salvador Female Male Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["Household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "and female refugees narrows as the location gets more remote. Female refugees are 8 percentage points less likely to be employed then male refugees in remote areas, but 13 percentage points in locations near Zone capital cities (Figure 6.9). Refugees in Woredas well-connected to markets have better prospects of being employed. Compared to other locations, Woredas with above- average market accessibility increase employment probability for refugees (Annex D, Table D.15). Regardless of education level and gender, the chance of being employed is below 25 percent for refugees living in a Woreda with a level of market access >1.5 standard deviations below the average. Moreover, the gender gap in employment persists at any level of market access but becomes more pronounced with decreased market accessibility. Female Male 2 .6 .5 .4 .3 .2 .1 12 22 32 42 52 62 72 82 92 102 112 122 132 142 Distance to nearest capital city (Km) Probability of being employed Figure 6.8: Distance to the nearest city and the chance of obtaining a job for refugees Source: World Bank Staff based on SESRE 2023. Note: Predicted marginal probabilities of being employed based on the distance to the nearest Zone capital city, tabulated by", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "22 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). To uncover the main drivers of the observed gender based differences in multidimensional poverty at country level, we study the absolute contribution of the gender difference in each indicator to the overall household gender gap (see Figure 6), calculated as the difference between the censored headcount ratio for males versus females. We find that in Ethiopia, the gender gap that disadvantages female-headed households is mostly driven by the difference in financial insecurity measures (lack of legal ID and bank account) and health measures (early marriage, physical safety, and food insecurity), which is further reinforced by the differences in the living standard and education measures. Female-headed refugee households are more food insecure, live in unimproved housing, have lower access to electricity, are more likely to be married at an early age, and have lower access to legal identification and a bank account. In South Sudan, gender gap that disadvantages female-headed households is mainly explained by the differential in the financial insecurity and health measures, but cumulative gaps in the living standard and education indicators also contribute to the overall gap at the household level.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["High Frequency Surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Therefore, in addition we adopted another adapted calculation method which is based on travel paths simulations using OD pairs from the JICA travel survey.\n\nThe conventional approach calculates shortest paths between every possible pair of nodes within the network whereas the adapted approach simulates the travel trajectories based on the multimodal transportation model and accounts for public transport waiting times, road speed limits, and designated origin and destination pairs acquired from the JICA commuter travel survey.\n\n**Acknowledgments:** We would like to thank GoMetro for leading the public transport mapping, and in\nparticular Bob Kabeya and Clayton Lane, as well as the enumerators that conducted these surveys. We thank\nShohei Nakamura, Takaaki Masaki and Mervy Ever Viboudoulou Vilpoux for their help in accessing\nKinshasa commuter and household surveys. We are grateful to Laurent Corroyer, Stephane Hallegatte and\nKirsten Homman for their inputs and feedback throughout this project. We are also grateful to Catalina\nOchoa for her review of an earlier draft of this paper and her thoughtful comments. This study was supported\nby the Global Facility for Disaster Risk Reduction and Recovery (GFDRR).\n\nThese data sets were combined with travel survey data containing travelers' socioeconomic attributes and trip parameters, as well as a high-resolution flood maps.", "output": {"entities": {"named_data": ["JICA travel survey", "JICA commuter travel survey"], "descriptive_data": ["Kinshasa commuter and household surveys", "travel survey data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Drawing on two years of panel survey data, this paper studies the dynamics of vaccine acceptance, its correlates, and reasons for hesitancy over time. The data come from multiple rounds of national High-Frequency Phone Surveys in five countries in East and West Africa (Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda), covering the period between 2020 and 2022.\n\nWe use data from High-Frequency Phone Surveys (HFPS) in five countries in Sub-Saharan Africa: Burkina Faso, Ethiopia, Malawi, Nigeria, and Uganda. The surveys were conducted by study countries' national statistical organizations (NSOs), supported by the World Bank's Living Standards Measurement Study (LSMS). Since May 2020, the LSMS-supported HFPS have collected cross-country comparable longitudinal data on a wide range of topics, focused on COVID-19 impacts on households and individuals.\n\nThe HFPS have national coverage and draw their samples from nationally representative samples of households interviewed in pre-pandemic face-to-face surveys of the LSMS-Integrated Survey on Agriculture (LSMS-ISA) series. In the settings at hand, mobile phone coverage is not universal, and the sample selection of phone surveys may not yield samples fully representative of the general population.\n\nWe use weather data taken from the _Terrestrial Air Temperature and Precipitation Version 4.01_ compiled\nby the University of Delaware (Willmott and Matsuura 2001) that has widely been used in the economics\nliterature (Dell Jones and Olken 2012, Burke Hsiang and Miguel 2015, among others). This data set provides\nmonthly total precipitation and temperature at a 0.5-degree spatial resolution. Data are available for each\nmonth between 1901 and 2014. Annual precipitation at the cell level between 1990 and 2014 ranged\nbetween 3mL in Sudan to 10,187mL in India.", "output": {"entities": {"named_data": ["High-Frequency Phone Surveys", "LSMS-Integrated Survey on Agriculture", "Terrestrial Air Temperature and Precipitation Version 4.01"], "descriptive_data": [], "vague_data": ["panel survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "C Details on outcome measures Outcome Variable Descriptions Psychological Well-being PHQ9 The standardized total score of 9 questions from the Patient Health Questionnaire-9 (PHQ9) Life Satisfaction Index A standardized average of survey responses to four questions from Di- ener ’ s standardized scale, responses made along a seven-point Likert scale. Stress Index The standardized total score from three elements of adapted from the Cohen Stress scale. “ How many of the last 7 days have you [been able to fall asleep peacefully / felt nervous / felt frustrated]? ” Sociability (Total) The total number of conversations in the past day with adults. Sociability (Positive) The total number of conversations in the past day with adults that the respondent felt were positive. Self-Worth Index The standardized total score from the responses on a scale from 1 to 10 to two questions: “ Think of a person you know who you most respect and who brings greatest value to your [family / community].", "output": {"entities": {"named_data": ["Patient Health Questionnaire-9"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Most importantly for the purpose of the analysis is the fact that the survey provides individual-level data on own schooling and parents ’ schooling for all adults in the sample, which is quite rare in household surveys from developing countries. Also, the survey provides the actual years of schooling completed and not only the highest educational degree attained, which allow observing the schooling variable with precision. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This distinction is also applied to foreign students, with those entering the system after February 2022 referred to as “ Migrant post-Feb 2022 ” and others referred as “ Migrant pre-Feb 2022 ”. 7 Administrative data from MoE. The Ministry data includes information for all students who enrolled at any point during the academic year. 8 This information covers academic year, grade, gender, birth date, birthplace, citizenship. They also include school-speciϐic information such as the name and identifying code of the institution where the student is enrolled. Furthermore, the dataset includes a variety of school outcome variables, including grades in English, Italian, Mathematics, overall GPA calculated as the average across all subjects, behavior scores from grade 9 to grade 12, guidance council evaluations from lower secondary school, records of absences, late entries, and early exits. Given the timing of this study, data for academic year 2022-23 are the most complete. For academic year 2022-2023, school enrollment data at the provincial level was provided for 4, 269, 348 enrolled students across the 8 years of Italian lower and upper secondary school, encompassing both public and private institutions. The dataset includes nearly all students in the country irrespective of their citizenship. 9 Table 1 shows the distribution of the different groups of students by grade. In the 7 For ease of reference, we refer to non-Italian and non-Ukrainian students as migrants. However, we acknowledge that some of these students may be refugees or displaced students. 8 At the time of writing this paper, both MoE and INVALSI data were not fully available and, as such, only the information on enrollment was used for the academic year 2023-24. 9These numbers do not include students enrolled in Provincial centers for adult education (CPIA). See footnote 5. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Protection monitoring for the northern corridor documented 487 households between January and March. Findings indicate that 41 percent of surveyed households face barriers to accessing documentation services, and 28 percent report incidents of property or land disputes with host community members. Protection monitoring results were shared with the district authorities and the national protection cluster secretariat, which incorporated the findings into its quarterly situation report and used them to support an advocacy letter to the Ministry of Interior requesting streamlined access to civil registration services.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Enumerators reminded respondents that the survey was part of an academic study and not connected to service provision decisions, which we hope alleviate some of these incentives. That said, it is important to note that this project analyzes self- reported perceptions of the presence of IDPs or refugees, not the conﬁrmed presence of displaced populations. Hosting Status Aggregate Individual % Respondents Displaced Currently Self-Reported Hosting % Respondents Recently Displaced Self Reported Hosting IDPs Self Reported Hosting Refugees Table 3: Operationalizing Hosting Status 24 Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "This allowed for consistent ranking the households into welfare quintiles and cross- tabulation of welfare status with household characteristics and indicators derived from the survey data. The imputation was carried out using s2sc algorithm in STATA. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Historical trends and patterns of forced displacement: Insights from available global data This section provides an overview of the available global data on conflict-induced forced displacement, drawing largely on UNHCR ’ s published data on asylum-seekers, refugees and IDPs. 41 Data are presented visually in a series of figures to highlight the scope and character of the current global forced displacement crisis and identify historical trends and anomalies. These data largely focus on the scale and trends in conflict-induced displacement (i. e. the numbers of forced displaced) with some coverage of other elements such as demographics, location and accommodation. Globally, there has been an unprecedented increase in the numbers of displaced people over the last decade, largely explained by the expansion in the number of reported IDPs. Historical data show a substantial increase in the numbers of forced displaced (see Figure 3), however the expanding geographical scope and quality of displacement monitoring systems are likely to account for much of the increase in forced displacement figures. The numbers of refugees under UNHCR ’ s mandate have recorded a number of variations over time, peaking in the early 1990s (at a level 10 percent over 2015 numbers) with the conflict and displacement associated with the end of the Cold War. The number of Palestinian refugees steadily has increased steadily over time, largely as a result of natural growth. IDP numbers (for which the underlying data are the least robust) have recorded the largest progression as a consequence of: (a) the expanded scope of monitoring efforts (IDPs were not counted before 1989 and methodologies were 41 UNHCR ’ s data only include IDPs protected or assisted by the agency.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["global data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "With this estimation strategy, we identify the ‘ pure ’ effects of genocide on fertility over and above conflict-related effects like urbanization, destroyed infrastructure, and weaker health infrastructure – which in turn also affect fertility. We study the short- and long-term effects of genocide and differentiate the analysis by cohort, kinship relation and gender of the deceased persons. Our approach is unique in that we estimate the effects of genocide on fertility using multiple measures of conflict exposure and disaggregating in more dimensions than done previously. In Rwanda, this is important as by law collecting a key conflict proxy, ethnicity, is not possible. In other contexts using conflict proxies may be important if data-sets, which do not contain any direct identification of conflict at the micro-level, are to be analyzed retrospectively. The paper has five major findings. First, we find a significant direct (or ‘ pure ’) effect of mass violent conflict in determining fertility. Of all conflict proxies studied, the loss of a child Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "schooling decisions, occupational choice, and savings (Cobb-Clark et al., 2016; Heckman et al., 2006). In Ethiopia, higher LOC has been shown to predict farmer adoption of modern agricultural technologies (Taffesse and Tadesse, 2017). In refugee populations, low LOC also correlate with depression, anxiety, and psychological distress (Schlechter et al., 2023; Tsionis et al., 2022). Higher LOC has also been found to improve employment and socio-economic integration among immigrants and refugees in Germany (Hahn et al., 2019; Thum, 2014). Compared to hosts, South Sudanese refugees perceive less personal control over their lives and destinies. Based on SESRE data, the index used to construct a measure of personal control over one’s life is an unweighted average of 10 LOC- related questions. The index (Likert scale) ranges from 1—”little control over one’s life”—to 4—”more control over one’s life”. The index is 0.12 points (.25 standard deviations) lower for refugees than hosts indicating they feel they have lower control over their lives and destinies, and this difference is statistically significant. This difference, however, is driven by South Sudanese refugees. When comparing LOC by country of origin, we find that there is only for South Sudanese there is a significant difference between refugees and hosts", "output": {"entities": {"named_data": ["SESRE data"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "8 agreements that did not fully satisfied either party (Ndayirukiye and Takeuchi 2014). This tension related to land, and who has a claim to the land, can lead to social tensions in communities with higher levels of return. Figure 3 – Refugees in Tanzania in 2005 by province of origin in Burundi Note: The number in brackets is the number of refugees in Tanzania in 2005 which was originally from the given province in Burundi. This information comes from (UNHCR 2021b). The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census. An important question for our hypotheses is the degree to which there is evidence of migration-related societal divisions in the country. There is no direct quantitative evidence on identity issues (i. e. returnees versus stayees), but we have data on attitudes towards emigration, remittances and return that can provide insights on these identities and even be a proxy for migration-related identity in some cases. Overall, attitudes towards emigration and return are mixed and show that there is scope for the existence of migration-related divisions. In Table 1 we report the share of respondents who agreed with different statements regarding emigration, remittances and return.", "output": {"entities": {"named_data": ["1990 Burundi Census"], "descriptive_data": ["data on attitudes towards emigration, remittances and return"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We measure time-use through the number of hours in the previous day a respondent reports spending idle, as well as the amount of time spent on a variety of other common activities one might do in the camps (including bathing, market, chores, collection of rations, eating, child-rearing, sitting at tea stalls, praying, sleeping, visiting friends / relatives, playing games, playing sport, sitting idle). Finally, we ask respondents how much they consume, borrow and save over the past week. We further consider changes in perceptions on gender and power in two ways. First, we generate a Household Power Index, composed of a set of questions on perceptions of gendered decision-making and intimate partner violence. The questions are drawn from Haushofer and Shapiro (2016), which are themselves adapted from the Demographic Health Surveys. In addition, we produce a Work Rights Index, composed of questions around whether respondents feel that women should be allowed to work inside or outside the home or the camp block. Each outcome is described in greater detail in Appendix C. The frequency with which each outcome is collected is also presented in Appendix C. Multiple hypothesis testing We utilize two approaches to address the issue of multiple hypothesis testing. First, we present our primary outcome, psychosocial well-being, as an inverse-covariance weighted index variable following Anderson (2008). We also generate index variables for other outcomes in which this is possible, such as the cognitive index, the household power index, and the work rights index. Our second strategy is to report the sharpened False Discovery Rate (FDR) q-values for all outcomes within a particular table, which control for the expected proportion of rejections that are type I errors, likewise 12", "output": {"entities": {"named_data": ["Demographic Health Surveys", "Work Rights Index"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The daily precipitation data used are from the 3- hourly data set from the Tropical Rainfall Measurement Mission Project (TRMM), which is aggregated up to daily data.\n\n\\n<!-- PAGE 28 -->\\ndata set product 3B42RT 3 hour product gives the best results in a basket of 8 near real-time rainfall products. The 3B42RT daily derived product is what is used in this paper.\n\n\\n<!-- PAGE 8 -->\\nthrough 2015. During the period from 1985 to 2016, the Dartmouth Flood Observatory (DFO) registered 3,808 floods of magnitude 4 or more and 1,175 floods of magnitude 6 and up. [3]\n\nAccording to the Indonesian National Disaster Management Authority (BNPB), there were more than 19,000 natural hazards in the period 2001 - 2015 (National Disaster Management Agency 2016), making Indonesia a useful country for any natural hazard analysis.\n\nAccording to the Global Facility for Disaster Reduction and Recovery (GFDRR), the Philippines is at high risk from several types of natural hazards (GFDRR 2019). Prime among them are cyclones, where an average of 20 make landfall every year. In 2013, typhoon Yolanda led to 6,000 casualties and damaged more than 1.1 million houses. The Philippines are also exposed to earthquake and flood risks.", "output": {"entities": {"named_data": ["Tropical Rainfall Measurement Mission Project (TRMM)", "3B42RT daily derived product"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "In the third (Rahim Yar Khan) there is a large difference, with the census reporting that 1 percent of all school-going children attended madrassas, and the LEAPS showing that the fraction is closer to 3. 7 percent (Table II). There are three potential explanations for this difference. First, the LEAPS data is not representative of the district and could be off the mark for districts with wide variation in madrassa enrollment across rural and urban samples. Second, the experience of the last five years could have varied dramatically across districts — in some, the enrollment fractions did not change and in others it increased substantially. Third, the data could point to systematic problems with the census estimates from certain districts, or the statistical problems that arise when we try to estimate low-probability events. 3. 3 Explaining the Differences A number of reasons could account for differences between the estimates presented here and those in the popular press. 1. Differences in the sampling unit. Our estimates are all based on household surveys — an interviewer goes to a household and asks about the enrollment status of every child. Some census estimates of home rather than religious schooling in the United States — the former ranges from 1 to 2 percent (Bauman 2001) while the latter is closer to 8 percent (National Center for Education Statistics, 2001). 10 In our own analysis, we find the quality of the data generated by the Federal Bureau of Statistics in Pakistan to be consistently high. We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages.", "output": {"entities": {"named_data": ["FBS Census of Private Schools", "LEAPS"], "descriptive_data": [], "vague_data": ["household surveys"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The countries that are the largest source of refugees include: Afghanistan, Syria, Somalia, South Sudan, Sudan, Democratic Republic of Congo, Central African Republic, Iraq, Myanmar, and Eritrea (UNHCR, 2017). These countries account for the vast majority of refugees worldwide. We will also consider three countries in Europe that are an important source of asylum seekers in Europe, and whose recognition rate is low, and account for many returnees – these are Kosovo, Serbia and Albania. Is return possible and safe, and if so, does the economic situation bode well to provide essentials, namely a job, housing, education and reliable public services? It is helpful to identify three groups of countries: ones that are still mired in war and / or high ‐ intensity civil conflicts, countries where medium ‐ intensity conflict persist and are already in the process of rebuilding, and countries which are not in conflict. According to the Armed Conflict Survey (IISS 2017), high ‐ intensity conflict is defined by frequent (daily) armed clashes between governments, government forces and insurgents, or among non ‐ state armed groups that control territory. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": ["Armed Conflict Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "(a) High frequency sample surveys using smartphone and cellular technologies. For example, a high-frequency survey initiative in Somalia employs a dynamic questionnaire loaded onto smartphones, which enables data to be collected from household interviews in 60 minutes. This approach was developed to overcome the challenges of insecurity, limited data gathering capacity and budgetary constraints. (b) The use of mobile phones to conduct surveys or follow up interviews following face-to-face household surveys. For example, a Bank paper on the impact of the 2012 crisis in Mali on IDPs, refugees and returnees used information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. This combination provided a mechanism to monitor the impact of conflict on hard-to-reach populations who at times live in areas inaccessible to enumerators. And in Sierra Leone and Liberia, the Bank supported the use of mobile phones to collect key socio-economic data on the effects of the Ebola virus. (c) Crowdsourcing data on displacement. Platforms such as the Kenyan Ushahidi has crowd- sourced data on displacement in Kenya and eastern DRC by encouraging IDPs and host communities to report incidents using their mobile phones or the internet, including information about living conditions. The platform references these reports geo-spatially. (d) Geo-mapping of data on displaced populations and affected host communities.", "output": {"entities": {"named_data": [], "descriptive_data": ["face-to-face household survey"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The algorithm is fed with 24 different satellite bands, measuring surface reflectance in the visible and infrared spectra, topographical data from the Shuttle Radar Topography Mission (SRTM), and road network data from the OpenStreetMap project.\n\nInformation on aquaculture production in Vietnam's two main river deltas was obtained from two data\nsources. In the Red River Delta, a vector dataset of the locations of individual aquaculture ponds was used.\nIt was created and provided by researchers of the German Aerospace Center (DLR). As part of their\nmethodology, an algorithm extracted individual ponds from time series of radar satellite data. Specifically,\n83 Sentinel-1 scenes from between 2014 and 2016 were processed. Radar data, such as the observations\nfrom the Sentinel-1 series of satellites, enables the detection of water-covered surfaces irrespective of\ndaylight and clouds.\n\nOverall, an\naccuracy of 0.84 is reported for pond detection in the Red River Delta (Ottinger et al. 2018). In the Mekong\nDelta, a vector dataset containing the locations of individual aquaculture ponds was provided by SWIRR.", "output": {"entities": {"named_data": ["Shuttle Radar Topography Mission", "OpenStreetMap project"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Not all cross-sectional studies have multiple rounds of comparable data, covering the period before and after the crisis. When comparing impacts between locations within a country, cross-sectional data also usually does not allow to capture impacts on those who moved out and to differentiate impacts between those who were already there before the shock and those who moved in afterwards. Some of the models based on administrative areas qualify as spatial econometrics models in that they use estimation methods that derive from this literature and are published in spatial econometrics journals. Studies that compare different areas within a country are not only confronted with the potential endogeneity of the size and skill composition of the inflow and the choice of destination, but also with the endogenous reactions of the host community. Local workers might respond to the labor supply shock by dropping out of the labor force, investing in education, occupational upgrading or moving to other areas and diffusing the impact of the inflow. Even if local workers do not respond to wage variations, capital flows may equalize capital / labor ratios within the country, labor-intensive industries might move towards the regions with a high refugee or IDP influx or firms might use more labor-intensive production technologies. The reactions of the host country workers, investors and firms are medium-to long-term in nature and will play less of a role in the short-term if there are large, sudden and geographically concentrated inflows. Some of the papers explicitly analyze these potential channels, notably migration of local workers, and, to a lesser extent, occupational upgrading. Outmigration of hosts is a critical complement to the labor market analysis and excluding this outcome can lead to an underestimation of the impacts of forced displacement on the labor market outcomes of natives. The papers we reviewed that looked at tasks complexities and the question of substitution vs complementarities between refugees and natives found occupational upgrading among Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 5: Significant Historical Crises as a Share of Total Forced Displaced 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes IDPs protected or assisted by UNHCR, asylum-seekers and refugees. Excludes IDPs not protected or assisted by UNHCR and Palestinian refugees under UNRWA ’ s mandate. Figure 6: Top 15 Countries of Origin end-2015 Source: IDMC Global Report on Internal Displacement 2016, UNHCR Global Trends 2015, UNRWA A small number of countries carry the burden of hosting the majority of refugees. Historically since 1991, 15 asylum countries, overwhelmingly in the developing world, have hosted more than 50 percent of refugees and asylum-seekers (see Figure 7). 44 By the end of 2015, while almost all countries in the world were hosting refugees, the burden was unevenly shared (see Figure 8). Only seven countries hosted more 44 Major host countries are identified based on the cumulative number of refugees and asylum-seekers over the period 1991-2015.", "output": {"entities": {"named_data": ["UNHCR Statistical Online Population Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "15 4. 1. 2. Empirical strategy In order to make use of the retrospective information on school attendance provided in the dataset, we have constructed what we have called an ‘ ad hoc panel ’, whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset. In this way, we are able to obtain observations for each individual over three academic years. All key education variables are time- variant, while other individuals and households characteristics are time-invariant. Within these three years, we focus our analysis on individuals that were of primary school age (between 7 and 12 years old) in each year. In practice, we keep all children aged at minimum 7 years old in 1998 and at maximum 12 years old in 2000. As a consequence, our panel data contains children aged 8-11 years in 1999, the year of the violence. 11 Since we are interested in looking at different effects across groups of individuals, we have split the sample between boys and girls and between younger children (aged 8-9 in 1999) and older children (aged 10-11 in 1999). Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": ["TLSS 2001 dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Respondents who were untraced were much more likely to be residing outside Kagera (43 %) compared to their counterparts who were re-interviewed (8 %). The consumption data come from an extensive consumption module administered in 1991 and again in 2004. The consumption aggregate includes home produced and purchased food and non-food expenditure. The non-food component includes a range of non-food purchases, as well as utilities, expenditure on clothing / personal items, transfers out and health expenditures. Funeral expenses and health expenses prior to the death of an ill person were excluded. Monetary levels were adjusted to account for spatial and temporal price differences, using price data collected in the Kagera survey in 1991 and 2004, and, for households outside Kagera, data from the National Household Budget Survey. Consumption is expressed in per capita, per annum terms. The poverty line is set at TZS 109, 663, calibrated to yield for our sample of respondents who remained in Kagera the same poverty rate as the 2000 / 1 National Household Budget Survey estimate for Kagera (29 %). 4. Growth, Poverty and Physical Mobility in Kagera In this section, we discuss changes in living standards overall, and the changes for four mutually exclusive groups based on residence in 2004: (i) still residing in the baseline community, (ii) residing in a", "output": {"entities": {"named_data": ["National Household Budget Survey", "Kagera survey"], "descriptive_data": [], "vague_data": ["price data", "consumption data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "namely those refugees who arrived in Addis Ababa after November 2020. Data collection took place between November 2022 and January 2023. Sample population The SESRE covers three types of groups, all of which require a distinct sampling procedure:55 (i) refugees in camps; (ii) refugees out-of-camps; and (iii) host communities. This section discusses the sampling frames of each group. (a) Refugees in Camps The sampling frame for refugee camps is based on UNHCR’s proGRES database. The refugee camps were grouped into three domains based on the concentration of refugees from the three major origin countries: South Sudan, Somalia, and Eritrea.56 The first sampling stage divided each camp into enumeration areas (EAs). Based on the proGRES database, we created pseudo EAs by taking 150-200 Annex C: Survey Design and Methodology 54 Formerly the Household Consumption and Expenditure Survey and Welfare Monitoring Survey. 55 Prior to the sampling process, the survey team conducted a pre-sampling assessment by visiting the camps to verify on-the-ground conditions. 56 Refugees from Sudan were not included in SESRE as, at the time of sampling, there were less than 50,000 Sudanese refugees in Ethiopia and inclusion was not deemed cost effective. Annexes 93 households in a row from the", "output": {"entities": {"named_data": ["proGRES database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We, therefore, first examine whether or not there is structure, both, to selection into the treatment group and attrition, which could undermine our econometric approach, where we rely on difference-in-difference estimators. Imbalance between treatment and control groups could undermine the key assumption of parallel trends. For example, given that men and women face different barriers in the labor market, we should not expect employment to evolve in the same way for men and women after the treatment. We would expect to observe a difference-in-differences for a treatment group where women are more common than in the reference group, even without the program. To test for imbalances, we run a simple regression of treatment and attrition indicators at baseline on the socio-economic and demographic controls, GRIT indicators, self-reported optimism, employment status and risk. Table 4 (Column 1 for the treatment analysis, Column 2 for the attrition analysis) shows some signs of structure. In particular, host status and risk preferences are significantly different between treatment and control, with 9 For example, “ hummus ” is used to refer to chickpeas in general but can also be used for the dish involving mashed chickpeas, tahini, lemon and garlic in Lebanon. In other dialects, some qualifiers are required to specify this dish (e. g. hummus ne ’ em, or smooth hummus). This is akin to identifying a British or American individual using similar variations in foodstuffs such as courgette / zucchini; coriander / cilantro; etc. 10 Specifically, we regress variables with missing observations on the list of all variables with a complete record. We then use the predicted values from this regression to populate the missing variables. Where appropriate, predicted values are rounded to the nearest integer and within answer codes of that variable. In a second round, this process is repeated on the full set of actual and predicted values from the first stage. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["GRIT indicators"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These surveys are part of a long-term data collection effort by the research team (Vinck & Pham 2014) and were collected separately from the focus group discussions. The focus group discussions thus did not directly inﬂuence the design of the surveys, but rather directed the analysis strategy of the surveys that our team has collected at regular intervals in eastern DRC. The survey data are analyzed in two ways. First, 11 surveys collected between 2017 and 2021 8Eastern DRC is a site of ongoing violence, raising a number of ethical, methodological, and practical concerns about collecting data. We discuss the ethical protections we implemented when collecting this survey data in the Appendix, Section B. 9Territoires are sub-provincial administrative units. Additional details on the structure of administrative units are available in the Appendix, Section A. 1. 21", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "First, I include a measure of whether citizens believe that a large portion of tax administrators is corrupt. Second, I in- clude a variable indicating whether citizens approve of how well their local government is handling the collection of license fees on bicycles, carts and barrows. 8 Third, both the size of a country and the size of the government may aﬀect a government ’ s ability to detect and punish evaders. I include the 7I also include a country-level indicator of government performance, the World Bank Governance indicator of government eﬀectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implemen- tation, and the credibility of the government ’ s commitment to such policies (Kaufmann, Kraay and Mastruzzi, 2006, 4). This variable is not signiﬁcant at the p <. 05 level. 8I included two additional measures in the model neither of which were signiﬁcant at the p < 0. 05 level. One is a measure of citizens ’ approval of how well their local government council provides citizens with the information about the councils budget (i. e. revenues and expenditures). The other, the World Bank governance indicator, control of corruption, measures the extent to which public power is exercised for private gain, as well as capture of the state by elites and private interest (Kaufmann, Kraay and Mastruzzi, 2006, 4). 13", "output": {"entities": {"named_data": ["World Bank Governance indicator"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The JLMPS sample is restricted, after matching it to the 2010 school census, to individuals born in Jordan who are aged 25 to 70 in 2010 and who have nonmissing information on age, subdistrict of birth, years of schooling, father ’ s schooling, mother ’ s schooling, and local supply of schools in subdistrict of birth. 9 These exclusions resulted in a sample of 4, 139 males and 4, 131 females, which are referred to as the male and female full samples, respectively. 7. Because of the absence of annual estimates of subdistrict populations, the population used to normalize the supply of schooling at the subdistrict level is the 2004 population of the subdistrict. There are 86 subdistricts in Jordan. If subdistrict populations are growing at different rates, this could introduce some measurement error of the true supply of schooling available to different cohorts. 8. Secondary schools include both general and vocational secondary schools. Public schools include schools under the jurisdiction of: (i) Ministry of Education, (ii) Ministry of Higher Education, (iii) Ministry of Defense, (iv) Ministry of Social Development, (v) Ministry of Religious Endowments (Awqaf), and (vi) UNRWA. 9. The original sample size of all individuals who are aged 25 to 70 years in 2010 and are born in Jordan is 8, 312 observations. The sample restrictions on the missing values result in the exclusion of 34 observations (missing age), 1 observation (missing father ’ s schooling), and 7 observations (missing mother ’ s schooling).", "output": {"entities": {"named_data": ["JLMPS", "2010 school census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "B Description of Variable ’ s Construction This section describes how we constructed each of the variables in our main analysis. B. A Outcome Variables Outcome variables were constructed using the data from the Afghanistan Welfare Moni- toring Survey conducted for this study in the fall of 2021. • Integration: we measured the integration dimension with three different types of questions. The first included the individual reported answer from a 1 to 5 scale, where 1 means strongly disagree, and 5 strongly agree of the following questions: (i) ” Would you feel comfortable if your child or grandchild were to socialize or be friends with children of Refugee community people? ”, (ii) ” Do you feel welcomed in this village / town / city? ”, (iii) ” Do you think that everyone living in this village / town / city feels like they are a part of this village? ”. The second, included the answer to the following question ” On a scale of 1 to 10, with 1 being not very much to 10 very much, how much do you feel that Tajikistan is your home? ”, and the third, the total number of refugee, and Tajik friends among all of their friends, respectively. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "C Details on outcome measures Outcome Variable Descriptions Psychological Well-being PHQ9 The standardized total score of 9 questions from the Patient Health Questionnaire-9 (PHQ9) Life Satisfaction Index A standardized average of survey responses to four questions from Di- ener ’ s standardized scale, responses made along a seven-point Likert scale. Stress Index The standardized total score from three elements of adapted from the Cohen Stress scale. “ How many of the last 7 days have you [been able to fall asleep peacefully / felt nervous / felt frustrated]? ” Sociability (Total) The total number of conversations in the past day with adults. Sociability (Positive) The total number of conversations in the past day with adults that the respondent felt were positive. Self-Worth Index The standardized total score from the responses on a scale from 1 to 10 to two questions: “ Think of a person you know who you most respect and who brings greatest value to your [family / community]. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Patient Health Questionnaire-9"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "7 Database. Where both censuses and population registers are available, censuses receive priority. Censuses, generally conducted decennially, are retrospective tools for surveying an entire population (or in some cases, a representative sample) at a single point in time. In addition to their universal coverage, their greatest strength is the inclusion of questions on place of birth and nationality. Censuses also typically aim to enumerate the resident population, whether documented or undocumented (Bilsborrow and others 1997). So although some migrants have a strong incentive to provide false information to enumerators, many undocumented migrants will be captured in these matrices. 7 The size and scope of the census questionnaires vary enormously, both over time and in different destination countries. And there is potential variation in the quality of censuses both across countries and over time. Richer countries have many resources at their disposal to design questionnaires, train interviewers, employ statisticians, and disseminate results. Researchers have little choice but to accept the data at face value. However, where the underlying census is clearly substandard (when there are errors that are obviously not coding errors or not easily corrected), these data are omitted from the analysis. Popular in many parts of Europe, population registers are continuous reporting systems providing up-to-date demographic and socioeconomic information for everyone surveyed. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["population registers", "censuses"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Host response to “Would you feel comfortable having a refugee as a neighbor?” by gender ��������������������������������������� 65 Figure 7.5: Share of hosts who agree refugees should have access to... 66 Figure 7.6: Host beliefs about refugee impact in Ethiopia ......................... 66 Figure 7.7: Negative experiences due to refugees �������������������������������������� 66 Figure 7.8: Positive experience due to refugees ����������������������������������������� 66 Figure 7.9: Are most Ethiopians/refugees in Ethiopia trustworthy? .......... 67 Figure 7.10: Host attitudes index................................................................... 67 Figure 7.11: Host attitudes index by gender ������������������������������������������������� 67 Figure 7.12: Share with family or friends in Ethiopia ������������������������������������� 68 Figure 7.13: Share with friends in Ethiopia by demographic group ............. 68 Figure 7.14: Share of refugees who think interactions with hosts are “easy to do” ........................................................................... 69 Figure 7.15: Who do refugees rely on in times of need �������������������������������� 69 Figure 7.16: Share of refugees who agree they are “culturally similar to hosts”....................................................................... 70 Figure 7.17: Share or refugees involved in a community representative body 70 Figure 7.18: Share or refugees engaged in a community representative body by demographic group ���������������������������������������������������� 70 Figure 7.19: Discrimination and harassment ������������������������������������������������ 71 Figure 7.20: Discrimination and harassment by demographic group ....... 71 Figure D.1: Age group by", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "26 Source: Listening to Displaced People Survey, 2014. 95 % of the refugees in Mauritania envision an independent or autonomous North and 26 % of the refugees in Mauritania even state the independence of Azawad (= the north) as a main condition for returning home. Stark differences can also be observed with regard to the discussion around a possible federalist solution for the North that was ongoing when the monthly phone interviews were conducted in October. As illustrated in the Figure 20 below, 80 % of the refugees in Mauritania support a federalist solution, while the majority of IDPs, returnees and refugees in Niger are not in favor. Of those who do not support a federalist solution (96 % of the IDPs, 88 % of the refugees in Niger and 95 % of the returnees), the majority of IDPs (61 %) and returnees (70 %) as well as 38 % of the refugees in Niger suggest decentralization as a possible solution to resolve the conflict. 13 % of the refugees in Niger also mention war and 27 % the integration of the North. Nonetheless, 49 % of IDPs, 86 % of returnees and 89 % of refugees believe a stable and sustainable peace accord can be achieved. by 4 % of the population living Timbuktu, 2 % in Gao and nobody in Kidal.", "output": {"entities": {"named_data": ["Displaced People Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "28 In section three of this study, we could not find taxes and regulations among the potential barriers to sales and employment growth that clearly separated the ups from the downs in the formal sec- tor. We also showed that the large sector has become a smaller share of the economy rather than a bigger share due to self-employment growth and due to the process of de-industrialisation. In substance, the CIS-7 may fit the developing countries scenario better than the transitional countries scenario in the Schneider and Klinglmair (2004) regressions. If this is the case, we should expect that the growth of the informal sector negatively contributes to growth. The data we have do not contradict this hypothesis given that the shadow economy has been on the rise during the recession period and has stabilised during the growth period. This digression on informality suggests that self-employment may partially act as an host to in- formal and illegal activities especially during recessions where self-employment may constitute a refuge for small informal and illegal businesses. Self-employment is also evidently a sector of ne- cessity for those who wish to keep health and pension records alive and do not want to formally register anywhere else. In times of growth this sector may instead function as a first step to for- mality, an entry gate to the formal sector given its lower entry barriers and taxes. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The UNHCR data identify principal applicants for each household and our analysis examines differences in household poverty between households with a female rather than male principal applicant. 6 The principal applicant is the person who receives assistance from UNHCR for the family and is self ‐ selected or selected by the family. This definition of female headship has advantages over the way that household headship is commonly identified in household surveys. An often ‐ noted drawback of the headship variable is that female headship may reflect the enumerators ’ perception about who should be considered a family head rather than who has the most responsibility for the family ’ s welfare in practice. 7 Social norms can also affect whether female respondents self ‐ identify as household heads. For example, some Eritrean returnees who would in other cultural settings be regarded as de jure female headed (single mothers, widows, divorcees, separated women) reported being male ‐ headed. Other Eritrean female returnees who would be considered de facto heads reported headship by absent husbands or male relatives (Kibreab, 2003). Our approach is therefore to distinguish between different types of female and male principal applicant households, using a typology that reflects some of the indicators of vulnerability used by UNHCR. We find that distinguishing between different types of female principal applicant households is important in the setting of Syrian refugees in Jordan. Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR data"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Data sources: UNHCR PRIMES, UNHCR Resettlement Statistics Report.   For more information or to contribute, please contact UNHCR RBSA DIMA (rsarbdima@unhc\n\n**1,729**\n\n**154**\n\n**57**\n\n**53**\n\n**34**\n\n**13**\n\n**12**\n\n**4**\n\n**3**\n\n**2**\n\n**COD**\n\n**BDI**\n\n**SOM**\n\n**RWA**\n\n**TUR**\n\n**ZAM**\n\n**ETH**\n\n**UGA**\n\n**ANG**\n\n**AFG**\n\n**COD**\n\n**BDI**\n\n**SOM**\n\n**RWA**\n\n**ETH**\n\n**PAK**\n\n**TUR**\n\n**CAR**\n\n**BOT**\n\n**ERT**\n\n**3,619**\n\n**263**\n\n**235**\n\n**220**\n\n**40**\n\n**36**\n\n**19**\n\n**14**\n\n**8**\n\n**8**", "output": {"entities": {"named_data": ["UNHCR PRIMES", "UNHCR Resettlement Statistics Report"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "We only found reliable approximations from the International Displacement Monitoring Center (IDMC) between 2003 and 2013. According to IDMC, there were about 12. 5 million internally displaced people in SSA at the end of 2013 (IDMC 2014), more than one third of the total number of IDPs and more than tripling the number of refugees in SSA. Although the number of IDPs in SSA is the highest since 2007, the share of IDPs in SSA has been decreasing from 53 % in 2003. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "33 Ratha, D., and W. Shaw. 2007. ― South-South Migration and Remittances. ‖ World Bank Working Paper 102, World Bank, Washington, DC. United Nations Statistics Division. 1998. Recommendations on Statistics of International Migration Revision 1. New York: United Nations. United Nations, Department of Economic and Social Affairs, Population Division. [2008]. United Nations Global Migration Database. New York: United Nations. http: / / esa. un. org / unmigration — — —. 2006. Trends in Total Migrant Stock 1960 – 2000, 2005 Revision. Database. POP / DB / MIG / Rev. 2005 / Doc. New York: United Nations. — — —. 2009. Trends in International Migrant Stock: The 2008 Revision. Database. POP / DB / MIG / Stock / Rev. 2008. New York: United Nations. http: / / www. un. org / esa / population /. — — —. 2010. ― World Population Prospects: The 2009 Revision, Highlights ‖, Working Paper No.", "output": {"entities": {"named_data": ["United Nations Global Migration Database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The GBV Action Plan specifies a case management protocol that must be followed for all SEAH allegations regardless of whether the alleged perpetrator is a project employee. The protocol prohibits confrontation between the alleged perpetrator and survivor, requires referral to a designated survivor support specialist within 24 hours of a report being received, and mandates that case management records be stored separately from the project's general MIS to protect survivor confidentiality. The PIU's social development officer is the designated GBV focal point responsible for implementing the protocol.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Sources of information This work includes a descriptive analysis that allows us to identify how the prevalence of non- standard employment (NSE) has evolved in the last two decades in different regions of the planet, as well as the evolution of the profile of workers in those roles, in terms of their educational level, salary per hour, and type of tasks performed. This analysis was based mainly on periodic surveys of households that included information regarding the employment and educational situation of individuals. Although the denomination of this type of surveys varies from country to country, in all the cases analyzed there is usually a survey of annual or higher frequency that includes information required to identify the labor status of the individuals as well as to analyze the salary profile and education of the employed. However, it should be noted that the identification of the type of work relationship (standard or non- standard) is frequently limited in these data sources. Indeed, it is only possible to identify part-time employment and temporary employment (not in all cases) within the non-standard forms of employment mentioned in the previous section. Usage context: primary mention, supporting mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["periodic surveys of households"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "using the Washington Group Short Set on Disability. 11 Sharing a home is common, likely as an economic coping mechanism, with on average of 1. 3 families living in the same house. Housing quality is low, with most houses in some state of disrepair or incomplete construction. Table 1 also illustrates descriptive statistics for the treatment versus control households to as- sess balance across groups, where the third column presents a mean difference test. Respondents across the study arms are largely balanced by age (with an average age of 34 years), marital status (84 % married), and incidence of a disability (Panel A). There is a slight imbalance in the share of respondents who are female, with the proportion slightly higher in the control group. Moreover, average household size is just above ﬁve members, with nearly three children on average, and these are balanced between treatment and control, as is the number of families per residential unit (Panel B). Refugee households in the sample face challenging and precarious housing conditions, but these characteristics are generally balanced across groups (Panel C). For example, only 66 % of households have access to piped water, 22 % have functional windows, and 44 % completed ﬂoors. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Our analysis exclusively uses data from the 1951-2015 UNHCR Population Statistics Reference database (extracted September 18 2015). Data were provided for 173 countries: 77 percent of these data were based on individual refugee registration, 13 percent on estimates, 5 percent on combined estimation and registration, and 5 percent on other sources. The data are structured as follows: for each situation, the database records annual numbers of persons of concern, which comprise “ Refugees (including refugee-like situations) ”, “ Asylum seekers ”, “ Internally Displaced Persons ”, “ Returnees ”, “ Stateless ” persons, and “ Others of concern ”. A situation is a pair country of origin / country of destination. For example, Somali refugees in Kenya account for one situation, Somali refugees in Ethiopia for another, and South Sudanese refugees in Kenya for yet another. Furthermore, a situation is considered major if it involves more than 25, 000 people. It is referred to as protracted if it is major for at least 5 continuous years. The database, and therefore our analysis, is limited to refugees under UNHCR protection. It does not include asylum seekers, i. e. individuals who have sought international protection under the 1951 Convention but whose claims for refugee status have not yet been determined, and persons in “ refugee-like situations ”, i. e. individuals outside their country or territory of origin who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained (e. g., undocumented Rohingya originating from Myanmar 4", "output": {"entities": {"named_data": ["UNHCR Population Statistics Reference database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "appear to be a relationship between perceptions of the helpfulness of donors and non-state actors and the willingness to defer to the police and to the courts. Individuals who believe that donors and non-state actors exert too much, rather than, too little inﬂuence over one ’ s government is associated with the willingness to defer to the court and to the police. Findings also suggest that citizens who believe non-state actors are responsible for provid- ing law and order are less likely to be willing to defer to the police and to the courts than respondents who believe the state is responsible for providing law and order. 7. 4 Conclusion This paper demonstrates that the logic of the ﬁscal contract is relevant to a wide variety of contemporary African states. Findings from a cross-national analysis of survey data from Africa link citizens ’ legitimating beliefs — in- dicated by a willingness to defer to the tax department, the police and the courts — to a government ’ s fulﬁllment of a ﬁscal contract. Citizens who are satisﬁed with their government ’ s provision of services and goods are more likely to be willing to defer to the tax department, courts and police than citizens who disapprove of government service provision.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["survey data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Coastline changes in the Mekong Delta, including Ho Chi Minh City and Bà Rịa-Vũng Tàu, were evaluated\nbased on data provided by the Viet Nam Disaster Management Authority in vector format [2] . By analyzing\nthe coastline in 1988 and 2015 in imagery from Landsat satellites, sediment changes in this period could\nbe quantified.\n\n\nSimilarly, coastal erosion data in the provinces further north were developed by companies Deltares and\nRoyal Haskoning DHV (Deltares at al. 2017). Coastline changes in the period between 1990 and 2015 were\ndetected automatically based on imagery from Landsat and Sentinel satellites. In this vector format\ndataset, average erosion or accretion is available by coastline segments with a length of 500 meters.\n\nTo analyze this hazard, a dataset in raster format describing the worst saline intrusion event in 2016 was provided by the (Southern Institute of Water Resources Research (SWIRR). With a resolution of about 50 by 50 meters, it shows the salinity level at each point in the delta during the 2016 event\n\nThe exposure of various types of agricultural production and urban areas is estimated based on land use\ndata with a very high spatial resolution. This dataset was developed by the Japanese Aerospace Exploration Agency and is openly and freely available online (JAXA EORC, 2018).\n\nThe land use map provides a fine-grained overview of land use for all of Vietnam for 2015, for northern regions and for central and south Vietnam in 2017.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["land use map"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "16 is the number of children born in the five years prior to the data collection. In the 2000 RDHS, this corresponds to the period between May 1995 and June 2000. This time span will be compared to the period between May 1987 and June 1992, when data collection for the 1992 RDHS began. Similarly, in the pooled 1992-2005 data, the time span of interest is the ten years prior to the starting date of the 2005 RDHS, which is the period between May 1995 and February 2005. This corresponds to the period from September 1982 to June 1992 in the 1992 RDHS. To explore the marriage market effects of conflict on fertility, the following equation is estimated: Ki = α0 + β1Xi + β2D + β3R + β4Year + β5Conflicti + β6Conflicti x Year + μi (1b) which additionally includes a time dummy Year for the survey year and an interaction term between the proxy for conflict exposure Conflicti and the time dummy. The estimated coefficient β6 captures the impact of actual conflict exposure on fertility after the genocide relative to women with similar risk of exposure to conflict before the genocide. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 Over the past three or four decades, the Arab world has experienced a massive expansion in educational attainment. According to the Barro and Lee educational attainment dataset, seven out of the top 20 countries in terms of increase in number of years of schooling from 1980 to 2010 were Arab countries (Barro and Lee 2013). 1 Jordan, the subject of this paper, had the seventh highest increase in educational attainment in the world, with an increase of about five years in the average years of schooling over the period. This increase is widely believed to be attributable to a massive public investment in the supply of schooling in the postindependence period in the context of a state-led development model, which virtually guaranteed employment in the public sector for graduates (Assaad 2014; Saleh 2016). The rapid increase in educational attainment has continued unabated despite the fall in returns to education that accompanied the demise in the state-led model and its employment guarantee schemes (Pritchett 2001). A slew of recent literature on the drivers of the Arab Spring protests, some of which occurred in Jordan, has identified the low economic returns to this massive increase in education as the single most important cause of the uprisings (Goldstone 2011; Campante and Chor 2012a, 2012b, 2014; Sanborn and Thyne 2014). Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["Barro and Lee educational attainment dataset"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "7 Figure 1: Map of regions in Ethiopia, location of refugee camps, and refugee source countries. Source: Database of the Global Administrative Areas (GADM) (https: / / gadm. org / data. html, accessed on November 20th, 2020) In Ethiopia, the Refugees and Returnees Services (RRS, former Agency for Refugees and Returnees Affairs (ARRA)) is responsible for managing refugee camps and its oversight by making sure that the commitment of the federal government is met (Nigusie and Carver 2019). Except for Eritrean refugees, most of whom are eligible for out of camp policy, arriving refugees, at the time the data was collected, were allocated to one of the 26 refugee camps spanning the five refugee hosting regions. Refugees living outside of camps represent about 10 percent of the refugees in Ethiopia (Abebe et al. 2018). The allocation tends to be based on shared identity between the refugee and the host communities and the distance of the refugee camps from the border of the source country. The South Sudanese refugees are hosted in the refugee settlements in Gambella, except the few who were relocated to the refugee camps in Benishangul-Gumuz region. Most of these refugees arrived during the civil conflict in South Sudan in 2013 (Nigusie and Carver 2019).", "output": {"entities": {"named_data": ["Database of the Global Administrative Areas"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5. 5 Ethnic diversity at different levels Despite the use of sampling weights in the construction of the diversity indices, we have no guarantee that our diversity indices are representative at the local level. Although similar ethnic diversity indices have been used at the local level (Nunn and Wantchekon, 2011; Rohner et al., 2013; Robinson, 2017; Desmet et al., 2020; Gomes, 2020b, a; Hodler et al., 2020), we cannot exclude the possibility that a lack of representativeness at the local level introduces some noise into our estimates. Ideally, we would have liked to construct our local diversity indices based on census data. However, such data are not available on an annual basis and only a minority of African countries include ethnicity questions on their censuses (Robinson, 2017). Robinson (2017) highlights other benefits but also warns against the risk of using non-random samples or of the size of samples introducing significant errors. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["census data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "% 83 % 28 % 9 % 14 % Equal contribution 93 % 81 % 83 % 16 % 17 % 17 % Majority male earners 78 % 56 % 57 % 19 % 15 % 16 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 8. Conclusion This paper contributes to the literature by analyzing multidimensional poverty among refugees and internally displaced populations. We observe that forcibly displaced communities are poorer than host communities in each of the five countries ’ sub-populations covered in the surveys, with the difference in incidence between displaced and non-displaced population ranging between 15 and 19 percentage points in South Sudan and Somalia to over 30 percentage points in Ethiopia and Sudan. Displaced communities also experience greater deprivations in nearly every indicator, although there is significant variation in which indicators are the most salient, with having a bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria showing the largest differences between the two populations. The results also indicate gender differences in the experience of multidimensional poverty, with female-headed households more likely to be poor than male-headed households in most of the countries. In addition, displaced households headed by women have a higher incidence of poverty and MPI than non-displaced female-headed households. Particularly, female-headed households in camps have higher multidimensional poverty and intensity compared to their counterparts living outside camps. Dissaggregating further, we find heterogeneity among de facto and de jure female heads. This variation lends itself to further research questions about", "output": {"entities": {"named_data": ["High Frequency Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "on violence using data from the Armed Conflict Location & Event Data Project (ACLED) on\n\nSecurity Council meeting and subsequent news reporting- Nigeria, Somalia, South Sudan, and Yemen (e.g., World Bank 2018b). 7In the 2016 IPC classification, the mVAM was actually the key component of determining food access across the country.\n\nAuthors' calculations using the 2014 Household Budget Survey. 16Registration for the World Bank's cash transfers program being implemented by UNICEF, which covers approximately one-quarter of the total population and is aimed at relatively poorer households, demonstrates the vast majority of households can be reached via phone (e.g., World Bank 2018c); evidence from different WFP surveys of food aid benefic\n\n<!-- PAGE 11 -->\nsurvey is very similar in its relative ranking of governorates based on food access as compared\n\nto the face-to-face Emergency Food and Nutrition Security Assessment that was undertaken\nduring the survey period. [20] Additionally, the regions that the WFP survey identifies as receiving\n\nproduced by another famine early warning system (FEWS NET 2017). These unofficial updates", "output": {"entities": {"named_data": ["Armed Conflict Location & Event Data Project (ACLED)", "2014 Household Budget Survey", "Emergency Food and Nutrition Security Assessment"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The Women ’ s Empowerment in Agriculture Index (WEAI) (Alkire et al. 2012) uses individual-level data, and the linked Gender Parity Index reflects inequalities across women and men ’ s deprivation scores within the same household. Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer. Espinoza-Delgado and Klasen (2018) create an individual-level MPI to understand differences in poverty between women and men in Nicaragua, finding similar overall incidence, but much higher intensity of poverty among women. Bessell (2015) and Pogge and Wisor (2016) explore deeply contextual gendered poverty measures and elucidate the ways that participatory consultations can inform the design and uses of gendered measures. Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa. Alkire, Ul Haq, and Alim (2019) use individual-level data alongside MPI data to expose gendered and intrahousehold differences among MPI poor and non-poor children. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "2 INTRODUCTION In 2015, an estimated 2. 2 million Syrians Under Temporary Protection (SUTPs) were residing in Turkey, the majority arriving in the country over the last 4 years. 2 Turkey ’ s national population is roughly 75 million; recent refugees account for approximately 3 percent of the population. For a country that has never experienced such a large-scale, sudden inflow of foreigners, demographic changes in the composition of the population and labor force will yield unprecedented implications. This paper examines, as data allows, the relationship between the size of the foreign-born population and host community poverty rates in Turkey. First, this paper finds the poverty rates of ‘ recent migrants ’ near the Syrian border (NSB) significantly increased from 2009 to 2013. Second, the number of foreign-born households being captured by the Labor Force Survey (LFS) is expanding, which suggests a growing number of foreign households that are likely to be Syrians. Third, with respect to poverty, the results show no negative impacts on the host community as a result of the increasing size of the foreign-born population. The impact of SUTPs has been both positive and negative. Overall, a significant negative impact on host communities ’ welfare is not observed in the data.", "output": {"entities": {"named_data": ["Labor Force Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "contributing to their overall vulnerability and lack of self-reliance. As this report shows, few refugees in Ethiopia work, and those who do work mostly inside camps. Research indicates that extended periods of forced unemployment negatively affect refugees’ longer- 8 As granted under Article 26 of the 1951 Geneva Refugee Convention. Introduction 6 term labor market participation (Hainmueller et al., 2016; Hvidtfeldt et al., 2018; Brell et al., 2020), thereby also hampering the host society through larger expenditure on assistance and forgone taxes (Marbach et al., 2018; Fasani et al., 2022). Enabling refugees’ labor market participation outside of refugee camps as early as possible is key to achieving their integration (Fasani et al., 2022; Slotwinski et al., 2019) by limiting long-term scarring effects, such as long-term unemployment or inactivity. Integrating refugee children into education soon after arrival avoids loss of valuable years of education and harm to human capital accumulation that hinders future prospects. Globally in 2019, almost half of all refugee children were out of school (UNHCR, 2020d). Of those attending school, most do not make it past basic education; gross enrolment in primary education stood at 77 percent in 2019. Yet, the contrast between primary and secondary school enrolment", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The countries that are the largest source of refugees include: Afghanistan, Syria, Somalia, South Sudan, Sudan, Democratic Republic of Congo, Central African Republic, Iraq, Myanmar, and Eritrea (UNHCR, 2017). These countries account for the vast majority of refugees worldwide. We will also consider three countries in Europe that are an important source of asylum seekers in Europe, and whose recognition rate is low, and account for many returnees – these are Kosovo, Serbia and Albania. Is return possible and safe, and if so, does the economic situation bode well to provide essentials, namely a job, housing, education and reliable public services? It is helpful to identify three groups of countries: ones that are still mired in war and / or high ‐ intensity civil conflicts, countries where medium ‐ intensity conflict persist and are already in the process of rebuilding, and countries which are not in conflict. According to the Armed Conflict Survey (IISS 2017), high ‐ intensity conflict is defined by frequent (daily) armed clashes between governments, government forces and insurgents, or among non ‐ state armed groups that control territory. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Armed Conflict Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "**Annex 5: Stakeholder Engagement Plan Summary**\n\nThe SEP was prepared in accordance with ESS10 and identifies four categories of project-affected stakeholders: (i) direct project beneficiaries in the 24 target communities; (ii) host community residents not enrolled in project services; (iii) national and subnational government agencies involved in implementation; and (iv) civil society organizations active in the project area. The SEP specifies communication channels, frequency of engagement, and mechanisms for recording and responding to feedback for each stakeholder category. A stakeholder engagement log is maintained in the MIS and reviewed at each Bank supervision mission.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "4. 2 Perceptions of Social Cohesion The outcome of interest is how respondents perceive various dimensions of social cohesion in their communities. The surveys capture each of the locally directed dimensions of social cohesion described above. Table 4 groups these measurement strategies by the dimensions of social cohesion from the qualitative exercise. Social Cohesion Dimension Relationships Solidarity Governance Perception of In-Group Relationships Participation in socio-cultural activities with Other Ethnic Groups Access to Basic Needs Perception of Out-Group Relationships Contact with Other Ethnic Groups Access to Services Table 4: Operationalizing the Locally-Led Deﬁnition of Social Cohesion with Survey Responses First, to measure how respondents perceive the quality of their relationships, the survey asked respondents to report their perceptions of the quality of their relationships with their own ethnic group and with other ethnic groups. Two binary variables based on each respondents ’ answers to these questions are used to measure relationships, with respondents answering either “ Good ” or “ Very Good ” coded as perceiving high-quality relationships. Second, respondents report their willingness to participate in socio-cultural activities / ceremonies, attend the same place of worship, work together, marry members of other ethnic groups, and how often they have contact with members of other ethnic groups to measure solidarity.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "These findings were also echoed in the context of other developing countries such as India (Allard et al., 2022) and Zimbabwe (Mabugu, Maisonnave, Henseler, Chitiga-Mabugu, & Makochekanwa, 2023). In the MENA economies ’ context, the evidence is considerably sparcer. Early work on the impact of the COVID-19 pandemic on labor markets in the MENA region re- lied on high-frequency phone surveys and highlight important job losses among wage workers and an uneven impact across industries (Krafft, Assaad, & Marouani, 2021, 2022). Providing evidence from Labor Force Surveys in the Islamic Republic of Iran, Dang and Salehi-Isfahani (2023) find that the pandemic exacerbated the pre-existing low participation of females in the labor force. Wahby and Assaad (2023), on the other hand, focus on the impact of the pandemic on Syrian refugees in Jordan and find a divergence in job finding and separation rates of Syrian refugees relative to their Jorda- nian hosts after the onset of the pandemic. Focusing on cross-border commuters in the West Bank and Gaza, Adnan and Etkes (2022) find that undocumented commuters benefited relative to their documented peers after the pandemic, as Israeli policies inadvertently created incentives for employers to favor the former. This sharply con- trasts the results by Borjas and Cassidy (2020) on the impact of the pandemic on immigrants in the United States. The rest of this paper is organized as follows. Section 2 provides background information on labor markets in the West Bank and Gaza, as well as background in- formation on the COVID-19 pandemic and government responses. Section 3 describes the data. Section 4 discusses our methodology. Section 5 presents the main regression results and investigates heterogeneous effects. Section 6 provides robustness checks. Finally, we provide concluding remarks in Section 7. 4 Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Ideally, we would conduct a comparative analysis of the characteristics of migrants residing in Medellin versus those in other parts of the coun- try to discern the extent of these differences. However, the lack of comprehensive data regarding the living conditions of this population makes such analysis unfeasible. To explore how this population compares with other migrant groups in the country, we turn to the only two available data sources on migrants. First, we use the Venezuelan Refugees Panel Survey (VenRePS), conducted by Ib ´ a ˜ nez et al. (2022), which captures a 19 Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Venezuelan Refugees Panel Survey"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "For refugees, the situation is more varied, with most staying close to their country of origin while a smaller minority fled to countries further away. UNHCR (2020) estimates that three-quarters of all refugees were hosted by neighboring countries. To reflect the increase in forced displacement over the last decade and enable sustainable and long-term solutions to refugee situations, the UN Statistical Commission approved a new indicator, SDG Indicator 10. 7. 4, in early 2020 to measure and track the “ proportion of population who are refugees, by country of origin ” (UNHCR 2020). While the specific challenges for displaced communities depend on the country or host community context, often, in new locations, key challenges confronting IDPs and refugees include food insecurity, lack of livelihood opportunities, and tensions and competition over resources with host communities. The multiplicity of deprivations faced by displaced Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": ["SDG Indicator 10. 7. 4"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "5km. Following EA selection, a fresh list of households was prepared at the beginning of this survey, which was used as a frame to choose sampled households from each sample EA. In Addis Ababa, a separate host domain was developed as refugees spatially concentrate in a few sub-cities and Woredas. We applied the ESS EA maps around the area where refugees in Addis Ababa are located. We selected EAs in the first stage and then conducted a complete listing. Sampling design The sample for this survey was 3,456 households from eight domains, with data was collected from 3,452 households (Table C.1).58 There are three domains for the three largest in-camp refugee groups—Eritreans, Somalis, and South Sudanese— three for host communities of these major refugee groups, and one for refugees and one for host communities in Addis Ababa. In all categories, a stratified, two-stage cluster sample design technique was used to select EAs and 12 households per EA, whereby the EAs were considered a Primary Sampling Unit and the households as the Secondary Sampling Unit. The SESRE is designed to estimate demographic, socioeconomic, welfare, and refugee- specific indicators of the eight domains. 57 The cartographic map (frame) was prepared in 2018", "output": {"entities": {"named_data": ["SESRE"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 The fundamental unit of observation in ACLED is the event. Figure 2 illustrates the ACLED data for Central Africa for the 1980s and the 1990s. Each location of a conflict event is represented by a symbol. In several of these locations, multiple events occured over the periods. Events always involve two actors – a rebel group and a government – and are coded to occur at a specific point location and on a specific day. Most of the events are battles, but the dataset also records other activities. The dataset includes information on and distinguishes between six types of events: battles resulting in no change of territory, battles resulting in a transfer of territory to the rebel actor, battles Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Female Male Figure 7.13: Share with friends in Ethiopia by demographic group Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 69 Despite the generally positive attitudes described, social integration—measured by the friends and family refugees have in Ethiopia—is low. Only 7 percent of refugees report having family in Ethiopia, and 25 percent report having an Ethiopian friend outside the refugee camp. This rate is slightly higher among OCP refugees in Addis Ababa but still relatively low at 11 for having family and 31 percent for having a friend. The share with Ethiopian friends is higher for men, but similar across age groups (though lower for refugees over age 64). This masks some variation across domains; refugees under age 30 are likely to have friends than older refugees in the Eritrean and South Sudan domains. In contrast, refugees under age 30 are less likely to have friends in Addis Ababa. Many refugees report that social interactions and sharing resources with hosts is “not easy,” especially in the South Sudanese domain. Overall, 30 percent of refugees say it is not easy to have social interactions with hosts, and 34 percent report that it is challenging to share resources such", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "1 POST-CONFLICT TRANSITIONS WORKING PAPER NO. 16 Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis * Håvard Hegre Centre for the Study of Civil War, PRIO (CSCW) Clionadh Raleigh CSCW, PRIO & University of Colorado at Boulder Abstract Why do larger countries have more armed conflict? This paper surveys three sets of hypotheses forwarded in the conflict literature regarding the relationship between the size and location of population groups: Hypotheses based on pure population mass, on distances, on population concentrations, and some residual state-level characteristics. The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events. The analysis covers 14 countries in Central Africa. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper develops a statistical method to analyze this type of data. The analysis confirms several of the hypotheses. World Bank Policy Research Working Paper 4243, June 2007 The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post- Conflict Transitions Project can be found at http: / / econ. worldbank. org / programs / conflict).", "output": {"entities": {"named_data": ["Armed Conflict Location and Events Dataset", "ACLED"], "descriptive_data": [], "vague_data": ["conflict event data", "geographically disaggregated data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "the West Bank and Gaza. Out of all the estimated coefficients for each period for both outcomes (job loss and job gain), we only find a negative effect on job gain in 2019Q4, which is very small in magnitude (1 percentage point). Taken altogether, the results presented in this section bolster our confidence that we are correctly identifying the effects of the pandemic shock on labor market outcomes. Figure 12: Placebo effect on labor market flows Notes: The figure shows the output of a placebo test with a set-up analogous to Figures 6 and 9. We perform the same regression as specified in Equation (2). Our sample includes data from 2018Q2 to 2020Q1 and assumes that the pandemic started in 2019Q2. Therefore, the post-pandemic period refers to the quarters between 2019Q2 to 2020Q1. The analysis is restricted to men aged 20-59. 7 Conclusion This paper examines the effect of the pandemic on labor markets in the West Bank and Gaza using quarterly labor market data provided by national labor force surveys. With a focus on men ’ s labor market outcomes, this paper sheds light on how labor markets in the West Bank and Gaza adjusted to the COVID-19 shock examining adjustments at the extensive (employment) and intensive (hours of work) margins. Usage context: supporting mention, primary mention, background mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["national labor force surveys", "quarterly labor market data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "List of acronyms CAR Central African Republic DHS Demographic and Health Surveys DRC Democratic Republic of Congo DTM Displacement Tracking Matrix FCS Fragile and Conflict-affected Situations GIDD Global Internal Displacement Database GIS Geographic Information Systems IASC Inter-Agency Standing Committee ICRC International Committee of the Red Cross IDMC Internal Displacement Monitoring Centre IDPs Internally Displaced Persons ILO IOM International Labour Organization International Organization for Migration IRRS International Recommendations for Refugee Statistics JIPs Joint IDP Profiling Service LSMS Living Standards Measurement Study MICS Multiple Indicator Cluster Surveys NGOs Non-Governmental Organizations NRC Norwegian Refugee Council OCHA Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat OAU Organization of African Unity ODA Official Development Assistance OECD Organisation for Economic Co-operation and Development SDG Sustainable Development Goal SKOPE Somalia Knowledge for Operations and Political Economy SuTPs Syrians under Temporary Protection UAV Unmanned Aerial Vehicle UNDP United Nations Development Programme UNHCR United Nations High Commissioner for Refugees UNITAR United Nations Institute for Training and Research UNOSAT UNITAR ’ s Operational Satellite Applications Programme UNRWA United Nations Relief and Works Agency for Palestine Refugees in the Near East UNSD United Nations Statistical Commission WFP World Food Programme Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["Global Internal Displacement Database", "Demographic and Health Surveys", "Geographic Information Systems", "Multiple Indicator Cluster Surveys"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "street vending (48 % of those with at least 1 IGA), food processing for sale, including baking, cooking, and drying (16 %), and home production of crops, livestock, and fish (11 %). It is important to note that the EPAG program was not targeted toward the most vulnerable segments of Liberian society, but rather toward young women with enough education to be able to benefit from a training program of this nature. Based on comparisons from the 2007 CWIQ (Core Welfare Indicators Questionnaire) survey, the average EPAG participant is more educated, more literate, more likely to be engaged in an income-generating activity, and owns more assets than the average Liberian women of the same age group: mobile phone ownership was high (63 %), as was the proportion reporting that they had some money of their own (79 %). Even compared to other similar residents of Monrovia, the EPAG participants are better educated and have higher income. A strong sense of female empowerment at baseline emerges from the sections of the survey instrument having to do with self-confidence and agency.", "output": {"entities": {"named_data": ["Core Welfare Indicators Questionnaire", "CWIQ"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "in-camp refugees, their hosts, and out-of-camp refugees (Table D.12 in Annex D). The predicted poverty rate decreases with the share of employed household members, indicating that employment is essential to lowering poverty for in- camp refugees (Figure 5.19). 0 10 20 30 40 50 60 70 80 90 100 Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts Electricity Mobile phone Any livestock Bank account Agricultural holding Nonfarm enterprise Percent Figure 5.17: Household wealth indicators by expenditure quintiles Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 Labor force participation Employment to working age population ratio Unemployment rate 0 10 20 30 40 50 60 70 80 90 100 Agriculture Industry Services 0 10 20 30 40 50 60 70 Poorest 2 3 4 Richest In Camp Refugees Inside the Camp Outside the Camp 0 10 20 30 40 50 60 70 80 Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Poorest 2 3 4 Richest Low-skill Medium-skill High-skill Percent Percent", "output": {"entities": {"named_data": ["SESRE 2023"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "7 All data collection is done by GISSE a research institute in Bamako. The anonymized unit record data of the baseline and the monthly surveys can be downloaded from www. gisse. org. The response rate for the phone interviews has been very high (Table 1): after 6 rounds of monthly interviews the original sample is almost entirely intact. The low level of attrition demonstrates that mobile phone samples can be maintained over prolonged periods without being unduly affected by (non-random) respondent drop-out. 3. Characteristics of the Displaced and Returnee Population According to the 2009 population census, the two most sizeable ethnic groups in northern Mali are the Songhai (45 %) and Kel Tamasheq (32 %)-- see Table 2. The crisis brought about an ethnic divide, which is reflected in the composition of the three sub-samples. The majority of IDPs and returnees are Songhai (75 % and 71 % respectively), while the majority of refugees are Kel Tamasheq. Results suggest that the decision of where to flee was determined by ethnicity: Kel Tamasheq and Arabs left the country; Songhai fled towards Bamako. Usage context: primary mention, background mention, supporting mention.", "output": {"entities": {"named_data": ["2009 population census"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "14 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 5. 2 Composition of poverty Unpacking the headline numbers further, important patterns emerge about the composition of multidimensional poverty among forcibly displaced and host communities in these countries. Overall, the censored headcount ratios (proportion of people who are poor and deprived in a given indicator) are lower among non-displaced communities than among refugees and IDPs, but there are large differences in which indicators are the most salient in different countries. The indicators with the largest difference between the two populations are bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria. These findings reinforce the need for policies and programming that take into account the measured experiences of IDPs and refugees. In this way, the MPI can function both as tool to monitor, track, and bear witness to the lived experiences of forcibly displaced communities, as well as advise on evidence-based interventions that address the needs of the local population. Figure 1 shows the censored headcounts of each indicator in Sudan ’ s MPI, with large differences appearing by displacement. Usage context: background mention, supporting mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Programs designed to enhance employment among young people can focus on the supply side of the labor market, including skills training programs for both wage employment and entrepreneurship training; or programs to augment demand, such as wage subsidies, public works, and community service programs; or programs to help the labor market clear, such as job search assistance and placement services. In addition, it may be that the constraints facing young people are not in the labor market itself, but in other markets, such as for credit. The vast majority of jobs programs have focused on the supply side: training programs make up about 79 percent of over 600 cases included in the World Bank ’ s Youth Employment Inventory database. 3 Although rigorous and general evidence of success is limited, it seems that successful skills training programs share a few key features: they are responsive to local market conditions, they provide more than just technical skills in a specific area (including, for example, “ life skills ”), and they include ancillary services that alleviate other constraints preventing successful labor market integration (e. g., access to credit). Among the most celebrated are the Jovenes programs that provide demand-driven technical training, plus social skills that help in the labor market, plus internships. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": ["Youth Employment Inventory database"], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The countries in our sample are Benin, Burkina Faso, Burundi, Cameroon, Gabon, Ghana, Guinea, Ivory Coast, Kenya, Liberia, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, Tanzania, Togo, Uganda, Zambia, and Zimbabwe. As described in Table B. 1, we also incorporate information on the quality of our refugee data, which is determined by comparison with official UNHCR bilateral data. Below we describe how these data have been used to define our main variables of interest and present some descriptive statistics in Table B. 2. 11 Conflict. In Equation 1, we first relate variation in ethnic diversity with data on conflict from ACLED (Linke et al., 2010). Two main definitions are used: the incidence of conflict and the intensity of conflict. Incidence is captured by an indicator equal to one if conflict occurred in a particular year within a pre-defined buffer around cluster j. Intensity is measured by summing the number of conflict events occurring in a particular year within the same buffer area. A conflict event is defined as a single altercation wherein force is used by one or more groups for a political end (Linke et al., 2010). We further describe events (non-exclusively) as violent events, non-violent events, violence against civilians, and riots. In our main analysis, we focus on violent conflicts (Section 5. 1) and report results for other outcomes as robustness tests (Section 5. 3). In doing so, we follow a recent and large literature that has combined the ACLED dataset with geographically disaggregated data in Africa (Besley and Reynal-Querol, 2014; Berman and Couttenier, 2015; Michaelopoulos and 11Panel A of Table B. 2 shows descriptive statistics for the data from refugee-hosting areas specifically, whereas panel B of Table B. 2 shows descriptive statistics for our data in all covered areas. 11 Usage context: background mention, primary mention, supporting mention.", "output": {"entities": {"named_data": [], "descriptive_data": ["data on conflict from ACLED"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "The data on total gross proﬁts and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key diﬀerence from the Chamber of Commerce data is that the sales and proﬁts data include all businesses including self- proprietorships. 9 Data were provided for the years between 2010 and 2014 and are re- ported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by deﬁnition and they are likely to be less accurate and complete for smaller ﬁrms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and proﬁt ﬁgures. 10 We scale the variables according to province size by dividing sales and proﬁts by the pop- ulation of the provinces. If we use sales and proﬁts in absolute terms, we get qualitatively similar results. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12", "output": {"entities": {"named_data": [], "descriptive_data": ["Chamber of Commerce data"], "vague_data": ["administrative taxation data"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Figure 5 Panel A illustrates the percent of FDP datasets that cover various topics in their questionnaire modules and Panel B shows differences in the percent of datasets covering each topic between FDP datasets and all other non-FDP datasets stored in the WB MDL. 32 The topic of health is by far the most common with roughly 78 percent of datasets mentioning keywords related to health in their descriptions of the questionnaire modules. Other common topics include food insecurity (49 percent) and water sanitation (39 percent). Less frequently included are topic areas related to labor and employment (19 percent); security and conflict (18 percent); finance, credit and debt (14 percent); agriculture and livestock (10 percent), and shocks (9 percent). The fact that topics such as security, conflict and shocks are often not included in the survey modules is not unique to FDP datasets. In fact, those topics are also relatively uncommon in other micro-level household surveys available in the WB MDL. When compared to the topic coverage of all non-FDP micro-level datasets in the WB MDL, FDP datasets are scarcer in such topics as labor, finance, agriculture / livestock / fishery, and education. As shown in Figure 5 Panel B, these topics are much more likely to be picked up in the non-FDP micro-level datasets. When it comes to labor and employment in particular, the gap is over 50 percentage points illuminating 32 See Annex C for further details on the methodology used to analyze topic coverage. For this topic coverage analysis, we exclude those datasets that are project specific or collected before 2010. Usage context: supporting mention, background mention, primary mention.", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "However, an endline survey was conducted after the second round of the EPAG program as per the timeline depicted in Figure 1. Examination of this endline survey data will permit a descriptive analysis of the outcomes of the first group of trainees 12 months after they completed the EPAG program, as well as examination of the outcomes of the second batch of trainees. The second round included not only the control group from this impact evaluation but also newly recruited participants who were offered brief basic literacy and numeracy training program prior to program entry. Work is already underway to design and implement the third round of EPAG, with a substantial redesign of the Job Skills track, an emphasis on reaching younger girls with lower literacy, and expansion to communities outside of Monrovia. If the high success rates found in this study are replicated for these future cohorts, the EPAG program should serve as a model for policy makers in Africa and the world seeking to improve lives and livelihoods of all youth, male and female. 25", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": ["endline survey data", "endline survey"]}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Ababa live in rented houses; the team faced challenges in tracing some refugees due to changes in their residential locations. Challenges related to identifying the eligible sample households were also observed due to outdated names of the household heads in UNHCR lists, and UNHCR’s registration of names which is not consistent with the Ethiopian context58. Thus, these challenges required additional effort by the team to ensure that sampled households and replacements were traced, identified, and interviewed. Another challenge was that some refugees were not willing to provide their current location due to personal security reasons, but these situations were resolved by reaffirming the confidentiality of the survey. 58 UNHCR register names starting with last name, whereas, in Ethiopia names starts with first name. Annexes 97 Annex D: Descriptive Statistics and Regression Results Results on Sociodemographic Profile Table D.1: Demographic characteristics by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Age group <15 39% 46% 54% 50% 47% 56% 15 to 24 19% 19% 17% 22% 21% 21% 25 to 44 26% 25% 21% 17% 21% 16% 45 to 64 13% 8% 7% 9% 8% 5% >=65 4% 2% 2% 2% 3% 1% Gender Male 48% 51%", "output": {"entities": {"named_data": [], "descriptive_data": ["UNHCR lists"], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "Still, despite these critiques, the evidence in this paper cautions against a naive switch to the HCES\ndirect method. In our survey experiment, we calculate hunger to range between 19 and 68 percent -this\n\nis a difference of more than 23 million people in Tanzania (a country with a population of 45 million\n\npresenting a strong challenge to both the HCES-direct and the FBS-CV methods. The FBS-CV is\n\nNote: estimates of Equation (1). Each column represents the results of a (separate) regression (OLS or LPM) of a selected HCES-derived measure (mentioned in the titles of the panels) on 7 module assignment dummies, a single selected household characteristic (mentioned in the column headings) and 7 interaction terms of that household characteristic with the module assignment dummies.\n\nSmith L. 1998. Can FAO's Measure of Chronic Undernourishment be Strengthened? _Food Policy_ 23(5):", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}
{"input": "about 333,000 Venezuelan children were enrolled in government schools in 2020 (about 3.4 percent of the total student population in Colombia) (UNHCR, 2021a). In Turkey, the government is supporting the transition of Syrian refugee children into the national school system, redirecting resources to locations with high concentrations of refugees (Abu-Ghaida and Silva, 2020), resulting in nearly 80 percent of Syrian primary school-aged refugee children being enrolled in education programs by 2020/2021 (UNHCR, 2021). In addition to benefiting refugee children, high school enrolment and learning outcomes increased for local Turkish students (Tumen, 2019; 2021). Educational attainment is low among both hosts and refugees, especially in camps. About 73 percent of in-camp adult refugees and 59 percent of adult hosts (aged 18 and above) either did not attend school or did not complete primary education. South Sudanese refugees have the worst educational attainment. Refugees in Addis Ababa have relatively better educational attainment compared to their hosts, with a higher percentage of refugees in Addis Ababa completing primary (45 percent) and secondary (27 percent) education (Figure 2.7). Although more educated, even youth’s (age 15 to 24) educational attainment is low, with large differences by survey domains. Many youth refugees and hosts have not", "output": {"entities": {"named_data": [], "descriptive_data": [], "vague_data": []}, "entity_descriptions": {"named_data": "A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.", "descriptive_data": "A data mention described by its characteristics or producer rather than a proper name.", "vague_data": "A data mention that only generally references data, information, or statistics."}}}