ai4data/datause-dataset-rechunked huggingface dataset:
composite of the effect of skills at Grade 6, as captured by ENLACE, on future outcomes

Furthermore, we find evidence that strongly supports the case that ENLACE captures

main specification and run a regression of ENLACE test scores in a particular subject

<!-- PAGE 8 -->
scribes the ENLACE national examination and the steps followed to construct the longi

tudinal data. Section 3 of the paper describes the empirical approach followed to study

We construct two longitudinal datasets for this paper: (1) the ENLACE panel and (2)
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.
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
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
3) Female teacher who is HIV�/ but not sick
should be allowed to continue teaching in
school.

PEPFAR

4) Would not want to keep the HIV�/ status PEPFAR
of a family member a secret.

Practices

Condom use 1) Percent of men and women (aged 15 �24)
who used a condom at last sex with a
non-marital, non-cohabiting partner, of
those who have had sex with a non-marital,
non-cohabiting partner in the last 12
months. [bc]

UNGASS, MDG, PEPFAR

a prior to the UNGASS indicators in 2002, the UNAIDS stated indicators did not specify youth
(15 �24 years).
b the MDG indicator replaces ‘have’ with ‘transmit’.
c the MDG indicators do not specify ‘non-marital, non-cohabiting’ but add ‘high risk’. PEFPAR
uses 15 �49 years.

with the surrounding host population response and a sub-regional approach
undertaken in order to take into account the displacement cycle (UNHCR 2005).
Timely and accurate data are needed to provide targeted and effective
interventions in conflict and post-conflict settings. Unfortunately, due to unstable
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.
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.
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
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.