ai4data/datause-dataset-rechunked huggingface dataset:
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
**Total Poverty** **Food Poverty** **Lower Total**
**Estimation** **Poverty**

Parametric R$287 R$507
Nonparametric R$287 R$503
_Source:_ Own calculations using POF 2017/18.

**Total Poverty** **Food** **Lower Total**
**Estimation** **Poverty** **Poverty**

Parametric R$251 R$443
Non-parametric R$251 R$441
_Source:_ Own calculations using POF 2017/18.

Deaton 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.

In 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.

In comparison with earlier work, mainly based on POF 2003, our poverty lines are generally similar in real
values, although methodologies differ, and consumption patterns have likely changed over time.
Converted to January 2018 prices and considering São Paulo (mostly metropolitan) lines in the case of
regional lines, previous estimates range from R$485 to R$532 (Rocha, 2007; Silveira et al., 2007). Ferreira
et al. (2003) used POF 1996 and estimated a lower poverty line of R$477 in January 2018 metropolitan
São Paulo prices. Only World Bank (2007) estimated a considerably lower poverty line of R$272 in January
2018 metropolitan São Paulo prices.
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.
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.
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.
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
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.
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.
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.
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
**in order to meet housegold food needs?**

**Sell HH assets/goods (radio, furniture, jewellery)**

**Reduce food expenditure**

**Spent household savings**

**Sold house/land**

**Accept degrading unsuitable high risk dangerous or exploitative work**

**Some members return to homeland**

##### **b) Food coping strategies**

**79%**

The reduced Coping Strategies Index (rCSI) includes the five most commonly used food-related coping strategies and
their order of severity as a proxy indicator to measure access to food. The higher the rCSI, the more coping strategies
households had to endure. The reduced food coping index score was the highest in the North with a value of 24.66,
followed by BML 21.4, South 16.58, and Bekaa was the lowest with the value of 10.25.

**Figure 9: Food coping reduced index score per area**

**24.66**

**21.41**

**18.74**

**16.58**

**10.25**

**Bekaa** **BML** **North** **South** **All**
food consumption expenditure in Tanzania based on the Household Budget Survey 2000/01. To make

representative 2006-07 Household Budget Survey (the comparison results are not presented here but

This 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).

The 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

Another  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
Notes: The graph plots local means of secondary school outcomes by ENLACE test score
percentile in grade 6 in 2007. The solid line shows a linear fit estimated using the grouped
data. Panel (a) reports the probability of on-time graduation from grades 9 and 12, proxied
by siting in the Enlace exam in those grades. Panel (b) reports Enlace test scores in grades
9 and 12 (normalised with mean 0 and SD 1) conditional on taking the Enlace exam in 2010
and 2013. Data: ENLACE panel.


38



<!-- PAGE 41 -->
Figure 4: ENLACE test scores and post-secondary school outcomes

(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

d<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|
|---|---|
|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
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|



Notes: The graph plots local means of post-secondary school outcomes by ENLACE test
score ventile in grade 12.
(henceforth CRU), provided by the Climatic Research Unit of the University of East

and satellite-based observations. The data enable us to characterize historical climate

<!-- PAGE 6 -->
**2.1 Assignment of reliability weights to the eight GCMs, based on their**
**historical "goodness of fit" to the CRU data**
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.
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
**7.3 Environmental and Social Commitment Plan (ESCP)**

The 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.
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.
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.
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.