ai4data/datause-dataset-rechunked huggingface dataset:
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).
 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.
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.
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.
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).
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.
Next, we evaluate firm level data on ownership to get a better understanding of what foreigners

2Aitken and Harrison (1999) argue a similar point. Using plant level data on productivity in Venezuela, they find

hypotheses testing. Data on mergers and acquisitions in Korea are from Securities Data Corporation

from Securities Data Corporation (2000) or have a foreign ownership share exceeding 25 percent.

Handbook of Listed Companies, Daewoo Securities (1998 and 1999). In cases where the World
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
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.

Myanmar Ministry of Planning and Finance & World Bank Group. (2017). Technical Poverty Estimation
Report: Myanmar Poverty and Living Conditions Survey. World Bank, Yangon.

Oliveira, 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.

Rodrigues, 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.
For example, the baseline year for children sampled in the 2007 DHS will be the year 2000.

We do not  have any particular expectations regarding the relationship between DHS years and the likelihood of  enrollment (Year [2011], Year [2014]).

[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.

Our 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.

We matched the children within each DHS year, using the Stata user-written program _cem_ introduced in Blackwell et al.
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.
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|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|
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|_Source:_ RGPH (General Population and Housing Census) 1998 and 2009.|_Source:_ RGPH (General Population and Housing Census) 1998 and 2009.|
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
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
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.
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.
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.
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
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.