ai4data/datause-dataset-rechunked huggingface dataset:
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.
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
and Fonden threshold information for 1,745. As shown in table 1, using 2000 census data, we

various administrative records that allow us to observe at the municipal level: the number of

**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.

To 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.

Note. Standard errors are clustered at the state level. Asterisks indicate statistical significance: _∗_ _p_ _<_ 0 _._ 10, _∗∗_
_p_ _<_ 0 _._ 05, _[∗∗∗]_ _p_ _<_ 0 _._ 01. Observations are at the municipality-year-level. Hectares sowed are defined as the total
hectares growing any rainfed agricultural crop as reported in SAGARPA production data.
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).
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.
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.
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.
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.
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.
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.
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
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
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.
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
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.

these reports when made by people who never transact in metric units. A typical HCES consumption

Once the food reported in an HCES is converted into calories, the household's calorie intake is compared

While the potential sources of mismeasurement in HCES are numerous, this study systematically

using a survey experiment conducted in Tanzania. There were a total of eight alternate designs, which
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.
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.

|Figure 12a Net primary enrollment (%)|Figure 12b Net primary enrollment (%) mining<br>communes|
|---|---|
|<br>|<br>|
|_Source:_RGPH (General Population and Housing Census) 1998 and 2009.|_Source:_RGPH (General Population and Housing Census) 1998 and 2009.|

5 We could not use the 1987 census data because communes were not created at that time.
**6.2 Baseline and Endline Survey Design**

The 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.
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
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.