ai4data/datause-dataset-rechunked huggingface dataset:
40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Electricity (meter) Electricity (meter, generator, solar) Percent Figure 2.24: Source of lighting Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Improved source of drinking water Improved bathing facilities Percent Figure 2.22: Access to drinking water and hygiene Source: World Bank Staff based on SESRE 2023. 37 Improved toilet facility includes toilets flush to septic tank/pit latrine/piped sewer system, pit latrine with slab, or composting toilet. 38 Improved waste disposal refers waste not thrown to field or yard, into river and burnt. 24 T his chapter presents findings on labor market outcomes and livelihood choices of refugees and hosts. It discusses how sociodemographic characteristics such as age, gender, education level, and location of residence relate with labor market outcomes. Ethiopia has experienced steady economic growth for much of the last two decades, but even before the country’s concurrent crises, economic growth did not transform the labor market structure. Between 2004 and 2020, Ethiopia’s GDP annual growth averaged 10 percent, helping to reduce the poverty by about ten percentage points.
Annual surveys funded under the previous project cycle provided panel data tracking changes in household income, agricultural production, and child schooling from 2017 to 2021. The annual surveys were administered by the same field firm across all rounds, ensuring consistent interviewer training and questionnaire interpretation. The final annual survey in 2021 achieved a response rate of 96 percent, with non-response concentrated among households that had migrated seasonally for employment, which may slightly understate income recovery among the most economically mobile households.
26 education Turkish to a subregion. In sum, there is some evidence that the inflow of Syrian refugees results in a decrease in the number of Turkish living in a subregion. The evidence, however, is weak and the impact unlikely to be very large. 5. PLACEBO TESTS AND ROBUSTNESS CHECKS 5. 1 Placebo Tests The key threat to the validity of our instrument is that there are subregion specific economic trends that are correlated with the instrument, and not fully controlled for by the inclusion of the log distance of a Turkish subregion from the Syrian border. A priori this seems unlikely since the instrument is also based on travel distances, but we can test for the existence of such trends in a pre-period. Specifically, we run regressions that are analogous to those reported in Tables 5, 6 and 7 using data from the LFS 2009 and 2011. As a placebo test we pretend that the Syrian refugees had arrived between 2009 and 2011, rather than between 2011 and 2014, to see if the instrument is correlated with Turkish outcomes in this pre-period. Table 10a presents the results of our placebo tests. For the overall sample there is no statistically significant trend that is correlated with subsequent (instrumented) refugee flows in formal or informal employment, or in log wages.
For rainfall data, I use a historical daily grid of rainfall, which is interpolated based on readings

that suddenly drop to zero purchasers is suspicious, especially since the BASIX data does not contain

error I run simulations where I assume that the BASIX data has been matched completely correctly,

I start by examining whether there is actual autocorrelation in the rainfall data. To test for
location in the past, interacted with the year of observation. This is a semi-parametric ver- sion of the traditional migrant network instrument as recommended by Goldsmith-Pinkham et al. (2020) and it allows the strength of the network eﬀect to potentially vary in each year. An overidentiﬁcation test can be used to examine whether the instrument has a consistent relationship over time. We ﬁnd robust evidence that increased immigration from Venezuela has a positive impact on labor market outcomes for Peruvians, with increased employment rates, incomes and expenditure in locations that receive more Venezuelans. Additionally, locations that receive more immigrants have lower levels of reported non-violent crime, improved reported quality of local services, greater reported trust in neighbors and higher reported community quality. On the other hand, we ﬁnd evidence that in locations with more Venezuelans, Peruvians report that their community likes diversity less. There are a number of potential explanations for these ﬁndings that we plan to explore in future work. 1 The arrival of Venezuelans may have expanded the economic opportunities for Peruvian because of their higher levels of potential productivity, due to higher human capital, and their concentration in low wage jobs.
tionnaire from Falk et al. (2022), along with a question on how much do the subjects in our sample trust the Colombian government. Additionally, we inquired about the ado- lescents ’ social networks by asking about their number of friends of each nationality and whether they have felt discriminated in school. While these outcomes were not the pri- mary focus of the VenReps Kids survey, they collectively provide a general assessment of social cohesion, a crucial aspect in understanding the overall well-being of children and adolescents. We measure the average differences in altruism between Venezuelan and Colombian ado- lescents following the estimation of equation 1. Table 8 presents the results for altruism, trust, discrimination and social ties in panels A, B and C respectively. As in the previous tables, the first column for each outcome reports the results of the estimates of equation 1 without controls, and the second and third columns report the results of the estimation with all controls and the propensity score matching respectively. As for altruism, we see that Venezuelan adolescents are on average willing to donate 17 % more of their imagi- nary money endowment to a good cause relative to Colombian adolescents. As for trust, the results are mixed when analyzing the trust items separately.
They contain a rich source of information on various attributes of datasets, including the country and dates of data collection, sampling strategy, survey modules, and other related aspects. While submission of datasets to the libraries is voluntary and thus does not guarantee an exhaustive list of all publicly available existing datasets on FDP, they are considered to be among the largest databases of microdata concerning development and forced displacement (Thompson 2010; EGRISS 2023). For the purpose of this study, microdata are defined as primary data collected from household surveys. Our focus lies in identifying micro-level datasets that are publicly available and designed to have a representative sample6 of FDP so that collected data can be disaggregated for refugees and / or IDPs specifically. By studying the geographical and thematic coverage of existing FDP microdata, we seek to also shed light on critical data gaps that remain to be filled with further data collection efforts. The paper identifies critical gaps in geographical and thematic coverage as well as compliance with international recommendations for proper identification of displacement status. The paper highlights that microdata is comparatively rich in Sub-Saharan Africa in contrast to other regions. However, data scarcity is notably pronounced in countries facing fragility and conflict and also among IDPs. There are also certain topics that are relatively lacking in the FDP microdata.
16 Investors actively trade-off disaster risk with gains from economic density. In addition to the city or location specific analysis, we examine how investors value risk from natural disasters. Data from a recently compiled dataset of a sample of global cities provides some insights. Gomez-Ibañez and Ruiz Nuñez (2006) constructed a dataset of central business district office rents for 155 cities around the world in 2005 to identify cities where rents seem elevated or depressed by poor land use or infrastructure policies. Their dataset also includes information on many factors that determine the supply and demand for central office space such as construction wage rates, steel and cement prices, geographic constraints, metropolitan populations and incomes. We link this information to the natural disasters hotspot dataset (Dilley et al. 2005), and examine if city demand – as reflected in office rents, is sensitive to risk from natural disasters. Gomez-Ibañez and Ruiz Nuñez (2006) focus on offices in the primary business district, which they define as the district having the highest density of employment; a very large, if not the largest, concentration of offices; and the highest rents in the metropolitan area. As we are interested in the tradeoff between economic density and disaster risk, using the central business district works well for our analysis.
The LFS reports employment status for individuals aged 15 to 64. The LFS does not cover institutionalized populations, individuals in collective living arrangements (dormitories, military barracks), or the de facto homeless population. These exclusions are standard for household-based labor force surveys and are unlikely to materially affect our estimates, since the excluded populations represent a small fraction of the working-age total and their labor market outcomes are not correlated with the shocks we study.
18 the local data on physical geography and population from the raster data. These data can then be imported into statistical programs for analysis. We aggregate all data up to a grid of 8. 6x8. 6km squares. Each grid square is assigned attributes of the country it is in along with information from data disaggregated to the level of the individual squares. Figure 3 illustrates this grid as a fictive country somewhat smaller than the average size in our dataset (50x50 squares, or 430x430 km) with a fairly representative but stylized population distribution. The country has three major cities, one of which is the capital, and two smaller ones. A rebel group has its headquarters at the Eastern border. The ACLED data for the Central African conflicts were aggregated up to the 8. 6x8. 6km squares and merged with information on other explanatory variables aggregated to the same level.
 Despite the challenges, the survey also reveals a positive trend of **youth engagement in their communities**
(20%), however, 17% are not accessing any form of work-related activity, situation that can also lead to
risks of recruitment or engagement in illegal activities (including smuggling), as well as drug abuse.

The results also show that a significant majority of young people, (82%), **do not believe that newly graduated**

**youth have the same working opportunities** as those who have already graduated or have previous work
experience. Lack of job opportunities represent the biggest barriers for recently graduated youth, most likely, in
the case of NWS, humanitarian organizations constitute the biggest employment alternative, however, lack of
experience can also represent a barrier for youth to access.

FDGs showed that:

 **Young women are especially vulnerable to sexual exploitation, harassment, and early or forced**

**marriages**, leading to social exclusion and lack of support, particularly for divorced women. Femaleheaded households and displaced individuals also face discrimination.

 The male group highlighted that **corruption, regionalism, and favoritism in employment** and resource
distribution exacerbate discrimination, with young people often being excluded from opportunities. They
also noted that the continuation of armed conflict increases vulnerability to exclusion.

**10**
**Return intention survey**

**Shalozan Tangi, Kurrum agency**

**June 2014**

**I.** **Background**

Kurrum Agency is the only tribal region in the country’s semi-autonomous seven tribal
territories which has a large number of Shiites - the rest of the six tribal agencies are
overwhelmingly inhabited by Sunni Muslims. According to official figures, its total
population is 500,000, with 58 percent Sunni and 42 percent Shiite. The majority of the
Shiites live in the upper part of the Kurrum Agency, while Sunnis inhabit lower and central
Kurrum. The population of Kurrum valley consists of a number of tribes, namely Turi,
Bangash, Parachamkani, Massozai, Alisherzai, Zaimusht, Mangal, Kharotai, Ghalgi and
Hazara. There was also a sizeable Sikh population but most of them have left the valley.
Sectarian violence is not a new phenomenon in Kurrum Agency where well over 4000
people have been killed in clashes between the Sunni and Shia tribes since the decade of
1980s. Kurrum Agency is divided into three tehsils: upper Kurrum, lower Kurrum and central
Kurrum.
If many people migrate to a speciﬁc location, such as the capital city, this is likely to aﬀect wages, incomes, and access to amenities in that location. 7 This would generate a potential endogeneity bias due to the fact that incomes and amenities in that location result in part from the decision of many migrants to locate there. To eliminate this bias, we use past data to estimate the income regression. More precisely, let T be the period for which we have income information and T + t the period at which we 5The dropped observation corresponds to the location of origin M h ii which, as explained earlier, we do not include in the analysis since including M h ii would mean de facto including the decision of whether to migrate or not. 6McFadden (1974) has shown that, in multiple choice problems of the kind studied here, the application of logit estimation is justiﬁed if (1) the errors in each latent choice equation follow the extreme value distribution and (2) errors are independent across choices. See Train (2003), Chapter 3 for a detailed discussion. The estimation of models with correlated errors across choices requires either multiple integration or the use of Bayesian estimation techniques relying on Gibbs sampling. With a choice of over 70 possible destinations, multiple integration is out of the question. Gibbs sampling remains a possibility but would require extensive programming. We choose instead to keep the logit approach but to correct the standard errors for possible correlation in errors across choices. In our case the possible eﬃciency gain achieved by Bayesian methods does not appear to justify the programming cost. 7The eﬀect could be negative — e. g., congestion — or positive — e. g., agglomeration externalities. 10
The data for this paper come from the European Social Survey (ESS) for the survey years 2002,

observations per country/year. The ESS covers 36 countries, 24 of which are included in our

Card et al (2012) investigates the drivers of attitudes towards immigrants in Europe using,


among others, the following variables of the ESS:
An alternative version of the database that has been mapped to the United Nations (2006, 2009) Trends in International Migrant Stock database is available from the authors. These data are standardized over time in terms of the years to which they refer. { Table 3 here} Calculating Missing Gender Splits Although common in the underlying data, bilateral migration data disaggregated by gender are sparser than aggregate migrant totals (see table 1). An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits; they are described in appendix 5. Combining Migrant Definitions Only a single definition of a migrant (foreign born or foreign citizen) can be applied to each destination country in the final matrices. Switching definitions over time 17 The subregions used for the disaggregations are the 21 UN regions (see http: / / unstats. un. org / unsd / methods / m49 / m49regin. htm, with the countries of Oceania aggregated into a single subregion. They do not match the large World Bank regions used in the analysis in section IV. 18 While this propensity measure is clearly inappropriate, less than 1 percent of all migrants and observations are assigned on this basis. This method is included so that every migrant in the underlying data is accounted for.
These findings were also echoed in the context of other developing countries such as India (Allard et al., 2022) and Zimbabwe (Mabugu, Maisonnave, Henseler, Chitiga-Mabugu, & Makochekanwa, 2023). In the MENA economies ’ context, the evidence is considerably sparcer. Early work on the impact of the COVID-19 pandemic on labor markets in the MENA region re- lied on high-frequency phone surveys and highlight important job losses among wage workers and an uneven impact across industries (Krafft, Assaad, & Marouani, 2021, 2022). Providing evidence from Labor Force Surveys in the Islamic Republic of Iran, Dang and Salehi-Isfahani (2023) find that the pandemic exacerbated the pre-existing low participation of females in the labor force. Wahby and Assaad (2023), on the other hand, focus on the impact of the pandemic on Syrian refugees in Jordan and find a divergence in job finding and separation rates of Syrian refugees relative to their Jorda- nian hosts after the onset of the pandemic. Focusing on cross-border commuters in the West Bank and Gaza, Adnan and Etkes (2022) find that undocumented commuters benefited relative to their documented peers after the pandemic, as Israeli policies inadvertently created incentives for employers to favor the former. This sharply con- trasts the results by Borjas and Cassidy (2020) on the impact of the pandemic on immigrants in the United States. The rest of this paper is organized as follows. Section 2 provides background information on labor markets in the West Bank and Gaza, as well as background in- formation on the COVID-19 pandemic and government responses. Section 3 describes the data. Section 4 discusses our methodology. Section 5 presents the main regression results and investigates heterogeneous effects. Section 6 provides robustness checks. Finally, we provide concluding remarks in Section 7. 4
Typically, registers have evolved over time (from parish records, for example). They were never developed specifically to record international migration information, and they vary considerably across countries. For example, the laws under which individuals are classified as migrants and the conditions under which they are inscribed or deregistered differ greatly (Bilsborrow and others 1997). The Raw Data The Global Migration Database is a vast collection of destination country data sources detailing migrant stocks from numerous origin countries and regions (United Nations [2008]). Compiling and maintaining the underlying primary sources require herculean efforts to scour the key census collections of the world and enter the data manually. In total, the database comprises records from some 3, 500 separate censuses from more than 230 migrant destination countries and territories, by sex and age.
such as education and caste. We also construct measures of social proximity between a migrant ’ s place of birth and each possible destination, using detailed available data on ethnicity, caste, language, and religion. We also investigate a number of factors that may inﬂuence the choice of migration destination but have not received much attention in the existing literature. Fafchamps and Shilpi (2009) have shown that the subjective welfare cost of geographical isolation is high. To investigate this issue, we include regressors controlling for population density and for the average distance to various amenities. Fafchamps and Shilpi (2008) have further shown that migrants are concerned with their welfare relative to that of their birth district as well as to that in their destination location. We examine whether relative welfare considerations inﬂuence the choice of migration destination. Additional controls include distance and prices. The empirical analysis is conducted using LSMS survey data as well as the 2001 population Census data from Nepal. The diverse terrain of Nepal along with geographical variation in amenities makes it ideal for our study. The mountainous nature of Nepal means that the country faces daunting challenges in the provision of transport and energy infrastructure. These challenges are unique to Nepal, however. Similar constraints are faced by many developing countries — or regions within such countries.
3 The exact list of socioeconomic variables in the DHS is not identical to that on the JFPR application form. We
derive the index of socioeconomic status in the DHS from variables describing: the ownership of a bicycle, cart,
boat, motorbike, car, truck, radio, television; the conditions of the dwelling such as hard roofing and finished
flooring; the availability of electric lighting; the main source of drinking water; the type of toilet facilities; and the
main type of cooking fuel used.

beneficiaries was done by the Local Management Committee (LMC) of a JFPR _secondary_ school. In

The JFPR scholarship program established a cut-off in the maximum number of scholarships

Finally, the paper presents evidence of heterogeneity in the JFPR program effects. For this
this way, profiling of IDP situations aims to underpin advocacy, protection and assistance activities as well as support the achievement of durable solutions by informing joint strategies between government, humanitarian and development actors. Profiling provides an overview of displacement-affected populations through the collection and analysis of minimum core data (number of IDPs, disaggregated by location, age and sex) and where possible additional quantitative and qualitative data (causes of displacement, patterns of displacement, protection concerns, humanitarian needs, vulnerabilities, and aspirations and prospects for durable solutions). Profiling may utilize data collection techniques at individual, household and community levels, often combining population estimation methods, a review of secondary data, focus group discussions, household surveys and key informant interviews targeted specifically at forcibly displaced populations (UNSD 2014). 70 Profiling methods focus on displacement situations, rather than only on displaced populations, and therefore includes comparisons to conditions in the host population. IDMC estimates that humanitarian profiling data forms the basis for 18 of their 60 country estimates and around 63 percent of their annual estimates (IDMC 2015), with the largest volume of data on conflict-induced internal displacement provided by OCHA followed by IOM. There are several practical challenges associated with IDP profiling exercises in displacement situations. Insecurity or terrain may impede access to displaced populations in conflict-affected or hard to reach areas.