ai4data/datause-dataset-rechunked huggingface dataset:
sanitation including a lack of latrines, and fragile livelihood strategies; others reside in shared and
overcrowded rental accommodation, or with relatives.

**Returnees** : In 2016, more than 600,000 documented and undocumented Afghans returned from
Pakistan and Iran including 372,577 registered refugees who returned under UNHCR’s facilitated
return program and were provided with UNHCR cash grants as part of their repatriation assistance
package. The majority of returnees have indicated Kabul, Nangarhar, Kandahar, Herat, Balkh, Ghazni,
Baghlan and Kunduz provinces as their intended destination for return, including areas subject to
attacks by armed groups. Returnees report a lack of land and adequate shelter, insufficient livelihoods,
insecurity, and poor access to services as obstacles to sustainable return and reintegration. These and
other factors have forced many returnees to undertake secondary movement to locations, particularly
in urban centers, other than their place of origin.
due to armed conﬂict but for more idiosyncratic reasons. We check robustness of this approach in two ways: (i) using all of the refugee stock observations available in the data set, and (ii) computing refugee ﬂows as the positive time diﬀerences in refugee stocks from year to year (setting negative time diﬀerences to zero). The results are robust to these two alternatives. 5 10 15 20 Number of refugees (million) 1987 1992 1997 2002 2007 2012 2017 Year Figure 1: Global refugee population, 1987-2017 Note: This ﬁgure plots the global stock of refugees. The data on bilateral distance and contiguity come from CEPII. The distance vari- able refers to the great circle distance between the most populated cities of each country in the pair. The contiguity indicator is equal to one if the two countries share a land border. Figure 1 charts the global refugee population over time. The sharp increase in the number of refugees over the past decade is evident. Such refugee movements can have signiﬁcant impacts on the destination countries.
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 challenges in comparing findings from the two surveys. Moreover, the two surveys are not comparable in other ways, including: (ii) Surveyed population: SESRE includes out-of-camp refugees living in Addis Ababa, while SPS does not. (iii) Survey scope: The methodology to sample and definitions of host communities varied between the two surveys. (iv) Survey design: SESRE’s questionnaire aimed at comparability with the national poverty survey, while SPS aimed at comparability across countries. This rendered differences in the contents of the surveys, where the SESRE employed the same survey instrument as the national poverty survey, while the SPS used an instrument specific to the survey. (v) Differences in consumption: SESRE includes a full consumption module while SPS relied on the Rapid Consumption methodology. Moreover, there are differences in the recall period for food consumption data collection. While SPS used the past 7 days recall period, SESRE collects food consumption data through two
problems. Previous literature has shown that immigrants are more likely to move to locations where they have a network of peers from the same country. We show below that this is true among Venezuelans in Peru as well. Our main interest is on β1, which represent the impact of the labor market conditions in centro poblado j on the discrimination experienced by Venezuelans. Venezuelans who arrive to the country clearly evaluate where to settle based on the labor market opportunities (among other reasons), and therefore to causally identify β1 we need a source of exogenous variation for the labor market at the local level. We use an instrumental variable strategy that exploits variation in the share of workers employed in diﬀerent industries in 2007, along with national level shocks to trade in speciﬁc industries between Oct 2016 and Oct 2017 when the census was collected. More precisely, the ﬁrst stage regression is given by: lnEmpj = α + νSharejk (2007) × ∆ lnExportk + ηXij + νZj + αo + ϵj (2) where Sharejk (t − 1) is the share of workers in centro poblado j employed in industry k in 2007, and ∆ Exportk represents the log change in national level exports in industry k between 2016 and 2017. The remaining control variables are similar to those in Equation 1.
Maps of soil salinity and waterlogging risk were produced by the national agricultural research institute using a combination of soil profile data from the national soil database and satellite-derived vegetation stress indicators. These maps were reviewed during site selection to identify agricultural plots within the project area that were unsuitable for the proposed irrigation investments due to soil degradation risks. Maps were updated with ground-truth observations collected by agronomists during field missions conducted in January and February.
The employment arm also signiﬁcantly improves cognitive function as measured through an index of memory and basic arithmetic tests, a ﬁnding consistent with a large psychol- ogy literature documenting the relationship between cognitive processes and depression (Semkovska et al., 2019). As with physical health, improvements to cognitive function are unlikely to be a direct product of the employment task itself, which was speciﬁcally de- signed to require no literacy or mathematical skill. Rather, these results are suggestive of a downstream impact to reducing depression through the experience of employment. Finally, we ﬁnd no change in time preferences: treated individuals are no more or less likely to discount the future relative to control counterparts, although results may have diﬀered had we engaged participants in an eﬀort or consumption-based time preference game rather than a ﬁnancial one. However, we ﬁnd a substantial increase in risk tolerance among the employed. A greater preference for risk-taking may be indicative of employment serving as a form of psychological ‘ insurance ’ that allows participants the mental bandwidth to exercise greater risk. This is consistent with the positive impacts of employment on stability as well as with a key motive underlying universal basic income (UBI) in the developing world (Banerjee, Niehaus, and Suri, 2019). Interestingly, however, we document no parallel increase in risk tolerance in the cash transfer arm. Our result on risk preference also echoes a potential consequence of depression and anxiety described in Ridley et al. (2020), although empirical 15
countries covered in this study. We next turn to analyzing the factors that helped or hindered the return of Syrian refugees until early 2018. 3 Data and Empirical Strategy Return migration decisions are potentially inﬂuenced by expected payoﬀs in both the country of origin and country of asylum, as well as the individual characteristics of refugees. To analyze these factors, we need a comprehensive data set, which is often not available especially in active conﬂict situations. In what follows, we describe the strategy we followed in exploiting the available information. 3. 1 Data With an active conﬂict situation in Syria, a complete longitudinal data set for conditions in countries of asylum and origin was not available. Thus, we adopt a pragmatic approach that combines diﬀerent sources and types of data. Refugee attributes: We use the Proﬁle Global Registration System (ProGres) database, which is compiled by UNHCR to record each person of concern who ap- proaches it. 7 Our version comprised about 2. 16 million Syrian refugees in the Middle East and North Africa region (Turkey and European countries are not included), with a cutoﬀdate of March 2018. The ProGres database is a limited administrative database, which functions like a civil register.
Table 2 reports average daily hours in the three locations for a representative sample of 4,612 individuals drawn from 600 households in rural, peri-urban and urban areas of seven Bangladeshi regions

A previous paper by the authors (Dasgupta, et al., 2004) has documented extensive indoor air-quality monitoring in the peri-urban area of Narshingdi (Dhaka region).

Drawing on information from continuous, 24-hour monitoring of PM10 in 27 households in Narshingdi, Figure 2 displays a typical daily pollution cycle as

closer to cooking-area concentrations, and our 24-hour monitoring data indicate that daily pollution
WDI data on electricity consumption only encompass output from power plants and hence may underestimate the intensity and volume of night lights in countries with a strong reliance on private generators.

What about the economy of Somalia? National accounts data for Somalia have been non-existing since 1991, when the country sunk into chaos following the toppling of the Badre regime.
For the remaining one-to-many relations, we keep these ethnicities in the Afrobarometer as such and consider them as a single ethnic group. Some manual treatment can even further improve 4
education. In addition, information on their registration status is recorded, including refugee status, arrival and, where applicable, return date, and sub-district-level location information for last residence in Syria and current residence in the country of asylum. It also identiﬁes kinship of individuals within each “ case"(e. g., familial relationships of everyone within a case to the principal applicant, ranging from members of the nuclear family to extended family, such as in-laws and aunts). Following the initial registration, entries are updated in subsequent contacts. Up- date frequencies vary from one operation to another, with at least 5 percent of all observations being updated in a given month. 8 Therefore, although information on single-shot events like arrival and return dates is ﬁxed, other information like occupa- tion, education and marital status may change over time. In the case of education and marital status, these changes are largely driven by the aging of the refugee. However, the occupation variable is more problematic, since it could refer to current employ- ment or past employment (including in Syria) depending on when it was last updated. Therefore, while we are able to use all of the demographic and registration information of the ProGres database, we exclude the occupation variable from the main analysis.
Existing research shows that due to the number of factors involved, no climatic or
environmental hazards inevitably result in migrations. Many authors note that even if
disasters become more frequent in the future, political efforts and measures of
protection will be able to lessen the need to emigrate provided that the necessary
financial means are made available. Even rising sea levels could be partially
counteracted by the erection of dykes or the filling in of threatened zones. The Stern
report is clear in this respect and states that "the exact number who will actually be
displaced or forced to migrate will depend on the level of investment, planning and
resources" (112), before estimating the cost of mitigation to be several billion dollars.

The overview we have carried out also shows that the very concept of climate or
environmental refugees, because of its connotations of urgency and unavoidability, is
to be handled with care. It actually evokes fantasies of uncontrollable waves of
migration that run the risk of stoking xenophobic reactions or serving as justification
for generalized policies of restriction for people seeking asylum.
17 Figure 10: Children aged 7-12 attending school (%) Source: Listening to Displaced People Survey, 2014 and 2015. 5. Challenges Faced by Returnees The results suggest that returnees were less affected by the crisis than IDPs and refugees. This is reflected in data on asset and livestock ownership, but also in the information pertaining to exposure to violence. Returnees reported fewer victims; fewer returnees reported to have lost income as a consequence of the crisis; more of their children were able to continue schooling; and relative to IDPs and refugees, fewer perceived being poorer in June 2014 than before the crisis. Returnees are also the group that feels most secure, that has high levels of trust in the Malian army and police and that has a positive attitude towards most government policies. 88 88 97 79 76 78 99 88 92 55 74 72 91 98 92 96 100 85 90 91 96 90 86 87 87 95 89 76 75 90 94 92 97 86 73 98 Bamako Gao Timbuktu Kidal Niger Mauritania IDPs Returnees Refugees 14-Aug 14-Oct 14-Nov 14-Dec 15-Jan 15-Feb
UNHCR (2021a) projects that the number of returnees will reach 141, 000 in 2021, up from 41, 000 in 2020. There are no datasets such as the one used in this study to explore the impact of post-2015 returnees on social cohesion and it is not possible to determine the degree to which our findings our applicable to this new context. However, it is possible to explore similarities and differences between the two contexts. Looking at a UNHCR report about the latest wave of returnees, it states that “ almost all returnee households rely on food obtained from their own gardens (93 %) and / or fields- households struggle to get food during the period they do not produce. 81 % of households declared that they are not satisfied with their level of food security because of the low dietary diversity ” (UNHCR 2021a). The report also suggests that “ 88 % of returnee heads of households are subsistence farmers, but most of them declared not having the adequate resources to produce their land. ” This high level of dependence on agriculture and prevalence of food insecurity are similar to the ones in our dataset for returnees and suggests that tensions related to access to agricultural land could also be present for post-2015 returnees. There are also signs of potential differences between current dynamics and the pre-2015 period.
Survey instruments enable detailed questions to be asked about the characteristics and situations of households, and if they identify displaced populations based on self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status. Additionally, more innovative tools and technologies for data collection, analysis and compilation should be explored and leveraged. For example, new methodologies (such as high resolution satellite imagery and unmanned drones) may expand the coverage of data collection efforts in insecure or inaccessible areas. Additionally, new techniques could be explored to improve the collection of robust data on flows of refugees and IDPs. Organizations such as the World Bank, UNHCR, IOM and IDMC are already exploring and in some cases are beginning to use more innovative data collection tools. These techniques include: 99 Several standardized international sample surveys have been designed for special purposes including Living Standards Measurement Studies, Labor Force Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys. The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner
List of acronyms CAR Central African Republic DHS Demographic and Health Surveys DRC Democratic Republic of Congo DTM Displacement Tracking Matrix FCS Fragile and Conflict-affected Situations GIDD Global Internal Displacement Database GIS Geographic Information Systems IASC Inter-Agency Standing Committee ICRC International Committee of the Red Cross IDMC Internal Displacement Monitoring Centre IDPs Internally Displaced Persons ILO IOM International Labour Organization International Organization for Migration IRRS International Recommendations for Refugee Statistics JIPs Joint IDP Profiling Service LSMS Living Standards Measurement Study MICS Multiple Indicator Cluster Surveys NGOs Non-Governmental Organizations NRC Norwegian Refugee Council OCHA Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat OAU Organization of African Unity ODA Official Development Assistance OECD Organisation for Economic Co-operation and Development SDG Sustainable Development Goal SKOPE Somalia Knowledge for Operations and Political Economy SuTPs Syrians under Temporary Protection UAV Unmanned Aerial Vehicle UNDP United Nations Development Programme UNHCR United Nations High Commissioner for Refugees UNITAR United Nations Institute for Training and Research UNOSAT UNITAR ’ s Operational Satellite Applications Programme UNRWA United Nations Relief and Works Agency for Palestine Refugees in the Near East UNSD United Nations Statistical Commission WFP World Food Programme
27 Source: Listening to Displaced People Survey, 2014. 7. Conclusion The 2012 crisis in northern Mali led to widespread displacement. The welfare of the displaced – and those who returned – is monitored by combining a baseline survey with structured monthly follow-up interviews carried out by mobile phone. This innovative approach allows tracking changes in welfare with high frequency – even for those who returned to areas that are insecure and inaccessible to enumerators. After 6 rounds of follow-up interviews attrition rates are very low (more than 99 % response rate), demonstrating that it is possible to collect robust and representative data from hard-to-reach, conflict-affected populations. The results show that those who fled were better educated, better off and less affected by violence than the average population in the North. Those who fled lost significant amounts of durable goods (20-60 %) and livestock (50-90 %); many of their children ended up being taken out of school and their welfare (measured subjectively and by the number of meals consumed) declined considerably. Over time, the impact of the crisis on welfare has lessened and by February 2015 the majority of eligible children of the displaced were going to school and levels of employment and number of meals consumed were at pre-crisis levels.
4 In this study, we apply their techniques to understand gender differences among adults as well. 2. 2 Intrahousehold analyses of multidimensional poverty The literature on multidimensional poverty measurement and intrahousehold analysis is limited. Espinoza-Delgado and Klasen (2018) propose an individual-based multidimensional poverty measure for Nicaragua and estimate gender gaps in headline statistics. Klasen and Lahoti (2016) question the neglect of intrahousehold inequality in multidimensional poverty indices by comparing a standard household-level MPI and an individual-level MPI to the MPIs proposed by Alkire and Santos (2014) and UNDP (2014), finding that females recorded a far higher poverty rate when using the individual measure and that age differentials in poverty were also larger. We follow their work of investigating poverty in the indicators for which individual data is available and compare the achievements of men and women and boys and girls living together.
**Appendix Table A3: LFS Sample Composition by Quarter and Region**

| Region | Q1 2019 | Q2 2019 | Q3 2019 | Q4 2019 |
|---|---|---|---|---|
| Northern | 2,418 | 2,391 | 2,340 | 2,306 |
| Central | 3,102 | 3,088 | 3,074 | 3,051 |
| Southern | 4,211 | 4,187 | 4,190 | 4,168 |
| Coastal | 1,890 | 1,878 | 1,855 | 1,843 |

Attrition across LFS quarters is minimal (less than 2 percent per quarter). The LFS uses an imputed replacement procedure for household members absent during a wave; imputed observations are weighted at 0.5 in the regression analysis.
ethnic group e at time t. It can also be expressed as one minus the Herfindahl index (Alesina et al., 2016). The EP index gives more weight to intergroup differences at the expense of within group homo- geneity. It can be defined as (Esteban and Ray, 1994, 1999; Montalvo and Reynal-Querol, 2005) 14 EPjt = Nrt X e = 1 (g2 et) (1 − get). (3) We compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity. In order to construct the revised refugee diversity indices according to ethnicity e, we first combine information about the country of origin of refugees hosted in refugee camps c in year t with the data from the EPR-ER 2019 dataset. The EPR-ER records the ethnic composition of refugee stocks originating from neighboring countries and countries in proximity to each other (maximal distance between country borders ≤ 950 km) with at least 2, 000 refugees and provides the ethnic composition of refugees (Vogt and Girardin, 2015). More specifically, the EPR-ER dataset gives us the share of refugees from ethnic group e moving from country o to country d at year t. The EPR-ER data gives us the three main ethnic groups.
Data on community characteristics were not collected. Due to confidentiality policies we only know the province in which a respondent currently resides, but not the administrative unit below the province level. This prevents us from merging the RDHS data with secondary data on geographical conflict intensity. Moreover, no information on income or consumption expenditure is recorded and households ’ physical asset endowments, such as owning a radio or the quality of roofing materials, is the only implicit measure available on household wealth. Instead, we construct a wealth index based on recorded household assets. Components of the index include durables, such as radio and bicycle, source of drinking water, characteristics of floor materials, and type of toilet facility. While the 2000 and 2005 surveys record a larger number of assets than the 1992 5 This description of the sample design refers to the 2005 RDHS, with slightly different designs used in the two previous RDHS waves. The 1992 RDHS builds on the 1991 Census as a sampling frame. At the time of the 1992 survey collection, a civil war was ongoing, with most actions of warfare taking place along the Ugandan-Rwandan border. Due to security concerns, 44 rural sectors in the provinces of Byumba and Ruhengeri in northern Rwanda were excluded from the sample frame at the outset. The 2000 RDHS builds on the listing of enumeration areas outlined for another household survey, the Enquête Intégrale sur les Conditions de Vie des Ménages (EICV) collected in 2000, as no other population records were available at the time. The sampling frame of the EICV itself is based on the pre-genocide Census of 1991. Three strata were used – Kigali, other urban areas, rural areas – and rural areas were further stratified into provinces, resulting in 13 strata (Ministère des Finances et la Planification Economique 2003). The sample design of the 2000 RDHS is only representative of rural areas of each province and Kigali City.
In the ALTA study, perception of tenure security is asked on a scale from 1 to 5, ranging from "not at all likely [to involuntarily lose land]" to 'extremely likely [to involuntarily lose land]", in line with the joint module by FAO, World Bank, and UN-Habitat (2019).

In the ALTA study, the non-response rates at the individual level for Arms 1 and 2 are 13% and 12%, respectively, with the difference not statistically different from zero.

This paper uses data from a methodological experiment in Armenia to assess the implications of survey design-namely, respondent strategy and the level of disaggregation of land data-on the measurement of individual land rights and SDG indicator monitoring.

Evidence from the Uganda Methodological Survey Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA), which fed into the development of the UN EDGE guidelines, illustrates the asymmetric impacts of respondent approach by gender.

3 For reports on the LSMS+ surveys in Tanzania, Malawi, Ethiopia, and Cambodia, see Hasanbasri et al. (2021a) and
Hasanbasri et al. (2021b).