ai4data/datause-dataset-rechunked huggingface dataset:
standard ﬁxed or random eﬀect approach is not suﬃcient to ensure correct inference; clustering standard errors by individual is necessary. This is what we do. Having described how the dependent variable is constructed, we turn to regressors. We begin by describing how we construct an estimate of g E [yhs | zh], the level of income (or consumption) yh s that a migrant with characteristics zh can expect to earn in district s. To construct such estimate, we use the 1995 / 96 NLSS data. The reason for using the 1995 / 96 data instead of the 2002 / 3 NLSS survey is to avoid reverse causation, i. e., migration causing a change in income patterns. Migrants are unlikely to be able to accurately predict the evolution of incomes in each district over time. Income and consumption levels observable before migration are thus a reasonable starting point.
The Normalized Deviation Vegetation Index (NDVI) is used to determine the exposure of households to
the drought. The NDVI is derived from satellite images measuring the health of vegetation.

In controlling for potential confounding factors, we rely on geo-coded conflict fatality data provided by the Armed Conflict Location Event Dataset (ACLED) and on data on the percentage of target beneficiaries reached with aid by pre-war region coming from the Food Security Cluster Somalia (Table A.1).
the degree to which the global response should include a development element. We ﬁnd that the average stood at around 10. 3 years at the end of 2015, with a median duration of 4 years, and signiﬁcant sensitivity to a few situations. Such numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete protracted situations. To produce these numbers, we rely on the Population Statistics Database compiled and main- tained by UNHCR. The database records the number of “ persons of interest ” to UNHCR in each year since 1951 and for each situation, where a situation consists of a pair host-origin countries. The calculation of duration of exile is obtained under a no-turnover assumption, whereby a de- crease in the number of refugees for any given situation is fully attributed to exits from refugee status, while increases are assumed to be fully accounted for by new cases. Although such ap- proach tends to over-estimate the true duration of exile, the lack of individual-level data on regis- tration precludes reﬁning the estimate further. Attempts to estimate similar statistics have been limited.
77% Proximity to resource hubs Nearest to zone 34% 93% Nearest to woreda 24% 72% Nearest to border 37% 81% Remote 33% 78% Source: World Bank Staff based on SESRE 2023. Annexes 113 Table D.12: Determinants of welfare (total expenditure per capita) (1) (2) (3) (4) In-camp refugees In-camp hosts OCP refugees OCP hosts Female headed 0.00 0.03 -0.01 0.02 (0.03) (0.03) (0.04) (0.05) Age of head (year) 0.00** 0.00 -0.00 -0.00 (0.00) (0.00) (0.00) (0.00) Household size -0.12*** -0.09*** -0.20*** -0.16*** (0.01) (0.01) (0.01) (0.02) Head years of schooling 0.02*** 0.01*** 0.03*** 0.03*** (0.00) (0.00) (0.01) (0.00) Mobile phone 0.10*** 0.19*** 0.39*** 0.17 (0.03) (0.03) (0.11) (0.15) Household has electricity 0.15*** 0.22*** 0.14 0.19 (0.04) (0.03) (0.27) (0.13) HH owns any livestock 0.04 0.03 -0.42* (0.03) (0.04) (0.25) HH member has bank account 0.13*** 0.13***
.13) HH owns any livestock 0.04 0.03 -0.42* (0.03) (0.04) (0.25) HH member has bank account 0.13*** 0.13*** 0.07 0.16 (0.03) (0.03) (0.10) (0.26) HH has agricultural holding -0.02 -0.08* -0.47* (0.03) (0.04) (0.24) HH operates a nonfarm enterprise 0.07** -0.00 -0.05 0.26*** (0.03) (0.03) (0.10) (0.07) Share of employed members 0.35*** 0.57*** 0.14** 0.06 (0.08) (0.06) (0.06) (0.08) Health shock 0.07 -0.02 -0.04 -0.20 (0.06) (0.06) (0.10) (0.18) Market shock -0.01 -0.09*** -0.04 -0.19*** (0.02) (0.03) (0.04) (0.05) Employment shock 0.01
Figure 22: Numbers of Refugees in Ongoing Refugee Situations end-2014 Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes Palestinian refugees under UNRWA ’ s mandate. D. Data on asylum-seekers, refugees and IDPs: sources, applications and credibility In this section, a distinction is made between: (a) the collection of source data; and (b) the compilation of data across sources (within a country or across countries). In general, there is a delineation of roles between data collectors and data compilers, however there are organizations, such as UNHCR, IOM and the Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat (OCHA), 59 that are involved in both data collection and compilation activities. Data collection: Sources for refugees, asylum-seekers and IDPs60 Collection of primary data on forcibly displaced persons is generally undertaken by national governments through their national statistical offices, line ministries or immigration agencies. However, where countries lack the capacity to undertake this work, they may rely on international organizations as well as international and local NGOs to collect data or undertake estimates. 61 In general, governments tend to collect data on refugees in developed countries, while UNHCR and NGOs tend to collect data on refugees in developing countries.
25 less cohesion. The increase in solidarity is less apparent among refugees where the majority (53 %) stated that the crisis had no effect on solidarity. Figure 18: Levels of trust, by group (June) (%) Source: Listening to Displaced People Survey, 2014. Perceptions that different groups have of others are important elements of peace. When asking for the degree to which neighbors, other villagers and people from other ethnic groups can be trusted the survey finds positive outcomes. Although all groups trust people from other ethnic groups slightly less, the general level of trust is high and it remains stable over time. Finally, consider how IDPs, refugees and returnees envision the future of Mali. The majority of refugees in Mauritania vie for an independent or autonomous North, while the majority of IDPs, returnees and refugees in Niger wish to see full government control over the North. 20 20This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013.
**KEY HIGHLIGHTS** **[1]**

 Most of the youth in NWS **face protection risks,** with 53% reporting homelessness and 55% experiencing
exploitation, while 44% face gender-based violence.

 75% of youth **lack access to protection services**, highlighting a significant gap in availability and
accessibility.
 77% of respondents indicate **no organizations offer tailored activities or services for youth** in NWS,

**Specific youth groups**, such as those with disabilities 70%, adolescent females 40%, IDPs 48%, and
homelessness 36%, are identified as experiencing significant protection risks.
 82% of **youth believe newly graduated youth have fewer working opportunities**, 66% of Youth actively
participate in community-based structures, with 74% volunteering and 53% involved in outreach teams,
promoting skill development, relationship building, and community contribution.

 76% of youth in northwest Syria **perceive unequal opportunities for social participation,** with
discrimination (85%), exclusion (34%), and stigmatization (15%) as key contributing factors.

 Only 12% of **youth have access to capacity-building or empowerment activities**,

 Expanded initiatives for **youth empowerment, leadership, and conflict resolution** are needed, with a
focus on essential skills development and resilience building.

 20% of **youth have been threatened or felt afraid**, while 24% sometimes experience such feelings,
impacting their well-being.

---
[1] The Protection Monitoring and Analysis working Group (PMA WG) of the Northwest Syria Protection Cluster, with support from the
Hosts Refugees Eritrean Somali South Sudanese Figure D.24: Household owns livestock Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.23: Household owns crops Source: World Bank Staff based on SESRE 2023. 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.25: Total value of livestock Source: World Bank Staff based on SESRE 2023. 0 5,000 10,000 15,000 20,000 25,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.26: Value per tropical livestock unit Source: World Bank Staff based on SESRE 2023. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.27: Household has non-farm business Source: World Bank Staff based on SESRE 2023. Annexes 110 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.28: Value of productive assets in households with business Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 Percent 70 80 90 100 Hosts Refugees Hosts Refugees
As in previous sections we follow Henderson et al. (2012) who argue that the relationship between GDP and night light at the country level can be expressed fairly well in a constant elasticity model in which an increase of night light by 1 percent implies an increase of GDP of about 0. 25 percent. Hodler and Raschky (2014) also look at the relationship between log nighttime light intensity and log GDP at the regional level using the panel data of regional GDP per capita assembled by Gennaioli et al. (2013) 48 and they conﬁrm that the relationship is linear and also ﬁnd an elasticity of around 0. 3. Access to political power is ranked on a scale from 1 to 7 in the GROWup dataset. Ethnic groups are"powerful"(monopoly of power or dominant group in power), have access to central power through a formal system of power sharing (as"Senior"or"Ju- nior"partner) or are “ excluded ” from power (self excluded, powerless or discriminated). Strong executive constraint is measured as a dummy indicating whether or not we have executive parity or subordination of the executive at the country level, a value 7 for “ xconst ” variable in Polity IV dataset.
Considering that these statistical standards and recommendations were established in recent years, it may not be surprising that the overall level of compliance with EGRISS recommendations is still evolving. Figure 6: Prevalence of Identifying Questions in FDP Datasets A) Refugee identification questions B) IDP identification questions Notes: Panel A shows the percent of FDP datasets containing each of the core identification questions recommended by EGRISS for refugees (in all publicly available datasets designed to sample from refugees that are not project specific and collected after 2010) whereas Panel B shows IDP equivalent of that. For refugees, we do not make a distinction between the question on the country of citizenship and the acquisition of citizenship because many datasets we study often collapse these questions into one by simply asking respondents to indicate their nationality (or nationalities). 5. Conclusion With an ever-growing number of people forced to flee their homes due to conflict, violence, fear of persecution, and human rights violations, data on FDP has become ever more important. A solid evidence base is crucial to inform effective policy responses to address their challenges and to establish such a base, 35 More than 95 percent of the FDP datasets asked questions on either the country of birth or citizenship. 36 While the country of birth does not change, nationality or citizenship can. And it is the country of citizenship or acquisition of citizenship that determines whether refugees and refugee related persons should still be regarded as refugees by the national authorities even though their country of birth may be different from that of their citizenship or nationality and how citizenship interplays with refugee status varies by country (EGRISS 2019, p. 28).
_returns to education are heterogeneous across countries. The second set of Mincerian returns are those estimated by Montenegro and Patrinos (2014)._
_Source: The data has been collected from PWT 9.0 (Feenstra, Inklaar and Timmer 2015)._

natural capital: (i) it represents only the contribution of the physical capital stock from PWT 9.0 in the conventional

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decomposition, and (ii) it shows the contribution of the physical capital stock (from PWT 9.0) and natural capital (Lange


et al. 2018) in the natural resource decomposition. The average annual rate of growth in output per worker for the

respectively- are then computed using the estimated relative income shares (which vary across groups) and the PWT   9.0 labor share (which varies across countries and over time).

Since  is calculated from the share of labor force in PWT 9.0 and 𝛼�1 �𝛾� is proxied by the ratio of natural resource
Polity data - political regime or patterns of authority. Countries with larger positive (negative) polity values have a more democratic (autocratic) system.

This paper describes an approach to forecasting future climate at the local level using historical weather station and satellite data and future projections of climate data from global climate models (GCMs) that is easily understandable by policymakers and planners.

policymakers. It draws on historical climate data from weather stations and satellites;

We begin with monthly temperature and rainfall data for the period 1961-2000
Proclamation of Ethiopia provided a long list of areas52 exclusively reserved for Ethiopian nationals, including small businesses that would have been of interest to refugees (FDRE, 2020). Hence, it allows many business activities to go beyond the reach of refugees. A draft MoU signed between RRS and MoLS/MoTRI/ MoR proposed exceptional treatment of refugees engaged in business due to their vulnerability. According to the MoU, refugees can own a business in agriculture, manufacturing, services, small and medium enterprises, handicrafts, and trade sectors through establishing private limited companies or cooperative societies. Hence, refugees can obtain a business license as a private business or association by providing proof of refugee ID, support letter from RRS on the type of business, source of capital, utilization of profits, qualification certification (depending on the sector), and tax identification number. A business license renewal requires a valid refugee ID, tax clearance, and audit report for a limited liability company. 52 Restaurants, tearooms, coffee shops, bars, nightclubs, catering services, producing bakery products and pastries, barbershop and beauty salon services, smithery, tailoring, sawmilling, timber manufacturing, brick and block manufacturing, quarrying, laundry services, translation secretarial services, security services, brokerage services, and attorney and legal consultancy. Annexes 87 Following the
However, another regressor indicates that security may still be pivotal: those who owned a weapon were up to 30 percentage points more likely to plan to go back. In addition to this, it is quite surprising that, if the respondent thought that the Northern Mali crisis was improving, he or she was less likely to plan a return to that area. From a technical point of view, we should point out that we have used an LPM even if the dependent variable was a binary outcome. This choice has been made since in this linear model it is straightforward to add fixed-effects. Furthermore, the coefficients can be interpreted as average partial effects. A simple logit or probit model would not have allowed the inclusion of individual fixed-effects because of the incidental parameter problem. An alternative approach would have been to estimate a conditional logit model. However, since the distribution of the fixed effects is unknown, it would not have been possible to estimate the average partial effects in this model, but only the effect of the regressors on the log-odds ratio. 13 We conclude by stressing that the monthly phone interviews were relatively short, so we did not have a rich panel data set. This may have led to omitted variable biases. Indeed, there may still be time varying factors which could have affected both the probability of being employed and the respondents ’ intentions to go back. Nevertheless, we believe that our model managed to control for 13 See (Wooldridge, 2010) page 639. Conclusions from the conditional logit model are qualitatively similar.
Colombian and Venezuelan migrant children and adolescents. VII Discussion In this study, we analyze novel and unique data on forcibly displaced migrants and hosts, focusing on children and adolescents, to highlight the disparities in human development between them. The structure our analysis in two parts. In the first part, we characterize the main trends in the data. We show that forcibly displaced households have a wealth distribution skewed towards lower values relative to Colombian households. This is likely explained by the assets ownership loss that forcibly displaced households expe- rienced after the migration episode. We also identify meaningful lags in human capital accumulation between Colombian and Venezuelan children and adolescents of approxi- mately 1 year. We further note that the Colombian government ’ s supportive policies for Venezuelan forced migrants are evident through high levels of service access and pro- gram participation for migrants. Nevertheless, it remains surprising that participation is not higher, suggesting significant potential for improvement in increasing sisb ´ en and health insurance enrollments. In a second part of our analysis, we document sizeable lags in physical and cognitive de- velopment of Venezuelan children and adolescents, relative to their Colombian counter- parts. However, we were not able to identify any gaps in the socioemotional and mental health between the two groups.
(b) Disaggregation and geo-mapping of data by location (current location and location of habitual residence; urban, peri-urban or rural location), accommodation (organized camp versus non- camp), and demographics (age and sex); (c) Expanded coverage of data collection exercises to include all areas of affected countries (security permitting); (d) Improved coverage and detailed data on displaced populations living outside of organized camps; (e) Improved coverage of ‘ flows ’, i. e. new displacement, durable solutions (returns, integration, resettlement), births, deaths, and in the case of IDPs, the numbers that flee across international borders becoming refugees; (f) Systematic data collection beginning from the earliest moment following displacement, following up as populations disperse, and continuing until sustainable / durable solutions have been achieved; and (g) Better aggregation, analysis and presentation of forced displacement data currently compiled separately by UNHCR, IOM, IDMC and UNRWA. Additional efforts are required to address the gaps in the data required for development policy and planning. These data are critical for informing the design of development policies and assistance programs.
A map highlighting Medell ´ ın ’ s geographic position and the locations of the households interviewed for this study is provided in Figure 2, offering visual context to our research setting. Representativeness and stratification. VenRepS-Kids is designed to be representative of two groups of youth. The first group consists of Colombian children and adolescents, aged 5 to 17, born to Colombian parents. The second group encompasses Venezuelan migrant children and adolescents of the same age range, born to Venezuelan parents, who mi- grated to Colombia between 2016 and 2020. The sample was further stratified by gender and socioeconomic levels, using Colombia ’ s neighborhood income-based classification system that ranges from 1 to 6, where six indicates the wealthiest neighborhoods. Our 13
High-skill occupations include managers, professionals, and associate professionals. Ln Earnings is the log of monthly earnings winsorized at the 1st and 99th percentiles. All other models are linear probability models. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 107 Table D.8: Refugee Household Reliance on NGOs/Donations (1) (2) (3) (4) All Eritrea Somali South Sudan Years in Ethiopia -0.007** -0.017** -0.011** -0.001 (0.003) (0.006) (0.005) (0.005) Member works outside the camp -0.080** -0.335*** -0.180*** -0.001 (0.035) (0.099) (0.051) (0.041) Region Fixed Effects Yes No No No Demographic Controls Yes Yes Yes Yes N 1252 423 412 417 Source: World Bank Staff based on SESRE 2023. Note: Each column is a linear probability model where the outcome is a binary indicator of whether the household relies primarily on donations for income. Demographic controls include the household size and the share within each age and education group. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Table D.9: Determinants of refugee-host earnings gap (1) (2) (3) Earnings Earnings Earnings Refugee (% difference from hosts) -24.9** -22.4** -18.5* (0.137) (0.115) (0.114)
The MIS was designed to serve as the single source of truth for beneficiary-level data across all three components. It captures individual registration records, service delivery events, transfer payments, and complaint submissions. Each implementing partner has read access to data from their own operation area, while the central PIU has system-wide read and write access. The MIS generates automated exception reports flagging duplicate registrations, inconsistent household composition records, and transfers issued outside the scheduled payment windows.
10 as the Demographic and Health Surveys (DHS), though researchers have reduced under-reporting by providing special training to interviewers, placing greater emphasis on respondents ’ privacy and safety, and allowing women multiple opportunities to disclose their experiences (Ellsberg et al., 2001a; Garcia Moreno et al., 2003; Ellsberg et al., forthcoming). Estimates of the magnitude of the problem Population-based surveys have found that between 10-70 % of women report being physically assaulted by an intimate male partner at some point in their lives (Heise, Ellsberg and Gottemoeller, 1999). See Annex A for estimates from many recent population based studies (Ellsberg et al., forthcoming). Findings from a multi-country study on domestic violence and women ’ s health carried out by the World Health Organization in fifteen sites and ten countries found that between 13-62 % of women had experienced physical violence by a partner over the course of their lifetime, and between 3-29 % of women reported violence within the past year (Figure 1. 1). Figure 1. 1.
observations. The ENILEMS-ENLACE panel also includes the information from ENOE

for all 3,714 matched observations plus information from ENLACE's context question

in means between the observations of ENILEMS that were merged with ENLACE scores