ai4data/datause-dataset-rechunked huggingface dataset:
5 narrative around the regressions and aims to explain why not more people migrate when benefits of doing so are so high. 2. The Setting: Tanzania and Kagera, 1994-2004 In the last decade, Tanzania has experienced a period of relatively rapid growth, attributed to liberalization, a renewed trade orientation, a stable political context, and a relatively positive business climate to boost economic performance. Real GDP growth was of the order of 4. 2 % per year between 1994 and 2004, while annual population growth was around 3. 2 % in the same period (URT, 2004). There is also evidence that growth had accelerated in the last few years compared to the 1990s. However, this growth has not been sufficiently broad-based to result in rapid poverty reduction. On the basis of the available evidence, poverty rates have declined only slightly and most of the poverty reduction progress has been made in urban areas. According to the Household Budget Survey (HBS), between 1991 and 2000 / 01, poverty declined from 39 percent to 36 percent in mainland Tanzania. The decline in poverty was steep in Dar es Salaam (from 28 % to 18 %) but minimal in rural Tanzania (from 41 % to 39 %).
Those who had moved out of the Kagera Region by 2004 experienced consumption growth that was 10 times higher compared to those who remained in their original community. These averages translate into very different poverty dynamics patterns for the physically mobile and immobile. For those who stayed in the community, poverty rates drop by about 4 percentage points over these 13 years. For those who moved elsewhere within the region, poverty rates drop by about 12 percentage points, and for those who moved out of the region, they drop by 23 percentage points. Had we not tracked and interviewed people who moved out of the community – a practice found in many panel surveys – we would have seriously underestimated the extent to which poverty has gone down over the past 13 years in the Kagera Region; we would have reported poverty reduction at about half of its true value. Clemens and Pritchett (2007) raise similar concerns in the context of income growth and international migration. In addition, the data would omit the part of the population with a high information content on pathways out of poverty. Still, these statistics are not evidence that moving out of the community leads to higher income growth. As noted above, we cannot observe the counterfactual: What would income growth have been for migrants had they not migrated? We exploit some unique features of these data to address concerns about unobserved heterogeneity. First, individual fixed effects regressions for movers and stayers produce a difference-in-difference estimation of the impact of physical movement, controlling for any fixed individual factors that affect consumption. Second, we can control for initial household fixed effects in the growth rate of consumption since we observe baseline households in which some individuals migrate
13 Proposition 7 State-Level Effects of Population Size: The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects. 3 Research Design 3. 1 Unit of Analysis To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8. 6 km x 8. 6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope'and `location'variables in the Uppsala / PRIO dataset.
ministries to achieve better outcomes for refugees and their hosts. ◆ Improve the coverage, accuracy, reliability, quality, and comparability of data to provide the analytical underpinning for policy decisions. Executive Summary 1 C onflict, political unrest, environmental disruption, and economic instability has forcibly displaced millions of people globally (Ferris, 2010; Black, 2001). Over the last decade, the number of forcibly displaced persons has continuously increased. In mid-2023, there were 36.4 million refugees worldwide (UNHCR, 2023d). As development reduces global poverty, extreme poverty is increasingly concentrated among vulnerable groups; refugees are among these vulnerable groups (World Bank, 2017). Therefore, the plight of the forcibly displaced poses significant challenges to broad development efforts to eradicate extreme poverty and achieve the Sustainable Development Goals (SDGs). Ethiopia has a long history of hosting refugees and has one of the largest refugee populations in Africa. The refugee situation in Ethiopia is characterized by both complex humanitarian emergencies and protracted refugee status. Forced displacement is a pressing issue in the country, a result of conflict, drought, flood, economic instability, and political instability in neighboring countries (Martin, 2010; UNHCR, 2020d; IPCC, 2019). As of year-end 2023, more than 922,000 refugees and asylum seekers 1. Introduction 0 200,000
Maternal depression was measured by the Self-Reporting Questionnaire (SRQ-20), which has previously been used in Pakistan and has been found to be valid and reliable for screening for depressive disorders in rural Pakistan (Husain et al., 2006). The SRQ-20 contains 20 yes / no questions, with higher scores indicating higher levels of depression, and was found to have a high internal consistency reliability, with a Cronbach ’ s alpha of. 89 in this sample. Anxiety. Maternal anxiety was assessed using the General Anxiety Disorder 7-item questionnaire (GAD-7). This 7-item questionnaire has been found to be valid in the Gilgit province of Pakistan (Ahmad et al., 2017) and has subsequently been used in other assessments of anxiety in Pakistan (Yasmin et al., 2021). The GAD-7 was found to have a high internal consistency reliability, with a Cronbach ’ s alpha of. 80 in this sample. Parenting Stress. Parenting stress was measured using an adapted version of the Parental Stress Scale (PSS), which has previously been validated in Urdu in Punjab (Bilal et al., 2021). The adapted version used in this survey was designed by Ugarte et al., 2024 for Rohingya refugees in Bangladesh.
-0.000* -0.001*** -0.001*** -0.000 0.000 -0.000 0.001 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) Educ: < Primary - - - - - - - - Educ: Primary -0.028 0.058*** 0.127 -0.042* 0.077** -0.104 -0.172 0.121 (0.020) (0.021) (0.107) (0.022) (0.032) (0.069) (0.122) (0.361) Educ: Secondary 0.115*** 0.506*** 0.700*** -0.045 0.460*** -0.200** 0.098 0.849*** (0.031) (0.046) (0.100) (0.045) (0.107) (0.080) (0.212) (0.311) Educ: Post-sec 0.231*** 0.716*** 0.812*** 0.004 0.493*** -0.073 0.222 0.528** (0.024) (0.037) (0.078) (0.117) (0.121) (0.149) (0.367) (0.232) Years in Ethiopia 0.009*** 0.001 0.001 0.003 0.006 (0.003) (0.002) (0.004) (0.012) (0.015) Work outside camp 0.352**
.009*** 0.001 0.001 0.003 0.006 (0.003) (0.002) (0.004) (0.012) (0.015) Work outside camp 0.352*** (0.128) Region fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Restrict to workers outside camp Yes N 3321 1626 494 3,069 830 975 238 73 Source: World Bank Staff based on SESRE 2023. Note: Columns 2 and 5-6 are restricted to working respondents. Columns 3 and 7-8 are restricted to employees. 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
global-trends-report-2021.html. UNHCR. (2022c). Ethiopia Country Refugee Response Plan (ECRRP) Jan Dec 2022. https://data.unhcr.org/en/documents/ details/94099 UNHCR. (2022d). Serdo Refugee Camp Profile April 2022. https://data.unhcr.org/fr/documents/details/92436 UNHCR. (2022e). “Global Report 2021.” http://reporting.unhcr.org/globalreport2021/pdf. 81 UNHCR. (2023). Ethiopia Global Refugee Forum Pledge Progress Report. UNHCR. (2023a). Addis Ababa Quarterly Urban Factsheet, April 2023. UNHCR. (2023b). UNHCR Resettlement Handbook. UNHCR. (2023c). Culture, Context and Mental Health and Psychological Well-being of Refugees and Internally Displaced Persons from South Sudan. UNHCR. (2023d). Mid-year Trends. Geneva. https://www.unhcr.org/mid-year-trends-report-2023 UNHCR. (2023e). Ethiopia Country Refugee Response Plan 2023. https://reporting.unhcr.org/ethiopia-country-refugee- response-plan-summary UNHCR. (2024). Global Refugee Forum 2023 Pledges. https://globalcompactrefugees.org/pledges-contributions UNHCR. (2024a). Press Release (Ethiopia Launches Inclusive ID System for Refugees, Boosts Access to National Services). https://www.unhcr.org/africa/news/press-releases/ethiopia-launches-inclusive-id-system-refugees-boosts-access- national-services UNHCR, and World Bank Group. (2020). Understanding the Socioeconomic Conditions of Refugees in Kenya Volume A: Kalobeyei Settlement. Results from the 2018 Kalobeyei Socioeconomic Survey. Washington, D.C. UNICEF. (2018). Situation and Access to Services of Persons with Disabilities in Addis Ababa. Briefing Note. UNICEF. (2019). Birth Registration for Every Child by 2030: Are WE on Track? New York. UNICEF. (2021). Education in South Sudan. Briefing Note. Vemuru, V., Sarkar, A., and Woodhouse, A.
. (2019). Birth Registration for Every Child by 2030: Are WE on Track? New York. UNICEF. (2021). Education in South Sudan. Briefing Note. Vemuru, V., Sarkar, A., and Woodhouse, A.F. (2020). Impact of Refugees on Hosting Communities in Ethiopia: A Social Analysis. World
To further control for geological and agro-ecological characteristics at the community level, elevation figures were included, taken from the GLOBE 1 kilometer elevation database (GLOBE Task Team et al. 1999). Elevation differences were also incorporated as a measure of terrain roughness. These were computed using the SRTM3 data (Jarvis et al. 2006) by subtracting the minimum elevation in a 1 kilometer grid from the maximum elevation in the 1 kilometer grid. Finally, urban/rural and regional indicator variables help capture unobserved (time invariant) location specific characteristics related to the urban and rural livelihood systems as well as those related to regional variation in the agro-ecological, economic and political environment. Year dummies were included to control for the survey year.

Given the focus here on riverine and coastal floods and to better pinpoint the locations that actually experienced riverine or coastal floods, the DFO flood maps were overlaid with the location of the rivers taken from the Hydroshed project (Lehner et al., 2008). Only the major rivers were mapped as these will have sufficiently large catchment areas exposing the downstream areas they traverse to (non-localized) flooding. Riverine and coastal flood bands were then derived by considering DFO flooded areas within 2, 5 and 8 meters elevation difference from the closest point on the major river or the coast using a Digital Elevation Model from GLOBE (GloBe Task team, 1999).

To reflect access to public services, indicator variables are included that take the value of one if clean water [32], sanitary latrines, [33] or electricity are present in the community, and zero otherwise. To proxy a household's integration in the overall economy, the community data were further augmented with the distance of the centroid of the community (in meters) to the nearest part of the nearest primary or secondary road, as designated in the VMAP0 dataset (NIMA 1997). These measures were computed that using Arc View 3.2. In addition, the estimated travel time to the nearest town or city of at least
the nearest part of the nearest primary or secondary road, as designated in the VMAP0 dataset (NIMA 1997). These measures were computed that using Arc View 3.2. In addition, the estimated travel time to the nearest town or city of at least 25,000 people, the nearest city of at least 100,000 people, and the nearest city of at least 500,000 people were included. The travel times were computed in Arc View from a friction grid that assigned differing travel times to various classes of roads, urban areas, water bodies, off-road, and crossing international boundaries. [34]

The expenditure data are expressed in January 2002 prices using the CPI provided by the Government Statistical Office. They are also corrected each year for regional and rural/urban differences across the 8 regions.
9 Visits (JD-HV). ProGres records demographic information of each individual refugee and their household at the time of registration with UNHCR including the relationship of each household member with the principal applicant. In this paper, we refer to the self-identified principal applicant as the household head – the term used in the poverty research literature. Self-identification is commonly used as a means of identifying the household head in household surveys of income, consumption and expenditure surveys which are used to estimate poverty rates (see Hanmer et al, (2020)). Home visits began at the same time as the UNHCR cash assistance program was launched. The program aimed to assist vulnerable Syrian households to meet their basic needs. During the home visits, UNHCR determined whether a household met the criteria to qualify for assistance, considering economic poverty and the presence of members in the household requiring protection. Since 2013, these visits have been made to every registered Syrian refugee household in Jordan outside the camps (UNHCR, 2014). At registration, each household is assigned a unique registration number that serves to cross- reference the initial registration data with Home Visits data. Home Visits data are collected for about one-third of the registered households.
**Electricity** **Capital** **GDP** **Consumption** **Stock** **Population** Median 3.84 3.65 3.45 1.44 Average 3.48 4.10 3.88 1.43 Standard Deviation 4.37 6.87 2.77 0.97 Skewness -1.58 1.56 0.81 0.11 Kurtosis 9.83 20.48 1.10 -0.33 _Sources: World Development Indicators, Penn World Tables Version 8, and author's calculations._

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#### **VII. Hypothesis Testing**

We explore two basic questions. First, could night-time lights data serve as a good proxy for any of the
economic variables considered in this paper?

A new satellite is generating superior night-time lights data. The data are generated by the Suomi National Polar-orbiting Partnership (SNPP) satellite series operated by the NASA - NOAA Joint Polar Satellite System that was launched in late 2011.

observed in cross-section surveys - even when controlling for other observable characteristics -   cannot directly be interpreted as reflecting a change in attitudes over the life-cycle.
Given that we control for the effects of conflict in addition to the usual socio-economic determinants of fertility and given that conflict may also affect these other variables, the estimated coefficient of the conflict variable can be interpreted as the ‘ pure conflict ’ effect. This makes our calculations conservative estimates of the total effects of conflict on fertility. For example, conflict is likely to reduce the educational attainment of children exposed to violence during their school-age (Akresh and de Walque 2008) and of girls in particular (Shemyakina 2006), hence inter alia raising their fertility. In both (1a) and (1b), measures of each woman ’ s socio-economic characteristics include three age categories (young: 15-24 years, middle: 25-34 years, and old: 35-49 years), the number of sons and daughters born before the time span of interest, her education (no education, some primary education, and some secondary or higher education), a dummy variable indicating whether she is currently in a union, 9 a dummy variable indicating whether she has had more than one union, a continuous household wealth index (see Section 4. 1), a dummy variable indicating whether she has always lived in the same community, a dummy variable indicating whether the current location is urban, mortality rates of children under five years at the district 9 This variable is not included in the estimation of equation (1b), given that widowhood is strongly correlated with being in union.
school report cards and a related online platform. Still, few schools viewed ENLACE

By design, ENLACE had a national mean score of 500 and a standard deviation of 100

and excellent. ENLACE's methodology followed item response theory (IRT), allowing

In 2008, SEP decided to use ENLACE scores to measure teacher performance in

subject to yearly evaluations. In 2008, ENLACE scores among their students were given
ratings. Where GDP per capita growth data have been recorded, it appears that FCS countries have stronger growth trends than non-FCS countries and in both groups of countries growth is correlated with stronger outcome ratings.

categorization is derived from the FCS lists for FY06 to FY13. [1] Partial FCS countries are those classified as FCS in at least two years during this period.

7For example that would be detected through the implementation status reports (ISRs).

we rely on the data from Melecky and Podpiera (2013) and the 2003, 2007, and 2012 Bank Regulation


and Supervision Surveys of the World Bank. For banking crisis classification, we rely on Laeven and


Valencia's (2013) database and cross-check our results against the crisis classification by Reinhart and

countries.


8



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_**Regression Model**_


We use the systemic banking crisis database of Laeven and Valencia (2013) to identify banking
### 4.2 GLSS Cluster-Level Aggregation

To construct community-level outcome variables from GLSS household data, we aggregate household-level observations to the GLSS enumeration area level using survey-weighted means. We restrict this aggregation to GLSS enumeration areas with at least 10 interviewed households to limit noise from small-cluster estimates. The resulting GLSS cluster-level dataset comprises 378 enumeration areas distributed across all ten regions, with an average of 18 interviewed households per cluster.
We only found reliable approximations from the International Displacement Monitoring Center (IDMC) between 2003 and 2013. According to IDMC, there were about 12. 5 million internally displaced people in SSA at the end of 2013 (IDMC 2014), more than one third of the total number of IDPs and more than tripling the number of refugees in SSA. Although the number of IDPs in SSA is the highest since 2007, the share of IDPs in SSA has been decreasing from 53 % in 2003.
prior to the pandemic, a period characterized by less distinct time trends in the labor market. Since the focus of the paper is on the impact of COVID on labor market stock and dynamics, we also restrict the sample of interest to include only prime-aged working adults (aged 20 to 59). The dataset contains standard variables expected of a labor force survey, including those denoting employment, unemployment, and inactivity. It also contains information on the intensive margin of the labor supply, including hours worked and full-time and part-time status. Information on employment sector, industry, contract status, health insurance coverage, and mode of work (distinguishing between employees and self-employed, for example) is also available, allowing us to construct indicators of formality and to differentiate different modes of employment. Information on occupation is also available, but only at the level of 2-digit ISCO-08 classification. This information is enough to distinguish between white- and blue-collar occupations but it is not enough to observe additional relevant pandemic-related job characteristics such as the degree of contact with the public. 3. 2 Descriptive statistics Figure (1) tracks the evolution of labor market stocks in the West Bank and Gaza respectively over time.
Data and Variables Definition The data used to conduct this study are the ARIS-REDS data from the National Council of Applied Economic Research (NCAER). Since 1971, the NCAER has been conducting surveys on a _sample_ of households in 232 villages in the 17 major states of India.

development improves. According to FAO data, in the last five decades per capita milk

development partners) as part of the Living Standard Measurement Study - Integrated Survey  on Agriculture (LSMS-ISA) program [2] . The paper aims to contribute to building an evidence

2 More information on the program is available at www.worldbank.org/lsms-isa.
Chew et al. (2018) use a baseline convolutional neural network model on a gridded population sampling frame to select a sample of households in Nigeria and Guatemala. The authors found this technique to be on par with human canvassing in terms of accuracy, and to outperform other machine learning models based on crowdsource or remote sensing data. Grais et al (2007) compared an unweighted random point selection methodology to a random walk in their study of vaccination rates in urban Niger. The authors do not find statistically significant differences between the methods, though the sample size was limited and both methods were non-probabilistic. 3. Design and Field Protocols 3. 1. Experiment Design This paper makes use of a dataset from the purposefully designed methodology experiment conducted in one section of the Protection of Civilians site 1 (PoC1, Figure 1), one of the largest IDP camps in Juba, South Sudan. To generate a gold standard as the basis of comparison, a household census was conducted between August and September 2017. During this exercise, 2, 655 households were interviewed using a questionnaire designed to collect demographic information, dwelling characteristics, household consumption, and perception data. At the end of each census interview, households received a unique barcode that could be used to identify them later in the experiment.
To avoid changes in camp composition, immediately following the completion of the census fieldwork, the interviewers returned to the field to implement the experiment. Teams used each of the sample selection methods to identify which households would have been selected had that method been used for a survey. To avoid respondent fatigue, instead of re-asking the questionnaire, the interviewers simply scanned the unique bar code of the selected household. Once scanned, the barcodes created an observation in the method-specific dataset with the information captured in the census. Each sampling technique targeted about 322 interviews so that comparisons could be made between the methods using an identical sample size. There was, however, some non-response for each method if interviewers were not able to contact a household member who could provide access to the barcode, if the barcode had not been retained by the household, or if the barcode was not scanned correctly. Protocols for each individual method are listed below.