ai4data/datause-dataset-rechunked huggingface dataset:
8 neighboring community, (iii) residing elsewhere in the Kagera Region and (iv) residing outside the Kagera Region. Table 3 shows that the basic needs poverty rate declined 8 percentage points in the full sample. This figure masks significant differences in changes between subgroups based on migration. For those found residing in the baseline community, poverty rates dropped by 4 percentage points, but rates dropped by 11, 13 and 23 percentage points for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. A similar pattern is found for consumption per capita. While consumption per capita grew by $ 65 overall, it grew by only $ 30 for those found in the same community and by $ 65, $ 100 and $ 287 for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. Dividing consumption into food and non-food components gives the same result. The most basic assessment of welfare changes would have been wrong if we had focused only on individuals still residing in the community, a practice found in many panel data surveys. We would have underestimated the growth in consumption by half of its true increase. The differences in consumption changes of groups in Table 3 are statistically significant, as shown in Table 4.
<!-- PAGE 10 --> ##### **3 District Expenditure Data** The financial data used are the District budget data for the years from 2001 to 2012 (Fiscal  Year of January-December every year).

They were derived from the Regional Financial In formation System (Sistem Informasi Keuangan Daerah, SIKD) of the Ministry of Finance.  The district expenditures are available for 12 different sectors/functions (such as agriculture,  health, education, etc.) and for four economic classifications (personnel, goods and services,  capital, and other).

- floods, earthquakes, volcanic eruptions and the 2004 tsunami - that are combined with  nightlight data - used as a proxy for economic activity - to construct an index that estimates  the impact on districts and provinces.  More specifically, the nightlight data used provide a normalized annual light value ranging  from 0 (no light) to 63 (maximum light) and are from the Defense Meteorological Satellite  Program (DMSP) satellites.

Raschky (2014) and Michalopoulos & Papaioannou (2014). In our case, the nightlight data  have been employed as a weight for the economic impact of disasters.  Floods are modeled through a combination of remote sensing images and GIS-modeling  using the Geospatial Stream Flow Model (GeoSFM).
**Annex 2: Project Cost by Component and Expenditure Category**

| Component | IDA (USD M) | Govt (USD M) | Total (USD M) |
|---|---|---|---|
| 1. Livelihoods and Social Protection | 18.5 | 2.0 | 20.5 |
| 2. Infrastructure and Service Delivery | 24.0 | 3.5 | 27.5 |
| 3. Institutional Strengthening | 4.0 | 0.5 | 4.5 |
| 4. Project Management and M&E | 3.5 | 0.0 | 3.5 |
| **Total** | **50.0** | **6.0** | **56.0** |

Project management costs include operation of the PIU, SIGAF licensing and maintenance fees, and the annual external audit. M&E costs cover baseline and endline surveys, the MIS and activity monitoring dashboard platform, and supervision of protection monitoring activities.
14 4. 1 Variation across Districts Pakistan is divided administratively into four provinces with 102 districts — Punjab, Balochistan, North-West Frontier Provinces (NWFP), and Sindh — plus the federal capital Islamabad, the Federally Administered Tribal Areas (FATA), the federally administered Northern Areas and Azad Jammu and Kashmir (AJK). The four provinces or Punjab, Balochistan, Sindh and NWFP, together with Islamabad, account for more than 97 percent of the population. Geographically, parts of Balochistan, the NWFP and FATA border Afghanistan. Sindh and Balochistan are sparsely populated provinces, with the exception of Karachi in Sindh, which is the single biggest metropolis in the country with a population approaching 10 million. We use data from the population census, 1998, as well as the census of private schooling, 2000, to provide estimates of madrassa, private, and government school enrollment in each district except for those in the province of FATA. The geographical dispersion of madrassa enrollment depends on how we define madrassa prevalence. There are three alternatives. We could present a geographical breakdown of the total number of children enrolled in madrassas. This number is related to the total population of the district, and may thus reflect only the size of the district relative to others.
be imputed into the LFS, it may be unclear, ex-ante, if SUTPs are adequately included in the survey. Despite a small sample of foreigners and other concerns, there is evidence that the LFS sample does include some “ recent foreign ” migrants, especially in the border regions (NUTS2) [TRC1-Gaziantep, Adiyaman, Kilis, TRC2-Sanliurfa, Diyarbakir, TRC3-Mardin, Batman, Sirnak, Siirt TR63-Hatay, Kahramanmaras, Osmaniye] (Map 1).
Publications Office of the European Union. Fasani, F., Frattini, T., and Minale, L. (2022). (The Struggle for) Refugee integration into the Labour Market: European Evidence. Journal of Economic Geography, 22(2), 351-393. Feyissa, D. (2015). Power and Its Discontents: Anywaa’s Reactions to The Expansion of The Ethiopia Statae, 1950 – 1991. International Journal of African Historical Studies, 48(1), 31 - 49 FDRE. (2004). Refugee Proclamation No. 409/2004. FDRE. (2019). Refugee Proclamation No. 1100/2019. FDRE. (2020). Investment Proclamation No. 1180/2020. Fuller, S. (2015). Do Pathways Matter? Linking Early Immigrant Employment Sequences and Later Economic Outcomes: Evidence from Canada. International Migration Review, 49(2), 355-405. Gebreegziabher, T., and Regassa, N. (2019). Ethiopia’s High Childhood Undernutrition Explained: Analysis of the prevalence and Key Correlates based on Recent Nationally Representative Data. Public Health Nutrition, 22(11), 2099–2109. .org/10.1017/ S1368980019000569 Gebru, F. K., Haileselassie, M. W., Temesgen, H. A., Seid, O. A., and Mulugeta, A. B. (2019). Determinants of Stunting among under-five Children in Ethiopia: A Multilevel Mixed-effects Analysis of 2016 Ethiopian Demographic and Health Survey Data. In BMC Pediatrics 19 (1). BioMed Central Ltd. .org/10.1186/s12887-019-1545-0 Ginn, T., Resstack, R., Dempster, H., Arnold-Femandez, E., Miller, S., Guerrero, M., and Kanyamanza, B. (2022). 2022 Global Refugee Work Rights Report.
Table 2: Ethnic composition of IDPs, refugees, returnees in the North Ethnicity IDPs in Bamako (%) Refugees Niger (%) Refugees Mauritania (%) Returnees (%) Total I + R + R (%) Ethnic composition of the North (%) Songhai 75 21- 71 43 45 Kel Tamasheq 12 56 69 12 38 32 Arab 3- 28 4 11 3 Peulh 4 21- 6 4 7 Other 6 11 3 7 4 12 Total (%) 100 100 100 100 100 100 Total (n) 100 81 100 220 501 1, 268, 009 Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census. The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94 % of returnees were displaced within Mali. Only 6 % returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5 % of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2 % lived in Gao town and 1 % in Kidal town. The remaining 92 % lived in 27 different towns and villages in northern Mali, locations not covered by the survey.
In contrast, districts of origin are distributed widely across the country. This reﬂects the fact that much work migration is from remote rural areas to towns and cities. The main characteristics of work migrants are reported in Table 1, together with those of non- migrant adult males. We see that work migrants are on average younger and better educated. The census contains detailed information about ethnicity, language, and religion. In the Nepal census, the term ‘ ethnicity ’ is used to capture a hodgepodge of caste and tribal distinctions. The census distinguishes up to 103 ethnic categories. Most of these categories only account for a tiny proportion of the total population. In terms of the total adult population, the most common ethnic categories are Chhetri, Brahmin, and Newar who, together, account for 35 % of 13
this approach to calculating hunger the HCES-direct method. Of note is that neither the FBS-CV nor the

HCES-direct method actually measures what individuals ate (as in a food intake survey) or ask about

The FBS-CV and HCES-direct methods both rely on household surveys. In the case of the FBS-CV method,

the second moment of the calorie distribution comes from the surveys, while the HCES-direct method

relies on the surveys for all moments. Yet, the design of HCES varies over several key dimensions, such
a devastating war, and frequent droughts and floods. SESRE data collection was carried out between November 2022 and January 2023, marked by drought, inflation, and insecurity, posing significant threats to the livelihoods of refugees and host communities in an already fragile economy. The conflict in Northern Ethiopia continued until 2022 and disrupted the socio- economic conditions in the North Gondar Zone, home to over 20,000 refugees. The November 2022 peace agreement between the federal government and Tigrayan authorities offered a glimmer of hope for ending the war in Northern Ethiopia. Still, tensions in other parts of Ethiopia continued. Additionally, following the outbreak of fighting in the Amhara region in 2023, refugees in the Alemwach camp in the Amhara region faced attacks by unidentified armed groups. Moreover, food assistance for refugees has been unstable due to funding shortfalls, resulting in reduced food rations for hundreds of thousands of refugees for several months in 2022 and 2023. Insecurity in the Gambella and Benishangul-Gumuz regions not only undermined refugee and host community livelihoods but also heightened tensions between them. The intensification of conflict in Western Oromia further disrupted humanitarian operations in Eastern Benishangul-Gumuz, blocking the transport of relief and commercial supplies and affecting
To take advan- tage of the much larger ProGres database of nearly 2. 2. million refugees, we compute area averages for the above-mentioned case-level host community conditions, aggregat- ing to the smallest possible geographic unit available (district level for Lebanon and governorate level for Jordan). This information is then matched with all refugees in the ProGres database that have location information in Lebanon and Jordan, yielding a sample of 1. 85 million refugees. Finally, we worry about reporting bias. Respondents may have felt they were more likely to receive assistance if they reported worse living conditions. The bias could also go the other way if refugees want to signal their gratitude for the assistance they receive. The problem is even more acute given our research question. Those who intend 13
Looking also at the impact of IDPs in Colombia on wages, Morales (2017) uses a labor force survey, census data and registry data to study short and long-term effects as follows: 𝑆ℎ𝑜𝑟𝑡 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ௧ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ௧ ି ଵ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ௧ ൅ 𝜆 ௠ 𝑋 ௠ ௧ ൅ 𝛾௧ ൅ 𝛿 ௠ ൅ 𝛿 ௠ 𝑇 ൅ 𝜀 ௜ ௠ ௧ 𝐿𝑜𝑛𝑔 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ൅ 𝜆 ௠ 𝑋 ௠ ൅ 𝛿 ஽ ௠ ൅ 𝜀 ௜ ௠ where y is the log of wages, i, m, and i are individuals, municipalities and time respectively, 𝑋 ௜ ௠ ௧ are individual controls, 𝑋 ௠ ௧ is the log of total population or other municipality controls, 𝛾௧ and 𝛿 ௠ are time and municipality fixed effects, 𝛿 ௠ 𝑇 are municipality time trends, 𝛿 ஽ ௠ are department fixed effects and d is the inflow of IDPs defined as 𝑑 ௠ ௧ ൌ 100 𝑝𝑜𝑝 ௠ ௧ 𝑓 ௠ ௧ where 𝑓 ௠ ௧ is the total number of IDPs arriving in municipality m at time t. The same variable without the t subscript is used for the long-run effects equation.
This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turk­ish Labour Force Survey to assess their labor market impact. Syrian refugees are overwhelmingly employed informally, since they were not issued work permits, making their arrival a well-defined supply shock to informal labor. Consistent with economic theory our instrumental variable estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupa­tional upgrading, there are increases in formal employment for the Turkish- though only for men without completed high school education. Women and the high-skilled are not in a good position to take advantage of lower cost informal labor. The low educated and women experience net dis­placement from the labor market and, together with those in the informal sector, declining earning opportunities.
cents. The universe of blocks (“ manzanas ”) was stratified by socioeconomic strata and a representative sample of blocks was selected at random without replacement. To en- sure a sample representative of Colombian children by age group, stratum, and sex, we followed a multi-stage random sampling process. 8 As mentioned earlier, one of the biggest constraints in characterizing the role of forced migration in children ’ s human development within developing countries is the difficulty of finding a representative sample of those migrants. This is specially true in contexts where migrants are not hosted in refugee camps, but are integrated in local communi- ties, which account for 80 % of refugees worldwide (Climate Center 2022). We address these difficulties, leveraging all available information on Venezuelan settlements across the country to construct the largest possible comprehensive listing. The listing included data on Venezuelan settlements from all available sources, such as the 2018 population census, migrant organizations, and settlements identified by iMMAP, a non-profit orga- nization. iMMAP uses multiple sources, including OIM, United Nations, local migrant organizations, and satellite images, to identify Venezuelan settlements geographically. 9 Hence, to create our sampling frame, our field team verified the geographic location of all the Venezuelan settlements in-person and implemented a snowball sampling procedure in all the settlements found.
complete census of land parcels in Barafu and Kati, known as the Tanzanian Land Rights


Survey (TLRS). Households were identified using records and maps from the Kinondoni


Municipality, which had created a listing of all households in the area to assist with the

**Notes:** data are from Tanzanian Land Rights survey. Sample restricted to dual-headed households in
treatment blocks.


levels of female land ownership: investigating the gender breakdown of land ownership in

using baseline data from the experimental intervention, which is discussed in more


detail in the following section. Households in two unplanned settlements in Dar


es Salaam were asked a series of questions about the _de_ _facto_ ownership of land,

5To avoid priming, households were not asked directly about female ownership. Instead, they were
asked to list all members of the household that were default owners, must be consulted before a sale, or
would be included on a CRO.
6Section 191(2) of the 1999 Land Act and section 58 of the (1971) Law of Marriage Act.
7Authors' calculations using data from the Kinondoni municipal data.
8Section 159(6) of the 1999 Land Act.


8



<!-- PAGE 12 -->
Table 1: Female land ownership in Dar es Salaam
seekers. 81 There are several challenges associated with using central population registers to estimate refugee and asylum-seeker populations, including: consistency of the definition of refugees with the definition in the 1951 Convention and 1967 Protocol; difficulty and cost of establishing and maintaining a population register (UNSD 2014); and confidentiality safeguards. Compilation of statistics on forcibly displaced populations Several international organizations are involved in the compilation, analysis and dissemination of statistics on forced displacement including UNHCR, 82 Eurostat, IDMC, OCHA, International Committee of the Red Cross (ICRC), 83 WFP84 and IOM. Each of these actors has their own thematic focus and specific objectives, and applies their own methodologies. Asylum-seekers and refugees UNHCR is the principal organization responsible for the compilation, analysis and dissemination of data on asylum-seekers and refugees. UNHCR maintains a publicly available statistical online database85 with data for the period 1951-2014 on refugees (including people in refugee-like situations), asylum-seekers (pending cases), returned refugees, IDPs protected or assisted by UNHCR, returned IDPs previously protected or assisted by UNHCR, stateless persons and others of concern to UNHCR, disaggregated by country of origin and asylum. 86 Data are also provided on demographics, location, asylum-seekers (refugee status determination and monthly data) and resettlement.
project had to meet a basic minimum literacy level in order to qualify for admission to the program; whereas only half of the 15-29-year-old female respondents to the nationally-representative Core Welfare Indicators Questionnaire (CWIQ) survey report that they can read and write (LISGIS 2007). Fewer than two percent of the girls in the EPAG study responded that they had no education, which is much lower than in the CWIQ survey. Second, the majority of adolescent girls and young women in Liberia reside in rural areas, whereas the survey participants were residing in urban and peri-urban areas, where access to basic social services may be much more improved. Consequently, the results are not representative of adolescent girls and young women in Liberia overall. The results are neither indicative of the average Liberian girl and young woman; nor are they indicative of the average Liberian girl or young woman in the project communities. They are only indicative of the average girl and young woman who are part of the EPAG project. Finally, many of the variables that we examine in this study are measures of self-assessed levels of satisfaction or belief. These are entirely subjective variables, and are subject to significant measurement error. There is considerable evidence that the wording of these questions can affect the answers given, as can the order in which the questions are asked.
The MHPSS referral pathway was tested through six clinical review sessions conducted jointly by the project's MHPSS technical advisor and the district psychiatric nurses. Case reviews identified bottlenecks at the step between community-based MHPSS workers and facility-based counselors, particularly for cases involving domestic violence survivors. Revised referral procedures were agreed, including a protocol for same-day warm handoffs in urgent cases and the designation of dedicated MHPSS consultation slots in three district hospital outpatient departments.
Using the NLSS data we begin by estimating a regression of the form: yk s = δs + α (ak s − a) + βs (Ek s − Es) + χs (Hk s − Hs) + vk s (4) where yk s is the log of income (or consumption) of household k residing in district s, coeﬃcients δs, βs and χs vary by district, ak s stands for the age and age squared of the household head, Ek s is the education level of the head measured in years of completed education, and Hk s = 1 if the head belongs to what we have earlier classiﬁed as a high caste (i. e., Brahmin, Chhetri or Newar). Since income or consumption are expressed in logs, βs and χs can be thought of as education and high caste premia, respectively. Female headed households are excluded from the regression since the focus is on migrant males. Vector a denotes the average age and age squared of observations across the sample. Variables E and Hs denote the district-speciﬁc averages of Ek s and Hk s. By demeaning regressors, we ensure that eδs measures the unconditional, district- speciﬁc average of yk s. Marital status, household size, and other household characteristics are 15
estimates, we also estimate the continuous time, semi-parametric Cox proportional hazard (Cox) model. The main advantage of the Cox model is that it allows us to estimate the relationship between the hazard rate and the determinants of refugee returns without having to make any assumptions about the shape of the baseline hazard function. A continuous time model might also be appropriate in this context, given that we have a long panel of 75 months. Using the same variable deﬁnition as in 3, we estimate h (t) = h0 (t) exp [β1chari + β2regi + β3foodsect + β4housingt + µl + δd + τt] (4) We restrict survival analysis to the Syrian refugees based in Jordan and Lebanon as their host country conditions are proxied by the geographical aggregates computed from the VAF and VASyr surveys. Despite this restriction, more than 85 percent of all refugees in our data set is included in the analysis. It is important to note that the conditions in the country of asylum may be the result of a refugee ’ s anticipated length of stay in the host country.
14 displacement. 55 % of the returnees reported to have been employed before the crisis and 36 % in June 2014. Over time the employment situation among the displaced has improved steadily and by December 2014 more people reported being employed than prior to the crisis. All the returnees were able to regain employment after returning. The employment situation of IDPs, returnees, and refugees in Niger is steadily improving; only for refugees in Mauritania does one notice a steady decrease, with 100 % reporting no employment during January and February. Source: Listening to Displaced People Survey, 2014 and 2015. The ownership of livestock and consumer durables was reduced significantly as a consequence of the crisis. Table 7 demonstrates this by showing the Tropical Livestock Units (TLU) 12 owned prior to the crisis and in June 2014 as well as the percentage of ‘ yes ’ responses on a question whether a given asset was owned by the household. 13 The loss on livestock has been enormous particularly amongst IDPs and refugees who lost respectively more than 90 % and 75 % of their animals. 12 TLU is a common unit to describe livestock numbers of various species as a single figure that expresses the total amount of livestock present – irrespective of the specific composition. 13 This was a ‘ yes / no ’ question meaning that if 56 % of the
graduates aged 18, 19, and 20. The objective of ENILEMS was to provide information on

The ENILEMS-ENLACE panel merges information from the respondents of the ENILEMS

2010 survey with their results in the ENLACE Grade 12 taken in May of 2008, 2009, or

2010. Although ENILEMS 2010 did not capture the CURP, it included all the necessary
For each of these food items we obtain calorie intake values from TBCA that are based on the descriptions of the food item in POF and TBCA. TBCA has calorie intake values for different preparation forms of the food item.

A second step is applied in order to increase the list of food items that have calorie intake assigned to
them-and thus contribute to the cost per calorie estimation. In this step, we create food groups by
aggregating POF's original 7-digit item code to a 5-digit item code. Based on this, we impute calorie intake
to items that were not mapped to calorie intake in step 1.

For instance, different types of rice ( _arroz_ _hibrido_, _arroz bica corrida_, _arroz quirera_ ) were not found in the TBCA, but by creating a 5-digit aggregation, we can assign them to the same group as _arroz polido,_ for which we do have a caloric intake estimate.

For all of these types we assign the average value of calorie intake for rice at this 5-digit item code that had calories assigned from TBCA in step 1 (in this case, _arroz polido_ ). After this step, we have raw calorie intake values for 1,413 items, which account for 86.6 percent of average total expenditures on food consumed at home and 58.1 percent of all food expenditures.