ai4data/datause-dataset-rechunked huggingface dataset:
status Source: World Bank Staff based on SESRE 2023. Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent 0 20 40 60 80 100 Camp Hosts Female Camp Refugees Female Addis Hosts Female Addis Refugees Female Figure 3.29: Female youth work status Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 39 T his chapter looks at how low sociodemographic and labor market outcomes shape how refugees perceive their future prospects and aspirations. While “resettlement” to a high-income country is an attractive solution, the share of refugees resettled globally is marginal. Resettlement is considered one of the three “durable solutions” for refugee protection under the 1951 Refugee Convention, alongside naturalization and return. Yet, the share of refugees resettled globally—including private sponsorship and other complementary pathways of refugee admission to third countries outside of UNHCR processes—was below 2 percent over the past twenty years (World Bank, 2023). According to government statistics, there has also been a downward global trend in the number of resettlement opportunities, fluctuating from 99,000 in 2010 to just 34,000 in 2020, even as the number of forcibly displaced persons increases globally. In Ethiopia, resettlement numbers are similarly low; in 2022,
10 statuses in February 2020, right before the pandemic. We deemed the SYPJ data to be more suited to our question since it allows to look at the trend of employment outcomes before the pandemic as well as right after the shock in the summer of 2020 and therefore allows investigating the effect of the pandemic on the two populations. The CMM data would be better suited to capture the recovery from the pandemic (Krafft et al., Forthcoming). The overlapping portion of the CMM data with the SYPJ data (February 2020 to February 2021) did not allow for a comparable analysis to the one we did in this paper. The overlap is not perfect. CMM data was collected a few months after the bulk of the SYPJ data only allowing for a comparison between the first half of 2021 (CMM) to the second half of 2020 rates (SYPJ). This is not a reasonable comparison especially in a period with rapidly changing events like the pandemic time. In addition, the time intervals are different as CMM data allows for examining annual change (February 2020 to February 2021) as opposed to semi-annual, and that is only one period which does not allow to compare the trend. In our analysis we focus on young males aged 15 and above who ever worked.
high informality. Some of these studies also ﬁnd that women and young workers were disproportionately aﬀected. More recently, researchers have shifted the focus to other dimensions of the eﬀects of Syrian refugee inﬂows into Turkey. Altindag et al. (2020) argue that refugee inﬂows had a positive impact on ﬁrm production, both in terms of the volume of production and the introduction of new product varieties. Akgunduz and Torun (2020) examine other channels by which Turkish workers and ﬁrms accommodated the inﬂows of Syrian refugees. The authors ﬁnd that skilled native workers increased their specialization in complex tasks, moving away from manual tasks, and domestic companies took advantage of the increased abundance of labor by reducing capital intensity. As shown in earlier studies, both mechanisms contribute to mitigate the eﬀects of immigration on the wages of the receiving country (Lewis (2005), Peri and Sparber (2009), Gonzalez and Ortega (2011) and Dustmann and Glitz (2015)). In the last few years, some researchers have begun to analyze the economic eﬀects of the exodus of Venezuelans on the surrounding countries but progress has been slow due to the diﬃculty of analyzing Venezuelan migrants equipped solely with government- provided data or the standard labor force surveys.
Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Health institutions (health center/hospital) Home Other Percent Figure D.10: Childbirth in health institutions (children under five years) Source: World Bank Staff based on SESRE 2023. Annexes 104 Hearing Walking or climbing steps Percent Refugees Eritrean Somali South Sudanese In Camp Addis Ababa Total Hosts Seeing Diﬀiculty with selfcare Communicating Remembering or concentrating Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts 50 40 30 20 90 100 80 70 60 10 0 Figure D.13: Types of disability Source: World Bank Staff based on SESRE 2023. - 10,000 20,000 30,000 40,000 50,000 60,000 Hosts Refugees Addis Ababa Total annual rent expenditure Per adult equivalent rent expenditure Figure D.14: Rent expenditure (Refugees and hosts in Addis Ababa) Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Eritrean Somali South Sudanese Addis Ababa Eritrean Somali South Sudanese Addis Ababa Eritrean Somali South Sudanese Addis Ababa Has hand washing place/item Water available Detergent available Hosts Refugees Percent Figure D.15: Hand washing facility Source: World Bank Staff based on SESRE 2023. Annexes 105 Table D.5: Labor force statistics by survey domains Eritrean Somali
Figure 3 also shows that there is considerable variation in both indexes within our sample when averaging these indexes at the regional level over the period of investigation. 16It is possible that our approximation is noisy and could potentially induce non-random measurement errors. In Section 4. 3, we propose an instrumental variable approach and estimate Shareodet from the EPR-ER data using a gravity model. Our findings concerning the number of ethnic groups across time for a given origin – destination pair are in line with the EPR-ER data. It seems that refugees of a given origin – destination pair mainly belong to two major ethnic groups. This also means that the variation in diversity in refugee-hosting areas is coming from the refugee composition at the camp level. Figure B. 5 shows the movements of refugees from origin to destination countries under scrutiny. Somalia, the Democratic Republic of Congo, Liberia, South Sudan, and Sudan are major source countries for refugees, while Kenya, Tanzania, Uganda, Zambia, and Ghana appear to be countries hosting most refugees. Representing refugees in camps per ethnic group for the top 5 asylum countries over the sample period, Figure B. 9 shows that there is considerable variation in ethnic composition across camps. 17
race or ethnic group as the majority (ESS). _Education_ is the average number of years of full-time education completed (ESS).   _Age_ is the median age of all respondents (ESS). All summary statistics use design and population size weights.

The estimates are small area estimations
based on the 2014 Demographic and Health Survey and



the latest population census. It is shown that small area
estimations are powerful predictors of undernutrition, even
controlling for household characteristics, such as wealth and
education, and hence a valuable targeting metric.

**Keywords:** Child malnutrition, Small Area Estimation, Demographic and Health Survey, Targeting, Ethiopia
Total Consumption (percent, Production) 66 66 66 73 81 78 77 1.2 _Source:_ Publications of and contacts with Public Services Regulatory Commission, Republic of Armenia

_Source:_ International Energy Agency, _Energy Statistics, 2007_

The 2008 ILCS data is used in the simulation of the impact of gasp price hike on April 1, 2010.

3 For detailed description of the 2008 ILCS, please refer to NSS (2009).

The ILCS collects data from nearly 8,000 Armenian households surveyed year round. It is based
36 7. Conclusions To make progress in promoting equality and improving the living standards of the population in Uganda, it is necessary to understand the effect of Uganda ’ s fiscal policy on inequality and poverty. This report uses the internationally comparable CEQ methodology to assess the individual and combined effects of taxes and public social spending, based on the microdata from the UNHS 2016 / 17 household survey. The main objective is to provide considerations for policy makers in terms of which fiscal instruments or, more specifically, which mix of fiscal instruments can contribute to reduce poverty and inequality. Overall, our results show that Uganda ’ s fiscal system is modestly equalizing. As a whole, taxes and transfers in 2016 / 17 reduced inequality by approximately 3. 23 Gini points. This result is comparable to Ghana, moderately lower than in Kenya and Tanzania, and much lower than the result observed for South Africa. The largest contributive factor to this equalizing effect is direct taxation (personal income tax or PAYE), followed by the education in-kind transfers (net transfers). This is not surprising, given the size of these fiscal interventions as a proportion of pre-fiscal income and their progressiveness. Direct transfers and social protection programs more generally contribute to the redistributive effect and are simulated as highly concentrated among the poorest households.
The 2024 survey data revealed that 62 percent of enrolled cooperatives had adopted at least one new post-harvest management practice introduced during the training-of-trainers workshops conducted in Year 1. Among cooperatives that adopted improved storage techniques, average post-harvest losses declined from an estimated 28 percent of harvested output to 14 percent, representing an annualized value retention gain of approximately USD 340 per metric ton of stored produce. These findings will be used to calibrate the economic rate of return estimates in the project's completion report.
IV. D Validity of the local continuity assumption Table 1 examines whether migrants who migrated just before and after the RAMV cutoff date were similar across a range of individual and household characteristics. For this pur- pose, a sharp RDD model was estimated with a set of pre-migration and pre-RAMV controls used in the RDD as the outcome variables. Only one out of 22 estimated coefﬁcients is sta- tistically signiﬁcant for the robust RDD estimator. The conventional, bias-corrected, and robust estimators, illustrated in Figure G. 2, further conﬁrm the validity of the local continu- ity assumption. Moreover, Tables B. 2 – B. 3 report the same exercise but restrict the sample of non-RAMV migrants obtained through referrals or refugee organizations. The data in both tables conﬁrms that the local continuity assumption holds regardless of the sample of non-RAMV migrants. Finally, we present robust evidence that the socioeconomic characteristics of migrants are uncorrelated with their arrival date during our period of analysis. For this purpose, we ﬁrst regress the arrival date on a rich set of baseline socioeconomic characteristics before the pro- gram onset (and the RAMV registration). The results show that the covariates are not jointly statistically signiﬁcant (Table C. 1).
**Table of Contents:**

**Background** ....................................................................................................................................... 3

**Overview of UNHCR’s Referral Guidelines** .......................................................................... 3

**Data Collection and Analysis** ...................................................................................................... 3

**Summary of Findings** ...............................
........... 3

**Summary of Findings** .................................................................................................................... 4

**i.** **Demographic Characteristics of Medical Referrals in January – December**

**2016 (n= 34,571)** ............................................................................................................................... 5

Figure 1. Medical referrals by admission category and final outcome on discharge .......... 5

Figure 2. Medical referrals per month; January – December 2016 (n= 34,571) ................. 5

Figure 3. Frequency of referrals per unique patient; January – December 2016 (n=
23,296) .......
34,571) ................. 5

Figure 3. Frequency of referrals per unique patient; January – December 2016 (n=
23,296) ........................................................................................................................................... 6

Figure 4. Proportion of referrals by gender and age group (n= 34,571) ............................... 6

Figure 5. Proportion of referrals by nationality (n= 34,571) .................................................. 7

Figure 6. Proportion of referrals by referral hospital (n= 34,571) ......................................... 7

Figure 7. Proportion of referrals by referring clinic (n= 34,571) ...........
...................... 7

Figure 7. Proportion of referrals by referring clinic (n= 34,571) ........................................... 8

Figure 8. Number of JHAS Madina clinic referrals by nationality (n= 9,488) ................... 8

Figure 9. Zaatri and Azraq referrals per month; January – December 2016 (n= 19,473) . 9

Figure 10. Proportion of referrals by diagnosis at discharge ............................................... 10

**ii.** **Mortality** ................................................................................................................................... 11

Figure 11. Number and proportion of mortalities by age group (n= 94) .......................
.......... 11

Figure 11. Number and proportion of mortalities by age group (n= 94) ............................ 11
28% 49% 54% 73% Secondary school (19 to 24 years) 34% 11% 30% 32% 32% 38% Enrollment rates Primary NER 81% 65% 65% 62% 82% 80% Secondary NER 47% 14% 44% 32% 28% 17% Primary GER 101% 86% 79% 84% 114% 116% Secondary GER 54% 14% 47% 36% 31% 18% Source: World Bank Staff based on SESRE 2023. Annexes 99 Table D.3: Health outcomes by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Faced any health problem 21% 20% 6% 7% 29% 25% Received medical assistance 86% 85% 85% 85% 85% 93% Child Nutrition Stunted 43% 52% 37% 47% 26% 26% Underweight 28% 37% 31% 38% 26% 19% Wasted 10% 10% 14% 19% 17% 12% Disability Seeing 2% 3% 1% 2% 3% 3% Hearing 1% 3% 1% 1% 2% 2% Walking or climbing steps 2% 2% 1% 2% 3% 2% Remembering or concentrating 1% 2% 1% 1% 1% 2% Difficulty with self-care 1% 1% 1% 1% 1% 2% Communicating 0% 1% 1% 1% 1% 1% Any disability 5% 8% 3% 4% 6% 5% Source: World Bank Staff based on SESRE 2023. Table D.4: Living conditions by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees
Table 3 shows summary statistics for a select group of baseline characteristics, as well as a

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land owners in all countries in sub-Saharan Africa for which survey data are available

generally lasts 35-40 days. During this time, rainfall data is collected daily at a designated weather

The data set consists of the entire set of BASIX's purchasers of rainfall index insurance from 2005-2007,

that season. The BASIX data covers 42 weather stations, and includes a total of 19,882 customers from
own/parents’ COB 0.05 (0.04) HH received humanitarian food aid in past 12 months -0.00 (0.04) Distance to woreda capital (log) -0.10*** (0.01) Distance to border (log) -0.03 (0.02) Medium market accessibility -0.02 (0.06) High market accessibility 0.29*** (0.05) Constant 11.10*** (0.34) Survey domain Yes Survey time Yes Observations 1,266 Source: World Bank Staff based on SESRE 2023. Note: Dependent variable is the log of total per capita consumption expenditure. All regressions include the controls in Table D.12. This table provides the results for the additional independent variables of interest. Standard errors in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 115 38% 60% 2% Drought prone, lowland, pastoralist Humid moisture reliable, lowland Moisture reliable, highland-Cereal Figure D.31: In-camp refugee locations by ecological Zone Source: World Bank Staff based on SESRE 2023 and Ethiopia Ecological Zone Classification from ESS. 0 10 20 30 40 50 60 70 80 90 100 Employment rate Unemployment rate LFP Share of individuals (%) 1-20km 20 -100km >100km 0 10 20 30 40 50 60 70 80 90 100 Share of individuals (%) 1-30km 30 -50km >50km Employment rate Unemployment rate LFP Figure D.32: Refugee’s labor market performance Source: World
Page 30 of 51 Figure 23: Temporary employment by gender. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys In summary, the evidence does not suggest a clear trend across countries regarding the prevalence of non-standard employment but rather identifies a heterogeneous panorama by countries and types of non-standard employment. 4. 2. 2 Profile of Non-Standard Employment In this section, we investigate possible changes in the profile of non-standard employment, usually associated with lower productivity and greater vulnerability. Like in the case of the LAC countries, the analysis of the employment profile was made based on the education, wages and the content of tasks performed. 9 From the educational point of view, a general tendency can be observed in the countries analyzed to improve the profile of workers linked to non-standard work contracts. In fact, in most of the countries in our sample, it is observed that taken together, the prevalence of workers with secondary and tertiary educational levels increases to the detriment of workers with a lower educational level.
Methods Research Design and Data Collection Strategy This household survey was commissioned by The World Bank and carried out by the Center for Evaluation and Development between December 2023 and February 2024 in Khyber Pakhtunkhwa, Pakistan. Households were selected based on being located in the catchment area of one of 200 public schools across Khyber Pakhtunkhwa (excluding newly merged districts). 2 In most cases, the boundaries of a catchment area represented a maximum of a 30-minute walk from the proximate school. Households living in a given catchment area who had at least one child under the age of 72 months (for either of the 0-35-month or 36-72-month age groups) available to be 2 The sample of 200 public schools was drawn for a different World Bank survey in 2022. These 200 schools are a representative sample of public schools across all districts of Khyber Pakhtunkhwa (excluding newly merged districts). The households in question are thus representative of those in the catchment area of a representative sample of schools.
formal labor market in the same way as nationals—Burkina Faso, Cameroon, Democratic Republic of the Congo, Djibouti, Mauritania, Niger, and Rwanda— often restrict access in practice by requiring certain identification documents, the country employers are reluctant to hire refugees (World Bank Group, 2021), or restrictions exist, such as wait periods, limited mobility, owning property, or accessing finance. In other countries—Burundi, Chad, Uganda, and Ethiopia—access to the labor market is limited by regulations, such as requiring work permits, caping the percentage of foreign workers, or restricting work to certain sectors of employment (World Bank Group, 2021). In addition to wage employment, self-employment can be an important avenue, but in many countries, access to self-employment is restricted for refugees, including in Ethiopia where refugees require business licenses. Overall, while the GoE has made progress in creating a legal framework for refugees to obtain work permits, refugees still face significant challenges accessing employment, contributing to their overall vulnerability and lack of self-reliance. As this report shows, few refugees in Ethiopia work, and those who do work mostly inside camps. Research indicates that extended periods of forced unemployment negatively affect refugees’ longer- 8 As granted under Article 26 of the 1951 Geneva Refugee Convention.
20 Note: Poverty is defined as the percent of the population living with less than $ 2 a day (World Development Indicators database). The annual number of refugees in each country is given by the Center for Systematic Peace (http: / / www. systemicpeace. org /). 4. 2. Lessons from Case Studies in Kenya, Tanzania, and Uganda Given the limits of cross ‐ country comparisons, we present below three short case studies on the impact of protracted refugee situations on hosting communities. These case studies were not chosen based on a systematic review but they are sufficiently close to each other to allow for comparative learning. These case studies are also those emerging from a growing literature on the quantitative assessment of the impact of refugees on hosting communities (Mabiso et al. 2014). Case Study # 1: The protracted refugee situations in Tanzania Tanzania has been known as a refugee ‐ hosting country for long due to its peaceful history and its location surrounded by conflict ‐ affected countries (Burundi, Rwanda, Uganda, Mozambique). The first president of Tanzania, Julius Nyerere, welcomed most of refugees as a sign of pan ‐ African solidarity in the post ‐ independence periods from many African nations.
The surveys were conducted
each year between May and November of the survey year, with expenditures referring to the past
12 months.


Real total per capita expenditure less per capita expenses on health care is taken as the
dependent variable.

In particular, using the historical daily rainfall records over 1980-2006 in 166 geo-referenced weather stations from across Vietnam (see Figure 1) [9], monthly rainfall grids of 0.1 degree resolution are constructed [10] using both inverse distance weighting (IDW) and inverse elevation difference weighting (IEW).

To also capture riverine floods, which may occur downstream following heavy rainfall in the mountains or as a result of storm surges in coastal areas, riverine flood indicators will be constructed based on the satellite based flood data from the Dartmouth Flood Observatory (DFO) (2008). The 1980-2006 geo- referenced UNEP/GRID-Europe storm track dataset is used to separately explore the effects from high winds and gusts from cyclones. As the damage from heavy rainfall associated with cyclones will already be captured by the localized and riverine flood indicators, these measures will capture the wind damage associated with cyclones. [5]

Tropical storms approach Vietnam from the east. They typically arrive during the Southeast and Northeast monsoon (Christiaensen et al., 2009), with 90 percent of them occurring between June and November, and almost half of them coming ashore in the northern part of the country. To generate annual cyclone maps, a GIS dataset of areas affected by hurricane force winds was developed from the UNEP/GRID-Europe (2007a, 2007b) tropical cyclones databases. In particular, the storm track and wind speed data were used to create symmetric polygons about each path, based on the algorithm by Klotzbach and Gray (no date). [21]

4 The frequently used CRU TS2.1 monthly rainfall database (Mitchell and Jones, 2005) only has a half degree resolution, which is around 55 kilometres at the equator. 5 See Thomas (2009) for a detailed description of the different data sources
]

4 The frequently used CRU TS2.1 monthly rainfall database (Mitchell and Jones, 2005) only has a half degree resolution, which is around 55 kilometres at the equator. 5 See Thomas (2009) for a detailed description of the different data sources and a more elaborate description of the construction of the different natural hazard maps. 6 The nearest neighbour method assigns the value of the weather station nearest to the grid cell centre. 7 Radial basis functions are functions of distance used for exact interpolation of point data.