ai4data/datause-dataset-rechunked huggingface dataset:
Following the approach used for the Global Costing of Refugee Inclusion, successful inclusion is defined as earning sufficient income to be no longer poor and to consume more than the (international) poverty line. This definition opens two tracks for investigation: first, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this question is found by identifying the poverty gap for refugees. This opens the second track which explores the factors that determine, or at least that are associated with, the size of the poverty gap. The note is organized as follows. The next section discusses data and presents some key descriptive statistics on refugees and host communities in Uganda. This is followed by a methodological section discussing how own income and aid are complements and how an analysis of poverty gaps informs about the need for assistance. This is followed by two analytical sections. The first identifies refugee poverty gaps, and assistance needs for refugees with distinct characteristics. The following section estimates how much has been saved by including refugees in the economy and explores how more could be saved. Conclusions follow. 2 The poverty numbers in World Bank (2019) are based on the official poverty line adopted in Uganda in 1997. There was a need to update this line as it was too old and producing a very low poverty rate. For example, using this line produced a national poverty rate of about 21 percent in 2019 / 20 compared to more the than 40 percent international poverty rate using the USD 2. 15 2017 PPP daily poverty line. In order to address this criticism, the poverty line was revised by the Uganda Bureau of Statistics in 2021, but it is not available for the 2018 Refugee and Host Communities Household Survey used in this note. Instead, we are using the international poverty line throughout.
An open empirical question remains on the relative importance of the three initial conditions (i. e., unemployment, co-ethnic enclaves and attitudes) when examined together. Moreover, we expect the effect of the individual factors to vary in the short, medium and long run over the 20 years examined. In the next section we empirically investigate these issues, and examine whether these assumptions are supported by the empirical evidence. 4 Research Design 4. 1 Data and Descriptive Statistics In our analysis we construct a unique longitudinal dataset which covers the universe of refugees and migrants in Switzerland over 1998-2018. It allows us to follow refugees over the life-cycle for 20 years, and allows us to follow them even after they change residence permit and status. To construct this longitudinal dataset we are combining three administrative datasets. The first dataset, that covers all asylum seekers is AUPER (Automatisierte Personen Registratursystem) which is provided by the State Secretariat for Migration. The data includes information about the residence permit, year of arrival, country of origin, canton of allocation and socio-demographic characteristics. Once an asylum seeker has obtained a residence permit other than permit N (asylum seekers) or F (provisionally admitted foreigners), he or she is registered in ZAR (Zentrales Ausländerregister). This longitudinal dataset is also coming from the SEM.
Increasing the schooling attainment of girls is a challenge in much of the developing world. In
this paper we evaluate the impact of a program that gives scholarships to girls making the transition
between the last year of primary school and the first year of secondary school in Cambodia. We show
that the scholarship program had a large, positive effect on the school enrollment and attendance of girls.
Our preferred set of estimates suggests program effects on enrollment and attendance at program schools
of 30 to 43 percentage points; scholarship recipients were also more likely to be enrolled at any school
(not just program schools) by a margin of 22 to 33 percentage points.

The impact of the JFPR program
appears to have been largest among girls with the lowest socioeconomic status at baseline. The results we
present are robust to a variety of controls for observable differences between scholarship recipients and
non-recipients, to unobserved heterogeneity across girls, and to selective attrition out of the sample.

to the 2000 Demographic and Health Survey (DHS), 85 percent of 15 to 19 year olds had completed
Although the OCO-2 satellite platform provides the best available database, its coverage for our 25-km grid cells is limited by its 16-day repeat cycle, relatively narrow observation track, and the frequent occurrence of cloud cover over some areas.

For the translation of regression residuals to emissions deviations, we use the EDGAR gridded database of CO2 emissions estimated from sectoral activity data and standard emissions parameters (Crippa et al.

We translate these residuals to emissions using the EDGAR global database of gridded CO2 emissions estimated from local activity measures and standard emissions parameters (Crippa et al.

2020). The current EDGAR database terminates in 2018, so we perform the conversion using data for 2015 - 2018, the period of overlap with our OCO-2 database.
are combined. The surveys follow a repeated cross-sectional design and are not panels (i. e. the same administrative units, not the same people, are re-sampled), so responses are aggregated to the groupement level, the lowest level at which the project consistently collect representative data. Table 2 provides a summary of the dates, sizes, and percent of respondents who report being displaced within each survey wave. This aggregated temporal analysis can show, associations between ﬂuctuations in displacement and perceptions of social cohesion over space and time at the groupement level. Question coverage varies across survey waves, but a battery of core questions enables consistent observation of how many individual respondents self-report being displaced at the time of the survey and being involuntarily moved within the past year. Poll Date N % Currently Displaced % Displaced Last Yr % Hosting Displacees # 11 July 2017 5834 4. 35 7. 42 – # 12 September-October 2017 4013 1. 62 2. 62 – # 13 December 2017 4883 3. 50 7. 97 – # 14 March-April 2018 1933 4. 97 8. 85 31. 35 # 15 June-July 2018 5951 3. 70 8. 35 30. 33 # 16 October 2018 1112 6. 47 4. 68 – # 17 December 2018 5918 5. 86 11. 20 – # 19 July-August 2019 5961 5
This distinction is also applied to foreign students, with those entering the system after February 2022 referred to as “ Migrant post-Feb 2022 ” and others referred as “ Migrant pre-Feb 2022 ”. 7 Administrative data from MoE. The Ministry data includes information for all students who enrolled at any point during the academic year. 8 This information covers academic year, grade, gender, birth date, birthplace, citizenship. They also include school-speciϐic information such as the name and identifying code of the institution where the student is enrolled. Furthermore, the dataset includes a variety of school outcome variables, including grades in English, Italian, Mathematics, overall GPA calculated as the average across all subjects, behavior scores from grade 9 to grade 12, guidance council evaluations from lower secondary school, records of absences, late entries, and early exits. Given the timing of this study, data for academic year 2022-23 are the most complete. For academic year 2022-2023, school enrollment data at the provincial level was provided for 4, 269, 348 enrolled students across the 8 years of Italian lower and upper secondary school, encompassing both public and private institutions. The dataset includes nearly all students in the country irrespective of their citizenship. 9 Table 1 shows the distribution of the different groups of students by grade. In the 7 For ease of reference, we refer to non-Italian and non-Ukrainian students as migrants. However, we acknowledge that some of these students may be refugees or displaced students. 8 At the time of writing this paper, both MoE and INVALSI data were not fully available and, as such, only the information on enrollment was used for the academic year 2023-24. 9These numbers do not include students enrolled in Provincial centers for adult education (CPIA). See footnote 5.
The decision to link it with CM made ENLACE a de facto high-stakes test encouraging

in grade inflation (Contreras and Backoff, 2014). Anomalies in ENLACE and the creation

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###### **2.2 The ENLACE Panel**

ENLACE created a unique personal identifier ( _Clave_ _Única_ _de_ _Registro_ _Poblacional_ or

the ENLACE dataset included a school identifier, socioeconomic information for each
(vi) ran out of food, (vii) adults were hungry but did not eat, (viii) went without eating for a whole day, (ix) restricted consumption so kids could eat, and (x) borrowed food or relied on friend/relative for help. 7.0 8.4 7.8 6.0 8.2 6.5 In Camp Addis Ababa Total Hosts Refugees 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Poor (0-21) Borderline (21-35) Acceptable ( > 35) Percent Figure 5.10: Dietary diversity and food consumption status Source: World Bank Staff based on SESRE 2023. Note: Dietary diversity score is calculated as the total number of food groups (out of 12) consumed by the household in the last seven days before the survey. The food groups are cereals, roots and tubers, vegetables, fruits, meat (including poultry and offal), eggs, fish and seafood, pulses and legumes and nuts, milk and milk products, oils and fats, sugar/honey, and others. Food consumption status is determined based on food consumption score. a. Dietary diversity score (out of 12 groups) b. Food consumption status Refugees’ Aspirations 49 Moreover, insecurity and displacement-related shocks are common for Eritrean refugees, with 14 percent having experienced
future year by the counterpart observation in the CRU benchmark dataset (from the most

representative temperature/rainfall combination, derived from CRU data for 1980-2000).

departure: the benchmark series from the CRU data. This translation step is necessary
Page 8 of 51 In the 9 cases mentioned, it was possible to identify part-time workers, while temporary workers were only identified in the cases of Argentina, Brazil, Chile, Mexico and El Salvador. 4. 1. 1 Variation of the NSE as a percentage of total employment The prevalence of NSE in the total employment has not shown very significant variations in the countries considered in the last two decades (Figure 1). Indeed, most of the countries analyzed show non-standard employment registers similar to those observed in the mid-1990s. The exceptions where the variation is a little more relevant are Brazil and Uruguay, where there are contractions in the incidence of the NSE of the order of 10 and 5 percentage points respectively and Mexico, where there is an increase of 5 percentage points. Figure 1: Prevalence of NSE among salaried employees. (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the prevalence of NSE by types of occupations, considering the ISCO classification at one digit, we find a quite similar pattern across countries. Indeed, in most of the considered countries, the “ Elementary Occupations ” are the category where the prevalence of NSE is higher.
of dependability (Heller and Kessler, 2022). In a recent study, Karpowitz et al. (2023) show how these soft skills interact with discriminatory practices. They find that assigning leadership roles to women in a classroom setting reduces gender discrimination. Similarly, Kaas and Manger (2012) use a correspondence study to show that presenting soft information, such as reference letters with information on conscientiousness and agreeableness, seems to mitigate discrimination. Second, unlike most correspondence studies, we exploit data on firm characteristics and decisions at multiple stages of the hiring process to conduct a rich heterogeneity analysis. Most studies are only able to observe if the candidate receives an interview offer. Hangartner et al. (2021) is an interesting exception, which tracks online employers ’ actions and collects information that allows them to study hiring decisions. We instead observe five stages in the hiring process: 1) if employers reject an application, 2) if they visit an applicant ’ s profile, 3) the number of visits to each profile, 4) if employers contact the candidate, and 5) if they offer an interview. These five outcomes provide a rich preview into the hiring decision.
report). Box 5.2: Disparity between refugee ration aid and reported consumption quantities 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Own production Market purchase Transfers/gifs Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Percent 0 10 20 30 40 50 60 70 80 90 100 Percent Own production Market purchase Transfers/gifs Figure 5.5: Food and non-food expenditures shares by sources Source: World Bank Staff based on SESRE 2023. a. Food expenditure b. Non-food expenditure Refugees’ Aspirations 46 5.1.3 Multidimensional poverty Refugees are more vulnerable to multidimensional poverty than hosts. The multidimensional poverty rate is relatively high among refugees, driven primarily by low living standards and poor access to education. Trends in monetary poverty are mirrored using the Multidimensional Poverty Index (MPI)—an index measuring deprivations across three dimensions of well-being: education, health, and standard of living (see Box 5.3). MPI provides a general picture of the extent of deprivation (Alkire et al., 2021). The results show that 50 percent of refugees and 23 percent of hosts are multidimensionally poor (Figure 5.7). Looking at in-camp refugees and their hosts, multidimensional poverty is 64 percent for
JDC Joint Data Center LFPR Labor Force Participation Rate LFS Labor Force Survey LoC Locus of Control LSMS Living Standards Measurement Study MoE Ministry of Education MoLS Ministry of Labor and Skills MoLSA Ministry of Labor and Social Affairs MoR Ministry of Revenue MoTRI Ministry of Trade and Regional Integration MoU Memorandum of Understanding MPI Multidimensional Poverty Index NEET Not in Employment, Education or Training NER Net Enrollment Rate NGO Non-governmental Organization OAU Organization of African Unity OCP Out-of-Camp Policy PPP Purchasing Power Parity RRS Refugees and Returnees Service SESRE Socio-economic Study of Refugees in Ethiopia TVET Technical and Vocational Education and Training UNHCR The United Nations Refugee Agency UNICEF The United Nations International Children’s Emergency Fund UPSNJP Urban Safety Net and Jobs Project WFP World Food Programme WHO World Health Organization i The report titled "Expanding development approaches to refugees and their hosts in Ethiopia" was prepared by a team of the Poverty and Equity Global Practice at the World Bank led by Christina Wieser (Senior Economist, World Bank), including Wondimagegn Mesfin Tesfaye (Economist, World Bank), Fikirte Girmachew (Consultant, World Bank), Jeremey Aaron Lebow (Young Professional, World Bank), and Manex Bule Yonis (Economist, World Bank), under the adept guidance
2.0 percentage points more likely to be enrolled at any school, JFPR or otherwise (p-value: 0.29). Note

scholarship receipt, therefore provide further evidence of a JFPR program effect.

the 2000 Demographic and Health Survey; here too there is a clear schooling gradient. [3] Figure 1 shows,

2 Comparisons for the full sample of girls, not just those with completed applications, also suggest that scholarship recipients were more likely to be enrolled and attending school: In the full sample, the difference in the probability of enrollment at a JFPR school is 22.1 percentage points; the difference in the probability of attending is 21.9 percentage; and the difference in the probability of enrollment at any school, not just a JFPR school, is 12.1 percentage points.
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**Appendix 1: Comparing applicants with full application forms to those with only partially**
**completed forms**

**All applicants** **Full application form**
**Outcomes**
Enrolled in JFPR school 0.81 0.82
Attending on the day of school visit 0.74 0.75
Enrolled in any school 0.87 0.87

Das, Jishnu, Quy-Toan Do, and Berk Özler. 2005. "Reassessing Conditional Cash Transfer Programs."
_World Bank Research Observer_ 20(1): 57-80.

Deolalikar, Anil. 1993. "Gender Differences in the Returns to Schooling and School Enrollment Rates in
Indonesia." _Journal of Human Resources_ . 28(4): 899-932.

"Educational Attainment and Enrollment Profiles: A Resource Book based on an Analysis of Demographic and Health Survey Data." Development Research Group.

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**Figure 1: Attendance and enrollment status by decile, JFPR application data**

JFPR applicants: Enrollment at a JFPR school, by JFPR applicants: Attendance on day of school visit, scholarship status and decile by scholarship status and decile
As summarized by Robinson (2017), “ fortunately, Afrobarometer respondents comprise stratified random samples at all levels, making population estimates based on them unbiased: thus, the major concern with using Afrobarometer sample data to construct demographic measures is unbiased measurement error. ” Based on a comparison of census-based and survey-based diversity indexes across five African countries, Robinson (2017, 224) found that a “ sample-based measure tends to underestimate the overall degree of diversity compared to census data ”. In theory, this should make it more difficult to observe the true relationship between ethnic diversity and some outcomes at the local level. Diversity indices are more likely to be measured with noise in highly diverse communities at the local level. We nonetheless argue that such a concern should not be overestimated, for three reasons. First, such noise cannot easily explain the contrast between the coefficients corresponding to the pre-revised and revised indices and the opposite results found for the revised refugee fractionalization and the revised polarization. This set of results can be explained by the fact that our identification comes from the annual changes in refugees flows. Second, the IV approach is likely to deal with the measurement errors if they are correlated with our main variables of interest. Our IV estimates therefore capture a local average treatment effect coming from the plausibly exogenous increase in annual refugee flows of particular ethnic groups. The similarity of the IV results to the OLS results supports this interpretation. Third, at the cost of introducing attenuation bias30, we also aggregate the number of conflict events at the regional level. Lines B and C of Table 7 confirm the negative and positive effects found for the revised fractionalization and polarization indexes, respectively, whether or not 30Another risk highlighted by Robinson (2017) is the fact that ethnic diversity may also capture different theoretical mechanisms at aggregated levels. 34
refugees and their host communities, this work aims to inform policies and operations (humanitarian actors, development partners, and government) to facilitate refugee integration and their lives, along with hosting communities. This report’s eight chapters aim to comprehensively provide an overview of SESRE results, with the final chapter highlighting policy implications. This Chapter 1 introduces the refugee situation in Ethiopia. Chapter 2 presents the sociodemographic profile of refugees and their hosts, including demographic characteristics, education, health, and living conditions. Chapter 3 provides an in-depth profile of jobs and livelihoods of refugees and their hosts, covering labor market outcomes for refugees inside and outside of camps and those of hosts living in the vicinity of refugees, as well as a subsection on labor market outcomes of youth. Chapter 4 dives deeper into refugees’ future aspirations and their feeling of personal control over their lives. Chapter 5 describes the welfare situation of refugees and their hosts by: (i) understanding different dimensions of welfare, such as monetary poverty, inequality, multidimensional poverty, food security, and shocks; and (ii) understanding determinants of welfare and estimating the cost to meet basic needs through a combination of assistance and some economic inclusion of refugees into national systems. Chapter
The Women ’ s Empowerment in Agriculture Index (WEAI) (Alkire et al. 2012) uses individual-level data, and the linked Gender Parity Index reflects inequalities across women and men ’ s deprivation scores within the same household. Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer. Espinoza-Delgado and Klasen (2018) create an individual-level MPI to understand differences in poverty between women and men in Nicaragua, finding similar overall incidence, but much higher intensity of poverty among women. Bessell (2015) and Pogge and Wisor (2016) explore deeply contextual gendered poverty measures and elucidate the ways that participatory consultations can inform the design and uses of gendered measures. Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa. Alkire, Ul Haq, and Alim (2019) use individual-level data alongside MPI data to expose gendered and intrahousehold differences among MPI poor and non-poor children.
The GLWD Coastal Wetlands area identified along the northern boundary of the project zone supports mangrove ecosystems that provide fish nursery habitat and protect the coastline from storm surge erosion. A coastal ecological assessment conducted during project preparation confirmed that proposed coastal access infrastructure would not directly encroach on the GLWD Coastal Wetlands perimeter. However, cumulative sedimentation risks from upstream construction activities were identified as a concern, prompting inclusion of erosion control and runoff management requirements in the ESMF.
**Environmental Baseline: Wetland Mapping**

Field survey results confirmed that the GLWD Coastal Wetlands boundary as currently mapped by the national environment authority is consistent with the extent of active mangrove and brackish marsh ecosystems observed on the ground. Three small encroachments of the GLWD Coastal Wetlands boundary by agricultural clearings were identified and reported to the environment authority. The project's ESMP requires monitoring of the GLWD Coastal Wetlands boundary at six-month intervals to detect any further encroachment attributable to project-induced land use change.
Table 5: Coverage of Published Data on Location, Accommodation and Demographics 2015 Population end- 2015 (millions) Urban or Rural Location Accommodatio n Age Sex Refugees and people in refugee-like situations 16. 1 85 % 83 % 58 % 68 % Other people of concern to UNHCR 47. 8 (including 37. 5 IDPs) 70 % 47 % 25 % 39 % UNHCR ’ s total population of concern 63. 994 73 % 56 % 33 % 46 % IDPs monitored by IDMC but not included in UNHCR's data 3. 3 0 % 0 % 0 % 0 % Source: UNHCR Global Trends 2015 Note: Other people of concern to UNHCR include asylum-seekers, IDPs and people in IDP-like situations protected or assisted by UNHCR, stateless persons, and ‘ other ’. Overall robustness of current data The robustness of data is difficult to estimate. A review of data collection and compilation methodologies shows broad variations in terms of the accuracy and reliability of the global estimates of forced displacement that are widely used. Headline figures on forced displacement are significant in shaping public opinion and are critical for sound decision making, both to inform the allocation of resources and to design effective humanitarian and development responses. However, the available estimates are potentially misleading and should not be referred to without appropriate caveats and qualifiers.