ai4data/datause-dataset-rechunked huggingface dataset:
This is used to analyze the impact of changes in conﬂict and luminosity patterns on return in an aggregate manner using ordinary least squares (OLS) and Poisson quasi maximum likelihood (PQML) count models. Second, we use the detailed information on refugee characteristics provided by ProGres together with conditions in countries of asylum, 4 to analyze individual return decisions. Given that we have arrival and- where applicable- return dates for each refugee, we can study their likelihood of return for a given month using both discrete 4Since the conditions in countries of asylum are only captured for a small sample of registered refugees in Lebanon and Jordan, we approximate host country conditions with district averages for the full sample. 3
measures of the adequacy of calorific intake for a survey sample or before the CV needed for the FAO

(2012b) present a useful list of various HCES in low and middle income countries highlighting substantial differences in their design across a select number of

HCES in all their relevant dimensions (requiring a 22-page form to cover all variations). Drawing on

Consequently, HCES with different methods of data capture (diary versus recall questionnaires), levels of

comparable. The survey experiment we use in this paper was designed in part to assess the extent to
Flood maps generated by the disaster risk reduction directorate were integrated into the project's site assessment tool to automate flood-risk flagging for proposed subproject locations. When a proposed site's coordinates fall within the 50-year inundation boundary on the flood maps, the tool generates an automatic alert requiring sign-off from the PIU's environmental specialist before the subproject can proceed to the design phase. Since rollout of the tool in June, 17 alerts have been generated, of which 12 were resolved through minor site adjustments and 5 required full site relocation.
Average share of primary school age children in primary education Average share of secondary school age children in secondary education Percent Figure D.3: Share of school-age children in education per household Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 Eritrean Somali South Sudanese Addis refugees Availability of education document Able to verify education document Percent Figure D.2: Refugees’ education document Source: World Bank Staff based on SESRE 2023. Annexes 101 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Primary school (7 to 14 years) Boys Primary school (7 to 14 years) Girls Secondary school (15 to 18 years) Boys Secondary school (15 to 18 years) Girls Percent Figure D.4: School-age children currently attending school by gender Source: World Bank Staff based on SESRE 2023. Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In Camp Addis Ababa Total Average household expenditure on education (children in school) Average household expenditure on education per child (school age (4 to 18 years)) - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18.000 Figure D.6: Average annual household education expenditure
The DFIL confirms that the project's Designated Account will be held at the National Bank and operated jointly by the PIU coordinator and the Ministry of Finance's project accountant. Withdrawals from the DA exceeding USD 500,000 require dual authorization and supporting documentation submitted through the Bank's Client Connection system. The DFIL also specifies that the project may request a one-time advance from the Bank prior to project effectiveness under the project preparation advance mechanism, subject to the conditions set out in the relevant Board approval.
Page 25 of 51 higher followed by the “ Professionals ” and “ Skilled agricultural, forestry and fishery workers ”. On the contrary, “ Managers ”, “ Technicians ”, “ Craft and related trades workers ” and “ Plant and machine operators and assemblers ” are the types of occupations with a lower incidence of NSE in the region. Figure 16: Prevalence of NSE among salaried employees. (Early 2000s / Mid-2010s) Source: Own calculations based on Household surveys Figure 17: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys. As in the case of Latin American countries, the observed prevalence of NSE across types of occupations suggests a strong heterogeneity across non-standard employees. Actually, as we will analyze in detail in next sections, the productivity and task profile of workers in the categories of “ Professionals ” and “ Elementary workers ” is very different, even though both types of occupations are characterized by a higher prevalence of non-standard employment arrangements. 0 % 5 % 10 % 15 % 20 % 25 % 30 % Russia Georgia Kyrgyz Republic Turkey Armenia Albania Moldova starting point ending point 0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % 40 % 45 % 50 % Managers Professionals Technicians Clerical Support Workers Services and Sales Workers Skilled
changes) and reducing the number of meals eaten daily (negative food intake). Borrowing food or cash from friends and relatives and purchasing food on credit second represent the second and third most common coping strategies. Refugees in camps and in Addis Ababa are more likely to rely on these coping strategies than their hosts (Figure 5.12). The results further show that both refugee and host households do not engage in adverse coping strategies, such as the sale of (productive) assets that would make them vulnerable to poverty. This could be because either they do not have enough assets to sell or because the strategies they utilize are enough to cope with the effects of shocks. 5.2 Determinants of welfare The poverty profile in this section compares the characteristics of poor compared to non-poor people. The previous section presents refugees’ and host communities’ poverty and welfare patterns. This section substantiates the earlier discussions on poverty levels by describing the demographic, geographic, and socioeconomic characteristics by expenditure quintiles for each refuge and host group separately, along with the poverty headcount rate across grouping variables (see Annex D, Table D.11). The descriptive statistics are substantiated by results from a regression analysis examining correlates
children (under age 15). The data further show that the poorest refugees and hosts are more likely to have married and older household heads compared with the richest counterparts (Figure 5.14). Richest in-camp and out of camp refugee households are more likely to have female- headed households compared to the poorest. There is no difference in the gender of the household head among the poorest and richest host households (Annex D, Table D.11). Location is an essential determinant of monetary poverty. Monetary poverty is highest among South Sudanese refugees (89 percent) (Figure 5.15). There is a significant difference in poverty rates between in-camp refugees and their hosts, the gap being the highest in the Eritrean domain. As discussed in Chapter 2, refugee households have larger household sizes than hosts, except in Addis Ababa. In light of the discussion above, the highest poverty incidence among South Sudanese refugees could be associated with their high dependency ratio and high number of female-headed households. In-camp refugees working inside the camp tend to exhibit lower poverty incidence (81 percent) than those working outside the camp (88 percent). The poor tend to live in households headed by individuals with limited education. This trend is evident
Geospatial data compiled from satellite imagery, field GPS surveys, and administrative boundary shapefiles were integrated into the targeting framework for rural livelihood subgrants. The geospatial data were processed using open-source tools and validated against paper maps held by district land offices. Where discrepancies between the geospatial data and official cadastral records were identified, the cadastral records were treated as authoritative and the geospatial data updated accordingly.
14 woman becomes a widow, it is likely that she does not marry again given a lack of suitable partners of similar age. 8 Finally, we calculate a demographic conflict proxy that captures the extent of deaths across age groups (SEXRATIO). The data on sex ratio comes from two secondary sources: the 1991 Census (which is matched with the 1992 RDHS) and the 2002 Census (which is matched with the 2000 RDHS and the 2005 RDHS). Women are assigned the average sex ratio (defined as the ratio of males to females) in the cohort of their potential partners in a given province, taking into account the typical age difference between spouses in Rwanda. More precisely, sex ratios in a woman ’ s five-year age group, one younger age group and two older age groups were averaged. These provincial, age group-specific sex ratios are the closest approximation to the local marriage market possible with publicly available data. Still, sex ratios derived from census data overestimate the number of men potentially available on the marriage market, as tens of thousands of male perpetrators of genocide were in jail (Ministry of Finance and Economic Planning et al. 2003). It is important to note that our analysis is based on a sample of survivors.
environment. Any issues discovered during these monitoring visits were brought to the attention of service providers and resolved swiftly in conjunction with the project coordination team at MoGD. Eligibility: The EPAG program was targeted to young women who: i) were age 16 to 27, ii) possessed basic literacy and numeracy skills, iii) were not enrolled in school within several months prior of the program initiation, and iv) resided in one of nine target communities in and around Monrovia. 6 These eligibility criteria stemmed from the project's objectives to reach young women at an early enough age to significantly improve the trajectory of their working years, to focus on girls who already had the basic literacy and numeracy skills needed to succeed in the labor market, and to avoid incentivizing applicants to drop out of school.
using purchasing power parity rates. It comes from the World Development Indicators

The governance indicators come from the International Country Risk Guide (ICRG)

Data on the Gini coefficient were drawn from a more complete World Bank source

100 km of ice-free coast) come from Gallup et al. (1999). Data on total forest area (km [2] ),

precipitation come from WDI (2010). Latitude (in absolute value), mean elevation (meters
These people are then expected to return to their place of origin once the conflict is over and governments are typically over optimistic about the duration of civil conflicts and about return of IDPs. In some cases, governments also have an interest in denying the very existence of IDPs for political purposes. Therefore, little time is spent in surveying IDPs or trying to find durable solutions in the place where they migrated. Moreover, national censuses are usually conducted every ten years and statistical agencies have little incentives to revise censuses, master samples and sample survey structure for situations that are perceived as short ‐ term. In most cases, new surveys are suspended or carried out under the pre ‐ crisis frameworks and, in either case, information on IDPs is not collected or poorly collected. This leaves specialized government agencies or international organizations in charge of IDP statistics (and care). However, unlike refugees, the IDPs do not benefit from a specialized international agency such as the UNHCR. IDP assistance is currently provided by a multitude of organizations including ministries of interior, specialized government agencies, the UNHCR, the International Organization for Migration (IOM), the UN Office for Humanitarian Affairs (UN ‐ OCHA), specialized NGOs and others. Some of these organizations collect information on IDPs and make this information public while others collect information that is not published and others do not collect information and focus on providing assistance. Most data collected are for the simple purpose of counting IDPs and do not include individual or
From 23 to 25 May 2014, 137 interviews were conducted with the Shalozan Tangi displaced
population. The Return Intention Survey (RIS) was conducted using a specific tool/
questionnaire developed in 2013 for previous consultations and slightly adapted to the
current situation.

Furthermore, during an inter cluster mission from 26 to 28 May, protection cluster
representative conducted additional consultations with populations displaced from
Shalozan Tangi (12 male key informants) and in areas of return in Shalozan Tangi (two male
key informants from amongst the population already returned to the area of origin in 2012).

**III.** **Profile of respondents**

Out of the 137 respondents, one was women (a female headed household) and 126 men. 69
% of the respondents were heads of households (including one female respondent) and also
included three community leaders.
The LFS question on usual occupation was re-coded using the International Standard Classification of Occupations (ISCO-08) at the two-digit level for comparability with other countries' labor force surveys. Occupational coding was conducted by the national statistics office's coding team using an automated coding software trained on prior LFS data. For unusual occupational descriptions, manual coding was applied after a verification call with the interviewer. Approximately 4.2 percent of the LFS occupation responses required manual review.
PREPAREDNESS
EMERGENCY
TRANSITION
LONG-TERM INCLUSION
OTHER ACTIVITIES (PUBLIC HEALTH, NUTRITION, FOOD SECURITY)
Review national health plans and policies 
and advocate that activities for refugees 
are included and equitably provided as 
nationals.
Measles, polio vaccination on arrival and vitamin A 
supplementation.
Expand vaccination to national immunization schedule (EPI) and supplementary immunization activities (SIA).
Additional support for vaccines and human resources may be needed and may be sought as per the GAVI, Fragility, 
Emergencies, and Refugees Policy.
UNICEF may support cold chain capacity, training of health workers and vaccine related activities
As above
Essential primary health care.
Integrated primary health services.
Gradually integrate refugees into national health services, support services if need be with human resources for 
health, medications, medical supplies and equipment.
Engage other UN agencies (UNICEF, UNFPA and WHO) to support efforts to include refugees in national program/
systems.
If refugee standalone facilities, aim for accreditation and inclusion in national system.
Engage supervision from Ministry of Health especially for malaria, TB, HIV, nutrition, reproductive health and 
immunization.
Use national clinical management protocols.
International (and in exceptional situations, local) procurement of medicines in line with UNHCR policy. Review of 
and support to MoH procurement protocols/systems and integrate where applicable (context-specific and based on 
quality assurance assessment).
8
PUBLIC HEALTH AND NUTRITION
Woreda Nearest to Zone Share of individuals (%) Trade Industry Other service Agriculture Trade Industry Other service Agriculture Low accessibility Medium accessibility High accessibility Share of individuals (%) 0 20 40 60 80 100 Markets and Opportunities 61 6.2 Effects of local factors on refugees’ labor market outcomes This section estimates the effect of local factors on refugees’ employment outcomes. It shows how local factors matter for employment opportunities by looking at refugees aged 18 to 64 not currently studying. More specifically, it sheds light on the importance of accessible locations and proximity to economic and resource hubs for refugees to perform better in local labor markets and to access sustainable economic opportunities. The analysis uses household and individual information from SESRE data and geospatial information. The estimation applies logistic regressions to predict the effects of the various indicators on the probability of being employed and working in different sectors of employment (see Annex D, Table D.14). Annex D, Table D.15 shows the average marginal effects of the explanatory variables. We discuss the results using predicted marginal probabilities of being employed based on various local factors. The local labor market structure affects49 the possibility of refugees finding jobs. Consistent with
However, the program itself was designed to fit within the young women ’ s lives, specifically with regard to their employment, education, and childcare duties. Training schedules were flexible, to allow participants to continue with their pre-existing educational and income-generating activities (and many participants did report continuing with these activities) and free childcare was provided. Hence, at least for these three dimensions of life, there would not have been much incentive to change one ’ s behavior prior to starting the program. While these explanations do not erase concerns about anticipatory behavior, they at least mitigate them. The generalizability of these results is also limited by the differences between the EPAG target group and the population of young women in Liberia. First, a high proportion of adolescent girls and young women in Liberia are illiterate or have very low literacy, while the participants recruited for the EPAG 17 Gender-based violence questions were administered in line with international ethical protocols, with additional informed consent procedures and referral mechanisms as needed. 18 See Ashenfelter (1978). 11
Section A.1, [9] we verify that the I2D2 database generates   global patterns that are broadly consistent with patterns observed when using other   global databases such as the _World Development Indicators_ (WDI) database of the World   Bank. More precisely, we find that our samples are globally representative in terms of   per capita incomes, age structure, education, and self-employment.

10For example, we find that log mean earnings in I2D2 are strongly correlated across country-years with log per capita GDP in WDI. We show that wage variance is in line with what is expected.

In our case, I2D2 calculates their wage as the amount of salary taken from the business; and (iii) public sector workers because they receive nonwage compensation and their wages may not reflect the full payment for their labor. 23Results hold if we use 12 or 14 years or information about high school graduation (Web Appx.

If   34Since most low-income countries are in sub-Saharan Africa and to ensure that our results are not driven by a lower quality of surveys from the region, we verify that the patterns that we obtain for subSaharan Africa in I2D2 are consistent with the patterns observed when using other global databases such as the _World Development Indicators_ database of the World Bank (see Web Appx. Section A.1).

We set the schooling transition probabilities _πs_ ( _·_ ) to match the schooling  distribution in developed and developing economies (I2D2). [49] We assume agents may   only accumulate up to 25 years of schooling as some countries only record schooling   up to 25.
Both the EHCVM sample and the COVID-19 phone survey include each of the eight FIES questions, allowing us to get a measure of food insecurity for both periods.

_Notes:_ These figures come from Google's COVID-19 Community Mobility Data. With the same kind of aggregated and anonymized data used in Google Maps, these data show changes for each day in time spent at specific types of places relative to the baseline period.

Baqueano, F., Christensen, C., Ajewole, K. and Backman, J. (2020). International food security


assessment, 2020-30. _GFA-31,_ _U.S._ _Department_ _of_ _Agriculture,_ _Economic_ _Research_ _Service_ .


Barrero, J., Bloom, N. and Davis, S. (2020). Covid-19 is also a reallocation shock. _NBER_ _Working_

The Kinshasa Commuter Travel Survey (CTS), conducted by the Japan International Cooperation Agency (JICA, 2018), reports information on local commuting patterns as well as socio-economic attributes at both household (income/expenditure, number of vehicles, members, etc.) and individual (age/sex, work/school type and place, industrial category, income, vehicle availability, etc.) levels.

Building on the resulting wet network, we then ran travel simulations for a total of 8,866 commuters [13] in Kinshasa using the origin and destination location information provided in the JICA commuter travel survey under both dry and wet conditions for five flood return periods.
**Table 4: Financial Management Capacity Assessment**

| Dimension | Rating | Key Weakness | Mitigation |
|---|---|---|---|
| Budgeting | Moderate | Delays in budget releases | Contingency drawdown procedure |
| Accounting | Moderate | Partial SIGAF rollout | PIU parallel records |
| Internal Controls | Substantial | Limited segregation of duties | Additional FM staff |
| Reporting | Moderate | IFR delays in prior project | Monthly reporting schedule |
| External Audit | Low | N/A | Competitive audit firm selection |

The overall financial management risk is rated **Moderate** following agreed mitigation measures. SIGAF will be fully operational within six months of project effectiveness.
35 Inputs: 1. Household Survey with consumption or income welfare aggregates 2. Project data / Other survey data without welfare aggregates 3. Set of harmonized common variables in both surveys Outputs: 1. Set of imputed welfare variables for project data / other survey for each household in the data 2. Imputed welfare variables can be used for poverty, distributional analysis (quintiles or more), profiling of the poor or group of interest Models: 1. Ordinary Least Squares (OLS) 2. Probit 3. Multiple Imputation (MI) Table 18. Model Specification Variables Demographic Share of children, share of adults, share of adults squared and share of old (omitted) Characteristics of head Age, gender, and level of education Interactions with urban dummy variable Level of education of the head, age of the head Geography Dummies for regions at NUTS 1 level (12 regions) Interactions with Geography Level of education of the head, age of the head interacted with regions at NUTS 1 level (12 regions) and urban-rural division 1. Validation and Robustness Check Figure 7.
Page 10 of 51 Source: Own calculations based on Household surveys Figure 4: Prevalence of Temporary employment among salaried employees (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the evolution of non-standard employment according to their age profile, we found a slight increase in the share of the older groups (Figure 5 and 6). This slight aging in the profile of non- standard workers is observed in both part-time and temporary employment. This finding is striking since, in principle, it was expected that the non-standard modalities of employment would show an increasing participation of the younger groups of the population. However, this change in the age composition of NSE is consistent with the age profile observed in total employment. In fact, the 0 % 5 % 10 % 15 % 20 % 25 % 30 % 35 % Argentina Brazil Peru Dominican Republic El Salvador Starting point 0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % Argentina Brazil Chile Mexico El Salvador Starting Point Ending Point