ai4data/datause-dataset-rechunked huggingface dataset:
The GIS-based beneficiary targeting map was reviewed during the midterm assessment to verify that the communities selected for Component 2 infrastructure investments remained those with the highest unmet need. Updated displacement figures from UNHCR and poverty scores from the Ministry of Social Welfare were overlaid in the GIS to check whether the original targeting ranks had shifted. The GIS reanalysis confirmed that 41 of the original 47 target communities remained in the highest-need tertile; the six communities that fell below the threshold were replaced through a targeted consultation process.
However, an endline survey was conducted after the second round of the EPAG program as per the timeline depicted in Figure 1. Examination of this endline survey data will permit a descriptive analysis of the outcomes of the first group of trainees 12 months after they completed the EPAG program, as well as examination of the outcomes of the second batch of trainees. The second round included not only the control group from this impact evaluation but also newly recruited participants who were offered brief basic literacy and numeracy training program prior to program entry. Work is already underway to design and implement the third round of EPAG, with a substantial redesign of the Job Skills track, an emphasis on reaching younger girls with lower literacy, and expansion to communities outside of Monrovia. If the high success rates found in this study are replicated for these future cohorts, the EPAG program should serve as a model for policy makers in Africa and the world seeking to improve lives and livelihoods of all youth, male and female. 25
Production data for the agricultural cooperative subcomponent were obtained from the Ministry of Agriculture's crop monitoring unit, which collects yield estimates from extension officers at the district level following each harvest season. These production data cover 14 crop types and are disaggregated by farm size category and irrigation status. Baseline production data will be compared against endline estimates to assess whether project-supported cooperatives achieved productivity gains consistent with the expected rate of return in the economic analysis.
A joint study conducted by HI and IOM in Bentiu in 2023 highlights the limited capacity of the South Sudanese authorities to
address the needs and rights of persons with disabilities, compounding the challenges. The gaps in disability disaggregated
data also hinder effective assistance, although at times there may be valid reasons for not reporting on specific vulnerabilities
when providing mass assistance like awareness raising or information sharing activities. The reported numbers of persons with
disabilities benefiting from individualized assistance services through the 5W system are very low. Together with the reliance
on the global estimate as the basis for planning, this indicates a need for improved data collection, analysis, and utilization to
better support persons with disabilities.

Data from the Protection Cluster's Protection Monitoring System (PMS) collected between October 2022 and March 2023
further emphasizes the severe impact on persons with disabilities. Approximately 76% of key informants consistently report
violations of persons with disabilities' ability to access humanitarian aid, especially in crucial areas such as food, shelter, and
health services. Moreover, according to the PMS data, they face disproportionate challenges in accessing justice and
addressing Housing, Land, and Property issues.
food item per month and cash transfers per person per month for each camp and period. The food items include cereal, wheat, maize, rice, sorghum, CSB/famex (CSB+), pulse, biscuit, date biscuit, dates, oil, vegetable oil, salt, and cash (Table E.4). We have computed the per person per month in-kind aid quantities into annual values using the household size and prices from SESRE and mapped them to the closest food item in SESRE (this was not straightforward as the items are different) considering food ration change periods. The food ration scaling factor is 50 percent vs. 84 percent. Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food aid admin data for every item except Biscuits. Refugee households still report lower quantities, even correcting for shares indicated as sold. Annexes 125 Table E.2: Food aid and consumption comparisons Items Quantity (per capita/year) Expenditure (per capita/year) SESRE UNHCR SESRE UNHCR Nonzero All Net of sold ration* Cereals/other cereals 23.2 186.1 175.5 493 12404 Wheat 60.5 133.2 116.6 1427 4710 Maize 39.6 125.5 118.3 312 4267 Rice 21.3 48.5
The UNHCR data identify principal applicants for each household and our analysis examines differences in household poverty between households with a female rather than male principal applicant. 6 The principal applicant is the person who receives assistance from UNHCR for the family and is self ‐ selected or selected by the family. This definition of female headship has advantages over the way that household headship is commonly identified in household surveys. An often ‐ noted drawback of the headship variable is that female headship may reflect the enumerators ’ perception about who should be considered a family head rather than who has the most responsibility for the family ’ s welfare in practice. 7 Social norms can also affect whether female respondents self ‐ identify as household heads. For example, some Eritrean returnees who would in other cultural settings be regarded as de jure female headed (single mothers, widows, divorcees, separated women) reported being male ‐ headed. Other Eritrean female returnees who would be considered de facto heads reported headship by absent husbands or male relatives (Kibreab, 2003). Our approach is therefore to distinguish between different types of female and male principal applicant households, using a typology that reflects some of the indicators of vulnerability used by UNHCR. We find that distinguishing between different types of female principal applicant households is important in the setting of Syrian refugees in Jordan.
29 should be entered immediately into the accounts; and (c) Payment must be recorded as soon as they are made. Budget implementation should be reviewed periodically to ensure that programs are implemented effectively and to identify any financial or policy derailment. The review of budget execution should cover financial, physical and other performance indicators. Development budgets are often beset by implementation problems because of insufficient implementation capacities and other factors such as delays in mobilizing external financing, overoptimistic implementation schedules or difficulties in importing supplies. It is thus important to have in place mechanisms for reviewing the most significant or problematic projects. These could consist of a regular monthly or quarterly review of projects within the line ministries and a midyear review involving line ministries and central agencies29. The government has taken steps to improve the tracking of budget expenditure until the intended destination, particularly investments spending, for which a tracking survey was entrusted in 2005 with the Ministry of infrastructures. In addition, the ministries took themselves certain internal initiatives, in particular in health and education sectors, but the action plans of these ministries were not updated as envisaged in 2005, and there are neither reliable benchmark, nor quantitative targets as regards improvement of the arrival of the expenditure at intended destination in these sectors.
10 Figure 3: Level of education age 25 +; compared with regional average (%) (*) INSTAT refers to census 2009 Source: Listening to Displaced People Survey, 2014. With regards to ownership of consumer durables, IDPs, refugees and returnees were better endowed than the average citizen of the North (see Figure 4). As was the case for education, they are more comparable to the average citizen in Bamako than to the average citizen in the regions of Gao, Timbuktu and Kidal. Figure 4: Asset ownership compared with regional average Source: Listening to Displaced People Survey, 2014 and EMOP 2011 (INSTAT). The main occupation of IDPs, refugees and returnees before the crisis was commerce (Table 5). This held for over half of the IDPs, 37 % of refugees and 34 % of returnees. 18 % of the refugees 51 85 60 47 85 89 87 15 6 18 29 11 8 8 34 9 22 25 5 3 5 IDPs Refugees Returnees Bamako (INSTAT) Gao (INSTAT) Timbuktu (INSTAT) Kidal (INSTAT) Secondary or Higher Primary None 0 100 200 300 400 500 600 IDPs Refugees Returnees Bamako (Instat) Gao (Instat) Timbuktu (Instat) Kidal (Instat) Percentage Mobile Phone Car / Motorized Vehicle Motorbike / scooter Bicycle Refridgerator TV CD
Recall that, by definition, attritors are not enrolled in a JFPR school. Arguably, the probability of

6 Smith and Welch parametrize this as the enrollment ratio between attritors and non-attritors. We report this ratio
in columns 2 and 5 of Table 3.


11



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probability of other girls who were turned down for scholarships and did not enroll in a JFPR school, but

scholarships and did not enroll in a JFPR school, but whose enrollment status could be established; this

enrollment of attrited recipient and non-recipient girls, the estimated JFPR program effect is 0.191. Note,

girls who received JFPR scholarships and other girls-they correspond to the raw difference in
34 Any statistics on the imputed welfare will based on the set of imputed welfares for each household. The estimator takes the form, with R denotes the number of simulation: ܪ ෡ ൌ 1 ܴ ෍ ݄ ሺݕ ෤ ௥ ሻ ோ ௥ ୀ ଵ where ݄ ሺݕሻ is a function that converts the vector y with (log) incomes for all households into a poverty measure (such as the head-count rate or bottom 40 %), and where ݕ ෤ ௥ denotes the r-th simulated imputed welfare. Figure 6. Survey-to-Survey Imputation Methodology, an illustration For the case of Turkey, we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey. Income is used instead of consumption for this paper ’ s analysis. The model included variables related to: household demographics (age, gender, age composition, etc.), household characteristics (education, labor activity, etc.), household head ’ s characteristics (age, gender, labor, education, marital status, etc.) and household assets holding (both livestock and durables). Based on that model the simulated values of consumption (at household level) were imputed for the households in the corruption survey.
displaced due to armed conflict, situations of generalized violence and violations of human rights. 28 Data on IDPs monitored by IDMC are disaggregated and currently published separately for conflict-induced displacement and disaster-induced displacement. 29 At the country level the IOM ’ s Displacement Tracking Matrix (DTM) 30 provides data on IDPs in both conflict and natural disaster settings (activated in all major natural disaster contexts in recent years). Global data on conflict-induced internal displacement reflect variations in how IDPs are defined across situations. There is no consensus on how far a person must flee in order to be considered internally displaced. The definition of internal displacement for nomadic populations, which account for a significant share of IDPs in the Horn of Africa and increasingly in the Sahel, is open to controversy. 31 Moreover, while some countries register IDP children born in displacement (e. g. Azerbaijan, Cyprus and Georgia), other countries do not (IDMC 2015). The crafting of a definition for IDPs and its application in a particular context may be heavily influenced by local and national politics in conflict and post-conflict countries, as well as the direct link between estimates of displaced populations and humanitarian assistance, which can lead to both over- and under-reporting.
Ethiopia. Box 1.1: Comparison of SPS 2017 and SESRE 2023 Sociodemographic Profile 9 2. Sociodemographic Profile T his chapter presents results on sociodemographic outcomes of refugees and hosts. It provides the context for refugees and their hosts, covering demographic characteristics, human capital, living conditions, and displacement experience, which are crucial to understand refugees’ context in Ethiopia. These results are presented across the eight domains: Eritrean, Somali, South Sudanese, and refugees in Addis Ababa and their hosts, as well as broad categories between in-camp and out-of-camp refugees and hosts. 2.1 Demographic characteristics In the SESRE sample, most refugees are from South Sudan, accounting for 53 percent of all refugees.11 South Sudanese refugees reside in camps in Gambella and Benishangul-Gumuz regions. Somali refugees living in camps in the Somali region constitute 30 percent of the refugee sample. Eritrean refugees who reside in camps in the Amhara and Afar regions and Addis Ababa under the Out-of-Camp Policy (OCP) constitute 5 and 12 percent of the sample, respectively (Figure 2.1). More than 30 percent of refugees in camps are born in Ethiopia (Figure 2.2). Somali refugees have a higher share of refugees born in Ethiopia (38 percent). According to UNHCR estimates (2023)12, around 1.9
The data on total gross proﬁts and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key diﬀerence from the Chamber of Commerce data is that the sales and proﬁts data include all businesses including self- proprietorships. 9 Data were provided for the years between 2010 and 2014 and are re- ported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by deﬁnition and they are likely to be less accurate and complete for smaller ﬁrms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and proﬁt ﬁgures. 10 We scale the variables according to province size by dividing sales and proﬁts by the pop- ulation of the provinces. If we use sales and proﬁts in absolute terms, we get qualitatively similar results. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12
13 genocide in 1994, with estimates that approximately 300, 000 to 400, 000 women suffered rape; Somalia in the early 1990s; the conflict in Kashmir; the 15-year-long civil war in Peru; and the recent civil war in Sudan (See, for example, McGinn 2000; El Jack 2003; Human Rights Watch 1995, 1996; McGinn 2000; Swiss and Giller 1993). A global review of 50 countries found significant increases in gender-based violence following major wars (World Bank 2011). Estimates of sexual and gender-based violence can suffer in both wartime and peacetime from serious underreporting (i. e., because people are unwilling or afraid to report gender-based violence, especially when the perpetrator is a family member) or overreporting, when incidence statistics are inflated because reporting improves with time (Nordås and Cohen 2011). This situation also occurs in peacetime, making it very difficult to accurately assess the increases in sexual and gender-based violence that are associated with conflict. Recent evidence highlights variations in the prevalence of sexual and gender-based violence in war situations and relates this variation to combatant norms and group cohesion. This evidence shows that Bosnia and Rwanda are anomalous cases of wartime rape being used as a war weapon for ethnic cleansing.
arise. The field visits helped to understand the camp administrative structure and environment of the teams facilitating the camp and to test the accessibility of sampled refugee households inside the camp, in Addis Ababa, and the identification of the host. ESS provided detailed feedback on the fieldwork procedures and adjustments made before the fieldwork began. For instance, in Afar (Asayita), the visit helped to identify challenges in tracing sampled refugee households and to take the necessary corrective measures. Likewise, in Addis Ababa, the visit assisted in designing an appropriate strategy to select host communities. The survey created a good opportunity for a collaborative effort between different government institutions and development partners. This collaboration allowed the sharing of experiences across institutions and knowledge for ESS to implement such unique surveys in the future. The survey process, from preparation to implementation, focused on ensuring data quality for refugee data collection. During preparation, ESS translated the survey instrument into different main languages and undertook an in- depth training of supervisors and enumerators for enumerators to understand better the concepts of the questions related to the refugee context. Moreover, a close follow-up and coordination in the field helped to get better quality data and
14 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 5. 2 Composition of poverty Unpacking the headline numbers further, important patterns emerge about the composition of multidimensional poverty among forcibly displaced and host communities in these countries. Overall, the censored headcount ratios (proportion of people who are poor and deprived in a given indicator) are lower among non-displaced communities than among refugees and IDPs, but there are large differences in which indicators are the most salient in different countries. The indicators with the largest difference between the two populations are bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria. These findings reinforce the need for policies and programming that take into account the measured experiences of IDPs and refugees. In this way, the MPI can function both as tool to monitor, track, and bear witness to the lived experiences of forcibly displaced communities, as well as advise on evidence-based interventions that address the needs of the local population. Figure 1 shows the censored headcounts of each indicator in Sudan ’ s MPI, with large differences appearing by displacement.
Ethiopia–Household Welfare Statistics Survey (HoWStat)—excludes the majority of displaced populations (Internally Displaced People [IDPs] or refugees) from its sample of households. Thus, we have limited in-depth information on the socio-economic outcomes—including on poverty— for refugees across all camps in Ethiopia to compare with Ethiopian hosts. SESRE collected data from November 2022 to January 2023, from a nationally representative sample of 3,452 refugee households and their hosts. The SESRE covers all currently operating refugee camps of major refugee groups: Eritreans, South Sudanese, and Somalis, as well as the out-of- camp refugees of Addis Ababa and their respective host communities. The survey was aligned with the HoWStat methodology, allowing comparability between refugees and their host communities. The World Bank, Ethiopia’s RRS, Ethiopia’s Statistical Service, and UNHCR collaborated to implement SESRE10 and was the first of its kind, building on the “Skills Profile Survey 2017, A Refugee and Host Community Survey” conducted in Ethiopia in 2017. The SPS 2017 was conducted in refugee camps and host communities in four regions in Ethiopia. The survey was used to draw a profile for skills and potential opportunities for refugees and host 9 See Annex C for detailed information on survey design and methodology. 10 Financial
Since the set of household members at baseline have subsequently moved, and usually not as a unit, the 2004 round results in more than 2, 700 household interviews (from the baseline sample of 912 households). Although the KHDS is a panel of respondents and the concept of a ‘ household ’ after 10-13 years is a vague notion, it is common in panel surveys to consider re-contact rates in terms of households. Excluding households in which all previous members are deceased (17 households with 27 people), the field team managed to re-contact 93 % of the baseline households. This is an excellent rate of recontact compared to panel surveys in low-income countries and high-income countries. The KHDS panel has an attrition rate that is much lower than that of other well-known panel survey summarized in Alderman et al. (2001) in which the rates ranged from 17. 5 % attrition per year to the lowest rate of 1. 5 % per year. Most of these surveys in Alderman et al. (2001) covered considerably shorter time periods (two to five years). Figure 1 charts the evolution of households from baseline to 2004. One-half of all households interviewed were tracking cases, meaning they did not reside in the baseline communities. Of those households tracked, only 38 % were located nearby the baseline community. Overall, 32 % of all households were not located in or relatively nearby the baseline communities. While tracking is costly, it is an important exercise because migration and dissolution of households are often hypothesized to be important
Treatment Randomization: The housing subsidy program was randomized geographically at the community level. 8 In the ﬁrst step, 158 communities in Irbid and Mafraq were randomized into treatment or control for HSP assistance, stratifying on governorate and district population quartile. One third of communities were randomly assigned to treatment, while the remaining two thirds were randomly assigned to control. All eligible applicants living in the treatment communities were assigned to treatment. In all analysis below, error terms are clustered at the community level. There were several reasons for the cluster randomized design, including the ability to streamline implementation (and thus lower program costs), to reduce conﬂict among refugee households as- signed to treatment versus control, and to improve the analysis of treatment spillovers onto host community members by boosting the local saturation of program assistance, thereby making it more salient to neighbors. Refugee Sample: The survey sample consists of all refugees assigned to treatment and an 7See AER RCT Registration Number AEARCTR-0006141. 8Communities correspond to the Jordanian government ’ s administrative unit of “ localities ”. Communities in Mafraq have 3, 866 people on average, while those in Irbid are somewhat larger at 14, 626 individuals on average. 10
The paper assembles a new, regionwide panel data set that measures local economic activity using nightlights, potential hurricane damages using a detailed hurricane windstorm model, and mangrove protection by mapping the width of mangrove forests on the path to the coast.

Specifically, we use remote sensing data on nightlights to measure local eco nomic activity. Nightlights have been shown to be a good proxy of economic activity (see Donaldson and Storeygard (2016) for a review), and their high spatial resolution is ideal because the economic impact of tropical cyclones has been shown to be highly localized

We measure potential hurricane damages using predicted wind speed from the wind field model of Pita et al. (2015). The model is calibrated for Central America and has been validated with historical data.

We measure local economic activity using imagery from four weather satellites that are part of the United States Air Force Defense Meteorological Satellite Program. These satellites record daily cloud formation by measuring the amount of moonlight reflected by clouds at night.

Specifically, we use the annual composites produced by the National Oceanic and Atmospheric Administration (NOAA). These composites predominantly measure man-made lights because they only use information from cloudfree days and because NOAA's methodology filters transient sources of light.