ai4data/datause-dataset-rechunked huggingface dataset:
17 significant for both selected and non-selected youth (see the 𝛽 ൅ 𝛿 estimate and the 𝛽 estimate for columns 2 & 3). Lack of results also holds for specification 3, where imputing missing data with upper and lower bound estimates to account for the potential bias introduced by attrition did not alter the lack of impact of the program, as well as for specification 4 (see the 𝛿 estimate). Figures 1, 2, and 3 plot the distribution of soft skills scores at baseline for both selected and non- selected youth. The figures indicate that scores across the three skills are concentrated towards the end of the scale, suggesting that soft skills training offered by NVSP might have been ineffective or too basic for this pool of volunteers. 19 Indeed, as table 1 shows, a high percentage of selected youth (71 percent) and non-selected youth (63 percent) had taken previous training in soft skills prior to NVSP. Results from a process evaluation conducted separately support this explanation. The majority of NVSP volunteers in focus group discussions and interviews mentioned that they would have welcomed more advanced trainings on soft skills, as well as on technical topics and job-relevant skills that can support their employability.
Figure 2. Location of Households in the VenReP-Kids Sample Notes: The figure depicts the exact geographic location of all the households in the VenRePs-Kids study. Green and blue dots depict the location of Colombian and Venezuelan households, respectively. The map in the upper right corner illustrates the location of Medell ´ ın (blue pin) with the department of Antioquia (highlighted in red). survey focuses on strata 1 through 4, intentionally omitting strata 5 and 6 to avoid bias toward higher-income groups which are less likely to include migrants in need of sup- port. 14
Operators/Assemblers Craf/Related Trade Workers Skilled Agricultural Workers Service/Sales Workers Clerical Support Workers Tech/Associate Professionals Managers/Professionals Percent Figure D.19: Occupation by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Inside the camp Outside the camp Percent Figure D.20: Work location by survey domains Source: World Bank Staff based on SESRE 2023. 0 5 10 15 20 25 30 35 40 45 50 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.21: Hours per week by survey domains Source: World Bank Staff based on SESRE 2023. Annexes 109 0 10 20 30 40 50 60 70 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.22: Hourly earnings by survey domains Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.24: Household owns livestock Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.23: Household owns crops Source: World Bank Staff based on SESRE 2023. 0
UNHS. Data were collected over a 12-month period. The sample used in this paper is

characteristics also differ between the two groups. Table 1 summarizes relevant socio
economic and child anthropometric variables by ownership and per capita consumption


8



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expenditure terciles for the 2005/06 UNHS and the 2009/10 UNPS. [3] Descriptive statistics on

DHS. (2011). Uganda Demographic and Health Surveys, 2011. Data accessed via & Hautvast, J. (1989)
Increased Risk of Vitamin B-12 and Iron Deficiency in Infants on Macrobiotic Diets. _The_
_American Journal of Clinical Nutrition_, 50, 818-824.

PAGE 31 --> Kabubo-Mariara, J., Ndenge, G. & Mwabu, D. (2009). Determinants of Children's Nutritional Status in Kenya: Evidence from Demographic and Health Surveys. _Journal of African_ _Economies_, 18(3), 363-387.
13 degree of group bias. Thus, should the program reduce bias, 𝜓𝜓7 < 0. 𝑋𝑋is the same 𝑛𝑛 × 𝑘𝑘 matrix of control variables. 𝜓𝜓 is a 𝑘𝑘 × 1 vector of regression coefficients; and 𝜔𝜔 is the idiosyncratic error. We employ two small deviations from these approaches to produce the full set of results. First of all, as we do not have two sets of control variables from outreach to baseline, we run a fixed effects analysis to understand the impact of assignment to treatment status on life and economic optimism. Second, due to a data collection error in the field, indicators of economic scarcity were not collected from all of the control group at baseline. Instead, we seek to approximate the effect of treatment on these indicators by triangulating comparisons in two dimensions. First, we test whether or not these indicators improved for the treatment group from baseline to endline. Second, we test whether or not there are differences between the treatment and control groups at endline. This stops short of causality but still reveals interesting information about the dynamics at play. We produce five outputs for each analysis, with the exception of the economic scarcity indicators. First, we use uncontrolled OLS. Second, we introduce control variables. Third, we remove the controls but add inverse probability weights.
Cross-sectoral programming designed to meet families ’ holistic needs would also increase the odds of maternal and child well-being, including increasing provision of cash transfers through BISP, expanding access to ECE programs, ensuring food availability and affordability for mothers, and implementing emergency-response programs for continuity of basic services in the context of floods and other disruptive disasters to protect vulnerable mothers and young children. Both direct interventions that provide mental health services and indirect interventions that reduce exposure to stressors within communities and households are approaches to alleviate mental health concerns among mothers of young children in the FCV setting of the current study. In addition to individual-level impacts, societal and economic impacts result from reduced maternal depression, anxiety and stress. For example, costing estimates from Pakistan suggest that there is a $ 16. 6 billion cost per cohort of unaddressed maternal mental health concerns (Bauer et al., 2024). Limitations The data collection coincided with a politically sensitive time in Khyber Pakhtunkhwa, inclusive of security concerns for enumerators, ongoing election campaigns, and occasional disruptions to data collection, which could have impacted the accuracy and consistency of data collected. Further, repatriation efforts being implemented during the data collection period led researchers to avoid self-report determination of refugee (documented and undocumented) status and rely on enumerator report using observable indicators as outlined above. This may have therefore impacted the accuracy of the data on identifying refugee households. There are also several measurement constraints to be considered. The tools selected to measure maternal mental health may not cover all relevant dimensions of symptoms and experiences specific to the region of Pakistan studied, even though tools were specifically selected based on previous adaptation, validation and use in the province.
There are different ways to arrive at a value for housing services consumed by nonrenters, which have different advantages and disadvantages (see Deaton and Zaidi, 2002). In POF, households that own their dwelling (even if still paying the mortgage) or that

_Temporal and spatial price adjustment._ To make the consumption aggregate and average costs per calorie
comparable across households, we adjust expenditure values temporally and spatially. POF was collected
throughout a full year and its reference periods go back up to 12 months. Figure 2 shows the temporal
variation of prices for different Brazilian states over the reference period of the survey. On average, prices
increased by 26.8 percent over the reference period - a monthly average of 1 percent. IBGE (2020)
provides temporal deflators in the publicly available data, which we use to deflate prices and the
consumption aggregate to express all monetary values in January 2018 values.

13 There are several differences between our consumption aggregate and other studies using POF data. In one of the
most recent ones (Oliveira et al. 2016), the authors aim to measure and analyze welfare, poverty (using the
administrative "poverty'' line equivalent to half a minimum wage), inequality, and vulnerability.

Midwest-Rural 0.997 0.671 0.933
_Source:_ Own calculations using POF 2017/18 data.
_Note:_ Prices in the urban Southeast are the reference prices. While we use household-specific price indices in our estimations,
for ease of presentation we calculated regional averages of household-specific indices and divided them by the
Southeast-Urban average.
Notes: The table displays the mean and standard deviations of several characteristics of all students
matched in the ENILEMS-ENLACE (column 1), students that reported to be in college (column 2),
out of college (column 3) or employed (column 4). Data: ENILEMS-ENLACE panel.


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Table 3: OLS - ENLACE test scores and secondary school outcomes: simple correlations

Notes: (1) The table displays the estimation of the effect of early test scores on grade 9 and 12 test scores and enrollment by subject and
grade. (2) Specifications for each subject and grade include as independent variables grade 6 test scores of the same subject. (3) Sample:
Twins (students in the same school in grade 6, with identical last names and birth date. (4) All specifications include twins fixed effects. (5)
Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05,  - p _<_ 0.1.



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Table A.4: OLS       - ENLACE mathematics and Spanish test scores and post-secondary school outcomes

Enlace Score 0.102*** -0.00750 0.0681** -0.000444 (0.0154) (0.0297) (0.0304) (0.0298) Upper Secondary GPA 0.0688*** 0.0412* -0.00579 0.00507 (0.0146) (0.0233) (0.0268) (0.0254) Girl -0.0500* -0.237*** -0.0321 0.00994 (0.0263) (0.0442) (0.0501) (0.0478) Private Upper Secondary 0.113*** -0.0962 0.0606 -0.0842 (0.0320) (0.
.00994 (0.0263) (0.0442) (0.0501) (0.0478) Private Upper Secondary 0.113*** -0.0962 0.0606 -0.0842 (0.0320) (0.0649) (0.0736) (0.0653) Urban resident 0.269*** 0.0373 0.0944* 0.137** (0.0375) (0.0472) (0.0535) (0.0566)
The age categories are 15 – 19, 20 – 24, 25 – 29, 30 – 34, 35 – 39, 40 – 44, 45 – 49, 50 – 54, 55 – 59, and 60 – 64 years. There are 183 groups since we exclude groups containing less than 40 observations. 24 An additional advantage of the IV approach is that it helps deal with measurement problems. Despite the improved measures of refugee numbers in Turkey by subregion starting in 2014, there is likely considerable measurement error, resulting in attenuation bias in the OLS estimates. For the IV estimates to be consistent, it is only necessary that- conditional on the fixed effects and control variables- the flows of Syrian refugees are uncorrelated with the instrument. 25 Using data from AFAD (2013) we can also weight the aggregate refugee numbers using the Syrian source governorates of refugees in 2012-13 (see Figure 2). Results are qualitatively robust to this alternative instrument and first-stage F-statistics about the same. We prefer the use of the pre-war distribution of population in Syria,
6. Robustness Checks 6. 1. Sample attrition Many challenges were encountered during survey implementation, due to the context – the densely populated and impoverished communities in and around Monrovia are difficult settings in which to find and track respondents — as well as the transience of a young study population. Despite vigorous efforts to track and interview each individual in the sample, a certain amount of survey attrition was expected. As the survey response rates in Table 1 show, 1622 (or 80 %) of the individuals in the study sample were successfully interviewed in both the baseline and midline surveys. Another 305 respondents were interviewed at baseline but not at midline and hence are not in the panel used for the analysis in this paper. 27 This survey attrition, while not much higher than other program evaluations in Africa, may cause concern that the results of this evaluation are biased, especially if the loss to follow up is correlated with individual characteristics that might affect the outcomes. To address this concern, Table 10 presents regressions on the likelihood of panel inclusion, that is, the likelihood of being interviewed at both baseline and midline. The first column indicates that treated individuals are significantly more likely than control to have been interviewed twice. This result persists even after controlling for individual characteristics and community dummies.
Firstly, the Hydrosheds variant of SRTM is used, both at 3 and 30 arc second resolutions. A number of additional corrections are applied to the terrain data including a systematic vegetation correction procedure in vegetated areas and an urban correction procedure in urbanized areas. A detailed description of the modeling framework is provided by (Sampson et al. 2015).

For this study, we developed flood hazard maps representing riverine, flash-flood and coastal flood risk for Vietnam. These flood hazard maps estimate the inundation depth at a grid cell level of 3 arc-seconds, (~ 90m) and provide coastal surge hazard layers, along with pluvial and fluvial layers. The maps provide information on the extent and depth of flood hazard for a specific location. For the coastal component, we explicitly model four return periods - 25, 50, 100, and 200 year events, under current and future climate conditions.

Data on natural capital is sourced from a World Bank dataset on national wealth. The dataset disaggregates the three components of national wealth-produced capital, intangible capital and natural capital-and it decomposes the natural wealth stock into renewable and nonrenewable resources.

The data on income inequality is sourced from the World Bank's "All-the-Ginis Database,"
which was last updated in 2014. [8] The database collects Gini indexes from multiple sources
into long time series. The data has been standardized for this analysis via the so-called
"choice-by-precedence approach," which reflects each dataset's reliability, degree of
variable standardization, and consistency of geographical coverage. GDP and population
figures have been collected from the United Nation's UNCTAD-STAT database. Table 2
presents the descriptive statistics for per capita GDP and the Gini index by country
income group. The Gini index peaks among the upper-middle-income group, falls among
the lower-middle-income and low-income groups and is lowest among the high-income
group. These data are consistent with the relationship between inequality and GDP
described by Kuznets (1955).
Abstract
Behavioural surveillance surveys (BSSs), an evolution from the knowledge �attitudes practice surveys (KAPs), are a tool to track trends in HIV/AIDS knowledge, attitudes
and risk behaviour among populations. The data collected support organizations in
targeting specific HIV/AIDS prevention and care activities, monitoring their effectiveness
and coverage, and allocating scarce resources. The objectives are to evaluate the quality and
standardization of BSS-like surveys undertaken in conflict and post-conflict situations, and
to provide recommendations to humanitarian agencies and governments on how to
improve their quality. Survey methodology was classified as reproducible if the populationbased sampling defined a sampling frame using probabilistic sampling. Survey indicators
were compared to internationally-accepted HIV indicators. The results showed that 14
(45.2%) of the 31 BSS-like surveys evaluated between 1998 and 2005 in 14 countries were
classified as reproducible. Surveys undertaken by non-governmental organizations
(NGOs) were significantly less reproducible than those undertaken by non-NGOs (p �/
0.05). The majority of surveys used at least one identical or similarly worded
internationally-accepted HIV indicator for prevention and misperception but not for
practice and attitudes. Few reported disaggregated indicators according to age or gender. It
was concluded that the majority of BSS-like surveys are of insufficient methodological rigor
to be reproducible. Few surveys reported internationally-accepted HIV indicators by
gender and age which makes interpretability and comparison difficult. United Nations
agencies, NGOs, and governments undertaking BSSs in conflict and post-conflict settings
should proceed with a BSS survey once the design and plan for execution has been prepared
by experienced and qualified experts. These experts should then oversee the survey, assure
data quality and incorporate training of others in the process. A practical and field userfriendly BSS manual is needed for conflict affected and displaced population situations,
one which is customized to take into account the special circumstances of such populations.
Figure A4: Participation certiﬁcate to boost ‘ resume ’ CERTIFICATE THIS ACKNOWLEDGES THAT I engaged with Pulse Bangladesh to do data collection Notes: The wording of the certiﬁcate was made such that it could be applied to both arms; cash-only arms participated in weekly surveys along with all other experiment participants, so technically also engaged in data collection for our project. 60
Page 14 of 51 Source: Own calculations based on Household surveys From the education perspective, an improvement in the profile of workers employed as NSE can be observed in the countries analyzed, even though there are some exceptions. In fact, in most of the countries of our sample, the prevalence of workers with secondary and tertiary education increases in detriment of workers with a lower educational level. However, there are some differences by type of non-standard employment. In the case of part-time employment (Figure 10), several countries show an obvious rise in the prevalence of workers with secondary and tertiary education (Argentina, Brazil, Peru, and Mexico). There is a second group of countries that presents a decrease in the prevalence of workers at the secondary level, but, this decrease is more than compensated by the greater incidence of tertiary workers, so that, taken together, workers with secondary or higher education increased their share within part-time employees (Uruguay and Chile). Therefore, we can also conclude from this group that part-time employment presents a better educational profile today than two decades ago. In the Dominican Republic and El Salvador, we find a rise in the prevalence of workers with secondary education but a decrease in the share of workers at the tertiary level.
during the sampling and data collection stages. Moreover, our thanks go to the following UNHCR colleagues for their invaluable comments on the various draft stages of this report: Emily Lugano, Annick-Laure Tchuendem, Jed Fix, Theresa Beltramo, Alessio Baldaccini, Anna Gaunt, Asaad Kadhum, Benoit d’Ansembourg, Berhanu Geneti, Campbell Macknight, Daniel Gebrekidan, Florah Bukania, Florence Nimoh, Johannes Abate, Joyce Wahome, Katie Ogwang, Nada Omeira, Nathalie Bussien, Robert Nyambaka and Yukta Kumar. Our thanks also go to our JDC colleagues Felix Schmieding (Senior Statistician, UNHCR) and Harriet Kasidi Mugera (Senior Data Scientist, World Bank) for their tireless support and advice during the preparation of SESRE. The report benefited from the insights of peer reviewers: Leslie Velez, Mulualem Desta, Takaaki Masaki (Senior Economist, World Bank), and Precious Zikhali (Senior Economist, World Bank). We are indebted to Nistha Sinha (Senior Economist, World Bank) for her constructive comments that significantly enhanced the report. We also recognize Aldo Morri for his excellent editorial support. Finally, we extend our deepest gratitude to the survey respondents for their willingness to share their experiences which has been instrumental in deepening our understanding of the challenges and needs faced by both refugees and host households. ACKNOWLEDGEMENTS ii Introduction E thiopia, with
Using Afrobarometer ’ s geocoded surveys, we focus on clusters as our unit of observation. 10 Our sample consists of 7, 547 such locations and 76, 518 individuals in 23 countries in Sub-Saharan Africa. “ The sampling universe normally includes all citizens age 18 and older. As a standard practice, they [we] exclude people living in institutionalized settings, such as students in dormitories, patients in hos- pitals, and persons in prisons or nursing homes. ” (Afrobarometer, https: / / afrobarometer. org / surveys − and − methods / sampling − principles) Since the sampling frame is based on recent censuses, with the aim of representing all citizens of voting age in a given country, the Afrobarometer samples are unlikely to include refugees. Note also that “ the sample design is a clustered, stratified, multi-stage, 8We explain the construction of theses indices in Section 4. 2. 9We test the robustness of our results with a smaller (40 km) and a larger (120 km) radius in Section 5. 3. This choice of buffer size assures us that between 75 percent and virtually all refugee camps fall within a cluster buffer. Other studies relying on Afrobarometer data construct buffers ranging from 25 km (e. g., Michaelopoulos and Papaioannou (2011), investigating ethnic-specific pre-colonial institutional structures) to 100 km (e. g., McGuirk and Burke (2020a), analyzing the impact of food-price shocks on conflict). 10Afrobarometer is a pan-African research network conducting public attitude surveys on democracy, governance, the economy, and society in African countries that are repeated on a regular basis (Afrobarometer, 2020). 10
### 6.1 Grievance Redress Mechanism

The project-level GRM will receive and process complaints related to beneficiary selection, service delivery, labor conditions, environmental impacts, and GBV incidents. A dedicated toll-free hotline, an SMS channel, and physical suggestion boxes at community centers will serve as intake points. All complaints will be logged in the MIS within 24 hours of receipt and assigned to the responsible district coordinator for investigation. Complainants will receive a response within 15 working days, with more complex cases referred to the PIU's E&S specialist for resolution.
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As we did not have meteorological data for each and every municipality in Chile, this
information was estimated. Since average annual temperature in any particular location
depends principally on distance from the equator and elevation above sea-level, we
estimated a simple model (see Table 2) using information on average annual temperature,
latitude, and altitude for all the Chilean stations for which we could obtain "normal"
temperature data (from www.worldclimate.org). In order to get sufficient variation in the
elevation variable, we included 4 stations located close to Chile (Catamarca, Mendoza,
Arequipa, and Oruro).

In this section we will analyze climate data for Chile from May 1948 to March 2008 to test
whether there are any significant trends, and whether these trends differ between regions.


We will use the Monthly Climatic Data for the World database collected by the National
Climatic Data Center (NCDC) in the US. This project started in May 1948 with 100
selected stations spread across the World, including five in Chile. Since then, many more
stations have been included in the data base, but only seven stations in Chile have
contributed systematically throughout the period, with only inconsequential gaps. These are
listed in Table 5, ordered from north to south.

According to the model simulations reported in Christensen et al (2007), temperatures are going to increase faster in the northern part of Chile than in the southern part

temperature data are extracted from the New et al. (2000) gridded 0.5 degree dataset. Calculation of

indices, such as the Palmer Drought Severity Index which uses precipitation and temperature to measure