ai4data/datause-dataset-rechunked huggingface dataset:
Gender Perceptions- Violence (IPV) The standardized total score of ﬁve questions regarding norms for inti- mate partner violence (IPV) from the Demographic and Health Survey (DHS) (The important decisions in the family should be made only by the men of the family. How often would you agree? The wife has the right to express her opinion even when she disagrees with what her husband is saying. How often would you agree? A wife should tolerate being beaten by her husband in order to keep the family together. How often would you agree? A husband has the right to beat his wife. How often would you agree? It is more important to send a son to school than it is to send a daughter. How often would you agree?). Financial Well-being Savings Response to the question “ How much money do you currently have in savings? ” During the collection surveys (midlines) this question instead asked “ How much money did you save in the past week? ” Borrowing Total amount of money the household has borrowed. Economic Decision Making Risk Preference Measured using incentivized responses to the multiple price list deci- sions adapted from Holt-Laury and Sprenger (2002).
**Procurement Performance**

As of the midterm review, all prior review contracts have been awarded and counter-signed, and 83 percent of goods and non-consulting services contracts have been awarded against the Year 2 procurement plan. Delays in two civil works contracts are attributed to a late ESMF clearance for one subproject and to difficulties in obtaining land access certification for a second. Both contracts are now unblocked, and the STEP system has been updated to reflect the revised award dates. No misprocurement has been identified during Bank reviews of contract documentation.
Our nightlights data set is composed of 22 satellite-year composites for the period 2000 2013. Each composite covers Central America and contains information on 604,473 one kilometer square grid-cells.

To measure the distribution of surface winds from hurricanes in Central America during our sample period, we use the wind field model developed by Pita et al. (2015). This model uses an asymmetric Holland equation that has been specifically calibrated for Central America.

Data on the distribution of mangroves come from two sources. The first is a collection of harmonized maps, 1960 to 1996, that was assembled for the Mangrove World Atlas (Spalding et al., 1997). We use this map to identify areas that have historically supported mangrove habitats.
24 According to Table 12, all countries but Sudan show a significant relationship for school- age children ’ s experience of intrahousehold inequality and their displacement status, although sample sizes mean that only Nigeria and Somalia ’ s results are robust. In Nigeria, most children experiencing intrahousehold inequality reside in non-displaced households – although it is crucial to note that these children constitute most of the school-age children (71. 1 %), and the levels of intrahousehold inequality are nonetheless far higher than anticipated if displacement status had no effect. In Somalia, displaced children are significantly more likely to experience intrahousehold inequality in school attendance, as they constitute 63. 5 % of school-age children experiencing intrahousehold inequality even though they only make up 35. 3 % of the school-age children population in the sample. For the years of schooling indicator, the overall lack of intrahousehold inequality among the MPI poor in years of schooling obscures meaningful or robust differences by displacement status. Gender and displacement status appear to jointly have significant impacts in school attendance in Ethiopia, Somalia, and South Sudan. In Northeast Nigeria, it appears that displacement status has larger effects than gender. In Somalia, forcibly displaced school children experience intrahousehold inequality more often than non-displaced children, to the disadvantage of girls.
2017 SESRE 2023 (In camp) Hosts Refugees Hosts Refugees Country of origin South Sudanese 23% 53% Somali 24% 30% Eritrean 25% 5% Sudan 28% Demographics Female headed 35% 66% 44% 73% Children (0 to 14) 55% 61% 47% 53% Youth (15 to 24) 13% 17% 19% 21% Adults (25 to 64) 29% 22% 31% 24% Elderly (>=65) 2% 1% 3% 2% Education Net primary enrollment 74% 79% 75% 69% Net secondary enrollment 35% 13% 39% 22% Living conditions Own a house 72% 5% 69% 14% Overcrowding 32% 59% 36% 56% Improved sources of water 96% 98% 91% 100% Access to electricity (grid) 46% 8% 41% 3% Improved toilet facility (shared/not shared) 51% 69% 38% 43% Employment Employed 61% 22% 48% 25% Unemployed 2% 6% 9% 19% Inactive, not in school 23% 44% 20% 23% Inactive, in school 14% 27% 24% 33% Poverty incidence US$1.9 per capita per day 27% 65% National Poverty line 32% 84% Food security High food insecurity 26% 67% Food insecurity scale 4.0 8.1 Social cohesion Economic competition 33% 49% Increased insecurity 37% 39% Source: Pape et al. (2018) and World Bank Staff based on SESRE 2023.
Landscan Population Data. Oak Ridge National
Laboratory.
Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D., Vogt-
Schilb, A., 2016.

At the national-level analysis, we overlay the flood hazard maps developed for this study with spatial socioeconomic data. For Vietnam, the World Bank has produced estimates of the number of people within each district who live below the poverty line: this "poverty map" is displayed in Map 4a, and the full methodology can be found in (Lanjouw, Marra, and Nguyen 2013). In addition, we use gridded population density data with a 1km resolution from Landscan (Geographic Information Science and Technology 2015). This "population map" is displayed in Map 4b.

The spatial socioeconomic data set used for Ho Chi Minh City is a data set of potential slum areas and of urban expansion from 2000 to 2010, from the Platform for Urban Management and Analysis (PUMA), a city-level data set developed by the World Bank (World Bank 2015).This data was collected via satellite in the year 2012, through a combination of visual interpretation of various sources and vintages of imagery.

The inundation maps were used in an earlier flood risk study of HCMC (Lasage et al. 2014), and were
composed with the MIKE 11 hydraulic modeling software (DHI 2003).
However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel. 19
The instrument becomes even weaker if we use refugee to population ratios rather than the number of refugees, therefore we use the absolute number of refugees throughout. 4Including 2014 data in the DD estimations generally results in more statistically signiﬁcant and larger coeﬃcients but does not change the direction of the results. 5The weights are calculated using the Stata package synth provided by the authors. We use the option nested to calculate the weights through the nested optimization procedure described in Abadie et al. (2011). 6Tunceli ’ s 2011 export and import values are missing for 2011, therefore only 2009 and 2010 values could be used in calculating Tunceli ’ s average. 7The Chamber of Commerce also provides information on the number of ﬁrms that shut down. However, reporting exits is not mandatory and the indicator is therefore less reliable. We found no signiﬁcant eﬀects in both the IV and DD estimates on the number of ﬁrms that shut down. 8Since the number of new foreign ﬁrms is 0 in several observations, we add 1 to the value. As an alternative, we used the hyperbolic inverse sine transformation which does not have the same problem with 0s as log transformation and found similar results (Burbidge et al., 1988). 9Publicly available data from the Ministry of Science, Industry and Technology indicate that less than 10 % of total revenue is from micro-establishments. Most of the net sales and gross proﬁts reported stem from larger ﬁrms that should be included in the Chamber of Commerce data. 10All ﬁrms exceeding 200, 000 Turkish Liras (ca. $ 85, 000) in sales are obligated to report detailed balance sheets. Smaller ﬁrms may still report their balance sheets but would be doing so on a voluntary 25
Oseni and Pollitt (2013) use cross-sectional data on electricity outages collected by the 2007 World Bank Enterprise Survey of 6,854 firms in 12 African countries.

** **Data** We used the social accounting matrix (SAM) of year 2007.

We use a choice experiment study to fill this knowledge gap and identify preferences for

goods, when market data are not available for assessing these valuations. A choice experiment


survey presents the respondent with choice scenarios where each choice scenario has

The enumerators spoke local languages and were trained in conducting choice   experiment surveys (and most had previous experience with collecting choice experiment data
After a short respite in November, Israel imposed a third lockdown from December to February the next year. Gradual easing continued throughout March. During this lockdown, vaccination was rolled out, including to commuters with valid permits. Given this context, we expect the main shock to occur in 2020Q2 in the West Bank, with negative effects of smaller magnitudes to persist over the next quarters. In Gaza, we expect another major shock in 2020Q3 that carries over to Q4. We expect the commuters ’ outcomes to instead track events in Israel and occupied territories more closely, with a major shock in 2020Q2, recovery in Q3, and another dip in Q4. 3 Data 3. 1 Data sources and definitions We employ data from the Labor Force Surveys (LFS) of the West Bank and Gaza, collected by the Palestinian Central Bureau of Statistics and prepared by the Economic Research Forum. The surveys are conducted on a quarterly basis, covering periods from 2000 onwards. The LFS is meant to represent all households whose ordinary residence is in the West Bank and Gaza, though their place of work need not be. The LFS is representative at the region level (respectively of the West Bank and Gaza), as well as at the level of locality types (urban, rural, and refugee camps) (Palestine- Labor Force Survey, LFS, 2021). Importantly for the purpose of our analysis, the data have a panel dimension, en- abling the study of labor market transitions. The sample rotation scheme is described 9
Refugees In camp Addis Ababa Total Attending school Primary school Secondary school 0 10 20 30 40 50 60 70 80 90 100 Primary school (7 to 14 years) Secondary school (15 to 18 years) Percent Percent Figure 2.10: Children currently attending school Source: World Bank Staff based on SESRE 2023. a. Attended education b. Education level a. All children b. School-age children 20 The Primary Gross Enrollment Rate (GER) is the ratio of the number of children enrolled in primary school irrespective of age to the number of children of primary school age (age 7 to 14). 21 The Primary Net Enrollment Rate (NER) is the ratio of the number of children of primary school age enrolled in primary school to the number of children of primary school age (age 7 to 14). 22 The secondary Gross Enrollment Rate (GER) is the ratio of the number of children enrolled in secondary school irrespective of age to the number of children of secondary school age (age 15 to 18). 23 Secondary Net Enrollment Rate (NER) is the ratio of the number of children of secondary school age enrolled in secondary school to the number of children of secondary school age
The UNHCR supported in engaging refugee communities and leaders. The RRS facilitated access to all camps for the survey teams; this is the first time that the RRS facilitated access to all camps for such an extensive survey. Notably, the UNHCR and RRS facilitated the collaboration of the field workers with refugee leaders in each camp and Addis Ababa to support the teams in identifying sampled households and maintaining the safety of the field workers, and the sampled households during the entire survey period. The World Bank team led the collaboration between ESS, UNHCR, and RRS and provided technical support to the ESS since the project’s inception. The SESRE used a logistics plan similar to HoWStat. Six ESS branches were responsible for administering the survey: the Asayita, Gondar, Jigjiga, Negele, Gambella, Assosa, and Addis Ababa branches. Twenty-four field teams carried out the fieldwork, each consisting of one statistician, one team supervisor, and four enumerators. All field staff involved in the SERSE participated in the HoWStat survey. Enumerators were knowledgeable about local cultures and languages and could detect inconsistencies and misunderstandings during interviews to ensure high-quality data. Supervisors were additionally trained on how to troubleshoot standard technical issues with tablets. The
Another ex- planation of these results could be the vulnerability of the Colombian population who in many cases has also a long history of internal forced displacement and violence. V. D Social Cohesion We also delve into the differences in secondary outcomes among Colombian and Venezue- lan adolescents concerning social cohesion. We focus on assessing altruism, trust, iden- tity towards specific domains, networks, and experiences of discrimination. To measure altruism and trust, we employ the questions from the Global Preference Survey, a tool developed by Falk et al. (2022) to elicit risk, time, and social preferences. Specifically, to measure altruism we ask the adolescents how much of a fictional endowment would they be willing to donate to a good cause. To measure trust, we include the 7-itme ques- 38
This assessment is made possible through the use of state of the art transportation modeling techniques and by combining three data sets: 1) An innovative General Transit Feed Specification (GTFS) data set collected for the purpose of this analysis under normal (dry) and flooded (wet) conditions, 2) a travel survey data set with travelers' socioeconomic attributes and Origin-Destination (OD) information, as well as 3) a set of high-resolution global flood maps that capture both the extent and depth of pluvial and fluvial floods.

**3.2** **General Transit Feed Specification (GTFS) Public Transit Feed**

Wong, J. C. (2013). _Use of the general transit feed specification (GTFS) in transit performance measurement_ [Thesis,
Georgia Institute of Technology]. https://smartech.gatech.edu/handle/1853/50341

To understand the impact of flooding on public transit, we compared the GTFS transit feed mapped under dry and wet conditions and evaluated changes in headways, blockages of roads and rerouting, and travel speeds.
Sudan domain (.48 standard deviations below the mean). It is higher for female than male hosts in both domains, by .14 standard deviations from average in the Somali domain and .19 standard deviations in the South Sudan domain. The difference in attitudes between male and female hosts is not statistically significant, and inter-group attitudes vary little by age or education. We can study the characteristics associated with attitudes by putting this index into a regression 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Addis Ababa All Hosts All Refugees Neither are trustworthy Only my group Only the other group Both are trustworthy Percent Figure 7.9: Are most Ethiopians/refugees in Ethiopia trustworthy? Source: World Bank Staff based on SESRE 2023. Note: Combines two questions regarding trust in Ethiopians and refugees, with identical wording to both Ethiopians and refugees. 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 Eritrean Somali South Sudanese Addis Ababa All Hosts Figure 7.10: Host attitudes Index Source: World Bank Staff based on SESRE 2023. Note: This index is an average of ten questions regarding beliefs about refugees’ character, the rights they should
#### 4. RESPONSE

The Protection Sector in Sudan comprises General Protection, Child
Protection, GBV and Mine action AoRs. There is also active Housing
Land and Property and Durable Solutions Working groups that work
closely with Protection Cluster. These structures meet every month
at the national and state level (five Darfur, South Kordofan and Blue
Nile). The Protection Sector developed its strategy and work plan.
The Sector developed the Protection of Civilians’ Incidents Tracking
tool in consultation with other agencies. It also publishes maps of

health care providers and called on Sudanese authorities to enforce
international”, Report of the Secretary-General, 2 March 2022
Excluding respondents who have relocated would omit those with the higher rates of income growth and poverty reduction. Table 5 reports confidence intervals for the incremental samples (which are not mutually exclusive); it gives a more detailed picture of how inference on consumption growth and poverty reduction would have changed if we had not tracked movers. It is apparent that inference from a ‘ simple ’ panel survey of respondents continuing to reside within the original communities would have produced underestimates of actual consumption growth and poverty reduction in this population. These conclusions are robust across the distribution of consumption, as well as at the mean and poverty line. Panel A in Figure 2 depicts the cumulative density function for consumption per capita for those people who remained living in the same community. Panels B, C and D make the same graph for respondents found residing in neighboring communities, elswehere in Kagera Region and outside Kagera Region. As respondents were located further from their location in 1991, so the difference between the 1991 and 2004 graphs becomes more pronounced. Note how, for people who remained in the baseline community, the 1991 and 2004 distributions lie close to each other under the poverty line and diverge above it, while for other mobility categories there is more divergence at the bottom of the graph.
impression of the state ’ s role in development and give credit to the state for helping to leverage external resources. Citizens are also likely to give the state credit where mechanisms to voice complaints about non-state actors exist and where bureaucrats are able to eﬀectively respond to complaints. Under these conditions, non-state service provision is likely to strengthen the ﬁscal contract. 5 Data and Methods I explore the relationship between external service provision and deference to government using Afrobarometer survey data from 19 Sub-Saharan African countries (see Table 1). Africa is an especially good place to examine these issues because of the large amount of variation both within and across African countries in the extent to which non-state actors, donors and other states are active in service provision and the extent to which governments are relatively eﬀective and fair. Government responsiveness, corruption and reliance on non-public resources vary considerably among localities with consequences for citizen understanding of and relationship to government (Gibson and Hoﬀman, 2005). This project relies on the fourth round of Afrobarometer data that surveys Africans ’ views towards democracy, economics, and civil society with random, stratiﬁed, nationally representative samples.
Chinese Exports in Agriculture and Food Products**   Value of Exports in Billion USD        _Source_ : COMTRADE Database, Authors calculation    19     <!-- PAGE 23 -->  **Table 1.

6 [http://faostat.fao.org/](http://faostat.fao.org/)   6     <!-- PAGE 10 -->  standards and heavy regulation (Chen and Findlay 2008).Chinese exports in agricultural and  food products are increasing.

Although there is no data on the implementation of  voluntary standards, the China Statistical Yearbook of Certification and Accreditation (cited in  Jin et al.
access for refugees by easing restrictions and providing work permits. ◆ Integrate refugee children into national education system to improve their long-term prospects. ◆ Strengthen inclusive healthcare systems to address the health needs of refugees. Focus on place-based interventions: ◆ Invest in refugee hosting areas to benefit both refugees and host communities. ◆ Direct additional educational resources to districts hosting refugees to support integration. ◆ Expand access to social safety nets for vulnerable refugees and hosts. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Comfortable Neutral Not comfortable Percent Figure ES.7: Host response to “Would you feel comfortable having a refugee as a neighbor?” Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Strongly agree Agree Disagree Strongly disagree Percent Figure ES.6: Host response to “Refugees are good people” Source: World Bank Staff based on SESRE 2023. Executive Summary viii Continue implementation of progressive policies: ◆ Implement concrete actions to fulfill Government pledges and proclamations to move away from encampment toward mobility based on economic opportunities. ◆ Harmonize national and sub-national
By highlighting socioeconomic interactions and outcomes, SESRE aims to guide development interventions and facilitate refugee integration. The survey covers various aspects, including demographic profiles, livelihoods, welfare patterns, and social cohesion, offering valuable insights for policymakers and humanitarian actors. SESRE is a separate but integrated survey alongside the Ethiopian Household Welfare Statistics Survey (HoWStat),1 the national household survey to measure poverty and other socio-economic outcomes. Like most national poverty surveys, HoWStat excludes displaced populations—Internally Displaced People (IDPs) or refugees—including in Ethiopia. To have up-to-date information on the socio-economic outcomes and poverty levels of refugees and to allow comparison to Ethiopian host communities, the SESRE applied the same questionnaire and data collection methods as the HoWStat, with some modifications. The World Bank, Ethiopia’s RRS, Ethiopia’s Statistical Service, and UNHCR collaborated to implement SESRE and was the first of its kind. This report uses data from the SESRE extensively to analyze the Ethiopian refugee situation and to devise policy directions. The SESRE covers three types of groups: (i) refugees in camps; (ii) refugees out-of-camps in Addis Ababa; and (iii) host communities; all of which require a distinct sampling procedure. The sampling frame for refugee camps is based on UNHCR’s proGRES database. SESRE is