ai4data/datause-dataset-rechunked huggingface dataset:
We sourced bilateral trade value data from WITS and bilateral tariff data from a medley of sources, presented in Appendix Table A1. As part of this procedure, all entries in the two composite regions (rest of Western Asia and rest of Northern Africa) were split and assigned the split values to the newly created economies, while all entries for the two composite regions from the GTAP database were removed from the database. Each entry was split using the most thematically relevant external source. Sectoral GDP shares were used to split consumption and production values, trade data were used to split export and import values, and tariff information was used to assign tariff values. Export shares were used to split further production and consumption information into the final set of industries presented in Table 1. For internal consistency purposes, the required accounting relationships were imposed on the split database 8
Staff based on SESRE 2023. Note: Columns 3-4 are restricted to workers, and Columns 5-6 are restricted to workers in camps. High-skill occupations include managers, professionals, and associate professionals (around 7% of refugees). Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 124 Table E.1: Food aid data/information received from UNHCR Item Remark Assumptions Cereal Not clear Other cereals Wheat Matched Maize Matched Rice Matched Sorghum Matched CSB/famex (CSB+) Not in SESRE Average of other cereals/pulses Pulse Not clear Peas Biscuit Matched Date biscuit Not in SESRE Merged with biscuits Dates Matched Oil Merged with edible oil Vegetable oil Merged with edible oil Salt Matched Cash - - Source: UNHCR Annex E: Robustness Checks of Refugees’ Consumption This Annex discusses assessing the disparity between refugee ration aid and reported consumption quantities. This is reported as a robustness check. As discussed earlier, the expenditure of in-camp refugees is almost half that of hosts despite sizeable food aid and significant investments made by the WFP and UNHCR in cash transfers (in selected camps). The significantly lower expenditures (food and non-food) among refugees compared to the host population led to higher poverty
In the recent past, the number of persons affected by drought has been comparable to
that of victims of hurricanes and floods (146 million, on average, between 2000 and
2005 according to the EM-DAT). The latest report of the IPCC predicts increased
water shortages in Africa (74 to 250 million people affected in 2020) and Asia:
"Freshwater availability in Central, South, East and Southeast Asia particularly in
large river basins is projected to decrease due to climate change which, along with
population growth and increasing demand arising from higher standards of living,
could adversely affect more than a billion people by the 2050s." (Intergovernmental
Panel on Climate Change 2007a, 10). Case studies, however, paint a contrasting
picture. The effect of a lack of drinking and irrigation water on migration is actually
less sudden than that of the meteorological events mentioned in the previous chapter,
and only generates progressive departures.
the main government department responsible for refugee affairs, housed within the former National Intelligence and Security Services (NISS). The former ARRA was elevated to an agency level in accordance with Proclamation No. 1097/2018, which defines the Powers and Duties of the Executive Organs of the Government and established the Agency for Refugees and Returnees Affairs (ARRA) under the Ministry of Peace in 2018. In 2021, a new government announced through the Definition of Powers and Duties of the Executive Organs Proclamation No. 1263/2021, during which it reestablished ARRA as Refugees and Returnees Service (RRS). The RRS became one of the executive organs accountable to the NISS which is accountable to the Prime Minister’s Office and oversees the Immigration and Citizenship Service other than RRS. Introduction 4 Refugees in Ethiopia have been severely affected by ongoing conflict and unrest across Ethiopia. The country has dealt with multiple crises, including rampant inflation, a devastating war, and frequent droughts and floods. SESRE data collection was carried out between November 2022 and January 2023, marked by drought, inflation, and insecurity, posing significant threats to the livelihoods of refugees and host communities in an already fragile economy. The conflict in Northern Ethiopia continued until 2022
analysis of social integration outcomes (1) (2) (3) (4) (5) Has in Ethiopia: Easy to do: Family Friend Market Interactions Social Interactions Sharing Resources Male 0.007 0.067** 0.033 0.010 -0.076** (0.017) (0.033) (0.027) (0.036) (0.035) Age Under 30 - - - - - Age 30-44 -0.030 0.033 0.007 0.004 -0.048* (0.021) (0.025) (0.025) (0.029) (0.028) Age 45-64 -0.041 0.024 0.002 0.011 0.012 (0.025) (0.038) (0.038) (0.050) (0.041) Age Over 64 -0.009 -0.062 0.100** 0.041 0.046 (0.046) (0.058) (0.043) (0.089) (0.073) Educ: Primary incomplete - - - - - Educ: Completed primary -0.004 0.121*** 0.008 0.028 0.063** (0.027) (0.033) (0.035) (0.041) (0.032) Educ: Completed secondary -0.026 0.218*** -0.020 0.061 0.060 (0.028) (0.054) (0.051) (0.060) (0.057) Educ: Completed post-sec. 0.003 0.238* 0.100** -0.020 0.238 (0.045) (0.131) (0.040) (0.095) (0.155) Years in
: Completed post-sec. 0.003 0.238* 0.100** -0.020 0.238 (0.045) (0.131) (0.040) (0.095) (0.155) Years in Ethiopia 0.003** 0.006* 0.008*** -0.001 0.006 (0.002) (0.004) (0.003) (0.004) (0.004) Agrees hosts culturally similar 0.021 0.047 0.020 -0.010 -0.030 (0.016) (0.039) (0.037) (0.051) (0.036) Region Fixed Effects Yes Yes Yes Yes Yes N 1667 1667 1667 1667 1667 Source: World Bank Staff based on SESRE 2023. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 123 Table D.20: Regression
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.
21 Results suggest that 66 % of the returnees trust the Malian police and army most when it comes to providing security in the North. Almost half believe that the Malian army is brave and well trained. The vast majority of returnees believe that the government ’ s policies regarding reconciliation, security and social cohesion are good or very good. They also support the government ’ s approach towards decentralization and providing infrastructure such as access to potable water and electricity. As the next section will illustrate this differs strongly with the opinions of refugees. 6. Prospects for Peace IDPs, refugees and returnees have comparable opinions with regard to the requirements for peace: (i) addressing the ongoing crisis, (ii) improving security and (iii) reconciliation. Although there is agreement on what needs to be done, there is little consensus on what happened during the crisis, who the culprits are and who the main victims. Figure 15: What is the most important problem the Government needs to resolve today? (%) Source: Listening to Displaced People Survey, 2014.
Our results suggest that any comparative assessment of hunger prevalence using HCES should clearly

from HCES, such as poverty counts and inequality measures (Beegle et al. 2012). The reason is that the

expected when measuring hunger directly from HCES and how some of these errors likely differ by

hunger numbers across the various arms of the survey experiment and verifies whether the magnitude

Agriculture Organization (FAO) of the United Nations in a series of reports tracking world hunger, with
Table 2 presents hunger estimates derived from HCES alone. The calorie measure displays a great

One of the arguments for favoring the HCES-direct method over the FBS-CV method is that it allows for

patterns of hunger are sensitive to the type of HCES that is used. For example, for each standard

our experiment to calculate a CV of calorie availability, following FAO (1996, Appendix 3). [11] Specifically,

we collapse the data to 10 deciles of daily per capita kilocalories available and then calculate the CV

FAO motivates these manipulations, which serve to lower the CV, by the desire to purge the CV of
3. Secondary outcomes among refugees: These include a broader set of variables that were grouped into 14 broad families in the PAP including: (1) dwelling characteristics and house- hold structure, (2) consumption and expenditure, (3) ﬁnancial participation, (4) earnings, labor, and occupational choice, (5) migration, (6) physical, mental health, and sleep, (7) marriage and fertility, (8) child outcomes, (9) social capital, (10) political attitudes, (11) time use, (12) education and cognition, (13) behavioral games and preferences, and ﬁnally, (14) speciﬁc COVID-19 related outcomes. The three primary outcomes noted above are a subset of these measures. Certain outcomes were collected in each of the three survey rounds, for instance, the food security measures (the number of meals eaten and the frequency of go- ing to bed hungry). Respondents also reported information on child school attendance, and completion of learning activities when schools were closed due to the COVID-19 pandemic.
We use the FAO Food Composition Tables for the Near East to convert daily food quantities into kilocalories; we then divide by the effective household size to get per capita daily caloric intake. [6]

The amount of assistance delivered during conflict is increas ing to support the growing share of the extreme poor living in these settings. However, little is known about how well assistance is targeted, which can have implications for conflict itself. Using a novel data source in Yemen that tracks food assistance, which accounts for the largest share of assistance in the country, the authors find that the share of households receiving food assistance significantly increased following the U.N. announcement of a food emergency and that the increases were larger in regions identified by the U.N. as being closer to famine. Furthermore, the increases in

This article investigates the change in humanitarian assistance following a particularly in teresting event- the announcement of a food emergency in Yemen in 2017. The United Na tions announced the 2017 Integrated Food Security and Phase Classification (IPC) in March

utilizes a novel mobile phone survey conducted by the World Food Programme (WFP) that pro vides high-frequency data on food assistance and food access that are regionally disaggregated.

_∗_ The authors thank the World Food Programme for sharing the data from the November 2017 Yemen mVAM survey and commend the team on the impressive data they have collected over the course of the conflict in Yemen. We thank the editor and two anonymous reviewers for excellent comments and suggestions.
To this eﬀect, we replace yh i with yh i / yi in equation (1) and proceed as outlined above. If migration decisions are based on relative rather than absolute income, then the coeﬃcients of eδs − eδi and (eηs − eηi) zh should be positive and signiﬁcant only when they are computed using yh i / yi. In addition to relative and absolute income diﬀerences, the analysis also examines the re- spective roles of various location characteristics such as housing and food prices, availability of public services, and density of human settlement. 8An alternative strategy for the estimation of pre-migration income distribution in cross-section data is sug- gested by Bayer, Khan and Timmins (2008). 11
1 POST-CONFLICT TRANSITIONS WORKING PAPER NO. 16 Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis * Håvard Hegre Centre for the Study of Civil War, PRIO (CSCW) Clionadh Raleigh CSCW, PRIO & University of Colorado at Boulder Abstract Why do larger countries have more armed conflict? This paper surveys three sets of hypotheses forwarded in the conflict literature regarding the relationship between the size and location of population groups: Hypotheses based on pure population mass, on distances, on population concentrations, and some residual state-level characteristics. The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events. The analysis covers 14 countries in Central Africa. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper develops a statistical method to analyze this type of data. The analysis confirms several of the hypotheses. World Bank Policy Research Working Paper 4243, June 2007 The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post- Conflict Transitions Project can be found at http: / / econ. worldbank. org / programs / conflict).
South Sudanese Addis Ababa All Refugees Discrimination/harassment in past year Victim of crime in past 2 weeks Figure 7.19: Discrimination and harassment Source: World Bank Staff based on SESRE 2023. 72 A ddressing the challenges refugees face in Ethiopia requires a concerted effort to promote their self-reliance, economic integration, and access to education. By leveraging data from initiatives like SESRE and adopting a comprehensive approach that considers the needs of both refugees and host communities, Ethiopia can maximize the benefits of hosting refugees while minimizing associated costs. The GoE has proven its strong commitment to protecting refugees, but the progressive policy framework has not yet translated into tangible socioeconomic outcomes for refugees. The encampment model previously followed in Ethiopia neglected how refugees affect socio-economic and environmental conditions of hosting communities, including the untapped potential for refugees to contribute to the local economy. Despite strong improvements in Ethiopia’s underlying legal framework to benefit refugee inclusion, and a strong international aid response, refugees still face various challenges accessing services and improving their socioeconomic outcomes. Refugees are unable to move to locations with better economic opportunities and require a work permit (which is difficult to get for work outside of refugee camps) to
thirty years of autocratic rule that followed the Congolese independence from Belgium colonial rule paved the way to a violent transition, culminating in two internationalized wars from 1996 to 1997, in the aftermath of the Rwandan genocide, and from 1998 to 2003. Since the end of the Second Congo War in 2003, eastern Congo has remained unstable and violent, with many domestic and foreign-backed armed groups – including elements of the state military, FARDC – using violence against civilians and each other (Autesserre 2010). The violence and instability have resulted in poor living conditions and regular forced displacement for Congolese civilians. This project focuses on three provinces of eastern DRC that are especially impacted by forced displacement and political violence: North Kivu, South Kivu, and Ituri. 4 These three provinces account for 4. 5 million out of an estimated 5. 268 million total (85 %) IDPs in DRC 2020 (UNHCR Operational Data Portal: Democratic Republic of Congo 2021). Other provinces not included in this study but hosting IDPs include southern and central provinces such as Kasai, Kasai-Central, Kasai-Oriental, Lomani, Sankuru, and Tanganyika. The analysis in this paper focuses exclusively on dynamics in eastern Congo where the authors collected data.
**3.3 Production Data for Economic Rate of Return Estimation**

Production data covering the five-year period from 2018 to 2022 were sourced from the Ministry of Agriculture's administrative records and validated through rapid field surveys covering 120 farms in the project area. The production data are available at commune level and capture planted area, harvested area, and yield by crop variety. A 12 percent farm-gate price increase was applied to the production data in the economic model to reflect the projected reduction in post-harvest losses attributable to improved storage infrastructure financed under the project.
Outcome Variable Collection Periods Basline Midline Weekly Endline Psychological Well-being PHQ9 X X Life Satisfaction Index X X Stress Index X X X Sociability (Total) X X X Sociability (Positive) X X X Self-Worth Index X X Locus of Control X X Allocation Decision Game X X Stability Index X X Physiological Well-being Index X X Gender Dynamics Gender Perceptions- Work X X Gender Perceptions- Violence (IPV) X X Financial Well-being Savings X X ∗ X Borrowing X X Economic Decision Making Risk Preference X X Time Preference X X Other Outcomes Cognitive Ability X X ∗ X Physical Health X X ∗ X Notes: The “ Baseline ” survey was conducted with respondents before treatment assignment was revealed. The “ Midline ” survey were questions asked immediately after treatment assignments were disclosed after the baseline survey, but before the work task had begun. “ Weekly ” surveys were conducted after each week of work (if any).
The relation- ships between displacement dynamics and perceptions of each manifestation of social cohesion are analyzed separately by running 12 models for each independent variable (6 regressions for each sample). The regressions are correlations and should not be interpreted causally. Hosting status and displacement ﬂows are likely related to perceptions of social cohesion in indirect ways and the structure of the survey data limit the ability to specify the channels through which these relation- ships run. Each regression controls for characteristics that may inﬂuence respondents ’ perceptions of social cohesion outside of the presence of IDPs or refugees in the local community such as province, gender, age, marital status, level of education, employment, and exposure to violence. 11Poll numbers correspond to the number wave in our larger project, as described and shown in Table 2. 28
26 Source: Listening to Displaced People Survey, 2014. 95 % of the refugees in Mauritania envision an independent or autonomous North and 26 % of the refugees in Mauritania even state the independence of Azawad (= the north) as a main condition for returning home. Stark differences can also be observed with regard to the discussion around a possible federalist solution for the North that was ongoing when the monthly phone interviews were conducted in October. As illustrated in the Figure 20 below, 80 % of the refugees in Mauritania support a federalist solution, while the majority of IDPs, returnees and refugees in Niger are not in favor. Of those who do not support a federalist solution (96 % of the IDPs, 88 % of the refugees in Niger and 95 % of the returnees), the majority of IDPs (61 %) and returnees (70 %) as well as 38 % of the refugees in Niger suggest decentralization as a possible solution to resolve the conflict. 13 % of the refugees in Niger also mention war and 27 % the integration of the North. Nonetheless, 49 % of IDPs, 86 % of returnees and 89 % of refugees believe a stable and sustainable peace accord can be achieved. by 4 % of the population living Timbuktu, 2 % in Gao and nobody in Kidal.
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 supervisors conducted reinterviews, consistency, spot-checking, and data syncing to the head office. Also, the statisticians from ESS branch offices were with the team all the time to support and monitor the fieldwork. The data collection system consisted of encrypted Android devices for prolonged usage in the field equipped with the chosen survey application and a GPS tracking application for EA delineations. Electronic data files were transferred daily to the ESS central office in Addis Ababa via the Annexes 96 secured link. The core team from ESS undertook field supervision and was responsible for the day-to- day field management. Also, the World Bank team undertook field supervision, providing on-time and on-the-spot guidance for the field teams whenever and wherever they encountered a challenge. (e) Challenges faced and lessons learned The SESRE served as a learning experience for including refugees in future rounds of the official household survey (HoWStat). Given the unique
For this study, data on the four types of capital are obtained for three periods - 1995, 2000 and 2005 and for 210 countries, from an updated database underpinning the wealth estimates of nations (World Bank, 2006; G. Ruta and K. Hamilton, personal communication, 2009). The data are measured in per capita values at 2005 constant prices. An econometric analysis is conducted for a panel data of capital, along with the data on the magnitude of natural disasters for the same 2 <!-- PAGE 5 --> periods. Because of limitations in the data on human capital related to education (HS), the intangible capital residual (HR) is used as a proxy measure of human capital in this study.
The report focuses on intent-to-treat (ITT) estimates, measuring the impact of offering volunteering opportunities and soft skills training independently of actual take-up. 9 We estimate the following individual-level intent-to-treat regression: 𝑌 ௜ ௧ ൌ 𝛼 + 𝜇 ௡ ൅ 𝛽𝑇௧ + 𝛾𝐷 ௜ + 𝛿ሺ𝑇 ∗ 𝐷ሻ ௜ ௧ + 𝜀 ௜ ௧ (1) where 𝑌 ௜ ௧ is the outcome of interest for respondent i in period t, 𝑇௧ is a post-treatment year binary variable, 𝐷 ௜ is a binary variable for being assigned to the treatment, and 𝜇 ௡ is a fixed effect for NGOs. 𝛼 represents the baseline average for the outcome of interest for non-selected youth. 𝛽 is the difference in after-and- before intervention in outcomes for non-selected youth. 𝛽 ൅ 𝛿 is the difference in after-and- before intervention in outcomes for selected youth. 𝛾 is the difference in 9 Due to some procurement delays that caused a big time-lag between baseline data collection and actual NGO project implementation, many of the volunteers who belonged to selected NGOs and who were randomly selected to participate in the impact evaluation study dropped out after their baseline data were collected and were replaced by other volunteers. Project monitoring data reveal that 23 percent of volunteers assigned to treatment did not actually end up participating in the NVSP. Given the relatively high number of non-compliance, we are unable to perform Local Average Treatment Effects (LATE) analyses to understand the impact of participating in NVSP on outcomes of interest.