ai4data/datause-dataset-rechunked huggingface dataset:
The observations of positive events contain more information than the non-event observations We therefore sample asymmetrically: We sample all of the transition events and 1. 0 % of the non-transition events. 3. 4 Disaggregated Independent Variables Local level data on land, population, and elevation is available in the geospatial format of raster files with a resolution of 1km. Using Geographic Information systems (GIS), attributes from raster and point data are associated with the grid square in which they lie. In this way, spatial data is georeferenced to a location that is defined by the grid cell. This process results in a data structure in which each row has within it combined information on a square defined by the grid, the national level information in which is it located, and () () () ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ ∑ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ∑ = = ∉ = t X t X t d d j j p j R i d j j p j w w t β β 1 1 exp exp at out breaks war a | square a in war Pr
The monthly precipitation data comes from the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric Association's Climate Prediction Center, is based on ground station measurements of precipitation over the period 1948-2001.

Soil data was obtained from FAO (2003). The FAO data provides information about the
major and minor soils in each location. Data concerning the hydrology was predicted from a
hydrological model for Africa (Strzepek & McCluskey 2006). The model calculated the
water flow through each district in the surveyed countries. Data on elevation at the centroid
of each district was obtained through GIS manipulation using data from the United States
Geological Survey (USGS, 2004). The USGS data are derived from a global digital elevation
model with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).

Weng F & Grody N, 1998. Physical retrieval of land surface temperature using the Special Sensor Microwave Imager. _Journal of Geophysical Research_ 103: 8839-8848. World Bank, 2003. Africa rainfall and temperature evaluation system (ARTES). World Bank, Washington DC.
Regional refugee response activities in the Eastern sub-region are coordinated through the Regional Refugee Response Plan, which aligns funding from bilateral donors and UN agencies against the Refugee Coordination Model's joint response priorities. The plan is revised annually following a joint needs assessment and endorsed by the Regional Refugee Coordinator. Implementation progress is tracked against the plan's indicators through a shared activity monitoring dashboard accessible to all participating agencies. The dashboard is updated monthly by designated focal points from each implementing partner.
In all speciﬁcations, conﬂict incidence correlates nega- tively with country-level economic performance. The estimated coeﬃcients of conﬂict incidence are statistically signiﬁcant and negative. We also ﬁnd that the coeﬃcients 7For a discussion see Henderson et al. (2012). In order to calculate light per capita we use popu- lation data that is provided by a World Bank dataset. 8The standard reference here is Miguel et al. (2004). Ciccone (2011) shows that high rainfall levels three years earlier seem to be best predictors of conﬂict in the reduced form. Miguel and Satyanath (2011) argue that lagged negative growth shocks are a predictor of conﬂict onset. In any case, there is no evidence from this literature that contemporaneous growth declines cause conﬂict. Bazzi and Blattman (2014) corroborate the view that the relationship between income shocks and conﬂict is not straightforward. They do not ﬁnd evidence of an eﬀect of price shocks on conﬂict onset and only weak evidence on incidence. 9Results from this are presented in the Appendix. 10We take the threshold from Mueller (2016) who shows that a threshold like this leads to a similar number of coded civil wars as the threshold of 1000 battle-related deaths often used in the conﬂict literature. In the context here, this is a conservative approach as it is not the threshold which yields the biggest diﬀerence between conﬂict and non-conﬂict countries. 10
would like to thank colleagues from RRS who provided invaluable comments on the various draft stages of this report: Bruhtesfa Mulugeta, Zewdu Bedada, Daniel Adefires, Anteneh Mekasha, Anteneh Gorfu, Yewulsew Nigussie, Fantaw Kabtamu, Biruk Kebede, Dr. Goitom Ademnur, Dr. Tagay Kelil, Daniel Darcha. Additionally, we acknowledge the Household Expenditure and Welfare Statistics core team at ESS for their exceptional work on the data collection, from the survey's inception to the report's culmination: Amare Legesse, Alemayehu Teferi, Efrem Belachew, Salah Yusuf, Seid Jemal, Hagos Haile, Zenaselase Siyum, Tsigab Halefom, Yirga Nigussie, Kassu Gebeyehu, Zemecha Abdella, Mengistu Abebe, Aklilu Fikre, and Sisay Guta. Our gratitude goes to Leslie Velez (Assistant Representative (Protection, UNHCR) for her unwavering support and encouragement throughout the whole SESRE process. We are particularly thankful to UNHCR colleagues Yonas Lemma, Yonatan Assefa, Michel Uwamahoro, Millicent Lusigi, and Mekdes Aschalew for enabling access to administrative refugee data and their support 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,
Afrobarometer finds that the vast majority of Malians want their country to remain a single and unified nation and that the attempt by armed groups to create a breakaway state in Mali ’ s northern territories is decisively rejected. See Afrobarometer Policy Paper 10 (Dec 2013). This difference with the Afrobarometer survey can be explained by the fact that the latter survey only focused on Malians inside the country and did not take the views of refugees into account. 5 93 2 6 86 75 20 2 3 94 Independence of the North Autonomy of the North Establish full government control over the North Figure 19: How do you envision the future of Mali? IDPs Refugees Niger Refguees Mauritania Returnees
Specifically, a 1 percent increase of the refugees ’ presence5 leads to a 2. 7 and 15. 9 percent increase in the diversification of livelihood activities as a secondary occupation and value of livestock product sale, respectively. It should be noted that this analysis is taking place during a period where refugees in Ethiopia were prohibited by law from seeking work outside designated camps. This has changed after 2019 because of the revised Ethiopian Refugee Law. These effects tend to be heterogeneous across regions and to a limited extent, vary depending on the gender of the household head. The negative effects tend to be concentrated in Gambella, a region that hosts most of the refugee population in Ethiopia and where the refugee population is as large as the population of the region. Overall, compared to women-headed households, households with a male head seem to benefit through increased diversification of activities as a secondary 4 Region refers to the administration level 1 from the Database of Global Administrative Areas (GADM). The nearest region to the refugee camp is identified as the one that has the shortest straight distance to the refugee camp among all neighboring regions in the major refugee source countries. 5 As explained above, refugee presence is the number of refugees (population) in the nearest refugee camp to the household location weighted by the household ’ s inverted distance to the camp.
The conflict in Sudan continues to steadily trigger waves of population outflows to
neighbouring countries. At the beginning of July, based on registration and government
statistics, almost 490,000 newly arrived refugees and asylum seekers were recorded in
neighbouring Chad, Egypt, Ethiopia and South Sudan, and almost 142,000 South
Sudanese were recorded as returnees in their country of origin.

_[Source: UNHCR Sudan Situation, Operational Data Portal https://data.unhcr.org/en/situations/sudansituation](https://data.unhcr.org/en/situations/sudansituation)_

Within Sudan, the internal movement of refugees fleeing insecurity and active conflict
continued unabated, particularly from Khartoum, which traditionally hosted the highest
numbers of refugees, mainly from Eritrea and Ethiopia [4] . UNHCR estimates that more than
187,000 refugees may have left their areas of residence to seek safety in other regions of
Sudan unaffected by the conflict. UNHCR continues to work with concerned authorities,
including the Commissioner for Refugees (COR) to identify their locations to provide the
needed support. The tracking and registration so far conducted highlighted how White Nile
State has been one of the main areas of initial destination, followed by other States of East
Sudan, notably Gedaref and Kassala, where refugees continue to seek shelter in the
existing sites, as well as the Red Sea State.
Bank and Israel and occupied territories strongly suggest that commuters would be differently affected during the pandemic when border closures were enacted. Another unique feature is the presence of a large refugee population in the West Bank and Gaza. However, it is important to note that refugees in this context are de- fined quite differently from other contexts. Here, not only the individuals immediately displaced are considered refugees, but also their patrilineal descendants, even if born many decades later. In particular, the LFS dataset follows the United Nations Relief and Works Agency (UNRWA) definition of refugees, which is “ persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948, and who lost both home and means of livelihood as a result of the 1948 conflict, ” as well as “ the descendants of Palestine refugee males, including adopted children ” (UNRWA, 2023). Consequently, most refugees are indistinguishable in socio-economic outcomes and labor market behavior from non-refugees. However, residence in refugee camps does make a significant difference. As of 2019Q4, 5 % of the West Bank ’ s residents live in refugee camps, as do 14 % in Gaza.
The cross-dimensional poverty cut-off is defined as k = 50 %, with those deprived in half or more of the weighted indicators identified as multidimensionally poor. 4. Data Data on forcibly displaced populations are scarce, with many household surveys excluding refugees and IDPs from the sample framework. To ensure that MPI results are representative of these communities and that they can be disaggregated for comparative analysis, an initial review of possible data sets was conducted. Feasibility was determined based on the availability of sufficient sample sizes for forcibly displaced persons for quantitative analyses, as well as inclusion of many of the indicators (on health, education, living standards, etc.) 14 A household is deprived if the respondent reports feeling moderately or very unsafe when alone at home, walking alone after dark, or walking around during the day. In Sudan, the indicator on the ‘ feeling safe from crime and violence when at home ’ was not available, and the indicator only considers answers to the questions on safety when walking alone. 15 Unprotected dug well, unprotected spring, carts with tank, tanker-truck, surface water, or other are considered as unsafe water sources according to international guidelines. See https: / / washdata. org / monitoring / drinking-water. 16 Pit latrine without slab, bucket, hanging toilet, and no facility (open defecation) are considered as unimproved sanitation facilities according to international guidelines. See https: / / washdata. org / monitoring / sanitation. 17 According to the ILO definition, those who did not participate in employment in the last four weeks (and have no work to return to) are actively looking for work and are available to start, or those currently waiting to start work are classed as unemployed. See https: / / www. ilo. org / ilostat-files / Documents / description_UR_EN. pdf.
5. Time Use and Child Labor: The fifth module examined how children spend their leisure time, their involvement in child labor, and interactions with peers. 6. Pro-social Preferences and Migration Outlook: The sixth module concentrated on adolescents ’ pro-social behaviors, such as altruism and trust, and explored their expectations and intentions regarding migration. 7. Socio-emotional and Mental Health: The final module involved the administration of various scales to assess socio-emotional well-being and mental health, includ- ing trauma, behavioral problems, anxiety, and depression. The scales include the Trauma Symptom Checklist for Young Children (TSCYC), Strengths and Difficulties Questionnaire (SDQ), General Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). All these scales and the corresponding outcomes that we evaluated are described in the next subsection. The survey also employed the Peabody vocabulary test to evaluate the cognitive devel- opment of all participating children and adolescents. A summary of the survey modules is depicted in Table A. 1. III. C Sample comparability While Medell ´ ın ranks as the third city with the highest migration in Colombia, it is crucial to recognize the degree to which migrants arriving in the city differ from those migrating to other regions in Colombia.
25 contributions to the local communities. More than other studies, this analysis points to the transfer of physical and human capital by refugees as an important source of benefits for the local economies. Interestingly, Kreibaum (2016) provides a more quantitative approach to the issue by assessing the impact of an increase in the presence of Congolese refugees on the hosting population in the Southern and Western parts of Uganda. The results indicate a positive ‐ although small in magnitude ‐ impact on the hosts ’ welfare (consumption per adult equivalent) but with distributional effects. Those depending on wage income and transfers experienced a deterioration in welfare, suggesting labor substitutability with rural landless workers. That seems to constitute a commonality with the Tanzanian case study. In addition, increase in the provision of private education services are also found, which is consistent with the move to the so ‐ called self ‐ reliance strategy in Uganda (see below). A major contribution of this paper is to contrast these results to the Ugandan households ’ perceptions in local communities. Conditional on assuming a common trend (that could not be tested with the available data), people are found to perceive their living conditions as having worsened off in areas with a higher number of refugees.
Yit is the outcome of interest for individual i and time t. Treati is an indicator which is equal to 1 for treated individuals and 0 otherwise. Postt is an indicator equal to 1 for midline observations and 0 for baseline. Xit is a vector of controls at baseline (t = 0), including individual characteristics (education, age, current pregnancy, marital, parental, and orphan status) and household characteristics (sex of household head and household size). β1 is the coefficient of interest that defines the “ impact ” of the program on individuals in the treatment group. The model also includes dummy variables for the communities where the program was implemented (and where the trainees resided) as well as the program track (business skills or job skills) to which the respondent was assigned. Finally, in order to control for household wealth, we compute an index based on household asset ownership at baseline using multiple component analysis, similar to the method described in Filmer and Pritchett (2001). After constructing the index, which includes thirteen household assets and six indicators of housing conditions, we control for the quintile of household ’ s overall asset position in all regressions. We augment this basic specification with an individual fixed effects model and find that the results are almost identical.
4 household displacement and the destruction of household dwellings during the violent events. We are also able to measure conflict intensity across time – including peaks of violence at various stages of the conflict – at the district level from event data on violence intensity during the conflict in Timor Leste. We focus on primary school effects because only a small percentage of the Timorese population attended secondary school. Our results show mixed evidence for the impact of violent conflict on educational outcomes. Mirroring some of the findings of Bellows and Miguel (2006) and others, we find evidence for a rapid recovery of the education sector in Timor Leste, and of educational outcomes, particularly for girls. However, in line with emerging results in the micro-level literature, we find that the 1999 wave of violence in Timor Leste – as well as peaks of violence in the 1970s and 1980s – resulted in negative effects on primary school attendance and attainment. This effect is particularly strong for boys. We attribute the first result to a process of educational catch-up among girls in Timor Leste that started before the conflict and continued despite the conflict.
13 Where 𝑌𝑌𝑖𝑖 represents one of the indicators of social cohesion explained above, 𝛿𝛿𝑗𝑗 is the province indicator, 𝑅𝑅𝑐𝑐 is the share of returnees in the community, 𝐻𝐻𝑖𝑖 indicates a series of household level controls and 𝐶𝐶𝑐𝑐 are a series of community level of controls. In the main regressions we estimate the share of returnees in the community, using the information from the survey (i. e. share who are returnees), but in the robustness section we show that results are robust to the use of an alternative indicator in which the information is provided by a community leader. The Appendix (Table A2) includes the descriptive statistics for the control variables. We present results for the full sample and divided by communities with lower / higher ethnic diversity, less / more pre-1993 war land availability and better / worse attitudes towards return. In the robustness checks we also present the results if we limit the analysis to stayees only. Limiting the sample in this way does not affect the main results of the paper. 4. 4 Identification As mentioned above, Tanzania mandated the return of all Burundian refugees from the 1993 conflict. Returnees also had a very strong incentive to return to their communities of origin as this was the place in which they were entitled to land, a very scarce resource in the country.
Page 16 of 51 Figure 11: Education profile of Temporary employees. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys However, it should be noted that the improvement of the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in Annex II of the paper. Figure 12 presents the Kernel distribution of the labor income per working hour by country and type of employment. When we analyze what has happened at the salary level and distribution in the period under analysis, two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by these economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases.. 6 The second trend identified as generalized in the countries of study is the increase in the variance of the wage distribution. In fact, in most of the countries considered, non-standard employment wages currently show a significantly greater dispersion than that registered a decade ago.
mean CEI over our sample period. Since return decisions are likely impacted by the persistence of conﬂict, we classify districts into high and low conﬂict districts, using the top 10th percentile of the mean CEI as a cut-oﬀpoint. 11 We will use this classiﬁcation to explore the extent to which return decisions of key social and demographic groups are impacted by the persistence of conﬂict. Survey of refugees in Lebanon and Jordan: In a survey of 900 Syrian refugees in Jordan and Lebanon, we randomly varied the details of the scenario or vignette presented to a given individual respondent. Some refugee families are certainly more predisposed to wanting to return than others. By describing hypothetical scenarios, but ones which hit fairly close to home, and varying key factors within those scenarios should help us identify what factors are important to many refugee families when deciding whether to return. For all respondents in all vignettes, we asked “ How likely is this family to return to Syria in the next 2 months? ” where the respondent could answer using a Likert scale, ranging from “ Very likely ” to “ Very unlikely ”. For the analysis below, we use an indicator which is equal to one if a respondent says the family is either very likely or likely to return, and 0 if the respondent says neutral, unlikely or very unlikely.
Self-reported data from this survey suggest that firms in Dar es Salaam, Dodoma, and Tanga incurred at least $7.6 million in direct losses and damages due to flooding on business premises in 2018.

The data collected through this survey suggest that the primary reasons for general supply and delivery delays in Tanzanian firms are upstream supply chain issues.

Rentschler et al. (2019a) build a microdata set of about 143,000 firms to estimate the monetary costs of infrastructure disruptions in 137 low- and middle-income countries.

The lack of reliable and resilient infrastructure systems causes economic efficiency losses. A global study
by Rentschler et al (2019) highlights the substantial drag that unreliable infrastructure imposes on firms
in developing countries. In Tanzania, firms are incurring estimated utilization losses of nearly $670 million
a year (1.8 percent of national GDP) from power and water outages and transport disruptions. The firm
survey collected for this study allows us to estimate the share of these utilization losses that are caused
by natural shocks.

We use consumption and price data from the National Risk and Vulnerability Assessment (NRVA) 2007/08, conducted by the Government of Afghanistan Central Statistics Organization and the Ministry of Rural Rehabilitation and Development. The survey was administered between August 2007 and September 2008 and covered over 20,500 households (over 150,000 individuals) in 2,572 communities in all 34 provinces of Afghanistan.
This assistance is crucial and has been reported to support a meal a day, a better roof, and dignity for Syrians who have fled to Jordan. 8 The JD ‐ HV database has detailed information on expenditure, sources of income, and indicators of household ‐ level welfare, for example, as reflected by recourse to coping mechanisms, standard of accommodation, or access to water, sanitation, and hygiene (WASH). JD ‐ HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need. Verme et al. (2016) draw attention to the precarious circumstances of Syrian refugees in Jordan and Lebanon. Around 55 percent of refugees in Jordan are vulnerable to monetary poverty and more than half are vulnerable to food shocks. Family size increases the probability of being poor, with the poverty rate almost doubling if the size of the family goes from one to two members and increasing by 17 percent when the number of children increases from one to two.