ai4data/datause-dataset-rechunked huggingface dataset:
Deciding on whether to flee or not to flee a conflict (the migration choice) can be an individual or household choice and risk coping strategies may include temporary migration, shuttling between places, migration of only selected members of the households or migration of the whole household. This implies that individuals may stay put throughout the period observed, join or leave the household during the period, or have several episodes of out and immigration. Households may decide to leave and come back several times. In econometric terms, this means that longitudinal data may be left and right censored and have spells within. They are therefore the most complex set of panel data possible and require particular treatment of data and modeling. Survival or duration models can usually accommodate many of these complexities but it is very rare to find similar data sets used in published articles. Collecting such type of data is also not obvious, particularly if conflict is intense and survey areas cannot be reached. This is an issue where empirical economics could provide a real contribution by defining the optimal data format and adapting panel models to this format. Macro models Macroeconomics has attempted to model forced migration using models borrowed from the trade and economic migration literature such as the gravitational model (Echevarria and Gardeazabal, 2016) or used other macro models to test the impact of refugees on trade (White and Tadess, 2010). A more recent body of work is adapting trade models to take into account stochastic shocks in a dynamic framework (Cameron et al., 2007; Artuc et al., 2008). These are rational expectations models that are able to model the unpredictability of shocks, and recent work has tried to adapt these models to the context of violent
Based on specification (1), we estimate total 2013 GDP for Kenya at $26.8 billion (2005 USD), close to its actual level of $26.9 billion (WDI, 2014). On county-level, we find that Nairobi had the highest overall GDP in 2013 ($3.4 billion), followed by Kiambu ($3.0 billion), Nakuru ($2.3 billion), Nyeri ($1 billion), and Kilifi ($1 billion). Counties with the lowest GDP are the sparsely populated counties of Isiolo ($56 million), Lamu ($58 million), Samburu ($67 million), Elgeyo Marakwet ($108 million), and Tharaka Nithi ($109 million). Nairobi has the highest contribution to national GDP (13 percent), followed by Kiambu (11 percent),

Based on specification (1), we estimate total 2013 GDP for Rwanda at $4.58 billion (2005 USD), only slightly higher than the national-accounts estimate of $4.57 billion (WDI, 2014). On the district-level, we find that Gasabo district had the highest GDP in 2013 ($925 million), followed by the two other districts of Kigali (Kicukiro at $538 million and Nyarugenge at $371 million). Districts in the secondary urban centers come next: Rubabu ($219 million), Rusizi ($186 million), Huye ($166 million), and

As evident from the title of this paper, we also tried to estimate poverty levels and changes based on nightlights data. The estimated associations were however not robust, and more time and effort would need to be invested in examining this relationship in a more detailed fashion.

Based on national labor survey data (EPAM 2010), it appears that mean income from mining activity for each active worker is higher than the average income for all other activities, especially the agricultural and industrial sectors in the Sikasso region.
18 Figure 3 Job finding, job separation and the change in employment for young men who ever worked between 2016 and 2020 by half year and nationality conditional on informal employment in their most recent job (Seasonally adjusted) Source: Authors ’ calculations using SYPJ data Figure 4 Job finding, job separation and the change in employment for Syrian young men who ever worked between 2016 and 2020 by half year and residency in camp (Seasonally adjusted) Source: Authors ’ calculations using SYPJ data
1 Introduction The political and economic crisis in Venezuela has given rise to a massive exodus, esti- mated at 4. 3 million people (as of the end of 2019). Many Venezuelans sought refuge in neighboring countries. It is estimated that 1. 5 million Venezuelans had settled in Colombia by the end of 2019, and over 2 million elsewhere in Latin America. Naturally, Venezuelan migrants have sought employment in the host countries but, because of their recent arrival and their transient status, our knowledge of their working conditions is scant. Our paper aims to ﬁll this gap focusing on Ecuador, where almost 400, 000 Venezuelans have settled and many more have transited through the country on the way to other destinations. More speciﬁcally, we provide the ﬁrst analysis of the labor-market conditions of Venezuelan migrants in Ecuador, based on a new nationally representative survey of this population that also includes information on the Ecuadorans living in the same localities. The survey (known by its Spanish acronym EPEC) was promoted by the World Bank and the government of Ecuador and implemented during the summer of 2019. The design and scope of the survey are unique in the context of the countries across Latin America hosting Venezuelan migrants.
The 2023 survey collected data on 1,980 rural enterprises across nine provinces using a stratified random sample drawn from the business registration database maintained by the national statistics office. The 2023 survey questionnaire covered enterprise age, legal form, employment, revenue, access to credit, and adoption of digital technologies. Due to budgetary constraints, the 2023 survey did not include a module on international trade linkages; this limitation is noted where relevant in the interpretation of findings on market integration.
The World Bank's online screening tool classified this operation as High Risk for GBV/SEA/SH due to the presence of civil works in isolated rural communities and a history of gender-based violence documented in the project area. In response, the project's ESMF includes an expanded GBV annex specifying minimum requirements for contractor codes of conduct, confidential reporting channels, survivor support services, and community sensitization activities. These requirements are contractually binding and subject to third-party verification on a biannual basis.
**Appendix E: LFS Harmonization Across Survey Rounds**

The LFS underwent a questionnaire redesign between round 4 (2015) and round 5 (2016), introducing changes to the employment status classification and the self-employment income module. To enable pooled analysis across rounds 1–7, we harmonize the LFS employment status variable using a crosswalk table developed by the national statistics office. The harmonization reduces the number of employment status categories from seven (rounds 5–7) to four (rounds 1–4), which is sufficient for the binary employed/not-employed distinction used in our main analysis.
in Bangladesh). The data set we use does not cover the 5. 1 million Palestinian refugees who are under the man- date of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UN- RWA). The deﬁnition of Palestinian refugees in international law is distinct from other refugees. Palestinian refugees are people “ whose normal place of residence was Mandatory Palestine be- tween June 1946 and May 1948, who lost both their homes and means of livelihood as a result of the 1948 Arab-Israeli conﬂict ”. Importantly, their patrilineal descendants are also considered refugees regardless of citizenship (UNRWA 2009). For the purpose of the analysis, we do not include Internally Displaced Persons (IDPs), who are deﬁned as “ persons who have been forced or obliged to ﬂee or leave their home or place of habitual residence, in particular as a result of or in order to avoid the effects of armed conﬂict, situations of generalized violence, violations of human rights or natural or human-made disas- ters, and who have not crossed an internationally recognized border ” (United Nations 2004). The categories “ returnees ”, “ Stateless ”, and “ Others of concern ” are also not included.
protracted nature of refugee hosting in Ethiopia, it is essential to better understand how vocational training programs and cooperatives can become self-sustainable, especially in the more remote border regions with poor market linkages. Box 3.3: Refugee Vocational Training and Cooperatives Jobs and Livelihoods 35 In line with high reliance on remittances, labor force participation and employment rates among OCP refugees are low, with rates similar for male and female refugees. Table 3.2 presents labor force participation (LFP) data; among those aged 15-64, LFP is 46 percent for refugees and 66 percent for hosts. This increases to 67 and 72 percent when you use the relaxed definition of unemployment, reflecting the large number of OCP refugees who say they are ready to work but are not actively searching. The unemployment rate is an astonishing 63 percent for refugees relative to 12 percent for hosts, and it increases to 75 percent under the relaxed definition. Only 17 percent of OCP refugees work, and 23 percent are inactive and not in school. Female refugees have similarly large rates of inactivity and unemployment, and the total share working is similar to male refugees. Female refugees also have high rates of self-employment relative to female
obtained from the Center for Research in Security Prices (CRSP). Stocks with prices less than

of all stocks included in CRSP.

Firm characteristics are obtained from COMPUSTAT. Size is computed as the log of total assets.

of stock returns on the Fama and French (2015) 5 factors.
The LFS is a rotating panel in which households are interviewed for two consecutive quarters, rotated out for two quarters, and then re-interviewed for two further quarters (a 2-2-2 rotation scheme). Because the LFS does not follow individuals across household moves, our analysis is restricted to non-movers — defined as individuals observed in the same dwelling across all four LFS interview waves. This restriction reduces the analytical sample from 18,400 to 14,230 individuals but eliminates the bias that would arise if mobility decisions are correlated with labor market outcomes.
Figure 18: Significant Refugee Returns by Country of Origin 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Countries selected based on their cumulative returns of refugees over the period 1991-2015. Return does not necessarily lead to the full reintegration of a person into their home country or area of origin. In the absence of global data on the success of reintegration following return, data on returns appear to be taken as indication of sustainable return. In reality, many returnees face impediments to reintegration and continue to have specific economic and social vulnerabilities linked to their displacement. They may not be able to reclaim land, access sufficient financial resources (e. g. accumulated during their displacement) or reestablish social networks in areas of origin, which are critical factors for successful reintegration (World Bank 2015). Sustainable refugee return is therefore not a one-off event but a process that provides returnees with adequate safety, housing, livelihoods and services that address their specific vulnerabilities and reduce the likelihood of secondary displacement (World Bank 2015). Figure 19: Voluntary Returns of Refugees 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of refugees and people in refugee- like situations protected or assisted by UNHCR.
Figure 20: Returns of IDPs Protected or Assisted by UNHCR 1993 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of IDPs and people in IDP-like situations assisted and protected by UNHCR. Consequently, the average length of protracted refugee situations has increased over the past two decades according to UNHCR estimates (see Table 2). UNHCR estimates that the average length of ongoing
The effort was inspired by the U.S. Drought Monitor (USDM) and involved USDM experts and experienced officials from Mexico, which had recently implemented a similar drought monitor process.

The World Bank performed a disaster risk financing diagnostic to gain a more nuanced understanding of the economic impacts of the drought throughout the Eswatini economy.

World Development 157, 105932
Anseeuw, W., Bache, W., Bru, T., Giger, M., Lay, J., Messerli, P., Nolte, K., 2012. Transnational land deals for agriculture in the
global South: Analytical report based on the land matrix database.

by SGC** 2015 1,935 1,530 28,345 0 0.54 2016 2,817 1,916 42,754 0 0.87 2017 3,388 2,139 43,358 0 0.84 2018 5,108 3,519 65,859 0.06 0.85 2019 8,710 5,327 83,269 0.51 0.69 2020 2,431 847 13,408 0 0.50 2021 7,805 5,232 58,442 0.09 0.38 Pre-Prozorro 7,094 4,774 54,425 0 0.41 Prozorro 711 458 4,017 1 0 2022 2,041 1,043 9,964 1 0 Total 34,235 21,553 345,399 0.22 0.61 _Source:_ Own computation from SGC and Prozorro data as described in the text.

0.322 0.255 0.469 0.202 0.174 0.319 0.203 Contract length (years) 8.61 8.33 9.51 8.84 9.04 7.98 7.08 _**Land use**_ Crops 0.48 0.48 0.47 0.65 0.66 0.61 0.69 Pasture 0.35 0.34 0.36 0.
.04 7.98 7.08 _**Land use**_ Crops 0.48 0.48 0.47 0.65 0.66 0.61 0.69 Pasture 0.35 0.34 0.36 0.29 0.27 0.37 0.38 Forest 0.15 0.15 0.13 0.09 0.09 0.08 0.06 _**Distance in km to**_ Main road 8.65 8.48 9.22 9.31 9.30 9.32 9.38 Nearest city 15.64 15.66 15.58 16.58 16.59 16.57 17.16 Grain elevator 13.39 13.55 12.84 13.56 13.50 13.84 14.43 Kyiv 297.22 295.17 304.04 312.33 309.85 323.26 334.55 _Source:_ Own computation from SGC and Prozorro data as described in the text.
Both treatment and control group respondents report equally high confidence in their ability to return to school “ should [she] decide to do so. ” These findings from the quantitative impact evaluation complement the results from a set of qualitative focus group discussions that were held with participants at the end of the 6 months of classroom training. Twenty-five percent of the trainees from Round 1 participated in a total of 34 focus group discussions that covered a variety of topics including their satisfaction with the program and their empowerment in both social and economic realms. The trainees overwhelmingly voiced a high degree of satisfaction with the training, and trainers commented on how the motivation or “ seriousness ” of the participants grew over the 6 month period. The trainees credited the transport allowance and free childcare in particular as features that facilitated their full participation; as one trainee commented, 22 Questions adapted from the Adolescent Self-Regulation Inventory, developed and validated for youth in the United States by Moilanen, 2006. The questions were revised and translated into an 11-item for the Liberian context. In the future, we plan to conduct basic testing on this scale on internal consistency and reliability. 17
To complete this analysis with information on how these losses are distributed in the population and especially on the poorest, a survey was conducted with informal dwellers affected by the 2005 floods. It was found that the aggregated losses they suffered from are about $250 million (for total losses due to the 2005 flood of about $2 billion), but the relative impact on their savings and consumption was extremely large, with average capital losses of the same order of magnitude than their average total savings (in other terms, their savings were totally wiped out by the event).

<!-- PAGE 12 --> _Figure 2: Roofer wages in an area where losses have been significant after the 2004 hurricane season in_ _Florida. Data from the Bureau of Labor Statistics, Occupational Employment Surveys in May 03, Nov 03,_ _May 04, Nov 04, May 05, May 06, May 07._

The box figure illustrates the effect of insurance penetration [13] on the household's budget, for a July 2005 like flood estimated using the ARIO model. Three scenarios are included: (i) γ=0, equivalent to the absence of insurance system, but with an access to credit; (ii) the current value of flood insurance penetration estimated by RMS (γ=0.08 for households, γ=0.15 for firms); and (iii) γ=1, representing the situation where all the reconstruction is paid for by insurance.

<!-- PAGE 5 --> methodologies and approaches, and they often reach quite different results. In the US, for instance, a systematic analysis by (Downton and Pielke, 2005) showed that loss estimates differ by a factor of 2 or more for half of the floods that cause less than $50 million in damages.

One of the appealing features of night lights data is their availability on every level. Night lights
data are measured on the so-called 30 arc sec level, corresponding to roughly 1 square
more for half of the floods that cause less than $50 million in damages.

One of the appealing features of night lights data is their availability on every level. Night lights
data are measured on the so-called 30 arc sec level, corresponding to roughly 1 square kilometer
at the equator.
most land cover by built-up and shops, and nearest to Zone capital city, respectively. All estimates are controlled for refugee camps. Standard errors in parentheses. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001 Variables Basic Model Local Market Proximity Market access Model I Model II Model I Model II Annexes 119 Table D.16: Proximity and market accessibility effects on engagement in agriculture activity: logit model Variables Proximity Market access Model I Model II Individual feature Male 0.1979*** 0.2016*** 0.1998*** (0.0289) (0.0286) (0.0297) Age -0.0307*** -0.0294*** -0.0296*** (0.0083) (0.0081) (0.0088) Age squared 0.0004*** 0.0004*** 0.0004** (0.0001) (0.0001) (0.0001) Some primary -0.1403*** -0.1436*** -0.1484*** (0.0305) (0.0298) (0.0307) Household feature HH size: member age [15,29] -0.0157 -0.0171 -0.0186 (0.0118) (0.0113) (0.0117) HH size: member age (30,44] 0.0422* 0.0414* 0.0320+ (0.0179) (0.0178) (0.0185) HH size: member age (45,64] 0.0187 0.0278 0.0314 (0.0221) (0.0226) (0.0237) HH access electricity -0.2980** -0.2655** -0.2658** (0.0929
] 0.0187 0.0278 0.0314 (0.0221) (0.0226) (0.0237) HH access electricity -0.2980** -0.2655** -0.2658** (0.0929) (0.0884) (0.0974) Proximity and market access Level two 0.2065*** (0.0607) Level three 0.2358** (0.0788) Level four 0.3559*** (0.0769) Distance to Zone city (Km) 0.0022*** (0.0006) Market accessibility indicator -0.0792* (0.0330) Observations 742 737 742 Chi-square test 0.0000 0.0000 0.0000 R2 0.2517 0.2435 0.2128 Source: World Bank Staff based on SESRE 2023. Standard errors in
Regrettably, until now almost all enrollment numbers cited have been based on establishment surveys which do just that. These data sources show that around 200, 000 children were enrolled full-time in madrassas before 2001. Since 2001, our school census suggests that these numbers may have increased somewhat, although the experience varies across districts. To put this number in context, total primary enrollment (grades 1-5) in public and private schools stood at 17. 4 million in 2003 (Government of Pakistan, Ministry of Finance, 2003). The choice of madrassa schooling viewed as either the percentage of eligible children or the percentage of enrolled children, is statistically insignificant for the average Pakistani household. Enrollment in madrassas accounts for approximately 0. 3 percent of all children between the ages of 5 and 19. Given that the overall enrollment rate for this age group is roughly 42 percent, this represents less than 0. 7 percent of all enrolled children, an order of magnitude less than the 33 percent cited by the International Crisis Group report (2002). 3 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 to provide statistics for private versus public enrollment. 4The PIHS is the equivalent of the widely used Living Standard Measurement Surveys (LSMS) implemented in various countries. See http: / / www. worldbank. org / lsms for extensive notes on the 1991 PIHS. See also www. statpak. gov. pk for information on the census and the Federal Bureau of Statistics data.
Figure 19: Trust and Conﬂict in the Cross Section The problem with a lack of trust and strong group identities is that they penetrate and pervert formal institutions. The ethnic politics analyzed by Burgess et al. (2015) is just one example. Shayo and Zussman (2011), for example, use data from Israeli small claims courts to show that Arab and Jewish judges displayed signiﬁcant judicial ingroup bias. Furthermore, this bias is strongly associated with terrorism intensity in the vicinity of the court in the year preceding the ruling. Conﬁdence-building is also a crucial ingredient for the establishment of a fertile investment climate, which in turn is a trigger of economic development post conﬂict. This is the core message of the World Bank Report by Mills and Fan (2006). An important role of increasing trust doubtlessly goes to the media. It has been shown, for example, that hate radio in Rwanda played a critical role in the extent of ethnic violence during the genocide. 59 Other research has shown that media coverage can have strong eﬀects on political preferences more generally. 60 Perhaps the most direct proof of the crucial role played by the media in the post-conﬂict situation comes from DellaVigna et al. (2014). The authors exploit variation in radio reception of na- tionalistic Serbian radio in border regions in Croatia.
NGOs. Initiatives such as JIPS, a collaborative project of UN and NGO actors, have also been established to support governments and operational organizations to design and implement data collection processes. There are a variety of data sources for generating statistics on forced displacement, each of which has strengths and weaknesses. Despite the significant challenges, large amounts of data are collected and disseminated every year. The main data sources and methods for the generation of statistics on forcibly displaced populations include: (a) registration of refugees and asylum-seekers; (b) registration of IDPs; (c) profiling of IDPs; (d) population movement tracking systems; (e) national population censuses; (f) sample surveys; (g) border crossings; (h) administrative records and registers; (i) general population registers; and (j) a variety of estimation methods for producing statistics when adequate and reliable data on individuals are unavailable (UNSD 2014). Several of these data sources might be used together to triangulate estimates of stocks and flows for a particular displacement situation.
data are collected from the World Bank's World Development Indicators (WDI). In this context, TFP growth is:

Oil Metal **Trade Openness** Percent of GDP 1990 - 94 64 95 65 56 2010 - 14 77 94 93 70 **Herfindahl Index** Product Concentration 1990 - 94 0.41 0.84 0.69 0.29 2010 - 14 0.30 0.67 0.46 0.21 Market Concentration 1990 - 94 0.20 0.30 0.08 0.19 2010 - 14 0.20 0.15 0.28 0.18 **Natural Resources** Percent of GDP 1990 - 94 17.0 46.9 26.8 9.8 2010 - 14 13.2 43.0 16.3 8.9 Percent of total exports 1990 - 94 0.82 0.99 0.95 0.78 2010 - 14 0.73 0.89 0.71 0.72 _Sources: WDI, and author's calculations using WITS, COMTRADE_