ai4data/datause-dataset-rechunked huggingface dataset:
described above, HSP households received not only full rental subsidies for 9 to 18 months but also physical upgrades to the home environment. These proximate outcome measures can be seen as capturing successful program implementation. To measure physical improvements to the shelter, we constructed an index of self-reported ﬂoor, roof, and wall quality; electricity and water access; and crowding. Housing expen- ditures were measured in two ways: First, in the midline phone survey, respondents re- ported their total monthly housing expenditures, including the payments provided by the implementing organization. Second, in the endline in-person survey, once assistance had ended, participants reported their monthly out-of-pocket housing expenditures (excluding the amount paid by the implementer). The latter is our preferred measure of the direct effect of the program on housing expenditures, because it allows us to test whether treated house- holds experienced rental savings. Because the endline measure was collected after assistance ended, any observed expenditure reductions would most likely be an underestimate of the beneﬁts experienced during the program. Recall that HSP also required the landlord to not raise rent for an additional year after the assistance ended, which could help account for any persistent effects on housing expenditures in the treatment group. 2.
In order to take account of the building types in Indonesia we use  information from the USGS building inventory for earthquake assessment, which provides  estimates of the fractions of building types observed by country; see Jaiswal & Wald (2008).  The data provides the share of 99 different building types within a country separately for  urban and rural areas, where - due to lack of other information - a homogenous distribution  of buildings is assumed.  Then fragility curves by building type are derived from the curves constructed by the  Global Earthquake Safety Initiative project; see GeoHazards International and United Na tions Centre for Regional Development (2001).

In order to use these vulnerability curves for Indonesia we first allocated each of the 99  building types given in the USGS building inventory to one of the 9 more aggregate cate gories of the GESI building classification.

of ongoing eruption as a threshold of when to include an eruption in the data set or not.  Second, images containing sulphur dioxide data from the OMI/AURA satellite are used to  model the intensity of the eruptions.

To construct an inundation map of the affected areas, a  map based on MODIS satellite pictures from Anderson _et_ _al._ (2004) is used with spatial al gorithms to detect the difference in color between inundated and non-inundated areas.
ASPIRE does not cover all countries in the world. To expand the set of countries in this study, we proceed as follows. First, we estimate an econometric model that explains the share of income from transfers, for poor and nonpoor people in each country by the share of GDP spent on social protection at the national level (from the International Labour Organization), an indicator of government effectiveness (from the Worldwide Governance Indicators), and whether the country used to be in the Soviet Union. (Other explanatory variables we tested include World Bank income category of the country, region, and GINI coefficient, but they turn out to have no explanatory power.) This provides an estimate of income from transfers in 26 additional countries (from 98 using only ASPIRE data).

Diversification also comes from financial inclusion. We use the fraction of the population with savings at a financial institution, from the Global Financial Inclusion Database (FINDEX, 2015). The Findex database provides these values for the bottom 40% and for the top 60%, which we use for poor and nonpoor people respectively. We assume that the fraction of income that is diversified increases by 10% for people who have bank accounts.

Rather, we proxy the ability of a country to borrow with sovereign credit rating, using the average (long-term) rating of from three agencies: S&P, Moody's and Fitch.

Our study takes advantage of newly available panel data from the Indian Human Development Survey (IHDS), which includes a question asking households to estimate the daily average duration of outages.
The univariate analysis showed that migrants on average move to destinations where they are on average less likely to ﬁnd people like them. The results presented in Table 4 present a diﬀerent picture. Conditional on the other regressors, the ethnicity and language proximity indices are signiﬁcant with the anticipated sign: social proximity between the migrant and the population of the destination district is higher than in alternative destinations. The religion proximity index is not signiﬁcant. Taken together, these results suggest that, conditional on material beneﬁts from migration, migrants prefer to move to a destination where they integrate more easily — and possibly enjoy network beneﬁts in terms of access to jobs and housing (Munshi 2003, Beaman 2006). 24
8 Flood return periods of 5, 10, 20, 50, 75, 100, 200, 250, 500 and 1000 are included in the Fathom data product.

We leverage a unique large-scale experiment on consumption measurement in Iraq designed for the Iraq Household and Socio-Economic Survey (IHSES) in 2012.

[1] In recall interviews, households are typically asked to report on food _consumption_ during the specified period and 1The World Bank's Living Standards Measurement Study (LSMS) survey finder includes a list of household consumption and expenditure surveys: of almost 90 surveys from more than 25 countries, more than 75 use recall.

(2017) compare recall questions on food spending from the Canadian Food Expenditure Survey to data from expenditure diaries.

The availability of repeated diary-recall measurements of food spending or acquisition is not unique to our setting, the Consumer Expenditure Surveys in the United States and the Canadian Food Expenditure Survey being notable examples.
We offer less restrictive assumptions and formal tests for these assumptions where data are available, provide more insights into the selection of control variables for model building, and offer simpler variance formulae. Our framework can be generally applied to imputation either from one survey 27 We also experimented with imputation from the HEIS into the DHS. However, one major issue is the latter survey ’ s most recent two rounds are in 2009 and 2012, which do not overlap with the HEIS, thus making it difficult to benchmark the DHS. We tried benchmarking both rounds of the DHS using the HEIS in 2010, and found a qualitatively similar decreasing trend in poverty across these two survey rounds. 28 It is also more demanding to make the distributions of the explanatory variables comparable for smaller population groups (e. g., as disaggregated by regional characteristics or other distributional characteristics such as quintiles) in surveys of different designs. We leave this extension for further research. 33
Ethiopia has experienced steady economic growth for much of the last two decades, but even before the country’s concurrent crises, economic growth did not transform the labor market structure. Between 2004 and 2020, Ethiopia’s GDP annual growth averaged 10 percent, helping to reduce the poverty by about ten percentage points. But during this period, the distribution of workers across sectors and geographies shifted very little. According to newly released 2021 Labor Force Survey (LFS) data, about 80 percent of Ethiopians live in rural areas, where roughly 75 percent work in agriculture. In urban areas, approximately 70 percent of people work in services. Nationally, self-employment accounts for about half of jobs, with unpaid family work second most common and wage work a distant third. Again, the urban context is different: wage employment is prevalent but often poorly paid. Over the last decade, negative repercussions from the concurrent overlapping crises have threatened this marginal progress. In 2020, the COVID pandemic closed markets, albeit relatively briefly, and this immediately decreased job opportunities. While most reentered the labor market, many changed their work situation, and some permanently exited the workforce or reduced working hours. At the same time, more and more people— rural women
9 Beans, for instance, which are one of the most important food purchases and items in Brazil, have a raw calorie intake of 306 kcal per 100 g in TBCA (Feijão, carioca), while in the prepared form, TBCA assigns 71 kcal per 100 g, assuming a composition of 50 percent beans and 50 percent water to the meal.

Table 2 shows the top 10 food groups in POF in terms of average calorie intake per 100g. It shows that
'Meat' is the most expensive food group, followed by 'Breads, Cakes and Pies'. On the other hand, 'Kitchen
Oil' is displayed as the most caloric food group, as expected. It is followed by 'Flour Derivatives' (which
includes pasta). The cheapest food group is 'Beans and Legumes', while the least caloric is 'Fruits'.
Therefore, the data - including our calorie mapping - show some expected patterns of caloric and price
distribution across food groups.

_Source:_ Own calculations using POF 2017/18 data and TBCA, based on food items categorizations by IBGE.

For this, we construct a consumption aggregate using the data from POF. The consumption aggregate is based on household expenditures on goods and services.
followed the same standards and procedures as the HoWStat. SESRE data was not collected alongside HoWStat due to security concerns at the time of data collection for HoWStat, especially in the refugee areas. The SESRE aimed to solve two problems: (i) gaps in data on the socioeconomic dimensions of refugees, and (ii) gaps in analytical studies presenting the socioeconomic outcomes of refugees and hosts. Lack of up-to-date evidence is a significant obstacle to designing effective policies and support for refugees and host communities. To this end, the availability of the data helps to analyze refugee hosting areas’ social dynamics and longer-term socioeconomic viability by focusing on the: (i) social impact of refugees on host communities, (ii) socioeconomic interaction, (iii) social inclusion, and (iv) social relations among refugees and between refugees and host communities. The data provides valuable information to development partners and governments to inform policies to facilitate refugees’ integration and improve their lives, along with refugee hosting communities. The SESRE covers all current major refugee camps: Eritreans, South Sudanese, and Somalis, as well as the out-of-camp refugees in Addis Ababa. In addition, the survey covers the respective host communities around the camps, including the host communities of Addis Ababa.
We test the two parts of the redistribution hypothesis using the most recent data from Luxembourg Income Study for 20 OECD countries covering the period 1967-2005 (total number of country/years is 110).

_Economy_ (Milanovic, 2000) and which for the first time used household level data derived from household income surveys to test the hypothesis, and in the process

individual national identification", which he argues, based on World Values Survey data, to be stronger among the poor voters, and which reduces their propensity to vote for

seemingly depending on author's preferences, availability of the data (OECD provides the 90-10, 90-50 and 50-10 gross wage ratios), or perhaps contingent on the formulation

is why, in their analysis of eight advanced economies, they use scores of "intended generosity" (developed by Scruggs, 2004) of the pension system, unemployment benefits, child benefits and social assistance.
the intensity of conflicts, and iv) population density in refugee host countries by region. Appendix B presents the list of variables along with description and summary statistics. 5. Empirical strategy To estimate the impact of refugee inflow19 on host community ’ s livelihood strategy choice, we use the following basic econometric model: 𝑌𝑌𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑅𝑅𝑅𝑅𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝜈𝜈 + 𝜀𝜀𝑖𝑖 (1) Where, 𝑖𝑖 indexes a household, 𝑌𝑌𝑖𝑖 is an outcome variable of interest (livelihood diversification or commercialization of agriculture), 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 is the measure of refugee inflow, i. e., the refugee population (average of 2017 and 2018) in the nearest refugee camp weighted by the inverse of distance of the household to the refugee camps, 𝑋𝑋𝑖𝑖 is a set of household controls, 𝜈𝜈 is kebele fixed effects, and 𝜀𝜀𝑖𝑖 is the error term. Several variables, from the DRDIP data set, were used as controls in our model.
**6. Conclusions**


This paper has used historical rainfall data to estimate a putative history of payouts on


Indian rainfall insurance policies. We have found that indemnities are concentrated in the

According to Census data (CNBS, 2001), shares of these crops in five of China's provinces

The climate data (monthly temperature and precipitation) were gathered from the National   Meteorological Information Center in China.  The data are based on actual measurements in 753   national meteorological stations that are located throughout China. The temperature and   precipitation data were collected from 1951 to 2001.

Socio-economic data come from China's National Bureau of Statistics (CNBS). The data   were collected by a highly trained, professional enumeration staff in 2001 as part of the annual,   nation-wide Household Income and Expenditure Survey (HIES). The data cover 45,700 farm   households in 4365 villages, 533 counties and 31 provinces.

account for soils, we downloaded a soil map from FAO's website. There are three major soil


types-clay, sand and loam soils. The final set of variables for our analysis was created by
to guide and define camp-specific cooking energy options. • In Afar, Gambella, and Melkadida, more than 382,000 refugees and 85,000 hosts have access to alternative cooking fuels and market-based clean electricity from solar-mini grids. • More than 1,739,726 seedlings were planted, 160m3 check dams were built, and 8 km of soil stone band were built to rehabilitate degraded land. Additional GoE GRF Pledges 2023 Climate Action: Protect and restore the environment, manage natural resources, afforestation of degraded lands, and expand renewable energy solutions for the benefit of both refugees and host communities. Recognize vulnerability of women to climate change, and prevent violence against women in all environmental policies and programs, and empower women to have agency and influence in environmental stewardship and adaptation to climate. Human Settlement: Transform selected refugee camps into sustainable urban settlements by enhancing the quality and availability of shelter, infrastructure, and public services, such as roads, electricity, water, sanitation, health, and education by aligning them with adjacent towns’ masterplan, by 2027. Inclusion of refugees into existing national systems: Enhance the capacity of GoE to include 1,000,000 refugees into the national Central Statistics Service (CSS), the national Gender-Based Violence (GBV) prevention and response programs, 814,000 refugees into
Since 2006, Rwanda is divided into 30 districts, which are the main service-delivery units. All
household surveys are representative at the district level, but there is no information on districtlevel economic growth. Estimating district-level growth based on nightlights is however
complicated by the low intensity of lights: Four districts did not emit any observable lights at all
during 2000-2011, and ten districts only have nonzero observations towards the end of the 20002011 period. As a result, we can only estimate nighlights-based GDP growth for 16 districts.

Notes: Dependent variable is the natural log of GDP in constant LCU. All specifications include country and
year fixed effects. Robust standard errors clustered by countries in brackets. ***: significant at 1%-level; **:
significant at 5%-level; *: significant at 10%-level. Data source: NGDC (2014) and WDI (2014).

In Column (3) we replace lights observed from space by data on electrical power consumption (in kilowatt hours, obtained from the World Development Indicators). As data on electricity consumption are only available for 22 countries in SSA, the number of observations drops from 966 to 425 (not all 22 countries have data for all 21 years). We find a strong and statistically significant association between electricity consumption and GDP in the reduced sample (elasticity of _0.37_ ). The coefficient is however considerably smaller than the one estimated between night lights and GDP ( _0.58_ ). This may potentially be explained by the relatively high use
Use of state level data on rental restrictions, together with a nationally representative survey from India, suggests that, contrary to original intentions, rental restrictions negatively affect productivity and equity.

1 Using census figures, Appu (1997) estimates that, to avoid having to give rights to tenants, landlords evicted about 30 mn tenants or about one third of the total agriculturally active population.

The data used in the analysis come from two rounds of NCAER's ARIS/REDS survey conducted in 1982 and 1999, respectively. This survey, the first rounds of which were implemented in 1968-71 to evaluate the impact of an agricultural development program, covers all of India's major states.

of observation 4980 1279 1613 670 1417 7476 1705 2479 1307 1985
_Source:_ Own computation from 1982 and 1999 ARIS/REDS surveys.
All values are in 1982 Rs with 1999 values having been deflated by state level deflators.
Integrating refugees into functioning national education systems can improve future outcomes for refugee children and their hosts (UNHCR, 2020; Piper et al., 2020; Abu-Ghaida and Silva, 2020; Crawford et al., 2015; Bilgili et al., 2019). As Chapter 2 highlighted, more than half of all refugees are children under the age of 15. Although over 70 percent of primary school age children attend primary education, they do not make it past primary education. Integrating refugee children into educational programs soon after their arrival in Ethiopia avoids the loss of valuable years of education and human capital accumulation, hindering prospects. It is critical to address the obstacles that hinder children from transitioning to secondary schooling, such as challenges in accessing school records, language barriers, or distance to schools. Supporting Regional Education Bureaus could increase the accessibility of secondary schools Policy Recommendations 74 Policy Recommendations to camp refugees.51 To improve the educational attainment of refugee children, the focus should be on increasing the number of qualified teachers in primary education, increasing the currently low compensation to incentive teachers with similar qualifications as nationals, improving primary-to- secondary transition rates, and reducing classroom overcrowding. Build an inclusive health system. Good health is an essential requirement
In settings with clearly distinguishable individual structures, these methods can also be reasonably accurate for the purposes of rapid estimation of displaced population (Checchi, et al. 2013). 101 The use of unmanned drones is also becoming more popular as the cost of this technology falls. This technique has been used by UNHCR to update its estimates of IDPs in Somalia ’ s Afgooye corridor (IDMC 2015) and by IOM to monitor disaster-induced displacement in Haiti, including the use of Unmanned Aerial Vehicles (UAVs) in collaboration with UNOSAT. (g) Open data initiatives. There are several initiatives to provide free and open data that enable Internet users to independently mine and analyze data and generate customized summaries, charts and visualizations. For example, the Bank has provided free, open access to its development data since the launch of its Open Data Initiative in 2010, however there is little open data on asylum-seekers, refugees and IDPs. JIPS has developed a web-based platform that allows users to explore, analyze and visualize profiling data online, and IDMC has 100 See http: / / www. flowminder. org /. 101 These methods are not effective in settings with connected structures, a complex pattern of roofs or multi-level buildings, as are prevalent in urban areas. Additionally, cloud cover and dense foliage can also obscure structures.
The data sources are (1) Romania MoF firm-level data for the period 2011-20, and (2) the World Bank Businesses of the State (BOS) database for Romania, and (3) the taxonomy of sectors developed by Dall'Olio et al.

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the period 2011 to 2020. It is based on financial statements and contains balance sheet information such as
firm tax identification number, year of incorporation, operating revenue, average number of employees,
number of employees at the end of the year, labor cost, fixed assets, total assets, amount of subsidies
received from the government, [6] 4-digit NACE industry code, and the county location of the firm.

The Romania MoF firm-level data did not include a variable that identifies the ownership status of the firm (whether the firm is an SOE), which is the key explanatory variable of interest.

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2014 428,618 1,031 0.24 86.8 13.2 25.2 71.2
2015 440,445 1,092 0.25 87.5 12.5 23.7 72.7
2016 457,273 1,136 0.25 88.1 11.9 22.9 73.7
2017 475,757 1,209 0.25 88.9 11.1 21.4 75.4
2018 491,289 1,277 0.26 89.4 10.6 20.5 76.4
2019 511,863 1,299 0.25 89.5 10.5 20.1 76.9
Total 4,027,783 0.24 87.6 12.4 23.7 72.8
Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. For purposes of providing the
summary statistics, the sample is restricted to firms with revenue and employment data. SOEs are defined as firms
where national or subnational governments have ownership stake of
.8
Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. For purposes of providing the
summary statistics, the sample is restricted to firms with revenue and employment data. SOEs are defined as firms
where national or subnational governments have ownership stake of at least 10%, while POEs are firms where national
or subnational governments own 0-9.9%. The percentages for centrally owned or locally owned may not add up to
100 because there are "other" SOEs that are neither owned by the central or local government.
We developed pseudo-EAs from the list by location (sub-city and Woreda); some EAs covered more than one Woreda, and multiple EAs were in a single Woreda. We selected a sample of EAs and households from each EAs in collaboration with UNHCR. Finding refugees in Addis Ababa was challenging, as they change their location frequently. To minimize the burden of searching for selected refugees, representatives of the selected households were contacted before the survey to ask them to come to a UNHCR center to collect preliminary information, including their current residential address. Since many Eritrean refugees in Addis Ababa had fled from the conflict in Tigray, out- of-camp refugees in Addis Ababa were stratified into two domains: refugees who arrived before the start of the conflict in November 2020, and those who arrived after November 2020. (c) Host Communities Populations around the refugee camps under each domain were meticulously identified in consultation with UNHCR and RRS. We used the ESS EA maps to assess the settlement of communities around camps, ensuring a precise fit to the definition of a “host community”. The assessment highlighted that using the list of EAs obtained from the new cartographic frame57 meets the definition of host