rafmacalaba/data-use-mentions-extended huggingface dataset:
8 The violence peaked with the 1994 genocide, when extremist Hutu militia known as Interahamwe, the Rwandan Armed Forces (FAR) and Rwandan police forces organized massacres against the Tutsi minority and, to a lesser degree, moderate Hutu intellectuals who were opposed to the regime of President Habyarimana. The human suffering during the genocide was enormous. Death toll estimates range between at least 500,000 deaths (Desforges 1999; Prunier 1999) to over a million deaths (African Rights 1995), about 10 percent of the 1994 population. Most of these individuals were Tutsi, killed in one-sided violence, causing the death of an estimated 75 percent of the Tutsi population (Desforges 1999). A smaller number of soldiers died in combat between the FAR and the rebel army, the Rwandan Patriotic Front (RPF), which eventually stopped the genocide and took power. The breakdown of the health care system and displacement also contributed (although to a much lower extent) to excess mortality (de Walque and Verwimp 2010). Sexual violence was widespread during the genocide, leading to a cohort of children conceived through rape (Nowrojee 1996).

Estimates of death tolls are politically sensitive in Rwanda and the available demographic data are scant. Reconciliation policies enacted after 1994 have strictly prohibited the collection of information on ethnicity and other characteristics that would facilitate reconstruction the demographic impact of the genocide in more detail. The few studies that attempt to differentiate deaths according to gender (e.g. de Walque and Verwimp 2010; Ministry for Local Government 2002) conclude that adult men made up the majority of casualties.<sup>4</sup> In the aftermath of the genocide, sex ratios (the ratio of males to females) became severely unbalanced. Primarily, this is because more men and boys than women and girls died. Further, about 2 million Rwandans, commonly referred to as _new caseload refugees_ , escaped to the Democratic Republic of Congo and Tanzania immediately after the genocide in fear of revenge by the RPF and persecution for their
jobseekers after their engagement with the private provider. To do this, the FEI drew on a database maintained by the Tax Administration. As described above, payment to the private employment service provider was based on independent verification of a jobseekers’ formal employment status. Whenever the private employment service submitted a payment request for a successful placement, FEI would independently verify the employment status of the jobseeker. This typically happened 3-6 months after the jobseeker started receiving program services and is used in this analysis as an indicator of employment in the short-term. In March 2023 (2125 months after program services were first delivered), another round of employment status verification was conducted which is used as a measure of medium-term employment outcomes. These data are available for the full sample of beneficiaries and includes 1,541 observations.

9
a regression exercise instead.<sup>8</sup> Therefore, it is expected that the new 2011 PPPs better reflect the spatial price differences of these particular economies.<sup>9</sup> For this reason, we think that poverty estimates need to be calculated based on the most up-to-date information on relative prices.<sup>10</sup> As Jolliffe and Prydz (2015) mention, there is a presumptive trade-off when changing the poverty line since it can be perceived as an intention to accelerate the achievement of specific goals (e.g., the World Bank’s goal of eliminating global poverty by 2030) instead of improving poverty measurement and cross-country comparison. However, the authors show that this trade-off is not relevant as the poverty headcount based on the 2005 PPPs changes slightly when using 2011 PPPs.

Second, the method used to estimate the current $2.5 and $4 per person per day regional poverty lines has not been properly documented. No description has ever been published about the source of the underlying official poverty lines used in the analysis or the robustness of the regional estimates. Gasparini, Cicowiez, and Escudero (2013) explain that these lines are obtained as the unweighted median value of the official national poverty lines of all countries in the LAC region converted to US dollars using 2005 PPPs.<sup>11</sup> Moreover, even when including a relatively large set of countries from LAC, several countries were excluded from the estimation of the $2.5 and $4 per person per day poverty lines in 2005 PPPs. For instance, Brazil, which represents approximately 38 percent of LAC’s population, was not considered in the pool of countries used to estimate the currently-used regional poverty lines. Similarly, Haiti, the poorest country in LAC, was also excluded from regional estimates of extreme and moderate poverty lines.<sup>12</sup> The exclusion of these important countries could materially affect the poverty status of
to check if the results hold when the lower threshold for children’s schooling is raised. We thus estimate the IGRC and IGC in a series of samples, starting with 14 years and raising the lower threshold incrementally by one year at a time up to 20 years. The evidence from this exercise is very reassuring; while the magnitudes differ across the samples, the main conclusions reached on the basis of 13-60 years age range remain intact. For the sake of brevity we report estimates for 16 and 20 years as the lower age threshold for children. A 16 year age cut-off implies potentially 10 years of schooling which coincides with one of the most important public examination in both Bangladesh and India (called Secondary School Certificate (S.S.C) or ‘Matriculation’ examination). After 12 years of schooling (18 years of age cut-off), the students sit for a second important public examination, called Higher Secondary Certificate (H.S.C) or Intermediate examination. In our Bangladesh data, about 10 percent of 20 years of age or older has 10 years or more schooling, and 5 percent has 12 years or more schooling.

For each age range, we present the bias estimates and omit the underlying estimates of IGRC and IGC for full and coresident samples. This allows us to reduce the number of tables by putting together the relevant estimates for both Bangladesh and India in a single table. However, all of the underlying estimates are available from the authors upon request. Note that we do not discuss statistical significance of the estimates, all of the estimates are significant at the 5 percent or lower level. But as noted earlier, the estimated standard errors in IGRC and IGC are very small reflecting the large sample size. Thus statistical significance is not very informative, and the focus should be on the magnitude of the bias.

The estimates for three different age ranges for the all children sample including both sons and daughters are presented in Table 4. The estimates, both for India and Bangladesh
consumption is modeled through the Linear Expenditure System over composite commodities (domestic and imported); exporters of each commodity face constant-elasticity<sup>1</sup> foreign demand schedules; production for exports or domestic markets are regulated by CET<sup>2</sup> functions for each firm, production is a nested Leontief/CES structure for primary factors and composite inputs, labor is a CES function of 10 different types of labor. This non-linear model is solved with the GEMPACK software, and distinguishes between 42 sectors and 52 commodities<sup>3</sup> ; 10 labor occupational categories.

All quantity variables in the model are disaggregated according to 27 regions within Brazil, using an elaboration of the top-down regional modeling method described in Chapter 6 of Dixon _et al._ (1982). This methodology recognizes local multiplier effects: many service goods are little traded between regions, so that local service output must follow local demand for services.

The CGE model is calibrated with data from the Brazilian economy for 1996, obtained from two main sources: the 1996 Brazilian Input-Output Matrix (IBGE. http://ibge.gov.br), and the Brazilian Agricultural Census (IBGE, 1996).

On the income generation side of the model, workers are divided into 10 different categories (occupations), according to their wages. These wage classes are then assigned to each regional industry in the model. Together with the revenues from other endowments (capital and land rents) these wages will be used to generate household incomes. Each activity uses a particular mix of the 10 different labor occupations (skills). Changes in activity level change

> 1 For the simulations reported here, we set the export demand elasticities to values derived from the GTAP model, so as to increase consistency between results for the world and Brazil models.

> 2 The domestic/exportable CET was set to infinity for the simulations reported below, to fit in with the assumptions of the GTAP model.

> 3 One of
to the United States is to pay $50,000, $100,000 or more for enrollment in a university program here, there are likely to be many people around the world for whom this could be a good investment, but who cannot pay the costs. As an even more extreme example, many countries offer special investor visas for those with enough money – which in the case of the U.S. EB-5 program, requires investing US$1.8 million (or $900,000 if investing in targeted employment areas).

_Explicit policy barriers that directly limit migration._ Policy barriers such as limits on legal migration can be viewed as making the cost term _C_ extremely large (or almost infinite) in some cases, so that no matter the gains from migrating, there is no way for some people to take advantage of them. One type of policy barrier is an explicit quota on the number of migrants who can come in through a certain category, or from a particular country. An ILO (2004) survey found that most quotas tend to be partial in nature, applying only to certain sectors, types of firms, or migration streams. Perhaps more prevalent are labor market tests that require employers to show that there is a lack of qualified applicants, or requiring that migrant workers be offered at least the prevailing 11
sexual violence by<br>age 18||19.9%|19.9%|
||16.7.1|2|Proportions of positions in national and local<br>institutions, including (a) the legislatures; (b) the<br>public service; and (c) the judiciary, compared<br>to national distributions, by sex, age, persons<br>with disabilities and population groups|Any data: Ratio for female members of parliamen<br>(Ratio of the proportion of women in parliament i<br>proportion of women in the national population w<br>the age of eligibility as a lower bound boundary),<br>Lower Chamber or Unicameral; AND/OR Ratio f<br>female members of parliaments (Ratio of the<br>proportion of women in parliament in the proporti<br>women in the national population with the age of<br>eligibility as a lower bound boundary), Upper<br>Chamber|NA|0.0%|
|Note:|16.7.2<br>Percent of co|2<br>untries|16.7.2 Proportion of population who believe<br>decision-making is inclusive and responsive, by<br>sex, age, disability and population group<br>with any reporting on the indicator in the five|years (2016-2020). NA indicates that the indica|0.0%<br>tor is not relevant|0.0%<br>in regards|

Note: Percent of countries with any reporting on the indicator in the five years (2016-2020). NA indicates that the indicator is not relevant in regards to sex disaggregation. Source: UN SDG Global Database. https://unstats.un.org/sdgs/dataportal 20
|-0.0308|-0.0055|
|program period|(0.9449)|(0.5366)|(0.2915)|(0.0922)|(0.0294)|(0.0262)|
|Program * (Poverty rate – 50) *|0.5014|0.2798|0.1230|0.0710|0.0277|0.0103|
|Post-program period|(0.7820)|(0.4410)|(0.2448)|(0.0762)|(0.0258)|(0.0290)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.4518|0.1390|0.2238|0.0707|0.0183|-2.6308***|
||(1.4249)|(0.5715)|(0.5626)|(0.2691)|(0.0394)|(0.0786)|
|Observations|2,625|2,625|2,625|2,625|2,625|2,279|
|R-squared|0.578|0.490|0.604|0.633|0.359|0.840|
|The mean value of the dependent<br>variables before the program|9.016|0.908|0.923|0.348|0.067|0.277|

Note: This table reports district fixed-effect RDD-DD regressions of density of firms and nighttime light data intensity of districts using district-level observations. The number and density of firms at the district level are computed using the annual Vietnam Enterprise Censuses from 2000 to 2017. Robust standard errors in parentheses. Standard errors are clustered by district level. *
According to the GHFPH, the general pattern holds, but with higher overall job losses: for all private sector workers, the reported rate of job loss was 50.9 percent for those with less than secondary education, and this falls to, 32.5 percent for those with university and 22.1 percent for those with post-graduate degree lost jobs (Table 5-24). The rate at which lower secondary- educated workers reported reduced income due to reduced

|_Table 5-23 Georgia: Distri_|_bution of PFPS Job Los_<br>**Wave 1**|_ses (Percentage of Jobs L_<br>**Wave 2**|_ost) by Educational Attainment_|
|---|---|---|---|
|||CFUWBES2|CFUWBES2|
||CFUWBES1<br>Modeled|Model-predicted|Survey Measured|
|**Primary or Lower**|25.6|13.4|13.2|
|**Lower Secondary**|22.2|14.0|12.4|
|**Secondary**|21.2|11.6|10.8|
|**University**|14.9|-3.3|2.4|
|**Post-Graduate**|16.2|-1.9|3.7|
|**Total**|19.0|5.9|7.8|
|Sources: CFUWBES and L|FS 2019.|||

working hours was nearly 50 percent, which was more than twice that of those with higher levels of education.

5.7 Projected Job Losses by Wage Level Many observers have predicted that the loss of jobs would be unequally shared, with the poor most greatly impacted by this crisis. Our method projects such unequal job losses in Jordan, but not in Georgia.

# **5.7.1 Jordan**

Our method projects that
2005-2010, it remained small as a percentage of GDP.

> 5 Our focus on publicly listed firms provides us with a more homogeneous group of firms that, vis-à-vis non-listed firms, are large, have already met the costly listing requirements, and are formal corporations that can raise capital. 6 We also use the Thomson Reuters’ Worldscope database to obtain information on capital expenditures and to check the robustness of the results.

3
conceptual underpinning to estimate housing deficit focused on inadequacy of housing across and within countries. Therefore, the estimation of the housing deficit presented in this section frames the challenge faced by households in securing housing that is adequate. We present the estimates of housing deficit in terms of number of housing units based on the _AHI_ , average HH size, and the country’s current population. This presentation of the deficit as the number of housing units offers practical applicability for policy makers drafting housing policies or planning subsidy programs for lower-income segments as well as private sector companies along the housing value chain. We refer to this number as the adequate housing _stock deficit_ . This deficit can be tackled by not only building new housing units but also by expanding infrastructure networks to reach all segments of the population, by providing consistent services to all aspects of housing adequacy, as well as by upgrading or replacing inadequate housing. Although our data does not allow to fully distinguish these two aspects contributing to the overall stock deficit, we present an indicative estimate of the subset of the deficit that may require new or replacement buildings, as opposed to just upgrade and refurbishments. Based on the stock assessment, under assumptions of continuation of business-asusual policies and practices, and using projection of urbanization rates and population growth, we also estimate the _flow deficit_ , which is the projected housing deficit by 2030.

# **_5.1 Stock Deficit_**

Making use of the _AHI’s_ interpretation at the aggregate level as the share of housing that is inadequate, we estimate the current stock deficit, defined here as the difference between the projected number of housing units in a country and those units that are currently inadequate. We approximate the number of current housing stock by drawing on available data of countries rural and urban population as well average HH size in rural and urban areas.<sup>19</sup> Based on these estimations, we estimate a country’s current housing stock deficit as where _D_ is the current housing stock deficit for country _c
sample are, by construction, the same for all migrant households in a given city. Both measures provide estimates of the average school fees that each migrant household is (exogenously) faced with when migrating to the city.

Yet, these school fee measures may still be endogenous at the city level if unobserved citylevel events occur that affect both a city’s school fees and its migrant workers’ decisions regarding child migration. As discussed earlier in the presentation of the empirical model, we address this issue by instrumenting school fees with the one-year lag of unexpected shocks to the city government’s education spending. We gathered the historical city-level education spending as a share of local public spending in the 15 cities (metropolitan areas) covered by RUMiC 2008 for the period 2002-2007 from the China City Statistical Yearbooks. For each city, using different detrending techniques (i.e., Hodrick-Prescott (HP) filter and linear filter), we decomposed the time series records into a trend component and a cyclical component.

We constructed a measure for the trend in city education services with the growth rate of the student-teacher ratio in 2007 based on the number of students and teachers in metropolitan areas in 2006 and in 2007 using the China City Statistical Yearbooks. As a proxy for migration distance between the original and the destination areas, we constructed a dummy variable that equals 1 if the migrant household is from a rural area within the same province and equals 0 if the migrant household is from another province. Since city-level Consumer Price Index data are not available for China, we proxy for living costs with city-level housing prices in 2007 from the China Urban Life and Price Yearbook 2008.

Table 1 shows the summary statistics for household and city characteristics. The average age of household heads in our sample is 36.7, with around one-fourth (26 percent) of households being female-headed. About half of all household heads are primary school graduates and less than one-third (29 percent) of them hold a junior high school diploma or higher. 38 percent
uses firm‐level data for eight aggregated regions in Russia and at the much disaggregated regional level. The data come from the RUSLANA database, which contains balance sheet information on companies in Russia, such as stock data and exports revenue. Although RUSLANA distinguishes between domestic and exporting firms, trade flows are not covered, so the regional analysis relies on the number of exporters and their revenue as the best proxy for the export activities of Russian firms. These data are also the basis for exploration of the countrywide distribution of foreign direct investment (FDI) in Russia, the majority of which goes into services. Data on FDI revenues refer to 2012; however, firms covered are those that exported to any country in the world at least once between 2009 and 2013.

Assessing the competitiveness of Russian services begins with evaluating their role in the domestic economy, which is discussed in the first section. An important preliminary assessment consists in cross‐ country comparison of basic indicators, such as the share of a country’s value added from services exports and its importance in relation to the domestic economy. Russia’s performance is compared with that of Brazil, China, India, and South Africa, the other BRICS (Brazil, Russia, China, India, and South Africa) countries. The second section assesses ability to be competitive in the two functions of services trade—as a source of (a) export diversification and (b) greater competitiveness within the Russian economy. The third section examines how much value‐added services and other exports contribute to the economy. Section 4 analyzes firm‐ and region‐level services trade, following in section 5 by a diagnostic of determinants of services performance, identifying factors that facilitate and constrain their competitiveness, linking performance and determining factors, and suggesting policy options to address specific constraints. The final section draws conclusions.

> 3 All monetary amounts are US$ unless otherwise indicated.

3
Policy Research Working Paper 9915

# **Abstract**

Using representative survey data including Iranians and Afghan refugees in Iran in 2011–2019, this paper explores the unequal impact of macroeconomic fluctuations due to Iran’s nuclear dispute on Afghan refugees and host communities. The paper finds that economic shock increases refugee’s exit and disproportionately reduces their consumption expenditure and aid received from the host community. In addition, bad and good economic cycles create asymmetric impacts. While negative shocks affect the economic outcomes of two communities homogenously, it hurts social cohesion between them. In contrast, economic recovery benefits refugees relatively more in terms of consumption and income, but the impact on social cohesion measures is insignificant. The findings also suggest that in turbulent economic times, both inter-community and intra-community inequalities go up.

This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mo.hoseini@imps.ac.ir.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 3: Effects on the use of childcare centers

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Household had any children<br>in a creche in<br>the past 24 hours|At least one child<br>attended a creche in<br>the past 12 months|At least one child<br>attended a public works creche<br>in the past 12 months|Maximum number of months<br>any child in the household<br>attended any creche|
|**Full sample**|||||
|ITT estimate|0.119***|0.241***|0.235***|0.720***|
||(0.026)|(0.026)|(0.027)|(0.145)|
|Observations|1961|1965|1965|1965|
|_R_<sup>2</sup>|0.051|0.127|0.181|0.081|
|Control mean|0.100|0.120|0.003|0.571|
|**Have child aged 0-2**|||||
|ITT estimate|0.112***|0.268***|0.253***|0.756***|
||(0.028)|(0.037)|(0.033)|(0.170)|
|Observations|1124|1126|1126|1126|
|_R_<sup>2</sup>|0.053|0.155|0.202|0.086|
|Control mean|0.094|0.091|0.003|0