rafmacalaba/data-use-mentions-extended huggingface dataset:
** . Since the X variables are selected from the baseline survey, their independence is guaranteed by randomization. However, these extra instruments could capture sources of variation that are not related to the variation in counterfactual enrollment, therefore causing a violation of the constant treatment assumption. To be valid, the conditional LATE approach therefore requires that the extra instruments capture the heterogeneity caused by the variation in counterfactual enrollment (i.e. the fact that some would have stayed at home and some would have enrolled in an ASP) but not other forms of more standard heterogeneity. Arguably, variables at the province or village level that capture infrastructure quality are less likely to be correlated with the standard heterogeneity associated with observed characteristics. Note that when there is more than one instrument in **_X_** , the validity of the constant treatment assumption can be tested using an over-identification test.

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Table 1: Tradeoffs among privatization methods

|Method|| Better Corporate<br>Governance|Speed and<br>Feasibility|Better Access to<br>Skills and_Capital|IMore Government<br> I<br>Revenue|| Greater<br>Fairness|
|---|---|---|---|---|---|
|Sale to Outsiders|+|-+||+||
|MEBO|||<br>+||||
|Mass Privatization|<br>?|+|v|||

## Source: World Development Report, 1996

The comparisons in Table 1 were based on conceptual, not empirical analysis. The main reason was the limited evidence. The different privatization methods were also country-specific. Thus, for example, the Czech Republic opted for mass privatization, Hungary went primarily for sales to foreign investors, while MEBOs were wide-spread in Poland. This made studies on the effectiveness of privatization methods difficult (if not impossible) since one could not control for the impact of the overall economic environment. In this paper we evaluate the effectiveness of different privatization methods in fostering firm restructuring in the light of new evidence from Slovak firms' case studies. Slovakia provides the best natural experiment among all transition economies since it is the only country which has adopted all three privatization methods over a large number of former state-owned firms.

# **III. The Data**

The evidence presented here builds on a series of visits to large Slovak enterprises undertaken by the authors in December, 1996. By that time all the initial macroeconomic shocks were over and the economy had registered high aggregate growth in two consecutive years 5. The twenty-one enterprises we visited were scattered throughout Central and Western Slovakia and displayed significant diversity in sector origin and ownership structure.

For further analysis of the Slovak stabilization and mass privatization program **s** ee Shafik (1995).

7
This allows us to calculate the aggregate indicator of interest as **W**<sup> ̃</sup> (<sup>(</sup> _r_<sup>_k_</sup> )<sup>)</sup> _,V_<sup>=</sup><sup>**W**(</sup><sup>_{y_ ̃</sup> _i,_<sup>(</sup><sup>_k_</sup> (<sup>)</sup> _r_ )<sup>_}i∈V_)</sup> in _r_ -th simulation. The point estimate and its associated standard error are estimated as the mean and standard deviation of **W**<sup> ̃</sup> (<sup>(</sup> _r_<sup>_k_</sup> )<sup>)takenoverthesimulation.Forexample,thepoint</sup> estimate of the prevalence of stunting for commune _κ_ is as follows:

# **4 Data**

Our basic building blocks comprise a survey dataset, a census dataset and a dataset of geographic variables. The survey dataset we used is CDHS 2000, which was designed to collect health and demographic information for the Cambodian population with a particular focus on women of childbearing age and young children. The sample covered 12,236 households in 17 strata across the country. Data collection took place over a six-month period between February and July in 2000. In addition to detailed information about each household, its members, and housing characteristics, one-quarter of these households were systematically selected to participate in the anthropometric data collection. All children under 60 months of age in the sub-sampled households were weighed and measured. After excluding children for which information on height or weight is missing or implausible, 3,596 observations were used for this analysis (for further details, see National Institute of Statistics et al.
panel data set, which became available in 2013 via the Integrated Household Panel Survey (IHPS). The Malawi National Statistical Office conducted the surveys on which our data set is based, with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS‐ISA) project. The baseline round is from a subsample of 3,247 households interviewed between March and November 2010 as part of the Third Integrated Household Survey (IHS3) 2010/11 survey, which is a nationally representative survey of 12,271 households fielded over 12 months (March 2010–March 2011). The follow‐up round is from the Integrated Household Panel Survey (IHPS) 2013 survey. The IHPS tracked households and individuals to new locations when necessary. Hence, the IHS3 panel sample grew from 3,247 households in 2010 to 4,007 households in 2013 mostly because households split and formed new households. The IHPS utilized a complex household sample design allowing for 6 key domains of inference: the combination of urban and rural areas with the 3 main regions in the country (North, Central, and South). The data set contains detailed information on a rich variety of topics including demographics, education, health, employment, housing, assets, and agricultural production, as well as self‐reported shocks, their impact and coping responses to them.

This paper uses an unbalanced panel on rural households only. This unbalanced panel consists of 2283 households in 2010, which had split into 2789 households in 2013. This characteristic of the data set is viewed as strengthening the results since it more accurately captures outcomes for all 2010 household members, as compared with simply choosing which 2013 household matches the 2010 household and ignoring members who have joined different households.

# **_Welfare Measures_**

This study employs household total consumption per capita, food consumption per capita, income per capita, and maize harvest per capita as the main outcome measures for detecting vulnerability. Consumption and income capture two different measurements of households’ economic
well as from reciprocity. Similarly, evidence for Romania shows that incumbents gained political support through a program aimed at helping poor families purchase a computer (Pop-Eleches and Pop-Eleches, 2009).

Using administrative data at the municipality level, analysis of Mexico’s _Oportunidades_ CCT suggests that turnout and incumbent vote share increased in villages that were randomly assigned to receive the program (De La O, 2009). Previous work for Colombia’s _Familias en Acci ́on_ (Nupia 2012) point to results in the same direction. The larger beneficiary rates in regions where Uribe won by more than 50% of the vote in 2002 are associated with an increase in the proportion of votes that go to the incumbent governing coalition; there is no statistically significant effect for swing regions.<sup>3</sup> Conditional and unconditional correlation analysis for Brazil suggests that the coverage of _Bolsa Familia_ , a large scale CCT program, is associated with Lula’s vote share when he sought a second term (Zucco, 2008).

Addressing causality between social policy and political participation and preferences is empirically challenging as it requires an exogenous source of variation in social transfer receipt and rich data on voting behavior. This paper exploits discontinuities in program eligibility and variation in participation across voting booths to estimate the effect of enrollment in _Familias en Acci ́on_ (FA), a Colombian CCT program, on the intent to vote, turn out and on electoral choice during the 2010 Colombian presidential election. The use of detailed data at the individual and voting booth levels together with the identification strategies (discussed in more detailed below) allow us to control for possible confounders such as policy endogeneity, individual selection in the take-up and reverse causality. Additionally, unlike other studies we use actual voting outcome data, as opposed to self-reported data which could suffer from reporting bias. We provide evidence that relative to non-participants, FA beneficiaries of voting age are 1.6-2.5 percentage

> 3This paper relies on variation at the municipality level. However, there
# **1 Introduction**

A growing body of evidence reveals that current economic underdevelopment can be traced back to specific historical episodes, with long-lasting impacts typically arising through either domestic institutions or cultural traits (Hall and Jones, 1999; Acemoglu et al., 2001; Dell, 2010; Nunn, 2008, 2009, 2014). Less attention has focused on uncovering specific channels by which a bad legacy can be turned into a relatively more prosperous future. In this paper, we identify the interaction between the dismantling of coercive institutions and the distribution of rents from natural resource exports as an important driver of the development path of marginalized communities following the end of apartheid, the system of racial segregation enforced in South Africa between 1948 and 1994.<sup>1</sup> Apartheid legislation classified inhabitants into racial groups and institutionalized segregation of residential areas, enforced by means of forced removals and strict control of population movements. In 1970, black people were deprived of their citizenship, legally becoming citizens of one of ten tribally based self-governing homelands, four of which became nominally independent states.<sup>2</sup> At the same time, they were prevented from working outside the corresponding homeland, unless a pass for a particular area was issued. Black inhabitants were granted inferior property rights, while labor legislation severely restricted their pay and access to skilled and semi-skilled occupations. Supply of public goods was segregated, with markedly inferior provision to black people. In the aftermath of the 1994 democratic elections, all homelands were legally reintegrated into South Africa, and the newly elected government assumed responsibility for the effective dismantling of coercive institutions previously imposed on marginalized racial groups.

Using community-level data from the 1996 and 2011 population censuses, we provide quantitative evidence on the legacy of apartheid. In October of 1996, just over one year after Nelson Mandela took office as the first President elected in a fully representative democratic election, communities located just-inside and just-outside the former homelands exhibited large differentials with regard to the racial composition of the
population, leading to limited benefits from using EB. Meanwhile, an important drawback of EB is the required assumptions that the components of the error term are distributed normally, which can reduce the accuracy of the estimates if the underlying distribution of the residuals is not normal. The choice of whether to use EB in a typical case, when the auxiliary data are a household census, must weigh the cost of assuming normality against the benefit of obtaining additional information from a relatively small sample.

The calculus changes, however, when the auxiliary data are specified at an aggregate geographic level, such as a subarea or area. In this case, EB estimation is essential to effectively combine the survey data with census-based predictions. Intuitively, the auxiliary data contribute less variation when they only vary across sub-areas, compared to the sample, because the sample, unlike the auxiliary data, contains household level information. In the EBP framework described above, the use of predictors aggregated to the sub-area level increases the variance of the area effect σ<sup>2</sup> , which also represents the covariance of unexplained welfare across households within the same area. This covariance rises when using sub-area predictors because all households within each sub-area share the same set of ηη characteristics used to predict welfare in the model. The increase in σ<sup>2</sup> due to the use of regionally aggregated predictors in turn increases the shrinkage factor γ, leading to a greater increase in precision when using an EBP model versus the traditional ELL estimator. ηη The assumption of normality is critical in the EBP framework, as a distributional assumption is required to condition the area level random effect on the average residual of the regression in that area. In the EBP model, this conditioning is equivalent in a Bayesian framework to specifying the average of the residuals as a prior distribution for the random effect under the assumption of normality. The normality assumption is also necessary for the Monte-Carlo simulations of the parameters, which are required because headcount poverty is a non-linear
international remittances is potentially high, given the impermanent nature of overseas migration to the Gulf. A mitigating factor, however, is that a

> 10 Migrant households must typically cover the costs of an airline ticket, visa, commission to the recruitment agency, and even bribes (KMS, 1998).

> 11 Investing in assets should provide a return to justify giving up immediate consumption. This need not be financial gains—for example, housing improvements would offer a stream of future utility to the intended beneficiaries, as would durable goods, such as a vehicle, refrigerator, etc.

> 12 For example in Qatar, a minimum monthly income of approximately US$2,192 is required to bring dependents into the country.

> 13 Recent data from a 2013 survey of workers residing in dormitory-style accommodations in Qatar show that nearly 60 percent of a worker’s annual income is remitted back home. Most of the low and unskilled migrants in the country live in this sort of dwelling.

7
4 income contribute to income inequality. This analysis finds that income from government employment represents the largest share of nonfarm income in both countries. Third, the study applies a new income decomposition procedure based on regression analysis to the data from rural Egypt. This procedure, which cannot be applied to Jordan because of the lack of data on landowning, provides a flexible and efficient way for quantifying the role of various household-level variables in "determining" the level of income inequality. This analysis finds that landownership, which is distributed very unevenly in rural Egypt, is negatively and significantly related to the determination of nonfarm income.

The study proceeds in six further sections. Section I presents the standard decomposition of the Gini coefficient. Section II discusses the household data sets from Egypt and Jordan. Section III uses the Gini decomposition to analyze the contribution of the different sources of income - including nonfarm income - to overall rural inequality. Section IV presents the new decomposition procedure based on regression analysis, and Section V uses this new procedure to pinpoint the contribution of landownership to nonfarm and agricultural income inequality in rural Egypt. Section VI concludes.

# I. Decomposition of Income Inequality Based on Gini Coefficient

According to the literature, any decomposable inequality measure should have five basic properties. They are: (1) Pigou-Dalton transfer sensitivity; (2) symmetry; (3) mean independence; (4) population homogeneity; and (5) decomposability.
<br>Investment Export GDP Investment Export GDP -4 China's investmentActual<br>Commodity exporters Commodity importers<br>-5<br>2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Sources: Haver Analytics, International Monetary Fund, Oxford Economics, World Input Output Database, World Bank estimates.

C. Cumulative impulse response of weighted average EMDE output growth after 1 year to a 1 percentage point decline in growth in real investment, real exports, and real GDP in China. Investment spillovers based on a Bayesian vector autoregression of world GDP growth (excluding China), the U.S. 10‐year sovereign bond yield, JP Morgan’s EMBI index, growth in the non‐investment component of China’s real GDP, China’s real investment growth, and real GDP growth in the spillover destination group. Oil price is exogenous. Exports and real GDP replace real investment in models that estimate spillovers from exports and output. Sample includes 18 EMDEs from 1998Q1‐2016Q2. Blue bars denote 16th‐84th percentile confidence interval, red dots denote median of posterior distribution.

D. Historical contribution of China’s investment and non‐investment growth based on model used for Figure C. Line denotes unweighted average demeaned GDP growth.

54
enterprises in 2,131 households in the 2018 wave.<sup>11</sup> For each enterprise, there are questions about enterprise characteristics, household members’ participation, employment of non-household members, net revenues and expenditures.

# **2.1.2. Economic Census and Establishment Census**

We use the public release 50% sub-sample of Egypt’s 2012/2013 EcC. Despite being called a census, the EcC is a survey of fixed establishments, with the largest establishments having a sampling rate of 100%. The 50% sub-sample we have access to includes 62,108 establishments. Although the 2017 EsC is truly a census of fixed establishments, we only have access to a 20% random sample of these establishments. Thus, our sample is made up of 772,432 establishments. The EcC and EsC cover all non-governmental establishments and therefore exclude any firms that are not in fixed establishments, i.e. ones that involve mobile workers or ones whose activities take place in a field or open worksite, such as the vast majority of firms engaged in construction, agriculture, and transportation activities.<sup>12</sup> The EcC and EsC include public enterprises, but we exclude those from our analyses since they are by definition formal. We also exclude the very small number of agricultural firms that show up in both data sources for comparability with the ELMPS. Since the ELMPS excludes the Frontier governorates, we likewise exclude these governorates from the EsC and EcC data. Since the EcC and EsC only include firms that operate in fixed establishments but the ELMPS includes firms that operate both in and out of establishments, most of our analysis is limited to firms operating within fixed establishments.

# **2.2. Measuring Formality**

Firm formality can be defined and operationalized in a variety of ways. We test two increasingly expansive measures of firm formality. First, our basic measure of formality is commercial registration. However, in the EcC, the registration question
>10</sup> Fourth, around the national lockdown announced on March 24, electricity consumption dropped strongly. Fifth, at the end of May electricity consumption first recovered before falling again.

## **3.2 Nighttime light intensity**

The nighttime light data are extracted from the VIIRS-DNB Cloud Free Monthly Composites (version 1) made available by the Earth Observation Group at the National Geophysical Data

> 9The figure is in mega units. A mega unit is one million units of electricity, where one unit is equal to one kilowatt hour.

> 10This decline at the end of 2019 has been linked to weakening GDP growth.

6
users of LPG**

<!-- Start of picture text --> Primary<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data.

_Note_ : The analysis is confined to those areas—nationally or urban areas—where more than one-fifth of the population lived in households citing LPG as the primary cooking fuel. For the first nine countries, the entire sample is taken because the one-fifth threshold is met nationally. In Burkina Faso, Kenya, and Nigeria, the one-fifth threshold is met only in urban areas. Households abandoning polluting fuels include very few LPG users who did not cite LPG as their primary cooking fuel. However, the sum of abandonment and primary bars in the figure equals the percentage of the total population citing LPG as the primary cooking fuel. Taking Cameroon as an example, the figure shows that 31 percent of the population was using LPG in any capacity, 20 percent of the population cited LPG as the primary cooking fuel, and 7 percent used LPG and no other polluting liquid or solid fuels. Abandonment = percentage of people living in households that used LPG and had abandoned all forms of polluting fuels; primary = percentage of people living in households that cited LPG as their primary cooking fuel but had retained the use of polluting fuels; other users = percentage of people who reported non-zero expenditures on LPG but did not cite it as their primary cooking fuel and had retained the use of polluting fuels; * = urban areas only.
public sector revenues (RG) and GDP (Y), using simple least squares.<sup>7</sup> The regressions are based on annual data from 1970 to 1996 from the national income accounts. These series are non-stationary, so the regressions were carried out on the differenced variables to avoid spurious correlation. The equations in Table 3 were also reestimated with Two-Stage-Least Squares, under the assumption that GDP was also an dependent variable (not shown here). This different procedure did not appreciably change the results.

Three issues are of interest in light of the earlier discussion: (a) whether public investment crowds out or crowds in private investment, (b) whether that impact occurs solely through its impact on the overall deficit (which leads to financial crowding out), or whether public investment exerts an additional influence, and (c) whether there has been any break in these relations after the Government embarked on its new economic strategy in the mid-1980. The main findings from the regression with regard to these issues are described next: 7These equations are similar to the ones estimated by Rodriguez (1994) for Argentina.
get larger, while the use of local and foreign commercial banks and state funds increases. Small and medium sized firms have a greater propensity to report moderate or major financial obstacles than do large firms, though not in relation specifically to high interest rates which is overall the most highly rated financial constraint. The other major constraints reported were lack of access to long-term funds, collateral requirements, lack of credit information on clients and bank paperwork. (Curiously, small firms had a poorer image of the quality of the central bank than medium or large firms).

An early use of the WBES data was to explore the impact of foreign bank penetration on firm access to credit (Clarke et al.2001). Since then there have been many other uses including examination of the relation between firm growth, firms’ perceived constraints and independently measured country environment variables such as bank concentration in RPED panel study of 6 African countries in the mid-1990s and selectively updated around 1999. This covered about 200 firms per country ranging from micro (less than five employees) up to large scale employing over 1000. In the survey, questions were asked about whether firms applied for loans, if not, why not and if they applied whether applications were approved. Only about 10 per cent of firms had received a loan in the past year (2 per cent of the micro firms, 5 per cent of the small) with trade credit and informal sector debts together about three times as important as bank debt in the balance sheet. Interestingly, the rejection rate for firms that applied for credit was about 45 per cent—75 per cent for micro firms, 60 per cent for small, 45 per cent for medium and 20 per cent for large. Fully 20 per cent of firms (38 per cent of micro) said they didn’t apply because they didn’t have collateral or thought they would be rejected. A further 8 per cent of firms (13 per cent of micro) didn’t apply because they thought the