rafmacalaba/data-use-mentions-extended huggingface dataset:
the household survey and tax data earnings distributions look closer than they are, and thus understates the extent of under-capturing of high earners.<sup>5</sup> Piketty et al. (2022) note that tax evasion and avoidance can reduce measured top income shares compared to the true number and that these can both vary across time and countries, making within and between country comparisons difficult. Jouste et al. (2023) show that an increase in the top marginal tax rate in Tanzania, from 30% to 40%, which affected the top 1% of earners, may have resulted in declines in reported incomes. In addition, that income in tax data does not include incomes important at the top of the distribution, like retained earnings in firms (Piketty et al., 2022), is also likely to be important in middle-income countries, although probably not for low-income countries with very large informal sectors.

A final challenge is the survey and tax data may not identify the same units, and tax and other forms of administrative data may not give a welfare measure that economists care about (Ravallion, 2022). Many countries have joint tax filing for married couples, and this can make it more difficult to use the tax data to complement surveys. Tax data also usually tell us little about households and thus cannot easily be used to create a measure of household per capita income. Ravallion (2022) points out that using tax data to improve surveys can often require researchers to define and use taxable income from the surveys to match what is available in the tax data, but that taxable income is not an appropriate measure of welfare, ignores transfers and is thus likely to overestimate income inequality.

# Measuring the Upper Tail of the Wealth Distribution

Thus far we have focused on the challenges when measuring the top of the income distribution. But much of the preceding discussion applies directly to wealth. In this section we focus on aspects of measuring the top of the wealth distribution where there is not a
Policy Research Working Paper 8972

# **Abstract**

Enumeration areas are the operational geographic units for the collection, dissemination, and analysis of census data and are often used as a national sampling frame for various types of surveys. Traditionally, enumeration areas are created by manually digitizing small geographic units on high-resolution satellite imagery or physically walking the boundaries of units, both of which are highly time, cost, and labor intensive. In addition, creating enumeration areas requires considering the size of the population and area within each unit. This is an optimization problem that can best be solved by a computer. This paper, for the first time, produces an automatic designation of predefined census enumeration areas based on high-resolution gridded population and settlement data sets and using publicly available natural and administrative boundaries. This automated approach is compared with manually digitized enumeration areas that were created in urban areas in Mogadishu and Hargeisa for the United Nations Population Estimation Survey for Somalia in 2014. The automatically generated enumeration areas are consistent with standard enumeration areas, including having identifiable boundaries to field teams on the ground, and appropriate sizing and population for coverage by an enumerator. Furthermore, the automated urban enumeration areas have no gaps. The paper extends this work to rural Somalia, for which no records exist of previous enumeration area demarcations. This work shows the time, labor, and cost-saving value of automated enumeration area delineation and points to the potential for broadly available tools that are suitable for low-income and data-poor settings but applicable to potentially wider contexts.

This paper is a product of the Poverty and Equity 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 upape@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
a small negative effect on birthweight despite an increase in fetal deaths (stillbirths).<sup>10</sup> This work in a developed country still leaves a gap in the evidence with respect to the strength of the selection effect in middle-income developing countries.

Methodologically, this literature shares the use of geographical and time variations in violence to identify the intended effect.<sup>11</sup> Most of these previous studies use measures of violence based on victims, such as the number of conflict-related fatalities or bomb casualties. In contrast, here, as in Dagnelie, Luca, and Maystadt (2018),<sup>12</sup> I choose violence events. Victim-based measures are more likely to capture the underlying intensity of the conflict in an area and are thus more likely to be endogenous. My approach, as will be explained in the methodological section, is to focus on a particular type of event —violent attacks against civilians— that is a better reflection of an exogenous local-violence shock, and less of the underlying violence intensity.

One aspect shared in the literature is the focus on aggregate violence exposure rather than on individual victimization. This emphasizes the role of stress as the most likely channel between

> 6 However, the effect is not statistically different from zero in a restricted sibling sample with a substantially reduced sample of approximately 500 births (40\% of the original sample).

> 7 Although she finds a negative effect on stillbirths, the increase in miscarriages is larger and the overall effect on fetal deaths is thus positive.

> 8 Also, the interaction with gender did not show a stronger positive health effect for boys as would be expected if the strongest had survived to birth.

9 Although there can be a selection of the babies surviving into late pregnancy as shown by the increase in miscarriages and the small decrease in stillbirths.

> 10 They use VRS data (1980-2003) and their most complete specification uses fixed-effects for province, time (month-year of conception) and province
Policy Research Working Paper 10174

# **Abstract**

The claim that social protection is a luxury good—with a national income elasticity exceeding unity—has been influential. The paper tests the “luxury good hypothesis” using newly-assembled data on social protection spending across countries since 1995, treating the pandemic period separately, as it entailed a large expansion in social protection efforts. While the mean income share devoted to social protection rises with income, this is attributable to multiple confounders, including relative prices, weak governance in low-income countries and access to information-communication technologies. Controlling for these, social protection is not a luxury good. This was also true during the pandemic.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region and the Department of Economics, Georgetown University. 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 mr1185@georgetown.edu, mlokshin@worldbank.org, and itorre@worldbank.org.

_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
are considered, including social (or anti‐social) behavior and social environment, (self‐reported) engagement in a bad/criminal activity, and the reported incidence of being a victim of such an activity. We also explore, to the extent possible given data limitations, potential channels underlying the results.

# _4.2 Data_

As part of the impact evaluation design of the UYEP, the Eligibility Baseline Survey (EBS) was administered during the 4<sup>th</sup> and 5<sup>th</sup> intakes to both treatment group and control groups. The treatment group consisted of youth who were selected for the 4<sup>th</sup> and 5<sup>th</sup> intake of the project, while the control group consisted of eligible youth from neighboring districts who had not participated in program at the time of the follow‐ up survey (FUS). This survey was conducted in the summer of 2015, about a year after participants’ completion of the program, to evaluate the changes in the variables of interest over time. The primary purpose of the FUS was to collect information on post‐intervention outcomes of treatments and controls to estimate various impacts of the program. A more detailed description of the ESS, EBS and FUS is provided in Annex 1.

9
|Controls and Governorate Fixed Effects|N|Y|Y|Y|Y|

Note: This table estimates how the average Reduced Coping Strategy Index changed following the announcement of the 2017 IPC in March 2017. Column (1) estimates a sparse specification regressing the average share of respondents in each governorate on an indicator equaling one if the survey was conducted after March 2017; column (2) adds governorate fixed effects and control variables; column (3) estimates an identical specification but replaces the post indicator with indicators equaling one if the survey happened during that quarter-year time period; column (4) interacts the post indicator with an indicator for the most severe IPC classification in the 2017 IPC announcement (IPC 4); and column (5) further interacts the post indicator with governorate indicators for each governorate that was classified as IPC 4 2017 IPC announcement. Control variables include the number of people killed in the month before the survey, and four lags of the same variable. Standard errors are clustered by governorate and reported in parentheses. * denotes significance at the 1 percent level; ** denotes <u>significance at the 5 percent level; and *** denotes significance at the 1 percent level.</u> 40
, WPS867, February 1992.

Clearly, as a form of financial intermediation, privatized pension systems seems rather expensive not only in the initial period of affiliation, but also over the entire 14 year period. The implications of this are particularly disturbing for other countries, since the performance of the Chilean economy has been exceptional during this period. Other countries such as Argentina, Peru and Colombia who are encouraging, rather than forcing as in Chile, participation in the privatized system will undoubtedly face greater challenges in convincing the workers to join the privatized system if their capital seems to erode rather than grow in the initial period.

Costs **and Efficiency.** While the average cost for a contributing affiliate over 1982-95 seems rather high based on available data, it may be somewhat overstated for several reasons. The average cost data published by the Superintendency are highly processed, and some experts have suggested possibilities of errors (which were raised with but not confirmed or denied by the Superintendency). Also, while above costs are correctly interpreted for contributors, contributors subsidize non-contributing affiliates to the extent no fees are charged on balances maintained, as is the case in Chile since 1987. However, during 1982-86, the AFPs did charge fixed and variable fees based on the balance as well as on new contributions; thus, during this period, such distortions are relatively smaller. Also, the self-employed who contribute more irregularly than the salaried affiliates, and the holders of voluntary savings accounts (some 961,000 who hold only 1.3% of total funds) who don't pay any commissions "free ride" compared to the regular affiliates. Pension funds also pay retirement, death and disability pensions, but they charge a separate commission for this. Pension recipients have grown from zero at the initiation of the system to 289,452, still only 5.5% of the affiliates.

AFPs incur some expenses in following up missing or misapplied payments. Such costs cannot be isolated from available system-wide data, but perhaps are not
consumption levels. Consequently, the total value of the subsidy that the household receives is the product of the subsidy per kilogram or kilowatt-hour and annual household consumption. For agricultural subsidies, the survey data are used to identify the direct beneficiaries of the agricultural services that the government provides to farmers. Then, the per capita spending of the program is calculated by dividing total expenditures of the program by total beneficiaries. Accordingly, the per capita amount is imputed to each beneficiary to represent the value of agricultural subsidies they receive.

The poorest benefit the most from agricultural subsidies in Mali, whereas the richest benefit the most in Niger. The poorest 50 percent receive 58.3 percent of total agricultural subsidies in Mali and 30.7 percent in Niger (figure 7.b). Moreover, agricultural subsidies are progressive in Mali. For instance, as a share of market income, the total benefit of agricultural spending decreases as income level rises, whereas in Niger, there is no evidence of the progressivity of agricultural subsidies, and the share of total agricultural subsidies in market income fluctuates with income level (figure 7.a).

12
level), a permanent contract (a large pooled effect of 38% due to an increase of 3 percentage points over a low control mean of 8%), and of working in a registered firm (a pooled effect of 14%).<sup>27</sup> Interestingly, the positive impact on having a permanent contract is driven by effects after 10 and 15 months, which could reflect improvements over time. However, no effects are detected on the probability of receiving job benefits that characterize formal jobs (contributions to Social Insurance, Health Insurance, and paid annual leave). The lack of program impacts on the probability of having a formal job is also confirmed when looking at a snapshot of administrative, Social Security records obtained 17 months after training.<sup>28</sup>

> 26There are also no effects on measures of job satisfaction asked only in the second and third follow-ups.

> 27Around 26% of employees in the formal sector report having informal jobs in Rwanda (Rwandan National Institute of Statistics, 2022).

> 28In November 2022, 17 months after training, we were able to recover information on registration to Social Security records for all 900 youth in our sample. The data indicate that 62% of youth in the control group were registered with Social Security at that time, which means they have had a formal job at some point in their lives. Appendix Table A13 shows a small treatment effect of 2 percentage points that is not statistically significant.

11
for application of a prioritization tool. These factors confirmed demand for prioritization of investments in infrastructure planning.

The IPF was applied to a selection of 35 proposed projects in water supply and sanitation and 19 in transport. These projects were identified in consultation with the Ministry of Economics and Finance. The pilot offered a key opportunity to replicate and refine the existing framework, in that it entailed decision analysis based on limited financial‐economic data, particularly for a portion of the water projects. In this way, it replicated a common input problem for infrastructure decision‐making, namely restrictions on data.

Planners from several agencies, in consultation with the Ministry of Economy and Finance and a team from the World Bank, agreed on a set of component SEI and FEI variables. The SEI variables initially selected included the number of beneficiaries (BEN), direct jobs created during implementation (EMP), the population of poor serviced by the project (POOR), social and environmental risks (SER), and the carbon footprint (CO2). The final analysis used only the first three, due to data problems and lack of specificity in the risk variable.<sup>17</sup>

> 17. See Marcelo, Mandri‐Perrott, & House, 2015 for a description of these challenges.

20
Data from the World Bank’s Debtor Reporting System (DRS) has been used to assess the validity of the BRI-debt estimates and the terms of BRI-related lending used in the analysis.<sup>14</sup> The DRS records all loans from Chinese institutions,<sup>15</sup> as reported by debtors’ countries. The loan information is not sufficient to identify whether a loan relates to a BRI project and, in addition, only countries which have data on both datasets are considered.<sup>16</sup> However, DRS data are available up to end-2017 and record all committed amounts for project loans. It is reasonable to assume that a loan contract has been signed for projects that are under construction. We therefore compare Chinese loan commitments in BRI countries with estimated BRI-debt financing derived from infrastructure projects under construction between 2013 and 2017. We find that our estimated commitments are lower than Chinese loan commitments recorded in the World Bank’s Debtor Reporting System: around 4 billion less in low-income developing countries (LIDCs) and around 20 billion less in Emerging Markets (EMs). The difference may arise from the fact that governments may have already signed contracts for some projects that are still at the planning stage. As expected, the amount of estimated BRI debt financed projects planned and under construction exceeds the loan commitments during this period by about USD10 billion (Figure 2). However, discrepancies between DRS data and our estimated historical debt financing are large in some countries (Figure 3).

Figure 3. Estimated BRI PPG debt financing and China’s PPG Loan Commitments, 2013-2017 (In billions of U.S. dollars) <!-- Start of picture text --> By country groups By countries<br> 80 Estimated BRI Debt financing - Planned 80 20<br>Estimated BRI Debt financing - Under constr. 18<br> 70 70<br>second 16<br> 60 60<br> 14<br> 50 50 12
gives them an endogenous poverty line, which is a determined by the inner product of the subsistence quantities and prices.

Hertel et al. (2004b) utilize a generalization of the LES demand system, AIDADS, which has been shown to perform well in predicting expenditure patterns across the income spectrum (Cranfield et al., 2002). They estimate this demand system using a combination of international cross-section data and household survey data from individual countries. The estimated demand system is then adjusted (calibrated) to exactly replicate the distribution of total expenditure and per capita spending by commodity in each individual country before proceeding with the policy analysis. The authors then proceed to use this demand system to predict the consumption impacts across households at different 27
# **A. Methodological Appendix**

## **_A1.Basic Katz and Murphy (1992) supply and demand framework with two inputs_**

The methodology in this document follows the literature initiated by Katz and Murphy’s (1992) seminal work and subsequent contributions. The basic framework assumes a CES production function with two factors: where _Q_ is output; _A_ is total factor productivity; _λ_ and _ρ_ are technology parameters; and _S_ and _U_ are the two factors that represent units of skilled and unskilled labor, respectively.

Under perfect competition, the wage skill premium ( _ws_ / _wu_ ) in year _t_ satisfies the following relationship: where _σ=(1-_ ρ _)_<sup>_-1_</sup> is the elasticity of substitution between the two types of labor, and _D_ ( _t_ ) is the relative demand for these two factors measured in log quantity units. Under the assumption that the economy operates on the demand curve given by equation (A1), a given value of the elasticity of substitution between factors implies a time series of demand shifts: The greater the elasticity of substitution between the two factors, the greater must be the fluctuations in _D_ ( _t_ ) to explain any given time series of relative prices for a given time series of observed relative quantities. The two logged terms on the right hand side of equation (A3) can be constructed from observational data. Following the literature initiated by Katz and Murphy (1992), equation (A3) can be used to impute _D_ ( _t_ ) (which is not observable) conditional on a range of values for _σ_ .

An estimate of the elasticity of substitution _σ_ is needed to implement (A3). Most of the literature gets that estimation from the inverse of the coefficient of the relative supply of skilled labor in a wage gap regression controlling by proxies of the relative demand – usually a time trend, linear or of higher order. The mean value of _σ_ in our regression analysis indicates values fluctuating around
) regional states. The three states cover approximately 70 percent of the land area and 80 percent of the population of Ethiopia.

Within each enumeration area, we randomly drew 14 households from a local census, and assigned them into treatment and control groups at the household level within each community. The 10 households in the treatment group received a _Mirt_ stove and were allocated an additional behavioral treatment based on price (free vs. subsidized), incentives based on use (none vs. payment for recorded use), or a social networking intervention (home training only vs. home training plus community training). The control and treatment households were balanced on covariates at baseline, as well as across arms within the treatment group (see Beyene et al., 2015 for detail on the sampling and baseline analysis).<sup>4</sup> Initial adoption of the stoves was assessed from both self-reports by the primary cook within

> 4 Balance implies baseline demographics are unrelated to treatment status. At the household level, a regression of treatment status on baseline characteristics returns a _p_ -value of 0.19 for the joint overall significance of household factors. A similar test at the individual level including age, gender, and parental characteristics returns a _p-_ value of 0.35.

5