rafmacalaba/data-use-mentions-extended huggingface dataset:
of internal and external validation.<sup>60</sup> In the context of this exercise, we compute the share of learners below the PISA minimum proficiency level (MPL), the average learning gap in respect to the MPL, and the average learning gap severity also in respect to the same MPL. The main advantage of the learning gap and learning gap severity is the greater sensitivity of the measure to the inequality among those students below the MPL.

> 57 For a detailed discussion of common errors made by economists while using large-scale international assessments see Jerrim et al (2017)

> 58 Kakwani (1980)

> 59 Villasenor and Arnold (1989)

> 60 Lorenz parameters were estimated using the user written Stata ADO function GROUPDATA, available at <u>https://github.com/jpazvd/groupdata</u>

19
characteristics.

This paper seeks to contribute to that literature by proposing two simple and closely-related measures of inequality - one for educational achievement and another for opportunity to education – and reporting them for all countries that participated in the 2006 wave of PISA surveys. To measure inequality in achievement, we propose simply using the variance or the standard deviation of test scores. But we arrive at this simple proposal by considering the implications of two issues specific to the distribution of test scores for the measurement of inequality. These two issues are: (i) the fact that many common inequality indices are not ordinally invariant in the standardization to which IRT-adjusted test scores are generally subjected; and (ii) the fact that PISA student samples are likely to suffer from non-trivial selection biases in a number of countries. The choice of the variance (or the standard deviation) addresses the first issue. We also propose two alternative two-sample non-parametric procedures to assess the robustness of the inequality measure to the sample selection biases, and implement them in the four countries for which PISA sample coverage (as a share of the total population of 15 year-olds) is smallest.

The proposed measure of inequality of educational opportunity draws on the recent literature on inequality of opportunity in the income space, but is also adapted to the specificities of

> 2 There is also an International Adult Literacy Survey (IALS), which is applied to adults long after they have left school.

3
**Figure 1.3. Total Income and Consumption per capita, RLMS 1994-2015**

<!-- Start of picture text --> Panel A: Cross-sectional samples Panel B: Balanced panel<br>9.5<br>Total consumption per capita<br>Total income per capita<br>9<br>8.5<br>8<br>7.5<br>Log of income(consumption)<br>1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 1994 1995 1996 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->

**Note:** Estimation results are obtained based on total household income per capita and total household consumption per capita. Household consumption is constructed from non-durable consumption, including food, alcoholic and non-alcoholic beverages, tobacco products, clothing and footwear, gasoline and other fuel, rents and utilities, services (such as transportation, repair, health care services, education, entertainment, etc. without lumpy expenditures on housing construction, refurbishment and ritual services) and durable consumption. All consumption measures are converted to a monthly base. To keep the consumption variable consistent over time, we exclude expenditure categories that became available after 1994. All numbers are deflated with December to December regional CPIs and weighted with population weights.

62
The second dataset, SIFA, is used by the national agency that runs FA for administration and monitoring purposes. The data set is a longitudinal census of program beneficiaries from 2001 to the present. There is information on nearly 2.5 million families who have participated or are currently participating in the program. We limit this dataset to all adults from beneficiary households with a valid identity card (needed to vote) and match it with the electoral census (using each person’s national identification number). Overall, most people in SIFA (96.5%, corresponding to 3,608,733 program participants) are registered to vote.

Finally, we use the _SISBEN_ to identify non-participants that are comparable to FA beneficiaries, namely that they live in households with similar socio-economic and demographic characteristics as determined by their corresponding poverty index score. These data are also matched with the electoral census. Then we append Sisben-electoral census with SIFA-electoral census to produce a sample that is comprised of individuals below and above the cutoff score that establishes eligibility to FA. The matched sample shows that 86% of the FA beneficiaries are registered in the _SISBEN_ . The rest is mostly comprised of displaced individuals and indigenous people, who by law are not required to have a score below the eligibility threshold to participate in FA, and thus are not included in our analysis.

The sample is restricted in two ways. First, considering that the program is offered only to families with children under 18 years, the data are restricted to only include adults 25-46 years old at the moment of the presidential election. This age range is determined by the age distribution of mothers in urban Colombia as reported in vital statistics records in 2010. This is done to ensure that we focus on the group of parents whose voting behavior is more likely to be influenced by FA given the timing in which the program was rolled out throughout the country. The second restriction limits the data to only urban households. The reason for doing this is that by design, the algorithm
Power How does this evidence contrast with the determinants of power winners (columns 5 to 8 in Table 8)? e Hypothesis 5: Returns to mother tongue Tetun rise strongly, while those to speaking Tetun decline. Power returns to education rise more than those for welfare returns, suggesting a greater importance of education for power than welfare. In contrast to welfare changes, the returns to initial economic status increase over time, suggesting that power gains are regressive. e Hypothesis 6: Negative household and village level shocks generally reduce power, although the coefficients are smaller in absolute terms than for welfare. °2 Resettlement has a larger impact on power than welfare changes, suggesting that the disruptive effect on social networks outweighs the loss of location-specific economic resources. Improvements in economic sectors leave power unchanged, while more political participation and higher community status raise power, as do less corruption and better own-health. °° Overall, the results suggest that power winners include those with strong social resources (Tetun as mother tongue rather than speaking Tetun and no displacement) (Hypothesis 5). Negative economic shocks diminish power but the impact is less than on welfare (Hypothesis 6).

Conclusion This paper evaluated changes in well-being in Timor-Leste between 1999 and 2001, covering the period from prior to the referendum on independence from Indonesia to the eve of independence at the end of 2001. It demonstrated how subjective, objective and recall data from a single cross-sectional survey can be combined to provide a rich characterization of the changes in economic and power status during a time of systemic economic and political transformations.

The mobility analysis established that overall self-rated welfare improved little, while self-rated power increased dramatically. However, the changes were not 52 The coefficient on household livestock loss is insignificant and close to zero. However, power changes are negatively correlated to livestock loss in the bivariate setting. Dropping regional controls (area dummies and language variables) from the regression, the coefficient on livestock loss is negative and significant at the 2-percent level. 53 The null hypothesis of equality
>25</sup> While the 2007-08 event was led by rice prices, exacerbated by export restrictions imposed by major rice producers, the 2010-11 food price surge was led by maize and wheat prices, triggered by adverse weather events in major wheat and maize producers in Australia and the Black Sea Basin. During 2007-08, large rice consumers, such as India, imposed export restrictions to contain domestic rice price increases. These were gradually unwound over the following years. In 2010-11, some large wheat and maize producers, such as Russia and Ukraine, introduced export restrictions and import bans to contain domestic price pressures.

24Results reported here do not take into account the impact of safety-net programs such as India’s Public Distribution System, which distributes food to poor households at fixed prices.

25World Bank (2012) estimate that the 2010-11 food price spike increased the number of poor by 50 million in the short run, and by 34 million in the long run. These higher estimates do not explicitly account for insulation policies and consider price increases of a wider range of food commodities (also beef, chicken, dairy, vegetable oils and soybean prices). In addition, there is uncertainty around poverty estimates due to systematic measurement errors in household surveys that may bias the poor’s dependence on food purchases (Headey and Martin 2016). Finally, Jacoby (2016) and Jacoby and Dasgupta (2018) highlight the importance of accounting for the endogenous agricultural wage response and spillover effects to non-agricultural wages (also accounted for in this paper).

21
Figure 7: Combustion power plant HDI relative to country HDI <!-- Start of picture text --> 0.2<br>0.1<br>0.0<br>−0.1<br>−0.2<br>0 600 1200 1800<br>Combustion power plants<br>Coal Gas Oil Biomass<br>HDI<br><!-- End of picture text --> _Notes:_ This figure plots for each combustion power plant the difference between the average Human Development Index (HDI) among the population residing within 50 km and the HDI of the country where the plant is located. The HDI was measured in 2019.

neighborhoods, or the adverse effects of past pollution exposure on HDI. Despite the varied reasons for this association, this exposure pattern is noteworthy because our CRFs do not capture the plausibly greater vulnerability for populations with lower HDI levels. Consequently, our counterfactual calculations of premature deaths are likely to underestimate the deaths caused by excess PM2 _._ 5 in the region.

# **4.2 Value of lives lost due to excess air pollution**

To monetize the cost of the lives lost previously documented, we calculate the Value of a Statistical Life (VSL) estimate at the country-year level in two steps. In the first step, we follow Banzhaf (2022) and compute a base VSL from a meta-meta-analysis of U.S. VSL estimates. In the second step, we follow Viscusi and Masterman (2017) and calculate an income-adjusted extrapolation for each country and year using our base VSL, the GNI per capita data from World Bank (2023b), and assuming that the income elasticity of the VSL is equal to 21
Central Africa. Insofar as international trips may face greater regulatory barriers or search frictions, domestic surveys may underestimate the overall rate.

Individual surveys may collect the share of trips which are empty, the share of miles traveled which are empty, or the probability of not finding a backhaul job. I discuss each type of measure below.

**Empty Miles** The most direct way to measure the amount of empty driving is in terms of vehicle-miles (vehicle-kilometers). This information may be recovered from firm surveys and records, either conducted by private industry associations as in Williams and Murray (2020) or by national governments as in Eurostat (2021). Site surveys can infer an estimate of empty vehicle-miles if they collect information about the distance of trips in addition to the loaded/empty status. Similarly, trip audits as used in McKinnon and Ge (2006) that collect distance information can provide empty mileage estimates. Detailed location data from apps may also allow researcher to reconstruct empty miles under some assumptions, as Heilmann (2020) does with Uber Freight data. I present studies of empty miles in Table 4.

**Empty Trips** The next measure of empty driving comes from studies that estimate the number of empty trips. These estimates are most often generated by site surveys, which randomly poll trucks passing through a site (often a checkpoint or a rest stop) and ask whether the truck is loaded or empty. Some trip audits, such as the Danish study used in Abate and Kveiborg (2013) and Abate (2014) or the Irish study used in Central Statistics Office (2019), also measure whether or not a trip was loaded, independent of the distance traveled. I present studies of empty trips in Table 5.

**Backhaul Probability** The third common measure of empty driving estimates the probability that a truck can find a load for a backhaul trip. Typically, these estimates are recovered by directly asking firms about how often they can find backhauls, as in Council (
rather than developing country agglomeration estimates being much higher than advanced countries as noted in country studies using wage data from China and India (e.g. Chauvin et al., 2017), there is no statistically significant difference between developing country and developed country estimates. Our novel estimates of urban dis-amenities suggest that although the elasticity of pollution and congestion in developing countries is comparable with developed countries, their levels are much higher. Higher costs in developing country cities are partly due to bad design and lack of capital investment, but also the fact that their growth is not driven by the process of structural transformation, which would create a critical mass of more productive firms that benefit from sharing, matching, and learning. Many developing country cities are not dense and productive—they are just crowded.

25
Policy Research Working Paper 9607

# **Abstract**

The COVID-19 pandemic has highlighted the centrality of primary care in protecting people’s health and well-being during and beyond crises. It has also provided an opportunity to strengthen and redesign primary care so that it will better serve its purpose. However, to-date there is limited evidence on the quality of service delivery in primary care. Service Delivery Indicators surveys have attempted to fill this gap. Using Service Delivery Indicators surveys of 7,810 health facilities and 66,151 health care providers in nine Sub-Saharan African countries, this paper investigates the quality of care across five domains to understand a citizen’s experience of primary care in his/her country. The results indicate substantial heterogeneity in the quality of primary care service delivery between and within countries. The availability of basic equipment, infrastructure, and essential medicines varies—public facilities, facilities in rural areas, and non-hospitals are more lacking compared with private facilities, urban facilities, and hospitals. In terms of patient care, health care providers’ ability to correctly diagnose and treat common health conditions is low and variably distributed. COVID-19 has catalyzed a long overdue health system redesign effort, and the Service Delivery Indicators surveys offer an opportunity to examine carefully the quality of service delivery, with an eye toward health system reform.

This paper is a product of the Human Development 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 sdi@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
not be interpreted as policy experiments but rather as accounting exercises.<sup>9</sup> Two non-structural studies share similarities with our approach. Bourguignon et al. (2008) decompose country-level income distribution differences. Although their exercise is static, they also use simulations to aggregate semi-parametric models of each determinant of income dispersion, and generate counterfactual distributions in which the differences in the parameters governing each individual factor are shut down one at a time. Gayle et al. (2012) implement a decomposition that explicitly captures individual career dynamics but the object of their decomposition is the gap in the cumulative earnings of U.S. male and female executives, rather than a cross-sectional firm-level pay gap and hiring pipelines. Also, their approach relies on stationary analytical formulae rather than micro-simulations.

A second contribution of the study is to produce new evidence on where pay gaps are generated within a large, multicultural organization, using an original panel data set of sizable length and width, extracted from recent personnel records.<sup>10</sup> The novelty of our approach is to “locate” and quantify, rather than “explain”, pay gaps. It allows to focus follow-up investigations on those organizational processes where the gaps are highest and more malleable. If, for instance, most of the gender gap arose in entry salaries, a classic Oaxaca–Blinder decomposition could then be applied to entry salaries.<sup>11</sup> If instead, salary growth was the main pay gap source, follow-

> 9In fact, we lack the data to examine such behavioral responses where the gender gap is most salient – historical hiring patterns. For instance, when there is a change in the president of the WBG, churn increases sharply. It may be that the applicant pool changes in anticipation of this churn or immediately after. Unfortunately, the institution does not retain data on the historical applicant pool, and therefore incorporating behavioral responses is currently outside the scope of our work.

10Existing gender pay gap
# **Box 1: The Euro‐area debt crisis and the sovereign‐bank nexus**

# **Context**

The Euro‐area debt crisis led to financial market fragmentation and revealed the flaws of an incomplete currency union in which responsibility for financial stability remains _de facto_ national. The 2010‐12 European debt crisis erupted before the Euro area could recover from the global financial crisis and was characterized by overly high government structural deficits and accelerating public debt levels. During 2012‐16, the average consolidated public debt to GDP ratio of the EU‐28 slightly decreased from 83.9% to 83.3% and subsequently 81.6 % of GDP in 2017. The Euro area also faced a wave of defaults by property developers and homeowners. Loan defaults, structural revenue shortfalls, and the currency union’s incomplete architecture left many Euro area economies with the challenges of managing liquidity risk, establishing fiscal sustainability, and addressing a tightening the sovereign – bank nexus.

To strengthen their balance sheets, EU banks had received guidance to improve their risk‐ weighted capital ratios. This was perhaps a contributing factor for banks to increase their sovereign debt holdings further. Despite having passed stress tests conducted by the Committee of European Banking Supervisors, banks in some countries continued to struggle with large impaired assets and nonperforming loans. Concerns also emerged that the stress tests did not fully incorporate sovereign default risk. Based on data from the European Banking Authority, sovereign exposures of large banks in the Euro area: i) did not change substantially between end‐2013 and mid‐2015; ii) banks still invest more than their own funds in sovereign debt; and iii) banks’ sovereign debt holdings exhibit a strong home bias, particularly in the southern European countries and in Germany.

# **Crisis management**

Crisis responses include: _ECB liquidity and credit support_ through traditional and unconventional facilities aimed at avoiding market dislocations; _European Financial Stability Facility_ (EFSF) was created to assist Euro area sovereigns for the
on taxpayers in various tax brackets and interpolation using the Pareto distribution. But Piketty (2003:1007) notes that he could check the reliability of such methods because tax microdata was available for later periods, in addition to the tax tables.

Piketty’s work stimulated the creation of the World Top Incomes Database (WTID) in 2011, which created top income inequality statistics for 30 countries using similar data to that used in Piketty’s original work (Alvaredo et al., 2016). WTID was later subsumed into the World Inequality Database, which extended WTID to create Distributional National Accounts (DINA). The DINA project aims to provide estimates of the distributions of income and wealth that are consistent with national accounts and harmonized over time and countries (Alvaredo et al. 2016). To do so requires household survey data, national accounts data and ideally either tax microdata or aggregated data. And since comparisons of these data sources often show that household surveys do under-capture incomes and wealth, some of the research on measuring the top tail of the income distribution and the challenges of doing so that we discuss in the next section has come from researchers associated with the DINA project directly.

# The Extent of Income Inequality

A reasonable definition of the top tail of the income distribution is the top 10 percent. Using the World Inequality Database (WID) we briefly describe the share of the top 10% of the income distribution, using the most recent estimate for each region or country. Across all countries for which there is data, and excluding extrapolated or interpolated statistics, the share of the top 10% is 47%. It is only 37% in Europe but is 48% in the US, 56% in Sub-Saharan Africa, 58% in Latin America, 43% in China and 57% in India. These estimates are from a variety of sources and are of varying quality, but the bottom line is that the top of the distribution receive a very large share of total income. The next section