rafmacalaba/data-use-mentions-extended huggingface dataset:
to FA translates into actual votes. Considering that voting is not compulsory, this point is of major importance in light of the large historical differences in registration and turnout rates in Colombia. For instance, 44.3% of the population registered in the electoral census casted a ballot in the 2010 Presidential elections<sup>17</sup> . Given the absence of voting records at the individual level, we exploit

> 16The average registration rate for women in the sample of analysis is 95.5 percent.

> 17Source: IDEA, website: `http://www.idea.int/vt/country_view.cfm?id=48#pres`

16
Policy Research Working Paper 7595

# **Abstract**

- This paper studies welfare dynamics, especially changes asso ciated with middle-class status in countries in the Middle East and North Africa, before and after the Arab Spring transitions, using objective and subjective welfare measures. Absent panel data, the analysis employs state-of-the-art synthetic panel techniques using repeated cross sections of expenditure data from household surveys and subjective well-being data from value surveys, which were conducted during the 2000s and the Arab Spring period. The objective welfare dynamics indicate mixed trends. About half the

poor in the 2000s moved out of poverty by the end of the decade, but chronic poverty remained high; upward mobility was strong in Syria and Tunisia, but downward mobility was pronounced in Yemen and Egypt. Subjective well-being dynamics suggest negative developments in most countries during the Arab Spring transitions. Low education achievement, informal worker status, and rural residency are positively associated with lower than average chances for upward mobility, and greater than average chances for downward mobility according to both types of welfare measures.

This paper is a joint product of the Poverty and Inequality Team, Development Research Group and the Office of the Chief Economist, Middle East and North Africa Region. 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://econ.worldbank.org. The authors may be contacted at hdang@worldbank.org or eianchovichina@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
# IV. Data

This study uses a firm-level unbalanced panel dataset from the Vietnam Economic Survey over the 20092016 period. The Vietnam Economic Survey is conducted annually by the Government of Vietnam’s General Statistics Office (GSO) and covers all manufacturing and services enterprises in the country with more than 20 employees and a representative sample of firms with fewer than 20 employees. Tax codes were used as a time-invariant identifier of firms.

Several steps were taken to clean the data prior to analysis. First, Only manufacturing firms were considered for analysis given that the cost of goods sold (COGS) is more easily interpretable as materials costs within manufacturing than in services. In addition, firms with missing or invalid financial data (namely turnover, total assets, cost of goods sold, and employment) were excluded, as were firms with invalid or missing tax codes. Third, Micro-firms with fewer than 10 employees were excluded as production function estimation is not reliable at such low levels of employment. Finally, firms with outlier or unreasonable markup estimates (less than zero or over three) were removed from the data set.<sup>7</sup> The resulting data set thus includes 117,363 observations with markup estimates across 35,659 firms over the 2010-2014 period and 2016. Information from 2015 was excluded due to incomplete cost of goods sold data, while markup estimates could not be made for 2009 (the earliest year of the data set) as markup estimates rely on lagged data (see discussion in the methodology section).

In addition, the data from the Vietnam Economic Survey were supplemented with further national and global data for analytical purposes. Input and output deflator data at the Vietnam Standard Industrial Classification (VSIC) two-digit level were sourced from the GSO, while input-output tables for Vietnam were sourced from the Asian Development Bank (ADB). Finally, fixed capital deflators were obtained from the Food and Agriculture Organization (FAO).

## Descriptive statistics

Table 1 displays summary statistics of various firm characteristics used for markup
On surveys, research has revealed the need to use improved methods for household and farm survey data collection for enhancing our understanding of the agricultural sector, particularly in low-income countries that stand to benefit the most from the resulting data (Abay et al. 2019; Carletto et al., 2015, 2017; Desiree and Jolliffe, 2018; Kilic et al., 2020). Gourlay et al. (2019) demonstrated that the inverse scale-productivity relationship in agriculture (i.e. the hypothesis that smallholders are more productive than their larger counterparts) may be a statistical artifact, driven by systematic measurement errors in farmer-reported survey data on crop production. The authors showcased these errors by comparing objectively-measured crop cutting and farmer-reported maize yields on the same sample of plots in Eastern Uganda.<sup>4</sup> Follow-up research has demonstrated that survey methods for measuring crop yields directly affect the utility of surveys for earth observation applications, and have provided unambiguous support for the use of objective survey methods to generate the required training and validation data for remote sensing models that integrate survey and satellite data to derive high-resolution estimates of crop yields (Lobell et al. 2019, 2020 in Uganda and Mali, respectively).

Despite the expanding knowledge base regarding the use of earth observation techniques in lowincome countries that are primarily characterized by smallholder farming, research studies have largely remained sub-national in scope and have exhibited heterogeneity in terms of the ground data used to fulfill comparable analytical objectives pursued in different settings. Lack of methodological research to identify the required volume of and approach to ground data collection for training and validating remote sensing models is arguably one of the hurdles against the scaleup of satellite-based estimation of agricultural outcomes across countries and expansive geographies. Identifying ground data requirements for key earth observation applications in lowincome countries, including high-resolution crop type mapping and crop yield estimation, would be important not only for assessing the utility of existing georeferenced household survey data for earth observation research but also informing the design of future large-scale household and farm surveys that can provide the
To summarize the discussion to this point, we are able to exploit three advantages over previous studies: (1) a large firm-level database drawn from extensive surveys which provide a precise measure of firm growth as well as valuable information on firm characteristics, (2) updated data on financial structure, which enables us to include a broader set of countries in our regressions than what was used in previous studies of bank-based versus market-based financial systems, and (3) a broader set of potential instruments, many of them tied directly to theoretical arguments about how endowments shape institutions, including financial ones.

Table 3 presents correlations between financial development indicators, potential instruments for financial development, and the firm characteristics that we include in the labor growth regressions. Table 4 presents regressions of financial development on firm characteristics and potential instruments, each added to the regression separately. The strongest correlations are negative ones between settler mortality and financial development (private credit/GDP; stock market capitalization/GDP). Next are the positive correlations between financial development and interpersonal trust. Note that the ethnic fractionalization measure was not strongly linked to financial development (results not in the table) and so we present only the trust measure in Tables 3 and 4.

Surprisingly, neither the legal origin variables nor our resource-based measures of endowments (commodities dummies, net oil exports) are significantly correlated with financial development. Though we tried a variety of variables, we found no strong positive relationships between grains and financial development, as we had expected from our reading of Engerman and Sokoloff, and thus we do not present the commodities dummies in the tables. We do present the net oil exports variable. Though it is not strongly correlated with our measures of financial development, it has some explanatory power for both variables (with t-statistics between 1.2 and 1.3). Similarly, though only English legal origin appears in the tables, we experimented with French legal origin and combinations of legal origins (e.g., non-English, non-French) and found no
woman according to the government’s data and 6.4 according to UN data. About half (48.7 percent) of Uganda’s population is below 15 years of age, well beyond Sub-Saharan Africa’s average of 43.2 percent and the world average of 26.8 percent. Both the high level of fertility and the youthfulness of the population have created a very high youth dependency ratio (World Bank 2011b).

Improvements in services—health, education, and infrastructure—have been unable to keep pace with rapid population growth. Sustaining the positive trends in growth will require massive investments in all of these areas, but in infrastructure in particular. Improved transport services will be needed to connect economically depressed areas and producers to markets and improve mobility of labor. Industry also needs reliable electricity to boost productivity and competitiveness. Levels of urbanization in Uganda in 2002 were relatively low compared to other African countries at 12 percent, but are expected to more than double over the next 15 years to reach almost 35 percent. Rapid urbanization needs to be accompanied by better urban services such as adequate access to water and sanitation.

The recent discovery of oil reserves promises significant fiscal and development gains to the country. Even under conservative price assumptions, oil production could increase annual government revenues by about 10 percent of GDP within 6–10 years (World Bank 2011a) Higher oil income presents greater economic opportunities as well as challenges. Increased oil income can be expected to have positive repercussions in the economy as a whole, but evidence across the world points to a mixed story. In particular most cases of increased oil wealth and dependence on natural resources have produced outcomes of increased income disparity, often resulting in political instability (Angola, Bolivia, and

> 1 The Gini coefficient is a measure of the inequality of income distribution. It ranges between zero (describing a situation where everybody has the same income level) to one (describing a situation where all income is concentrated in the hands of one person).
# 1. Introduction

A country’s human capital is critical for its economic success. A recent report found that human capital comprises the largest component of a nation’s wealth, at 64 percent (World Bank 2018). In this paper, we focus on one measure of human capital: learning outcomes. Fifteen years of literature demonstrates that learning outcomes, as well as years of schooling, matter substantially for growth (World Bank, 2018; Hanushek and Kimko, 2000; Pritchett, 2001; Hanushek and Woessmann, 2008, 2012). This insight comes at a time when the availability of international student achievement tests (ISATs) is growing. These tests are psychometrically designed, standardized assessments of cognitive skills and enable credible cross-country and over-time comparisons of learning. However, traditional ISATs often exclude developing countries – the countries that have the most potential to gain from human capital accumulation.

In this paper, we address this gap by including the largest number of developing countries in a globally comparable database of learning outcomes to date. We build on a literature producing comparable measures of learning across countries and over time (Barro and Lee, 1996; Hanushek and Kimko, 2000; Barro and Lee, 2001, 2015; Altinok and Murseli, 2007; Hanushek and Woessmann, 2012, 2016; Angrist, Patrinos and Schlotter, 2013; Altinok, Diebolt and de Meulemeester, 2014; UIS, 2017; Altinok, Angrist and Patrinos, 2018). Our database covers 164 countries/territories from 2000-2017.

The central intuition behind the methodology we use is the production of a conversion factor between international standardized achievement tests (ISATs) such as PISA, TIMSS, and PIRLS and their regional counterparts (RSATs) such as SACMEQ, LLECE, and PASEC. This conversion factor is derived by comparing scores for countries that participate in both an RSAT and an ISAT in a given time period, schooling level (primary and secondary), and subject. Following Altinok, Angrist and Patrinos (2018), we call these countries “
to assess the program’s impacts or to demonstrate causal impacts of the program.<sup>3</sup> Notably, we cannot detect what would have happened to MSMEs’ access to finance in absence of the KUR program. Moreover, while we see that borrowers access KUR loans repeatedly, we cannot determine whether repeated use of KUR loans is due to their favorable conditions or whether firms struggle to find unsubsidized commercial lending that can meet their needs. Despite these limitations, the study illuminates important trends based on a large, comprehensive administrative database of all KUR borrowers between 2015 and 2020 and a nationally representative survey of KUR borrowers.

Following the initial release of findings of this study in 2022, several key policy changes were introduced to the KUR program. In January 2023, a new regulation introduced a pathway to graduation from KUR loans, by reducing subsidies and increasing interest rates for each repeat loan from a single borrower.<sup>4</sup> The regulation also introduced penalties for participating banks that contravened program guidelines by requesting collateral from borrowers when it was not required and introduced other policy reforms to optimize the impact of the program and its outreach to first-time borrowers. Both lenders and firms now have greater incentives to access KUR as a time-bound subsidy, that can help unbanked firms climb the ladder of financial inclusion. The Government of Indonesia’s administrative data from the KUR program for the year 2023 shows that 72% of KUR borrowers were first-time borrowers, 49% were female, and 53% were graduating to larger KUR loans or commercial lending.<sup>5</sup> Our paper confirms some findings from existing literature on KUR while nuancing others. It also complements the literature by using large-scale, nationally representative data on KUR borrowers. Existing studies of the KUR program rely on internal program monitoring data, interviews with government officials, financial institutions, or small samples of KUR borrowers. Our findings on KUR’s large collateral requirements echo those in other studies (De Braw et al
*|_School Deprived_and_Learning Deprived_<br>_=_<br>**Learning poor**|

Note: Given the anchoring on the learning poverty measure on the SDGs, learning deprivation at the End of Primary is measured using schoolbased learning assessments. Hence, this measure requires the combination of two data sources, namely, school-based learning assessments used 14 World Bank 2018.

15 UNICEF 2020; WFP 2020.

16 Bhutta et al (2013) and Adelman et al (2008).

17 Blau and Currier (2006) and Blau and Robins (1988).

18 Easterly et al (2006).

19 Khan (2016).

20 Combing an ordinal (binary) and a cardinal variable always implicitly puts more weight on the ordinal data, since they always get the full score of 1. This is a problem since the measure always puts more weight on the deprivation in the ordinal variable. Alkire and Foster (2011) handle this problem by only using the ordinal form of the cardinal variable as well. The headcount measure of Learning Poverty uses the MPL to transform learning from cardinal to ordinal.

21 This measure combines a direct measure and an indirect measure to identify an underlying learning poverty concept, using two deprivation indicators, schooling and learning, in the space of reading at the end of primary. The direct measure identifies those whose basic need for schooling fails to meet the accepted minimum standard—to be enrolled in school when at the proper age to be at the end of primary. The indirect measure, learning, identifies those who do not to meet a minimum proficiency level as defined by their reading assessment for that age and grade. A learning poor child, on this approach, is one who is not in school or whose reading is not adequate to meet the specified minimum as defined by the GAML process. The indirect measure has the advantage of providing a metric of learning shortfalls through numerical distances from an agreed threshold—something the direct measure does not provide. The indirect method is stringent
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regional SPVs including OMVS (Organisation pour la Mise en Valeur du fleuve Sénégal) purchased by Mali, Mauritania and Senegal; Ruzizi purchased by Burundi, the Democratic Republic of Congo, and Rwanda; MOTRACO (Mozambique Transmission Company) purchased by South Africa, Swaziland, and Mozambique; CEB (Communauté Electrique du Bénin) purchased by Benin and Togo; and a small portion through the day-ahead market facilitated by the Southern African Power Pool.

# 3.4 Availability factors and reserve margins

Inadequate electricity is mostly the result of inadequate investment in new power generation capacity, but can also be the result of deteriorating performance of existing generation plants. One of the casualties of insufficient revenue is maintenance expenditure. Utility managers often have to choose between paying salaries, buying fuel, or purchasing spares (forcing them to cannibalize parts from functional equipment). The weighted average available capacity—which is lower than the nominal capacity due to age as well as mechanical and other constraints—for a subset of 20 countries with available data is 87 percent of installed capacity, falling to 73 percent if South Africa is excluded.

Reserve margins—available capacity over and above the capacity needed to meet normal peak demand—are small in many countries. Data are available for a subset of 14 countries, with a weighted average reserve margin of 18 percent, which is within the ideal reserve margin of 15–20 percent. However, utility reported estimates for peak demand are likely underestimated, because it is difficult to estimate unserved demand with customers not yet connected to the grid. In addition, there is a positive selection bias built into this estimate, as those utilities reporting available capacity and peak demand tend to be those better managed with the systems in place to track these data. Ethiopia, Kenya, Liberia, Mauritius, Senegal, and South Africa are the only countries with a reserve margin greater than 15 percent. Namibia and Zimbabwe have supply shortages in excess of 20 percent of peak demand.

# 3.5 Consumption

Consumption data are available for 46
- 42 -

same amount to increasing earnings, and that no additional benefit is derived from obtaining diplomas or certificates. Existing evidence indicates that the returns to primary, secondary, and higher education differ in developing countries (Psacharopoulos, 1985). Also, employers often screen prospective employees and set starcing salaries - or sometimes entire remuneration schedules - based on their diplomas or certificates. We therefore also estimate the equation below: lnWi - a + bjSij + cEi + dEi2 + 1 ekTik + fAi + j gjDij + ui (3) where j indexes the type or level of schooling and k indexes the type or level of training (no differentiation can be made within apprenticeships); DJit a caLtu!grical variau±e _L-_ ij if cne dipiowma ror curriculum j was obtained, zero otherwise.14/ Means and standard deviations of the variables in the earnings function are shown in Table 16. Most of the human capital variables have already been discussed in Sections 3-5 of this paper. With regards to experience, the CILSS provides two direct measures: the time worked in the

- 14/ An alternative to this procedure would be to estimate equation (3) minus the T and A variables for four groups of people: those with only general education, those with T, those with A, and those with both T and A. This procedure has the advantage of capturing the interaction between training and education, but it has the disadvantage of not showing the effect of the number of years of training and of any diplomas obtained. In principle, this disadvantage could be overcome by estimating earnings functions for groups defined for each year of training and by whether or not a diploma or certificate was obtained. In practice, there are insufficient observations in the CILSS data to implement this procedure.