rafmacalaba/datause-encoder-data huggingface dataset:
A recent exception is Gertler, Shelef, Wolfram, and Fuchs (2016), which uses household survey data in several large developing countries to document an S-shaped relationship between the income distribution and both consumer durable ownership and energy use, in the cross-section and over time as income grows. They then make use of a cash-transfer program-generated exogenous shock to incomes in Mexico to establish that such relationship is indeed causal. In a related paper, Davis and Gertler (2015) analyze energy consumption using Mexican household data, looking at both the extensive margin (whether households adopt air conditioning or not) and the intensive margin (the dollar amount of electricity consumption).

Lee, Miguel, and Wolfram (2016) use an experiment to derive the demand for electricity connections among poor rural households in Kenya, and find that consumer surplus is far less than total costs at all price levels, suggesting that residential electrification may reduce social welfare—at least in the absence of externalities.

Our contribution is first to systematically document infrastructure access and asset ownership across a panel of 14 countries in Latin America over 20 years, in the four main infrastructure sectors. The SEDLAC database provides a harmonized sample of surveys, allowing in particular for the comparison of households at similar levels of income across countries of the region. While the household surveys used here are not following the same households over time, we are able to construct a pseudo-panel by grouping households by income quantiles and looking at the within-quantile evolution of infrastructure access over time. This then allows for the estimation of income elasticities of infrastructure access in a dynamic setting.

This enables us to document extensive inequality of access, within countries across the income distribution, and across countries in Latin America for households at similar levels of income. In addition, the scope of the dataset allows us to test the relevance of country fundamentals vs. policies in explaining these differences, and to provide comparative estimates of the time needed to reach universal coverage.

This paper focuses exclusively on the extensive margin, i.e., whether households have access to services or own durables, and provides descriptive evidence on infrastructure demand in a context in which household income has experienced strong and sustained growth. It therefore stops short of providing insights on the intensive margin as captured by the value of consumption expenditures on these services. Finally, it does so in the context of potential binding supply constraints on the infrastructure provision side, and credit constraints on the demand side. Addressing these issues is the object of ongoing work.

4
Dollar, Kleineberg, and Kraay (2016) update their analysis on the systematic relationship between average growth and growth of the poorest groups, examining 151 countries from 1967 to 2011. Similar to the result in Dollar and Kraay (2002), they find that the income in the poorest deciles varies in equal proportions with average incomes (Figure 5, panel e). They also find that on average, the shares of income accruing to the poorest 20th percentile and 40th percentile are fairly stable over time. These results emphasize the idea that policies aimed directly at increasing economic growth rates are indeed “pro-poor,” in the sense that they lift the average income in the lowest deciles of the income distribution.

More recent literature has corroborated the importance of economic growth in reducing poverty. Analyzing the dynamics of the extreme poverty rate (PPP $1.90 per day poverty line) in 135 countries from 1974 to 2018, Bergstrom (2020) finds that 90 percent of the variation of poverty rates can be explained by changes in GDP per capita, while much of the rest is accounted for by changes in inequality.<sup>7</sup> At the same time, a 1 percent decline in inequality (measured as the standard deviation of log income) reduces poverty more than a 1 percent increase in GDP per capita for most countries in the sample. These results are reconciled by the fact that changes in mean growth have been substantially larger than observed changes in inequality. The study confirms that although growth has been the dominant force in poverty reduction, reductions in inequality have great potential in reducing poverty rates.

While both economic growth and inequality have an impact on social welfare, growth has been the dominant force. Dollar, Kleineberg, and Kraay (2015) construct social welfare functions that are sensitive to the bottom deciles, where welfare depends positively on income growth and negatively on inequality. Focusing on five decades of data for 151 countries, they find that most of the variation in welfare across countries is driven by the average growth of income. The role played by inequality is relatively minor—again because changes in inequality have been small and generally uncorrelated with growth. These results imply that policies aimed at reducing inequality will improve welfare as long as they are not detrimental to growth but may reduce social welfare if they reduce growth. Complementary results from Jones and Klenow (2016) show that GDP per capita is a good indicator of welfare for most countries, as these two variables have a correlation of 0.98. Moreover, they find that welfare inequality is greater than income inequality across countries. The mortality rate is the most important factor driving the dispersion in welfare.

> 7 Additional studies such as Bluhm, de Crombrugghe, and Szirmai (2018) and Fosu (2017) also find that poverty reduction has been driven primarily by economic growth, with changes in income distribution playing a secondary, albeit important, role.

11
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- Brambilla, Irene, and Darío Tortarolo. 2018. “Investment in ICT, productivity, and labor demand: the case of Argentina.” _World Bank Group Policy Research_ Working Paper No. 8325.

- Bresnahan, Timothy F, Erik Brynjolfsson, and Lorin M Hitt. 2002. “Information technology, workplace organization, and the demand for skilled labor: firm-level evidence.” _The Quarterly Journal of Economics_ 117 (1): 339–376.

- Brynjolfsson, Erik, Avinash Collis, Erwin Diewert, Felix Eggers, and Kevin J Fox. 2020. “Measuring the impact of free goods on real household consumption.” _AEA Papers and Proceedings_ 110:25–30.

- Brynjolfsson, Erik, and Andrew McAfee. 2014. _The second machine age: work, progress, and prosperity in a time of brilliant technologies._ WW Norton & Company.

- Brynjolfsson, Erik, Daniel Rock, and Chad Syverson. 2017. “Artificial intelligence and the modern productivity paradox: a clash of expectations and statistics.” _National Bureau of Economic Research_ The Economics of Artificial Intelligence: An Agenda:23–57.

- Bustos, Paula. 2011. “Trade liberalization, exports, and technology upgrading: evidence on the impact of MERCOSUR on Argentinian firms.” _American Economic Review_ 101 (1): 304–40.

- Byrne, David M, John G Fernald, and Marshall B Reinsdorf. 2016. “Does the United States have a productivity slowdown or a measurement problem?” _The Brookings Institution:_ 109–182.

- Cameron, A Colin, and Pravin K Trivedi. 2005. _Microeconometrics: methods and applications._ Cambridge University Press.

34
are unable to measure _directly_ if textbooks or teacher training are offered by the state TA program (PAIC) or a local program in the municipality. So, we only measure TA components indirectly, as textbooks and teacher training can be similarly offered in municipalities on both sides of the border. As a next step, we interacted these variables with _Cearac_ to account for differences at the border between municipalities in _Ceará_ and adjacent states (see Figure 1), exactly as performed in Eq. (1). We therefore estimate the impact of each TA component by using the difference _−_ in _−_ differences in learning scores of: (i) the difference in performance between schools/teachers reporting one TA component at the border; and (ii) the difference in performance between schools at the border during the RBF periods. Table 3 presents the estimated impacts using this strategy for 5<sup>_th_</sup> and 9<sup>_th_</sup> grades.

Results from columns (1) _−_ (4) in Table 3 add important details to our previous findings. Combining the _RBF_ incentives for local mayors with the provision of _textbooks_ to teachers seems to account for a great part of TA effects on learning scores in Panel B. As indicated in columns (1) and (2) in Panel B, if all teachers received textbooks, test scores would increase by 0 _._ 162SD for Portuguese and 0 _._ 253SD for Mathematics, equivalent to around 4 months of learning. In contrast, participation in the knowledge exchange program or providing teacher training does not show significant impacts. A possible explanation for absent impacts of teacher training may emerge from the results in Panel C. After the RBF takes into account clear thresholds of minimum student performance at schools, teacher training shows a strong and significant effect on test scores. For example, for 5<sup>_th_</sup> grade, its impacts are twice as large as providing textbooks to teachers: an increase of 0 _._ 244SD for Portuguese and 0 _._ 290SD for Mathematics, or around 5 months of learning equivalent. The impacts are similarly high for students in 9<sup>_th_</sup> grade, as seen in columns (3) and (4).

26
**4**

among firms declines by the end of the period. While large firms have seen a re?axing of the extent to which investment decisions are constrained by financing, in Indonesia small and large firms enjoyed this benefit of reform, as these firms were more efficient than their medium size counterparts. Smn1a firms are precisely these that might be expected to be discriminated against in formal directed credit programs, even in programs designed to direct credit to this group.<sup>7</sup> _Although these results do not prove that financial reform increases - allocational efficiency after all, they are only for afew countries and cover a short period -- they do represen_ **_t_** _he first micro level evidence of the posidve real effects offinancial refonn, effects which have been assumed but never substantiated._ In sum, these results, along with the cross-country studies of Gelb (1989) and King and Levine (1991), are the most promising evidence to date of efficiency gains from financial reform. Together, the findings should influence the way in which development economists think about finance.

The next section presents a summary of the lessons from the studies in Caprio, Atiyas, and Hanson. The key points are that:

- the performance of the financial sector is inextricably linked to that of the real sector, in particular through the evolution of borrower net worth. Reform programs should be designed and modified to take account of these linkages;

- initial conditions in finance -- the portfolio of banks, their "information capital," their human capital, and their internal incentive systems -- play a key role in determining the success of reform efforts, and implicitly offer a blueprint for the design of reform programs;

- these two points create a bias in favor of moving promptly on various aspects of institution building in finance and more gradually (but still steadily) on interest rate deregulation and the removal of portfolio restrictions;

- a variety of sequences of financial reforms have been tried, and in particular
# **List of Tables**

|1|Macroeconomic SAM, 2004 (billions of lempiras) . . . . . . . . . . . . . . . . . . . .|5|
|---|---|---|
|2|Labor Market Segmentation Estimation Results . . . . . . . . . . . . . . . . . . . . .|9|
|3|Honduran Import Tarifs and Import Values, 2004 (Percent and Millions of Lempiras)|10|
|4|Foreign Tarifs on Honduran Exports and Honduran Export Values, 2004 (Percent<br>and Millions of Lempiras) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|11|
|5|Sectoral Composition of Production, Exports, and Imports, 2004 (Percent)<br>. . . . .|12|
|6|Initial Levels and Changes in Macroeconomic Aggregates, Trade Scenarios . . . . . .|20|
|7|Sectoral Adjustments, Percent Change with Respect to Final Year in _BaU_, Trade||
||Scenarios<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|25|
|8|Initial Levels and Changes in Factor Prices and Migration, Trade Scenarios . . . . .|26|
|9|Congruence between DR-CAFTA and Full Trade Liberalization . . . . . . . . . . . .|29|
|10|Initial Levels and Changes in Macroeconomic Aggregates, FDI Scenarios . . . . . . .|37|
|11|Sectoral Adjustments, Percent Change with Respect to Final Year in _BaU_, FDI||
||Scenarios<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|39|
|12|Initial Levels and Changes in Factor Prices and Migration, FDI Scenarios . . . . . .|42|
|13|Microeconomic SAM Accounts<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|49|

# **List of Figures**

|1<br>Nested Structure of Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|.<br>14|
|---|---|
|2<br>Pre- and post-NAFTA FDI in Mexico<br>. . . . . . . . . . . . . . . . . . . . . . . . .|.<br>33|
2

# **I Introduction**

This paper develops a statistical model of graduation from International Development Association (IDA) assistance and uses it to analyze the factors that affect countries’ graduation as well as the effects of the shift in graduation policy which occurred in FY1999.<sup>1</sup> IDA provides concessional financing (credits, grants, and guarantees) to the world’s poorest countries to help reduce poverty and improve living standards. IDA’s eligibility criteria include (a) absence of International Bank for Reconstruction and Development (IBRD) creditworthiness and (b) gross national income (GNI) per capita below the IDA operational cutoff (US$1,175 for FY2020).

Before FY1999, the two major criteria for graduation from IDA were creditworthiness for IBRD for several years and a level of GNI per capita that exceeded the operational cutoff set in 1987 and adjusted since then annually based on GNI per capita calculations in accordance with the Atlas conversion factor methodology. However, the experience of eight reverse graduations which occurred in the 1990s, including that of Indonesia following the Asian financial crisis, led to a change in policy in FY1999, intent on limiting future reverse graduations. The set of factors affecting graduation decisions broadened to include other factors to account more precisely for country-specific circumstances. The only reverse graduation after this policy change was that of Syria in 2017.

Similar to the investigation of IBRD graduation policy conducted by Heckelman, Knack, and Rogers (2012), this paper takes a positive approach to IDA graduation policy, analyzing factors that influenced IDA’s decisions on graduations and reverse graduations over the past three decades through a pooled logit estimation using panel data covering IDA-eligible countries for 1987–2016. We find that throughout the sample the probability of being a graduate is positively and statistically significantly associated with income per capita, creditworthiness, and country size. We account for the shift of policy in FY1999. Using an interaction dummy variable to capture the pre-FY1999 period, we find that life expectancy, reduced poverty, urbanization, and institutional development have been positive and significant predictors of graduation status in the later period.

# **II Background**

IDA graduation policy aims to strike a balance between two objectives. The demand for IDA resources outstrips supply, and so there is a need to graduate countries as soon as they are ready, to focus the assistance where it is needed the most. It is also important to avoid reverse graduations—making

> 1 Denotes fiscal year 1999. World Bank fiscal years run from July 1 to June 30, and thus, FY2019 ended on June 30, 2019.
Note that all the variables linking the CGE and the microsimulations are at the aggregate levels apart from the household specific employment bundle ��,� . Given that the model employed here is of a static nature, the total amounts of skilled and unskilled workers do not change, and neither do the endowments of these two labor types at the household level. However, because of the shock, employment by sector changes. The CGE model produces the economy‐wide new allocation of workers across sectors, and the microsimulation is used to determine which specific worker moves from one sector to the other. More specifically, at the micro level, workers are reallocated among the agriculture and non‐agriculture sectors by means of a probit model, where the probability of switching sectors is estimated as a function of several personal and household characteristics. Workers are allowed to switch between labor market segments until the CGE‐estimated differences in labor allocations between the benchmark and oil price scenarios are achieved. For workers who switch, a labor income is imputed on the basis of observable characteristics and the return of them prevailing in the receiving labor market segment. For example, if a worker joins a new sector, that worker will be imputed a wage based on his or her observable characteristics such as age, gender, and education.

The impact of the oil shock can be assessed by comparing the standard inequality and poverty indicators estimated for both �� and �<sup>�</sup> � .

# Results

## _Macro‐simulation: A reverse Dutch Disease_

Oil revenues are concentrated in a few companies and in the government accounts and a small percentage of the total work force; less than 2 percent in the case of the Russian Federation, is employed in the oil sector. However, the fall of the international price of oil deeply affects the structure of the Russian economy and has a pervasive impact on all households through several channels.<sup>8</sup> The first channel is represented by the terms of trade loss. Because imports have become more expensive, the cost of living rises and reductions of real income and consumption are much larger than contraction of production (GDP) and employment. The income loss and reduction of demand triggers a series of additional effects in factors and goods markets in line with a reverse Dutch disease. Economic activity

> 8 It actually has an impact far beyond its borders as it is affecting neighboring countries through a reduction in the value of the remittances they are receiving. See for example World Bank (2016) where the case of the Kyrgyz Republic, a remittance dependent country with most of its migrant workers employed in Russia, is examined.

10
# **5. Conclusion**

Using firm-level data for 87 developing countries and focusing on the proportion of female top managers of firms, we find that the percentage of female managers is much higher in the service sector than the manufacturing sector. However, this result is entirely due to firms in the retail sector, which has more female managers when compared with the manufacturing sector and also when compared with the remaining service sectors. We also find that the higher percentage of female managers in the retail sector vis-à-vis manufacturing is not uniform - it is much larger for the relatively smaller firms and for firms located in the relatively smaller cities. We hope that the present paper motivates future work to better understand the determinants and consequences of the gender composition of the top managers of private firms.

14
this is rarely the case; none of the indirect sources provides a log of the amendments made to the original data received, or engages in a systematic feedback process with DMOs to ensure original records are also corrected.

# “ _4.5 Hidden debt”_

Finally, not all debt instruments may be disclosed (“hidden debt”). Compared to the dimensions listed above, the contribution of “hidden debt” is hard to quantify. Non-disclosed loans can be identified in a timely fashion only when granular information is provided by the creditor, which is rarely the case (see Section 4). Otherwise, given the existing voluntary-based data disclosure framework, progress can only be triggered by parallel engagements of external stakeholders (e.g., analysis of project funding) that may allow some form of cross-validation.

# 5. Implications of the current data disclosure framework and policy recommendations

The debt data recording and disclosure framework described above results in the multiplication of debt statistics that differ across sources. In the absence of enforceable international definitions, these idiosyncratic differences are explained by agents’ needs to present debt data according to their own standards.

The international community has been trying to address these data challenges by (i) independently targeting broader<sup>37,38</sup> coverage of debt statistics in each country, and (ii) providing technical assistance (TA) to countries to help increase transparency. This has greatly contributed to an overall expansion of debt data disclosure, but uncoordinated interactions between direct and (multiple) indirect reporting may lead to the following major shortcomings: _1- Erroneous sense of statistical homogeneity and cross-country comparability in debt statistics_ , which can lead to major misunderstandings in the fiscal and debt policy debate. This paper shows that debt statistics are not comparable across sources, (see Section 3). Even when external agents try to centralize and standardize records, they need to rely on the availability of each country’s public debt data. For instance, the WB’s IDS are supposed to cover public sector PPG external debt. However, as shown in section 2, the majority of LICDs only have central government debt data available. Additionally, external agents cannot easily validate the accuracy of the coverage of data shared and are left to assume that it coincides with their requests. The lack of homogeneity and comparability may impact the analysis of debt statistics that

> 37 E.g., the upcoming Review of Data Provision to the Fund for Surveillance Purposes in 2021 could consider proposals to extend the minimum data provision obligation under Article VIII, Section 5 of IMF Articles of Agreement to include data on general government debt and the broader non-financial public sector to enhance IMF capacity to assess fiscal space and analyze debt risks.

> 38 The WB is examining DRS reporting requirements for domestic debt data—launching a survey on domestic debt reporting, with pilots from November 2020—and broadening external debt data requirements, e.g., the collateralization features of loans.