rafmacalaba/data-use-mentions-extended huggingface dataset:
microfinance firms account between them for more than 90 per cent of the clients served worldwide by the 234 top firms (and hence for more than three-quarters of those served by all of the 2572 firms reporting to the Microcredit Summit).” While we cannot make a similar comparison here, Honohan’s evidence suggests that during the sample period the banks here served over half of all microfinance customers worldwide.

An important feature of our data is qualitative information on the lending style employed by the MFI, the range of the services it offers, its profit status, ownership structure, and sources of funds. These detailed data enable us to offer a more complete analysis of MFI performance by lending type than has been possible before.<sup>7</sup> Lending methods vary across regions, as shown in Table 3. There are no village banks in East Asia in the sample, for example. Individual-based lending predominates in East Asia and the Pacific, while institutions in South Asia and Sub-Saharan Africa tend to lend through group mechanisms. Institutions in Eastern Europe and North Africa do not strongly favor either individual-based or group lending.

Summary statistics at the bottom of Table 1 indicate that, with the possible exception of Latin America and the Caribbean (LAC), our sample is reasonably balanced across regions. Seventeen percent of the institutions come from Eastern Europe and Central Asia, another seventeen from Sub-Saharan Africa (AFR). South Asian (SA) institutions comprise ten percent of the sample, while institutions from East Asia and the Pacific (EAP) and the Middle East and North Africa (MENA) comprise nine and seven percent, respectively.<sup>8</sup> Institutions from Latin America and the Caribbean comprise forty

> 7 For example, Hartaska and Nadolnyak (2005) use a subset of the MIX data to investigate whether the regulatory environment affects the operational self-sustainability and outreach of MFIs, but they are unable to disaggregate by lending type and their self-sustainability measure is unadjusted. 8 The regional classifications and acronyms are
Policy Research Working Paper 7511

# **Abstract**

Donors increasingly fund interventions to counteract inequality in developing countries, where they fear it can foment instability and undermine nation-building efforts. To succeed, aid relies on the principle of upward accountability to donors. But federalism shifts the accountability of subnational officials downward to regional and local voters. What happens when aid agencies fund anti-inequality programs in federal countries? Does federalism undermine aid? Does aid undermine federalism? Or can the political and fiscal relations that define a federal system resolve the contradiction internally? This study explores this paradox via the Promotion of Basic Services program in Ethiopia, the largest donor-financed investment program in the world. Using an original panel database comprising the universe of Ethiopian woredas (districts), the study finds that horizontal (geographic) inequality decreased substantially. Donor-financed block grants to woredas increased the availability of primary education and health care services in the bottom 20 percent of woredas. Weaker evidence from household surveys suggests that vertical inequality across wealth groups (within woredas) also declined, implying that individuals from the poorest households benefit disproportionately from increasing access to and utilization of such services. The evidence suggests that by combining strong upward accountability over public investment with extensive citizen engagement on local issues, Ethiopia’s federal system resolves the instrumental dissonance posed by aid-funded programs to combat inequality in a federation.

This paper is a product of the Social Protection and Labor Global Practice Group. 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: qkhan@worldbank.org, J.P.faguet@lse.ac.uk, aambel@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
itself to quantile regressions to examine whether λ is different for light and heavy users of gas.

# **5. Results**

# _5.A. Effect of HUS on Energy Expenditure Burden_

> 19 In May 2019, the HUS was monetized, i.e., it became a cash payment and was no longer directly applied to the bills. Unfortunately, we only have annual expenditures, income and HUS for 2019, and the quality of the 2019 HBS is doubtful, which means that we are unable to assess the effect of the monetization using the HBS data.
Figure 3: Employment distribution: Economy-wide <!-- Start of picture text --> Burkina Faso (2015) Cameroon (2008) Ghana (2013)<br>Rwanda (2013) USA (2013)<br>1 − 4 5 − 910 − 1920 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>1 − 4 5 − 910 − 1920 − 99 100+ 1 − 4 5 − 9 10 − 19 20 − 99 100+<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>50 50<br>40 40<br>30 30<br>20 20<br>Share of employment (%)<br>10 10<br>0 0<br>50<br>40<br>30<br>20<br>Share of employment (%)<br>10<br>0<br><!-- End of picture text --> _Source:_ Establishment censuses obtained from the statistical agencies of the select countries; see section 3. For the U.S., the data comes from the 2019 BDS dataset. _Note:_ The figure is constructed based on data for establishments in all sectors irrespective of their state or foreign ownership status. Establishments with missing employment data are excluded.

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in which BOS operate and their performance, innovation, employment and employment growth relative to privately-owned firms. Section 4 estimates the wage premium associated with workers in BOS relative to the private sector using firm-level and worker-level data. Section 4.3 uses privatizations of public firms as a natural experiment for providing additional evidence on the BOS wage premium, and estimates the effects of these events on worker and firm-level outcomes. Section 5 presents empirical evidence on business dynamism in Brazil over the last decade, and estimates how the presence of BOS affects multiple measures of dynamism at the industry level. Section 6 concludes.

# **2 Data**

Our main data source is the Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais (RAIS), an annual census of active working contracts for all formal employees in Brazil. The dataset includes information on over 3 million establishments and 40 million workers annually. Each entry in the RAIS dataset is an employee-employer match, with both establishments and individuals having a unique identifier. Establishments’ identifiers include 14-digits, with the first 8-digits representing the firm. Throughout our analysis, we will concentrate on firm-level data. The primary use of the RAIS is to compute federal wage supplements, social security benefits, and unemployment insurance. Data collection is mandatory, and given its importance, firms and workers provide high-quality information, especially on wages.

We use data from 2010 to 2020 and focus on the productive sector – we exclude workers in public administration, non-profit organizations, extraterritorial organizations, and specific categories for individual entities such as political candidates and individual rural producers. We use worker-level information including age, level of education, tenure, wage, gender, date of admission, and occupation (CBO classification codes). At the firm-level, we measure employment as the number of active workers on December 31st of each reference year, and we include only establishments with positive employment in the sample. We also use the information on the 4-digit CNAE sector classification codes<sup>2</sup>
# 3. Coverage of the Balance Sheet

Countries that prepare a balance sheet must determine its scope and identify the entities that will be included. At one extreme, a limited balance sheet would simply detail the assets and liabilities of the central government. A broader public‐sector balance sheet could include assets and liabilities of subnational governments, state‐owned enterprises, and even sovereign wealth funds, which may be substantial and have a significant impact on public sector assets particularly in resource‐rich commodity exporting countries. A sovereign balance sheet may jointly report the assets and liabilities of the government with the central bank.

All respondents indicated that the sovereign balance sheet includes central government. In more than half the cases (56 percent of respondents), the balance sheet also covers state‐owned enterprises (SOEs). One‐third of the responding countries included subnationals, and 24 percent included the central bank. Sovereign wealth funds (SWF) are included in sovereign balance sheets by 24 percent of the respondents. In some cases, sovereign balance sheets also include insurance companies, social security funds and pension plans (see graph 3).

<!-- Start of picture text --> Graph 3 ‐ Coverage of Balance Sheet<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>25<br>25<br>20<br>14<br>15<br>10 8<br>6 6 6<br>5<br>0<br>Central Central bank SOEs Subnationals SWFs Other<br>government<br><!-- End of picture text --> Source: Responses to SALM survey The inclusion of central banks in the sovereign balance is a complex issue. On one hand, the inclusion could be perceived as an ambiguous signal with regards to the independence of the central bank vs. the central government. On the other hand, the foreign reserves managed by the
data, typically at the level of the municipio or equivalent. The current version, GPW3, then assumes homogeneous distribution of population within the administrative unit. Table 6 reports, by country, the administrative level and the total number of units used. A forthcoming version, GRUMP v1<sup>4</sup> , documented in Balk _et al._ (2004) allocates reported within-unit urban population to grid cells with known light areas (to proxy for built up areas), based night-time illumination spots, coordinates of cities, and satellite imagery. It assumes homogeneous distribution of the remaining population to non-illuminated grid cells, within administrative areas.

A summary table comparing the data sources is displayed below. This draft is based on GPW3. GRUMP v1 is expected to be released shortly, and we intend to use it to reevaluate the results when it becomes available. The reader should keep in mind that inter-country 2 0.5 arc-minutes; there are 60 arc-minutes in a degree of latitude or longitude. At the equator, a one-degree grid cell is about 111 km on a side, so 0.5’ is approximately a kilometer at the equator. The length of a degree of longitude shrinks towards 0 at the poles, so grid cells vary in size.

3 Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT), 2004. Gridded Population of the World, version 3, Palisades, NY: CIESIN, Columbia University. available from http://beta.sedac.ciesin.columbia.edu/gpw 4 Center for International Earth Science Information Network (CIESIN), Columbia University; International Food Policy Research Institute (IPFRI),the World Bank; and Centro Internacional de Agricultura Tropical (CIAT),2004. Global Rural-Urban Mapping Project (GRUMP): Gridded Population of theWorld, version 3, with Urban Reallocation (GPW-UR). Palisades, NY: CIESIN,Columbia University
countries -what some have called _negative_ trade diversion (Baldwin, 2014). For example, by harmonizing product and technical standards, a deep trade agreement can generate economies of scale for firms that need to comply with one set of standards to reach multiple destinations. Ultimately, the impact on third-country firms of RTAs is an empirical question that in part depends on the content of these agreements.

Using firm-level data from Costa Rica for the period between 1998 and 2012, we first estimate the effect of RTAs of which Costa Rica was not a member on its exports to member countries.<sup>1</sup> We find evidence of a positive spillover effect, corroborating the view of a negative trade diversion. Firms are more likely to export or start exporting to a new destination market if in the previous year they exported to a destination’s RTA partner. Moreover, we find that this positive spillover effect increases with the

> 1The focus on Costa Rican firms has two main advantages. First, the firm-level data cover an extended period of time. Second, Costa Rica is a diversified economy in terms of export products and number of destination markets. During the 1998–2012 period, 14,253 Costa Rican firms exported 4,939 HS6-products to 211 economies. The quality of the firm-level data thus allows us to investigate the impact of trade agreements on different types of products over a long period of time.

1
is due to the shrinkage factor . 33 We focus on leaders with tenures of 3 years or more because annual growth data introduce substantial rounding  We focus on leaders with tenures of 3 years or more because annual growth data introduce substantial rounding ψψ

33 We focus on leaders with tenures of 3 years or more because annual growth data introduce substantial rounding We focus on leaders with tenures of 3 years or more because annual growth data introduce substantial rounding errors for leaders with shorter tenures. We also exclude leaders where we have missing growth data for some of their tenures - leaders who are in office outside 1951-2014 - as we do not observe their full growth record.

34 Nothing guarantees that only a small fraction of leaders are significant. Monte Carlo evidence shows that if growth were less noisy (an iid SD of 1% rather than 5%), 40% of leaders would be statistically significant at the 95% level.

23
of US$10,000. The reason for focusing on developing countries is that we want to have a common support with regard to a country’s wealth. If a rich country shows outstanding results, perhaps it may be of interest to other rich countries which do not do as well, but it is hardly of great interest to a poor country. But if a poor country does very well, and stands out from the pack of poor countries that mostly do poorly in PISA, readers from poor countries want to know what can explain such a phenomenon, since it clearly cannot be attributed to the wealth of the country (as captured albeit imperfectly by per capita GDP). We start by looking at the Mathematics scores, with the identical approach being used for the other two PISA disciplines; Reading and Science.

# **3.1 Mathematics**

We estimate a weighted least squares regression of student level test scores as follows:<sup>8</sup> A key estimate of interest is _γ_ , the coefficient on _V IETNAM_ , a 0 or 1 dummy variable. Regressions are run in a sequence, starting from one without any covariates in _X_ , and then adding variables in groups to expand _X_ in consecutive columns in Table 10. Column (1) in Table 10 shows that the Vietnam dummy has a coefficient of 128.05, when no other covariates are added. By construction, this is the absolute difference in means between Vietnam and the Dev7 countries. Next, we want to see the extent to which observable variables included in the PISA dataset can help to explain this large gap of 128.05.<sup>9</sup> The first set of variables included in the regression reported in column (2) concern the students themselves. The student characteristics were - if students went to pre-school, repeated a grade in the past, and how often they are late for school (ST08Q01) or skipped classes (ST115Q01). With these variables included,
productsi, t|||||||<br>-0.007<br>(0.015)|<br>0.032***<br>(0.006)|
|Industry, Year FEs|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Observations|14,808|13,483|13,831|13,905|14,119|10,995|10,695|10,359|
|Adjusted R<sup>2</sup>|0.153|0.146|0.056|0.079|0.082|0.039|0.206|0.497|

Note: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticity-corrected standard errors clustered at the country-industry level in columns. The subscript i represents firm, j represents industry, c represents country, and t represents during the COVID-19 epidemic and t-1 represents a time before the COVID-19 outbreak. Year FEs labeled as Yes means that fixed effects of the year when implemented the lagged ES surveys (cover 2016-2019) are controlled for. The following tables are the same.

28
Kee and Tang (2016). The results also hold when accounting for the presence of zeros in firm capital goods imports, further reinforcing the robustness of the estimates.

To illuminate the mechanism and trace the formation of IO linkages within domestic supply chains following HSR connections, this paper introduces a novel measure that captures firm-level IO relationships using customs transaction data. Previous studies, such as Javorcik (2004), Amiti and Weinstein (2011), Kee (2015), Bernard et al. (2019), and Alfaro-Ureña et al. (2022), rely on either industry IO tables or unique firm datasets with names or tax information to link firms and their suppliers. However, these approaches are often constrained by data limitations and applicability issues. Firm-level IO linkage in this paper is captured by identifying the _potential domestic suppliers_ for each exporting firm based on matching the product codes of the firms’ imports and exports in the customs transaction data. For each exporting firm, the potential domestic suppliers are those firms that export products sharing the same Harmonized System (HS) 6-digit codes with the imported materials of the exporting firm. Instead of using imported materials, exporting firms could have potentially sourced these materials that the same the domestically from firms export products, thereby capturing firmlevel IO linkages along the supply chains. Unlike traditional methods that rely on industry IO tables or detailed firm-level supplier data, this approach accounts for firm heterogeneity, recognizing that each exporting firm within the same industry may have a unique set of domestic suppliers. Moreover, it avoids selection bias since no actual transactions happen between the exporting firms and their potential suppliers. It is also considerably easier in terms of data requirements.

Leveraging the staggered rollout of HSR networks across Chinese cities from 2008 to 2016, this paper presents robust evidence that firm-level DVAR increases as the proportion of potential domestic suppliers connected via HSR grows. These effects are particularly strong when HSR links firms with domestic suppliers over medium to long distances, suggesting that HSR is the dominant mode of transportation for firm-to-firm interactions
and Mansuri, p. 7).

In sum, these two observational studies —the only studies of which we are aware that examine the effect on achievement of increasing or decreasing the salience of the caste order— indicate that when one’s traditional status in the social order seems more fixed, both high- and low-caste individuals are less likely to enroll in school.

# **7.   Conclusion**

The experimental data show that being hard-working or clever is not a trait of the person, in the sense that it is always there, in a fixed manner. Instead, situational cues to one’s place in the

> 11 We thank Anjini Kochar for bringing her work to our attention.

> 12 Especially girls, for whom honor considerations restrict the freedom to travel outside their own hamlet.

32
significantly across firms even in the same sector because of differences in physical and intangible capital such as ICT use, modern management practices, accumulated knowledge, and other technical competencies. This perspective on technology and its associated demands for different kinds of complementary skills generates a rich menu of hypotheses for the analyses of skill deficits, skill strategies, and their impacts on firm performance, which are captured by the following four sets of equations.

- _Skill share equations_ . Given this production function, cost minimization for a given level of output yields skills demand equations for the wage share of each labor type in total payroll. In this framework, the relative shares of each labor type are related to total output, the relative (shadow) wages of each labor type, and the technology used in the firm. Given the paucity of reliable wage data on the different skill groups,<sup>24</sup> we use a closely related specification in which skills demand is expressed in terms of skill shares of the total workforce. In its empirical implementation, skill shares are related to a set of control variables for firm size (a proxy for output), sector and region, and two kinds of technology measures: firm-specific productivity attributes mentioned earlier and indicator variables for export orientation and innovation. The analyses might distinguish skills by level of educational attainment or, alternatively, by broad occupational skill categories. The coefficients of these share equations provide insights into how firms’ productivity attributes, export orientation, and innovation might affect their demand for these different types of skills.

- _Skill deficit equations_ . At any point in time, firms’ actual skills mix may diverge from their desired skills mix as reflected in the skill share equations. It may take time for actual skill mix to reach desired levels because employers have limited flexibility to replace incumbent workers with new hires who have the requisite skills.<sup>25</sup> Temporary or even persistent skill gaps can arise with constraints in the local supply of certain skills and with technology
Start of picture text --> Figure 2. Frequency of care categories Figure 3. Frequency of five fiscal categories<br>No workers, no<br>No dependents dependents<br>11 14<br>26 No workers, children<br>in school<br>Single adult, 36<br>12<br>dependents<br>Pensioners<br>5<br>Married couple, 12<br>dependents One/two worker, no<br>58 dependents<br>26<br>Other care One/two worker,<br>categories children in school<br>Source: 2017-18 Household Expenditure and Income Survey (HEIS) and World Bank calculations<br><!-- End of picture text -->

**_Table 3. Fiscal and care category comparison (percentage of total households)_**

|||No<br>workers, no<br>dependents|**FIS**<br>No<br>workers,<br>children<br>in school|**CAL CATEGOR**<br>Pensioners|**IES**<br>Worker(s),<br>no<br>dependents|Worker(s),<br>children in<br>school|
|---|---|---|---|---|---|---|
|<br>**IES**|No dependents|5|4|7|7|4|
|**ARE**<br>**GOR**|Single adult, dependents|2|0|1|1|0|
|**C**<br>**ATE**|Married couple, dependents|1|6|2|18|31|
|**C**|Other|5|2|2|