rafmacalaba/data-use-mentions-extended huggingface dataset:
focus on collecting unconsolidated financial information to avoid double counting issues that may overestimate the footprint of the state. When only consolidated information is available, we document that in the consolidation variable and indicate in the respective subsidiaries to avoid any overestimations.

The steps to construct the BOS Global Database financial and governance modules are the following:

- 1) For each country, identify the set of unique company identifiers in the SOE registry (using their BvD ID numbers) and export the list of firms of interest to the ORBIS interface.

- 2) Retrieve the key unconsolidated variables defined in section 2 using the ORBIS interface as of 2019 for the firms of interest, to be consistent with the ownership links created as of 2019.

- 3) For firms identified through supplementary sources, allocate a unique identifier using a similar structure as ORBIS and use complementary databases (as EMIS, Pitchbook, and those described in section 3) to retrieve the variables of interest (e.g., sector, year of incorporation) and financial unconsolidated information as much as possible for employment, revenues, net after tax profits/ losses.<sup>40</sup>

- 4) In case that a specific company is found both in ORBIS and in the SOE Supplementary Database, but where there are information discrepancies, use the official data collected through the SOE Supplementary Database.

- 5) Although the main focus of financial data is to obtain unconsolidated financial statements for all companies, when we can only find consolidated information despite the use of supplementary databases, the financial information is added to the database and indicated as C1/C2 (consolidated) in the consolidation code variable and mark as n.a. (not available) for the subsidiaries to indicate that those are covered under the financials provided by the parent company.

- 6) To differentiate between corporatized and non-corporatized SOEs, create a variable (Corporate type) which specifies whether a company is corporatized or not based on the analysis of the national legal forms and/or the company
about 6 percent when reference prices were very low in 1985. The shares of the individual subsidies in reference prices are illustrated in Fig 24 .

The details of the ESC estimates are given in Annex 23 and the results are summarised in Table 14. As noted previously, as are the EPC estimates, they have been calculated for the importable hypothesis only. The exportable hypothesis ESCs could easily be estimated from the spread sheet data. Comparing incentives between the three states, the ESCs make little difference to the result suggested by the EPCs i.e that during this period UP farmers were considerably more protected (average ESC = 2.65) than farmers in Maharashtra (average ESC = 1.20 ) and TN (average ESC = 1.48).

Tablel4: **Effective subsidy coefficients for cane, 1981-90**

||1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|Avg.|
|---|---|---|---|---|---|---|---|---|---|---|---|
|UP|0.56|1.05|1.11|1.98|8.13|6.18|3.98|1.80|0.94|0.80|2.65|
|Maharashtra|0.46|0.79|0.95|1.39|2.29|1.81|1.74|1.35|0.73|0.53|1.20|
|TN|0.51|0.90|1.33|1.86|3.84|2.57|1.60|0.97|0.53|0.65|1.48|
|All India|0.60|1.12|
1983). Table 2 presents the differences in under-5 mortality by educational status of the mother derived from forty-five Demographic and Health Surveys which imply that, for the average of this sample, mothers who have secondary schooling in addition to primary schooling (with usually about four years between levels) have child mortality rates 35.8 percent lower. Our cross country results, where mean female schooling is 4.97 years, imply increasing female schooling by 4 years would lead to 39.2 percent fall in under-5 mortality.

Table 2: Mean (standard deviations) under-5 mortality by education level of the mother: DHS results

||Mothers<br>with no<br>schooling|Mothers<br>with<br>primary<br>schooling|Change<br>(percent)|Mothers with<br>secondary<br>schooling|Change<br>(percent)|Number of..<br>countries|
|---|---|---|---|---|---|---|
|East Asia and<br>Pacific|119.0<br>(40.3)|69.9<br>(18.1)|41.3|39.2<br>(16.0)|43.9|3<br>___|
|Latin America and<br>Caribbean|110.9<br>(52.5)|79.3<br>(43.1)|28.5|49.4<br>(26.1)|37.7|10|
|Middle East and<br>North Africa|94.5<br>(34.3)|62.1<br>(14.7)|34.3|41.4<br>(15.6)|33.3|6|
|South Asia|127
# **4.3.Sensitivity to missing data**

The degree of missing data in the GMD varies by variable. **(** Table 9). The variable most severely affected by missing data is sector of work. This variable is reported in only 64 out the 89 countries in the sample, representing 2.1 billion of the full sample’s 5.25 billion people. For the purposes of this exercise, we consider education across all ages, which is reported in 83 out the 89 countries. Education is not asked for very young children, however, with the cutoff age varying across countries. The education profile, therefore, only represents 4.7 of the 5.25 billion people covered by the full sample.

The results reported above use a separate sample for each profiling variable. To see if using a fixed sample would significantly alter the results, we construct key profile statistics based on two fixed 28
respectively) and 57 percent for tobacco growers in Malawi. Fertilizer usage data are not available for Rwanda and are missing for a large share of the Kenya coffee sample.

In addition to information on harvest and input use, this analysis also requires an estimate of the date of harvest. Since our long recall indicator will refer to only one harvest, we restrict the analysis to crops that are harvested only once during the agricultural calendar. Coffee and tobacco are both single harvest crops. Maize and sorghum, however, can have multiple harvests during the same year. In Rwanda, where the month of harvest information is recorded in the questionnaire for staple crops, we restrict the analysis to households that indicated only one month of harvest, or two consecutive harvest months for maize or sorghum. In Malawi, seasonal rain patterns allow for one main and one secondary ( _dimba_ ) harvest per year. We include only the main harvest from rain-fed plots. In Kenya, the calculation of the harvest date is complicated by high degrees of variability in agricultural zones within the country. The analysis in Kenya is therefore limited to areas and crops with only one harvest per year and reliable information on the harvest month for the crop.<sup>5</sup> This includes only the Rift Valley for maize production, and the Eastern, Central, Western and Rift Valley regions for coffee production. For all three countries, the household interview can pertain to one of two agricultural seasons, depending on the last completed season relative to the month of the interview.<sup>6</sup> Thus, the results are not specific to only one agricultural season.

For the harvest date assigned to households, we would ideally use the actual harvest month for each household (a second best option would be the village-specific harvest month). In the Rwanda EICV, we have and use the direct reporting of the household’s harvest month for maize and sorghum. In Malawi and Kenya, and for coffee in Rwanda, we have no
# 1 Introduction

Evidence from high- and middle-income countries suggests that the quality of school leadership has measurable impacts on teacher behaviors and on student learning achievement (Leithwood and Sun, 2012; Branch et al., 2012; Bloom et al., 2015; Fryer Jr, 2017; Leaver et al., 2019). In a meta-analysis of six rigorous studies based on data from the United States, Grissom et al. (2021) find the impact of improvements in principals’ practices on student achievement to be nearly as large as those of a comparable improvement in teacher’s practices identified in similar studies.<sup>1</sup> The authors identify three key areas through which school leaders achieve these impacts: organizing resources strategically around people and instruction; overseeing and providing feedback on teaching; and managing and effectively communicating with teachers, parents and the community. Adopting these three broad areas as a framework, we note that the literature finds that school leaders can improve the performance of teachers by (i) motivating them to attend school regularly and fulfil their assigned duties (Leithwood and Sun, 2012; Leaver et al., 2019), and (ii) directly supporting improvement in pedagogical practices, for example by observing lessons and providing feedback (Grissom et al., 2021; Hallinger, 2005; Ingersoll, Dougherty, and Sirinides, Ingersoll et al.; Grissom et al., 2013). Finally, school leaders can inculcate school cultures that are conducive to learning and support strong identification and communication between teachers, students and parents (Akerlof and Kranton, 2002; for Economic Co-operation and Development, 2005). However, there is limited evidence on the impacts of school leadership quality on educational outcomes in low-income countries.<sup>2</sup> The apparent importance of school leadership has led to a growing body of experimental evidence on interventions to improve school leadership skills, with mixed findings. Fryer Jr (2017), de Hoyos et al. (2020) and Tavares
0.05|0.05|

a! Excludes data for countries where primary and secondary are combined as basic education.

Note: The R<sup>2</sup> corresponds, in column (1), to a regression of the form noted in footnote 5, based on data for the whole sample; and in column (2), to the same regression based on data for countries with a per capita income below $4,000 in 1993.

Consider now the allocation of spending by expenditure category. The available data pertain to spending on financial aid for students (in the form of grants, scholarships, bursaries and welfare services); and on pedagogical materials. Figure 2 (panel A) shows that the share of financial aid in total spending on education follows a U-shape, falling from about 10 percent in countries at $200 in per capita GNP, to about _5_ percent among countries at $3,000, and then rising to about 8 percent among countries at $20,000. The U-shape is even more pronounced if we exclude the data for primary education where hardly any spending is used to provide student financial aid. The relation between the share claimed by student aid and the per capita GNP is relatively loose, however, with a R<sup>2</sup> statistic of only 0.06.9 It means that country wealth have little influence on the pattern of allocation.

With regard to spending on textbooks and pedagogical materials, we focus on the pattern at the primary and secondary levels where these inputs are a key determinant of student achievement. The figure (panel B) shows that as countries grow richer this category claims an 9 The R2 statistic for separate regressions on secondary and higher education is, respectively, 0.23 and 0.38.
MONGOLIA'S INFORMAL SECTOR Appendix A: Micro-studies of Informality 32 drivers and passengers. (These fares are adjusted by the market periodically, in step with large and infrequent changes in the price of fuel. This price adjustment mechanism is a great example of a strong informal institution.) Every vehicle in Ulaanbaatar is a potential taxicab.

88. Taxicabs are subject to the _Informal Sector Income Tax Law,_ and are required to pay a flat tax of 1 ,100 togrogs per month in the case of Ulaanbaatar passenger vehicles, more if the vehicle is large or carries passengers outside of Ulaanbaatar city, less if the vehicle calls some place other than Ulaanbaatar home. The Mongolian authorities have relatively more success in collecting the tax from vehicles that travel outside of Ulaanbaatar, since papers can be verified at the _aimag_ border points. Within Ulaanbaatar, tax officials have the more difficult task of discerning those vehicles used primarily as taxicabs from those that are simply personal transportation. The Ulaanbaatar Tax Administration periodically cooperates with the Road Police, doing random checks of vehicles: those that appear to be carrying passengers are required to provide evidence that they have paid their taxes. Due to the high rate of moonlighting, only 37% of the taxicabs surveyed as part of this study said they are officially registered as taxicabs. Full-time taxicabs are somewhat more compliant. Among the subset that drive more than 45 hours per week, 74% are officially registered.

89. Taxicabs provide contrast to other forms of informal activity in several respects. First, driving a taxicab is very amenable to moonlighting, as discussed earlier. Second, the nature of competition is different: entry and exit are completely costless for anyone who owns a vehicle, and price competition only exists periodically when changes in the price of fuel spark changes in taxicab fares. Third, the return to driving a taxi represents a combined return to both labor and capital. These contrasts, and the apparent popularity of taxicab driving as an informal activity, motivated the survey of 308 taxicabs undertaken in February
consistent estimates of the true average treatment effect (Goodman-Bacon, 2021). Therefore, we estimate (1) on a stacked dataset as in Cengiz et al. (2019) to alleviate concerns related to the staggered nature of our treatment. We create a specific dataset for each treatment cohort (defined as a group of countries that enacted their childcare law in a given year), which includes all observations for those countries after the law enactment, and the appropriate controls, i.e., never-treated units, and not-yet treated units. This means that for a given treatment cohort _k_ the dataset includes all periods for the countries that enact the law in year _k_ , only the periods before law enactment for countries that get the law in a year different from _k_ , and all periods for countries that never enact a childcare law in our period of analysis. Then, we stack the cohort-specific datasets and estimate equation (1) with country-cohort and year-cohort fixed effects.

Sun and Abraham (2021) show that when dynamic treatment effects are heterogeneous across adoption cohorts, the coefficients from equation (2) become difficult to interpret. Thus, for example, problems may arise if the average treatment effect in the third year after adoption of the law is different for countries that adopted the childcare law in 2010 as it is for countries that adopted in 2015. Therefore, for robustness checks, we also examine alternative estimators presented in Callaway and Sant’Anna (2021) and Sun and Abraham (2021), that deliver consistent estimates even in the presence of heterogeneity in treatment effects. Additionally, we present an estimator proposed by De Chaisemartin and d’Haultfoeuille (2020), though given the unbalanced nature of our panel, these estimates are considerably noisier.

13
We assume that wage returns to skills are constant across one’s working life (Buchmann et al. 2016). Improvement from the same intervention I translates to a ∆L ∙β� predicted wage increase. The average annual income of a worker in country j is w� , measured by the labor share of the Gross National Income (GNI) per capita. Hence, intervention I yields an additional income of ∆L ∙β� ∙w� per year, assuming no wage growth,<sup>8</sup> following Miguel and Kremer (2004) and Muralidharan and Sundararaman (2011). Students receive the intervention at age a� and they are expected to enter the labor market at age 20 (Muralidharan and Sundararaman 2011). Wage gains will be further discounted by (20‐ a� ) years. The net present value of additional wage gains can be written as: where _n_ = number of years in the workforce and _i_ = discount rate.

In this paper, the expected work life is assumed to be 40 years and discount rate is taken at 3%, a standard social discount rate in public finance (Hanushek and Woessmann 2010; Hagist et al. 2005; Börsch‐Supan 2000).

## 2.3 Cost Effectiveness of Learning Interventions

Building on the impact metrics, this paper also presents cost‐effectiveness analysis and benefit‐cost ratios of a range of programs with available cost data from J‐PAL (2014). The cost‐effectiveness of selected interventions is estimated in terms of EYOS per US$100. For instance, we first multiply the point estimate of improved learning by ∆L to get the amount of EYOS for each participant by the program. We then divide the present value of total costs of the program (including costs to both the implementer and the beneficiary) by the number of participants to determine the program cost per beneficiary. And we divide the amount of EYOS by the cost per individual, then multiply by 100 to
The baseline summary statistics in table 1 show that about 20 percent firms had used any SEBRAE service during the year before our intervention took place. More firms received material from SEBRAE (about 16 percent) than participated in training (about 9 percent).

Also, from SEBRAE-PR, we use data from the questionnaire on 29 business practices applied during the first NaN visit (between April and September 2018) to construct our baseline measure of business practices: an index that corresponds to the percentage of business practices a firm uses. At baseline, the average firm used about half of the 29 business practices (table 1).

Second, we obtained access to RAIS, a dataset from the Brazilian Ministry of Economy (Ministério da Economiado Trabalho) for one baseline and one follow-up year: 2015 and 2019. All formally registered firms in Brazil are required to report information about their employees to RAIS annually. RAIS data includes the number of formal employees, their characteristics, and wages, as well as firms’ opening date and sector. In 2015, which was the latest year of RAIS available at the start of our study, the average firm in our sample had about four employees, earning 1.6 times the minimum wage (table 1). The average employee was about 34 years old in 2015. About 45 percent of employees were male and 7 percent had a college degree or higher. The average firm was close to 13 years old in 2018, calculated based on the registration date reported in 2015. About half of the firms operated in the commerce sector, selling a wide range of products, with the most common ones being clothing and auto parts. Another 39 percent of firms performed services, most commonly restaurants, followed by accountants and medical practices. The remaining 11 percent of firms were mostly in manufacturing and construction. Although reporting to RAIS is mandatory, not all firms report to RAIS consistently year after year. Indeed, we only find 782 of the 866 study firms in the RAIS data at follow-up,
3 years it is around four times that of agriculture.' The composition of GDP by industrial origin has changed very little over the past 15 years. In 1994, the distribution among agriculture, industry, and services was 15 percent, 36 percent, and 48 perclent, respectively (WDR; 1996).

**Figure 1 Sectoral Composition of Output in millions of ZIM$ (1987 prices)**

<!-- Start of picture text --> 9.2<br>8.7<br>8.2 Manufacturing<br>5.7<br>5.2<br>4.7<br>% S o~ o S 0; - (N C' *n C C 0 ° 0 - EN Z' E 8 C E' ° ° - o (N o<br>Data source: CSO and African Regional Database (World Bank).<br><!-- End of picture text -->

# **2.2 Employment**

The active population has increased nearly threefold in the period from 1964 to 1994. Total wage employment increased by only 72 percent (see Figure 2), and, thus, not been able to absorb the growing labor force. The result is extended informal sector activities and unemployment.

**1** See Blunch and Verner (1998) for an analysis of long run sub-Saharan sectoral growth, including the case of Zimbabwe.

4
margin.<sup>10</sup> While the FactSet database offers extensive coverage, it exhibits a potential sample bias towards larger, publicly listed companies due to its reliance on publicly available disclosures. Figure A.11 shows that public companies make up about 25% of all agribusi-

> 9The nutraceutical market refers to the industry centered around foods or food products that provide both nutritional and medicinal benefits. These products, known as nutraceuticals, combine aspects of nutrition and pharmaceuticals.

> 10We are aware that intensive margin link creation might still be influenced by extensive margins entrants and exits, and will keep this possible bias in mind in discussing results.

9
20 where _pi_ and _qi_ are the values of the _i_ -th indicator for country p and q, respectively. For a robustness check, the calculations also employ the Mahalanobis distance, which is a generalized version of the Euclidean distance that corrects for correlations among the variables.

Another crucial issue in this analysis is the number of clusters. Generally, the higher is the number of clusters, the higher the ̳precision‘ of the analysis, i.e. the higher the similarity of the points within the cluster (and dissimilarity to points outside of the cluster). But with a very high number of clusters, the informational value of the analysis becomes small, and it becomes more difficult to synthesize and communicate the results in a meaningful way. For these reasons, and to examine the robustness of the results with respects to the number of clusters, results for 3, 4, and 5 clusters are examined.

Once the number is set, the clustering analysis is a relatively straightforward (but computationally demanding) exercise in finding an allocation of the world‘s countries into 3 (or 4 or 5) sets so as to minimize the sum of distances for all pairs of countries within the same set: min , (3), where _k_ is total number of clusters and Sc denotes an individual cluster.

# **5. Selected Findings**

Appendix II illustrates the recent country-by-country data in the Global Financial Development Database (2008–10) for the individual characteristics of financial systems. It shows individual country data in 8 columns: 4 columns for financial institutions and 4 for financial markets.<sup>11</sup> Overall comparisons by levels of development and by region (Table 3<sup>12</sup> and Figure 3<sup>13</sup> ) confirm that while developing economy financial systems tend to be much less deep and also somewhat less efficient and providing less access, their stability has been comparable to developed country financial systems.

> 11 The Appendix
. Indeed, a widely accepted emprical fact is that the observed average cost curves in many industries appear to be linear for a wide range of output. However, as we argue below, although the implied e¤ect of contestability will be weaker in industries with U-shaped cost curves with a ‡at bottom or L-shaped curves, it will not entirely break down.

Our econometric model relates measures of ...rm size dispersion to proxies for industry-speci...c sunk costs. The results of the paper, which are based on a crosssection of four-digit US manufacturing industries, lend support for the hypothesis that the structure of markets depends continuously on the degree to which they exhibit imperfect contestability–smaller dispersions of ...rm sizes are observed in industries whose share of investment that is composed of sunk capital is low (i.e., industries exhibiting high degrees of contestability).<sup>3</sup> The paper’s empirical ...ndings are robust to reasonable changes in model speci...cation as well as to variations in the measures of ...rm size dispersion and sunk costs.

> 3 The ...nding that sunk costs in‡uence the size distribution of ...rms is also consistent with the predictions of the Stackelberg-Spence-Dixit model of entry deterrence. Sunk costs allow early entrants to exploit their headstart and limit the scale of entry of other ...rms by strategically investing beyond their steady-state Nash levels.

2