rafmacalaba/data-use-mentions-extended huggingface dataset:
compression posed a problem in attracting qualified staff to central government, the Bank sometimes recommend selective upward wage adjustments, with varying degrees of success. Other mechanisms for attracting higher level personnel were also explored. These included senior executive services, the use of local consultants, secondment from public enterprise3, and compensation through non-wage allowance and benefits systems.

Some of the operational techniques financed by the Bank to carry out the above reforms included improvements in the data collection capacity of personnel management systems, civil service censuses, functional reviews of whole governments or selected ministries, diagnostic studies of various aspects of pay and employment issues in the civil service.

The institutional arrangements for pay and employment reform have varied, but in general, reform efforts located in more autonomous institutions with greater access to higher levels of government have a better chance of succeeding.
comparing simple averages, we find that women-owned businesses have lower sales compared to their male counterparts, although the difference is not statistically significant. At the same time, men also report higher costs of goods sold and other expenses such as interest and depreciation expenses. Consequently, net profit and tax declarations (payable and to be paid) are not statistically different across genders.<sup>5</sup> When we consider the actual magnitude, men-owned businesses have, on average, 20% higher sales (see Table 4). This figure is significantly lower than what has been reported by World Bank (2019). The study, which is based on the Ethiopian Socio-Economic survey, reports a 79% difference in sales between women and men-managed businesses. However, World Bank (2019) primarily covers small and informal businesses.

Looking at the other side of the equation, the cost of goods sold by menowned businesses is also higher than those of their women counterparts by about 16%. Moreover, reported expenses by men and women are statistically different and high (where expense reported by men is almost double). As a result, the difference in reported tax liabilities between men and women is much smaller than the difference in sales. Finally, the difference in tax payment by men and women—a variable influenced by enforcement measure —is negligible and is also not statistically significant.

The fact that men reported the same tax liabilities, despite substantial differences in gross profits, highlights gender disparities in tax declaration behaviors. Inflating costs and expenses is a well-documented strategy for reducing tax liabilities (as observed in Mascagni et al., 2021; Slemrod et al., 2017). Our analysis reveals that men tend to report considerably higher expenses compared to women-owned businesses, and this difference is statistically significant.

> 5Tax payable and tax due are closely related but distinct measures of income tax liabilities. These measures differ due to the presence of tax credits and are defined in Table A.1 in the Appendix.

14
also when the law does not include any of the elements that are tracked in that pillar. A score equal to one means that the country’s law has been enacted and includes all possible elements that are tracked for that pillar. We then standardize each score across the full sample.

_Labor Market Outcomes_ We use ILOSTAT indicators on FLFP rates and other labor market indicators reported by the International Labor Organization. The data is available on a yearly basis for 80 of the 95 countries covered in the WBL dataset but its time coverage varies across countries, resulting in an unbalanced panel. The LFP rate is reported for 10-year age groups as well as by two aggregate categories of marital status where women are categorized as single or married.<sup>6</sup> Our sample comprises women aged 25 or above.

_Socio-demographic variables._ Our analysis also requires country-year-level information on marriage rates and educational attainment. We obtain data on marriage rate per 1000 people and women’s average age at marriage from the Our World In Data database. The marriage rate corresponds to the ratio of the number of marriages during a given year to total The at for women to the mean population. average age marriage corresponds age at first marriage for OECD countries. For other countries, figures are estimated indirectly from the proportions of single women by age.

The education data comes from the 2021 update of the Barro-Lee Educational Attainment dataset (Barro and Lee, 2013), which covers the years between 1995 and 2015. We obtain the share of women with primary education, secondary education, and tertiary

> 6Women in a civil union or cohabiting women are categorized as married; and widowed or divorced women are considered single.

8
2022; however, the addresses are distant because the EAs are large. Regardless of its strengths and limitations, this frame is inaccessible as the data is only available on a restricted computer at ArmStat (Pettersson, 2023).

Therefore, our focus is on the latter two datasets, including the 2011 settlement-based frame and the 2023 electoral precinct-based frame, and their advantages and disadvantages are described below.

# **2.1 Census Settlements**

The most granular spatial information in this dataset is “settlement”. There are 1,037 settlements (980 villages, 45 towns, and 12 districts in Yerevan). The main advantage of this frame is that it identifies slightly over 1,000 geographical areas in the country, which is better than having only regions or _marzes_ .

Suppose that 400 settlements were selected in the first stage of the two-stage stratified cluster sampling design as primary sampling units (PSUs), and all 12 districts in Yerevan are likely to be selected via probabilities proportional to size (PPS) approach in the first stage due to the large population in these large districts. Supposedly, if 10 households were randomly selected from each district in the second stage, the sample size from Yerevan would be 120 households, only 3% of the total sample of 4,000 households. However, approximately 35% of the population and 38% of total households live in Yerevan according to the 2011 Armenia Census. One can select a disproportionate number of households from each PSU in the second stage to account for the variations in the size of the PSUs in the first stage. For example, selecting 100 households from each district in Yerevan would yield 1,200 households from Yerevan, which is 30% of the sample.

It is better than having spatial information only at the level of administrative units or strata (strata is defined as a combination of _marz_ and settlement types–urban or rural status–following other official and major surveys) like in several other easily accessible datasets. The primary and more serious
of over 600 firms to use in the matching exercise. We focus on the period 2009– 18, for which the World Bank Infrastructure SOEs Database has better coverage.

One concern we had was whether our database of private firms included state-owned firms, despite our efforts to try to eliminate them. Because Capital IQ does not provide information on the ultimate ownership of firms, in a first iteration we use their “public ownership” indicator to remove some obvious SOEs from the comparison set. We used the database of Lazzarini and 14
throughout the analysis to correct for oversampling and ensure that the sample is representative of the non-agricultural private sector of the economy.

We focus on the sample of manufacturing firms and all countries for which data are available.<sup>1</sup> The two most recent rounds of the ES in the country are used in the regressions. Note that since the ES do not track firms over time, the sample used is a repeated cross-section rather than firm-panel. Our baseline sample consists of 44,539 manufacturing firms. We complement the ES with other data sources such as World Development Indicators (WDI), World Bank, Doing Business (World Bank), World Value Surveys, and so on. In the online appendix, Table A1 provides the list of countries in our baseline sample; Table A2 provides the summary statistics of all the variables used in the regressions; and Table A3 contains the correlations between our main explanatory variable, exports (defined below), and other variables used as controls.

All our regressions use Huber-White robust standard errors clustered at the country-ES time-industry level, where “ES time” is the final or initial ES (survey round) in the country; industry grouping is at the 2-digit ISIC Rev. 3.1 level (31 industries in the baseline sample).

# 3.1 _Estimation method_

The baseline regression exercise involves estimating the following equation:

> 1 Enterprise Surveys do not cover the primary sector, public sector, mining, and services sectors such as education and health.

9
Whether we estimate the CPI change on the subsidized product or on all products, the general equilibrium case implies deflating household expenditure and government revenues by the inflation rate generated by the change in the price of the subsidized good. With inflation adjustments, the final government revenues are simply described as Note that .

## **Estimating elasticities**

We have seen that to estimate changes in total revenues we need to know the demand elasticity of the subsidized good to changes in prices. This elasticity may be known for the particular good treated. For example, country and sector specialists may already know rather well demand elasticities for specific subsidized goods and, in this case, all we need to do is to use these known elasticities. However, in many cases, demand elasticity is not known in advance and will have to be estimated. The economic framework is simply the classic demand schedule where the slope of the demand and the elasticity is given by - _d_ q/ _d_ p or. With only a cross-section survey, we may have variation in price (say across regions or districts). In such case it is possible to estimate the demand elasticity with a simple equation as proposed by Deaton (1997): where is the expenditure (income) elasticity and is the own-price elasticity, which is our parameter of interest. This equation is a very general equation that can rarely be applied as such. Depending on the type of good considered, we may want to control for various factors such as substitutes goods or use fixed effects for spatial indicators such as urban or rural areas or municipalities. Moreover, with subsidized goods, it is rare to find variation in prices across regions or districts in cross-section surveys. Indeed, this would imply different subsidies across areas. Therefore, the equation above can only be considered as a broad baseline equation that need to be adjusted and tailored to the particular good and country treated.

The equation above also assumes that demand elasticity is equal for all consumers while we may want to consider different elasticities
claimants in specific instances. The LLSV theory focuses on the differences between five influential legal traditions: the British common law, the French civil law, the German civil law, the Scandinavian civil law and the Socialist law countries. To identify the legal origin of the country, we use five different dummy variables to capture each type of legal system using data from LLSV (1998, 1999). We refer to the effect of legal origin on property rights as the **_Law and Finance view._** Acemoglu, Johnson, and Robinson (2001) (henceforth AJR) argue that in many countries, especially former colonies, the legal system was not designed to protect property rights. Instead, its purpose was to facilitate the extraction of resources from the indigenous population. Thus, two systems with the same legal origin may in practice offer very different protections. AJR (2001, 2002) and Engerman and Sokoloff (1997) contend that European colonization offers a natural experiment to test this hypothesis. Europeans set up extractive systems in colonies which were not attractive for colonial settlement because of high settler mortality due to natural causes at the time of colonization or because the indigenous population was relatively large. In colonies where settlement was feasible the judicial systems were set up so as to protect the property rights of the settlers. This theory emphasizes the role of geography (latitude and natural endowments) and disease environment (which affected the settler mortality) in shaping property rights. We refer to this theory as the **_Endowment view_** .

To measure geographical endowments, we use Latitude, which is the absolute value of the latitude of the country scaled between 0 and 1, from LLSV (1999). Countries closer to the equator tend to have a more tropical climate that was inhospitable to European settlers and therefore fostered “extractive” institutions. We use data on Settler Mortality from Acemoglu, Johnson, and Robinson (2001) where settler mortality is a measure of the death rates faced by European settlers in former colonies
As in other successfully industrializing countries, Chinese government investment has been a key component of industry growth. Public investment, as reported by flow‐of‐funds data, averaged 6 percent of GDP during 1995–2007, including capital transfers. Rising levels of public investment were supported by strong growth in tax collections and other revenues (for example, land transfers by local governments) and by limits on current spending. As a substantial amount of investment is financed through special‐purpose municipal investment companies, total public investment was even higher.

China’s traditional capital‐intensive, industry‐led growth pattern is amplified by its policies. The government has favored industry and investment over the services sector and domestic consumption in several ways: • _Policies have encouraged saving and investment._ Government spending has been geared to investment in physical infrastructure instead of current spending on health and education. Apart from its obvious direct effect, this has increased precautionary saving by households. In addition, investment has been encouraged by several features of the tax system, by the exemption of SOEs from paying dividends, and by the channeling of capital transfers and loans to SOEs and SOE‐type entities.

• _Investment in industry has been encouraged through the financial system._ Commercial bank lending rates have been kept low for those with access to financial markets. Access has been easier for large industrial firms, which has promoted capital‐intensive industrial development. The People’s Bank of China reports that small and medium enterprises)—which are far more prevalent in services than in industry—account for more than half of GDP, but receive less than 10 percent of total bank loans.

• _Industrialization has also been promoted by keeping the prices of key inputs low._ Besides capital, these inputs include energy, electricity, utilities (including water), land, and low pricing of environmental impacts.

• _Promoting industrial development has meant that services­sector development was not given priority._<sup>5</sup> Recognizing this,
Klein, M. W., C. Moser, and D. M. Urban. 2010. “The Contribution of Trade to Wage Inequality: The Role of Skill, Gender, and Nationality.” NBER Working Paper 15985, National Bureau of Economic Research, Cambridge, MA.

Leibovici, F., and J. Crews. 2018. “Trade Liberalization and Economic Development.” Economic Synopses, No. 13, Economic Research, Federal Reserve Bank of St. Louis. https://research.stlouisfed.org/publications/economic-synopses/2018/04/20/tradeliberalization-and-economic-development.

Li, X. 2004. “Trade Liberalization and Real Exchange Rate Movement,” _IMF Staff Papers_ 51(3): 553-584.

Ostry, J. D., and A. K. Rose. 1992. “An Empirical Evaluation of the Macroeconomic Effects of Tariffs.” _Journal of International Money and Finance_ 11: 63-79.

Ostry, J. D., A. Prati, and A. Spilimbergo. 2009. “Structural Reforms and Economic Performance in Advanced and Developing Countries.” IMF Occasional Paper No. 268, International Monetary Fund, Washington, D.

Pavcnik, N. 2017. “The Impact of Trade on Inequality in Developing Countries.” The Jackson Hole Economic Symposium Proceedings, Fostering a Dynamic Global Economy, pp. 61-114.

Prati, A., M. G. Onorato, and C. Papageorgiou. 2013. “Which Reforms Work and Under What Institutional Environment? Evidence from a New Data Set on Structural Reforms.” _Review of Economics and Statistics_ 95(3): 946-968.

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**<mark>Figure 8. Changes in per capita household income between 2019 and 2020 by quintiles of the</mark> initial income distribution using different employment data. In percentages**

<!-- Start of picture text -->
-14.0% -12.0% -10.0% -8.0% -6.0% -4.0% -2.0% 0.0% 2.0%<br>Actual data<br>Elasticities<br>Actual data<br>Elasticities<br>Actual data<br>Elasticities<br>Actual data<br>Elasticities<br>Q1 Q2 Q3 Q4 Q5<br>Turkey<br>Philippines<br>South Africa<br>Brazil<br><!-- End of picture text -->

Notes: Projections following the methodology detailed in section 3.1. Estimates based on reported employment data. Estimates do not include changes in non-labor income.

<mark>Figure 9 compares changes in poverty, vulnerability, and the middle class between 2019 and 2020 when using the methodology of Section 3 and when using employment projections based on employment elasticities as an input in the macro-micro simulation model. The results indicate that the use of employment elasticities leads to: (i) smaller poverty increases and even poverty reductions in some cases (for instance, the share of people below the 6.85 USD-a-day poverty line in the Türkiye); (ii) smaller reductions in the share of the vulnerable population and increases in some cases (Philippines, South Africa and Brazil); and (iii) smaller reductions in the size of the middle class in some cases (Philippines, South Africa and Brazil) and a an increase in some other</mark> (Türkiye).

<mark>For Brazil specifically, the differences are substantial. Using 2020 employment data, our simulations project poverty increases ranging between 3 and 5 percentage points depending on the
5th percentile of the quality distribution and the 95th percentile is 40 percentage points. About 80 percent of this variation in competence is within-country or within-state. Second, the correlation between workload and clinical competence is weak. Across 11 countries in Sub-Saharan Africa, against a mean of seven patients per provider per day, higher-knowledge providers have higher caseloads only in Tanzania (two additional patients per standard deviation in competence) and in Kenya and Sierra Leone (one additional patient). In Mozambique and Malawi, each additional standard deviation of provider competence is associated with two _fewer_ patients per day. The remaining countries all exhibit effectively no relationship between provider knowledge and outpatient caseload.

Daniels, Das and Gatti (2022) use the data from Sub-Saharan Africa and India to present a mechanical calculation of the potential gains from relocating doctors, whereby the highest quality doctors are posted to the busiest clinics, the 2<sup>nd</sup> highest to the 2<sup>nd</sup> busiest and so on, always ensuring that the relocations are within country (or state in India) and sector. They find that patient-weighted quality could increase by 0.75 standard deviations in the Sub-Saharan Africa sample and by 0.5sd in the Indian sample, which 17
# **<mark>Pre-registration</mark>**

<mark>We prospectively registered a protocol of our analysis in the Center for Open Science (OSF) on 10 February 2023 (https://osf.io/jcp2b).</mark>

# **<mark>References</mark>**

<mark>Agostinelli, F., Doepke, M., Sorrenti, G., Zilibotti, F. (2022). When the great equalizer shuts down: Schools, peers, and parents in pandemic times.</mark> _<mark>Journal of Public Economics</mark>_ <mark>206 104574.</mark> <mark>Angrist, N., Djankov, S., Goldberg, P.K., Patrinos, H.A. (2021). Measuring human capital using global learning data.</mark> _<mark>Nature</mark>_ <mark>592(7854): 403-408.</mark> <mark>Angrist, N., Djankov, S., Goldberg, P., Patrinos, H. (2022). Mapping the Global Learning Crisis: Despite record enrollments in school worldwide, learning is limited.</mark> _<mark>Education Next</mark>_ <mark>22(2): 50-56.</mark> Azevedo, J.P., Hasan, A., Goldemberg, D., Geven, K., Iqbal, S.A. (2021). Simulating the potential impacts of COVID-19 school closures on schooling and learning outcomes: A set of global estimates. _The World Bank Research Observer_ 36(1): 1-40.

Belot, M., Webbink, D. (2010). Do Teacher Strikes Harm Educational Attainment of Students? _Labour_ 24(4): 391–406.

Betthäuser, B.A., Bach-Mortensen, A.M.,
intermediate input classified as either “Differentiated" or “Homogeneous" according to Rauch (1999).

Second, we study whether intermediate inputs that require highly-specified contracts and, therefore, are likely to be traded in locations with better institutional quality display higher complementarity (Boehm, 2022; Boehm and Oberfield, 2020). To test this hypothesis, we use the geographic variation in institutional quality across Indian districts. We classify districts into terciles ( _Low_ , _Medium_ , and _High_ ) according to the average time it takes for courts in each Indian district until a first hearing from the DevData Lab database, and we estimate our firm-level elas-

> 28For instance, in Elliott and Golub (2022), firms need specific knowledge about input requirements and capabilities from their trading partners and therefore need an ongoing relationship to overcome moral hazard problems.

30
children between 0 and 8. We specifically use a set of variables measuring households’ attitudes towards the future that were asked following the structure of the CESD questions, a widely-used measure of depression (Radloff, 1977).<sup>9</sup>

# _Methodology_

Given the randomized assignment and the high take-up rates, we can estimate intent-to-treat estimates that are very close to average treatment effects, without concerns about potential selectivity into treatment. Specifically, we consider the impacts of the vocational training package and the productive investment package and estimate the following intent-to-treat household-level regressions: where Y are outcomes of interest, _Grant_ and _Training_ capture the random treatment assignment, Z is the set of baseline variables for which we analyze treatment heterogeneity, and X includes additional baseline controls as well as block stratification dummies.<sup>10</sup> All errors are clustered at the community level.

The outcomes of interest Y include the participation and returns to nonagricultural wage and selfemployment activities two years after the end of the intervention. As the interventions were targeted at the household level, the analysis focuses on household level outcomes. Participation in nonagricultural wage employment takes the value 1 if at least one household member had a non-agricultural wage job at some point during the last 12 months, and 0 otherwise. Participation in nonagricultural self-employment takes the value 1 if at least one household member had a nonagricultural self-employment activity at some point during the last 12 months and 0 otherwise. We use two different variables to measure returns to nonagricultural self-employment, one that measures profits of any non-agricultural self-employment activity the household was active in at the moment of the interview and a second that aggregates gross income of all nonagricultural self-employment income in the last 12 months. While the latter has the disadvantage of not accounting for costs, it provides a longer reference period. The type of nonagricultural selfemployment activities captured by these variables include activities such as food production, small manufacturing, commerce, services, or rearing of small livestock and selling of derived products. All

> 9 In addition