rafmacalaba/data-use-mentions-extended huggingface dataset:
# **B. Appendix: Empirical calculations of wage premia**

## **_B1.The SEDLAC dataset and sample_**

The evidence is drawn from the analysis of microdata from a large set of household surveys that are part of the _Socioeconomic Database for Latin America and the Caribbean_ (SEDLAC), jointly developed by CEDLAS at the Universidad Nacional de La Plata (Argentina) and the World Bank’s LAC poverty group (LCSPP). This database contains information on more than 200 official household surveys in the region (see _sedlac.econo.unlp.edu.ar_ ). All variables in SEDLAC are constructed using consistent criteria across countries and years, subject to the constraint of the survey questionnaires, and identical programming routines.

The document covers a set of 16 Latin American economies: Argentina, Brazil, Bolivia, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay and Venezuela. Only Guatemala is missing among the continental Latin American countries. The sample represents 97.5 percent of the total Latin American population.

In this study, the sample for each country includes all individuals aged 26-56 that have fully coherent answers about income and education in the corresponding household surveys. For the wage premia, and following Manacorda _et.al_ (2010), the analysis attempts to control for the secular increase in female labor force participation by computing premiums from a sample of male workers only.

## **_B2.Construction of wage premia_**

Skilled workers are defined as those with some college education (complete or incomplete), and the unskilled as those up to complete secondary education – but without college. Unskilled labor is further divided into high school graduates and those without a high school diploma – also referred to as “dropouts” in the analysis.

These wage premiums are estimated by means of a Mincer wage regression based on individual worker’s microdata. The Mincer equation consists of a regression of the logarithm of the
# Figure 16. MNA: Potential output growth

A. Potential output growth B. Potential output growth by different estimates <!-- Start of picture text --> Percent Percent<br>2000-10 2011-21 2000-21 2011-21<br>8 6<br>2022-30 2000-21<br>6<br>4<br>4<br>2<br>2<br>0 0<br>EMDEs MNA PF MVF UVF Exp.<br>C. Investment growth D. Female labor force participation<br>Percent Real fixed investment $/bbl Percent<br>25 Brent crude oil price (RHS) 120 50<br>20 100<br>40<br>15<br>80<br>10 30<br>60<br>5<br>40 20<br>0<br>-5 20 10<br>-10 0<br>0<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 EMDEs MNA<br><!-- End of picture text --> Sources: International Monetary Fund; Penn World Tables; UN Population Prospects; World Bank. Note: EMDEs = emerging market and developing economies; MNA = Middle East and North Africa. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates). A.B. Period averages of annual GDP-weighted averages.

A. Estimates based on the production function approach. Sample includes 53 EMDEs, of which 5 countries are from MNA region.

B. MVF = multivariate filter; PF = production function approach; UVF = univariate filter (specifically, the Hodrick-Prescott filter). Expectations-based estimates (“Esp.”) are potential growth proxied by five-year-ahead IMF World Economic Outlook growth forecasts. Sample includes three economies (Jordan, Morocco, and Tunisia). C. Based on growth rate of real fixed investment and Brent crude oil price.

D. Based on female
the country unemployment rate was at 9.7% (20.8% for youth 15-24 and 10.5% among women). In urban area, the figures of unemployment are even worst for youth (39%) and for women (21.7%).

> 4 According to the Doing Business Dataset this ratio was at 0.77 in year 2013 (www.doingbussines.org)

> 5 International evidence indicates that a 10 percent increase in the level of minimum wage is estimated to reduce youth employment by a range of 1 to 4 percent in an average labor market (Angel-Urdinola and Kuddo, 2010).

2
data and per capita GDP growth-based projection, and 0.62 between survey data and phone survey-based projection. Yet there are clear outliers. The change in poverty is 4.5 percentage points higher in Colombia in survey data compared to per capita GDP growth-based projection and 4.8 percentage points higher compared to phone survey-based projection. On the other extreme, survey data estimates a 3.8 percentage point lower poverty in Brazil compared to the per capita GDP growth projection. The lower poverty in Brazil in the survey data can be explained by the extraordinarily large social protection measures implemented during the pandemic (see Castanada et al. 2022).

The poverty rate and size of increases in poverty using the distribution-neutral per capita GDP projection are similar to those using phone surveys. These are reported in Figure B.9. This similarity in estimated poverty is due to the similar magnitude of changes in growth between those calculated using per capita GDP and those calculated using sectoral growth rates, which are used in the phone survey simulations. The fact that the magnitude of the changes is similar and the phone surveys potentially provide additional signal on the distributional changes is an added benefit of using the phone surveys in conjunction with the sectoral growth rates compared to the distribution-neutral per capital GDP growth rates.

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11|10|—|6.5|—|—|18|15|
|Crane productivity<br>(tonnes/hour)|11|7.5|—|16|9|—|15|25|
|**arge**<br>Container cargo (ship to<br>gate, $/TEU)|155|125|138|320|—|110|258|258|
|**ng ch**<br>General cargo ($/tonne)|6|6.5|6-7|8.5|6|15|—|8.4|
|**ndli**<br>Drybulk($/tonne)|2-3|2.5|—|5|3|5|6.3|1.4|
|**Ha**<br>Liquid bulk ($/tonne)|0.5- 1.0|0.8|1|—|—|2|0.4|—|

_Source_ : AICD ports database (www.infrastructureafrica.org/aicd/tools/data. _Note_ : TEU = 20-foot equivalent unit. — = Not <u>available.</u> Handling fees in Mozambique are relatively low. As of 2006, the container cargo fare was in the range of $125–$155 per TEU, second lowest after the Walvis Bay port (Namibia). After the Cape Town port (South Africa), dry bulk handling charges in the Maputo and Beira ports are the lowest in the region (table 8).

There is widespread compliance with International Ship and Port Facility Security (ISPS) regulations in Mozambique. Generally, the ports run by private companies promote good security, as is demonstrated by the measures now in place at the Port of Maputo, which include increased electric fencing and gates, an increase in the number of land- and water-based security patrols,
Are Public Sector Workers in Developing Countries Overpaid? Evidence from a New Global Data Set<sup>#</sup> T. H. Gindling<sup>*</sup> , Zahid Hasnain<sup>‡</sup> , David Newhouse<sup>‡</sup> , and Rong Shi<sup>‡</sup> JEL classification: J31, J38 Keywords: Wages, Public sector labor markets, Developing country labor markets

> ‡ The World Bank. Email: <u>zhasnain@worldbank.org.</u>

> ‡ The World Bank. Email: <u>dnewhouse@worldbank.org.</u>

> ‡ The World Bank. Email: <u>rshi@worldbank.org.</u>

> * UMBC. Email : <u>gindling@umbc.edu</u>

# The authors thank Gary Fields and the participants of the October 2017 IZA Workshop on Labor and Development; and Daniel Rogger, Richard Disney and participants of the Innovating Bureaucracy conference organized by the World Bank in November 2017 and December 2018 for helpful comments.
find that, while output falls steeply immediately after conflict (6 percent on average), the economy recovers relatively soon afterwards. Mueller (2012) shows that this, fairly optimistic, view is due to a coding error. Economies contract by more than 18 percent in a civil war and this is not recovered within a decade. Mueller et al. (2017) confirm that there does not seem to be a distinct recovery growth period in the economic growth data. Countries appear to return to their average growth level in the absence of conflict. While this is good news in the sense that there is positive growth on average, it is also bad news in the sense that there seems to be, on average, no adjustment back to steady state once conflict is over. This is in contradiction with findings on the quick rebuilding of capital stock found in the literature. A possible theoretical explanation for this disparity between the micro and macro evidence comes from Barro and Sala-i-Martin (2003) who predict that the speed of recovery depends on the type of capital that is destroyed, with a slower recovery if human capital is destroyed, because it has a higher adjustment cost.

This is particularly relevant as there is evidence in the literature that exposure to conflict during early childhood or adolescence has a huge and persistent direct effect on health, education and productivity of children. Leon (2012) shows that the average person exposed to political violence in Peru before school-age accumulated 0.31 fewer years of schooling upon reaching adulthood. This suggests that children who were affected by violence during early childhood suffer irreversible effects. Duflo (2001) finds that the effect of a huge school construction program in Indonesia on school attainment in the long-run is of a slightly smaller magnitude. Each school constructed per 1,000 children led to an increase of 0.12 to 0.19 year of education. This means that if one wanted to mitigate the long-run effect of conflict in Peru on schooling by opening more schools, one would need to build more
firm value added, with no hint at a strong explicable pattern. Similarly, in column 2, we detect no significant effects on value added from Landscan ambient population. Our results for employment are more interesting. First, they suggest that it is employment density that matters for firm productivity, not the resident population or a composite of the resident population. In column 3, there appears to be a negative effect of greater employment density in the owncell, with positive spillover effects occurring from the second ring in the radius around 2km away from the reference cells. Experimentation suggested employment effects were driven by own industry effects. In column 4 on own-employment density, we also see the

> 20Obviously the Landscan data are for much later. We do note the simple correlation cofficients on own indsutry employment in Table 11 right panel, between 2002 and 2012 census measures are all over 0.97 for the 4 rings.

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to, among other things, streamline the process of business incorporation, implement e-payment facilities, and facilitate trade. More recently, PEMUDAH has provided healthcare industry players with the requisite information on the submission process and harmonization of technical requirements for setting up private hospitals in Malaysia (Kuriakose and Eknath 2020).

# **6. Implications for future research**

This paper is the first step in a longer research agenda on the mandate, structure, and composition of reform committees. As such, the current data lays a foundation that could be further refined and expanded to answer different research questions. The types of questions the data in its present form can address include further analysis of descriptive data to provide an even more granular understanding of what the current data suggests. The data could be used to design typologies of reform committees or related indicators which could be analyzed using different statistical methods. Statistical correlations could also be generated for further empirical testing. For example, is there a correlation between the existence of a reform committee and the number of business regulatory reforms enacted, or the type or effectiveness of business regulatory reforms implemented over the course of the existence of the committee? Similarly, is there a correlation between the position of the committee within the government (housed at the Prime Minister’s office, at a Ministerial level or other) and governance effectiveness or regulatory compliance as measured by either sector-specific improvements or broad national indicators such as the Worldwide Governance indicators, Fraser Institute’s Economic Freedom indicators, or Business Ready (the new World Bank Group’s benchmarking project assessing the business and investment environment worldwide<sup>13</sup> )?

An important contribution of the current project is the relationships and contacts that have already been established with the appropriate institutions. They could provide additional information and data which, in

> 12 Website of the Special Task Force to Facilitate Business or PEMUDAH (Pasukan Petugas Khas Pemudahcara Perniagaan) <u>https://www.pemudah.gov.my/about<
sub-sectors from which they sourced inputs, and an index of restrictiveness toward FDI in those same service sub-sectors (see Figure 9, a formal description of the way in which the index is calculated is presented below).

4. We then explored the link between these two indicators, controlling for sector specific effects.

## **4.2.2. Services Reform and Manufacturing Productivity**

To assess the impact that restrictive policies towards foreign direct investment in the service sector may have on the performance of the manufacturing sector, we proceed in two stages. First, we use input-output tables to get a sense of the importance that each service sector has for the input costs of each manufacturing sector. The service restrictiveness (rest) that each sector in manufacturing faces is calculated as the weighted average of each service sector’s OECD restrictiveness index, where the weights are given by the share in the total input bill of a given manufacturing sector ‘s’ accounted for the service ‘j’. Formally, we proceed as in equation (1): 10
Figure 1: Average shares by deciles of per capita consumption <!-- Start of picture text --> 27.0%<br>22.0%<br>17.0%<br>12.0%<br>7.0%<br>2.0%<br>1 2 3 4 5 6 7 8 9 10<br>Lower bound - imputed Upper bound - imputed<br>Lower bound 2022 Upper bound 2022<br><!-- End of picture text -->

# Source: Own estimations based on 2016 and 2022 HIES

The analysis reveals that the imputation method performs well in preserving the distribution of imputed parameters for both food and non-food components. Therefore, the 2016 simulated consumption aggregate, which incorporates the imputed components, is expected to exhibit an approximate increase of 15 percent compared to the official consumption aggregate for the bottom 40 of the distribution.

- b. How should the poverty incidence be estimated in 2016 to reconstruct trends?

The 2016 consumption distribution has shifted to the right due to the inclusion of new components in the welfare aggregate (Figure 2). Comparing it with previous 2016 official poverty thresholds will underestimate the incidence of poverty by construction. The 2016 poverty lines were drawn from the 2005 consumption aggregate based on 2005 HIES and updated by inflation. These reflect old consumption patterns and missing components of the welfare aggregate measured in 2022.<sup>8</sup> Thus, the re-estimated 2022 poverty lines should be contrasted to the imputed 2016 aggregate for estimating comparable poverty and extreme poverty headcount levels and trends.

> 8 The 2016 official poverty lines were approximately 17 years old, constructed using HIES 2005, and later updated mainly by inflation. The table below provides how the poverty lines for the last five HIES rounds were estimated over the years.

|Year|2000|2005|2010|2016/17|2022|
|---|---|---|--
Edu represents father’s years of schooling, which was calculated based on the education level completed. Family background is measured by father’s years of schooling. The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of 43 schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table; full table, with all coefficients, is available upon request. Data sources: Indonesia’s full count census 2000 and Duflo (2001).
7. _Drop observations with relevant missing information_ eliminate observations that at the BvD.ID-year level have missing information in all the following variables: operating revenue (turnover), sales, employment, total assets.

8. _Drop duplicates and keep most updated information_ : keep observations with the most recent closing date if there are duplicates at the BvD.ID-year-first letter of consolidation code (e.g., C, U) level.

9. _Drop duplicates and keep information from annual reports_ : keep observations with annual report in _Use FillingType_ variable if there are still duplicates and keep the standardized information. Using annual reports (IFRS preferred, instead of local reports) guarantees standardization of reporting protocol at international level.

10. _Eliminate firms with noisy data_ : drop all the observations corresponding to a specific BvD.ID if any of the following variables has a negative value in a specific year – total fixed assets, tangible fixed assets, intangible fixed assets, other fixed assets, current assets, sales, and employment.

11. _Deflate values_ : use country GDP deflators from the World Bank database to deflate nominal variables and set year 2005 as the base year.<sup>23</sup> 12. _Harmonize currencies_ : convert values in local currency to USD dollars, using the average of the monthly exchange rate for year 2005.

23 https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS.

32
trimmed to the geographical areas that were covered in both decades. This substantially affects Argentina and Uruguay, which thus become representative of the main urban areas,<sup>18</sup> and excludes less well populated areas in Brazil and Colombia.<sup>19</sup> The latter rule reduces the share of each of these countries in population-weighted regional averages, but not in the unweighted averages.

The following analysis also offers benchmarking of Latin American countries using a set of middle- and high-income countries and data of the International Labour Organization (ILO) Global Wage Report Gini database (for more than 30 European and middle-income countries). We also conduct our analysis of the changes in returns using data from the World Bank International Income Distribution Dataset (I2D2), consisting of harmonized microdata available for a subset of countries, including the United States and Turkey. Finally, we also incorporate analysis from South Africa built from the Post Apartheid Labor Market Series– v2.0 and the Russian Federation based on the Russia Longitudinal Monitoring Survey. The trends in labor income inequality found in these countries are similar to those discussed in the literature.<sup>20</sup> These countries are used as reference points in the study rather than as points of interest. Thus, harmonization, the control of comparability, and the validation of trends over time have been limited to guarantee a rough estimate of differences with respect to relations and trends in the Latin America and Caribbean region (see annex A.2).

The variable of interest is the labor income after taxes associated with the main occupation of workers during the month previous to the survey. The survey question is asked of all employees and of the selfemployed. The objective is to use this variable to approximate the labor earnings structure. As Székely and Hilgert (2007) point out, the cross-country comparison of labor income inequality is meaningful and shows a

> 16 The countries in which the effect was present and in which it was feasible to control for the
women also rate their own level of financial literacy more conservatively; self-assessment of financial literacy in the overall population tends to be more inflated.) This is true across almost all countries in both the developed and developing world. One exception is Bucher-Koenen and Lusardi (2011), which finds no differences by gender in East Germany.

In Africa, the FinScope surveys likewise indicate disparities by gender in terms of access to financial services, which could also translate into disparities in levels of financial literacy.<sup>4</sup> In Malawi, for instance, 17 percent of females are banked compared to 21 percent of males. A similar difference is found in many other countries, including Mozambique, South Africa, and Zambia, although the picture varies by type of service and country. When they do have access to finance, females are often more likely than males to rely on informal versus formal services. The InterMedia (2010) survey of youth in Ghana and Kenya (described in greater detail in the next section) also finds disparities between males and females in terms of access to financial services.

There are fewer surveys that focus specifically on women, particularly in developing countries. One exception in this regard is MasterCard Worldwide (2011), which reports on an index of financial literacy for women in 24 countries across Asia, the Middle East and North Africa, and Sub-Saharan Africa. The index measures knowledge of money management (weighted 50 percent), financial planning (30 percent), and investment (20 percent), and sampled approximately 10,500 women (although it is not clear how the sample was chosen). Interestingly, they find that financial literacy is not necessarily correlated with the level of income in each country; within Asia, women in Thailand scored the highest and women in Japan and Korea scored the lowest. However, women in African countries and some Middle Eastern countries generally had lower scores than those in Asia.

The gender gap in financial literacy is of particular concern as women are also
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis analysis focuses on resource dependence rather than resource abundance, as the former is more immediately relevant to economic growth and income inequality.

Table 2: Average Nonrenewable Resource Dependence and Abundance by Income Group (1980-2012)

|**D**|**ependence**|**Log_Abundance**|
|---|---|---|
|High income|2%|7.8|
|Upper middle income|15%|8.6|
|Lower middle income|8%|7.3|
|Low income|11%|6.6|

The data on income inequality is sourced from the World Bank’s “All-the-Ginis Database,” which was last updated in 2014.<sup>8</sup> The database collects Gini indexes from multiple sources into long time series. The data has been standardized for this analysis via the so-called “choice-by-precedence approach,” which reflects each dataset’s reliability, degree of variable standardization, and consistency of geographical coverage. GDP and population figures have been collected from the United Nation’s UNCTAD-STAT database. Table 2 presents the descriptive statistics for per capita GDP and the Gini index by country income group. The Gini index peaks among the upper-middle-income group, falls among the lower-middle-income and low-income groups and is lowest among the high-income group. These data are consistent with the relationship between inequality and GDP described by Kuznets (1955).

Table 3: GDP per capita and Gini index for Countries in Dataset, classified by Income Group (1980-2012)

|**GDP per capita**<br>**(US$ thous)**|**Gini index**|
|---|---|
|High income<br>34.5|33.9|
|Upper middle income<br>8.4|49.1|
|Lower middle income<br>2.
own computation with data from PISA<br>40<br>20<br>0<br>-20<br>Change in Reading Test Scores<br>-40<br>-60<br><!-- End of picture text -->

# **IV. Methodology**

Since the pioneering work of Coleman et al. (1966), researchers have used several methodological approaches to estimate what has been called the education production function. The aim of this approach is to link learning outcomes (outputs) with observable characteristics 6 Since PISA results are normalized, the y-axis in Figure 2 depicts the absolute change in PISA test scores as opposed to the percentage change as would be the case in a conventional GIC.

10