metadata: authors: 0: author: Rodriguez Castelan,Carlos
1: author: Lopez-Calva,Luis-Felipe
2: author: Lustig,Nora
3: author: Valderrama,Daniel
count: Latin America
docna: 0: docna: Understanding the dynamics of labor income
            inequality in Latin America
docty: Policy Research Working Paper
subtopic: Labor Markets,Educational Sciences,Poverty Reduction Strategies,Gender and Development,Rural Labor Markets
abstracts: cdata!: Since the early 2000s, after a long
            period of wide and persistent gaps, Latin America has
            experienced a steady decline in income inequality. This
            paper presents evidence of a trend reversal in labor income
            inequality, which is considered the main factor behind such
            a decline in income inequality across the region. The
            analysis shows that, while labor income inequality increased
            during the 1990s, with heterogeneous experiences across
            countries, it fell in a synchronized way across countries
            beginning in the early 2000s. This systematic decline was
            supported by an expansion in real hourly earnings among the
            bottom of the wage distribution and, to a lesser extent, the
            middle part of the earnings distribution, thus reducing
            upper and lower tail inequality. This trend reversal is
            explained by a lower dispersion of earnings among workers
            with observable different attributes and by a much less
            extensive dispersion of residual labor inequality. Regarding
            the earnings differentials among workers with observable
            different attributes, the analysis concludes that the
            decline in labor inequality in Latin America has been
            closely associated with a reduction in the college/primary
            education premium and in the urban-rural earnings gap,
            coupled with a steady drop in the high school/primary
            education premium, which accelerated markedly since the
            2000s, as well as a reduction in the experience premium
            across all age groups.
repnme: repnme: Understanding the dynamics of labor income
            inequality in Latin America
display_title: Understanding the dynamics of labor
            income inequality in Latin America
listing_relative_url: /research/2016/08/26695881/understanding-dynamics-labor-income-inequality-latin-america
topicv3: Labor Markets m1326488 855,Education m1327048 470,Gender m1327621 615,Income Inequality m1503891 2668,Labor Earnings m1504041 2738
publishtoextweb_dt: 2016-08-15T00:00:00Z
no_of_pages: 48
author_keys: 0: author_key: 1180219
1: author_key: 435254
2: author_key: 451471
model_extractions: WPS7795 Policy Research Working Paper 7795 Understanding the Dynamics of Labor Income Inequality in Latin America _Carlos Rodríguez-Castelán Luis F. López-Calva Nora Lustig Daniel Valderrama_ Poverty and Equity Global Practice Group August 2016
Policy Research Working Paper 7795

# **Abstract**

Since the early 2000s, after a long period of wide and persistent gaps, Latin America has experienced a steady decline in income inequality. This paper presents evidence of a trend reversal in labor income inequality, which is considered the main factor behind such a decline in income inequality across the region. The analysis shows that, while labor income inequality increased during the 1990s, with heterogeneous experiences across countries, it fell in a synchronized way across countries beginning in the early 2000s. This systematic decline was supported by an expansion in real hourly earnings among the bottom of the wage distribution and, to a lesser extent, the middle part of the earnings distribution, thus reducing upper and lower tail inequality. This trend reversal is explained by a lower dispersion of earnings among workers with observable different attributes and by a much less extensive dispersion of residual labor inequality. Regarding the earnings differentials among workers with observable different attributes, the analysis concludes that the decline in labor inequality in Latin America has been closely associated with a reduction in the college/primary education premium and in the urban-rural earnings gap, coupled with a steady drop in the high school/primary education premium, which accelerated markedly since the 2000s, as well as a reduction in the experience premium across all age groups.

This paper is a product of the Poverty and Equity 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 crodriguezc@worldbank.org and lflopezcalva@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 out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Understanding the Dynamics of Labor Income Inequality in Latin America

Carlos Rodríguez-Castelán,<sup>*</sup> Luis F. López-Calva,<sup>†</sup> Nora Lustig,<sup>‡</sup> and Daniel Valderrama<sup>§</sup>

**Keywords:** Inequality, Labor Incomes, Education Premium, Experience Premium, Latin America **JEL codes:** D63, E24, J21, J31, O54

> The authors would like to thank Oscar Barriga Cabanillas, Sarah Knob, Julián Messina, Joana Silva, Robert Zimmermann, and the participants at both the author workshop and the decision meeting of the Regional Study on Wage Inequality organized by the Office of the Chief Economist, Latin America and Caribbean Region, World Bank. The findings, interpretations, and conclusions in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank Group, its Executive Directors, or the countries they represent.

> * Senior Economist, Poverty and Equity Global Practice, World Bank. E-mail: crodriguezc@worldbank.org.

> † Lead Economist, Poverty and Equity Global Practice, World Bank. E-mail: lflopezcalva@worldbank.org.

> ‡ Samuel Z. Stone Professor of Latin American Economics and Director of the Commitment to Equity Institute at Tulane University. Professor Lustig is also nonresident senior fellow at the Center for Global Development and the Inter-American Dialogue. E-mail nlustig@tulane.edu.

> § Research Analyst, Poverty and Equity Global Practice, World Bank. E-mail: dvalderramagonza@worldbank.org.
# 1. Introduction

Latin America has been characterized as a region with an _excess of inequality_ for being a middle-income region (Londoño and Székely 2000). It ranks as the second most unequal region in the world, after Sub-Saharan Africa, a position that holds independent of the metric used to measure welfare, whether consumption or income (Alvaredo and Gasparini 2015). Although inequality in Latin America is still substantial today, the region is also known to have experienced a _turning point_ in the trend in the early 2000s (Gasparini et al. 2011), changing from a slightly increasing trend during the 1990s to a steady decline since the early 2000s (López-Calva and Lustig 2010). This represents a sharp contrast with what has occurred in other developing regions, particularly in Eastern Europe and Central Asia and in East Asia and the Pacific, where income inequality has been on the rise since the early 2000s (Alvaredo and Gasparini 2015).<sup>5</sup> The literature on the rise and fall of income inequality is vast. Following the seminal studies of Gasparini et al. (2009) and López-Calva and Lustig (2010), a growing body of literature has documented the turning point in total income inequality and has hypothesized about the potential supply, demand, and institutional factors that may be associated with the unique equalizing momentum of the region during the 2000s.<sup>6</sup> Together with this literature, a large number of country-level studies have gone deeper in exploring the main factors associated with the observed narrowing in inequality. These factors include fiscal policy (Higgins and Pereira 2014; Jaramillo 2014; Lustig and Pessino 2014; Scott 2014), economic cycles (Ferreira and Schady 2009; Gasparini and Cruces 2010), institutional changes (Fairris 2003; Maurizio 2014), trade liberalization (Attanasio, Goldberg, and Pavcnik 2004; Bastos and Silva 2008; Esquivel and Rodríguez-López 2003; Gonzaga, Menezes, and Terra 2006; Revenga 1997; Verhoogen 2008), and labor misallocations due to an unbalanced expansion of the coverage of education (Campos Vázquez, López-Calva, and Lustig 2016; Levy and López-Calva 2016). Most of these papers examine how variations in policies, institutions, and external conditions influence reductions in income inequality through their major components, including labor earnings, nonlabor incomes (such as land, capital income, rents, and remittances), and demographics.

However, the ample evidence documenting changes in total income inequality in Latin America sharply contrasts with the scarce evidence about the facts behind changes in labor income inequality across the region. Little is known about the trends in labor income inequality in the last two decades, and most studies concentrate solely on understanding the drivers behind the fall in the education premium (Manacorda, Sánchez-Páramo, and Schady 2010; Gasparini et al. 2011),<sup>7</sup> or the potential effect of a tertiary education expansion (Batiston et al. 2011). Even where the education premium is an essential piece, it is not the bulk of labor income inequality.<sup>8</sup> This study aims to fill this gap by taking stock of the main determinants of labor income inequality and the earnings structure in Latin America over the past two decades with an emphasis on the trend reversal in labor income inequality observed across the region since the early 2000s.

This paper is related to Azevedo et al. (2013), although that paper focuses the analysis on the decomposition method proposed by John, Murphy, and Pierce (1993) rather than documenting changes in labor inequality in Latin America.<sup>9</sup> Our study aims to fill several gaps in the literature on the stylized facts

> 5 See also Lakner and Milanovic (2013); Williamson (2015).

> 6 See Gasparini and Lustig (2011); Lustig, López-Calva, and Ortiz-Juárez (2013, 2015); World Bank (2011, 2013); Cornia (2014); de la Torre et al. (2014, 2015); Cord et al. (2016); Cord, Genoni, and Rodríguez-Castelán (2015); Székely and Mendoza (2016).

> 7 These studies have used a supply and demand framework to estimate a Tinbergen ratio between supply and technology (1974). In this framework proposed by Katz and Murphy (1992), an increase in the relative supply of skilled workers should result in a fall in their relative remuneration, while a positive change in the relative demand for this type of worker would increase the wage-skill premium; here, skill is defined by education because perfect substitutability across workers with different experience is assumed. Several studies have applied this framework to Latin American countries, for instance, Mexico (Montes Rojas 2006), Chile (Gallego 2011), Panama (Galiani 2009), Manacorda et al. (2010) on the five largest economies in Latin America, and Gasparini et al. (2011), which is the broadest study in terms of spatial coverage (17 Latin American countries) and time coverage (1990s and 2000s). Recently, Fernandez and Messina (2016) applied the framework, including variations in the experience premium, to selected Latin American countries.

> 8 A typical Mincer equation controlling only for education levels will lead to an R-squared of less than 0.3, independent of the country and year of the sample. This fact generally holds across the world.

9 Although, at the country level, there is a growing literature on the main drivers behind changes in labor income inequality, most of these studies are heterogeneous in terms of indicators, sample, data sources, and trimming criteria, making regional conclusions a difficult 2
around the trend in earnings inequality in Latin America during the last two decades. To a lesser degree, it also seeks to contrast these trends with those of other middle- and high-income countries in the world. To analyze the trend in labor income inequality, we aim to document changes in the inequality of the earnings structure (relative returns of different skills/attributes). We also analyze the context of these changes in terms of real earnings growth because different movements in real earnings could lead to the same change in relative returns to different attributes, but not to the same conclusions about the underlying causes. Finally, our paper presents a set of stylized facts on the variance in earnings across workers of observable different characteristics (the earnings structure) and the variance across workers with observable equal characteristics, which is defined in the literature as residual earnings inequality and corresponds to the variance that cannot be explained by the differences among workers in a set of observed characteristics. Unlike Azevedo et al. (2013), we are not imposing any assumption about the dynamics of the residual distribution.

By conducting our analysis at the regional level, we enhance the external validity of our results, while obtaining broad estimates of a common underlying trend in the distribution of labor market earnings in the region. In particular, we use data from the SEDLAC database on 17 countries in Latin America, which cover around 90 percent of the population. This allows us to identify underlying factors that are beyond specific country contexts (political, institutional, or economic). In addition, analyzing data since the early 1990s allows us to define whether the factors considered important in the 2000s were also present during the previous decade, when labor market inequality showed a different trend. In other words, by taking a long-term perspective, we can test whether the reverse of previous trends in overall inequality implied a reverse in previous trends in other factors, or a surge in new forces that were previously absent.

Our main finding is that, after a period of modest expansion in wage dispersion, Latin America experienced a trend reversal in labor income inequality after 2002. We find evidence of a parallel movement between total income inequality and labor income inequality, which is no surprise because labor earnings represent about 80 percent of the total household income distribution in any Latin American country.<sup>10</sup> In particular, the unweighted average Gini coefficient of labor income inequality fell from 0.473 in 2002 to 0.410 in 2013. According to our analysis, this significant drop in earnings inequality is robust to the choice of aggregation method,<sup>11</sup> inequality measure,<sup>12</sup> period of analysis, or the selection of countries in the construction of the regional aggregate. This phenomenon of declining labor inequality was observed in 16 of the 17 countries on which we have comparable data for the 2000s. Only Costa Rica experienced an increase in labor income inequality over the last decade.

Our analysis also shows that the decline in labor income inequality was supported by a substantial expansion in real hourly earnings at the bottom of the distribution.<sup>13</sup> In particular, since 2002, the labor incomes associated with relatively low-paying jobs (those in the bottom decile of the earnings distribution) have risen by more than 50 percent in real terms, which is significantly higher than the 15 percent growth among more well paid workers (those in the top decile of the earnings distribution) and also higher than the 32 percent growth rate experienced in the median of the distribution. In contrast, the increase in regional labor income inequality during the 1990s was supported by a reduction in real earnings at the bottom of the distribution, while the rest showed null or slightly positive growth in real earnings.

Our second set of results highlights a steady decline in the education premium in Latin America since the early 2000s that has been driven by larger growth in labor earnings among less well educated workers relative

> task. Moreover, most of these studies only explain the trend in the 2000s, which leads to a bias in their findings because they do not observe a period with different trends—the 1990s—to test their hypotheses. See, for instance, Paz and Urrutia (2015) on Peru; Ariztizabal et al. (2015) on Bolivia, Colombia, and Ecuador. Among these studies, Messina and Fernandez (2016) is the only one that analyzes the long-term trends in Argentina, Brazil, and Chile.

> 10 See, for instance, World Bank (2011) and Cord et al. (2016), which document that labor earnings represent about 80 percent of total household income in most Latin American countries.

> 11 Both the weighted and unweighted regional averages converge qualitatively in the fall of labor income inequality, albeit they report some differences in terms of the trend during the 1990s.

> 12 Our analysis also reaches this conclusion based on the Theil index, the mean log deviation, the Atkinson index, and the 90/10 ratio.

> 13 Labor earnings and wages represent different concepts. Our analysis focuses on labor earnings, which consider the earnings of both wage employees and self-employed workers. Even if we may sometimes use the term wage or wages, our results always refer to labor earnings.

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to workers with high school or college educational attainment. This drop in the education premium has been coupled with a steady fall in the experience premium, which has been accelerating since the early 2000s, a novel fact from a regional perspective. The most experienced workers have seen a reduction by almost half in their experience premium with respect to younger workers. Indeed, after 2002, most countries in the region experienced rapid reductions in these premiums. Moreover, we find evidence that the gender wage gap has narrowed consistently since the mid-1990s, but it has been almost stagnant during the first decade of the 2000s, which suggests a weak relation between the gender earnings gap and the trend reversal. We also find that the urban-rural earnings gap widened in the 1990s, but then narrowed sharply during the 2000s, closely related with what happened with overall labor inequality.

Our third set of results shows evidence that the narrowing in the earnings dispersion across different workers, although important, is still only a minor part of the reduction in earnings inequality. In contrast, more than half of the reduction in labor income inequality was derived from a reduction in the variance of the earnings of workers with similar characteristics, what is known in the literature as residual earnings inequality. This latter fact requires further investigation to clarify the extent to which this has been due to compositional changes.

The reminder of this paper is organized as follows. Section 2 describes the data and sample criteria. Section 3 documents the trend in labor income inequality observed in Latin America over the last two decades, its most salient characteristics, and a comparison with other countries in the world. Section 4 presents a diagnostic of how changes in the earnings structure, specifically, changes in the relative returns to different types of workers, may have played a role in the trend in labor income inequality in Latin America; it also documents how residual labor inequality is the most important factor in this trend. Section 5 concludes.

# 2. Data: From noise to signal for identifying regional trends

Most stylized facts on Latin America are produced using harmonized national household surveys from the SEDLAC Project.<sup>14</sup> The surveys are collected by the respective national statistics offices (NSOs) and harmonized through the SEDLAC Project. These microdata cover 17 countries over a span of 20 years, which account for 90 percent of the population in the region. Annex A provides more detail on the microdata used in this study for each country and each year. In addition, part of the analysis in section 3 uses aggregated information on República Bolivariana de Venezuela to present a more comprehensive picture on labor income inequality in Latin America. There is information on a yearly basis for all countries with the exception of Chile and Mexico, which collect data every two or three years, and Guatemala and Nicaragua, which collect household survey data about every five years.<sup>15</sup> All the surveys have a labor module to collect information on labor income after taxes, which is the main variable of interest in this study. Next, we provide a detailed description of the use we make of this variable.

An additional contribution of this study is the effort to minimize the intrinsic noncomparability of surveys over long periods of time. A salient characteristic of household surveys in developing countries is that they are living tools that undergo changes in structure, the phrasing of questions, sample design, and frequency, among other features, from time to time. There is thus a cost of comparability in the country series for those indicators that are more sensitive to the specific change. The large majority of the studies that are not country specific do not mention how they deal with the effect that the changes in the survey features have on the comparability of their estimates. We break with this tradition and work on four different areas, which are described below.

First, we use unique information about the comparability of surveys on the same country in terms of income aggregates (see table A.1) to define circa years (see table B.1) that minimize the measurement error of

> 14 For more information, see the SEDLAC Project webpage, at http://sedlac.econo.unlp.edu.ar/eng/.

> 15 The SEDLAC dataset allows cross-country comparisons of household welfare. The data cover 17 countries, which account for 90 percent of the population in the region. The countries are Argentina (urban only), Bolivia, Brazil, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay (urban only). See annex A for more details on the specific combination of country-year surveys included in the analysis. See SEDLAC (SocioEconomic Database for Latin America and the Caribbean), Center for Distributive, Labor, and Social Studies, Universidad Nacional de La Plata, La Plata, Argentina and World Bank, Washington, DC, http://sedlac.econo.unlp.edu.ar/eng/statistics.php.

4
changes during the 1990s and 2000s.<sup>16</sup> This information has been provided by the Team for Statistical Development for Latin America at the World Bank.<sup>17</sup> This allows us to identify more accurate trends at the country level. Since the breaks in comparability are not synchronized in the timing or the direction of the impact in our indicator of interest (labor income distribution and the earnings structure), there is no expected important effect on the regional aggregate constructed on a yearly basis.

Second, we use the most recent population projections based on the most recent census to adjust surveys that have not been updated. This procedure is carried out by each NSO years after the release of the census. However, the timing of the adjustment is different for each country. We have applied the last census projections of the urban and rural populations to the surveys to guarantee comparability at this level.

Third, we exclude some country surveys for certain years in which the parameter of interest included outliers by a simple routine of using as a benchmark the observations for the same country during the period. The parameters used are the proportion of missing labor income among the employed population, average earnings, and the sociodemographic composition of certain groups by education, experience group, sex, and area. This leads to the exclusion of Brazil in 1992 and 2007 and Paraguay in 1990.

Finally, we control for changes in terms of coverage even if these have been minor (similar to previous studies). In addition, in terms of coverage, almost all the surveys are representative at the national level during the last two decades. In the cases where the coverage of the surveys changes during the period of analysis, the surveys were 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 effect are those on which data exist on two years in the same period, one for the series before the change and the other for the series after the change.

> 17 This information has been constructed based on a knowledge of the SEDLAC Project and survey metadata and in close contact with the staff of each NSO.

> 18 This change limited the sample in Argentina to the urban areas covered since 1992, Gran Buenos Aires, and other major urban areas that include around 50 percent of the population. It also limited the sample in Uruguay to the urban population in the metropolitan area of Montevideo (about 70 percent of the population).

> 19 Among other trimming procedures, the north rural areas of Brazil were excluded because these were not covered in the 1990s (less than 2 percent of the population); also urban areas in new departments in the Amazon and Oriental regions in Colombia, only recently covered (less than 4 percent of the population).

> 20 See Bakis and Sezgin (2015) for the case of Turkey, with a fall between 2002 and 2004 and a slight increase in inequality since 2004. See Leibbrandt Arden, and Woolard (2013) for the case of South Africa, with a steady increase since the 1990s and a slight increase during the first decade of the 2000s. See Autor, Katz, and Kearney (2008) for the case of the United States, with a slight increase from the 1990s to the end of 2010. See Calvo, López-Calva, and Posadas (2015) for the case of the Russian Federation, with a slight increase at the end of the 1990s, but a steady fall during the first decade of the 2000s.

5
relatively lower ratio between noise and signal than the analysis of total income, which is affected by the lower quality of the information on nonlabor incomes. Labor income inequality is the most tractable variable for long periods of time. It is typically constructed using similar questions and recall periods. It also analyzes the same type of income (monetary income after taxes) and suffers from relatively lower underreporting issues than total income inequality across countries.<sup>21</sup> Monthly labor income is expressed as earnings per hour worked, which is a standard unit value traded in the labor market. Since the objective is to have the most accurate estimates of how the labor market assigns a price to certain skills either observable or unobservable (earnings structure), we limit our analysis to the sample of full-time workers (35+ hours per week) between 15 and 64 years of age. Depending on the country, this accounts for 75 percent to 85 percent of all workers, as has been traditionally done in other countries.<sup>22</sup> There are several advantages of focusing the analysis on the sample of full-time workers: (a) it reduces the dispersion of hourly earnings that could be caused by measurement error, (b) it allows those workers to be tracked who are strongly attached to the labor force and who are more similar both in observables and unobservables relative to part-time workers, and (c) it isolates the particularities of the demand- and supply-side forces that frame parttime jobs in the estimation of labor market prices, which is the main goal of this study. The main consequence of considering only full-time workers is that labor income inequality may be lower, and the trend may not be necessarily the same if we include part-time workers in the analysis. However, as mentioned above, we are interested in disentangling the trends in labor inequality derived from the inequality of the earnings structure rather than the labor supply decisions of workers.

Finally, the data series are spatially deflated between urban and rural areas using a factor of 15 percent of the differences in the cost of living<sup>23</sup> because the lack of the use of spatial deflators overstates income inequality, although the size of spatial price differences depends on the country,<sup>24</sup> and the use of a regional factor is a second-best method, but is better than not deflating spatially.

# 3. Trends in labor earnings inequality

Except for Sub-Saharan Africa, Latin America is the most unequal region in the world (Alvaredo and Gasparini 2015; Lakner and Milanovic 2013; Williamson 2015). This holds even after one controls for the fact that inequality in the region is measured through income, while, in the rest of the developing world, it is measured using consumption data. Following a method proposed by Alvaredo and Gasparini (2015) to generate a consumption Gini from income Gini measures, the left panel of figure 1 shows Latin America as the second most unequal region in the world. This position is statistically significant, and it is robust to the exclusion of extreme values.

> 21 On the other hand, the underreporting of incomes among top earners, a classical drawback in the literature that measures inequality using household surveys, is less pronounced if labor income is considered because a larger share of the incomes of the top of the distribution is derived from capital gains. Moreover, it has been shown that the current underreporting of these income sources is not an important source of variation in the ranking across countries.

> 22 See Katz and Murphy (1992) and Katz and Autor (1999). Autor et al. (2008) and Acemoglu and Autor (2011) argue for the use of full-time workers, while Lemieux (2006) proposes using all employees.

> 23 This spatial deflator has been defined within the SEDLAC Project as the average for Latin America. The ideal analysis should not only consider specific price differences by country, but also the differences among regions depending on differences in the size of cities. 24 See Bourguignon, Ferreira, and Lustig (2005) for Brazil; Arndt, Sam, and Vicenzo (2015) for Mozambique; Démurger, Fournier, and Li (2006) for China; and Slesnick (2002) for the United States.

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and the East Asia and the Pacific region experienced widening income inequality over the past decade, while data on the Sub-Saharan Africa region and the South Asia region show almost no change in inequality over the same period. Data on the Middle East and North Africa region reveal a modest decline during the 2000s, but the sample in this region only includes five countries, and data collection there is rather infrequent. The authors conclude, then, that the narrowing in income inequality in Latin America in the 2000s has been remarkable.

To analyze regional trends in several parts of this study, we use unweighted averages of the countryspecific trends for which data are available (unless otherwise indicated). This means that each analysis presented will be the average of the same indicator at the country level. Since not all the countries have information for all the years, a simple linear interpolation is conducted to have balanced panel data. If data are missing at the beginning or at the end of each country series, they are replaced by the value of the nearest year on which information is available. However, excluding these interpolations and extrapolations does not change qualitatively the results of the long-term trends.

Our analysis of the Latin America region (figure 2), which is based on comprehensive harmonized household survey data (the SEDLAC database), reaches a conclusion similar to the conclusions of previous studies, showing that the total income Gini coefficient in the region rose moderately during the 1990s (Londoño and Székely 2000), dropped significantly during the 2000s (Gasparini et al. 2009; López-Calva and Lustig 2010), and experienced a slowdown and possible stagnation during the early 2010s (Cord et al. 2016; Gasparini et al. 2016). Although it may still be too early to claim with any certainty that stagnation has occurred in the regional trends in income inequality, both regional aggregates in figure 2 represent evidence of a slower pace in inequality reduction since 2010, more marked in the case of the weighted average because of the upward inequality in Mexico and the slowdown in the decline in inequality experienced in Brazil (Cord et al. 2016).

The decline in income inequality observed in the region during the period is the most synchronized of these three trends. It is robust to the inequality measure<sup>25</sup> and to the aggregation method, both weighted and unweighted averages of country values. Moreover, the decline occurred in 16 of 17 countries in the region,<sup>26</sup> and it holds once the comparability issues of the country-specific trends are taken into account. In contrast, the increase in regional inequality during the 1990s and the slowdown after 2010 hides a certain amount of heterogeneity at the country level. For instance, during the 1990s, Brazil, Mexico, and Chile experienced a slight fall in total income inequality, in contrast with other countries such as Argentina, Uruguay, and Honduras, which witnessed sharp increases. Moreover, the still recent slowdown in the 2010s has meant different trends in some countries such as Bolivia, Costa Rica, and Mexico, where inequality has increased, while other countries experienced a slowdown, such as Chile and Brazil.

> 25 Our analysis also reaches this conclusion based on the Theil index, the mean log deviation, the Atkinson index, and the 90/10 ratio. 26 Only Costa Rica exhibited an increase in inequality over the first decade of the 2000s (figure C.1, panel b).

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<!-- Start of picture text --> T tT<br><!-- End of picture text --> <!-- Start of picture text --> o<br><!-- End of picture text --> <!-- Start of picture text --> v<br><!-- End of picture text --> <!-- Start of picture text --> 99<br><!-- End of picture text --> <!-- Start of picture text --> 93 95 97 99 O01 03 O05 OF O9 11 13 93 95 97 99 01 03 O05 OF O9 11 13<br><!-- End of picture text --> <!-- Start of picture text --> ® Difference respectto Labor earnings Gini in 2002 ° Difference respect to Labor earnings Gini in 2002<br>(Confidence interval -95% | Confidence interval -95%<br><!-- End of picture text -->
Figure 5. Annual changes in the labor income Gini, Latin America and selected countries, ca. 1993–2003 and ca. 2003–13 <!-- Start of picture text --> 0.8 Argentina Uruguay Peru<br>Colombia<br>0.4 SwedenDenmark United Kingdom United StatesPanama Mexico Honduras Paraguay<br>0.0 FinlandSwitzerland KazakhstanItaly Chile<br>Greece Israel Nicaragua<br>-0.4 Germany Ireland El Salvador Bolivia<br>Mauritius<br>Philippines Brazil<br>-0.8<br>-1.2<br>-1.6<br>15 20 25 30 35 40 45 50 55<br>Labor earnings Gini, ca 1993 (Gini points)<br>0.8 Germany France<br>Denmark<br>0.4 Switzerland Sri Lanka Costa Rica<br>Ireland<br>United States<br>0.0 Finland Sweden Poland Israel Mexico Honduras<br>El Salvador<br>-0.4 Greece Colombia Brazil<br>-0.8 Kazakhstan Kyrgyz Republic Bolivia<br>Argentina Ecuador Peru<br>-1.2 Nicaragua<br>Venezuela, RB<br>-1.6<br>15 20 25 30 35 40 45 50 55<br>Labor earnings Gini, ca 2003 (Gini points)<br>LAC Non-LAC<br>ca 1993-2003 (Gini points)<br>Annual change in labor earnings Gini,<br>ca 2003-2013 (Gini points)<br>Annual change in labor earnings Gini,<br><!-- End of picture text --> _Source:_ Venezuela, RB and the non–Latin American countries: Global Wage Report, International Labour Organization. Seventeen Latin American countries: SEDLAC database. See annex A.3 for details about the underlying data and Annex B for circa periods. _Note:_ The data have been multiplied by 100. For SEDLAC countries, the sample covers full-time, wage, and self-employed workers 15– 64 years of age. The values of the 1st and 100th percentiles of the earnings distribution were trimmed by each gender-education cell. The Global Wage Report data are not strictly comparable with SEDLAC data. In some countries, different types of surveys were used, and the sample and trimming criteria are different. The size of the bubbles represent the population of the country.

The most pronounced drop in earnings inequality since 2003 was observed in República Bolivariana de Venezuela, although the relevant data are not strictly comparable because they are provided through a harmonization process that differs relative to the process in the rest of Latin America (an International Labour Organization method instead of the SEDLAC method). República Bolivariana de Venezuela is followed by Uruguay (urban), Nicaragua, Peru, Ecuador, and Argentina (urban). The sharp contrast among the trends in labor income inequality in Argentina, Nicaragua, and Peru, which experienced large increases in labor income inequality over the 1990s, are of particular interest. In contrast, Costa Rica was the only country in Latin America on which data are available that experienced a widening in labor income inequality in the 2000s and, thus, across two consecutive decades.

The trend reversal in labor inequality can be illustrated through a graphic on the growth of real hourly earnings in Latin America. Thus, figure 6 shows that average earnings in Latin America rose at the bottom, the middle, and the top of the labor income distribution over 2002–13, after experiencing no change or even a slight reductions between the 1990s and early 2000s. The largest increase occurred among workers at the bottom of the earnings distribution, who experienced a rise of more 50 percent in real earnings in 2002–13. In contrast, the most well paid workers—those at the top of the labor income distribution—experienced a modest average earnings increase of 16 percent over the decade. Moreover, workers at the median of the earnings 12
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in the productivity of workers, which is closely related with the differences in the attributes of workers, plus discrepancies that are usually called an error term. Some of these attributes can be easily observed (such as education and experience), while others are more difficult to observe or measure (such as ability and soft skills). Using the framework of Autor and Katz (1999), Lemieux (2006), and Autor, Katz, and Kearney (2008), this section analyzes overall inequality by separating the range in the earnings of workers with different observed attributes and the range in the earnings of workers with similar observed attributes. The latter term—residual earnings inequality—may be a product of differences in the unobserved skills among otherwise equal observable workers, an idiosyncratic variance in certain types of workers, asymmetries in the information about human capital prices across firms, or a kind of measurement error. For our analysis, we estimate standard Mincer equations (Mincer 1974), but in a semiparametric way using a multiple dummy specification, as follows: log( _W) = f(education, experience, gender, region, e)_ , (1) where _W_ corresponds to the real hourly earnings of full-time workers to minimize the potential impact that measurement errors in hours or salary can have on the hourly wages of part-time workers. In addition, considering only full-time workers allows us to control partially by the unobservables of those who are not fully attached to the labor market. Education is measured through three educational categories: college, high school, and primary education. Experience refers to potential experience and is divided into five groups: 0–5 years, 6– 10 years, 11–20 years, 21–30 years, and 31+ years. Gender and urban are dummies for men and for urban residence. Regarding the definition of the education categories mentioned above, we follow country-specific definitions rather than homogenous cutoffs based on years of education as was proposed by Gasparini et al. (2011).<sup>33</sup> Under this country-specific definition, college refers to university degrees or completed official postsecondary education; high school refers to upper-secondary education, incomplete college education, or no formal education; and primary refers to no education, incomplete or complete primary education, and incomplete secondary education. Experience refers to potential experience, which is age in years, minus years of education, minus 6; it assumes short spells of unemployment during the average adult life.

For simplicity, we assume _f(*)_ is a linear function so that the parameters associated with each covariate can be interpreted as the returns to worker characteristics. We note that the mechanisms through which returns to human capital (education and experience) and other worker characteristics change are the result of interactions among demand, supply, and institutional factors. For instance, an observed reduction in the returns to college may arise because of either a less well educated labor force or a higher demand for workers with higher educational attainment.

# **The trends in explained and unexplained earnings inequality**

Figure 8 plots inequality measured through the variance in the log of the hourly wage and residual earnings inequality, which is defined as the part of inequality that is not fully explained by worker attributes. The first finding that arises from a comparison between overall and residual earnings inequality, according to figure 8, is that the inequality in labor earnings in the average country is mostly a result of the inequality across workers with similar observable attributes. About 70 percent of the overall inequality, the dark line in figure 8, in a particular year arises from the residual component, the blue line in figure 8, rather than from differences in the earnings between workers with different attributes. This result is not surprising, since the literature on labor market returns find that the Mincer model has limited explanatory power on the variance in wages. Even with a fully specified model with interactions between all variables and using sector and occupation, which are highly correlated with education, the size of the residual explains at least half the total variance (Silva et al. 2016).

> 33 The reason to change this tradition is that a country’s labor market may offer pay based on educational attainment according to the structure of the country’s educational system rather than international standards in the description of primary or secondary education.

15
<!-- Start of picture text --> 7957 — Overall — Residual<br>74<br>3<br>Cc<br>6 69.72<br>5 66<br>><br>a<br>0<br>oO<br>: 61.0<br>= 58 55.7 .<br>a<br>o<br>=<br>2 |495<br>50<br>44.6<br>42<br>1993 1997 2001 2005 2009 2013<br><!-- End of picture text -->
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workers with secondary education and workers with primary education, which was stable during the 1990s and then narrowed after 2002.

Both panels of figure 10 plot the estimated variations in the returns to schooling for the 17 countries in Latin America, plus Russia, South Africa, Turkey, and the United States. In particular, the left panel shows the annual changes in the country-specific labor earnings premiums of workers with secondary education relative to workers with primary or less education, and the right panel shows the annual changes in the country-specific labor earnings premiums of college-educated workers relative to workers with primary or less education. The variations have been truncated at 20 percent in absolute terms to control for outliers.

Figure 10 Annual growth rate of the education premium and labor income inequality, selected countries, 1990–2003 and 2003–10 _Sources:_ Seventeen Latin American countries: SEDLAC database; Russia: the Russia Longitudinal Monitoring Survey; Post Apartheid Labor Market Series: South Africa; Turkey: I2D2-LFS; the United States: I2D2-IPUMS. See annexes A.1 and A.2 for details. _Note:_ The underlying data represent the change in wage inequality and the change in the earnings premiums for each country and circa period. The years selected for each country-circa combination may be different depending on survey availability and to assure, to the extent possible, within-country comparability for each period; see annex B.1 for details. In the case of the y-axis, the growth rate of the education premium is plotted. For the x-axis, the growth rate of the labor income Gini is plotted. The educational categories—college, high school, and primary education or less (reference category)—follow country-specific education systems; see section 3 and equation 1 for details. The sample covers full time, wage, and self-employed workers 15–64 years of age. The values of the 1st and 100th percentiles of the earnings distribution were trimmed by each gender-education cell. The size of the bubbles represent the population of the country.

Our results show that countries with the most substantial decline in labor income inequality during the past decade (the countries on the left side of the x-axes) are also the countries that experienced negative expansion in the earnings gap (the blue bars). This pattern is more consistent in the declines in the earnings premiums of workers with college education, with the notable exception of South Africa, Russia, and some Latin American economies during the 1990s: Ecuador and, to a lesser extent, Chile and Uruguay (figure 10, panel b). The case of Costa Rica is also noteworthy because Costa Rica is the only country in which labor earnings inequality did not narrow during the last decade. In Costa Rica, the earnings premium of workers with secondary education widened relative to workers with primary education, although the earnings gap between workers with college education and workers with primary education narrowed. Russia and Bolivia, the countries exhibiting the largest decline in earnings inequality during the 2000s, showed a significant reduction in both education premiums. Earnings gaps—both between workers with secondary education and workers with primary education and between workers with college education and workers with primary education—were heterogeneous and less volatile between 1990 and 2003, although there was no clear pattern.

18
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suggests that the commodity boom may have translated into earnings growth among workers in rural areas who were specialized in the tradable sectors (including primary activities, mining, and manufacturing). These countries, plus Costa Rica, El Salvador, Nicaragua, and Panama, showed the largest narrowing in the urbanrural wage gap after 2003 (figure 12, panel b).

Figure 14. The average gender and urban-rural wage gaps, Latin America, 1993–2013 a. Gender labor earnings gap b. Urban-rural labor earnings gap _Source:_ Seventeen Latin American countries: SEDLAC database; Russia: the Russia Longitudinal Monitoring Survey; Post Apartheid Labor Market Series: South Africa; Turkey: I2D2-LFS; the United States: I2D2-IPUMS. See annexes A.1 and A.2 for details. _Note:_ The underlying data represent the change in wage inequality and the change in the earnings premiums for each country and circa period. The years selected for each country-circa combination may be different depending on the survey availability and to assure, to the extent possible, within country comparability for each period; see annex B.1 for details. In the case of the y-axis, the growth rate of the gender and area of residence gap is plotted. For the x-axis, the growth rate of the labor income Gini is plotted. The reference category of the gender gap is a woman, and the reference category of the residence earnings gap is rural areas. See section 3 and equation 1 for details. The sample covers full-time, wage, and self-employed workers 15–64 years of age. The values of the 1st and 100th percentiles of the earnings distribution were trimmed by each gender-education cell. The size of the bubbles represent the population of the country.

# **Summary and correlations: Earnings inequality and observed labor market structure**

Table 1 presents a summary of the main findings on the dynamics of labor market returns in Latin America over the last two decades. We can conclude that the four measures of earnings gaps that we have analyzed— the education premium, the experience premium, the gender earnings gap, and the urban-rural earnings gap— have exhibited significant declines since the early 2000s (with the exception of the gender wage gap in 2010– 13) relative to the changes observed in 1993–2003. Moreover, if we split the later period into 2003–2010 and 2010–13, we are able to uncover additional dynamics across the various measures of labor market returns. First, both measures of the education premium analyzed in this study—college relative to primary educational attainment and secondary relative to primary educational attainment—registered a more rapid reduction during 2003–10 than during 2010–13. Second, the same dynamics occurred in the urban-rural labor earnings gap. Indeed, this gap remained steady during the more recent years after a substantive compression during the commodity boom (2003–10). Third, all four measures of the experience premium showed a significant decline in 2003 that accelerated in 2010. One plausible factor in the dynamics of this phenomenon is skills obsolescence among older age-groups because of the rapid developments in information and communication technology in labor markets and the increasing mechanization and automation in sectors that have traditionally been intensive classified the Dominican Republic, El Salvador, Guatemala, Honduras, Mexico, Paraguay, and Uruguay as noncommodity boom countries, while excluding Costa Rica, Nicaragua, and Panama from the analysis because of data limitations.

22
in the use of labor (manufacturing and services). Likewise, the rate of decline in the experience premium has been larger at the high end of years of experience relative to the low end (0–5 years of experience). Finally, the gender earnings gap showed the largest decline in the earlier period, 1993–2003, but this was partially reverted in 2010–13. More research is definitely needed in this area.

Table 1. Average changes, measures of the earnings gaps, Latin America, 1993–2013

|Relative returns||Average<br>|growth rate<br>|by year(pe<br>|rcentage)<br>||
|---|---|---|---|---|---|---|
||1993–99|1999–03|2003–10|2010–13|1993–03|2003–13|
|**Education**|||||||
|High school|−0.5|−0.7|−2.9|−2.1|−0.6|−2.5|
|Tertiary|0.3|1.4|−2.8|−2.1|0.8|−2.7|
|**Potential experience**|||||||
|6–10 YoE|−0.8|−1.3|−1.9|−3.4|−1.0|−2.4|
|11–20 YoE|−1.6|0.3|−2.8|−3.7|−0.9|−3.1|
|21–30 YoE|−2.1|−0.3|−2.9|−4.9|−1.4|−3.4|
|31+ YoE|−2.1|−0.8|−3.6|−4.9|−1.5|−3.9|
|**Gender and area of residence**|||||||
|Male/Female|−1.4|−4.2|−1.1|−0.2|−2.3|−0.9|
|Urban/Rural|2.8|−3.8|−3.9|−0.5|−0.1|−3.2|

_Source:_ Calculations based on the SEDLAC database. See annexes A.1 and A.2 for details.

_Note:_ The underlying data represent the differences in the trends plotted in figures 9, 11, and 13. The bases for each category are primary or less in education, between 0 and 5 years of age for the potential experience, females for gender, and rural areas for area of residence. YoE = years of experience.

# 5. Conclusions

In this paper, we examine trends in labor income inequality in Latin America during the last 20 years. To estimate the inequality derived from the way the labor market values the different skills and demographic characteristics, we focus on the hourly earnings inequality of the main occupation of full-time workers between 15 and 65 years of age.

We identify a trend reversal in labor income inequality beginning in the early 2000s that followed a slight increase during the early 1990s. The increase of labor income inequality during the 1990s was associated with a reduction in real wages, most severe for the workers on less well paid jobs, particularly for those in the left half of the earnings distribution. On the other hand, we find evidence that this decline in labor income inequality in the 2000s was associated with more rapid growth rates in the earnings of less well paid jobs relative to labor incomes among the more well-paid earners. This expansion at the bottom of the wage distribution that narrowed the inequality in the lower tail of the earnings distribution was accompanied by a smaller, but still significant expansion in the middle part of the wage distribution that also helped narrow the inequality at the upper tail of the labor income distribution. In contrast to the increase in labor income inequality in the 1990s, the trend reversal in labor income inequality observed during the 2000s is robust to the selection of inequality measure, the aggregation method of country indicators (population weighted or unweighted), the countries selected to define the regional trend, and the definition of the time interval.

We also find that the trend reversal in labor income inequality is correlated with a steady compositional change in labor force attributes along the two decades of analysis: workers are becoming generally more educated, more highly experienced, and more urbanized, and more women are participating in the workforce. Because this compositional change has been fairly constant during the two decades, it does not explain alone the trend reversal in earnings inequality, suggesting that the influence of demand and institutional factors are among the forces behind the regional trend.

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The increase and subsequent reduction of the variance in labor earnings occurred in the variance of the earnings between workers with different observed attributes as well as in the variance of the residual earnings inequality. The latter explains at least half of the increase and the reduction, a phenomenon that calls for further investigation.

Our main conclusion is that the drop in the college/primary education premium and the acceleration in the decline of the high school/primary education premium are part of the story in the reversal of labor income inequality. Also a noticeable decline in the experience premium across all age groups, observed beginning in the early 2000s, is part of the main observable factors associated with the trend reversal in income inequality in Latin America. Finally, we find strong evidence that the urban-rural earnings gap may have also influenced the decline in earnings inequality in the region. Since the gender gap fell mostly during the 1990s, it is probably not directly related with the regional trend reversal, although it could explain the reduction of inequality in some countries in the region.

More research is needed to test the demand, supply, and institutional factors that may help explain the trend reversal in earnings inequality in Latin America. However, although our main findings should not be understood to imply causality, they represent a fundamental result that focuses attention on the importance of relative returns of different attributes in explaining regional patterns in labor income inequality, which goes beyond country-specific explanations.

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