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.

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