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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