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From: Brookings Papers on Economic Activity
Spring 2013
pp. 123-142 | 10.1353/eca.2013.0008

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Comment by Greg Kaplan

This paper by Jason DeBacker and coauthors provides a new perspective on the much-documented rise in income inequality in the United States, by exploiting confidential data on labor earnings and household income from the Internal Revenue Service (IRS). The IRS data contain information from a large panel of tax returns over the period from 1987 to 2009. The authors use these data to ask whether the recent rise in inequality is mostly due to persistent or to transitory factors. Other authors have answered this question using survey data, predominantly from the Panel Study of Income Dynamics (PSID), and for earlier periods. But this paper breaks new ground in its use of high-quality administrative data to decompose the rise in inequality in the 1990s and 2000s.

DeBacker and his coauthors reach a stark conclusion: all of the recent rise in inequality in male earnings is due to persistent factors; transitory factors have made no contribution to the increase in inequality. Their findings for total household income are similar but less extreme. The authors reach these conclusions using two different approaches. First, they employ simple nonparametric methods, which effectively measure the persistent component of income as a rolling average of income in a given number of adjacent years, and the transitory component as the residual from this rolling average. Second, they estimate error components models (ECMs) for earnings. The ECM approach involves specifying and estimating the parameters of a time-varying stochastic process for income. The persistent and transitory components are then inferred from the estimated model. The authors' conclusions about the relative importance of persistent versus transitory factors are consistent across the two methods.

In this discussion I will elaborate on three issues that are related to these findings, focusing exclusively on the ECM analysis of male labor earnings. First, I will use data from the PSID to investigate how the particular choice of ECM framework may have influenced the authors' conclusions. In doing so I will distinguish between factors that are fixed at the time of entry into the labor market, and shocks that are realized after entry. I will attempt to shed light on which of these factors is responsible for the increase in the persistent variance. I will also explain how an increase in the variance of shocks that occurred before 1987 could be responsible for the observed increase in inequality from 1987 to 2009 even in the absence of any changes in the labor market during this period. Second, I will use the PSID data to investigate the importance of changes in the returns to education in accounting for the authors' findings. I will show that the findings are mostly consistent across the two data sets and are not substantially affected by controlling for education. Third, I will highlight an issue that the authors do not address, but that is a natural one to raise in light of their findings, and given their access to the IRS data: in which part of the income distribution is the recent rise in inequality concentrated? I will conduct a simple decomposition using the PSID data to investigate this issue.

How do the publicly available PSID data compare with the confidential IRS data used by the authors? The baseline sample of male earners from the IRS contains 221,099 person-year observations on 20,859 individuals over the period 1987-2009. In all of the analyses that follow, I use a sample of male heads of households from the PSID that imposes the same selection criteria for age and minimum annual earnings as the authors impose on the IRS data. The resulting sample contains 70,479 person-year observations on 6,778 individuals over the period 1970-2008 (the data are biennial after 1996). Thus, the IRS sample is about three times the size of the PSID sample, both in terms of individuals and in terms of individual-year observations.

My figure 1 plots inequality in male earnings, as measured by the standard deviation of the logarithm, in the two data sets over time. For the period over which the two samples overlap, the trends in inequality are very similar. The level of inequality is...



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