A substantial part of this paper was completed while Guvenen was a visiting economist at the Federal Reserve Bank of Chicago, whose hospitality is gratefully acknowledged. The views expressed herein are those of the authors and not necessarily those of the Social Security Administration, the Federal Reserve Bank of Chicago, the Federal Reserve System, or the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe study the evolution of individual labor earnings over the life cycle, using a large panel data set of earnings histories drawn from U.S. administrative records. Using fully nonparametric methods, our analysis reaches two broad conclusions. First, earnings shocks display substantial deviations from lognormality-the standard assumption in the literature on incomplete markets. In particular, earnings shocks display strong negative skewness and extremely high kurtosis-as high as 30 compared with 3 for a Gaussian distribution. The high kurtosis implies that, in a given year, most individuals experience very small earnings shocks, and a small but non-negligible number experience very large shocks. Second, these statistical properties vary significantly both over the life cycle and with the earnings level of individuals. We also estimate impulse response functions of earnings shocks and find important asymmetries: Positive shocks to high-income individuals are quite transitory, whereas negative shocks are very persistent; the opposite is true for low-income individuals. Finally, we use these rich sets of moments to estimate econometric processes with increasing generality to capture these salient features of earnings dynamics.

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Wage inequality has been significantly higher in the United States than in continental European countries (CEU) since the 1970s. Moreover, this inequality gap has further widened during this period as the US has experienced a large increase in wage inequality, whereas the CEU has seen only modest changes. This paper studies the role of labor income tax policies for understanding these facts. We begin by documenting two new empirical facts that link these inequality differences to tax policies. First, we show that countries with more progressive labor income tax schedules have significantly lower before-tax wage inequality at different points in time. Second, progressivity is also negatively correlated with the rise in wage inequality during this period. We then construct a life cycle model in which individuals decide each period whether to go to school, work, or be unemployed. Individuals can accumulate skills either in school or while working. Wage inequality arises from differences across individuals in their ability to learn new skills as well as from idiosyncratic shocks. Progressive taxation compresses the (after-tax) wage structure, thereby distorting the incentives to accumulate human capital, in turn reducing the cross-sectional dispersion of (before-tax) wages. We find that these policies can account for half of the difference between the US and the CEU in overall wage inequality and 76% of the difference in inequality at the upper end (log 90-50 differential). When this economy experiences skill-biased technological change, progressivity also dampens the rise in wage dispersion over time. The model explains 41% of the difference in the total rise in inequality and 58% of the difference at the upper end.