What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Risk?

Fatih Guvenen, Fatih Karahan, Serdar Ozkan and Jae Song ()

No 719, Working Papers from Federal Reserve Bank of Minneapolis

Abstract: We 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 incomplete markets literature. 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.

Keywords: Earnings dynamics; Life-cycle earnings risk; Nonparametric estimation; Kurtosis; Skewness; Non-Guassian shocks; Normal mixture (search for similar items in EconPapers)
JEL-codes: E24 J24 J31 (search for similar items in EconPapers)
Pages: 71 pages
Date: 2015-01-30
New Economics Papers: this item is included in nep-mac
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Citations: View citations in EconPapers (52)

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Working Paper: What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Risk? (2015)
Working Paper: What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Risk? (2015)
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