What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Dynamics?
Fatih Guvenen (),
Fatih Karahan (),
Serdar Ozkan () and
Jae Song ()
No 710, Staff Reports from Federal Reserve Bank of New York
We study individual earnings dynamics over the life cycle using panel data on millions of U.S. workers. Using nonparametric methods, we first show that the distribution of earnings changes exhibits substantial deviations from lognormality, such as negative skewness and very high kurtosis. Further, the extent of these nonnormalities varies significantly with age and earnings level, peaking around age 50 and between the 70th and 90th percentiles of the earnings distribution. Second, we estimate nonparametric impulse response functions and find important asymmetries: positive changes for high-income individuals are quite transitory, whereas negative ones are very persistent; the opposite is true for low-income individuals. Third, we turn to long-run outcomes and find substantial heterogeneity in the cumulative growth rates of earnings and total years individuals spend nonemployed between ages 25 and 55. Finally, by targeting these rich sets of moments, we estimate stochastic processes for earnings that range from the simple to the complex. Our preferred specification features normal mixture innovations to both persistent and transitory components and includes long-term nonemployment shocks with a realization probability that varies with age and earnings.
Keywords: earnings dynamics; higher-order earnings risk; kurtosis; skewness; non-Gaussian shocks; normal mixture (search for similar items in EconPapers)
JEL-codes: E24 J24 J31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-lab, nep-lma and nep-mac
Date: 2015-02-01, Revised 2019-09-01
Note: Previous title: "What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Risk?"
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