Fatih Guvenen

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

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.

Do Stockholders Share Risk More Effectively than Nonstockholders

This paper analyzes the extent of risk-sharing among stockholders and nonstockholders. To evaluate the empirical importance of market incompleteness, it is essential to determine whether idiosyncratic shocks are important for the wealthy who have access to better insurance opportunities, but also face different risks, than the average household. We study a model where each period households decide whether to participate in the stock market by paying a fixed cost. Due to this endogenous entry decision, the testable implications of perfect risk-sharing take the form of a sample selection model, which we estimate using a semiparametric GMM estimator proposed by Kyriazidou (2001). Using data from PSID, we strongly reject perfect risk-sharing among stockholders, but perhaps surprisingly, do not find evidence against it among nonstockholders. This result appears to be robust to several extensions. This finding suggests further focus on risk factors that primarily affect the wealthy, such as entrepreneurial income risk.