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

By Fatih Guvenen, Fatih Karahan, Serdar Ozkan, Jae Song. 2015

289 citations recorded by the source corpus.

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.

Subfields: Accounting, Demography, General Health Professions

View Paper: https://www.semanticscholar.org/paper/4564782fe5a0757bd31f2852cb7f8decbca12e55

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

In Demography

In General Health Professions

Citations in the corpus

Monetary Policy According to HANK (2018, methodology): “27 We restrict attention to a symmetric process since Guvenen et al. (2015) find only a small amount of negative skewness in 1-year and 5-year annual changes.”
Saving and Wealth Inequality (2017, unknown): “…they compare the wealth distribution generated by the model in the case in which earnings follow: (1) follow the AR(1) process used by Huggett (1996); or, alternatively, (2) the rich earnings process implied by the estimates in Guvenen et al. (2015a).10 Table 5 summarizes their findings.”
CENTRAL BANK POLICIES AND INCOME AND WEALTH INEQUALITY: A SURVEY (2019, unknown): “Using administrative records, like those of the Social Security Administration in the United States (see Guvenen et al. , 2015) would be a good alternative to using survey data.”
The Dynamics of Inequality (2016, unknown): “This has a similar flavor to the mixture specification advocated by Guvenen et al. (2015). We build on Luttmer (2011), who studies a related framework applied to firm dynamics and argues that persistent heterogeneity in mean firm growth rates is needed to account for the relatively young age of very”
The evolution of wealth inequality over half a century: The role of taxes, transfers and technology (2016, background): “Guvenen et al. (2015) provide a detailed analysis of the distribution of earnings growth.”
Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework (2015, result): “They are also consistent with some recent results independently obtained using administrative tax records (see Guvenen et al., 2015, and references therein), and they are all at odds with linear models commonly used in the earnings dynamics literature.”
When Inequality Matters for Macro and Macro Matters for Inequality (2018, unknown): “Recent work by Guvenen et al. (2015) shows that changes in annual labor income are extremely leptokurtic, meaning that most absolute annual income changes are small but a small number are very large.”
A Schumpeterian Model of Top Income Inequality (2014, unknown): “Guvenen, Kaplan and Song (2014a) study the role of gender differences in the rise in top earnings inequality. These same authors (in progress) are working to estimate a rich model of micro income dynamics and tie it more closely to the rise in top income inequality. Related work using the administra”
Skewed Wealth Distributions: Theory and Empirics (2018, unknown): “In the United States this is well documented by Piketty and Saez (2003) and especially by Guvenen et al. (2015) in their detailed study of the Social Security Administration panel data covering 1978 to 2013; see also De Nardi, Fella, and Pardo (2016) for an overview.”