The Nature of Countercyclical Income Risk
NBER Working Paper No. 18035
This paper studies the cyclical nature of individual income risk using a confidential dataset from the U.S. Social Security Administration, which contains (uncapped) earnings histories for millions of individuals. The base sample is a nationally representative panel containing 10 percent of all U.S. males from 1978 to 2010. We use these data to decompose individual income growth during recessions into "between-group" and "within-group" components. We begin with the behavior of within-group shocks. Contrary to past research, we do not find the variance of idiosyncratic income shocks to be countercyclical. Instead, it is the left-skewness of shocks that is strongly countercyclical. That is, during recessions, the upper end of the shock distribution collapses--large upward income movements become less likely--whereas the bottom end expands--large drops in income become more likely. Thus, while the dispersion of shocks does not increase, shocks become more left skewed and, hence, risky during recessions. Second, to study between-group differences, we group individuals based on several observable characteristics at the time a recession hits. One of these characteristics--the average income of an individual at the beginning of a business cycle episode--proves to be an especially good predictor of fortunes during a recession: prime-age workers that enter a recession with high average earnings suffer substantially less compared with those who enter with low average earnings (which is not the case during expansions). Finally, we find that the cyclical nature of income risk is dramatically different for the top 1 percent compared with all other individuals--even relative to those in the top 2 to 5 percent.
Document Object Identifier (DOI): 10.3386/w18035
Published: The Nature of Countercyclical Income Risk (with S. Ozkan and J. Song), Journal of Political Economy, 2014, Vol. 122, No. 3, pp. 621-660. citation courtesy of
Users who downloaded this paper also downloaded* these: