The Nature of Countercyclical Income Risk
University of Minnesota - Department of Economics; National Bureau of Economic Research (NBER)
Federal Reserve Board
U.S. Social Security Administration
September 18, 2013
This paper studies the nature of business cycle variation in individual earnings risk using a unique and 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 2011. We use these data to decompose individual earnings growth during recessions into “between-group” and “within-group” components. We begin with the behavior of within-group (idiosyncratic) shocks. Contrary to past research, we do not find the variance of idiosyncratic earnings 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 earnings movements become less likely --- whereas the bottom end expands --- large drops in earnings 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 earnings of an individual at the beginning of a business cycle episode --- proves to be a 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 earnings 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.
Number of Pages in PDF File: 73working papers series
Date posted: April 27, 2012 ; Last revised: September 19, 2013
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