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The Nature of Countercyclical Income Risk

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  • Fatih Guvenen
  • Serdar Ozkan
  • Jae Song

Abstract

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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 18035.

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Date of creation: May 2012
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Handle: RePEc:nbr:nberwo:18035

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References

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  1. Almut Balleer & Thijs van Rens, 2013. "Skill-Biased Technological Change and the Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 95(4), pages 1222-1237, October.
  2. Wojciech Kopczuk & Emmanuel Saez & Jae Song, 2010. "Earnings Inequality and Mobility in the United States: Evidence from Social Security Data since 1937," The Quarterly Journal of Economics, MIT Press, vol. 125(1), pages 91-128, February.
  3. Katharine G. Abraham & Lawrence F. Katz, 1987. "Cyclical Unemployment: Sectoral Shifts or Aggregate Disturbances?," NBER Working Papers 1410, National Bureau of Economic Research, Inc.
  4. CASTRO, Rui & COEN-PIRANI, Daniele, 2005. "Why Have Aggregate Skilled Hours Become so Cyclical since the Mid-1980’s?," Cahiers de recherche 24-2005, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  5. Karen E. Dynan & Douglas W. Elmendorf & Daniel E. Sichel, 2007. "The evolution of household income volatility," Finance and Economics Discussion Series 2007-61, Board of Governors of the Federal Reserve System (U.S.).
  6. Rui Castro & Daniele Coen-Pirani, . "Why Have Aggregate Skilled Hours," GSIA Working Papers 2006-E27, Carnegie Mellon University, Tepper School of Business.
  7. Mankiw, N. Gregory, 1986. "The equity premium and the concentration of aggregate shocks," Journal of Financial Economics, Elsevier, vol. 17(1), pages 211-219, September.
  8. Lilien, David M, 1982. "Sectoral Shifts and Cyclical Unemployment," Journal of Political Economy, University of Chicago Press, vol. 90(4), pages 777-93, August.
  9. Sam Schulhofer-Wohl, 2011. "Heterogeneity and Tests of Risk Sharing," Journal of Political Economy, University of Chicago Press, vol. 119(5), pages 925 - 958.
  10. Kjetil Storesletten & Chris I. Telmer & Amir Yaron, 2004. "Cyclical Dynamics in Idiosyncratic Labor Market Risk," Journal of Political Economy, University of Chicago Press, vol. 112(3), pages 695-717, June.
  11. Constantinides,George & Duffie,Darrel, 1992. "Asset pricing with heterogeneous consumers," Discussion Paper Serie A 381, University of Bonn, Germany.
  12. Sabelhaus, John & Song, Jae, 2010. "The great moderation in micro labor earnings," Journal of Monetary Economics, Elsevier, vol. 57(4), pages 391-403, May.
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Cited by:
  1. Lars Ljungvist & Thomas Sargent, 2014. "Career Length: Effects of Curvature of Earnings Profiles, Earnings Shocks, Taxes, and Social Security," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(1), pages 1-20, January.
  2. Nicholas Bloom, 2013. "Fluctuations in Uncertainty," NBER Working Papers 19714, National Bureau of Economic Research, Inc.
  3. Nikolai Roussanov & Michael Michaux & Hui Chen, 2011. "Houses as ATMs? Mortgage Refinancing and Macroeconomic Uncertainty," 2011 Meeting Papers 1369, Society for Economic Dynamics.
  4. Fatih Guvenen & Greg Kaplan & Jae Song, 2014. "How Risky Are Recessions for Top Earners?," NBER Working Papers 19864, National Bureau of Economic Research, Inc.
  5. Bonhomme, Stephane & Hospido, Laura, 2012. "The Cycle of Earnings Inequality: Evidence from Spanish Social Security Data," IZA Discussion Papers 6669, Institute for the Study of Labor (IZA).
  6. Facundo Piguillem & Anderson Schneider, 2013. "Heterogeneous Labor Skills, The Median Voter and Labor Taxes," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 16(2), pages 332-349, April.
  7. Kim, Seewon, 2013. "Prudent consumers: New evidence from the Consumer Expenditure Survey," Economic Modelling, Elsevier, vol. 33(C), pages 77-85.

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