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The Evolution of Inequality, Heterogeneity and Uncertainty in Labor Earnings in the U.S. Economy

  • Cunha, Flavio

    ()

    (University of Pennsylvania)

  • Heckman, James J.

    ()

    (University of Chicago)

A large empirical literature documents a rise in wage inequality in the American economy. It is silent on whether the increase in inequality is due to greater heterogeneity in the components of earnings that are predictable by agents or whether it is due to greater uncertainty faced by agents. Applying the methodology of Cunha, Heckman, and Navarro (2005) to data on agents making schooling decisions in different economic environments, we join choice data with earnings data to estimate the fraction of future earnings that is forecastable and how this fraction has changed over time. We find that both predictable and unpredictable components of earnings have increased in recent years. The increase in uncertainty is substantially greater for unskilled workers. For less skilled workers, roughly 60% of the increase in wage variability is due to uncertainty. For more skilled workers, only 8% of the increase in wage variability is due to uncertainty. Roughly 26% of the increase in the variance of returns to schooling is due to increased uncertainty. Using conventional measures of income inequality masks the contribution of rising uncertainty to the rise in the inequality of earnings for less educated groups.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 3115.

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Length: 55 pages
Date of creation: Oct 2007
Date of revision:
Handle: RePEc:iza:izadps:dp3115
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  1. MaCurdy, Thomas, 2007. "A Practitioner's Approach to Estimating Intertemporal Relationships Using Longitudinal Data: Lessons from Applications in Wage Dynamics," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 62 Elsevier.
  2. James J. Heckman & Paul A. LaFontaine, 2008. "The American High School Graduation Rate: Trends And Levels," Working Papers 200828, Geary Institute, University College Dublin.
  3. MaCurdy, Thomas E., 1982. "The use of time series processes to model the error structure of earnings in a longitudinal data analysis," Journal of Econometrics, Elsevier, vol. 18(1), pages 83-114, January.
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