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Are Earnings Inequality and Mobility Overstated? The Impact of Nonclassical Measurement Error

  • Peter Gottschalk

    (Boston College)

  • Minh Huynh

    (National Institutes of Health)

Measures of inequality and mobility based on self-reported earnings reflect attributes of both the joint distribution of earnings across time and the joint distribution of measurement error and earnings. While classical measurement error would increase measures of inequality and mobility, there is substantial evidence that measurement error in earnings is not classical. In this paper, we present the analytical links between nonclassical measurement error and some summary measures of inequality and mobility. The empirical importance of nonclassical measurement error is explored using the Survey of Income and Program Participation (SIPP) matched to tax records. We find that the effects of nonclassical measurement error are large. However, these nonclassical effects are largely offsetting when estimating mobility, as measured by the intertemporal correlation in earnings. As a result, SIPP estimates of the correlation are similar to estimates based on tax records, though SIPP estimates of inequality are smaller than estimates based on tax records. © 2010 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/rest.2010.11232
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Article provided by MIT Press in its journal The Review of Economics and Statistics.

Volume (Year): 92 (2010)
Issue (Month): 2 (May)
Pages: 302-315

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Handle: RePEc:tpr:restat:v:92:y:2010:i:2:p:302-315
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  1. Shorrocks, Anthony, 1978. "Income inequality and income mobility," Journal of Economic Theory, Elsevier, vol. 19(2), pages 376-393, December.
  2. John Bound & Alan B. Krueger, 1989. "The Extent of Measurement Error In Longitudinal Earnings Data: Do Two Wrongs Make A Right?," NBER Working Papers 2885, National Bureau of Economic Research, Inc.
  3. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843 Elsevier.
  4. Pischke, J.S., 1994. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Working papers 94-01, Massachusetts Institute of Technology (MIT), Department of Economics.
  5. Duncan, Greg J & Hill, Daniel H, 1985. "An Investigation of the Extent and Consequences of Measurement Error in Labor-Economic Survey Data," Journal of Labor Economics, University of Chicago Press, vol. 3(4), pages 508-32, October.
  6. 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.
  7. Bound, John, et al, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-68, July.
  8. Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-94, July.
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