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

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  • Peter Gottschalk

    (Boston College)

  • Minh Huynh

    (National Institutes of Health)

Abstract

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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Bibliographic Info

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. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
  2. 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.
  3. 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.
  4. Shorrocks, Anthony, 1978. "Income inequality and income mobility," Journal of Economic Theory, Elsevier, vol. 19(2), pages 376-393, December.
  5. 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.
  6. 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.
  7. 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.
  8. 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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Cited by:
  1. Akee, Randall, 2011. "Errors in self-reported earnings: The role of previous earnings volatility and individual characteristics," Journal of Development Economics, Elsevier, vol. 96(2), pages 409-421, November.
  2. Markus Jantti & Stephen P. Jenkins, 2014. "Income Mobility," Working Papers 319, ECINEQ, Society for the Study of Economic Inequality.
  3. Jo Blanden & Paul Gregg & Lindsey Macmillan, 2011. "Intergenerational Persistence in Income and Social Class: The Impact of Within-Group Inequality," The Centre for Market and Public Organisation 11/277, Department of Economics, University of Bristol, UK.
  4. Audra J. Bowlus & Jean-Marc Robin, 2011. "An International Comparison of Lifetime Inequality: How Continental Europe Resembles North America," University of Western Ontario, CIBC Centre for Human Capital and Productivity Working Papers 20116, University of Western Ontario, CIBC Centre for Human Capital and Productivity.
  5. Kruppe, Thomas & Matthes, Britta & Unger, Stefanie, 2014. "Effectiveness of data correction rules in process-produced data : the case of educational attainment," IAB Discussion Paper 201415, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
  6. Akee, Randall K. Q., 2007. "Errors in Self-Reported Earnings: The Role of Previous Earnings Volatility," IZA Discussion Papers 3263, Institute for the Study of Labor (IZA).
  7. Kässi, Otto, 2011. "Earnings Dynamics of Men and Women in Finland: Permanent Inequality versus Earnings Instability," MPRA Paper 34301, University Library of Munich, Germany.
  8. Van Kerm, Philippe & Pi Alperin, Maria Noel, 2013. "Inequality, growth and mobility: The intertemporal distribution of income in European countries 2003–2007," Economic Modelling, Elsevier, vol. 35(C), pages 931-939.
  9. repec:ese:iserwp:2013-23 is not listed on IDEAS
  10. Daniel D. Schnitzlein, 2009. "Struktur und Ausmaß der intergenerationalen Einkommensmobilität in Deutschland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 229(4), pages 450-466, August.

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