IDEAS home Printed from
   My bibliography  Save this paper

Are Earnings Inequality and Mobility Overstated? The Impact of Non-Classical Measurement Error


  • Gottschalk, Peter T.

    () (Boston College)

  • Huynh, Minh

    () (U.S. Social Security Administration)


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 non-classical measurement error and measures of inequality and mobility. The empirical importance of non-classical measurement error is explored using the Survey of Income and Program Participation matched to tax records. We find that the effects of non-classical measurement error are large. However, these non-classical effects are largely offsetting when estimating mobility. As a result SIPP estimates of mobility are similar to estimates based on tax records, though SIPP estimates of inequality are smaller than estimates based on tax records.

Suggested Citation

  • Gottschalk, Peter T. & Huynh, Minh, 2006. "Are Earnings Inequality and Mobility Overstated? The Impact of Non-Classical Measurement Error," IZA Discussion Papers 2327, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp2327

    Download full text from publisher

    File URL:
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    1. 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.
    2. 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.
    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.
    4. Pischke, Jorn-Steffen, 1995. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 305-314, July.
    5. Shorrocks, Anthony, 1978. "Income inequality and income mobility," Journal of Economic Theory, Elsevier, vol. 19(2), pages 376-393, December.
    6. 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 576-594, July.
    7. 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 508-532, October.
    8. 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 345-368, July.
    Full references (including those not matched with items on IDEAS)

    More about this item


    earnings mobility and inequality; measurement error;

    JEL classification:

    • J30 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp2327. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mark Fallak). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.