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Occupation inflation in the Current Population Survey

Author

Listed:
  • Fisher, Jonathan D.

    (U.S. Census Bureau, New York Census Research Data Center, New York, NY, USA)

  • Houseworth, Christina A.

    (Economics Department, Hobart and William Smith Colleges, Geneva, NY, USA)

Abstract

A common caveat often accompanying results relying on household surveys regards respondent error. There is research using administrative data to estimate the extent of error, the correlates of error, and potential corrections for the error. The authors investigate measurement error in occupation classification in the Current Population Survey (CPS) using the panel component of the CPS to identify those who incorrectly report changing occupation. We find evidence that individuals are inflating their occupation to higher skilled and higher paying occupations than the ones they actually perform. Occupation inflation biases the education and race coefficients in standard Mincer equation results within occupations.

Suggested Citation

  • Fisher, Jonathan D. & Houseworth, Christina A., 2013. "Occupation inflation in the Current Population Survey," Journal of Economic and Social Measurement, IOS Press, issue 3, pages 243-261.
    • Handle: RePEc:ris:iosjes:0003
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    References listed on IDEAS

    as
    1. Christopher R. Bollinger & Martin H. David, 2005. "I didn't tell, and I won't tell: dynamic response error in the SIPP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 563-569, May.
    2. Christopher R. Bollinger & Barry T. Hirsch, 2013. "Is Earnings Nonresponse Ignorable?," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 407-416, May.
    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. 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.
    5. Jonathan Fisher & Christina Houseworth, 2012. "The reverse wage gap among educated White and Black women," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(4), pages 449-470, December.
    6. Macpherson, David A & Hirsch, Barry T, 1995. "Wages and Gender Composition: Why Do Women's Jobs Pay Less?," Journal of Labor Economics, University of Chicago Press, vol. 13(3), pages 426-471, July.
    7. Christopher R. Bollinger & Barry T. Hirsch, 2006. "Match Bias from Earnings Imputation in the Current Population Survey: The Case of Imperfect Matching," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 483-520, 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 576-594, July.
    9. Mellow, Wesley & Sider, Hal, 1983. "Accuracy of Response in Labor Market Surveys: Evidence and Implications," Journal of Labor Economics, University of Chicago Press, vol. 1(4), pages 331-344, October.
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    Cited by:

    1. Leonard Nakamura & Jon Samuels & Rachel Soloveichik, 2017. "Measuring the “Free” Digital Economy within the GDP and Productivity Accounts," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2017-03, Economic Statistics Centre of Excellence (ESCoE).
    2. Ikudo, Akina & Lane, Julia & Staudt, Joseph & Weinberg, Bruce A., 2018. "Occupational Classifications: A Machine Learning Approach," IZA Discussion Papers 11738, Institute of Labor Economics (IZA).
    3. Rachel Soloveichik, 2019. "Accounting for Improved Brick and Mortar Shopping Experiences," BEA Working Papers 0165, Bureau of Economic Analysis.
    4. Xia, Xing, 2021. "Barrier to Entry or Signal of Quality? The Effects of Occupational Licensing on Minority Dental Assistants," Labour Economics, Elsevier, vol. 71(C).

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    More about this item

    Keywords

    Measurement error; occupation; mincer equation;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • J30 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - General

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