IDEAS home Printed from https://ideas.repec.org/a/ris/iosjes/0003.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bollinger, Christopher R. & Hirsch, Barry & Hokayem, Charles M. & Ziliak, James P., 2018. "Trouble in the Tails? What We Know about Earnings Nonresponse Thirty Years after Lillard, Smith, and Welch," IZA Discussion Papers 11710, Institute of Labor Economics (IZA).
    2. Michele Lalla & Patrizio Frederic & Daniela Mantovani, 2022. "The inextricable association of measurement errors and tax evasion as examined through a microanalysis of survey data matched with fiscal data: a case study," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1375-1401, December.
    3. Christopher R. Bollinger, 2001. "Response Error and the Union Wage Differential," Southern Economic Journal, John Wiley & Sons, vol. 68(1), pages 60-76, July.
    4. Christopher R. Bollinger & Barry T. Hirsch, 2010. "GDP & Beyond – die europäische Perspektive," RatSWD Working Papers 165, German Data Forum (RatSWD).
    5. Nicolas Frémeaux, 2023. "The More, the Better? Individual and Joint Interviewing in Surveys," Annals of Economics and Statistics, GENES, issue 149, pages 63-96.
    6. 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.
    7. Michele Lalla & Maddalena Cavicchioli, 2020. "Nonresponse and measurement errors in income: matching individual survey data with administrative tax data," Department of Economics 0170, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    8. Jeremy J. Nalewaik, 2014. "Missing Variation in the Great Moderation: Lack of Signal Error and OLS Regression," Finance and Economics Discussion Series 2014-27, Board of Governors of the Federal Reserve System (U.S.).
    9. Bruce D. Meyer & Nikolas Mittag, 2015. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," NBER Working Papers 21676, National Bureau of Economic Research, Inc.
    10. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," IZA Discussion Papers 10943, Institute of Labor Economics (IZA).
    11. Annette Jäckle & Emanuela Sala & Stephen P. Jenkins & Peter Lynn, 2005. "Validation of Survey Data on Income and Employment: The ISMIE Experience," Discussion Papers of DIW Berlin 488, DIW Berlin, German Institute for Economic Research.
    12. Liu, Long, 2009. "On hourly wages and weekly earnings in the current population survey," Economics Letters, Elsevier, vol. 105(1), pages 113-116, October.
    13. Bruce Meyer & Nikolas Mittag, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," Working Papers 2017-075, Human Capital and Economic Opportunity Working Group.
    14. Kristensen, Nicolai & Westergård-Nielsen, Niels C., 2006. "A Large-Scale Validation Study of Measurement Errors in Longitudinal Survey Data," IZA Discussion Papers 2329, Institute of Labor Economics (IZA).
    15. John Abowd & Martha Stinson, 2011. "Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data," Working Papers 11-20, Center for Economic Studies, U.S. Census Bureau.
    16. Mittag, Nikolas, 2016. "Correcting for Misreporting of Government Benefits," IZA Discussion Papers 10266, Institute of Labor Economics (IZA).
    17. Peter Gottschalk & Minh Huynh, 2010. "Are Earnings Inequality and Mobility Overstated? The Impact of Nonclassical Measurement Error," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 302-315, May.
    18. Hu, Yingyao & Schennach, Susanne & Shiu, Ji-Liang, 2022. "Identification of nonparametric monotonic regression models with continuous nonclassical measurement errors," Journal of Econometrics, Elsevier, vol. 226(2), pages 269-294.
    19. An, Yonghong & Hu, Yingyao, 2012. "Well-posedness of measurement error models for self-reported data," Journal of Econometrics, Elsevier, vol. 168(2), pages 259-269.
    20. Barry T. Hirsch & David A. Macpherson, 2004. "Wages, Sorting on Skill, and the Racial Composition of Jobs," Journal of Labor Economics, University of Chicago Press, vol. 22(1), pages 189-210, January.

    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

    Statistics

    Access and download statistics

    Corrections

    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:ris:iosjes:0003. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Saskia van Wijngaarden (email available below). General contact details of provider: http://www.iospress.nl/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.