IDEAS home Printed from https://ideas.repec.org/a/tpr/restat/v95y2013i2p407-416.html
   My bibliography  Save this article

Is Earnings Nonresponse Ignorable?

Author

Listed:
  • Christopher R. Bollinger

    (University of Kentucky)

  • Barry T. Hirsch

    (Georgia State University)

Abstract

Earnings nonresponse in the Current Population Survey is roughly 30% in the monthly surveys and 20% in the March survey. If nonresponse is ignorable, unbiased estimates can be achieved by omitting nonrespondents. Little is known about whether CPS nonresponse is ignorable. Using sample frame measures to identify selection, we find clear-cut evidence among men but limited evidence among women for negative selection into response. Wage equation slope coefficients are affected little by selection, but because of intercept shifts, wages for men and, to a lesser extent, women are understated, as are gender gaps. Selection is least severe among household heads. © 2013 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • 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.
  • Handle: RePEc:tpr:restat:v:95:y:2013:i:2:p:407-416
    as

    Download full text from publisher

    File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/REST_a_00264
    File Function: link to full text PDF
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    2. Barry T. Hirsch & Edward J. Schumacher, 2004. "Match Bias in Wage Gap Estimates Due to Earnings Imputation," Journal of Labor Economics, University of Chicago Press, vol. 22(3), pages 689-722, July.
    3. 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.
    4. Cheti Nicoletti & Franco Peracchi, 2005. "Survey response and survey characteristics: microlevel evidence from the European Community Household Panel," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(4), pages 763-781.
    5. Korinek, Anton & Mistiaen, Johan A. & Ravallion, Martin, 2007. "An econometric method of correcting for unit nonresponse bias in surveys," Journal of Econometrics, Elsevier, vol. 136(1), pages 213-235, January.
    6. James J. Heckman & Paul A. LaFontaine, 2006. "Bias-Corrected Estimates of GED Returns," Journal of Labor Economics, University of Chicago Press, vol. 24(3), pages 661-700, July.
    7. Jungmin Lee & Sokbae Lee, 2012. "Does it Matter WHO Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited," ILR Review, Cornell University, ILR School, vol. 65(1), pages 148-160, January.
    8. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," Review of Economic Studies, Oxford University Press, vol. 79(3), pages 1053-1079.
    9. Giuseppe De Luca & Franco Peracchi, 2007. "A sample selection model for unit and item nonresponse in cross-sectional surveys," CEIS Research Paper 95, Tor Vergata University, CEIS.
    10. Hamermesh, Daniel S. & Donald, Stephen G., 2008. "The effect of college curriculum on earnings: An affinity identifier for non-ignorable non-response bias," Journal of Econometrics, Elsevier, vol. 144(2), pages 479-491, June.
    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. Ziliak, James P. & Hardy, Bradley & Bollinger, Christopher, 2011. "Earnings volatility in America: Evidence from matched CPS," Labour Economics, Elsevier, vol. 18(6), pages 742-754.
    2. Fertig, Michael & Görlitz, Katja, 2013. "Missing wages: How to test for biased estimates in wage functions?," Economics Letters, Elsevier, vol. 118(2), pages 269-271.
    3. repec:eee:econom:v:199:y:2017:i:2:p:117-130 is not listed on IDEAS
    4. Jon Jellema & Nora Lustig & Astrid Haas & Sebastian Wolf, 2016. "The Impact of Taxes, Transfers, and Subsidies on Inequality and Poverty in Uganda," Working Papers 1614, Tulane University, Department of Economics, revised Aug 2017.
    5. Jon Jellema & Nora Lustig & Astrid Haas & Sebastian Wolf, 2016. "The Impact of Taxes, Transfers, and Subsidies on Inequality and Poverty in Uganda," Commitment to Equity (CEQ) Working Paper Series 1353, Tulane University, Department of Economics.
    6. Barry T. Hirsch & John V. Winters, 2014. "An Anatomy Of Racial and Ethnic Trends in Male Earnings in the U.S," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(4), pages 930-947, December.
    7. Hirsch, Barry & Manzella, Julia, 2014. "Who Cares – and Does It Matter? Measuring Wage Penalties for Caring Work," IZA Discussion Papers 8388, Institute for the Study of Labor (IZA).
    8. Emily Isenberg & Liana Christin Landivar & Esther Mezey, 2013. "A Comparison Of Person-Reported Industry To Employer-Reported Industry In Survey And Administrative Data," Working Papers 13-47, Center for Economic Studies, U.S. Census Bureau.
    9. Drechsler, Jörg & Kiesl, Hans, 2014. "Beat the heap - an imputation strategy for valid inferences from rounded income data," IAB Discussion Paper 201402, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

    More about this item

    Keywords

    earnings nonresponse; Current Population Survey; wages;

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

    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:tpr:restat:v:95:y:2013:i:2:p:407-416. 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: (Kristin Waites). General contact details of provider: http://mitpress.mit.edu/journals/ .

    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.