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Is Earnings Nonresponse Ignorable?

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  • Bollinger, Christopher R.

    ()
    (University of Kentucky)

  • Hirsch, Barry

    ()
    (Georgia State University)

Abstract

Earnings nonresponse in the Current Population Survey is roughly 30% in the monthly surveys and 20% in the annual March survey. Even if nonresponse is random, severe bias attaches to wage equation coefficient estimates on attributes not matched in the earnings imputation hot deck. If nonresponse is ignorable, unbiased estimates can be achieved by omitting imputed earners, yet little is known about whether or not 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 wage gaps. Selection is less severe among household heads/co-heads than among other household members.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 5347.

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Length: 23 pages
Date of creation: Nov 2010
Date of revision:
Publication status: published in: Review of Economics and Statistics, 2013, 95 (2), 407-416
Handle: RePEc:iza:izadps:dp5347

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Keywords: response bias; earnings nonresponse; gender gap; imputation; CPS;

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References

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  1. 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, Elsevier, vol. 144(2), pages 479-491, June.
  2. 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, University of Chicago Press, vol. 24(3), pages 483-520, July.
  3. 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.
  4. Jungmin Lee & Sokbae 'Simon' Lee, 2011. "Does it matter who responded to the survey? Trends in the U.S. gender earnings gap revisited," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP05/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  5. 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.
  6. Hirsch, Barry & Schumacher, Edward J., 2003. "Match Bias in Wage Gap Estimates Due to Earnings Imputation," IZA Discussion Papers 783, Institute for the Study of Labor (IZA).
  7. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 6(3), pages 287-96, July.
  8. James J. Heckman & Paul LaFontaine, 2006. "Bias Corrected Estimates of GED Returns," NBER Working Papers 12018, National Bureau of Economic Research, Inc.
  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. Korinek, Anton & Mistiaen, Johan A. & Ravallion, Martin, 2005. "An econometric method of correcting for unit nonresponse bias in surveys," Policy Research Working Paper Series 3711, The World Bank.
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Cited by:
  1. 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.
  2. 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].
  3. 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).
  4. Ziliak, James P. & Hardy, Bradley & Bollinger, Christopher, 2011. "Earnings volatility in America: Evidence from matched CPS," Labour Economics, Elsevier, Elsevier, vol. 18(6), pages 742-754.

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