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Is Earnings Nonresponse Ignorable?

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  • 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.

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Bibliographic Info

Article provided by MIT Press in its journal Review of Economics and Statistics.

Volume (Year): 95 (2013)
Issue (Month): 2 (May)
Pages: 407-416

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

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Keywords: earnings nonresponse; Current Population Survey; wages;

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References

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  1. 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.
  2. 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.
  3. 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.
  4. James J. Heckman & Paul LaFontaine, 2006. "Bias Corrected Estimates of GED Returns," NBER Working Papers 12018, National Bureau of Economic Research, Inc.
  5. Bryan S. Graham & Cristine Campos de Xavier Pinto & Daniel Egel, 2008. "Inverse Probability Tilting for Moment Condition Models with Missing Data," NBER Working Papers 13981, National Bureau of Economic Research, Inc.
  6. Jungmin Lee & Sokbae Lee, 2012. "Does It Matter Who Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited," Industrial and Labor Relations Review, ILR Review, Cornell University, ILR School, vol. 65(1), pages 148-160, January.
  7. 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.
  8. 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.
  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.
  10. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-96, July.
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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. 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.
  3. 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].

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