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GDP & Beyond – die europäische Perspektive

  • Christopher R. Bollinger
  • Barry T. Hirsch

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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Paper provided by German Council for Social and Economic Data (RatSWD) in its series Working Paper Series of the German Council for Social and Economic Data with number 165.

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Length: 24
Date of creation: 2010
Date of revision:
Handle: RePEc:rsw:rswwps:rswwps165
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  1. James J. Heckman & Paul LaFontaine, 2006. "Bias Corrected Estimates of GED Returns," NBER Working Papers 12018, National Bureau of Economic Research, Inc.
  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, vol. 24(3), pages 483-520, July.
  3. 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.
  4. 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.
  5. 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.
  6. Lee, Jungmin & Lee, Sokbae, 2011. "Does It Matter Who Responded to the Survey? Trends in the U.S. Gender Earnings Gap Revisited," IZA Discussion Papers 5512, Institute for the Study of Labor (IZA).
  7. 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.
  8. 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.
  9. 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.
  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.
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