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Measurement Error and Misclassification: A Comparison of Survey and Administrative Data

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  • Arie Kapteyn
  • Jelmer Y. Ypma

Abstract

We provide both a theoretical and empirical analysis of the relation between administrative and survey data. By distinguishing between different sources of deviations between survey and administrative data we are able to reproduce several stylized facts. We illustrate the implications of different error sources for estimation in (simple) econometric models and find potentially very substantial biases. This article shows the sensitivity of some findings in the literature for the assumption that administrative data represent the truth. In particular, the common finding of substantial mean reversion in survey data largely goes away once we allow for a richer error structure.

Suggested Citation

  • Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25, pages 513-551.
    • Handle: RePEc:ucp:jlabec:v:25:y:2007:p:513-551
    DOI: 10.1086/513298
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    References listed on IDEAS

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    1. 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.
    2. Martha Stinson, 2002. "Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Survey and SSA Administrative Data," Longitudinal Employer-Household Dynamics Technical Papers 2002-24, Center for Economic Studies, U.S. Census Bureau.
    3. Duncan, Greg J & Hill, Daniel H, 1985. "An Investigation of the Extent and Consequences of Measurement Error in Labor-Economic Survey Data," Journal of Labor Economics, University of Chicago Press, vol. 3(4), pages 508-532, October.
    4. Bekker, Paul & Kapteyn, Arie & Wansbeek, Tom, 1987. "Consistent Sets of Estimates for Regressions with Correlated or Uncorrelated Measurement Errors in Arbitrary Subsets of All Variables," Econometrica, Econometric Society, vol. 55(5), pages 1223-1230, September.
    5. 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.
    6. Michael Hurd & F. Thomas Juster & James P. Smith, 2004. "Enhancing the Quality of Data on Income: Recent Developments in Survey Methodology," Labor and Demography 0412001, University Library of Munich, Germany.
    7. Klepper, Steven & Leamer, Edward E, 1984. "Consistent Sets of Estimates for Regressions with Errors in All Variables," Econometrica, Econometric Society, vol. 52(1), pages 163-183, January.
    8. Johansson, Fredrik & Klevmarken, Anders, 2006. "Explaining the size and nature of response in a survey on health status and economic standard," Working Paper Series 2006:2, Uppsala University, Department of Economics.
    9. Bonggeun Kim & Gary Solon, 2005. "Implications of Mean-Reverting Measurement Error for Longitudinal Studies of Wages and Employment," The Review of Economics and Statistics, MIT Press, vol. 87(1), pages 193-196, February.
    10. Abowd, John M. & Vilhuber, Lars, 2005. "The Sensitivity of Economic Statistics to Coding Errors in Personal Identifiers," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 133-152, April.
    11. Bound, John & Brown, Charles & Duncan, Greg J & Rodgers, Willard L, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-368, July.
    12. Pischke, Jorn-Steffen, 1995. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 305-314, July.
    13. Xiaohong Chen & Han Hong & Elie Tamer, 2005. "Measurement Error Models with Auxiliary Data," Review of Economic Studies, Oxford University Press, vol. 72(2), pages 343-366.
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