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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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    Cited by:

    1. Erik Meijer & Susann Rohwedder & Tom Wansbeek, 2008. "Prediction of Latent Variables in a Mixture of Structural Equation Models, with an Application to the Discrepancy Between Survey and Register Data," Working Papers 584, RAND Corporation.
    2. Bruce D. Meyer & Nikolas Mittag, 2015. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," Upjohn Working Papers and Journal Articles 15-242, W.E. Upjohn Institute for Employment Research.
    3. Andreasch Michael & Lindner Peter, 2016. "Micro- and Macrodata: a Comparison of the Household Finance and Consumption Survey with Financial Accounts in Austria," Journal of Official Statistics, De Gruyter Open, vol. 32(1), pages 1-28, March.
    4. repec:eee:ecolet:v:155:y:2017:i:c:p:19-23 is not listed on IDEAS
    5. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," IZA Discussion Papers 10943, Institute for the Study of Labor (IZA).
    6. John Ameriks & Andrew Caplin & Minjoon Lee & Matthew D. Shapiro & Christopher Tonetti, 2015. "The Wealth of Wealthholders," NBER Working Papers 20972, National Bureau of Economic Research, Inc.
    7. Felix Schmutz, 2016. "Measuring the Invisible: An Overview of and Outlook for Tax Non-Compliance Estimates and Measurement Methods for Switzerland," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 152(II), pages 125-177, June.
    8. Paulus, Alari, 2015. "Tax evasion and measurement error: An econometric analysis of survey data linked with tax records," ISER Working Paper Series 2015-10, Institute for Social and Economic Research.
    9. Whitaker, Stephan, 2015. "Big Data versus a Survey," Working Paper 1440, Federal Reserve Bank of Cleveland.
    10. David Card & David S. Lee & Zhuan Pei & Andrea Weber, 2015. "Inference on Causal Effects in a Generalized Regression Kink Design," Econometrica, Econometric Society, vol. 83, pages 2453-2483, November.
    11. Pudney, Stephen, 2009. "Participation in disability benefit programmes: a partial identification analysis of the British Attendance Allowance system," ISER Working Paper Series 2009-19, Institute for Social and Economic Research.
    12. Deborah A. Cobb‐Clark & Stefanie Schurer, 2013. "Two Economists' Musings on the Stability of Locus of Control," Economic Journal, Royal Economic Society, vol. 0, pages 358-400, August.
    13. Van-Ha Le & Jakob de Haan & Erik Dietzenbacher, 2013. "Do Higher Government Wages Reduce Corruption? Evidence Based on a Novel Dataset," CESifo Working Paper Series 4254, CESifo Group Munich.
    14. Dean Hyslop & Wilbur Townsend, 2016. "Earnings Dynamics and Measurement Error in Matched Survey and Administrative Data," Working Papers 16_18, Motu Economic and Public Policy Research.
    15. Jeffrey A. Groen, 2011. "Seasonal Differences in Employment between Survey and Administrative Data," Working Papers 443, U.S. Bureau of Labor Statistics.
    16. Jesse Bricker & Gary V. Engelhardt, 2007. "Measurement Error in Earnings Data in the Health and Retirement Study," Working Papers, Center for Retirement Research at Boston College wp2007-16, Center for Retirement Research, revised Oct 2007.
    17. Dieter Vandelannoote & André Decoster & Toon Vanheukelom & Gerlinde Verbist, 2016. "Evaluating The Quality Of Gross Incomes In SILC: Compare Them With Fiscal Data And Re-calibrate Them Using EUROMOD," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 5-34.
    18. Bruce Meyer & Nikolas Mittag, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," Working Papers 2017-075, Human Capital and Economic Opportunity Working Group.
    19. Hyslop, Dean R. & Townsend, Wilbur, 2017. "Employment misclassification in survey and administrative reports," Economics Letters, Elsevier, vol. 155(C), pages 19-23.

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