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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.

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File URL: http://www.journals.uchicago.edu/cgi-bin/resolve?id=doi:10.1086/513298
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Bibliographic Info

Article provided by University of Chicago Press in its journal Journal of Labor Economics.

Volume (Year): 25 (2007)
Issue (Month): ()
Pages: 513-551

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Handle: RePEc:ucp:jlabec:v:25:y:2007:p:513-551

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Web page: http://www.journals.uchicago.edu/JOLE/

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Cited by:
  1. Deborah Cobb-Clark & Stefanie Schurer, 2011. "Two Economists' Musings on the Stability of Locus of Control," Melbourne Institute Working Paper Series wp2011n09, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  2. 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.
  3. 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.
  4. 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.
  5. 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 Publications Department.

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