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Measurement Error in Earnings Data: Using a Mixture Model Approach to Combine Survey and Register Data

Author

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  • Erik Meijer
  • Susann Rohwedder
  • Tom Wansbeek

Abstract

Survey data on earnings tend to contain measurement error. Administrative data are superior in principle, but are worthless in case of a mismatch. We develop methods for prediction in mixture factor analysis models that combine both data sources to arrive at a single earnings figure. We apply the methods to a Swedish data set. Our results show that register earnings data perform poorly if there is a (small) probability of a mismatch. Survey earnings data are more reliable, despite their measurement error. Predictors that combine both and take conditional class probabilities into account outperform all other predictors. This article has supplementary material online.

Suggested Citation

  • Erik Meijer & Susann Rohwedder & Tom Wansbeek, 2011. "Measurement Error in Earnings Data: Using a Mixture Model Approach to Combine Survey and Register Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 191-201, February.
    • Handle: RePEc:taf:jnlbes:v:30:y:2011:i:2:p:191-201
    DOI: 10.1198/jbes.2011.08166
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    Cited by:

    1. 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 15-242, W.E. Upjohn Institute for Employment Research.
    2. 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.

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