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Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data

Author

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  • John M. Abowd

    (Cornell University, Census Bureau NBER, CREST/INSEE, and IZA)

  • Martha H. Stinson

    (U.S. Census Bureau)

Abstract

We propose a new methodology that does not assume a prior specification of the statistical properties of the measurement errors and treats all sources as noisy measures of some underlying true value. The unobservable true value can be represented as a weighted average of all available measures, using weights that must be specified a priori unless there has been a truth audit. The Census Bureau's Survey of Income and Program Participation (SIPP) survey jobs are linked to Social Security Administration earnings data, creating two potential annual earnings observations. The reliability statistics for both sources are quite similar except for cases where the SIPP used imputations for some missing monthly earnings reports. © 2013 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • John M. Abowd & Martha H. Stinson, 2013. "Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1451-1467, December.
    • Handle: RePEc:tpr:restat:v:95:y:2013:i:5:p:1451-1467
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    More about this item

    Keywords

    measurement error; earnings; matched survey-administrative data; linear mixed-effects model;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs

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