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Errors in Survey and Administrative Data on Employment Earnings: Austria and the United Kingdom Compared

Author

Listed:
  • Bollinger, Christopher R.

    (University of Kentucky)

  • Jenkins, Stephen P.

    (London School of Economics)

  • Rios-Avila, Fernando

    (Levy Economics Institute)

  • Tasseva, Iva V.

    (LSE)

Abstract

We contribute new cross-national evidence about the nature of measurement errors in employment earnings, fitting the same error components model to harmonised earnings data for Austria and the UK. The model allows for measurement error in the administrative data and linkage error as well as survey measurement error. We find several cross-national similarities in error structure but also intriguing differences in error component probabilities, means, and dispersions.

Suggested Citation

  • Bollinger, Christopher R. & Jenkins, Stephen P. & Rios-Avila, Fernando & Tasseva, Iva V., 2025. "Errors in Survey and Administrative Data on Employment Earnings: Austria and the United Kingdom Compared," IZA Discussion Papers 18129, IZA Network @ LISER.
    • Handle: RePEc:iza:izadps:dp18129
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    References listed on IDEAS

    as
    1. Stephen P. Jenkins & Fernando Rios‐Avila, 2021. "Measurement error in earnings data: Replication of Meijer, Rohwedder, and Wansbeek's mixture model approach to combining survey and register data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(4), pages 474-483, June.
    2. Jenkins, Stephen P. & Rios-Avila, Fernando, 2020. "Modelling errors in survey and administrative data on employment earnings: Sensitivity to the fraction assumed to have error-free earnings," Economics Letters, Elsevier, vol. 192(C).
    3. Maddalena Cavicchioli & Michele Lalla, 2022. "Evidences from survey data and fiscal data: nonresponse and measurement errors in annual incomes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 587-615, September.
    4. 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.
    5. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    6. repec:taf:jnlbes:v:30:y:2012:i:2:p:191-201 is not listed on IDEAS
    7. R. Bollinger, Christopher & Valentinova Tasseva, Iva, 2022. "Income source confusion using the SILC," ISER Working Paper Series 2022-04, Institute for Social and Economic Research.
    8. Jenkins, Stephen P. & Rios-Avila, Fernando, 2021. "Reconciling Reports: Modelling Employment Earnings and Measurement Errors Using Linked Survey and Administrative Data," IZA Discussion Papers 14405, IZA Network @ LISER.
    9. 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(3), pages 513-551.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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