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Modelling Errors in Survey and Administrative Data on Employment Earnings: Sensitivity to the Fraction Assumed to Have Error-Free Earnings

Author

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  • Jenkins, Stephen P.

    (London School of Economics)

  • Rios-Avila, Fernando

    (Levy Economics Institute)

Abstract

Kapteyn and Ypma (Journal of Labour Economics 2007) is an influential study of errors in survey and administrative data on employment earnings. To fit their mixture models, Kapteyn and Ypma assume a specific fraction of their sample have error-free earnings. Using a new UK dataset, we assess the sensitivity of model estimates and post-estimation statistics to variations in this fraction and find some lack of robustness.

Suggested Citation

  • 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," IZA Discussion Papers 13196, IZA Network @ LISER.
    • Handle: RePEc:iza:izadps:dp13196
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    References listed on IDEAS

    as
    1. 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.
    2. Dean R. Hyslop & Wilbur Townsend, 2020. "Earnings Dynamics and Measurement Error in Matched Survey and Administrative Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 457-469, April.
    3. repec:taf:jnlbes:v:30:y:2012:i:2:p:191-201 is not listed on IDEAS
    4. 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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    Cited by:

    1. Stephen P. Jenkins & Fernando Rios-Avila, 2023. "Finite mixture models for linked survey and administrative data: Estimation and postestimation," Stata Journal, StataCorp LLC, vol. 23(1), pages 53-85, March.
    2. Bergstrom, Katy & Dodds, William & Lacoste, Nicholas & Rios, Juan, 2026. "Estimating the welfare cost of labor supply frictions," Journal of Public Economics, Elsevier, vol. 253(C).
    3. Stella Martin, 2025. "We Might Both Be Wrong - Reconciliation of Survey and Administrative Earnings Measurements," CQE Working Papers 11025, Center for Quantitative Economics (CQE), University of Muenster.
    4. Nico Thurow, 2025. "Characterizing Measurement Error in the German Socio-Economic Panel Using Linked Survey and Administrative Data," Papers 2501.03015, arXiv.org, revised Aug 2025.
    5. Apostolos Davillas & Victor Hugo Oliveira & Andrew M. Jones, 2024. "A model of errors in BMI based on self-reported and measured anthropometrics with evidence from Brazilian data," Empirical Economics, Springer, vol. 67(5), pages 2371-2410, November.
    6. Nora Lustig & Andrea Vigorito, 2025. "The "Missing Rich" in Household Surveys: Causes and Correction Approaches. Extended Version with Technical Appendixes," Documentos de Trabajo (working papers) 25-03, Instituto de Economía - IECON.
    7. Crossley, Thomas F. & Fisher, Paul & Hussein, Omar, 2023. "Assessing data from summary questions about earnings and income," Labour Economics, Elsevier, vol. 81(C).
    8. Ferreira, Francisco H. G. & Brunori, Paolo, 2024. "Inherited inequality, meritocracy, and the purpose of economic growth," LSE Research Online Documents on Economics 126263, London School of Economics and Political Science, LSE Library.
    9. Emmanuel Flachaire & Nora Lustig & Andrea Vigorito, 2023. "Underreporting of Top Incomes and Inequality: A Comparison of Correction Methods using Simulations and Linked Survey and Tax Data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(4), pages 1033-1059, December.
    10. 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.
    11. 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.
    12. Nora Lustig, 2019. "The “Missing Rich” in Household Surveys: Causes and Correction Approaches," Commitment to Equity (CEQ) Working Paper Series 75, Tulane University, Department of Economics.

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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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