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Reconciling Reports: Modelling Employment Earnings and Measurement Errors Using Linked Survey and Administrative Data

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  • Jenkins, Stephen P.

    (London School of Economics)

  • Rios-Avila, Fernando

    (Levy Economics Institute)

Abstract

We contribute new UK evidence about measurement errors and employment earnings to a field dominated by findings about the USA. We develop and apply new econometric models for linked survey and administrative data that generalize those of Kapteyn and Ypma (Journal of Labor Economics, 2007). Our models incorporate mean-reverting measurement error in administrative data in addition to linkage mismatch and mean-reverting survey measurement error and 'reference period' error, while also allowing error distributions to vary across individuals. Annualised survey earnings underestimate true annual earnings on average. Mean-reversion in survey measurement errors is absent. Both earnings sources underestimate true earnings inequality. The survey earning measure is more reliable than the administrative data earnings measure, but hybrid earnings predictors based on both sources are distinctly more reliable than either source-specific measure. The models with heterogeneous measurement error distributions indicate how data quality may be improved. For example, for survey quality, our results highlight how respondents showing payslips to interviewers have smaller survey error variances. For administrative data, our results suggest that greater error variances are associated with non-standard jobs, private sector jobs, and employers without good payroll systems.

Suggested Citation

  • 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, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp14405
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    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. Stephen P. Jenkins & Fernando Rios-Avila, 2023. "Finite mixture models for linked survey and administrative data: Estimation and postestimation," Stata Journal, StataCorp LP, vol. 23(1), pages 53-85, March.
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    21. Christopher R. Bollinger & Barry T. Hirsch & Charles M. Hokayem & James P. Ziliak, 2019. "Trouble in the Tails? What We Know about Earnings Nonresponse 30 Years after Lillard, Smith, and Welch," Journal of Political Economy, University of Chicago Press, vol. 127(5), pages 2143-2185.
    22. Wojciech Kopczuk & Emmanuel Saez & Jae Song, 2010. "Earnings Inequality and Mobility in the United States: Evidence from Social Security Data Since 1937," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(1), pages 91-128.
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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 LP, vol. 23(1), pages 53-85, March.
    2. Ha Trong Nguyen & Huong Thu Le & Luke Connelly & Francis Mitrou, 2023. "Accuracy of self‐reported private health insurance coverage," Health Economics, John Wiley & Sons, Ltd., vol. 32(12), pages 2709-2729, December.
    3. 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.
    4. Evan S. Totty & Thor Watson, 2024. "Privacy Protection and Accuracy: What Do We Know? Do We Know Things?? Let's Find Out!," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, National Bureau of Economic Research, Inc.
    5. Stephen P. Jenkins, 2022. "Top-income adjustments and official statistics on income distribution: the case of the UK," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 151-168, March.
    6. 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, Institute of Labor Economics (IZA).
    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. Alessio Fusco & Philippe Van Kerm, 2023. "Measuring poverty persistence," Chapters, in: Jacques Silber (ed.), Research Handbook on Measuring Poverty and Deprivation, chapter 18, pages 192-200, Edward Elgar Publishing.

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    More about this item

    Keywords

    measurement error; earnings; survey data; administrative data; finite mixture models;
    All these keywords.

    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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