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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.
  • Rios-Avila, Fernando

Abstract

We develop and apply new statistical models for linked survey and administrative data on employment earnings that generalize those of Kapteyn and Ypma (Journal of Labor Economics, 2007). Our models incorporate four types of measurement error: mean-reverting measurement error in the survey data; ‘reference period’ error (mismatch between survey and administrative data definitions); error in the linking of survey and administrative data; and mean-reverting measurement error in the administrative data. In addition, we allow error distributions to differ with individual characteristics, which improves model fit and allows us to investigate substantive hypotheses about factors associated with error bias and variance. Using individual-level data from the 2011/12 Family Resources Survey linked with administrative data based on Pay As You Earn records (P14 data) for the same individuals, we contribute the first UK evidence to a field dominated by findings about the USA. We show that measurement errors are pervasive, but the four types are quite different in nature. We also document substantial heterogeneity in each of the error distributions.

Suggested Citation

  • Jenkins, Stephen P. & Rios-Avila, Fernando, 2023. "Reconciling reports: modelling employment earnings and measurement errors using linked survey and administrative data," LSE Research Online Documents on Economics 117213, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:117213
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    File URL: http://eprints.lse.ac.uk/117213/
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    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, 2021. "Reconciling Reports: Modelling Employment Earnings and Measurement Errors Using Linked Survey and Administrative Data," IZA Discussion Papers 14405, Institute of Labor Economics (IZA).
    3. 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).
    4. 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.
    5. 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.
    6. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    7. 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.
    8. repec:taf:jnlbes:v:30:y:2012:i:2:p:191-201 is not listed on IDEAS
    9. Paul Bingley & Alessandro Martinello, 2017. "Measurement Error in Income and Schooling and the Bias of Linear Estimators," Journal of Labor Economics, University of Chicago Press, vol. 35(4), pages 1117-1148.
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    16. Levy, Frank & Murnane, Richard J, 1992. "U.S. Earnings Levels and Earnings Inequality: A Review of Recent Trends and Proposed Explanations," Journal of Economic Literature, American Economic Association, vol. 30(3), pages 1333-1381, September.
    17. Barry T. Hirsch & Edward J. Schumacher, 2004. "Match Bias in Wage Gap Estimates Due to Earnings Imputation," Journal of Labor Economics, University of Chicago Press, vol. 22(3), pages 689-722, July.
    18. Blundell, Richard & Joyce, Robert & Norris Keiller, Agnes & Ziliak, James P., 2018. "Income inequality and the labour market in Britain and the US," Journal of Public Economics, Elsevier, vol. 162(C), pages 48-62.
    19. 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.
    20. Stefan Angel & Franziska Disslbacher & Stefan Humer & Matthias Schnetzer, 2019. "What did you really earn last year?: explaining measurement error in survey income data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1411-1437, October.
    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. 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.
    4. 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.
    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. 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.
    7. 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.
    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, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    measurement error; linkage error; earnings; linked data and administrative data; finite mixture models; Family Resources Survey; P14 data; PAYE data;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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