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Measurement error in earnings data: Replication of Meijer, Rohwedder, and Wansbeek's mixture model approach to combining survey and register data

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  • Stephen P. Jenkins
  • Fernando Rios‐Avila

Abstract

Meijer, Rohwedder, and Wansbeek (MRW, Journal of Business & Economic Statistics, 2012) developed methods for prediction of a single earnings figure per worker from mixture factor models fitted using earnings data from multiple linked data sources. MRW applied their method using parameter estimates of Kapteyn and Ypma's mixture factor model (KY, Journal of Labour Economics 2007) fitted to earnings data for Swedish workers aged 50+. First, we replicate MRW's empirical analysis using the Swedish model estimates. Second, we check the generality of their empirical finding with a new application. Using estimates of a KY model fit to a linked dataset on earnings for UK employees of all ages, we confirm that MRW's principal findings about the performance of their various predictors of true earnings also hold in this different setting.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:japmet:v:36:y:2021:i:4:p:474-483
    DOI: 10.1002/jae.2811
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    References listed on IDEAS

    as
    1. 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.
    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. repec:taf:jnlbes:v:30:y:2012:i:2:p:191-201 is not listed 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 LP, vol. 23(1), pages 53-85, March.
    2. Davillas, Apostolos & de Oliveira, Victor Hugo & Jones, Andrew M., 2022. "Model of Errors in BMI Based on Self‐reported and Measured Anthropometrics with Evidence from Brazilian Data," CINCH Working Paper Series (since 2020) 76143, Duisburg-Essen University Library, DuEPublico.
    3. 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).
    4. Luis Ayala & Ana Pérez & Mercedes Prieto-Alaiz, 2022. "The impact of different data sources on the level and structure of income inequality," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(3), pages 583-611, September.
    5. 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.

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    More about this item

    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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