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Finite mixture models for linked survey and administrative data: Estimation and postestimation

Author

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  • Stephen P. Jenkins

    (London School of Economics and Political Science)

  • Fernando Rios-Avila

    (The Levy Economics Institute of Bard College)

Abstract

Researchers use finite mixture models to analyze linked survey and administrative data on labor earnings, while also accounting for various types of measurement error in each data source. Different combinations of error-ridden and error-free observations characterize latent classes. Latent class probabilities depend on the probabilities of the different types of error. We introduce a suite of commands to fit finite mixture models to linked survey-administrative data: there is a general model and seven simpler variants. We also provide postestimation commands for assessment of reliability, marginal effects, data simulation, and pre- diction of hybrid variables that combine information from both data sources about the outcome of interest. Our commands can also be used to study measurement errors in other variables besides labor earnings.

Suggested Citation

  • 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.
    • Handle: RePEc:tsj:stataj:v:23:y:2023:i:1:p:53-85
    DOI: 10.1177/1536867X231161976
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj23-1/st0701/
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    File URL: http://hdl.handle.net/10.1177/1536867X231161976
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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. 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).
    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. 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.
    7. repec:taf:jnlbes:v:30:y:2012:i:2:p:191-201 is not listed on IDEAS
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    Cited by:

    1. 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).
    2. 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.

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    More about this item

    Keywords

    ky_fit; ky_estat; ky_sim; linked survey and administrative data; measurement error; finite mixture models; latent class 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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