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

Author

Listed:
  • 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 LLC, 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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    Cited by:

    1. 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.
    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.
    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, IZA Network @ LISER.

    More about this item

    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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