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Correcting for Misclassified Binary Regressors Using Instrumental Variables

Author

Listed:
  • Steven J. Haider
  • Melvin Stephens

Abstract

Estimators that exploit an instrumental variable to correct for misclassification in a binary regressor typically assume that the misclassification rates are invariant across all values of the instrument. We show this assumption is invalid in routine empirical settings. We derive a new estimator which allows misclassification rates to vary across values of the instrumental variable. Our key identifying assumption, that the sum of misclassification rates remains constant across instrument values, follows from the empirical examples we present. We also show this assumption can be relaxed using moment inequalities that arise from our model. We demonstrate the usefulness of our estimator through Monte Carlo simulations and a reanalysis of the extent to which Medicaid eligibility crowds out other forms of health insurance. Correcting for measurement error substantially reduces estimates of crowd out and the extent to which Medicaid eligibility lowers the share of the uninsured.

Suggested Citation

  • Steven J. Haider & Melvin Stephens, 2025. "Correcting for Misclassified Binary Regressors Using Instrumental Variables," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 43(3), pages 592-602, July.
    • Handle: RePEc:taf:jnlbes:v:43:y:2025:i:3:p:592-602
    DOI: 10.1080/07350015.2024.2415102

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    Cited by:

    1. is not listed on IDEAS
    2. Akanksha Negi & Digvijay S. Negi, 2025. "Difference‐in‐Differences With a Misclassified Treatment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(4), pages 411-423, June.
    3. Katherine Harris‐Lagoudakis & Hannah Wich, 2024. "Purchases over the SNAP benefit cycle: Evidence from supermarket panel data," Economic Inquiry, Western Economic Association International, vol. 62(4), pages 1426-1448, October.
    4. Acerenza, Santiago & Ban, Kyunghoon & Kedagni, Desire, 2021. "Marginal Treatment Effects with Misclassified Treatment," ISU General Staff Papers 202106180700001132, Iowa State University, Department of Economics.
    5. Denni Tommasi & Lina Zhang, 2024. "Identifying program benefits when participation is misreported," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(6), pages 1123-1148, September.

    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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