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Estimating treatment effects when program participation is misreported

Author

Listed:
  • Christopher F Baum

    (Boston College)

  • Denni Tommasi

    (University of Bologna)

  • Lina Zhang

    (University of Amsterdam)

Abstract

Instrumental variables are commonly used to estimate treatment effects in cases of imperfect compliance. However, if participation in the program is misreported, standard techniques can yield severely biased results. We present a new command, ivreg2m, that implements the mismeasured robust local average treatment-effect estimator developed by Calvi, Lewbel, and Tommasi (2022, Jour- nal of Business and Economic Statistics 40: 1701–1717) and Tommasi and Zhang (2024b, Journal of Applied Econometrics, https://doi.org/10.1002/jae.3079), to estimate the heterogeneous treatment effect of a program in the presence of treat- ment noncompliance and misreporting. The ivreg2m command can be used as the preferred strategy in cases of exogenous (nondifferential) misclassification.

Suggested Citation

  • Christopher F Baum & Denni Tommasi & Lina Zhang, 2024. "Estimating treatment effects when program participation is misreported," Stata Journal, StataCorp LP, vol. 24(4), pages 614-629, December.
    • Handle: RePEc:tsj:stataj:v:24:y:2024:i:4:p:614-629
    DOI: 10.1177/1536867X241297916
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