Estimating treatment effects when program participation is misreported

Christopher Baum, Denni Tommasi and Lina Zhang

Stata Journal, 2024, vol. 24, issue 4, 614-629

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.

Keywords: heterogeneous treatment effects; LATE; misreporting; instrumental variables (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1177/1536867X241297916

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