Estimating treatment effects when program participation is misreported

Open Access
Authors
Publication date 12-2024
Journal The STATA Journal
Volume | Issue number 24 | 4
Pages (from-to) 614-629
Organisations
  • Faculty of Economics and Business (FEB) - Amsterdam School of Economics Research Institute (ASE-RI)
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, Journal 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 treatment noncompliance and misreporting. The ivreg2m command can be used as the preferred strategy in cases of exogenous (nondifferential) misclassification.
Document type Article
Language English
Published at https://doi.org/10.1177/1536867X241297916
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