In cases of noncompliance with an assigned treatment, estimates of causal effects typically rely on instrumental variables (IV). However, when participation is also misreported, the IV estimand may become a nonconvex combination of local average treatment effects that fails to satisfy even a minimal condition for being causal. The aim of our paper is to generalize the MR-LATE approach. This is an alternative IV estimand that is more robust in cases of noncompliance and nondifferential misclassification of the treatment variable. Our generalization is threefold: First, we incorporate discrete and multiple-discrete instrument(s); second, we consider the use of instrument(s) under a weaker, partial monotonicity condition; third, we provide a general inferential procedure. Under relatively stringent assumptions, MR-LATE is either identical to the IV estimand or less biased than the naïve IV estimand. Under less stringent assumptions, the MR-LATE estimand can identify the sign of the IV estimand. We conclude with the use of a dedicated Stata command, ivreg2m, to assess the return on education in the United Kingdom.
CITATION STYLE
Tommasi, D., & Zhang, L. (2024). Identifying program benefits when participation is misreported. Journal of Applied Econometrics. https://doi.org/10.1002/jae.3079
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