Noncompliance in randomized control trials without exclusion restrictions
For randomized experiments with noncompliance, I propose a method to identify treatment effects without exclusion restrictions. It exploits a baseline survey that is commonly available in randomized experiments. I show the identification of the average treatment effect on the treated (ATT) and the local average treatment effect (LATE), assuming that a baseline variable maintains similar rank orders to the control outcome. I apply this strategy to a microcredit experiment with one-sided noncompliance to identify the ATT. I find that the instrumental variable (IV) estimate of log revenue is 2.2 times larger than my preferred estimate. Supplemental materials are available online.