  This study proposes a method to identify treatment effects without exclusion
restrictions in randomized experiments with noncompliance. Exploiting a
baseline survey commonly available in randomized experiments, I decompose the
intention-to-treat effects conditional on the endogenous treatment status. I
then identify these parameters to understand the effects of the assignment and
treatment. The key assumption is that a baseline variable maintains rank orders
similar to the control outcome. I also reveal that the change-in-changes
strategy may work without repeated outcomes. Finally, I propose a new estimator
that flexibly incorporates covariates and demonstrate its properties using two
experimental studies.
