A1031
Title: Randomization inference on policy assignments
Authors: EunYi Chung - University of Illinois at Urbana Champaign (United States) [presenting]
Abstract: Randomization inference is quickly becoming a widely used statistical approach in the social, behavioral, and natural sciences. In the setting of regression kink designs, propose a randomization test that is constructed based on random kink points assigned by a policy. The limitation of their method is that researchers are assumed to know the policy data-generating process that selects the kink point and use that distribution to simulate critical values for the test. Although the randomization test has an exact size under such an assumption, the test is no longer valid, even asymptotically, if the researcher misspecifies the policy assignment distribution. The first contribution is to provide a general framework for randomization tests based on policy assignments of individuals into treatment and control groups. The framework includes not only regression discontinuity and kink designs but also bunching and difference-in-differences models. The proposed test controls size in large samples even when the researcher does not know the policy assignment distribution; it retains the exactness property of the randomization test when the policy distribution is known. Simulations show desirable finite sample properties, and an empirical application illustrates the procedure.
