Pre-test with Caution: Event-study Estimates After Testing for Parallel Trends


Tests for pre-existing trends ("pre-trends") are a common way of assessing the plausibility of the parallel trends assumption in difference-in-differences and related research designs. This paper highlights some important limitations of pre-trends testing. From a theoretical perspective, I analyze the distribution of conventional estimates and confidence intervals conditional on surviving a pre-test for pre-trends. I show that in non-pathological cases, the bias of conventional estimates conditional on passing a pre-test can be worse than the unconditional bias. Thus, pre-tests meant to mitigate bias and coverage issues in published work can in fact exacerbate them. I empirically investigate the practical relevance of these concerns in simulations based on a systematic review of recent papers in leading economics journals. I find that conventional pre-tests are often underpowered against plausible violations of parallel trends that produce bias of a similar magnitude as the estimated treatment effect. Distortions from pre-testing can also be substantial. Finally, I discuss alternative approaches that can improve upon the standard practice of relying on pre-trends testing.
Last updated on 09/10/2020