Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity

Abstract

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, inference in DID models when there are few treated groups is still an open question. We show that usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, when there is variation in the number of observations per group, we show that inference methods designed to work when there are few treated groups would tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups would have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) dataset to show that this problem is relevant even in datasets with large number of observations per group. Then we derive alternative inference methods that provide accurate hypothesis testing in situations of few treated groups and many control groups in the presence of heteroskedasticity (including the case of only one treated group). The main assumption is that we know how the heteroskedasticity is generated, which is the case when it is generated by variation in the number of observations per group. (AU)