A0310
Title: Treatment effects in staggered adoption designs with non-parallel trends
Authors: Emmanuel Tsyawo - Universite Mohammed VI Polytechnique (Morocco) [presenting]
Brantly Callaway - University of Georgia (United States)
Abstract: The purpose is to consider identifying and estimating causal effect parameters in a staggered treatment adoption setting - that is, where a researcher has access to panel data and treatment timing varies across units. The case is considered where untreated potential outcomes may follow non-parallel trends over time across groups. This implies that the identifying assumptions of leading approaches, such as difference-in-differences, do not hold. The main focus is on the case where untreated potential outcomes are generated by an interactive fixed effects model and show that variation in treatment timing provides additional moment conditions that can be used to recover a large class of target causal effect parameters. The approach exploits the variation in treatment timing without requiring either (i) A large number of time periods or (ii) Any extra exclusion restrictions. This is in contrast to essentially all of the literature on interactive fixed effects models, which requires at least one of these extra conditions. Rather, the approach directly applies in settings where there is variation in treatment timing. Although the main focus is on a model with interactive fixed effects, the idea of using variation in treatment timing to recover causal effect parameters is quite general and could be adapted to other settings with non-parallel trends across groups, such as dynamic panel data models.
