QUANTITATIVE RESEARCH METHODS WORKSHOP
Abstract: This paper analyzes difference-in-differences setups with a continuous treatment. We show that treatment effect on the treated-type parameters can be identified under a generalized parallel trends assumption that is similar to the binary treatment setup. However, interpreting differences in these parameters across different values of the treatment can be particularly challenging due to treatment effect heterogeneity. We discuss alternative, typically stronger, assumptions that alleviate these challenges. We also provide a variety of treatment effect decomposition results, highlighting that parameters associated with popular linear two-way fixed-effect (TWFE) specifications can be hard to interpret, even when there are only two time periods. We introduce alternative estimation procedures that do not suffer from these TWFE drawbacks. Our empirical application reveals that these differences can be practically meaningful.
Pedro H. C. Sant’Anna is an Associate Professor of Economics at Emory University. His research interests lie in Applied Econometrics, particularly Causal Inference. His recent work is primarily focused on better understanding and improving Difference-in-Differences methods. He has published papers in leading journals, including Journal of Econometrics and Econometrica, and his papers have received over 5,000 citations since 2018. Before joining Emory, he was an Assistant Professor at Vanderbilt University, a Principal Economist at Microsoft, and an Amazon Visiting Academic. He received his Ph.D. in economics in 2015 from Universidad Carlos III de Madrid, Spain.
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This series is sponsored by the ISPS Center for the Study of American Politics and The Whitney and Betty MacMillan Center for International and Area Studies at Yale with support from the Edward J. and Dorothy Clarke Kempf Fund.