The canonical difference-in-differences (DD) model contains two time periods, "pre" and "post", and two groups, "treatment" and "control". Most DD applications, however, exploit variation across groups of units that receive treatment at different times. This paper derives an expression for this...

This paper uses the rollout of the first Community Health Centers (CHCs) to study the longer-term health effects of increasing access to primary care. Within ten years, CHCs are associated with a reduction in age-adjusted mortality rates of 2 percent among those 50 and older. The implied 7 to 13...

This paper analyzes difference-in-differences designs 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...

This paper considers methods for defining aggregate parameters of interest in a difference-in-differences design with a continuous and staggered treatment. It also discusses how aggregation choices often simplify estimation