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Difference-in-Differences Estimators of Intertemporal Treatment Effects

Clément de Chaisemartin and Xavier D'Haultfoeuille

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Abstract: We study treatment-effect estimation, with a panel where groups may experience multiple changes of their treatment dose. We make parallel trends assumptions, but do not restrict treatment effect heterogeneity, unlike the linear regressions that have been used in such designs. We extend the event-study approach for binary-and-staggered treatments, by redefining the event as the first time a group's treatment changes. This yields an event-study graph, with reduced-form estimates of the effect of having been exposed to a weakly higher amount of treatment for $\ell$ periods. We show that the reduced-form estimates can be combined into an economically interpretable cost-benefit ratio.

Date: 2020-07, Revised 2022-03
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Handle: RePEc:arx:papers:2007.04267