Learning the Effect of Persuasion via Difference-In-Differences

We develop a difference-in-differences framework to measure the persuasive impact of informational treatments on behavior. We introduce two causal parameters, the forward and backward average persuasion rates on the treated, which refine the average treatment effect on the treated. The forward rate excludes cases of "preaching to the converted," while the backward rate omits "talking to a brick wall" cases. We propose both regression-based and semiparametrically efficient estimators. The framework applies to both two-period and staggered treatment settings, including event studies, and we demonstrate its usefulness with applications to a British election and a Chinese curriculum reform.