We set up an econometric model of persuasion and study identification of key parameters under various scenarios of data availability. We find that a commonly used measure of persuasion does not estimate the persuasion rate of any population in general. We provide formal identification results,...

We set up an econometric model of persuasion and study identification of key parameters under various scenarios of data availability. We find that a commonly used measure of persuasion does not estimate the persuasion rate of any population in general. We provide formal identification results,...

This paper examines a commonly used measure of persuasion whose precise interpretation has been obscure in the literature. By using the potential outcome framework, we define the causal persuasion rate by a proper conditional probability of taking the action of interest with a persuasive message...

This paper examines a commonly used measure of persuasion whose precise interpretation has been obscure in the literature. By using the potential outcome framework, we define the causal persuasion rate by a proper conditional probability of taking the action of interest with a persuasive message...

We set up an econometric model of persuasion and study identification of key parameters under various scenarios of data availability. We find that a commonly used measure of persuasion does not estimate the persuasion rate of any population in general. We provide formal identification results,...

We set up an econometric model of persuasion and study identification of key parameters under various scenarios of data availability. We find that a commonly used measure of persuasion does not estimate the persuasion rate of any population in general. We provide formal identification results,...

We propose the sharp identifiable bounds of the distribution functions of potential outcomes using a panel with fixed T. We allow for the possibility that the statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use...

We propose inference procedures for partially identified population features for which the population identification region can be written as a transformation of the Aumann expectation of a properly defined set valued random variable (SVRV). An SVRV is a mapping that associates a set (rather...