Dear Applied Statistics Workshop Community, Just a quick reminder, our next meeting is Wednesday, April 3 (12:00 EST). Zeyang Yu will present "A Binary IV Model for Persuasion: Profiling Persuasion Types among Compliers." <When> April 3, 12:00 to 1:30 PM, EST Lunch will be available for pick-up inside CGIS K354. <Where> In-person: CGIS K354 Zoom: https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09 <Abstract> In the empirical study of persuasion, researchers often use a binary instrument to encourage individuals to consume information and take some action. We show that with the Imbens-Angrist instrumental variable model assumptions and the monotone treatment response assumption, it is possible to identify the joint distributions of potential outcomes among compliers. This is necessary to identify the percentage of persuaded individuals and their statistical characteristics. Specifically, we develop a weighting method that helps researchers identify the statistical characteristics of persuasion types: compliers and always-persuaded, compliers and persuaded, and compliers and never-persuaded. These findings extend the ”κ weighting” results in Abadie (2003). We also provide a sharp test on the two sets of identification assumptions. The test boils down to testing whether there exists a nonnegative solution to a possibly under-determined system of linear equations with known coefficients. An application based on Green et al. (2003) is provided. The result shows that among compliers, roughly 10% voters are persuaded. The results are consistent with the findings that voters’ voting behaviors are highly persistent. Link to the paper: yu_2023local.pdf <https://arthurzeyangyu.github.io/jmp/yu_2023local.pdf> <2023-2024 Schedule> GOV 3009 Website: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Calendar: https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm… Best, Jialu -- Jialu Li Department of Government Harvard University *https://jialul.github.io/ <https://jialul.github.io/>*