The Case of Polarization Among U.S. Congressional Candidates
Michael Bailey will present one of his recent works about Polarization Among U.S. Congressional Candidates at the médialab seminar
Event, Research Seminar
Amphi Jean Moulin, 13 rue de l'Université, 75007 Paris
This paper presents a method to estimate political ideology based on the the words and phrases candidates use on their websites and Twitter. The method enables estimation for both incumbents and challengers and, unlike methods based on campaign contributions or follower counts, the method is built on behavior under the control of candidates that directly relates to how they are perceived by voters. In contrast to existing approaches, the approach can accommodate important heterogeneity across parties in the ideological implications of many terms (i.e., liberal Democrats and conservative Republicans both use terms such as "black lives matter" or "President Trump" more than moderates). The process produces term parameter and ideal point estimates that have high face, convergent and construct validity. To demonstrate the utility of these estimates, this paper presents evidence that ideological moderation was electorally beneficial in the 2020 congressional general election.
Michael A. Bailey is at the médialab while on sabbatical from Georgetown University where he is the Colonel William J. Walsh Professor of American Government in the Department of Government and McCourt School of Public Policy.
His work covering trade, Congress, election law and the Supreme Court, methodology and inter-state policy competition has been published in the American Political Science Review, the American Journal of Political Science, the Journal of Politics, World Politics and elsewhere. Bailey is co-author with Forrest Maltzman of The Constrained Court: Law, Politics and the Decisions Justices Make. He is also the author of two statistics books: Real Stats and Real Econometrics. The goal of these books is to get to interesting and useful statistical material as quickly as possible. To be useful, the books focus on endogeneity (“correlation is not causation”) and to be interesting, the books quickly start using real data sets to answer important questions.