Seminar Details

Seminar Details


Dec 3

3:30 pm

Bayesian Learning About Ideal Points of U.S. Supreme Court Justices, 1953-1999

Kevin Quinn


University of Washington - Department of Statistics & Political Science

We employ Markov chain Monte Carlo methods to fit Bayesian measurement models of judicial preferences for all justices serving on the U.S. Supreme Court from 1953 to 1999. We are particularly interested in making inferences about the extent to which the policy preferences of justices change throughout their tenure on the Court and how the policy content of the cases they decide also changes across time. To do so, we fit a series of longitudinal measurement models that include dynamic specifications for the justices' ideal points (i.e., policy preferences) and the case-specific parameters. Our results suggest that justices do not have temporally constant ideal points, even after controlling for the changing policy content of the cases that come before the Court. We also compare the ideological composition of the current court to that of earlier courts.