Comparable Preference Estimates across Time and Institutions for the Court, Congress, and Presidency

Abstract

Empirically oriented scholars often struggle with how to measure preferences across time and institutional contexts. This article characterizes these difficulties and provides a measurement approach that incorporates information that bridges time and institutions in a Bayesian Markov Chain Monte Carlo approach to ideal point measurement. The resulting preference estimates for presidents, senators, representatives, and Supreme Court justices are comparable across time and institutions. These estimates are useful in a variety of important research projects, including research on statutory interpretation, executive influence on the Supreme Court, and Senate influence on court appointments.