Get access

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

Authors


  • This research was supported by the National Science Foundation (SES-0351469). I appreciate helpful comments from Kelly Chang, John Ferejohn, Barry Friedman, Eric Lawrence, Jeff Lewis, Forrest Maltzman, Barry Weingast, and participants at seminars at Washington University in St. Louis, New York University Law School, William and Mary, Dartmouth, George Washington, the University of Pittsburgh, and the 2005 Summer Meeting of the Society for Political Methodology. Keith Poole generously provided congressional vote data. I also appreciate the assistance of Laura Miller, Chris Drewry, Caroline Wells, Mike Griffin, Alexis Teagarden, Cynthia Fleming, Stephen de Man, and Matt Hard.

Michael A. Bailey is the Colonel Walsh Associate Professor of Government and Public Policy Institute, ICC, Suite 681, Georgetown University, Washington, DC 20057 (baileyma@georgetown.edu).

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.

Ancillary