Testing the Predictions of the Multidimensional Spatial Voting Model with Roll Call Data

e-mail: gjeong{at}artsci.wustl.edu This paper develops a procedure for locating proposals and legislatorsin a multidimensional policy space by applying agenda-constrainedideal point estimation. Placing proposals and legislators onthe same scale allows an empirical test of the predictions ofthe spatial voting model. I illustrate this procedure by testingthe predictive power of the uncovered set—a solution conceptof the multidimensional spatial voting model—using rollcall data from the U.S. Senate. Since empirical tests of thepredictive power of the uncovered set have been limited to experimentaldata, this is the first empirical test of the concept's predictivepower using real-world data. Author's note: An earlier version of this paper was presentedat the 2006 Annual Meeting of Political Methodology Society.I am grateful to Andrew Martin, Gary Miller, Dan O'Neill, DavidPark, Robert Walker, and three anonymous reviewers for theirhelpful comments. I am especially indebted to Gary Miller forhis insights and advice. All remaining errors are my own.