Spatial voting models in circular spaces: A case study of the U.S. House of Representatives

53 Pages Posted: 29 May 2019 Last revised: 17 Feb 2021

See all articles by Xingchen Yu

Xingchen Yu

University of California, Santa Cruz

Abel Rodriguez

University of California, Santa Cruz

Date Written: April 8, 2019

Abstract

The use of spatial models for inferring member's preferences from voting data has become widespread in the study of deliberative bodies such as legislatures. Most established spatial voting models assume that ideal points belong to a (possibly multidimensional) Euclidean policy space. However, the geometry of Euclidean spaces cannot accommodate situations in which members at the opposite ends of the ideological spectrum reveal similar preferences by voting together against the rest of the legislature. This kind of voting behavior can arise, for example, when extreme conservatives oppose a measure because they see it as being too costly, while extreme liberals oppose it as for not going far enough for them. This paper introduces a new class of spatial voting models in which preferences live in a circular space. Our formulation includes the one-dimensional version of the Euclidean model as a special (limiting case), allowing the data to inform us about the geometry of the underlying space. A circular structure for the latent space is motivated by both theoretical (the so-called "horseshoe theory'' of political thinking) and empirical (goodness of fit) considerations. In particular, by applying the model to roll-call voting data from the U.S. Congress between 1988 and 2019, we demonstrate that circular latent spaces provide a better explanation for the political process in the House of Representatives than Euclidean models, that policy spaces have become increasingly circular in recent years (and, especially, since 2010), and that legislators's rankings generated through the use of the circular geometry tend to be more consistent with their stated policy positions.

Keywords: Spatial Voting Model; Roll-Call Votes; U.S. Congress; Factor Model; Spherical Geometry; Geodesic Hamiltonian Monte Carlo.

Suggested Citation

Yu, Xingchen and Rodriguez, Abel, Spatial voting models in circular spaces: A case study of the U.S. House of Representatives (April 8, 2019). Available at SSRN: https://ssrn.com/abstract=3381925 or http://dx.doi.org/10.2139/ssrn.3381925

Xingchen Yu (Contact Author)

University of California, Santa Cruz ( email )

1156 High St
Santa Cruz, CA 95064
United States

Abel Rodriguez

University of California, Santa Cruz ( email )

1156 High St
Santa Cruz, CA 95064
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
110
Abstract Views
875
Rank
452,862
PlumX Metrics