Explaining Differences in Voting Patterns Across Voting Domains Using Hierarchical Bayesian Models

TL;DR

This paper extends hierarchical Bayesian spatial voting models to identify covariates explaining differences in legislators' ideal points across voting domains, revealing party and constituency effects on voting behavior.

Contribution

The paper introduces a novel extension to Bayesian spatial voting models that accounts for covariates influencing differences in legislators' ideal points across voting types.

Findings

Minority party legislators show greater differences in ideal points between vote types.

Moderate constituency legislators exhibit more variation in ideal points across votes.

The model provides insights into how party and constituency influence voting behavior.

Abstract

Spatial voting models of legislators' preferences are used in political science to test theories about their voting behavior. These models posit that legislators' ideologies as well as the ideologies reflected in votes for and against a bill or measure exist as points in some low dimensional space, and that legislators vote for positions that are close to their own ideologies. Bayesian spatial voting models have been developed to test sharp hypotheses about whether a legislator's revealed ideal point differs for two distinct sets of bills. This project extends such a model to identify covariates that explain whether legislators exhibit such differences in ideal points. We use our method to examine voting behavior on procedural versus final passage votes in the U.S. house of representatives for the 93rd through 113th congresses. The analysis provides evidence that legislators in the minority party as well as legislators with a moderate constituency are more likely to have different ideal points for procedural versus final passage votes.