The voting patterns of the nine justices on the United States Supreme Court continue to fascinate and perplex observers of the Court. While it is commonly understood that the division of the justices into a liberal branch and a conservative branch inevitably drives many case outcomes, there are finer, less transparent divisions within these two main branches that have proven difficult to extract empirically. This study imports methods from evolutionary biology to help illuminate the intricate and often overlooked branching structure of the justices’ voting behavior. Specifically, phylogenetic tree estimation based on voting disagreement rates is used to extend ideal point estimation to the non-Euclidean setting of hyperbolic metrics. After introducing this framework, comparing it to one- and two-dimensional multidimensional scaling, and arguing that it flexibly captures important higher-dimensional voting behavior, a handful of potential ways to apply this tool are presented. The emphasis throughout is on interpreting these judicial trees and extracting qualitative insights from them.
Understanding Complex Legislative and Judicial Behaviour via Hierarchical Ideal Point Estimation
Ideal point estimation is an important tool to study legislative and judicial voting behaviours. We propose a hierarchical ideal point estimation framework that directly models complex voting behaviours on the basis of the characteristics of the political actors and the votes that they cast. Through simulations and empirical examples we show that this framework holds good promise for resolving many unsettled issues, such as the multi-dimensional aspects of ideology, and the effects of political parties. As a companion to this paper, we offer an easy-to-use R package that implements the methods discussed.
- Publication Date:
- NSF-PAR ID:
- 10402019
- Journal Name:
- Journal of the Royal Statistical Society Series C: Applied Statistics
- Volume:
- 60
- Issue:
- 1
- Page Range or eLocation-ID:
- p. 93-107
- ISSN:
- 0035-9254
- Publisher:
- Oxford University Press
- Sponsoring Org:
- National Science Foundation
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