Party Polarization in Congress: A Network Science Approach

TL;DR

This paper introduces a network science measure called modularity to quantify polarization in the US Congress, revealing dynamic group structures and predicting political shifts better than traditional methods.

Contribution

It applies modularity to congressional networks, demonstrating its effectiveness in capturing polarization, predicting majority changes, and forecasting reelection success, surpassing existing measures.

Findings

Modularity varies over time, reflecting shifts in party influence.

Higher modularity predicts changes in majority party.

Divisiveness and solidarity predict reelection success.

Abstract

We measure polarization in the United States Congress using the network science concept of modularity. Modularity provides a conceptually-clear measure of polarization that reveals both the number of relevant groups and the strength of inter-group divisions without making restrictive assumptions about the structure of the party system or the shape of legislator utilities. We show that party influence on Congressional blocs varies widely throughout history, and that existing measures underestimate polarization in periods with weak party structures. We demonstrate that modularity is a significant predictor of changes in majority party and that turnover is more prevalent at medium levels of modularity. We show that two variables related to modularity, called `divisiveness' and `solidarity,' are significant predictors of reelection success for individual House members. Our results suggest that modularity can serve as an early warning of changing group dynamics, which are reflected only later by changes in party labels.