Party Polarization in Congress: A Social Networks Approach
Abstract
We use the network science concept of modularity to measure polarization in the United States Congress. As a measure of the relationship between intra-community and extra-community ties, modularity provides a conceptually-clear measure of polarization that directly reveals both the number of relevant groups and the strength of their divisions. Moreover, unlike measures based on spatial models, modularity does not require predefined assumptions about the number of coalitions or parties, the shape of legislator utilities, or the structure of the party system. Importantly, modularity can be used to measure polarization across all Congresses, including those without a clear party divide, thereby permitting the investigation of partisan polarization across a broader range of historical contexts. Using this novel measure of polarization, we show that party influence on Congressional communities varies widely over time, especially in the Senate. We compare modularity to extant polarization measures, noting that existing methods underestimate polarization in periods in which party structures are weak, leading to artificial exaggerations of the extremeness of the recent rise in polarization. We show that modularity is a significant predictor of future majority party changes in the House and Senate and that turnover is more prevalent at medium levels of modularity. We utilize two individual-level variables, which we call "divisiveness" and "solidarity," from modularity and show that they are significant predictors of reelection success for individual House members, helping to explain why partially-polarized Congresses are less stable. Our results suggest that modularity can serve as an early-warning signal of changing group dynamics, which are reflected only later by changes in formal party labels.