Party Polarization in Congress: A Network Science Approach

Forthcoming

56 Pages Posted: 21 Jul 2009 Last revised: 26 Jul 2011

Andrew Scott Waugh

University of California, San Diego (UCSD) - Department of Political Science

Liuyi Pei

California Institute of Technology

James H. Fowler

UC San Diego Division of Social Sciences; UC San Diego School of Medicine

Peter J. Mucha

University of North Carolina (UNC) at Chapel Hill

Mason Alexander Porter

University of Oxford

Date Written: July 20, 2009

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.

Keywords: modularity, congress, networks, polarization, parties, ideology, community detection, roll-call

Suggested Citation

Waugh, Andrew Scott and Pei, Liuyi and Fowler, James H. and Mucha, Peter J. and Porter, Mason Alexander, Party Polarization in Congress: A Network Science Approach (July 20, 2009). Forthcoming. Available at SSRN: https://ssrn.com/abstract=1437055

Andrew Scott Waugh (Contact Author)

University of California, San Diego (UCSD) - Department of Political Science ( email )

9500 Gilman Drive
Code 0521
La Jolla, CA 92093-0521
United States

Liuyi Pei

California Institute of Technology ( email )

Pasadena, CA 91125
United States

James H. Fowler

UC San Diego Division of Social Sciences ( email )

9500 Gilman Drive
Code 0521
La Jolla, CA 92093-0521
United States

HOME PAGE: http://jhfowler.ucsd.edu

UC San Diego School of Medicine ( email )

9500 Gilman Drive
MC 0507
La Jolla, CA 92093
United States

HOME PAGE: http://jhfowler.ucsd.edu

Peter J. Mucha

University of North Carolina (UNC) at Chapel Hill ( email )

102 Ridge Road
Chapel Hill, NC NC 27514
United States

Mason Alexander Porter

University of Oxford ( email )

Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom

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