The ideological nationalization of partisan subconstituencies in the American States
Since the mid-twentieth century, elite political behavior in the United States has become much more nationalized. In Congress, for example, within-party geographic cleavages have declined, roll-call voting has become more one-dimensional, and Democrats and Republicans have diverged along this main dimension of national partisan conflict. The existing literature finds that citizens have only weakly and belatedly mimicked elite trends. We show, however, that a different picture emerges if we focus not on individual citizens, but on the aggregate characteristics of geographic constituencies. Using biennial estimates of the economic, racial, and social policy liberalism of the average Democrat and Republican in each state over the past six decades, we demonstrate a surprisingly close correspondence between mass and elite trends. Specifically, we find that: (1) ideological divergence between Democrats and Republicans has widened dramatically within each domain, just as it has in Congress; (2) ideological variation across senators’ partisan subconstituencies is now explained almost completely by party rather than state, closely tracking trends in the Senate; and (3) economic, racial, and social liberalism have become highly correlated across partisan subconstituencies, just as they have across members of Congress. Overall, our findings contradict the reigning consensus that polarization in Congress has proceeded much more rapidly and extensively than polarization in the mass public.
KeywordsRepresentation Public opinion Ideology Nationalization Congress State politics
JEL ClassificationD72 H1 R50
We are grateful for helpful conversations with Chris Tausanovitch and for feedback from Howard Rosenthal and participants at the 2016 ASU Goldwater Conference on Campaigns, Elections and Representation and the 2016 Midwest Political Science Association and American Political Science Association conferences. We appreciate the research assistance of Melissa Meek, Rob Pressel, Stephen Brown, Alex Copulsky, Kelly Alexander, Aneesh Anand, Tiffany Chung, Emma Frank, Joseff Kolman, Mathew Peterson, Charlotte Swasey, Lauren Ullmann, Amy Wickett, Julie Kim, Julia Han, Olivia H. Zhao, Mustafa Ben, Szabolcs Kiss, and Dylan DiGiacomo-Stumm. Upon publication, the data and code necessary to replicate the analysis in this article will be posted in the Harvard Dataverse.
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