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Policy and the structure of roll call voting in the US house

Published online by Cambridge University Press:  27 March 2020

Scott de Marchi Open the ORCID record for Scott de Marchi [Opens in a new window] ,
Spencer Dorsey  and
Michael J. Ensley
Scott de Marchi*
Affiliation:
Department of Political Science, Duke University, NC, USA
Spencer Dorsey
Affiliation:
Department of Political Science, Duke University, NC, USA
Michael J. Ensley
Affiliation:
Department of Political Science, Kent State University, OH, USA
*
*Corresponding author. E-mail: demarchi@duke.edu
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Abstract

Competition in the US Congress has been characterised along a single, left-right ideological dimension. We challenge this characterisation by showing that the content of legislation has far more predictive power than alternative measures, most notably legislators’ ideological positions derived from scaling roll call votes. Using a machine learning approach, we identify a topic model for final passage votes in the 111th through the 113th House of Representatives and conduct out-of-sample tests to evaluate the predictive power of bill topics relative to other measures. We find that bill topics and congressional committees are important for predicting roll call votes but that other variables, including member ideology, lack predictive power. These findings raise serious doubts about the claim that congressional politics can be boiled down to competition along a single left-right continuum and shed new light on the debate about levels of polarisation in Congress.

Keywords

congressideologymachine learningNOMINATEpredictiontopic modelling
Type
Research Article
Information
Journal of Public Policy , Volume 41 , Issue 2 , June 2021 , pp. 384 - 408
Copyright
© The Author(s) 2020. Published by Cambridge University Press

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