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

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.

Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis

“…Recent progress in natural language processing and computational social science have pushed political science research into new frontiers. For example, scholars have studied language use in presidential elections (Acree et al, 2018), legislative text in Congress (de Marchi et al, 2018), and similarities in national constitutions (Elkins and Shaffer, 2019). However, datasets used by political scientists are mostly homogeneous in terms of subject (e.g., immigration) or document type (e.g., constitutions).…”