Revisiting Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech

TL;DR

This paper replicates and extends prior analysis of partisanship in congressional speech by introducing an unsupervised language model that reveals topical evolution and key partisan phrases over time.

Contribution

It proposes a novel unsupervised language model combining topic and ideal point models, providing new insights into topical changes and partisan phrases in congressional speech.

Findings

Replicated GST's results on partisanship evolution

Provided data-driven insights into topical content changes

Identified key partisan phrases at the topic level

Abstract

Gentzkow, Shapiro and Taddy, Econometrica Vol 87, No 4, 2019 (henceforth GST) use a supervised text-based regression model to assess changes in partisanship in U.S. congressional speech over time. Their estimates imply that partisanship is far greater in recent years than in the past, and that it increased sharply in the early 1990s. The paper at hand provides a replication in the wide sense of GST by complementing their analysis in three ways. First, we propose an alternative unsupervised language model, which combines ideas of topic models and ideal point models, to analyze the change in partisanship over time. We apply this model to the Senate speech data used in GST ranging from 1981-2017. Using our model we replicate their results on the specific evolution of partisanship. Second, our model provides additional insights such as the data-driven estimation of evolvement of topical contents over time. Third, we identify key phrases of partisanship on topic level.