Data Science Institute

Choices: Method and Application to Congressional Speech

Research Project

We study the problem of measuring group differences in choices when the dimensionality of the choice set is large.

MicrophoneWe show that standard approaches suffer from a severe finite-sample bias, and we propose an estimator that applies to recent advances in machine learning to address this bias. We apply this method to measure trends in the partisanship of congressional speech from 1873 to 2016, defining partisanship to be the ease with which an observer could infer a congressperson's party from a single utterance. Our estimates imply that partisanship is far greater in recent years than in the past and that it increased sharply in the early 1900s after remaining low and relatively constant over the preceding century. 

Research Leads

Matthew GentzkowProfessor of Economics at Stanford University

Jesse ShapiroProfessor of Economics at Brown University

Matt TaddyVP NA Chief Economist at Amazon

Funding Sources

The Initiative on Global Markets and the Stigler Center at Chicago Booth, NSF, Brown University PSTC, SIEPR.