Measuring Polarization in High-Dimensional Data: Method and Application to Congressional Speech
Published By: NATIONAL BUREAU OF ECONOMIC RESEARCH on eSS | Published Date: July , 2016This paper studies trends in the partisanship of Congressional speech from 1873 to 2009. It defines partisanship to be the ease with which an observer could infer a congressperson’s party from a fixed amount of speech, and estimates it using a structural choice model and methods from machine learning. This paper applies tools from structural estimation and machine learning to study the partisanship of language in the US Congress. [Working Paper 22423]
Author(s): Matthew Gentzkow, Jesse Shapiro, Matt Taddy | Posted on: Jul 21, 2016 | Views()