We study the problem of measuring group differences in choices when the dimensionality of the choice set is large. We 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.
Matthew Gentzkow, Professor of Economics at Stanford University
Jesse Shapiro, Professor of Economics at Brown University
Matt Taddy, VP NA Chief Economist at Amazon
The Initiative on Global Markets and the Stigler Center at Chicago Booth, NSF, Brown University PSTC, SIEPR.