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

47 Pages Posted: 18 Jul 2016

See all articles by Matthew Gentzkow

Matthew Gentzkow

Stanford University

Jesse M. Shapiro

Brown University - Department of Economics; National Bureau of Economic Research (NBER)

Matt Taddy

University of Chicago

Date Written: July 2016

Abstract

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 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 1990s after remaining low and relatively constant over the preceding century.

Suggested Citation

Gentzkow, Matthew and Shapiro, Jesse M. and Taddy, Matt, Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech (July 2016). NBER Working Paper No. w22423. Available at SSRN: https://ssrn.com/abstract=2810933

Matthew Gentzkow (Contact Author)

Stanford University ( email )

Jesse M. Shapiro

Brown University - Department of Economics ( email )

64 Waterman Street
Providence, RI 02912
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Matt Taddy

University of Chicago ( email )

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