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

Matthew Gentzkow, Jesse Shapiro and Matt Taddy

No 22423, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: D72 (search for similar items in EconPapers)
Date: 2016-07
New Economics Papers: this item is included in nep-cul and nep-pol
Note: POL
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Published as Matthew Gentzkow & Jesse M. Shapiro & Matt Taddy, 2019. "Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech," Econometrica, Econometric Society, vol. 87(4), pages 1307-1340, July.

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Journal Article: Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech (2019)
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