Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech
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.
Previously circulated as "Measuring Polarization in High-Dimensional Data: Method and Application to Congressional Speech." We acknowledge funding from the Initiative on Global Markets and the Stigler Center at Chicago Booth, the National Science Foundation, the Brown University Population Studies and Training Center, and the Stanford Institute for Economic Policy Research (SIEPR).We thank Egor Abramov, Brian Knight, John Marshall, Suresh Naidu, Vincent Pons, Justin Rao, and Gaurav Sood for their comments and suggestions. We thank Frances Lee for sharing her data on congressional communications staff. We also thank numerous seminar audiences and our many dedicated research assistants for their contributions to this project. This work was completed in part with resources provided by the University of Chicago Research Computing Center and the Stanford Research Computing Center. The data providers and funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
I am a member of the Toulouse Network of Information Technology, a research group funded by Microsoft.Jesse M. Shapiro
I have served as a compensated consultant for a firm that may develop an online news product and I have been a paid visitor at Microsoft Research.
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. citation courtesy of