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

• 成果类型: Article
• 署名作者: Gentzkow, Matthew; Shapiro, Jesse M.; Taddy, Matt
• 署名单位: Stanford University; National Bureau of Economic Research; Brown University; Amazon.com
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA16566
• 发表日期: 2019
• 页码: 1307-1340
• 关键词: LOG-LINEAR-MODELS; MEASURING SEGREGATION; public-opinion; political polarization; TOPIC MODEL; media; language; frequency; positions; BEHAVIOR

摘要

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.