Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech
Measuring Group Differences in High-Dimensional Choices: Method and Application to Congressional Speech
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DOI：
10.3982/ecta16566
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发表时间：
2019-07-01
期刊：
影响因子：
6.1
通讯作者：
Taddy, Matt
中科院分区：
文献类型：
--
作者：
Gentzkow, Matthew;Shapiro, Jesse M.;Taddy, Matt
我们研究当选择集的维数很大时测量选择的群体差异的问题。我们证明标准方法存在严重的有限样本偏差，并且我们提出了一种估计器，该估计器应用机器学习的最新进展来解决这种偏差。我们应用这种方法来衡量 1873 年至 2016 年国会演讲的党派倾向趋势，将党派倾向定义为观察者从单一言论中推断出国会议员所在政党的难易程度。我们的估计表明，近年来党派之争比过去严重得多，并且在上个世纪保持较低水平且相对稳定之后，在 20 世纪 90 年代初急剧增加。
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.