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Measuring Polarization in High-Dimensional Data: Method and Application to Congressional Speech

Author

Listed:
  • Matthew Gentzkow
  • Jesse Shapiro
  • Matt Taddy

Abstract

This paper studies trends in the partisanship of Congressional speech from 1873 to 2009. It defines partisanship to be the ease with which an observer could infer a congressperson’s party from a fixed amount of speech, and estimates it using a structural choice model and methods from machine learning. This paper applies tools from structural estimation and machine learning to study the partisanship of language in the US Congress. [Working Paper 22423]

Suggested Citation

  • Matthew Gentzkow & Jesse Shapiro & Matt Taddy, 2016. "Measuring Polarization in High-Dimensional Data: Method and Application to Congressional Speech," Working Papers id:11114, eSocialSciences.
    • Handle: RePEc:ess:wpaper:id:11114
    Note: Institutional Papers
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    File URL: http://www.esocialsciences.org/Articles/show_Article.aspx?acat=InstitutionalPapers&aid=11114
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