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

Author

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  • 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
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    Citations

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    Cited by:

    1. Charles Angelucci & Julia Cage & Michael Sinkinson, 2020. "Media Competition and News Diets," Sciences Po publications 2020-03, Sciences Po.
    2. Cagé, Julia, 2020. "Media competition, information provision and political participation: Evidence from French local newspapers and elections, 1944–2014," Journal of Public Economics, Elsevier, vol. 185(C).
    3. Niklas Potrafke, 2018. "Government ideology and economic policy-making in the United States—a survey," Public Choice, Springer, vol. 174(1), pages 145-207, January.
    4. Benjamin Ogden, 2017. "The Imperfect Beliefs Voting Model," Working Papers ECARES ECARES 2017-20, ULB -- Universite Libre de Bruxelles.
    5. Jerch, Rhiannon & Kahn, Matthew E. & Li, Shanjun, 2017. "The efficiency of local government: The role of privatization and public sector unions," Journal of Public Economics, Elsevier, vol. 154(C), pages 95-121.
    6. Michalopoulos, Stelios & Xue, Melanie Meng, 2019. "Folklore," CEPR Discussion Papers 13425, C.E.P.R. Discussion Papers.
    7. Draca, Mirko & Schwarz, Carlo, 2019. "How Polarized are Citizens? Measuring Ideology from the Ground-Up," CAGE Online Working Paper Series 432, Competitive Advantage in the Global Economy (CAGE).
    8. Khai Xiang Chiong & Matthew Shum, 2019. "Random Projection Estimation of Discrete-Choice Models with Large Choice Sets," Management Science, INFORMS, vol. 65(1), pages 256-271, January.
    9. Draca, Mirko & Schwarz, Carlo, 2019. "How Polarized are Citizens? Measuring Ideology from the Ground-Up," The Warwick Economics Research Paper Series (TWERPS) 1218, University of Warwick, Department of Economics.
    10. Bleemer, Zachary, 2018. "THE UC CLIOMETRIC HISTORY PROJECT AND FORMATTED OPTICAL CHARACTER RECOGNITION by Zachary Bleemer, UC Berkeley CSHE 3.18 (February 2018)," University of California at Berkeley, Center for Studies in Higher Education qt2bd8d25p, Center for Studies in Higher Education, UC Berkeley.
    11. Draca, Mirko & Schwarz, Carlo, 2021. "How Polarized are Citizens? Measuring Ideology from the Ground-Up," QAPEC Discussion Papers 07, Quantitative and Analytical Political Economy Research Centre.
    12. Bleemer, Zachary, 2018. "The University of California ClioMetric History Project and Formatted Optical Character Recognition," University of California at Berkeley, Center for Studies in Higher Education qt1xp6g8nj, Center for Studies in Higher Education, UC Berkeley.
    13. Saia, Alessandro, 2018. "Random interactions in the Chamber: Legislators' behavior and political distance," Journal of Public Economics, Elsevier, vol. 164(C), pages 225-240.
    14. Marianne Bertrand & Matilde Bombardini & Raymond Fisman & Brad Hackinen & Francesco Trebbi, 2021. "Hall of Mirrors: Corporate Philanthropy and Strategic Advocacy," The Quarterly Journal of Economics, Oxford University Press, vol. 136(4), pages 2413-2465.
    15. Ignacio-Jesús Serrano-Contreras & Javier García-Marín & Óscar G. Luengo, 2020. "Measuring Online Political Dialogue: Does Polarization Trigger More Deliberation?," Media and Communication, Cogitatio Press, vol. 8(4), pages 63-72.
    16. Macartney, Hugh & Singleton, John D., 2018. "School boards and student segregation," Journal of Public Economics, Elsevier, vol. 164(C), pages 165-182.
    17. Klaus Desmet & Romain Wacziarg, 2021. "The Cultural Divide," Economic Journal, Royal Economic Society, vol. 131(637), pages 2058-2088.
    18. Niklas Potrafke, 2017. "Government Ideology and Economic Policy-Making in the United States," CESifo Working Paper Series 6444, CESifo.
    19. Dodge Cahan & Niklas Potrafke, 2021. "The Democrat-Republican presidential growth gap and the partisan balance of the state governments," Public Choice, Springer, vol. 189(3), pages 577-601, December.
    20. Carlos Casanova & Alvaro Ortiz & Tomasa Rodrigo & Le Xia & Joaquín Iglesias, 2017. "Tracking chinese vulnerability in real time using Big Data," Working Papers 17/13, BBVA Bank, Economic Research Department.
    21. Robert J. Shiller, 2017. "Narrative Economics," American Economic Review, American Economic Association, vol. 107(4), pages 967-1004, April.
    22. Matthew Gentzkow & Bryan T. Kelly & Matt Taddy, 2017. "Text as Data," NBER Working Papers 23276, National Bureau of Economic Research, Inc.
    23. Bleemer, Zachary, 2018. "The Uc Cliometric History Project And Formatted Optical Character Recognition," University of California at Berkeley, Center for Studies in Higher Education qt9xz1748q, Center for Studies in Higher Education, UC Berkeley.
    24. Stone, Daniel F., 2019. "“Unmotivated bias” and partisan hostility: Empirical evidence," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 79(C), pages 12-26.
    25. Ang, Desmond, 2018. "Do 40-Year-Old Facts Still Matter? Long-Run Effects of Federal Oversight under the Voting Rights Act," Working Paper Series rwp18-033, Harvard University, John F. Kennedy School of Government.

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