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Estimating Spatial Preferences from Votes and Text

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  • Kim, In Song
  • Londregan, John
  • Ratkovic, Marc

Abstract

We introduce a model that extends the standard vote choice model to encompass text. In our model, votes and speech are generated from a common set of underlying preference parameters. We estimate the parameters with a sparse Gaussian copula factor model that estimates the number of latent dimensions, is robust to outliers, and accounts for zero inflation in the data. To illustrate its workings, we apply our estimator to roll call votes and floor speech from recent sessions of the US Senate. We uncover two stable dimensions: one ideological and the other reflecting to Senators’ leadership roles. We then show how the method can leverage common speech in order to impute missing data, recovering reliable preference estimates for rank-and-file Senators given only leadership votes.

Suggested Citation

  • Kim, In Song & Londregan, John & Ratkovic, Marc, 2018. "Estimating Spatial Preferences from Votes and Text," Political Analysis, Cambridge University Press, vol. 26(2), pages 210-229, April.
    • Handle: RePEc:cup:polals:v:26:y:2018:i:02:p:210-229_00
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    Cited by:

    1. Teodóra Szép & Sander Cranenburgh & Caspar Chorus, 2024. "Moral rhetoric in discrete choice models: a Natural Language Processing approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(1), pages 179-206, February.
    2. Lee, Seungpeel & Kim, Jina & Kim, Dongjae & Kim, Ki Joon & Park, Eunil, 2023. "Computational approaches to developing the implicit media bias dataset: Assessing political orientations of nonpolitical news articles," Applied Mathematics and Computation, Elsevier, vol. 458(C).
    3. Gavin Abercrombie & Riza Batista-Navarro, 2020. "Sentiment and position-taking analysis of parliamentary debates: a systematic literature review," Journal of Computational Social Science, Springer, vol. 3(1), pages 245-270, April.

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