Estimating Spatial Preferences from Votes and Text

In Song Kim, John Londregan, Marc Ratkovic

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)210-229
Number of pages20
JournalPolitical Analysis
Issue number2
StatePublished - Apr 1 2018

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Political Science and International Relations


  • discrete choice models
  • multidimensional scaling
  • spatial voting model
  • statistical analysis of texts


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