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Estimating Spatial Preferences from Votes and Text

Published online by Cambridge University Press:  03 May 2018

In Song Kim
Affiliation:
Assistant Professor, Department of Political Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. Email: insong@mit.EDU, URL: http://web.mit.edu/insong/www/
John Londregan
Affiliation:
Professor of Politics and International Affairs, Woodrow Wilson School, Princeton University, Princeton, NJ 08544, USA. Email: jbl@princeton.edu, URL: http://www.princeton.edu/∼jbl/
Marc Ratkovic*
Affiliation:
Assistant Professor, Department of Politics, Princeton University, Princeton, NJ 08544, USA. Email: ratkovic@princeton.edu, URL: https://scholar.princeton.edu/ratkovic

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.

Type
Articles
Copyright
Copyright © The Author(s) 2018. Published by Cambridge University Press on behalf of the Society for Political Methodology. 

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Footnotes

Authors’ note: We thank Jong Hee Park, Alex Tahk, Brandon Stewart, Arthur Spirling, Ben Johnson, Tolya Levin, Michael Peress, Kosuke Imai, and seminar audiences at Princeton University, the Universidad de Desarollo, and the annual meeting of the Society for Political Methodology for comments on this and an earlier draft. Replication data available through the Harvard Dataverse doi:10.7910/DVN/AGUVBE.

Contributing Editor: Jonathan N. Katz

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