@article{81086,
  author = {In Song Kim and John Londregan and Marc Ratkovic},
  title = {Estimating Ideal Points from Votes and Text},
  abstract = { <p>
	We introduce a framework for combining vote data and text data within a single formal and statistical framework.~ Formally, we model vote choice and word choice in terms of a common set of underlying preference parameters.~ Statistically, we implement a method for recovering these preference and location parameters.~ The method estimates the number of underlying ideological dimensions, models zero inflation, and is robust to extreme outliers.~ We apply the method to rollcall and floor speech from recent US Senates.~ We find two stable dimensions, one ideological and the other capturing leadership. We then show how the method can leverage common speech in order to impute missing data, to estimate rank-and-file ideal points using only their words and the vote history of party leaders, and even to scale newspaper editorials.
</p>
 },
  year = {2018},
  journal = {Political Analysis},
  volume = {26},
  pages = {210-229},
  language = {eng},
}