Scaling hand-coded political texts to learn more about left-right policy content

Abstract

Manual annotation of the policy content of political texts forms the basis for one of the most widely used empirical measures in comparative politics: left-right policy positions. Bridging automated “text as data” approaches and qualitative content analysis, we apply statistical scaling to this data to learn more about the association of specific policy dimensions to the left-right super-dimension, in a way that minimizes ex ante assumptions about the substantive content of left-right policy. We apply a Bayesian negative binomial variant of Slapin and Proksch’s (2008) “wordfish” model to category counts from party manifestos coded by the Manifesto Project, providing a data-driven approach that offers new insights into the policy content of left and right. We demonstrate how this method also works with content not originally designed for measuring positions. In addition, we show how the approach can be extended to measure the policy content of two latent dimensions, with some categories contributing to both.