Combining voter preferences with party position estimates from different sources for studying voting behavior and representation

Abstract

Researchers interested in policy voting and substantive representation face the challenge to combine party positions with voter preference data on a common scale. One solution is to rely on voters' perceptions of parties' policy positions, as reported in surveys. However, this kind of data is often only available for the common left-right dimension, but not for more concrete policy scales, and it suffers from bias. We first discuss how to free perceptual data from bias by relying on a Bayesian version of the Aldrich-McKelvey rescaling technique. Then we discuss two prominent alternative sources of party position estimates: expert survey positions, and positions based on the CMP coding scheme of the manifesto project. While both types of party position estimates are considered to be of good quality, it is unclear how they fit into voter preference scales. This paper presents a simple rescaling technique that improves the matching.