Objectifying the Measurement of Voter Ideology With Expert Data

Abstract

Many surveys require respondents to place themselves on a left-right ideology scale. However, non-experts may not understand the scale or their ’objective’ position. Furthermore, a uni-dimensional approach may not suffice to describe ideology coherently. We thus propose a novel way to measure voter ideology: Combining expert and voter survey data, we use machine learning to infer how political experts would place voters on three axes: general left-right, economic left-right and social/cultural ’GAL-TAN’. Our analysis suggests that i) voters are more likely to place themselves at the political center than we would pre-dict experts to do, ii) voters are ideologically most fragmented along the ’GAL-TAN’ axis, iii) European countries differ significantly in all ideological dimensions, and iv) ’objective’ ideology as predicted by our models improves the predictive power of simple spatial voting models even after accounting for the subjective ideological distance between voters and parties as perceived by the voters themselves.