Will the real populists please stand up? A machine learning index of party populism

The existing literature on populism has seen numerous attempts to empirically quantify this somewhat ambiguous concept. Despite notable advances, continuous measures of populism with a clear theoretical background and a considerable coverage are still hard to come by. This paper proposes a novel approach to measuring party populism by combining several different expert-surveys via supervised machine learning techniques. Employing the random forest regression algorithm, we greatly expand the geographical and temporal coverage of two well-known populism indicators, which are based on the discursive and the ideational approach, respectively. The resulting multidimensional measures capture party-level populism on a continuous 0–10 scale, covering 1920 parties in 169 countries from 1970 to 2019. Our measures accurately replicate both definitions of populism, although the indicators may be more suitable for predicting populist outcomes in Western countries, as compared to non-Western ones.