Abstract

Although widespread, the recent populist wave in Western countries is a heterogeneous phenomenon in terms of individual features of populist voters – the stereotype is «older, working-class, white, poorly educated, who live on low incomes» – as well as geographical characteristics of populist hotspots – «lagging-behind, stagnating and low productivity regions». This study leverages nonlinear statistical learning techniques to detect recurrent individual and geographical patterns of populist voting across Italy. Using the Chapel Hill expert survey classification, we analyse the most prominent voting patterns during the 2019 European elections in all Italian local labour markets. We map the Italian geography of discontent, highlighting how it seems to be shaped by the interaction between individual- and territorial-level predictors. Our study promotes the adoption of flexible and nonparametric predictive algorithms to «diagnose» the main factors linked to the spatial distribution and evolution of populist hotspots