We present an exploratory machine learning analysis of populist votes at municipality level in the 2018 Italian general elections, in which populist parties gained almost 50% of the votes. Starting from a comprehensive set of local characteristics, we use an algorithm based on BIC to obtain a reduced set of predictors for each of the two populist parties (Five-Star Movement and Lega) and the two traditional ones (Democratic Party and Forza Italia). Differences and similarities between the sets of predictors further provide evidence on 1) heterogeneity in populisms, 2) if this heterogeneity is related to the traditional left/right divide. The Five-Star Movement is stronger in larger and unsafer municipalities, where people are younger, more unemployed and work more in services. On the contrary, Lega thrives in smaller and safer municipalities, where people are less educated and employed more in manufacturing and commerce. These differences do not correspond to differences between the Democratic Party and Forza Italia, providing evidence that heterogeneity in populisms does not correspond to a left/right divide. As robustness tests, we use an alternative machine learning technique (lasso) and apply our predictions to France as to confront them with candidates' actual votes in 2017 presidential elections.
Voting Populism Economic insecurity Political Economy