Predicting municipality defaults with hybrid machine learning models

This paper deals with the prediction analysis of municipality default in Italy using the hybridization of weighted machine learning algorithms with Stacking techniques for classification. Amongst the 32 variables considered in our study, the presence of mafia is identified as a key default predictor of municipality default. Mafia organizations increase municipality default by using violence against corruption of politicians and bureaucrats and then curbing the allocation of public funds aligned with their interests. In addition, mafia presence reduces tax autonomy and increases external dependence of municipalities. Indeed, the relevance of the variables in predicting municipality default is similar to that in predicting municipalities dissolved by mafia infiltration. Overall, the paper suggests that to improve efficiency of local public institutions and to reestablish democracy in mafia-dominated territories, it is necessary to enforce new rules that drastically reduce the mafia presence.