In this paper we analyze the predictability of the bankruptcy of 7795 Italianmunicipalities in the period 2009-2016. The prediction task is extremely harddue to the small number of bankruptcy cases, on which learning is possible.Besides historical financial data for each municipality, we use alternative insti-tutional data along with the socio-demographic and economic context. The pre-dictability is analyzed through the performance of the statistical and machinelearning models with a receiver operating characteristic curve and the precision-recall curve. Our results suggest that it is possible to make out-of-sample pre-dictions with a high true positive rate and low false-positive rate. The modelshows that some non-financial features (e.g. geographical area) are more im-portant than many financial features to predict the default of municipalities.Among financial indicators, the important features are mainly connected to theDeficit and the Debt of Municipalities. Among the socio-demographic charac-teristics of administrators, the gender and the age of members in council areamong the top 10 features in terms of importance for predicting municipaldefaults.

Predicting bankruptcy of local government: A machine learning approach

Resce G.
2021-01-01

Abstract

In this paper we analyze the predictability of the bankruptcy of 7795 Italianmunicipalities in the period 2009-2016. The prediction task is extremely harddue to the small number of bankruptcy cases, on which learning is possible.Besides historical financial data for each municipality, we use alternative insti-tutional data along with the socio-demographic and economic context. The pre-dictability is analyzed through the performance of the statistical and machinelearning models with a receiver operating characteristic curve and the precision-recall curve. Our results suggest that it is possible to make out-of-sample pre-dictions with a high true positive rate and low false-positive rate. The modelshows that some non-financial features (e.g. geographical area) are more im-portant than many financial features to predict the default of municipalities.Among financial indicators, the important features are mainly connected to theDeficit and the Debt of Municipalities. Among the socio-demographic charac-teristics of administrators, the gender and the age of members in council areamong the top 10 features in terms of importance for predicting municipaldefaults.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11695/96058
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact