Predicting bankruptcy of local government: A machine learning approach

Nino Antulov-Fantulin, Raffaele Lagravinese and Giuliano Resce ()

Journal of Economic Behavior & Organization, 2021, vol. 183, issue C, 681-699

Abstract: In this paper we analyze the predictability of the bankruptcy of 7795 Italian municipalities in the period 2009–2016. The prediction task is extremely hard due to the small number of bankruptcy cases, on which learning is possible. Besides historical financial data for each municipality, we use alternative institutional data along with the socio-demographic and economic context. The predictability is analyzed through the performance of the statistical and machine learning models with a receiver operating characteristic curve and the precision-recall curve. Our results suggest that it is possible to make out-of-sample predictions with a high true positive rate and low false-positive rate. The model shows that some non-financial features (e.g. geographical area) are more important than many financial features to predict the default of municipalities. Among financial indicators, the important features are mainly connected to the Deficit and the Debt of Municipalities. Among the socio-demographic characteristics of administrators, the gender and the age of members in council are among the top 10 features in terms of importance for predicting municipal defaults.

Keywords: Financial distress; Public sector; Machine learning (search for similar items in EconPapers)
JEL-codes: C53 H72 H74 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:183:y:2021:i:c:p:681-699

DOI: 10.1016/j.jebo.2021.01.014

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