Predicting local taxation decision-making: evidence from Italian municipalities
研究用机器学习预测意大利市政的个人所得税附加税决策，发现税收选择受人口、财政、社会经济和制度因素驱动，南部地区预测更准，表明增税更多与财政约束相关。
This study investigates whether local taxation decisions can be reliably predicted using machine learning (ML), focusing on the personal income tax sur charge set by Italian municipalities. We evaluate the predictive performance of ML models and identify key determinants of tax-setting behaviour. Results show that municipal tax choices follow systematic and predictable patterns driven by demographic, fiscal, socio-economic and institutional factors. A North–South comparison reveals stronger predictive accuracy in Southern regions, suggesting that tax increases are more closely linked to fiscal constraints than to discretionary political choices. Our findings highlight the usefulness of ML for understanding and supporting local fiscal planning.