A New Index for the Efficiency in Collecting the Waste Fee (TARI) by Italian Municipalities and Analyses through a Regression Tree Algorithm

The efficiency of revenue collection is a critical concern for local governments, affecting their financial health and service delivery capabilities. This study introduces a novel index to measure the efficiency of TARI (waste fee) collection across Italian municipalities by exploiting the interoperability of different databases. Unlike traditional econometric approaches, which focus on identifying causal determinants, we use the Classification and Regression Tree (CART) algorithm primarily as a predictive tool for imputing missing data. CART enables a robust estimation of the expected TARI collection efficiency for municipalities with incomplete records by leveraging observed patterns in municipalities with similar socio-economic characteristics, tax collection systems, and waste service management. Beyond data imputation, the CART model also facilitates a structured classification of municipalities into homogeneous groups, supporting targeted policy interventions and gap analysis. This classification provides local administrations with a benchmarking tool to compare their collection efficiency against similar municipalities and identify areas for improvement, aiming to achieve complete waste fee collection. By adopting this approach, policymakers gain a pragmatic method for managing incomplete fiscal data while enhancing the strategic planning of tax collection policies. Ultimately, this study contributes to improving fiscal governance by offering municipalities a data-driven framework to optimize their TARI revenue collection strategies.