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Is Local Taxation Predictable? A Machine Learning Approach

Author

Listed:
  • Caravaggio, Nicola
  • Resce, Giuliano
  • Idola Francesca, Spanò

Abstract

This paper investigates determinants of local tax policy, with a particular focus on personal income tax rates in Italian municipalities. By employing seven Machine Learning (ML) algorithms, we assess and predict tax rate decisions, identifying Random Forest as the most accurate model. Results underscore the critical influence of demographic dynamics, fiscal health, socioeconomic conditions, and institutional quality on tax policy formulation. The findings not only showcase the power of ML in enhancing predictive precision in public finance but also provide actionable insights for policymakers and stakeholders, enabling more informed decision-making and the mitigation of fiscal uncertainties.

Suggested Citation

  • Caravaggio, Nicola & Resce, Giuliano & Idola Francesca, Spanò, 2024. "Is Local Taxation Predictable? A Machine Learning Approach," Economics & Statistics Discussion Papers esdp24098, University of Molise, Department of Economics.
    • Handle: RePEc:mol:ecsdps:esdp24098
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    More about this item

    Keywords

    Local taxation; Machine learning; Municipalities.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • H24 - Public Economics - - Taxation, Subsidies, and Revenue - - - Personal Income and Other Nonbusiness Taxes and Subsidies
    • H71 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Taxation, Subsidies, and Revenue

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