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Spatial Clustering with Functional Trend Data: An Application to Italian Health Tax Detractions

Author

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  • Mauro, M.;
  • Porcelli, F.;
  • Vidoli, F.;

Abstract

The integration of functional and spatial data in clustering methods is increasingly relevant in regional and urban economics. This paper introduces a novel spatial clustering algorithm that simultaneously considers geographical proximity and the similarity of temporal trends to identify territorially coherent clusters. The methodology, validated through simulations and applied to realworld data on Italian municipal health tax detractions, reveals significant geographical groupings characterized by similar levels and dynamics of tax benefits. Findings highlight that tax detractions align more closely with the economic capacity of individuals and areas rather than actual healthcare needs, raising concerns about equity and the territorial distribution of fiscal advantages. This divergence between formal universal healthcare principles and practical fiscal outcomes underscores the need for spatially aware policy tools. The proposed approach offers a replicable framework for analyzing spatio-temporal patterns across various domains, balancing interpretability and methodological rigor.

Suggested Citation

  • Mauro, M.; & Porcelli, F.; & Vidoli, F.;, 2025. "Spatial Clustering with Functional Trend Data: An Application to Italian Health Tax Detractions," Health, Econometrics and Data Group (HEDG) Working Papers 25/05, HEDG, c/o Department of Economics, University of York.
    • Handle: RePEc:yor:hectdg:25/05
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    References listed on IDEAS

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    1. Marie Chavent & Vanessa Kuentz-Simonet & Amaury Labenne & Jérôme Saracco, 2018. "ClustGeo: an R package for hierarchical clustering with spatial constraints," Computational Statistics, Springer, vol. 33(4), pages 1799-1822, December.
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    4. Marè, M.; & Porcelli, F.; & Vidoli, F.;, 2024. "Does private supply drive personal health choices? A spatial approach of health tax detractions at municipal level," Health, Econometrics and Data Group (HEDG) Working Papers 24/03, HEDG, c/o Department of Economics, University of York.
    5. R. Giraldo & P. Delicado & J. Mateu, 2012. "Hierarchical clustering of spatially correlated functional data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(4), pages 403-421, November.
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    More about this item

    Keywords

    spatial clustering; functional data analysis; tax expenditures; health economics;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • H71 - Public Economics - - State and Local Government; Intergovernmental Relations - - - State and Local Taxation, Subsidies, and Revenue

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