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Balancing the equity-efficiency trade-off in personal income taxation: an evolutionary approach

Author

Listed:
  • Simone Pellegrino

    (University of Turin)

  • Guido Perboli

    (Politecnico di Torino
    CIRRELT)

  • Giovanni Squillero

    (Politecnico di Torino)

Abstract

In this paper we propose a multi-objective evolutionary algorithm for supporting the definition of a personal income tax reform. As a case study, we apply this methodology to the Italian income tax, and consider a recently implemented tax cut. Our optimization algorithm can determine a set of tax structures that maximize the redistributive effect of the tax while minimizing its inefficiency—considering for the former the Reynolds–Smolensky index and for the latter the weighted average of taxpayers’ effective marginal tax rates. The approach also takes into account two additional factors: the tax has to guarantee a specific revenue and to minimize the share of losing taxpayers with respect to the pre-reform situation. Experimental results demonstrate that the methodology we employ can support the policymaker’s decisions in complex, real-world situations.

Suggested Citation

  • Simone Pellegrino & Guido Perboli & Giovanni Squillero, 2019. "Balancing the equity-efficiency trade-off in personal income taxation: an evolutionary approach," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 36(1), pages 37-64, April.
    • Handle: RePEc:spr:epolit:v:36:y:2019:i:1:d:10.1007_s40888-018-0132-4
    DOI: 10.1007/s40888-018-0132-4
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    1. Morini, Matteo & Pellegrino, Simone, 2018. "Personal income tax reforms: A genetic algorithm approach," European Journal of Operational Research, Elsevier, vol. 264(3), pages 994-1004.
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    Citations

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    Cited by:

    1. Francesca Greselin & Simone Pellegrino & Achille Vernizzi, 2017. "Lorenz versus Zenga Inequality Curves: a New Approach to Measuring Tax Redistribution and Progressivity," Working papers 046, Department of Economics, Social Studies, Applied Mathematics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
    2. Daniela Mantovani & Simone Pellegrino & Achille Vernizzi, 2020. "A note on the maximum value of the Kakwani index," Empirical Economics, Springer, vol. 58(2), pages 869-874, February.
    3. Simone Pellegrino & Achille Vernizzi, 2018. "Decomposing the Redistributive Effect of Taxation to Reveal Axiom Violations," Working papers 049, Department of Economics, Social Studies, Applied Mathematics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino.
    4. Edyta Małecka-Ziembińska & Radosław Ziembiński, 2020. "Application of Genetic Algorithm to Optimal Income Taxation," JRFM, MDPI, vol. 13(11), pages 1-24, October.

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    More about this item

    Keywords

    Personal Income Tax; Evolutionary algorithms; Multi-objective optimization;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • H23 - Public Economics - - Taxation, Subsidies, and Revenue - - - Externalities; Redistributive Effects; Environmental Taxes and Subsidies
    • H24 - Public Economics - - Taxation, Subsidies, and Revenue - - - Personal Income and Other Nonbusiness Taxes and Subsidies

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