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Personal Income Tax Reforms: a Genetic Algorithm Approach

Author

Listed:
  • Matteo Morini

    (ENS Lyon and University of Torino)

  • Simone Pellegrino

    (University of Torino)

Abstract
Given a settled reduction in the present level of tax revenue, and by exploring a very large combinatorial space of tax structures, in this paper we employ a genetic algorithm in order to determine the ‘best’ structure of a real world personal income tax that allows for the maximization of the redistributive effect of the tax, while preventing all taxpayers being worse off than with the present tax structure. We take Italy as a case study.

Suggested Citation

  • Matteo Morini & Simone Pellegrino, 2014. "Personal Income Tax Reforms: a Genetic Algorithm Approach," CeRP Working Papers 147, Center for Research on Pensions and Welfare Policies, Turin (Italy).
    • Handle: RePEc:crp:wpaper:147
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    References listed on IDEAS

    as
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    Cited by:

    1. 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.
    2. Saeed Solaymani, 2020. "Assessing the economic and social impacts of fiscal policies," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 47(3), pages 671-694, March.
    3. Fernando Di Nicola & Giorgio Mongelli & Simone Pellegrino, 2015. "The static microsimulation model of the Italian Department of Finance: Structure and first results regarding income and housing taxation," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2015(2), pages 125-157.
    4. S. N. Alpysbaeva & G. V. Stroeva & Sh. Zh. Shuneev & A. A. Bakdolotov, 2020. "Transition to the Progressive Scale of Individual Income Tax in Kazakhstan: Opportunities and Limitations," Studies on Russian Economic Development, Springer, vol. 31(1), pages 120-127, January.
    5. 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.
    6. Edyta Małecka-Ziembińska & Radosław Ziembiński, 2020. "Application of Genetic Algorithm to Optimal Income Taxation," JRFM, MDPI, vol. 13(11), pages 1-24, October.
    7. Paolo Di Caro, 2017. "Analisi distributiva dell?IRPEF utilizzando i microdati di fonte fiscale," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2017(1), pages 35-59.

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    More about this item

    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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