Personal Income Tax Reforms: A Genetic Algorithm Approach

Matteo Morini and Simone Pellegrino

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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 maximisation 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.

Keywords: Genetic algorithms; Personal income taxation; Micro-simulation models; Reynolds–Smolensky index; Tax reforms (search for similar items in EconPapers)
Date: 2016-08-09
New Economics Papers: this item is included in nep-cmp and nep-pbe
Note: View the original document on HAL open archive server: https://inria.hal.science/hal-01388958v1
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Published in European Journal of Operational Research, 2016, ⟨10.1016/j.ejor.2016.07.059⟩

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Related works:
Journal Article: Personal income tax reforms: A genetic algorithm approach (2018)
Working Paper: Personal Income Tax Reforms: a Genetic Algorithm Approach (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01388958

DOI: 10.1016/j.ejor.2016.07.059

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