Personal Income Tax Reforms: a Genetic Algorithm Approach

Matteo Morini and Simone Pellegrino

No 26, Working papers from Department of Economics, Social Studies, Applied Mathematics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), 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 optimal structure of a personal income tax that allows 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.

Keywords: Personal income taxation; Genetic algorithms; Micro-simulation models; Reynolds-Smolensky index; Tax reforms (search for similar items in EconPapers)
JEL-codes: C63 C81 H23 H24 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2014-09
New Economics Papers: this item is included in nep-acc, nep-cmp, nep-ore, nep-pbe and nep-pub
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.bemservizi.unito.it/repec/tur/wpapnw/m26.pdf First version, 2014 (application/pdf)

Related works:
Journal Article: Personal income tax reforms: A genetic algorithm approach (2018)
Working Paper: Personal Income Tax Reforms: A Genetic Algorithm Approach (2016)
Working Paper: Personal Income Tax Reforms: a Genetic Algorithm Approach (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tur:wpapnw:026

Access Statistics for this paper

More papers in Working papers from Department of Economics, Social Studies, Applied Mathematics and Statistics (Dipartimento di Scienze Economico-Sociali e Matematico-Statistiche), University of Torino Contact information at EDIRC.
Bibliographic data for series maintained by Daniele Pennesi ().