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.
Balancing the equity-efficiency trade-off in personal income taxation: an evolutionary approach / Pellegrino, Simone; Perboli, Guido; Squillero, Giovanni. - In: ECONOMIA POLITICA. - ISSN 1120-2890. - STAMPA. - 36:1(2019), pp. 37-64. [10.1007/s40888-018-0132-4]
Balancing the equity-efficiency trade-off in personal income taxation: an evolutionary approach
Perboli, Guido;Squillero, Giovanni
2019
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.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2713390