A utilitarian perspective on Rawls's difference principle
- Authors
- Kwon, H.Y.; Ryu, H.K.; Slottje, D.J.
- Issue Date
- 2017
- Publisher
- Allied Academies
- Keywords
- Gini coefficient; Individual capabilities; Maximum entropy method; Rawls' s difference principle; Utilitarianism
- Citation
- Journal of Management Information and Decision Science, v.20, no.Specialissue1
- Indexed
- SCOPUS
- Journal Title
- Journal of Management Information and Decision Science
- Volume
- 20
- Number
- Specialissue1
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/86080
- ISSN
- 1524-7252
- Abstract
- This paper introduces a new perspective on Rawls's Difference Principle. We link two distinctive approaches to analysing the social welfare implications of alternative policy actions; the utilitarian approach and the Rawlsian distributive justice approach, together in a cohesive way. While there is a large optimal tax literature, that literature generally treats the decision maker as a utilitarian. This paper adds a different dimension in that we compare and contrast what would happen if a Rawlsian government (RG), had as its objective function maximizing the utility of the poorest social group and compare that economic state to one where a utilitarian government (UG) existed that had as its principle objective maximizing the total utility of its entire society but subject to the constraint of a targeted level of inequality, using the maximum entropy method to capture the distribution of individual ability. Each government chooses tax parameters to achieve their respective goals under balanced budget constraints. Individuals in both regimes have different capabilities and maximize utility through suitable labor/leisure choices. The exercise shows that Gini coefficients are lower but hours worked increase under both RG and UG regimes even if people are allowed to work while they receive welfare payments. There are, however, differences in the total utility and inequality levels achieved under the different regimes. © 2017 Journal of Management Information and Decision Sciences.
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