Optimal progressive income taxation in a Bewley-Grossman framework
Links to Fileshttps://ideas.repec.org/p/tow/wpaper/2017-01.html
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Type of Workapplication/vnd.openxmlformats-officedocument.wordprocessingml.document
DepartmentTowson University. Department of Economics
Citation of Original PublicationJuergen Jung & Chung Tran, 2017. "Optimal Progressive Income Taxation in a Bewley-Grossman Framework," Working Papers 2017-01, Towson University, Department of Economics, revised Apr 2018.
SubjectsHealth and income risks
We study the optimal income tax progressivity in a Bewley-Grossman model where individuals are exposed to income and health risks over the lifecycle. Our results, based on a calibration for the US economy, indicate that the presence of health shocks requires the government to set higher optimal levels of tax progressivity in order to provide more social insurance for unhealthy low income individuals who have limited access to health insurance. The optimal progressive income tax system includes a tax break for income below $36, 400 and high marginal tax rates of over 50 percent for income earners above $200, 000. The tax progressivity (Suits) index—a Gini coefficient for income tax contributions by income—of the optimal tax system is around 0.53, compared to 0.17 in the benchmark tax system. Welfare gains from switching to the optimal tax system amount to over 5 percent of compensating lifetime consumption. The presence of health risk amplifies the social insurance role of the progressive income tax system. The optimal tax system in our model with health risk is more progressive than the optimal tax systems in models without health risk (e.g., Conesa and Krueger (2006) and Heathcote, Storesletten and Violante (2017)). When health risk is removed from the model, the optimal tax system becomes less progressive and thus more similar to the optimal progressivity levels reported in the literature. In addition, the optimal level of tax progressivity is strongly affected by the design of the health insurance system.