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OPTIMAL DYNAMIC NONLINEAR INCOME TAXATION UNDER LOOSE COMMITMENT

Published online by Cambridge University Press:  21 May 2013

Jang-Ting Guo  and
Alan Krause
Jang-Ting Guo*
Affiliation:
University of California, Riverside
Alan Krause
Affiliation:
University of York
*
Address correspondence to: Jang-Ting Guo, Department of Economics, University of California, Riverside, Riverside, CA 92521-0427, USA; e-mail: [email protected].
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Abstract

This paper examines an infinite-horizon model of nonlinear income taxation in which the probability that the government can commit is high, but not certain. In this “loose commitment” environment, we find that even a little uncertainty over whether the government can commit yields substantial effects on the optimal dynamic nonlinear income tax system. This result holds even though separating taxation remains optimal, as in the case of full commitment. Under an empirically plausible parameterization, our numerical simulations show that high-skilled individuals must be subsidized in the short run, despite the government's redistributive objective, unless the probability of commitment is higher than 98%. Loose commitment also reverses the short-run welfare effects of changes in most of the model's parameters, and yields some counterintuitive outcomes. For example, all individuals are worse off, rather than better off, in the short run when the proportion of high-skilled individuals in the economy increases.

Keywords

Dynamic Nonlinear Income TaxationLoose Commitment
Type
Articles
Information
Macroeconomic Dynamics , Volume 18 , Issue 6 , September 2014 , pp. 1403 - 1427
Copyright
Copyright © Cambridge University Press 2013 

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