Optimal Dynamic Nonlinear Income Taxation under Loose Commitment

Jang-Ting Guo and Alan Krause

Discussion Papers from Department of Economics, University of York

Abstract: This paper examines an infinite-horizon model of dynamic nonlinear income taxation in which there exists a small probability that the government cannot commit to its future tax policy. 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. Under an empirically plausible parameterization, numerical simulations show that high-skill 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 model parameters. In particular, all individuals are worse-off, rather than better-off, in the short run when the proportion of high-skill individuals in the economy increases. Finally, our main findings remain qualitatively robust to a setting in which loose commitment is modelled as a Markov switching process.

Keywords: Dynamic Income Taxation; Loose Commitment (search for similar items in EconPapers)
JEL-codes: H21 H24 (search for similar items in EconPapers)
Date: 2010-10
New Economics Papers: this item is included in nep-acc, nep-cmp, nep-pbe and nep-pub
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Citations: View citations in EconPapers (6)

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Journal Article: OPTIMAL DYNAMIC NONLINEAR INCOME TAXATION UNDER LOOSE COMMITMENT (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:yor:yorken:10/23

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