Optimal Dynamic Nonlinear Income Taxation under Loose Commitment
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.
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- Jang-Ting Guo & Alan Krause, 2010.
"Dynamic Income Taxation without Commitment: Comparing Alternative Tax Systems,"
201005, University of California at Riverside, Department of Economics, revised Jun 2010.
- Guo, Jang-Ting & Krause, Alan, 2015. "Dynamic income taxation without commitment: Comparing alternative tax systems," Economic Modelling, Elsevier, vol. 47(C), pages 319-326.
- J-T Guo & A Krause, 2010. "Dynamic Income Taxation without Commitment: Comparing Alternative Tax Systems," Discussion Papers 10/15, Department of Economics, University of York.
- Jang‐Ting Guo & Alan Krause, 2011.
"Optimal Nonlinear Income Taxation with Habit Formation,"
Journal of Public Economic Theory,
Association for Public Economic Theory, vol. 13(3), pages 463-480, 06.
- Jang-Ting Guo & Alan Krause, 2008. "Optimal Nonlinear Income Taxation with Habit Formation," Working Papers 200810, University of California at Riverside, Department of Economics, revised Aug 2008.
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