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Optimal Taxation under Income Uncertainty

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  • Xianhua Dai

    (School of Law and Business, Wuhan Institute of Technology)

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    Abstract

    Optimal taxation under income uncertainty has been extensively developed in expected utility theory, but it is still open for inseparable utility function between income and effort. As an alternative of decision-making under uncertainty, prospect theory (Kahneman and Tversky (1979), Tversky and Kahneman (1992)) has been obtained empirical support, for example, Kahneman and Tversky (1979), and Camerer and Lowenstein (2003). It is beginning to explore optimal taxation in the context of prospect theory, for example, Oswald (1983), Tuomala (1990) in conventional setting without utility interdependence, and Kanbur, Pirttila, and Tuomala (2008) for separable value functions between income and effort. It is challenging in the prospect theory to treat with optimal taxation for inseparable value function between income and effort. In this paper, we model taxation under income uncertainty by sufficiently considering government¡¯s risk aversion and individuals¡¯ loss aversion. We obtain its sufficient condition for the first order approach to general value functions including inseparable value function between income and effort, hence generalizing Oswald (1983), Tuomala (1990) to optimal taxation with utility interdependence, and Kanbur, Pirttila, and Tuomala (2008) to inseparable value functions between income and effort.

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    Bibliographic Info

    Article provided by Society for AEF in its journal Annals of Economics and Finance.

    Volume (Year): 12 (2011)
    Issue (Month): 1 (May)
    Pages: 121-138

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    Handle: RePEc:cuf:journl:y:2011:v:12:i:1:p:121-138

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    Related research

    Keywords: Optimal taxation; Income uncertainty; Moral hazard; Expected utility theory; Prospect theory; Risk aversion; Loss aversion;

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    3. Tversky, Amos & Kahneman, Daniel, 1992. " Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, Springer, vol. 5(4), pages 297-323, October.
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