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A UNIFIED FRAMEWORK FOR OPTIMAL TAXATION WITH UNDIVERSIFIABLE RISK

Published online by Cambridge University Press:  09 September 2019

Vasia Panousi  and
Catarina Reis
Vasia Panousi
Affiliation:
University of Montreal
Catarina Reis*
Affiliation:
Católica Lisbon School of Business & Economics
*
Address correspondence to: Catarina Reis, CATOLICA-LISBON School of Business and Economics, Universidade Católica Portuguesa, Palma de Cima, 1649-023 Lisboa, Portugal. e-mail: [email protected]. Phone: +351 965 863 097.
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Abstract

This paper considers a model of linear capital taxation for an economy where capital and labor income are subject to idiosyncratic uninsurable risk. To keep the model tractable, we assume that investment decisions are made before uncertainty is realized, so that the realization of the capital and labor income shocks only affects current consumption. In this setting, we are able to jointly analyze capital and labor income risk and derive analytical results regarding the optimal taxation of capital. We find that the optimal capital tax is positive in the long run if there is only capital income risk. The reason for this is that the capital tax provides insurance against capital income risk. Furthermore, for high levels of risk, increasing the capital tax may actually induce capital accumulation. On the other hand, if there is only labor income risk, the optimal capital tax is zero. The sign of the optimal tax can only be negative if the two types of risk are negatively correlated and labor income risk is large enough.

Keywords

Fiscal PolicyOptimal TaxationRisk
Type
Articles
Information
Macroeconomic Dynamics , Volume 25 , Issue 6 , September 2021 , pp. 1331 - 1345
Copyright
© Cambridge University Press 2019

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Footnotes

A previous version of this paper with only capital income risk was circulated with the title “Optimal taxation with idiosyncratic capital income risk.” We are grateful to Isabel Correia, Mikhail Golosov, Leonor Modesto, Pedro Teles, Ivan Werning, Dirk Krueger, and Daniel Belchior for helpful comments. We would also like to thank seminar and conference participants at Vanderbuilt University, The SAET Meeting in 2011, the SED meeting in 2016, and the PET meeting in 2017. Financial support from the ADEMU (H2020, No 649396) project and Fundação para a Ciência e Tecnologia is gratefully acknowledged.

References

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