On Optimal Commodity Taxation in a Spatial Setting

33 Pages Posted: 10 Jun 2019

See all articles by Tatsuhito Kono

Tatsuhito Kono

Tohoku University - Graduate School of Information Sciences

David Pines

Tel Aviv University - Eitan Berglas School of Economics

Date Written: May 24, 2019

Abstract

This paper is concerned with optimal commodity taxation in a spatial setting, represented by a monocentric urban model with homogenous people. It shows the need for an adjustment of the basic optimal taxation formulas, i.e. Ramsey (1927) and Atkinson and Stiglitz (1980), in a spatial setting and the reasons for this. The main finding is that the adjustment is not required to increase efficiency but rather to render the allocation competitively sustainable. Applying the basic (unadjusted) formula to a spatial setting requires income redistribution that is spatially differentiated and self-financed. This instrument is, by our premise, unavailable. Moreover, if this instrument were available, that is, spatially-differentiated head taxes and subsidies, it would render commodity taxation redundant in the first place. Unequal treatment of equals is reminiscent of Mirrlees (1972), but the reason for the two cases is completely different.

Keywords: Commodity taxation; Spatial economy

JEL Classification: H21; R14

Suggested Citation

Kono, Tatsuhito and Pines, David, On Optimal Commodity Taxation in a Spatial Setting (May 24, 2019). Available at SSRN: https://ssrn.com/abstract=3393612 or http://dx.doi.org/10.2139/ssrn.3393612

Tatsuhito Kono (Contact Author)

Tohoku University - Graduate School of Information Sciences ( email )

Aoba 6-3-9
Aoba-ku, Sendai, 980-8579
Japan
81-22-795-4477 (Phone)
81-22-795-4497 (Fax)

HOME PAGE: http://www.se.is.tohoku.ac.jp/~kono/t-kono.html

David Pines

Tel Aviv University - Eitan Berglas School of Economics ( email )

P.O. Box 39040
Ramat Aviv, Tel Aviv, 69978
Israel

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
47
Abstract Views
572
PlumX Metrics