Home About Latest Browse RSS Advanced Search

Federal Reserve Bank of San Francisco
Working Papers in Applied Economic Theory
Optimal redistributive capital taxation in a neoclassical growth model
Kevin J. Lansing
Abstract

This paper provides a counterexample to the simplest version of the redistribution models considered by Judd (1985) in which the government chooses an optimal distortionary tax on capitalists to finance a lump-sum payment to workers. I show that the steady-state optimal tax on capital income is generally non-zero when the capitalists' utility is logarithmic and the government faces a balanced-budget constraint. With log utility, agents' optimal decisions depend solely on the current rate of return, not any future rates of return or tax rates. This feature of the economy effectively deprives the government of a useful policy instrument because promises about future tax rates can no longer influence current allocations. When combined with a lack of other suitable policy instruments (such as government bonds), the result is an inability to decentralize the allocations that are consistent with a zero limiting capital tax. I show that the standard approach to solving the dynamic optimal tax problem yields the wrong answer in this (knife-edge) case because it fails to properly enforce the constraints associated with the competitive equilibrium. Specifically, the standard approach lets in an additional policy instrument through the back door.


No download available
Cite this item
Kevin J. Lansing, Optimal redistributive capital taxation in a neoclassical growth model, Federal Reserve Bank of San Francisco, Working Papers in Applied Economic Theory 99-01, 1998.
More from this series
JEL Classification:
Subject headings:
Keywords: Fiscal policy
For corrections, contact Federal Reserve Bank of San Francisco Research Library ()
Fed-in-Print is the central catalog of publications within the Federal Reserve System. It is managed and hosted by the Economic Research Division, Federal Reserve Bank of St. Louis.

Privacy Legal