Zero Expected Wealth Taxes: A Mirrlees Approach to Dynamic Optimal Taxation

Narayana Kocherlakota

Econometrica, 2005, vol. 73, issue 5, 1587-1621

Abstract: In this paper, I consider a dynamic economy in which a government needs to finance a stochastic process of purchases. The agents in the economy are privately informed about their skills, which evolve stochastically over time; I impose no restriction on the stochastic evolution of skills. I construct a tax system that implements a symmetric constrained Pareto optimal allocation. The tax system is constrained to be linear in an agent's wealth, but can be arbitrarily nonlinear in his current and past labor incomes. I find that wealth taxes in a given period depend on the individual's labor income in that period and previous ones. However, in any period, the expectation of an agent's wealth tax rate in the following period is zero. As well, the government never collects any net revenue from wealth taxes. Copyright The Econometric Society 2005.

Date: 2005
References: Add references at CitEc
Citations: View citations in EconPapers (233)

Downloads: (external link)
http://hdl.handle.net/10.1111/j.1468-0262.2005.00630.x link to full text (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Zero Expected Wealth Taxes: A Mirrlees Approach to Dynamic Optimal Taxation (2004)
Working Paper: Zero Expected Wealth Taxes: A Mirrlees Approach to Dynamic Optimal Taxation (2003)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ecm:emetrp:v:73:y:2005:i:5:p:1587-1621

Ordering information: This journal article can be ordered from
https://www.economet ... ordering-back-issues

Access Statistics for this article

Econometrica is currently edited by Guido Imbens

More articles in Econometrica from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().