Social Security Privatization with Elastic Labor Supply and Second-Best Taxes

Kent Smetters ()

Working Papers from University of Michigan, Michigan Retirement Research Center

Abstract: This paper shows that many common methods of privatizing social security fail to reduce labor market distortions when taxes are second best, challenging a key reason to privatize. Ironically, providing “transition relief” to workers alive at the time of the reform, in an effort to protect their previous contributions, undercuts potential efficiency gains. Chile’s reform -- the first major privatization that also served as a model for subsequent countries -- actually increased distortions. It is then shown that privatization with limited transition relief can reduce labor market distortions and produce gains to current and future generations without hurting initial retirees, i.e., a Pareto gain even with second-best taxes.

Pages: 26 pages
Date: 2005-01
New Economics Papers: this item is included in nep-lab and nep-pub
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://mrdrc.isr.umich.edu/publications/Papers/pdf/wp092.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 403 Forbidden (http://mrdrc.isr.umich.edu/publications/Papers/pdf/wp092.pdf [301 Moved Permanently]--> https://mrdrc.isr.umich.edu/publications/Papers/pdf/wp092.pdf)

Related works:
Working Paper: Social Security Privatization with Elastic Labor Supply and Second-Best Taxes (2005)
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:mrr:papers:wp092

Access Statistics for this paper

More papers in Working Papers from University of Michigan, Michigan Retirement Research Center P.O. Box 1248, Ann Arbor, MI 48104. Contact information at EDIRC.
Bibliographic data for series maintained by MRRC Administrator ().