mic Efficiency and Pareto Optimality in a Stochastic OLG Model with Production and Social Security

Martin Barbie, Marcus Hagedorn and Ashok Kaul

Bonn Econ Discussion Papers from University of Bonn, Germany

Abstract: We analyze the interaction between risk sharing and capital accumulation in a stochastic OLG model with production. We give a complete characterization of interim Pareto optimality. Our characterization also subsumes equilibria with a PAYG social security system. In a competitive equilibrium interim Pareto optimality is equivalent to intergenerational exchange efficiency, which in turn implies dynamic efficiency. Furthermore, dynamic efficiency does not rule out a Pareto-improving role for a social security system. Social security can provide insurance against macroeconomic risk, namely aggregate productivity risk in the second period of life (old age) through dynamic risk sharing. We briefly relate our results to models without uncertainty where the notions of exchange efficiency, dynamic efficiency and interim Pareto optimality are all equivalent in a competitive equilibrium.

Keywords: Stochastic OLG Model; Dynamic Efficiency; Interim Pareto Optimality; Social Security; Risk Sharing (search for similar items in EconPapers)
JEL-codes: D61 H55 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge
Date: 2000-02, Revised 2000-06
View list of references View citations in EconPapers

Downloads: (external link)
ftp://web.bgse.uni-bonn.de/pub/RePEc/bon/bonedp/bgse8_2000.pdf (application/pdf)

Related works:
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: http://EconPapers.repec.org/RePEc:bon:bonedp:bgse8_2000

Access Statistics for this paper

More papers in Bonn Econ Discussion Papers from University of Bonn, Germany
Address: Bonn Graduate School of Economics, University of Bonn, Adenauerallee 24 - 26, 53113 Bonn, Germany
Series data maintained by Daniel Park ().