Credit in a Random Matching Model with Private Information

S. Aiyagari and Stephen Williamson

Review of Economic Dynamics, 1999, vol. 2, issue 1, 36-64

Abstract: We consider a random matching model where agents have complete access to each others' histories. Exchange is motivated by risk sharing given random unobservable incomes. There is capital accumulation and an endogenous interest rate. The key feature of this environment is that information is mobile across locations while there are frictions associated with transporting goods. Optimal allocations in the dynamics private information environment resemble real-world credit arrangements in that there are credit balance, credit limits, and installment payments. The steady state has the property that there is a limiting distribution of expected utility entitlements with mobility and a positive fraction of agents who are credit constrained. (Copyright: Elsevier)

JEL-codes: D8 E1 (search for similar items in EconPapers)
Date: 1999
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

Downloads: (external link)
http://dx.doi.org/10.1006/redy.1998.0034 Full text (application/pdf)
Access to full texts is restricted to ScienceDirect subscribers and ScienceDirect institutional members. See http://www.sciencedirect.com/ for details.

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: https://EconPapers.repec.org/RePEc:red:issued:v:2:y:1999:i:1:p:36-64

Ordering information: This journal article can be ordered from
https://www.economic ... ription-information/

DOI: 10.1006/redy.1998.0034

Access Statistics for this article

Review of Economic Dynamics is currently edited by Loukas Karabarbounis

More articles in Review of Economic Dynamics from Elsevier for the Society for Economic Dynamics Contact information at EDIRC.
Bibliographic data for series maintained by Christian Zimmermann ().