Overcoming Information Asymmetries in Low-Income Lending: Lessons from the 'Working Wheels' Program

Jessica Holmes (), Jonathan Isham () and Jessica Wasilewski

Middlebury College Working Paper Series from Middlebury College, Department of Economics

Abstract: Without access to reliable transportation, the welfare-to-work transition for low-income households is nearly impossible, yet very little is known about the effectiveness of targeted loan programs designed to improve their access to credit. Since 1998, Vermont's TANF funds have been used to provide automobile loans to low-income residents through the "Working Wheels" program of the Vermont development Credit Union. In this paper, we take advantage of unique micro-level data on Working Wheels loan applications and loan performance to explore how such programs can cost-effectively provide car loans to those who are unable to obtain affordable loans elsewhere. Our results verify the importance of relationship lending, particularly among those without documented credit histories. In the presence of pronounced information asymmetries about credit history, our results justify a loan officer's increased trust in a client with whom the bank has had a stronger relationship; such clients, ceteris paribus, are less likely to default. We conclude that in the current climate of welfare reform, policymakers should consider programs that encourage welfare recipients to establish and maintain relationships with financial institutions in order to facilitate access to affordable credit and to minimize the risk of loan default.

Keywords: low-income lending; relationship lending; information asymmetries; automobile loans; credit-rationing; social capital (search for similar items in EconPapers)
JEL-codes: H53 I38 R42 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2004-04
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Working Paper: Overcoming Information Asymmetries in Low-Income Lending: Lessons from the "Working Wheels" Program (2002)
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Persistent link: https://EconPapers.repec.org/RePEc:mdl:mdlpap:0244r

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