Borrower-Lender Distance, Credit Scoring, and the Performance of Small Business Loans

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Abstract
We develop a theoretical model of decision-making under risk and uncertainty in which bank lenders have both imperfect information about loan applications and imperfect ability to make decisions based on that information. We test the loan-default implications of the model for a large random sample of small business loans made by U.S. banks between 1984 and 2001 under the SBA 7(a)loan program. As predicted by our model, both borrower-lender distance and credit-scoring contribute to greater loan defaults; the former finding suggests that distance interferes with information collection and monitoring, while the latter finding implies production efficiencies that encourage credit-scoring lenders to make riskier loans at the margin. However, we also find that credit-scoring dampens the harmful effects of distance, consistent with the conjecture that information generated by credit scoring models improves the ability of lenders to assess and price default risk.