Abstract

Credit to small businesses is an important underpinning for job creation and macroeconomic growth. We develop a theoretical model of decision-making under risk and uncertainty in which agents (bank lenders) have imperfect information about loan applications, and also have imperfect ability to make decisions based on that information. The model yields testable implications related to ongoing trends in small business lending, including the recent increases in the physical distance between borrowers and lenders that may be exacerbating information-related adverse selection problems, and the implementation of small business credit scoring models that may be mitigating these information problems. The model also yields testable implications for the effects of government loan loss subsidies on efficient allocation of credit to small businesses. We test these implications for a sample of 29,577 loans to small businesses made under the SBA 7(a) loan program between 1984 and 2001. We believe this is the first study to test the impact of borrower-lender distance, credit-scoring models, and the tradeoff between these two phenomena on the probability of loan default. Our findings offer substantial support for the predictions of our theory. We find that borrower- lender distance is positively associated with loan default, and that the adoption of credit-scoring dampens this relationship. However, we find that credit-scoring lenders experience higher default rates on average, which suggests that ancillary benefits associated with high-volume, credit-scored lending strategies (e.g., scale economies, portfolio diversification, cross-sales opportunities) may be offsetting the costs of higher expected default rates. We also find that more generous government loan guarantees, as well as more competitive local lending markets, are associated with higher loan default rates. These findings have implications for bank competition policy and for the funding and management of government subsidy programs for small business loans.

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