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Credit Scoring and Competitive Pricing of Default Risk

by Satyajit Chatterjee of the Federal Reserve Bank of Philadelphia,
Dean Corbae of the University of Texas at Austin, and
José-Víctor Ríos-Rull of the University of Pennsylvania & CAERP

January 2007

Abstract: When people cannot commit to pay back their loans and there is limited information about their characteristics, lending institutions must draw inferences about their likelihood of default. In this paper, we examine how this inference problem impacts consumption smoothing. In particular, we study an environment populated by two types of people who differ with respect to their rates of time preference and receive idiosyncratic earnings shocks. Impatient types are more likely to borrow and default than patient types. Lenders cannot directly observe a person's type but make probabilistic assessments of it based on the person's credit history. The model delivers an integrated theory of terms of credit and credit scoring that seems broadly consistent with the data. We also examine the impact of legal restrictions on the length of time adverse events can remain on one's credit record for consumption smoothing and welfare.

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