Multiple versus Single Banking Relationships: Theory and Evidence


  • Enrica Detragiache,

  • Paolo Garella,

  • Luigi Guiso

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    • International Monetary Fund, Universita' di Bologna, and Universita' di Sassari and Ente Luigi Einaudi, respectively. We wish to thank Marco Da Rin, Mathias Dewatripont, Giovanni Ferri, Andrea Generale, Andrea Gerali. Giorgio Gobbi, Raghuram Rajan, Andrei Shleifer, Alessandro Sembenelli, and participants in seminars at the Chicago FED, Igier, the University of Brescia, the BIS, and in the 1997 Alicante ASSET meeting; the 1st European Corporate Governance Network Conference; the 1997 CEPR Conference on Information and Learning, Financial Intermediation, and the Macroeconomy; the Center for Financial Studies Conference on Credit Risk Management and Relationship Banking; and the 1998 meetings of the American Finance Association for helpful comments on a previous version of this paper. Valuable suggestions were also received from the editor and from an anonymous referee. This paper is part of a project on “Legal Enforcement and the Structure of Bank-Firm Relations.” Financial support was provided by MURST. The Federal Reserve Board has kindly provided us with the public release of the 1988 National Survey of Small Business Finances. The views expressed in the paper are the authors' own, and do not necessarily reflect those of the International Monetary Fund. The data analysis in this work was performed while Luigi Guiso was at the Bank of Italy.


A theory of the optimal number of banking relationships is developed and tested using matched bank-firm data. According to the theory, relationship banks may be unable to continue funding profitable projects owing to internal problems and a firm may thus have to refinance from nonrelationship banks. The latter, however, face an adverse selection problem, as they do not know the quality of the project, and may refuse to lend. In these circumstances, multiple banking can reduce the probability of an early liquidation of the project. The empirical evidence supports the predictions of the model.