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Distance, Bank Organizational Structure, and Lending Decisions

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The Changing Geography of Banking and Finance

Abstract

We survey the extant literature on the effects of both a bank's organizational structure and the physical distance separating it from the borrower on lending decisions. The available evidence suggests that banks engage in spatial pricing, which can be rationalized by the existence of transportation costs and information asymmetries. Moreover, their ability to price-discriminate seems to be bounded by the reach of the lending technology of surrounding competitors. It is not entirely clear from an empirical viewpoint that small, decentralized banks have a comparative advantage in relationship lending. This advantage is motivated theoretically by the existence of agency and communication costs within a bank. However, differences in data and methodology may explain the inconclusive evidence.

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Notes

  1. 1.

    Alessandrini et al. (2009: Chapter 5, this volume) discuss the differences between functional distance and operational distance and provide empirical evidence on their effects on innovation adoption by firms.

  2. 2.

    Brevoort and Wolken (2009: Chapter 3, this volume) discuss in greater detail the relevance of distance in banking.

  3. 3.

    See Udell (2009: Chapter 2, this volume).

  4. 4.

    In the subsequent theoretical exposition, we disregard long-run dynamics by treating the number of banks (or the level of competition) as given. This assumption is implicit in most empirical studies we will analyze, as they employ samples spanning short time periods. Harsher competition should translate into lower loan rates, since it reduces the average distances between all possible combinations of firms and neighboring banks. On the other hand, an increase in the number of banks aggravates the adverse selection problem by enabling low-quality firms to obtain financing (Broecker 1990) and may result in a retrenchment toward relationship lending (Hauswald and Marquez 2006), resulting in higher loan rates.

  5. 5.

    Notice that location is not exogenous in these models. See for instance Hoover (1936) for a spatial price discrimination model with fixed locations.

  6. 6.

    The cost of one traveling minute equals 3.5 basis points in Degryse and Ongena (2005) and about 5.4 basis points in Petersen and Rajan (2002) (we infer the average speed in the United States from Agarwal and Hauswald (2006)).

  7. 7.

    For instance, they find that borrowers located in densely populated (i.e. urban) areas experience discrimination twice as harshly, which is probably related to higher traveling times in urban areas due to traffic congestion.

  8. 8.

    Recent evidence by Casolaro and Mistrulli (2007) seems to support this view. They find with an Italian dataset that spatial pricing is mainly confined to transactional loans.

  9. 9.

    The bank’s internal credit score itself could also be the avenue through which loan officers price discriminate, a possibility not addressed in their paper.

  10. 10.

    Udell (2009: Chapter 2, this volume) analyzes the effect of technological innovation on small business lending.

  11. 11.

    See Brevoort and Wolken (2009: Chapter 3, this volume).

  12. 12.

    An important set of theoretical models motivates collateral as arising from information gaps between borrowers and lenders. In particular, collateral may offset problems of adverse selection (Bester 1985, Chan and Kanatas 1985, Besanko and Thakor 1987) and/or moral hazard (Boot et al.1991) in credit markets.

  13. 13.

    A third possibility is addressed in Inderst and Mueller (2007). In their model the use of collateral is limited to loans granted by local lenders that have superior information over more distantly located competitors.

  14. 14.

    This analysis implicitly assumes that the choice of a lending bank located at a given distance from the firm precedes the design of the loan contract. This is always the case when the firm has a pre-established relationship with the bank.

  15. 15.

    Unfortunately, the two datasets contain different types of information, which restrains us from performing a totally controlled (i.e. ceteris paribus) empirical test.

  16. 16.

    We use a specification similar to that in Chakraborty and Hu (2006) (model (1) in Table 2, p. 97), who also employ the 1993 NSSBF, with the following differences: (i) we use the variable Main Bank as a proxy for the scope of the bank-firm relationship rather than the number of financial services, (ii) we correct the age of the firm by the duration of the relationship between bank and firm, and (iii) we add to the model the bank-firm distance, as well as a variable indicating whether the firm is located in a Metropolitan Statistical Area.

  17. 17.

    There is ample evidence of the importance of a bank relationship to small firms in terms of credit availability (Petersen and Rajan 1994), lower loan rates (Berger and Udell 1995, Degryse and Van Cayseele 2000) (in relationship duration and scope, respectively), reduced collateral requirements (Berger and Udell 1995) and intertemporal risk sharing (Petersen and Rajan 1995).

  18. 18.

    See, for instance, Becker and Murphy (1992), Bolton and Dewatripont (1994), Radner (1993) and Garicano (2000).

  19. 19.

    Petersen (2004) argues that the categorization of information into “hard” and “soft” is often too restrictive. He further suggests that “hard” and “soft” information are the extremes of a continuum along which information can be classified. An illustrative example of hardening “soft” information is a loan officer filling a report where he evaluates several attributes of an applicant (e.g. honesty and managerial competence).

  20. 20.

    These agency problems may result in the collusion between the loan officer and the firm (Tirole 1986), manipulation of “soft” information (Godbillon-Camus and Godlewski 2005, Ozbas 2005), excessive use of “discretion” in defining loan terms (Cerqueiro et al. 2007), and overlending or hiding a deteriorating condition of a borrower (Berger and Udell 2002).

  21. 21.

    Godbillon-Camus and Godlewski (2005) use a principal-agent framework to study a loan officer’s incentives to manipulate the signals conveyed about potential borrowers, which are based on “soft” information. They suggest that an adequate compensation scheme solves ex ante these agency problems. Ozbas (2005) analyze the optimal level of organizational integration when the agents’ (i.e. loan officers’) access to resources depends on the signals they communicate to their superiors.

  22. 22.

    See also Liberti (2005).

  23. 23.

    Important changes in bank organizational structure have resulted from technological innovation and to the recent wave of bank consolidation in recent decades. The effects of these phenomena on small business lending are covered by Udell (2009: Chapter 2, this volume).

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Acknowledgments

We gratefully acknowledge financial support from NWO-The Netherlands and FWO-Flanders.

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  1. Department of Finance, CentER, Tilburg University, Tilburg, The Netherlands

    Geraldo Cerqueiro

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  1. Geraldo Cerqueiro
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  2. Hans Degryse
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  3. Steven Ongena
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Correspondence to Geraldo Cerqueiro .

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    Cerqueiro, G., Degryse, H., Ongena, S. (2009). Distance, Bank Organizational Structure, and Lending Decisions. In: Zazzaro, A., Fratianni, M., Alessandrini, P. (eds) The Changing Geography of Banking and Finance. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-98078-2_4

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