The Impact Of Competition On Bank Orientation

THEME : Bank competition

TITLE : The impact of competition on bank orientation

AUTHOR :

1.      Hans Degryse

CentER-Tilburg University, TILEC and KU Leuven,

PO Box 90153, 5000 LE Tilburg, Netherlands

2.      Steven Ongena

CentER-Tilburg University and CEPR,

PO Box 90153, 5000 LE Tilburg, Netherlands

Published : 5 April 2007

INTRODUCTION

A bank offering a relationship loan augments a borrower’s success probability. Relationship lending then allows extracting higher rents from the borrower. Fiercer interbank competition pushes banks into offering more relationship lending, as this activity permits banks to shield their rents better. In their model stiffer interbank competition also reduces bank industry specialization in relationship loans as on the margin the returns to industry specialization decline.

1.      Hence, the value added of a relationship loan for the borrower also decreases.

2.      Relationship lending is further non-monotonically related to the degree of concentration in banking.

3.      Hence, whether interbank competition and relationships are inimical and how competition affects bank orientation seems ultimately an empirical question.

We analyze a unique data set consisting of all loans granted by an important Belgian bank. This data set is ideally suited to investigate the effect of interbank competition on bank orientation.

PROBLEM
We find, in line with Boot and Thakor (2000), that is it true when local interbank competition is fiercer a borrower is more likely to be engaged in relationship banking but that more intense compete only marginally increases industry specialization of the bank branches. Borrowers located physically closer to the bank branch are also more likely to consume other bank services and to be engaged over a longer time period.

PURPOSE

The data set allows us to:

(1) adequately measure local concentration, multi-market contact of banks across postal zones, and borrower—bank distance, respectively.

(2) construct multiple objective measures of relationship lending. Ongena and Smith (2000), for example, define a bank relationship to be the connection between a bank and customer that goes beyond the execution of simple, anonymous, financial transactions and argue that a bank relationship can be more specifically measured along two dimensions.

DATA AND RESEARCH METHOD

Data

The unique data set we analyze consists of loans granted to 13,098 firms by an important Belgian bank that operates all over Belgium. The sample includes all existing loans at the bank as of August 10, 1997 that were initiated after January 1, 1995.

Level of analysis
We a priori choose to analyze the competition–orientation correspondence at the local level. Most sample firms are small, implying that loan applications by firms and loan decisions by banks are taken locally both at the data-granting bank as well as at rival banks.

Dependent variables measuring bank orientation

We employ as our central dependent variable of bank orientation a dummy Relationship Banking. This dummy is equal to one if the length of the relationship with the borrower exceeds one year

Distance variables: Location may determine the degree of competition for a borrower when either borrower (Hotelling, 1929; Salop, 1979) or lender (Sussman and Zeira, 1995) face transportation costs.

Control variables: We introduce bank branch size, postal zone variables, and key firm characteristics in the base regressions. Start with the variable Branch Size.

RESULT AND ANALYSIS

banks may take into account exactly whom their competitors are in the postal zone given contact in other postal zones, i.e., banks may care about multi-market contact. To control for either pro- or anti-competitive effects arising from Multi-Market Contact we also include this contact variable. Proximity could encourage firms to frequent the same bank for multiple services during a longer time period.

As 95% of all postal zones in Belgium have an HHI below 0.37 (where the minimum percent Relationship Banking occurs), these findings confirm a key result in Boot and Thakor (2000), but are at odds with Petersen and Rajan (1995). Branches seemingly engage in more relationship banking when facing fiercer banking competition.

we replace Branch Size (a variable to be discussed in the next section) by random branch effects (employing OLS).13 Branch effects could capture omitted variables that could be correlated with bank orientation, such as branch service quality and local firm presence and/or competition (Cetorelli, 2001), for which we could not construct reasonable proxies. However, results are unaffected; if anything they are even more “striking” in statistical significance and economic relevance.

We first assume that industry specialization should be measured only for the portfolio containing these relationship borrowers. We rerun all specifications  but choose not to report. Most coefficients are similar in sign and size, but somewhat less statistically significant. Next we measure industry specialization for the entire loan portfolio of the branch (assuming some positive knowledge spillovers from transactional lending) and re-run all models for the same sets of relationship borrowers. Results are virtually unaffected and again we choose not to tabulate them.

CONCLUSION

There is significant disagreement on whether interbank competition and relationship banking are inimical or not both in theory and in empirical findings. Interbank competition seemingly affects bank branch orientation (and to a much lesser extent bank branch industry specialization). Finally, larger bank branches lend substantially more on a transactional basis but are less likely to be specialized in particular industries.