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Bank Competition and Collateral: Theory and Evidence

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Abstract

We investigate the impact of bank competition on the use of collateral in loan contracts. We analyze asymmetric information about the borrowers’ type in a Salop model in which banks choose between screening the borrower and asking for collateral. We show that the presence of collateral is more likely when bank competition is low. We then test this prediction empirically on a sample of bank loans from 70 countries. We perform logit regressions of the presence of collateral on bank competition, measured by the Lerner index. Our empirical tests corroborate the theoretical predictions that bank competition reduces the presence of collateral. These findings survive several robustness checks.

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Notes

  1. See the special issue of the Journal of Money, Credit and Banking in 2004 (vol. 36, n°3) on this topic.

  2. A similar setup is used in Degryse et al. (2008).

  3. Qualitatively we would obtain the same result in a model in which the firms differ in their preferences for the bank offering a screening contract (for instance, because they have relationships of different intensity) but do not differ with respect to the bank offering a collateralized credit contract.

  4. We would obtain the same qualitative result if firms had to incur some costs of application.

  5. The countries are : Algeria, Angola, Argentina, Australia, Austria, Belgium, Bangladesh, Bolivia, Brazil, Bulgaria, Cameroon, Chile, China, Colombia, Croatia, Czech Republic, Denmark, Ecuador, Egypt, Finland, France, Germany, Ghana, Greece, Guatemala, Hong Kong, Hungary, India, Indonesia, Ireland, Iran, Israel, Italy, Ivory Coast, Jamaica, Japan, Kazakhstan, Korea, Lithuania, Malaysia, Mexico, Morocco, Netherlands, Norway, New Zealand, Oman, Pakistan, Panama, Papua New Guinea, Peru, Philippines, Poland, Portugal, Romania, Russia, Saudi Arabia, Singapore, Slovenia, Slovakia, South Africa, Spain, Sri Lanka, Sweden, Switzerland, Thailand, Tunisia, Turkey, United Kingdom, Venezuela, Viet Nam.

  6. A term loan is defined in Dealscan as an instalment loan where amounts repaid may not be reborrowed.

  7. As our sample includes many syndicated loans in which arrangers play a more active role than participant banks, we have also performed our estimations by considering only the number of arrangers involved in the loan. Results are totally similar.

  8. This can lower performance in terms of social welfare because higher interest rates worsen firms’ incentives (Schnitzer 1999).

  9. As the Bankscope database does not provide information on the number of employees, we use this proxy variable for the price of labor following Maudos and Fernandez de Guevara (2004, 2007).

  10. We adopt the logit model rather than the probit model, because Akaike and Schwarz information criteria are lower in logit regressions. However, we have also performed the probit regressions as a robustness check, and found similar results.

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Correspondence to Laurent Weill.

Additional information

Financial support by the German Science Foundation, under SFB-Transregio 15 and HA 3039/2-1, is gratefully acknowledged. We received substantive comments during seminar presentations at the University of Munich, the ifo Institute Munich, the XVII International Tor Vergata Conference on Banking and Finance in Rome, the 2009 CESifo Area Conference on Applied Microeconomics in Munich, the 2009 Northern Finance Association Meeting in Niagara on the Lake, the 2009 Southern Finance Association Meeting in Captiva Island. Of course, all remaining errors are our own.

Appendix

Appendix

1.1 Proof of Lemma 1

From (IC.BF_L) we know that L = 1. Moreover, the participation constraint of the good firm must hold:

$$ \begin{array}{*{20}{c}} {{\text{p}}\left( {{\text{A}} + {\text{X}} - {{{\text{R}}}^{{\text{L}}}}} \right) + \left( {1 - {\text{p}}} \right)\left( {{\text{A}} - 1} \right) \geqslant {\text{A}}} \hfill \\ {{\text{p}}\left( {{\text{X}} - {{{\text{R}}}^{{\text{L}}}}} \right) - \left( {1 - {\text{p}}} \right) \geqslant {\text{0}}\quad \quad \quad \quad \quad {\text{(PC}}.{\text{GF}}\_{\text{L}})} \hfill \\ \end{array} $$

If the project is successful, a good firm repays R L from the payoff X it generates. In the case of failure, collateralized assets in the amount of 1 are seized by the bank. The expected payoff when the investment is credit-financed has to be at least as high as the payoff from not investing (which is A).

Bank 1 will participate if it makes at least zero expected profit with the contract offered. The bank’s expected profit by each (good) firm is given by

$$ p{R^L} + \alpha \left( {1 - p} \right) - 1 \geqslant 0{ }\left( {{\text{PC}}{\text{.B\_ L}}} \right){ } $$

which implies that it needs a repayment of at least \( {R^L} \). Since all banks are identical bank 1 cannot demand a higher repayment without losing all its customers. Q.E.D.

1.2 Proof of Lemma 2

When bank 1 offers a screening contract it must take into account that the other banks offer a collateralized contract. Given this outside option of the firm, bank 1 can demand a repayment of \( R_1^S \) without losing the firm as a customer:

$$ \begin{array}{*{20}{c}} {{\text{p}}\left( {X - R_{1}^{S}} \right) = {\text{p}}\left( {X - \frac{{\left( {1 - \left( {1 - p} \right)\alpha } \right)}}{p}} \right) - \left( {{\text{1}} - {\text{p}}} \right)\,{\text{or}}} \hfill \\ {R_{1}^{S} = \frac{{1 + \left( {1 - p} \right)\left( {1 - \alpha } \right)}}{p}} \hfill \\ \end{array} $$

Bank 1’s expected profit per borrower with this repayment is:

$$ \Pi_1^S = \left( {1 - p} \right)\left( {1 - \alpha } \right) - cd{ } $$

which is non-negative if \( d \leqslant \frac{{\left( {1 - p} \right)\left( {1 - \alpha } \right)}}{c} \). For firms located further away, the costs of producing information are lower with a collateralized contract. This means that bank 1 cannot undercut the expected repayment of a collateralized contract because it would need a repayment of at least \( { }R_1^S = { }1 + \frac{{cd}}{p} \). As a result, firms at a distance \( d > \frac{{\left( {1 - p} \right)\left( {1 - \alpha } \right)}}{c} \), apply for a collateralized loan from bank 2 which makes zero expected profits. Q.E.D.

1.3 Proof of Proposition 2

All banks are identical. Bank 1 finances the following fraction of firms through a screening contract:

$$ \begin{array}{*{20}{c}} {\frac{{\widetilde{d}}}{{\frac{1}{{2N}}}} = \frac{{2N\left( {1 - p} \right)\left( {1 - \alpha } \right)}}{c}\,{\text{and}}} \hfill \\ {\frac{{\partial \left( {\frac{{\widetilde{d}}}{{\frac{1}{{2N}}}}} \right)}}{{\partial N}} = \frac{{2\left( {1 - p} \right)\left( {1 - \alpha } \right)}}{c} > 0} \hfill \\ \end{array} $$

As a result, the fraction of firms financed through a screening contract increases as the number of banks increases.

Moreover, a collateralized contract is a viable alternative only if \( \left( {1 - p} \right)\left( {1 - \alpha } \right) < \frac{c}{{2N}} \). Thus, as N increases, the parameter range in which this condition holds, decreases.

Q.E.D.

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Hainz, C., Weill, L. & Godlewski, C.J. Bank Competition and Collateral: Theory and Evidence. J Financ Serv Res 44, 131–148 (2013). https://doi.org/10.1007/s10693-012-0141-3

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