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Collateral, relationship lending and family firms

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Abstract

Prior research suggested that relationship lending could play a role in solving asymmetric information problems between borrower and lender. Other studies suggest a relationship between family ownership and the shareholder–bondholder agency conflict. The present paper investigates the impact of relationship characteristics, family ownership and their interaction effects upon the use of collateral in SME lending. We examine the determinants of collateral as well as the determinants of the choice between business and personal collateral using decision tree analysis. The results reveal that relationship characteristics have a significant influence, but not always in the direction as expected. Moreover, they do not seem to be the primary determinants in our classification models. The most important determinants in both classification models seem to be the loan amount, total assets and the family versus non-family firm distinction. In addition, we differentiate between line-of-credit and non-line-of-credit loans and find significant differences between these decision trees.

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Notes

  1. The statistical significance of the interaction term is usually not calculated by standard software (Ai and Norton 2003).

  2. The magnitude and sign of the marginal effect may be different dependent on the observation. Possible non-linear effects can be easily deducted from the decision tree, whereas in a logit model, one should first calculate the interaction effect on various meaningful levels of the covariates (Hoetker 2007).

  3. Jiménez et al. (2006) found that if a firm works with more banks, it increases the probability of pledging collateral for long-term loans, while it decreases the probability of pledging collateral when acquiring short-term loans.

  4. Note that engaging in a long-term relationship can have the same impact as exclusively dealing with one bank. It may also result in a ‘lock in’ effect. Ending the long-term relationship with the bank would create switching costs or give a negative signal to external parties. So, SMEs that have a long-term relationship with a bank may also cope with a hold-up problem as cited above.

  5. The 1998 NSSBF database allows us to compose an elaborate database consisting of several firm, loan and relationship characteristics for 2,525 loans granted to US SME’s. However, this database has very incomplete data on maturity or interest rate which are often considered as possible substitutes for collateral pledging (Ortiz-Molina and Penas 2008; Cressy and Toivanen 2001; Toivanen and Cressy 2001). These characteristics of the loan contract are only provided for the ‘most recent loan’ approved for each SME that would dramatically decrease the sample size when used (e.g. less than 200 cases for some submodels). We discuss the possible limitations of this database further in Sect. 6.

  6. On the contrary, in a comparable study by Voordeckers and Steijvers (2006) on the determinants of collateral, the number of banks (COMPETITION) does have a significant (negative) impact on the probability of pledging collateral. In that same study, the loan amount (AMOUNT) only appears to have a significant influence in the business vs. personal collateral decision, whereas in the current study the loan amount is an important variable in both decision trees (collateral vs. no collateral and business vs. personal collateral). However, results of both studies may differ since (1) different databases on different countries (US vs. Belgium) are used; (2) the US is characterized as a market-based financial system where mainly transaction based lending technologies are used, based on hard quantitative data on opaque SME’s (Berger and Frame 2007). Whereas relationship duration as well as the main bank status improves the possibility of gathering hard data by the bank, the number of banks the SME negotiates with does not. On the contrary, Belgium is characterized as a bank based financial system, where relationship lending based on soft data often prevails. Working with more banks seems to induce a very strict analysis of the SME before any loan would be granted. The bank does not dispose of an information monopoly which makes it impossible to exploit any kind of market power in the future (Sharpe 1990). So, only low-risk loans will be granted, which explains the less strict collateral requirements when working with multiple banks. (3) Another important reason for the difference in results between both studies might be the inclusion of interaction effects in the current paper that were not taken into account in Voordeckers and Steijvers (2006). The current study reveals that the inclusion of these interaction effects provides a significant value added and introduces new insights in this research domain.

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Acknowledgements

The authors greatly appreciate comments of two anonymous referees which improved the paper substantially.

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Authors and Affiliations

  1. KIZOK Research Center, Hasselt University, Agoralaan–Building D, Diepenbeek, 3590, Belgium

    Tensie Steijvers & Wim Voordeckers

  2. IMOB, Hasselt University, Wetenschapspark 5, bus 6, 3590, Diepenbeek, Belgium

    Koen Vanhoof

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  1. Tensie Steijvers
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  2. Wim Voordeckers
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  3. Koen Vanhoof
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Correspondence to Wim Voordeckers.

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Steijvers, T., Voordeckers, W. & Vanhoof, K. Collateral, relationship lending and family firms. Small Bus Econ 34, 243–259 (2010). https://doi.org/10.1007/s11187-008-9124-z

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