Skip to main content
SpringerLink
Log in

Does Collateral Help Mitigate Adverse Selection? A Cross-Country Analysis

  • Published:
Journal of Financial Services Research Aims and scope Submit manuscript

Abstract

In this paper, we empirically investigate whether collateral mitigates adverse selection problems in a loan market. Theory predicts a negative relation between the presence of collateral and the interest spread of a loan. However, bankers’view and most empirical evidence contradict this prediction and support the observed-risk hypothesis instead. We provide new evidence from a sample of 4,940 bank loans from 31 countries. We test whether the degree of information asymmetry affects the positive link between collateral and the loan spread and find that a greater degree of information asymmetry reduces this positive relation. This finding provides support for both the adverse selection and observed-risk hypotheses.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. It is defined as the weighted average normalized to a scale of 0 to 100 of survey responses to the question, “Generally speaking, would you say that most people can be trusted or that you can’t be too careful in dealing with people?” Where a response of one indicates that most people can be trusted and zero indicates that you can’t be too careful.

  2. Liberti and Mian (2010) also support evidence on the role of financial development in reducing information asymmetries. They show in their analysis that greater financial development reduces the difference in rates of collateralization between high and low risk loans in a country.

  3. Data provided by Djankov et al. (2007) are for each year from 1991 to 2002, and we consider the 2002 value for the following years.

  4. Data for the rule of law indicator are available for 1996, 1998, 2000, 2002, 2003, 2004, 2005, and 2006. Therefore, we extrapolate the value for the index for the missing years, taking into account the fact that law enforcement slowly changes over time.

  5. A term loan is defined in Dealscan as an instalment loan where amounts repaid may not be re-borrowed.

  6. With one nonsignificant coefficient for Private Bureau with the full conditioning information set.

  7. We only comment the thresholds for estimations with the full conditioning information set (odd-numbered estimations), but the findings are similar when the thresholds are considered for estimations with the simple conditioning information set (even-numbered estimations).

  8. We have also carefully checked the cross-correlation matrix for firm, loan, and country-level variables.

  9. We have also performed regressions with firm characteristics for the baseline specifications without interaction terms leading to similar results regarding Collateral as in Table 2. Furthermore, using different specifications regarding firm characteristics variables (for instance with Firm size, Leverage and Liquidity ratio only) lead to similar results.

  10. The DWH test is implemented by including the residuals of each endogenous right-hand side variable (Loan Spread and Collateral respectively) as a function of all exogenous variables (see equation 1), in a regression of the original model. A coefficient of the residual statistically different from zero indicates that the endogenous regressor is correlated with the regression error and that OLS is not consistent.

  11. The Collateral equation is estimated using a Probit regression.

  12. The Loan Spread equation is estimated using an OLS regression.

  13. The Hansen-Sargan test is implemented in the following way. We obtain the 2SLS residuals by regressing Loan Spread against the set of explanatory and control variables and the fitted value of Collateral. The latter is obtained with a Probit regression in a first stage (Angrist 2001). Then we regress the residuals on all the explanatory and control variables and instruments to obtain its R². The Hansen-Sargan statistic is computed as this R² times the number of observations less the number of regressors. This statistic is chi-square distributed with degrees of freedom equal to the difference between the number of instruments and the number of endogenous variables. If the Hansen-Sargan statistic is not significant then the instrumental variables are valid and our system of equations is well-identified.

References

  • Allen T (1990) Developments in the international syndicated loan market in the 1980. Bank Engl Q Bull Feb 71–77

  • Angrist J (2001) Estimation of limited dependant variable models with dummy endogenous regressors: simple strategies for empirical practice. J Bus Econ Stat 19:2–16

    Article  Google Scholar 

  • Barth J, Caprio G, Levine R (2004) Bank regulation and supervision: what works best? A new database on financial development and structure. J Fin Intermed 13:205–248

    Article  Google Scholar 

  • Beck T, Levine R (2004) Stock markets, banks, and growth: panel evidence. J Bank Finance 28:423–442

    Google Scholar 

  • Beck T, Demirgüc-Kunt A, Levine R (2000) A new database on financial development and structure. World Bank Econ Rev 14:597–605

    Google Scholar 

  • Berger A, Udell G (1990) Collateral, loan quality, and bank risk. J Mon Econ 25:21–42

    Article  Google Scholar 

  • Berger A, Udell G (1995) Relationship lending and lines of credit in small firm finance. J Bus 68:351–382

    Article  Google Scholar 

  • Besanko D, Thakor A (1987) Collateral and rationing: sorting equilibria in monopolistic and competitive credit markets. Inter Econ Rev 28:671–689

    Article  Google Scholar 

  • Bester H (1985) Screening vs. rationing in credit markets with imperfect information. Amer Econ Rev 25:21–42

    Google Scholar 

  • Bharath S, Dahiya S, Saunders A, Srinivasan A (2010) Lending relationships and loan contract terms. Rev of Finan Studies forthcoming

  • Boot A, Thakor A, Udell G (1991) Secured lending and default risk: equilibrium analysis, policy implications and empirical results. Econ J 101:458–472

    Article  Google Scholar 

  • Bowden R, Turkington D (1984) Instrumental variables. Cambridge University Press

  • Brick I, Palia D (2007) Evidence of jointness in the terms of relationship banking. J Finan Inter 16:452–476

    Google Scholar 

  • Brown M, Jappelli T, Pagano M (2009) Information-sharing and credit: firm-level evidence from transition countries. J Finan Inter 18:151–172

    Google Scholar 

  • Bushman R, Piotroski J, Smith A (2004) What determines corporate transparency? J Acc Res 42:207–252

    Article  Google Scholar 

  • Chan YS, Kanatas G (1985) Asymmetric valuation and the role of collateral in loan agreements. J Mon Credit Bank 17:85–95

    Google Scholar 

  • Davidson R, MacKinnon J (1993) Estimation and inference in econometrics. Oxford University Press

  • Davydenko S, Franks J (2008) Do bankruptcy codes matter? A study of defaults in France, Germany and the UK. J Finance 63:565–608

    Article  Google Scholar 

  • Degryse H, Van Cayseele P (2000) Relationship lending within a bank-based system: evidence from European small business data. J Finan Inter 9:90–109

    Google Scholar 

  • Dennis S, Sharpe I (2005) Firm size dependence in the determinants of bank term loan maturity. J Bus Finance Acc 32:31–64

    Article  Google Scholar 

  • Dennis S, Nandy B, Sharpe I (2000) Determinants of contract terms in bank revolving lines of credit. J Finan Quan Anal 35:87–109

    Article  Google Scholar 

  • Djankov S, La Porta R, Lopez-de-Silanes F, Shleifer A (2003) Courts. Quar J Econ 118:453–517

    Article  Google Scholar 

  • Djankov S, McLiesh C, Shleifer A (2007) Private credit in 129 countries. J Finan Econ 84:299–329

    Article  Google Scholar 

  • Harhoff D, Körting T (1998) Lending relationships in Germany—empirical evidence from survey data. J Bank Finance 22:1317–1353

    Article  Google Scholar 

  • Inderst R, Mueller H (2007) A lender-based theory of collateral. J Finan Econ 84:826–859

    Article  Google Scholar 

  • Ivashina V (2009) Asymmetric information effects on loan spreads. J Finan Econ 92:300–319

    Article  Google Scholar 

  • Jappelli T, Pagano M (2002) Information sharing, lending and defaults: cross-country evidence. J Bank Finance 26:2017–2045

    Article  Google Scholar 

  • Jimenez G, Saurina J (2004) Collateral, type of lender and relationship banking as determinants of credit risk. J Bank Finance 28:2191–2212

    Article  Google Scholar 

  • Jimenez G, Salas V, Saurina J (2006) Determinants of collateral. J Finan Econ 81:255–281

    Article  Google Scholar 

  • Kaufmann D, Kraay A, Zoido-Lobaton P (1999a) Aggregating governance indicators. World Bank, Working Paper n°2195

  • Kaufmann D, Kraay A, Zoido-Lobaton P (1999b) Governance matters. World Bank, Working Paper n°2196

  • Kaufmann D, Aart K, Mastruzzi M (2007) Governance matters VI: Governance indicators for 1996–2006. World Bank, Policy Research Paper n°4280

  • Knack S, Keefer P (1997) Does social capital have an economic payoff? A cross-country investigation. Quar J Econ 112:1251–1288

    Article  Google Scholar 

  • La Porta R, Lopez-de-Silanes F, Shleifer A, Vishny R (1997a) Legal determinants of external finance. J Finance 52:1131–1150

    Article  Google Scholar 

  • La Porta R, Lopez-de-Silanes F, Shleifer A, Vishny R (1997b) Trust in large organizations. Amer Econ Rev 87:333–338

    Google Scholar 

  • La Porta R, Lopez-de-Silanes F, Shleifer A, Vishny R (1998) Law and finance. J Pol Econ 106:1113–1155

    Article  Google Scholar 

  • Levine R, Loayza N, Beck T (2000) Financial intermediation and growth: causality and causes. J Mon Econ 46:31–77

    Article  Google Scholar 

  • Liberti J, Mian A (2010) Collateral spread and financial development. J Finance 65:147–177

    Article  Google Scholar 

  • Nelson F, Olson L (1978) Specification and estimation of a simultaneous-equation model with limited dependent variables. Inter Rev Econ 19:695–709

    Article  Google Scholar 

  • Pagano M, Jappelli T (1993) Information sharing in credit markets. J Finance 48:1693–1718

    Google Scholar 

  • Qian J, Strahan P (2007) How laws and institutions shape financial contracts: the case of bank loans. J Finance 62:2803–2834

    Article  Google Scholar 

  • Stiglitz J, Weiss A (1981) Credit rationing in markets with imperfect information. Amer Econ Rev 71:393–410

    Google Scholar 

  • Stulz R, Williamson R (2003) Culture, openness and finance. J Finan Econ 70:261–300

    Article  Google Scholar 

  • Zak P, Knack S (2001) Trust and growth. Econ J 111:295–321

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Strasbourg (LaRGE) & EM Strasbourg Business School, Strasbourg, France

    Christophe J. Godlewski & Laurent Weill

  2. University of Strasbourg (LaRGE) & EM Strasbourg Business School, 47 avenue de la Forêt Noire, 67082, Strasbourg Cedex, France

    Laurent Weill

Authors
  1. Christophe J. Godlewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laurent Weill
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laurent Weill.

Appendix

Appendix

Table 11 Brief description of all variables and their sources
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Godlewski, C.J., Weill, L. Does Collateral Help Mitigate Adverse Selection? A Cross-Country Analysis. J Financ Serv Res 40, 49–78 (2011). https://doi.org/10.1007/s10693-010-0099-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10693-010-0099-y

Keywords

JEL

Search

Navigation