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Distance and Information Asymmetries in Lending Decisions

Distance and Information Asymmetries in Lending Decisions. by Sumit Agarwal and Robert Hauswald (& sons) Discussant Hans Degryse CentER – Tilburg University, TILEC, K.U. Leuven and CESIfo TILEC-AFM Chair on Financial Market Regulation

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Distance and Information Asymmetries in Lending Decisions

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  1. Distance and Information Asymmetries in Lending Decisions by Sumit Agarwal and Robert Hauswald (& sons) Discussant Hans Degryse CentER – Tilburg University, TILEC, K.U. Leuven and CESIfo TILEC-AFM Chair on Financial Market Regulation Conference on the “Changing Geography of Banking”, Ancona, September 2006

  2. General Issue • Does distance still matter? • Petersen and Rajan (JF2002): US • No, as distance between lender and borrower has quadrupled from 1970’s to 90s • Yes, as loan rate decreases in distance • Degryse and Ongena (JF2005): Belgium • Yes, distance between lender and borrowers did not increase substantially over from 1975-97 • Yes, as loan rate hinges on distance to lender and distance to competitor • This paper: US • Loan pricing: when controlling for score, no • Loan volume and switching: yes

  3. General Issue: evidence

  4. General Issue: why should distance matter? • What does distance capture? • Petersen and Rajan (JF2002): US • No, as distance between lender and borrower has quadrupled from 1970’s to 90s • Yes as loan rate decreases in distance • Degryse and Ongena (JF2005): Belgium • Yes; distance between lender and borrowers did not decrease dramatically • Yes, as loan rate hinges on distance to lender and distance to competitor

  5. This paper’s findings • Loan rate determinants • decreases in borrower-lender distance (DL) and increases in borrower-competitor distance (DC) • Distance loses statistical significance when introducing the internal score of lender • Interaction between score, and DL and DC shows that higher scored firms that are closer pay higher rates, hinting at asymmetric information being the driver • Nice treatment of potential selection issues • Decision to offer credit • Decrease in DL and increase in DC • Increase in score; distance remains significant

  6. This paper’s findings • “Switchers away from Bank” • Increase in DL and decrease in DC • Interaction of score, and DC and DL suggest that higher scored firms are more likely to switch when DC is large, suggesting asymmetric information • delinquency: further away borrowers are more delinquent

  7. Comments:Setting more inclined towards asymmetric information? “transportation costs” vs. “asymmetric information” • Degryse and Ongena (JF2005): Belgian sample: 44% borrowed from closest branch • US: “borrowers do not turn to the closest branch but prefer to borrow from further away”

  8. Comments:Setting more inclined towards asymmetric information? • US versus Belgium: absolute differences in distance • US versus Belgium: huge relative differences in DL versus DC => how would results be in a restricted sample with “similar” absolute and/or relative differences

  9. Comments: asymmetric information? • Inclusion of “score” in loan pricing model • Removes statistical significance of distance as standard errors increase • Magnitude of coefficients and economic significance, however, often remains • Main Bank and Duration of Relationship (Months on Books) • Relatively short duration: median of 30 months • both have a negative coefficient in loan rate regression => asymmetric information would suggest a positive coefficient

  10. Comments: Is it asymmetric information? • Suppose branch managers know that the bank applies the following pricing model: Loan rate = f (Score) => Branch managers reflect local market power in score => borrowers where the branch manager asserts to enjoy a lot of market power get low score (see Ioannidou and Ongena (2006): competing bank credit scores get adjusted to allow loan officers to give borrowers lower rate to attract borrowers) => distance and competitive setting gets reflected into score • On p. 8-9 the authors mention: our bank estimates that, on average, 20 to 30% of our bank’s scores consists of soft information • Suggestion: orthogonalize score to investigate the separate impact of distance and check in how far “internal score” differs from “external score”

  11. Comments: Technology and potential selection • Loans are from January 2002-April 2003, a recent period when new technologies may already be in place • Is the bank only granting loans via its branches, or are also loans through other channels like • Internet • Online banking • Other stores/shops, available => i.e. do you cover all the loan applications and approvals of the bank? => if not, potential selection issues • Similar issues and question for rival banks?

  12. Minor Comments • Do “competing branches” include own bank’s branches? • “Switching away from bank” versus “switching towards bank” • Should “months on books” and “main bank” and some other variables still play a role when internal score is good proxy of private information?

  13. Summary • Interesting topic? Yes! • Interesting paper? Yes! • Do I recommend that you read it? Yes! • Am I convinced? May be … Not Yet

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