1. 1 The Impact of Organizational Structure & Lending Technology on Banking Competition Hans Degryse CentER - Tilburg University, TILEC & CESIfo TILEC-AFM Chair on Financial Market Regulation Luc Laeven International Monetary Fund, CEPR & ECGI Steven Ongena CentER - Tilburg University & CEPR World Bank Conference – Small Business Finance – What works, What Doesn’t - May 5-6, 2008

2. 2 Point of Departure  Allocation of control within organizations shapes agents’ incentives Grossman & Hart (JPE 1986); Hart & Moore (JPE 2005); Hart (1995) –to collect information and to communicate Stein (JF 2002); Aghion & Tirole (JPE 1997)  Organizational form matters Rajan & Wulf (REStat 2007)

3. 3 Bank Organization  Bank’s internal organization matters for lending Stein (JF 2002); Takats (ECB 2004); Liberti (2004); Liberti & Mian (2007, RFSforth) –Opaque (small) firms borrow from close banks Petersen & Rajan (JF 2002); Saunders & Allen (2002) –Large, centralized banks lend to distant, large firms using hard information Berger, Miller, Petersen, Rajan & Stein (JFE 2005); Cole, Goldberg & White (JFQA 2004)  Geography may be relevant in banking Petersen & Rajan (JF 2002); Degryse & Ongena (JF 2005); Bharath, Dahiya, Saunders & Srinivasan (JFE 2006), Agarwal & Hauswald (2006) Bank organization matters for branch reach and spatial pricing of loans Also rival banks’ organization matters This paper!

4. 4 What We Do  Introduce differential transportation costs in standard spatial price discrimination model –Motivation: banks require different number of visits –Results: surprising number of interesting and testable hypotheses  Test some of these hypotheses of the simple model combining two unique datasets –one bank, >15,000 loans to mainly small firms –information on rival banks’ organization

5. 5 What We Find: Simple Model  Market shares or branch reach & the slope of spatial loan pricing also depend on the characteristics of the competing banks Market share & slope decrease, for example, when rival banks require fewer visits (e.g. hard information)

6. 6 What We Find: Empirical Exercises  Branch reach shrinks: Rival branch is part of a large, more hierarchical bank, with a smaller span, fewer layers to telex (more authority at rival), or with a fax  Spatial pricing possibly softens: Rival branch is part of a large bank, but more layers to telex (less authority at rival)

7. 7 “Hard” Information “Travels Better” than “Soft” Information Transportation Costs Differ: “Number of visits” or “mode of communication” By borrower to bank By bank to borrower may differ if banks’ organization implies different types of information Soft information: more visits Hard information: fewer or no visits

8. 8 Branches A, B located at endpoints of line with length 1 Borrower at location x Cost visiting A: t A x Cost visiting B: t B (1-x) Cost taking loan at A: r Ax + t A x Cost taking loan at B: r Bx + t B (1-x) Borrower indifferent when: r Ax + t A x = r Bx + t B (1-x) Illustration: Linear Transportation Cost Model (in the paper we develop a more general model) Bhaskar & To (RAND JE 2004)

9. 9 Equal Linear Transportation Costs 0 1 1/2 Branch A Branch B t MC = 0 -2t Loan Rate Distance Now introduction different costs, but for graphical purposes, we assume t A = t.

10. 10 Drop in t B (e.g., bank B is more hierarchical) : A’s reach shrinks and spatial pricing becomes softer 0 1 1/2 Branch A Branch B MC = 0 t A + t B Loan Rate Distance tAtA tBtB - (t A + t B ) t B / (t A + t B )

11. 11 Branch reach and loan rates at bank A Branch A’s loan portfolio: y = t B / (t A + t B ) For borrowers x to the left of y: r Ax = t B – (t A + t B )x We want to test if the market share (or branch reach) & the slope of spatial loan pricing depends on the characteristics of the own and competing branches.

12. 12 We Combine Two Data Sets: 17,776 loans to 13,104 borrowers in August 1997 Degryse & Van Cayseele (JFI 2000); Degryse & Ongena (JF 2005) sole proprietorships (81%), small, medium, and large firms  Loan Characteristics –Origination Date, Loan Rate, Collateral, Repayment Duration, Purpose, Other  Relationship Characteristics –Main Bank and Duration  Firm Characteristics and Identity (incl. Address) Bank Loan Contract Portfolio (source: one Belgian Bank)

13. 13 Postal Zone Borrower 6 km Lender Competitors 837 Postal Zones; 7,477 Bank Branches also possible

14. 14 Distance = shortest traveling time, in minutes  to Lender  to Closest quartile (Bank) Competitor in the borrower’s postal zone 17,776 + 293,170 borrower - bank branch combinations Recording errors; 801 at closing branches; 1% - screen (postal zone check) 612 contracts in postal zones without competitors 15,044 remaining contracts

15. 15 Dependent Variables

16. 16 Bank Organization Dataset (source: Belgian Bankers` Association)

17. 17 Control Variables

18. 18 Table 3: Impact on Branch Reach

19. 19 Robustness  Maximum Reach, Number of Loans  Small Loans (< 200,000 BEF)  Instrumental variable estimation –Branch organization could be determined by geographical considerations: instruments –a dummy that equals one if the postal zone is in and around Brussels for each postal zone –the average firm size in terms of total assets –the average firm employment in terms of number of employees –the average firm leverage –the industry concentration index –a bank multi-market contact index

20. 20 Robustness: IV estimation (Table 5)  Maximum Reach, Number of Loans  Small Loans (< 200,000 BEF)  Instrumental variable estimation –Branch organization could be determined by geographical considerations: instruments –a dummy that equals one if the postal zone is in and around Brussels for each postal zone –the average firm size in terms of total assets –the average firm employment in terms of number of employees –the average firm leverage –the industry concentration index –a bank multi-market contact index

21. 21 Spatial Pricing:  Theory suggest “less spatial pricing when hard information is important” –Large banks –Hierarchical banks –Less authority (large levels to telex), …

22. 22 Table 6

23. 23 Conclusions  Simple model shows that: –Branch reach and severity of spatial loan pricing depend on the organization of competing banks  Empirical Tests: –Branch reach shrinks Rival branch of large, hierarchical bank, with few layers to telex and with a fax –Spatial pricing softens Rival branch of large bank with more layers to telex