The Impact of Organizational Structure & Lending Technology on Banking Competition

1. The Impact ofOrganizational 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. 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. 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. 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. 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. 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. “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. Illustration: Linear Transportation Cost Model(in the paper we develop a more general model)Bhaskar & To (RAND JE 2004) Branches A, B located at endpoints of line with length 1 Borrower at location x Cost visiting A: tAx Cost visiting B: tB(1-x) Cost taking loan at A: rAx + tAx Cost taking loan at B: rBx + tB(1-x) Borrower indifferent when: rAx + tAx= rBx + tB(1-x)

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

10. Drop in tB (e.g., bank B is more hierarchical):A’s reach shrinks and spatial pricing becomes softer Loan Rate tA tB tA+ tB - (tA+ tB) MC = 0 0 1/2 tB / (tA+ tB) 1 Branch A Branch B Distance

11. Branch reach and loan rates at bank A Branch A’s loan portfolio: y = tB/ (tA + tB) For borrowers x to the left of y: rAx = tB– (tA + tB)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. We Combine Two Data Sets: Bank Loan Contract Portfolio (source: one Belgian Bank) 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)

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

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. Dependent Variables

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

17. Control Variables

18. Table 3: Impact on Branch Reach

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

22. Table 6

23. Conclusions • Simple model shows that: • Branch reach and severityof spatial loan pricingdepend 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