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The Impact ofOrganizational Structure & Lending Technology

on Banking Competition

Hans DegryseCentER - Tilburg University, TILEC, K.U. Leuven & CESIfo

TILEC-AFM Chair on Financial Market Regulation

Luc LaevenIMF & CEPR

Steven OngenaCentER - Tilburg University, TILEC & CEPR

Con ference on ”The Changing Geography of Banking”, Ancona, September 2006

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Motivation: Bank Organization & Spatial Pricing

Bank’s internal organization matters for lending Stein (JF 2002); Takats (ECB 2004); Liberti (2004); Liberti & Mian (2005), Berger et al. (JFE 2005)

– Opaque (small) firms borrow from close banksPetersen & Rajan (JF 2002); Saunders & Allen (2002)

– Large, centralized banks lend to distant, large firms using hard informationBerger, Miller, Petersen, Rajan & Stein (JFE 2005); Cole, Goldberg & White (JFQA 2004)

Banks spatially price loansPetersen & Rajan (JF 2002); Degryse & Ongena (JF 2005); Bharath, Dahiya, Saunders & Srinivasan (JFE 2006), Agarwal & Hauswald (2006)

Bank’s internal organization matters for branch reach and spatial pricing of loans

Also rival banks’organization matters

This paper!

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“Objective” Information “Travels Better” than “Subjective” Information

Transportation Costs Differ:

“Number of visits” or “mode of communication”By borrower to bankBy bank to borrower

may differ

if banks’ organization implies different types of informationSubjective or Soft information: more visits

Objective or Hard information: fewer or no visits

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Transportation Cost Model: stylized version with linear transportation costs

Banks A, B located at endpoints of line (length 1)

Transportation costs ti per unit of distance, shaped by internal organization of bank i

Consider 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)

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Equal Linear Transportation Costs

0 11/2

Bank A Bank B

t

MC = 0

-2t

Loan Rate

Distance

Now introduction different costs, but for graphical purposes, we assume tA= t.

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Drop in tB (e.g., B employs more objective information):A’s reach shrinks and spatial pricing becomes softer

0 11/2

Bank A Bank B

MC = 0

tA+ tB

Loan Rate

Distance

tA

tB

- (tA+ tB)

tB / (tA+ tB)

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In the paper, we also model:

Non-Linear transportation costs

Banks with different marginal costs

“Soft Firm – Hard Firm” and “Soft Bank – Hard Bank” – “Soft firms” generate more soft information

– If bank A has a comparative advantage in soft information:

• Bank A’s reach decreases when B becomes “harder”

• But less so for “soft firms”

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Data: We combine two data sets

Portfolio from one Belgian bank

17,776 loans to 13,104 borrowers in August 1997Degryse & 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)

1

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Postal Zone

Borrower

6 km

6 km

Lender

Competitors

837 Postal Zones; 7,477 Bank Branches

also possible

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Distance

= shortest traveling time, in minutes= 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 combinationsRecording errors; 801 at closing branches; 1% - screen (postal zone check)

612 contracts in postal zones without competitors15,044 remaining contracts

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Dependent Variables

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Bank organization variables(source: Belgian Bankers` Association)

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Other Control Variables

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Other Control Variables (cont’d)

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Table 2: Impact on Branch Reach

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Table 4: Spatial PricingModel

(Independent) Variable

ln (1 + Distance to Lender) -9.98 *** -25.62 *** -12.50 ** -10.65 -10.38 *** -4.84 -7.88 **(2.99) (9.55) (5.30) (11.92) (3.16) (9.19) (3.19)

ln(1 + Distance to Lender) * Variable 25.18 * 6.91 -2.16 9.11 -6.14 -16.37 *(14.68) (11.98) (7.53) (23.55) (5.86) (8.87)

ln(1 + Distance to Closest Competitor) 20.37 *** 43.17 *** 23.52 *** 11.38 20.56 *** 17.87 19.33 ***(4.35) (12.17) (6.91) (15.73) (4.45) (11.34) (4.50)

ln(1 + Distance to Closest Competitor) * Variable -36.42 ** -8.68 3.73 1.89 0.40 7.68(18.14) (14.84) (9.73) (27.90) (7.23) (17.93)

Relationship Characteristics Yes Yes Yes Yes Yes Yes YesOther Control Variables Yes Yes Yes No Yes No YesFixed Postal Zone Effects Yes Yes Yes Yes Yes Yes YesAdjusted R-squared 0.17 0.17 0.17 0.07 0.17 0.07 0.17

Spatial PricingSoft Rival -25.62 *** -12.50 ** -10.65 -10.38 *** -4.84Hard Rival -0.44 -5.59 -21.45 -1.27 -10.98

I II IV

Large Bank

IIIHierarchy

Branch

Hierarchy

Bank

V

Foreign Bank

VII

Urban

VI

Fax

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Differential Effects & work in progress

Effect of hierarchy on branch reach is smaller for small firms with limited liability

Effect of hierarchy on spatial pricing is larger for small firms with limited liability

Not incorporated in paper yet:– Layers to Telex softens spatial pricing– Span of control: little impact

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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 bank is large, hierarchical, or has fax

– Spatial pricing softens• Rival bank is large or hierarchical