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BANK SIZE, LENDING TECHNOLOGIES, AND SMALL BUSINESS FINANCE

Allen N. BergerUniversity of South Carolina

Wharton Financial Institutions Center

Lamont K. BlackBoard of Governors of the Federal Reserve System

The opinions expressed do not necessarily reflect those of the Federal Reserve Board or its staff. The authors thank Dan Grodzicki and Phil Ostromogolsky for valuable research assistance.

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OBJECTIVES

• Discuss current paradigm for small business lending research.– Focus on the most restrictive assumptions regarding

the lending technologies used by different sized banks.

• Show how we may relax these assumptions to generalize the basic paradigm.

• Test implications of the current paradigm by:– 1) Identifying the lending technologies used on bank

loans in the 1998 SSBF.– 2) Analyzing comparative advantages of large and small

banks in using these technologies on firms of different sizes.

• Draw some research and policy conclusions.

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THE CURRENT PARADIGM IN A NUTSHELL

Bank size Lending technologies Firm size

Large banks Comparative advantage Advantage in

in hard-information serving larger, technologies, more

transparent represented by firms.

financial statement

lending.

Small banks Comparative advantage Advantage in in soft-information

serving smaller

technologies, more opaque

usually just relationship firms.

lending.

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OUR RELAXATION OF FOUR ASSUMPTIONS OF THE PARADIGM

• 1) Allow for possibility that large banks may not have comparative advantages in all hard technologies.– All technologies employ combination of hard and soft

information.

• 2) Relax (often implicit) assumption that hard technologies as a whole may be represented by financial statement lending.– Hard technologies may be based primarily on other

types of hard information.

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RELAXATION OF ASSUMPTIONS (CONTINUED)

• 3) Allow the comparative advantage of large banks in hard technologies as a whole to be increasing or decreasing in firm size.– Depends on the relative abilities of large and small

banks to employ hard versus soft technologies as firm size increases.

• 4) Ease assumption that relationship lending is only important soft-information technology.– Judgment lending – Loan officer may use judgment

based on training and personal experience to lend to firms without strong banking relationships and without significant hard information.

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IDENTIFICATION OF THELENDING TECHNOLOGIES

• Identify a technology by principal source of information used to evaluate the loan.

• Main variables used in identification:

– Loan contract – contract type (lease versus loan), type of collateral pledged (if any), and credit size.

– Firm – firm size and leverage.

– Firm owner – personal bankruptcy/delinquency.

– Relationship strength – combination of relationship length and breadth.

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PRINCIPLES USED IN IDENTIFICATION PROCESS

• The bank chooses the lending technology that is most efficient for that firm based on the available information.– The bank generally chooses a hard-information technology over

a soft-information technology if hard information is available.

– Soft-information techniques tend to be labor-intensive.

• Lending based on the values of fixed assets (“immovables”) that are leased or pledged as collateral is generally more efficient than other hard-information lending technologies if this collateral is available.– Real estate, motor vehicles, and equipment.

– Strong incentive for firms to pay and bank can usually collect.

• Thus, we first identify the fixed-asset technologies (Step 1), then other hard-information technologies (Step 2), then soft-information technologies (Step 3).

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Step 1: Identifying Fixed-Asset Technologies

• Identify over 50% of loans as made using fixed-asset lending technologies.• Very clean identification – uses very simple loan contract terms only.• High degree of certainty because of collection priority.

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Step 2: Identifying Other Hard-Information Technologies

• Identify about 30% of loans as made using other hard-information technologies.• Identification not as clean and certain – requires information on the firm and bank and our intuition.

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Step 3: Identifying Soft-Information Technologies

• Identify about 12% of loans as made using soft-information technologies.

• Identification the least clean and certain.– Requires that hard-information technologies total is accurate.– Strong relationship is defined somewhat arbitrarily.

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• “Informal” test of comparative advantage – large banks have about 60% of loans, about 60% of bank branch offices.

• By convenience alone, expect about 60% of loans using each technology should be made by large banks under the null of no advantages.

• If >> 60%, then comparative advantage for large banks, if << 60%, then advantage for small.

• Large banks – significant comparative advantage in leasing, “other” hard technologies.

• Note: Advantage in SBCS is “engineered in” by assumption. Run regressions two ways to account for this.

• Small banks – significant comparative advantage in soft technologies.

Frequency Distribution of Technologies Used by Banks to Lend to Small Businesses by Bank Size

Small Banks Large Banks All BanksGTA ≤ $1B GTA>$1B

(4) (5) (6)

Hard-Information Lending TechnologiesFixed Asset Technologies

Leasing LEASE 16.81 83.19 100.00Commercial Real Estate Lending CRE 49.74 50.26 100.00Residential Real Estate Lending RRE 43.62 56.38 100.00Motor Vehicle Lending MV 46.47 53.53 100.00Equipment Lending EQ 48.93 51.07 100.00MV or EQ Lending MV or EQ 54.30 45.70 100.00Fixed Asset Totals 45.73 54.27 100.00

Other Hard Information TechnologiesAsset-Based Lending ABL 27.48 72.52 100.00Financial Statement Lending FSL 32.27 67.73 100.00Small Business Credit Scoring SBCS 0.00 100.00 100.00Other Hard Information Totals 18.01 81.99 100.00

Hard-Information Totals 36.37 63.63 100.00

Soft-Information Lending TechnologiesRelationship Lending RELATE 75.00 25.00 100.00Judgment Lending JUDGE 65.43 34.57 100.00Soft-Information Totals 68.75 31.25 100.00

Column Totals 40.16 59.84 100.00

Percent of Total 40.16 59.84 100.00

Number of Loans 988 1472 2460

Conditional on Lending Technology

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METHODOLOGY – EMPIRICAL TESTS OF CURRENT PARADIGM

• Logit model of probability that a given bank loan is made by a large bank:

ln [P(loan is from a large bank) / (1 - P(loan is from a large bank))]=

f(firm size, lending technology, firm size ▪ lending technology, large bank branch market share, bank market concentration,

MSA dummy)

• Most general null hypothesis – no comparative advantage or disadvantage of large banks in using any technology or serving any size class.– Coefficients of all the technology, firm size, and interaction

variables would be zero.– P(large bank) determined only by competitive conditions.

• We interpret a significantly higher probability of a loan being made by a large bank, conditional on competitive conditions, as evidence of a comparative advantage for large banks.

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Table 6: Tests of Hard versus Soft Technologies

• (1) Nonmonotonic effect of firm size on probability of borrowing from a large bank. Inconsistent with paradigm.

• (2) Large banks have comparative advantage in hard – consistent with paradigm.• (4) Comparative advantage of large banks is decreasing in firm size. Essentially 0 for large firms.

Inconsistent with current paradigm’s prediction of increasing in firm size.

Dependent variable: Large Bank

(1) (2) (3) (4)Firm Size

Medium Firm -0.428 -0.442 0.182[3.376]*** [3.380]*** [0.549]

Large Firm 0.044 -0.157 1.603[0.347] [1.181] [3.293]***

TechnologyHard 1.389 1.344 2.073

[9.528]*** [9.003]*** [6.759]***

InteractionsMedium Firm * Hard -0.753

[2.075]**Large Firm * Hard -1.927

[3.799]***

Market CharacteristicsLarge Bank Branch Market Share 3.300 3.293 3.33 3.341

[16.072]*** [15.788]*** [15.905]*** [15.896]***Herfindahl 0.571 0.544 0.466 0.457

[1.133] [1.065] [0.911] [0.889]MSA 0.294 0.334 0.307 0.297

[2.370]** [2.651]*** [2.430]** [2.341]**

Pseudo R^2 0.135 0.157 0.161 0.165Observations 2434 2434 2434 2434

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Table 6: Tests of Hard vs. Soft Technologies (Panel B) Tests of Predicted Probabilities of Large Bank by Firm Size and Lending Technology

(1) (2) (3)

Soft Hard Hard - Soft

Small Firm 0.247 0.723 0.476[6.76]***

Medium Firm 0.283 0.596 0.313[6.76]***

Large Firm 0.620 0.654 0.034[0.36]

F-Test for differences across size classes (2 restrictions) 14.46***

• For small firms, predicted probability of loan being made by large bank increases from 24.7% to 72.3% – statistically significant rise of 47.6% – as lending shifts from soft to hard technology.

• For large firms, not statistically significant.• Decreasing comparative advantage for large banks in hard

technologies is statistically significant (F test).– Bottom line – for small firms, large banks do well with hard technologies

and poorly with soft technologies. For large firms, technology does not matter as much.

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Table 8: Tests of Other Fixed-Asset Lending Technologies vs. Leasing(Panel A) Regression Results

• (1) Nonmonotonic effect of firm size on probability of borrowing from a large bank, inconsistent with paradigm.• (2) Comparative advantage of large banks in leasing relative to other fixed-asset lending technologies.• (4) Comparative advantage of large banks in leasing relative to other fixed-asset lending does not hold for smallest

firms.• (4) Other differences in comparative advantage for larger firms by size class (e.g., equipment lending interactions

not significant).• Results are inconsistent with current paradigm that effectively treats all hard-information technologies as if the

comparative advantages were the same.

Dependent Variable: Large Bank(1) (2) (3) (4)

Firm SizeMedium Firm -0.530 -0.431 0.908

[2.949]*** [2.340]** [1.336]Large Firm -0.026 0.086 1.527

[0.143] [0.451] [2.323]**

TechnologyCommercial Real Estate Lending (CRE) -1.439 -1.404 0.065

[5.060]*** [4.900]*** [0.102]Residential Real Estate Lending (RRE) -1.154 -1.056 0.178

[3.756]*** [3.401]*** [0.317]Motor Vehicle Loan (MV) -1.246 -1.182 -0.171

[4.375]*** [4.128]*** [0.318]Equipment Loan (EQ) -1.361 -1.349 -0.510

[4.575]*** [4.503]*** [0.765]

InteractionsMedium Firm * CRE -1.877

[2.281]**Medium Firm * RRE -1.685

[2.177]**Medium Firm * MV -1.097

[1.481]Medium Firm * EQ -1.351

[1.575]Large Firm * CRE -1.870

[2.335]**Large Firm * RRE -1.628

[1.994]**Large Firm * MV -1.716

[2.359]**Large Firm * EQ -1.085

Pseudo R^2 0.126 0.135 0.143 0.151Observations 1269 1269 1269 1269

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Table 9: Tests of Relationship Lending vs. Judgment Lending

• RELATE and interaction terms are not statistically significant – cannot reject null of no differences in comparative advantages between two soft technologies.

– May be due in part to small numbers of observations.• Finding suggests that there may be a significant bias in current research that just looks at

the effects of relationship strength – in effect groups judgment lending with hard technologies.

Dependent Variable: Large Bank(1) (2) (3) (4)

Firm SizeMedium Firm 0.139 0.152 0.059

[0.686] [0.661] [0.880]Large Firm 1.585 1.597 1.283

[0.002]*** [0.002]*** [0.030]**

TechnologyRelationship Lending (RELATE) -0.102 -0.133 -0.562

[0.741] [0.678] [0.448]

InteractionsMedium Firm * RELATE 0.425

[0.612]Large Firm * RELATE 1.130

[0.336]

Pseudo R^2 0.204 0.169 0.204 0.207Observations 287 287 287 287

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CONCLUSION• We relax some of the current paradigm’s most restrictive

assumptions regarding bank size, lending technologies, and firm size.

• We show that:– Large banks’ comparative advantages extend beyond lending

to large, transparent firms.• Hard information is available in forms other than financial

statements.• Large banks may be able to lend to opaque small firms using hard

information about the firm’s collateral or owner without using significant hard information about the firm itself.

– Small banks’ comparative advantages extend beyond relationship lending.

• All lending technologies have both hard and soft components, so small banks may have advantages in some hard-information technologies based on the soft-information component.

• Small banks also have a comparative advantage in an important soft-information technology that is neglected by the paradigm – judgment lending.

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CONCLUSION (2)

• Policy implications.– The current paradigm implies that the consolidation of

the banking industry may result in reduced credit to the smallest, least transparent firms, as large banks are disadvantaged in serving these firms.

– When we relax some of the most restrictive assumptions, we allow for the possibility that large banks can and do lend to these firms using hard-information lending technologies.