BANK SIZE, LENDING TECHNOLOGIES, AND SMALL BUSINESS FINANCE

1. BANK SIZE, LENDING TECHNOLOGIES, AND SMALL BUSINESS FINANCE Allen N. BergerUniversity of South Carolina Wharton Financial Institutions Center Lamont K. Black Board 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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).

8. 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.

9. 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.

10. 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.

11. Frequency Distribution of Technologies Used by Banks to Lend to Small Businesses by Bank Size • “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.

12. 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.

13. 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.

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

15. 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.

16. 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.

17. 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.

18. 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.