Allen N. Bergera,*, Lamont K. Blackb
a
Moore School of Business, University of South Carolina, Columbia, SC 29208, U.S.A. a
Wharton Financial Institutions Center, Philadelphia, PA 19104, U.S.A. a
CentER, Tilburg University, The Netherlands b
Board of Governors of the Federal Reserve System, Washington, DC 20551 U.S.A.
This version: August 12, 2010
Abstract
Under the current paradigm in small business lending research, large banks tend to specialize in lending to relatively large, informationally transparent firms using “hard” information, while small banks have advantages in lending to smaller, less transparent firms using “soft” information. We go beyond this paradigm to analyze the comparative advantages of large and small banks in specific lending technologies. Our analysis begins with the identification of fixed-asset lending technologies used to make small business loans. Our results suggest that large banks do not have equal advantages in all of these hard lending technologies and these advantages are not all increasing monotonically in firm size, contrary to the predictions of the current paradigm. We also analyze lines of credit without fixed-asset collateral to focus on relationship lending. We confirm that small banks have a comparative advantage in relationship lending, but this appears to be strongest for lending to the largest firms.
JEL classification: G21; G28; G34; L14
Keywords: Banks; Lending technologies; Relationship lending; Small business.
The views expressed do not necessarily reflect those of the Federal Reserve Board or its staff. The authors thank the anonymous referee, Bob Avery, Brian Bucks, Bob DeYoung, Traci Mach, Greg Udell, John Wolken, and participants at a seminar at the Federal Reserve Board for helpful comments and suggestions, and Dan Grodzicki and Phil Ostromogolsky for valuable research assistance.
* Corresponding author. Tel: +1 803 576 8440; fax: +1 803 777 6876.
1. Introduction
The current research paradigm in small business lending emphasizes the advantages of large banks in lending to large, informationally transparent firms and the advantages of small banks in lending to small, opaque firms. In this paradigm, loan officers at large banks are hypothesized to focus on lending to large, transparent firms using their comparative advantages in lending technologies based primarily on “hard” quantitative information that the loan officers may credibly communicate to others in the bank – such as financial ratios from certified audited financial statements, collateral values, and credit scores. Loan officers at small banks have more flexibility to evaluate credit using techniques based primarily on “soft” qualitative information that is difficult to quantify and communicate by the loan officers – such as personal knowledge about the subjective circumstances of the firm, its owner, and its management.
In this paper, we go beyond the current paradigm to analyze bank size and the use of different lending technologies in small business lending. Our tests allow for the possibility that large banks have a comparative advantage in lending to small businesses, including the smallest, least transparent firms, using hard-information lending technologies. We allow for the possibility that large banks use techniques such as the leasing of assets and lending based primarily on collateral values to lend to the smallest firms. We also analyze more closely the comparative advantages of small banks in using soft information to lend to small firms. Our results provide new evidence that does not always fit the predictions of the current paradigm.
small businesses by relying on “hard” information such as owners’ personal credit scores (Enrich 2007). As well, recent research shows that large banks provide large amounts of funding and other services to small firms in other nations (e.g., de la Torre, Martínez Pería, and Schmuckler 2010). Our data and data from bank regulatory reports are consistent with the fact that most small business loans are made by large banks. We find in our data that banks with over $1 billion in assets make about 60% of all small business loans, similar to their share of bank branch offices. Likewise, the June 2006 Call Report shows that over 65% of the dollar value of business loans of $1 million or less and over 68% of the value of such loans of $100,000 or less were made by banks with over $1 billion in assets.
Our empirical analysis matches data on U.S. small businesses, the banks that lend to them, the contract characteristics of these loans, and information from several other data sources to test the empirical implications of the current paradigm. The data include information about the loan contract, the borrower, the bank, and the bank-borrower relationship for 1811 small business loans.
size yields some new and interesting findings.
Under the current paradigm, large banks generally have a comparative advantage in using hard-information lending technologies – also known as transactions-based lending. The reasons for this comparative advantage are discussed below. Loan officers at large banks are hypothesized to make lending decisions using lending technologies based primarily on hard information. In most cases, the research tends to lump these hard technologies together, which often originates from an assumption that hard technologies may be represented by a single technology – financial statement lending – which relies primarily on statistics in firms’ financial statements. In contrast, we allow for the possibility that large banks may not have equal advantages in all of the individual hard technologies. This implies that financial statement lending may not be representative of hard technologies as a whole.
The assumption about the representativeness of financial statement lending implies that large banks’ comparative advantage in using hard-information lending technologies should be monotonically increasing in the size of the firm. As firms increase in size, they tend to have higher-quality financial statements, yielding an implied increasing advantage in hard technologies (see Berger and Udell 2006 for a summary of the current paradigm). However, we permit the comparative advantage of large banks to be increasing, decreasing, or nonmonotonic in firm size. If financial statement lending is not representative of hard-information lending technologies, then large banks may have differing comparative advantages across these technologies when lending to firms of different sizes.
“soft” qualitative information that is difficult to quantify and communicate by the loan officers – such as personal knowledge about the subjective circumstances of the firm, its owner, and its management. In particular, relationship lending – which is based primarily on information gathered over the course of a bank-borrower relationship, such as the owner’s character or reliability – is often analyzed as a soft-information technology. We take a step beyond this analysis by allowing the comparative advantage of small banks in relationship lending to be increasing, decreasing, or nonmonotonic in firm size.
Our main empirical findings are:
1) Large banks appear to have different comparative advantages in each of the fixed-asset lending technologies, which implies that no single hard technology is representative of all of the hard lending technologies;
2) The measured comparative advantages of large banks in hard technologies do not all appear to be monotonically increasing in firm size; and
3) Small banks appear to have a comparative advantage in relationship lending, but this advantage seems to be strongest for lending to the largest firms.
All of these major results are new to the literature and conflict with the predictions of the current paradigm.
those tests. Section 6 concludes.
2. The literature and our contribution
The current paradigm for small business lending concentrates mainly on two categories of lending technologies, hard- and soft-information technologies. It is often explicitly or implicitly assumed under the current paradigm that hard technologies as a whole may be represented by the financial statement lending technology alone. Based on this assumption, the conclusion is often drawn that hard technologies are best suited for serving the largest, most transparent small businesses that tend to have the highest quality financial statements. Thus, for most of the research in the current paradigm, as firms increase in size and transparency, banks tend to substitute from the use of a soft technology to one of the hard technologies.
The assumptions employed about the technologies in the current paradigm may result in biased or misleading empirical results. The empirical research in most cases does not separately identify the individual hard-information technologies employed by the lending banks. Instead, researchers often focus solely on the soft technology of relationship lending (e.g., Petersen and Rajan 1994, Berger and Udell 1995, Degryse and van Cayseele 2000). This research generally uses a measure of bank-borrower relationship strength, such as relationship length or breadth, as a continuous indicator of the degree to which the relationship lending technology versus a hard technology is effectively applied. This practice effectively groups the hard-information technologies together, so any measured effect of these technologies at best reflects an overall average effect across the individual lending methods, and may not accurately measure the effects of financial statement lending or any other single hard technology.
inclusion of the effects of soft technologies other than relationship lending. That is, the measured effect of hard technologies may also mix in the effects of soft technologies that are associated with weak banking relationships. We postulate a soft-information technology that we call “judgment lending,” which is lending based primarily on the judgment of a loan officer relying on experience and training, as well as any other available hard and soft information. While judgment of the loan officer is important for virtually any lending technology, it may be the principal information source for lending to some firms, such as small businesses that do not have significant hard information available and have not established a strong banking relationship. The exclusion of soft technologies such as judgment lending suggests that measured effects of relationship lending may not accurately reflect the effects of soft technologies as a whole and/or may give biased effects of relationship lending.
Some recent research recognizes the possibility that financial statement lending may not represent hard technologies as a whole, and that some of the other hard technologies may be particularly useful in lending to the smallest, least transparent firms (e.g., Berger and Udell 2006). To illustrate, small business credit scoring may be applied even when there is very limited information about the overall quality of the firm, as long as the firm has a good credit score based mostly on the credit history of the owner. Similarly, fixed-asset lending can be used to extend credit when the firm has high-quality fixed assets (real estate, motor vehicles, or equipment) that may be leased or pledged as collateral, even if the small business is not sufficiently transparent based on other hard and soft information.
relationship lending. All lending technologies employ some combination of hard and soft information, but hard and soft technologies are defined by the principal or most critical source of information employed in the screening, underwriting, and monitoring of the credit.1 As described above, the principal data source for financial statement lending is a firm’s financial statements. For fixed-asset lending, the main data are appraised values of the real estate, motor vehicles, or equipment leased or pledged as collateral, while the key data for asset-based lending are valuations of accounts receivable and/or inventory pledged. Small business credit scoring decisions are based principally on credit scores generated from the owner’s personal credit history and limited financial data on the firm. Relationship lending is based on proprietary information gathered over the course of the relationship. As noted above, judgment lending is based mainly on the judgment of a loan officer relying on experience and training.
A few recent empirical studies have made progress by identifying one or two specific lending technologies, rather than simply using a measure of relationship strength to separate relationship lending from hard technologies as a whole. For example, some studies empirically identify small business credit scoring based on survey data regarding whether, when, and how U.S. banks employ this lending technology (e.g., Frame, Srinivasan, and Woosley 2001, Berger, Frame, and Miller 2005, Berger, Espinosa-Vega, Frame, and Miller 2005, forthcoming, DeYoung, Glennon, and Nigro 2008, DeYoung, Frame, Glennon, and Nigro forthcoming). These studies confirm the possibility of banks using a hard technology to expand their small business lending or improve their information sets about very small customers, depending on how the technology is implemented. Some recent studies of Japan have information on whether small businesses have certified audited financial statements, which may be an indicator that their banks
1 Underwriting any loan requires at least some numbers about the firm, the owner, and/or the collateral
use financial statement lending on the loans to these customers (e.g., Kano, Uchida, Udell, and Watanabe 2006). This research finds, for example, that the beneficial effect of relationship length is smaller for firms with audited statements, consistent with a predicted shifting of technologies from relationship lending to financial statement lending.2
The current paradigm also relates to the role of banks’ organizational structure in determining their comparative advantages in different lending technologies. Large banks are considered to have comparative advantages in hard technologies because they have economies of scale in the processing and transmission of hard information, and may be better able to quantify and diversify the portfolio risks associated with hard-information loans. Conversely, large banks may be disadvantaged in processing and transmitting soft information through the communication channels of large organizations (e.g., Stein 2002). Lending based on soft information may also be associated with agency problems within the financial institution because the loan officer is the main repository of the information, giving a comparative advantage to small institutions with fewer layers of management (e.g., Berger and Udell 2002) or less hierarchical distance between the loan officer and the manager that approves the loans (e.g., Liberti and Mian 2009).3
In this paper, we go beyond the current paradigm in three ways. First, we allow for the possibility that large and small banks may have different comparative advantages for individual
2 One study of Japan identifies the use of six lending technologies, but takes a very different approach
from the one employed here. While we focus principally on contract terms and the bank-borrower relationship, Uchida, Udell, and Yamori (2008) focus principally on the borrower’s perception of how much the bank relied on different information. In their application, a loan may be made using multiple technologies. They find no significant differences in comparative advantages for large and small banks in the different technologies in Japan, which is very different from our findings below. The reasons for these different findings may be the different methodology, the application to a different country, or some combination of these.
3 One recent paper finds results suggesting that banks may use different lending technologies when
hard technologies. As explained above, all hard technologies employ some combination of both hard and soft information. Thus, for some hard-information technologies, the comparative advantage of large banks in using the hard-information component may be offset by a comparative advantage of small banks in using the soft-information component. For example, commercial real estate lending is a hard-information technology which is based mainly on the appraised value of the property. However, there may also be a significant soft-information component. Large banks may have only a slight comparative advantage in obtaining and processing the appraised values, whereas small banks may have a significant advantage in the soft-information component, based on relationships with the borrowing firms or loan officers’ knowledge of the market and local business conditions. This implies that large banks may not have comparative advantages in hard technologies with significant soft-information components. In our analysis, we examine whether large banks have equal comparative advantages in different fixed-asset lending technologies.
scoring for small business loans by small institutions (Berger, Cowan, and Frame forthcoming). Third, we analyze the effects of relationships on the comparative advantages of large and small banks apart from hard information based on fixed-asset valuations. To accomplish this step in our analysis, we analyze lines of credit without fixed-asset collateral. As explained in the next section, we argue that a bank will use a hard-information lending technology over a soft-information lending technology if sufficient hard soft-information is available. Therefore, we exclude lines of credit with fixed-asset collateral because these loans were likely underwritten on the value of the fixed assets. This is an important step forward for analyzing the comparative advantage of banks of different sizes based on the strength of a relationship between a bank and a firm. When lines of credit secured by fixed assets are included in a sample of lines of credit used to analyze relationship lending, results may be significantly biased or misleading. Our analysis provides a cleaner test of the effects of relationships on the comparative advantages of large and small banks among loans that are the most likely to involve relationship lending.
3. Data and lending technologies
In this section, we describe our data on small businesses and our approach toward identifying some of the technologies used by banks to lend to these firms. Although we are not able to identify all of the technologies which are likely used by banks, we believe that our approach provides a framework for moving the current paradigm forward. By incorporating lending technologies into our analysis, we are able to evaluate the comparative advantages of large and small banks in the context of specific types of hard and soft information.
SSBF collects information on small businesses (fewer than 500 employees) in the United States.4 Because we are interested in the size of the lending institutions as a proxy for their organizational form, we first reduce the dataset to loans from banks for which we are able to match the bank that provided the credit.5 This involves merging the 1998 SSBF data on small businesses with the December 1997 Call Report data for banks. Although some firms have loans from other types of financial institutions, we confine attention to bank loans because banks are the only institutions that use almost all the major technologies, giving the best representation of the use of comparative advantages in the most technologies. Banks are also the most popular source of loans, and at least some banks are conveniently available to virtually every small business. We also merge the data with market characteristics taken from the 1997 Summary of Deposits data.6
As explained below, we further reduce the dataset to a sample of fixed-asset loans (Step 1) and a sample of lines of credit without fixed-asset collateral (Step 2). We focus on these two samples because the fixed-asset loans provide a clean test of some of the hard technologies and the lines of credit without fixed-asset collateral provide a relatively clean test of one of the soft technologies. Table 1 shows descriptive statistics for these samples, including brief descriptions and the means and standard deviations of the variables used in the analysis. Column (1) gives the statistics for the sample of 1000 fixed-asset loans, which is over 40 percent of the full
4 It oversamples certain types of firms, including larger small businesses, which have been shown to use
leasing less often than smaller firms (see Eisfeldt and Rampini 2009).
5 About 13% of the bank loans are excluded because the bank could not be determined. We also exclude
five bank loans to firms with total assets ≤ 0 and another four for which the type of collateral could not be determined.
6 Note that we do not analyze the lending technologies that are used or would be used on borrowers
sample. Column (2) gives the statistics for the sample of 811 non-fixed-asset lines of credit.7 Excluded from these samples are 649 loans which do not fall into either of the two categories.8
Before considering lending technologies, we first observe the average bank size, firm characteristics, and market characteristics within each sample shown in Table 1. The mean of the large bank dummy is smaller for the sample of fixed-asset loans than for the sample of non-fixed-asset lines of credit, 55.4% and 69.7%, respectively. As for firm characteristics, the two samples have similar proportions of small, medium, and large firms – around 20% of loans to small firms (total assets (TA) ≤ $100,000), and about 40% each to medium firms ($100,000 < TA ≤ $1 million), and large firms (TA > $1 million). The data on firms’ return on equity (ROE) are winsorized at the 10% level for both samples to reduce the influence of extreme observations. While the winsorized means for ROE of 37% and 43% are still relatively high, the corresponding medians of 14% and 17% are much lower. One reason for the relatively high ROE for small firms is that our sample only contains firms that survive and leaves out the relatively large percentage of small firms that fail and tend to have significant negative returns.9 The market characteristics are similar across both the samples. Large banks have branch market shares between 55% and 65%, the Herfindahl concentration index averages about 0.20, and about 70% of the firms are in metropolitan competitive environments.
We follow two basic principles in identifying the lending technologies. First, we argue that to evaluate each potential borrower, the bank will choose the lending technology that is most
7 Return on equity is missing for one observation in the fixed-asset loan sample and two observations in
the non-fixed-asset lines of credit sample, which reduces the number of observations in the regressions using this variable.
8 These excluded loans include term loans used to purchase a fixed asset but not backed by the fixed asset
itself and lines of credit backed by fixed assets.
9 Recent statistics show that only 51 percent of new employer establishments born to new firms in 2000
efficient for that firm based on the available collateral and other information that the firm brings to the table, as well as any information that the bank already has about the firm. Based on this principle, we argue that the bank will generally choose a hard-information technology over a soft-information technology if sufficient hard information is available. Soft-information techniques tend to be labor-intensive on the part of the loan officer (high processing costs) and the information generated is difficult to communicate, so a hard-information technology will be chosen if possible.
Second, we argue that lending based on the values of fixed assets that are leased or pledged as collateral is generally more efficient than other hard-information lending technologies if this collateral is available. Fixed assets are long-lived assets that are not sold in the normal course of business (i.e., are “immovable”), and are uniquely identified by a serial number or a deed. These include real estate, motor vehicles, and equipment. The value of fixed assets meets the definition of hard information as quantitative data that may be credibly transmitted by the loan officer. We argue that fixed-asset lending is more efficient than other hard technologies because a bank with a loan secured by fixed assets can usually collect most of its owed repayment with higher priority before other creditors in the event of default or bankruptcy. Fixed-asset lending may also be particularly effective by providing a strong incentive for firms to make their payments – in many cases, the businesses may be crippled without access to their real estate, motor vehicles, or equipment. The threat of removal of fixed assets may therefore provide a powerful incentive for borrows to repay their loans.
lines of credit from this sample in order to reduce the possibility of mixing cases of relationship lending into our analysis of fixed-asset lending. Based on this approach, we are reasonably certain of the identification of the technology used for over 40 percent of the loans – term loans using valuations of fixed assets leased or pledged as collateral. Researchers often focus on the comparative advantages of large and small banks without specifying the lending technology, so this may offer a first opportunity to examine banks’ comparative advantages using a more detailed breakout of the fixed-asset technologies.
In Step 1, we analyze fixed-asset technologies. To identify these technologies, we use the contract type (lease versus loan) and the type of collateral pledged. The fixed-asset lending technologies include leasing (LEASE) as well as loans with fixed assets pledged as collateral – commercial real estate lending (CRE), residential real estate lending (RRE), motor vehicle lending (MV), and equipment lending (EQ). For convenience, we refer to leasing as a lending technology, although the asset is directly owned by the financing institution, and no loan is issued. However, a lease is similar to a loan with an almost “perfect” collateral lien – the bank owns the asset and can sell it or lease it to another customer if the loan is not repaid.10,11 We consider leasing to be a fixed-asset lending technology, because the leased assets are generally fixed. We also note that residential real estate collateral is somewhat different from the other fixed assets in that residential real estate is generally outside collateral (owned outside the firm), whereas the other types are generally inside collateral (owned by the firm). The collateral literature has found different results when using outside collateral versus inside collateral (e.g.,
10 We acknowledge that there are other differences between leasing and lending against fixed assets such
as real estate, motor vehicles, and equipment. For example, under leasing, the leasee/borrower can usually deduct the payment from taxable income, and the lessor can claim depreciation, whereas when a secured loan is made, the borrower deducts the interest and claims the depreciation.
11 The argument that leasing provides a more perfect lien than pledging collateral on fixed assets is not
John, Lynch, and Puri 2003, Brick and Palia 2007).
Among the fixed-asset technologies, we first identify the use of leasing (LEASE), which is simply based on whether the contract type is a lease. For leases, the principal source of information used to evaluate the credit is the valuation of the real estate, motor vehicles, or equipment that is leased. We hypothesize that large banks have the strongest comparative advantage in leasing, because the almost “perfect” collateral lien against the assets means that the bank has to rely very little on any secondary sources of hard or soft information.
The remaining fixed-asset technologies – commercial real estate lending (CRE), residential real estate lending (RRE), motor vehicle lending (MV), and equipment lending (EQ) – are identified by loan purpose and collateral. For instance, if the purpose of the loan is to purchase specific motor vehicles and those motor vehicles are pledged as collateral, then we identify the lending technology as MV, whether or not another type of collateral is pledged. The same is true for real estate loans secured by the real estate being purchased (CRE and RRE) and equipment loans secured by the equipment being purchased (EQ). Because these fixed-asset technologies have a less perfect lien than in the case of leasing, they may depend more on a soft-information component in addition to the hard soft-information about the value of the fixed asset. For instance, knowledge of local neighborhood business conditions may have value in underwriting a commercial real estate loan.
In Step 2, we analyze lines of credit (LCs) which do not have fixed assets as collateral. Berger and Udell (1995) argue that lines of credit are ideally suited for relationship lending and support this by showing that small firms are more likely to have all of their LCs consolidated at a single lender than other types of loans. The principal source of information in relationship lending is the loan officer’s processing of information through contact over time with the firm, its owner, and others in the local community that may be suppliers or customers, and so forth. The information is primarily soft because such information cannot be easily reduced to hard numbers that can be easily communicated by the loan officer.
We only analyze LCs without fixed assets as collateral because this eliminates one of the sources of hard information which banks might use in lending. Specifically, we exclude LCs collateralized by real estate, equipment, or a motor vehicle. As we explained in our principles for identifying lending technologies, a bank will generally choose a hard-information technology over a soft-information technology if sufficient hard information is available. In this case, even for lines of credit, the banks would likely use the valuations of the fixed assets as the primary source of information. Based on this principle, the exclusion of LCs secured by fixed-asset collateral can be an important first step for avoiding biased and misleading results in an analysis of the effect of relationship strength. With this exclusion, we can compare the advantages of banks which have strong bank-borrower relationships relative to banks that do not have strong relationships. The absence of a strong relationship suggests that the bank may be relying on technologies other than relationship lending, such as financial statement lending, small business credit scoring, asset-based lending, or judgment lending.
strength as well as individual relationship characteristics. Although we cannot identify relationship lending directly because the bank may be relying on other sources of hard and soft information, we rely on the strength of the relationship as an indicator of the importance of the relationship as a source of information. For instance, lines of credit underwritten using small business credit scoring are not as likely to have long relationships. Therefore, our approach to using relationship strength will help identify the comparative advantage of these banks in relationship lending.
We first quantify relationship strength based on an overall indicator of a “strong relationship.” To measure whether a relationship is “strong,” we combine several measures of the length, breadth, and exclusivity of the relationship between the firm and the bank extending the loan. Most measures of relationship strength in the literature focus on length, as longer relationships allow more time for the lending bank to garner proprietary soft information about the firm (e.g., Petersen and Rajan 1994, Berger and Udell 1995). Some strength measures include breadth in the form of a checking account through which the bank may gain information from monitoring the firm’s cash flows (e.g., Mester, Nakamura, and Renault 2007). Others focus on lender exclusivity, the accumulation of all of a firm’s credits in a single bank, which may maximize the information advantage of that bank (e.g. Berger, Klapper, and Udell 2001, Berger, Miller, Petersen, Rajan, and Stein 2005).
both breadth and exclusivity, such that the bank must have a checking account of the firm and be the exclusive lender. If the relationship is short – less than 5 years – or there is neither breadth nor exclusivity, the firm does not have a strong relationship with the bank. As shown in column (2) of Table 1, 40.7% of the non-fixed-asset lines of credit are to firms with strong relationships with their lending banks by our definition.
The category of loans without a strong relationship likely includes several types of lending technologies. As already explained, we focus in this part of our analysis solely on lines of credit and we have eliminated lines of credit which use fixed assets as collateral. However, these LCs may still have accounts receivable/inventory pledged as collateral. Additionally, the loan could be based on financial statements, model-based credit scores, or non-relationship-based soft information. Therefore, the excluded group of loans likely contains several types of lending technologies, including asset-based lending, financial statement lending, small business credit scoring, and judgment lending. The identification of these other technologies is not pursued here because their characteristics are not as easily observed.
4. Methodology
Our empirical methodology is designed to test the empirical predictions of the current paradigm regarding whether large versus small banks have comparative advantages in the different lending technologies, and how these advantages differ by firm size. In this section, we briefly describe the general model used in our empirical tests and how we change the specification to test different hypotheses when different subsets of the small business loans are included.
Our first specification is an analysis of the lending technologies associated with fixed-asset loans. The five fixed-fixed-asset technologies include leasing (LEASE), commercial real estate lending (CRE), residential real estate lending (RRE), motor vehicle lending (MV), and equipment lending (EQ).
In this specification, we model the probability that a given bank loan is made by a large bank as a function of firm size, lending technology, interactions of firm size and technology, and control variables for firm profitability, banking market competitive conditions, and firm industry. 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. The general specification is a logit equation of the following form:
(1) ln [P(loan is from a large bank) / (1 - P(loan is from a large bank))]
where P(•) indicates probability, “loan is from a large bank” is a dummy variable that is one if the loan is made by a large bank and zero if it is made by a small bank.12
The key exogenous variables are dummies for firm size class, lending technology employed, and their interactions, denoted by firm size ▪ lending technology. These dummies allow for tests of whether large or small banks have net comparative advantages in lending to different firm sizes, and in using the different technologies. The interaction terms are particularly important here because some of the empirical predictions of the current paradigm concern how the comparative advantages in the lending technologies vary with firm size class.
The remaining variables in equation (1) control for firm profitability, local market competitive conditions, and firm industry. We include return on equity (ROE) as a measure of firm profitability to control for the possibility that profitability would affect the bank from which the firm borrows. ROE is generally hard information, and so it is expected that large banks would more often lend to firms with high ROE relative to small banks. Consistent with prior research and anti-trust guidelines, we define the firm’s local banking market as the Metropolitan Statistical Area (MSA) or non-MSA rural county in which the small business is located.13 The market conditions specified are the large bank market share of branches, the Herfindahl concentration index of market bank deposits, and an MSA indicator dummy. It is important to control for the large-bank branch share because this accounts for the relative presence of large
12 In some cases, multiple bank loans to the same firm are included and may be made using different
lending technologies.
13 In some cases, we use New England County Metropolitan Areas (NECMAs), but for convenience, we
and small banks.14 Prior research shows that local market share of large banks is a powerful predictor of lending bank size (e.g., Berger, Rosen, and Udell 2007), which suggests that firms may generally choose an institution based on convenience. The Herfindahl index is the most standard measure of market power used in bank research and anti-trust analysis, and the MSA dummy proxies for the generally greater degree of competition in metropolitan markets. The effect of market concentration may be either favorable or unfavorable for small business borrowers (e.g., see Scott and Dunkelberg forthcoming). We also add 8 industry dummies for the industry of the borrower to control for differences in transparency, tangibility of assets, and loan types across industries. In the interest of brevity, the industry dummies are not shown in the tables and their results are not discussed.
The regression analysis of the five fixed-asset technologies allows us to test the prediction of the current paradigm that large banks have equal comparative advantages in all of these hard technologies versus our hypothesis above that large banks are likely to have the strongest comparative advantage in leasing. As explained above, we hypothesize that large banks have the strongest comparative advantage in leasing because of the almost “perfect” collateral lien against the assets. If we find different comparative advantages for individual fixed-asset technologies, this suggests that hard technologies as a whole may not be well represented by financial statement lending.
In our analysis of the fixed-asset loans, we also test whether the comparative advantages of large banks in these hard technologies are all monotonically increasing in firm size. Under the current paradigm, the comparative advantages of large banks in hard technologies are predicted to increase monotonically for all hard technologies. Our analysis extends beyond the current
14 The large-bank branch market share variable plays a similar role here to the log of median bank size in
paradigm by allowing for the possibility that large banks’ comparative advantages in the fixed-asset technologies may depend differentially on the size of the borrower. As noted above, some of the recent literature recognizes the possibility that some of the hard technologies other than financial statement lending may be particularly useful in lending to the smallest, least transparent firms, but this has rarely been applied in practice.
Our second specification is an analysis of lines of credit without fixed-asset collateral. The sample for this specification includes loans underwritten using relationship lending as well as financial statement lending, small business credit scoring, asset-based lending, and judgment lending. Our exclusion of fixed-asset-secured lines of credit allows us to rule out fixed-asset technologies, which is a step forward from previous empirical work. However, we cannot identify relationship lending directly from the data because the remaining lines of credit may have been underwritten based on one of the other technologies. Therefore, we focus our analysis specifically on measured relationship strength.
In this specification, we model the probability that a given bank loan is made by a large bank as a function of firm size, relationship strength, interactions of firm size and relationship strength, and the same control variables as in equation (1):
(2) ln [P(loan is from a large bank) / (1 - P(loan is from a large bank))]
= g(firm size, relationship strength, firm size ▪ relationship strength, firm ROE, large-bank branch market share, bank market concentration, MSA dummy, industry dummies)
index measure of “Strong Relationship.” This allows us to test the predictions of the current paradigm that small banks have a comparative advantage in relationship lending and that their comparative advantage should be greatest for the smallest firms. We also take a step further to analyze the underlying relationship characteristics used to define a relationship loan for the sample of non-fixed-asset lines of credit. In this specification, we use the same model of the probability that a given bank loan is made by a large bank, but we replace the strong relationship indicator variable with the three separate characteristics: relationship length, checking account, and exclusive lender. This specification allows us to identify the characteristics that generate a comparative advantage in relationship lending based on the strength of the relationship.
5. Empirical results
Tables 2 through 4 show the regression results. In Table 2 Panel A, we show four columns based on the specification in equation (1) – one with firm size dummies only, one with technology dummies only, one with both sets of dummies, and one complete specification with all the dummies and the interaction terms. Tables 3 and 4 Panels A also show four columns, but replace lending technologies with relationship strength, as specified in equation (2). All estimations include the control variables for banking market characteristics and firm industry. The control variable for firm profitability is included as an additional firm characteristic in all estimations that include the firm size dummies. For the complete specifications in column (4), we also show the predicted probabilities for each firm size when combined with either a lending technology or relationship-strength indicator, as explained below.
less than) 1, a higher level of the variable is associated with higher (lower) odds of the loan being from a large bank. We also report the absolute value of robust z-statistics based on the Huber-White sandwich method to correct for heteroskedasticity.
We first turn to Table 2 Panel A, which shows the results of testing the five fixed-asset technologies. We exclude the small-firm dummy and the LEASE dummy as the base case and conduct most of our tests on the comparative advantage differences of large and small banks for the four collateral-based fixed-asset technologies versus leasing. As discussed above, we hypothesize that large banks have the strongest comparative advantage for LEASE because of the almost “perfect” collateral lien against the leased assets. This characteristic of leasing makes it the purest hard information technology with the least requirement for secondary sources of soft information for which small banks may have the advantage.
Column (1) of Table 2 Panel A shows the logit regression with firm size and control variables only. This version of the model is the most similar to prior empirical research, which focuses on the effects of firm size without identifying or separating out the effects of the different technologies. One key difference here is that we include three firm size classes and allow for a nonmonotonic relationship between firm size and bank size, rather than using a continuous measure of firm size that forces a monotonic relationship. Our goal is to allow for the possibility that large banks may tailor specific fixed-asset technologies to reach different firm sizes.
banks with comparative advantages in hard technologies.15 When some of the most restrictive assumptions of the paradigm are relaxed, the comparative advantages of large banks in hard technologies may differ across firm sizes in various ways. The relationship between firm size and bank size may be increasing, decreasing, or nonmonotonic, depending on the sizes of firms for which large banks may best use their advantages in the different technologies.
The results in column (1) show a nonmonotonic effect of firm size on the probability of borrowing from a large bank, conditional on the control variables. The odds ratio on the medium firm dummy is significantly less than 1 and the odds ratio on the large firm dummy is statistically insignificant, which suggests that, all else held equal, small firms and large firms are more likely to borrow from large banks and medium firms are more likely to borrow from small banks.16 These findings are not consistent with the current paradigm, which predicts that the comparative advantage of large banks in hard technologies should be increasing in the size of the firm. Importantly, our findings suggest that large banks may use different lending technologies to serve the different firm sizes, which are not broken out in column (1). To evaluate the economic significance of these results, we note that the odds ratio for medium firm is 0.691. This suggests that the odds of a medium firm borrowing from a large bank are about 69% as high as for a small firm.
Column (2) shows the regression with the dummies for fixed-asset technologies and control variables only. Under the current paradigm, large banks are predicted to have the same comparative advantage in all fixed-asset technologies, yielding an odds ratio of 1 on the lending technology variables. In contrast, we predict an odds ratio less than 1 on the included fixed-asset
15 This monotonically increasing effect may also be accentuated by legal lending limits or problems of
diversification of small banks in lending to large firms, giving an additional advantage to large banks in lending to larger firms that is unrelated to the lending technologies.
16 When the firm size dummies are replaced with loan size dummies, we find similar results. Therefore,
technologies, based on our hypothesis that large banks have the strongest comparative advantage in the hardest technology, which is the excluded category of leasing. The statistically significant odds ratios of less than 1 are consistent with our prediction; therefore, column (2) confirms expectations of a comparative advantage for large banks in leasing relative to all of the other fixed-asset technologies. Given that the size classes are excluded from this specification, the results reflect an average effect of technology type across the firm size classes.
Column (3) shows the regression with both firm size and lending technologies, but without the interactions. The hypotheses are essentially the same and the odds ratios are similar to columns (1) and (2). The slight difference is that the medium firm odds ratio is no longer significant at the 10 percent level.
Column (4) shows the complete specification with firm size, lending technologies, and the interactions. Under the current paradigm, large banks are predicted to have the same comparative advantages in all fixed-asset technologies and these advantages should apply with monotonically increasing magnitude as firm size increases. In column (4), we relax some of the assumptions of the paradigm and allow the magnitude of the large banks’ comparative advantages in each fixed-asset technology to vary in any pattern with firm size.
the effect of non-leasing fixed-asset technologies is found to be strongest for medium and large firms as loans made to medium and large firms using a non-leasing fixed-asset technology are much less likely to be from large banks than leases.
These results clarify the findings in column (2) by showing that the difference between leasing and the other fixed-asset technologies is unique to medium and large firms. The implication is that the soft-information component in the other fixed-asset technologies is important for larger firms, but not for small firms. As noted above, the advantages in a given technology may differ by firm size because the relative importance of the hard and soft information components may differ by the size of the borrowing firm. The results in column (4) suggest that the benefit of assessing the soft-information component related to information other than the appraised value of the fixed asset may only be cost effective for larger firms.
Finally, we turn to the control variables in Panel A of Table 2 for firm profitability and banking market conditions. The odds ratio on ROE is always significantly greater than 1, consistent with the expectation that hard information about high ROE increases the probability of a firm borrowing from a large bank. The odds ratio on large-bank branch market share is greater than 1 and statistically significant, consistent with the convenience argument. Firms are more likely to borrow from a large bank if the branch offices of large banks are more convenient than those of small banks, and large-bank branch market share is a proxy for this relative convenience. The odds ratios on the Herfindahl index and MSA dummy are not significant, indicating that the competitive environment does not appear to be an important factor in determining the use of large versus small banks for fixed-asset lending.
a firm’s loan will be from a large bank using predicted values from the complete specification in column (4) of Panel A, where we assign the control variables to their means in all cases. The top four rows of Panel B show that for small firms, none of the predicted probabilities of the loan being made by a large bank change in a statistically significant fashion as the lending shifts from LEASE to another fixed-asset technology. The second set of rows shows that for medium firms, the predicted probability of a large bank decreases in a statistically and economically significant manner when technology shifts from LEASE to any of the other fixed-asset technologies except residential real estate. For example, the predicted probability of a loan being made by a large bank decreases from 80.4% to 36.6% – a statistically significant decline of 43.8% – as the lending shifts from leasing to commercial real-estate lending. For large firms, the findings are similar to those for medium firms. Thus, the use of other fixed-asset technologies decreases the probability of loan being from a large bank for both medium and large firms.
Next, we turn to Panel A of Table 3, which has the results of testing for the effect of strong relationships among non-fixed-asset lines of credit. As in Table 2, the results in column (1) show a nonmonotonic effect of firm size on the probability of borrowing from a large bank, conditional on the control variables. This similarity in results between columns (1) in Tables 2 and 3 is interesting, because it is the same empirical specification applied to two totally separate samples. This suggests that our finding – a nonmonotonic comparative advantage of large banks in lending to different sized firms – is identical across both fixed-asset loans and lines of credit excluding fixed asset collateral. In both samples, there is little difference in large banks’ comparative advantage in lending to small and large firms, but medium firms are less likely to borrow from large banks.
likelihood of a firm borrowing from a large bank. As in Table 2, column (2) excludes firm size whereas column (3) includes it. Under the current paradigm, small banks are predicted to have a comparative advantage in relationship lending. Although we cannot identify relationship lending directly, we use the strength of the bank-firm relationship as a proxy for the likelihood that the loan was underwritten using information from the relationship. The prediction of the current paradigm implies an odds ratio less than 1 on the relationship strength variable. Here, in Table 3, we measure relationship strength by “Strong Relationship,” which is a dummy variable based on relationship length, breadth, and exclusivity. In both columns, the odds ratio on strong relationship is less than 1 and significant at the 1% level, suggesting a comparative advantage for small banks in relationship lending. Therefore, the results of these two columns are consistent with the current paradigm. In terms of economic significance, the odds ratio for strong relationship is 0.570, which suggests that the odds of a firm with a strong relationship borrowing from a large bank are 57% as high as for a firm without a strong relationship.
relationships has less value for the smallest firms, which might appear to be a counterintuitive result. However, it appears to be consistent with our findings in column (1) that small banks are more likely to lend to medium firms than to small firms. It is also consistent with our results in Table 2 which suggest that the collection of soft information has greater value for larger firms.
The control variables in Panel A of Table 3 show that firm profitability is not a relevant factor in the likelihood of a line of credit without fixed-asset collateral being issued by a large bank, but that the banking market conditions are relevant. Unlike in the fixed-asset regressions, the odds ratios on ROE are not significant, perhaps because hard information like ROE is less important than other types of soft information for non-fixed-asset lines of credit. The odds ratio on large-bank branch market share is significantly greater than 1, again supporting the convenience argument. The odds ratios on the Herfindahl index and MSA dummies are greater than 1 and statistically significant in the non-fixed-asset line of credit sample, whereas they were not significant in the fixed-asset loan sample. This suggests that the competitiveness of the local banking market may be more important for determining the comparative advantage of banks of different sizes when lending is based on soft-information. The odds ratio greater than 1 on the Herfindahl index, the measure of local market banking concentration, suggests that firms in more concentrated bank markets are more likely to have their non-fixed-asset line of credit at a large bank. The odds ratio greater than 1 on the MSA dummy suggests that firms in metropolitan markets are also more likely to have their non-fixed-asset line of credit at a large bank.
probability of borrowing from a large bank is almost double the effect for small firms. Thus, the presence of a strong relationship appears to have an economically significant role in increasing the likelihood of a medium firm having their non-fixed-asset line of credit at a small bank.
Table 4 Panel A also shows the results for the sample of lines of credit without fixed-asset collateral, but here we use the separate components of strong relationship to measure relationship strength. The purpose of Table 4 is to see which elements of a strong relationship – length, breadth, or exclusivity – are most important to the comparative advantage of small banks in relationship lending. Column (1) is identical to column (1) of Table 3 Panel A, because it is the same sample and we only include firm size. Column (2) only includes the individual components of relationship strength and column (3) includes these components along with firm size. As discussed for Table 3, the current paradigm predicts that small banks have a comparative advantage in relationship lending, which implies that the odds ratios on the relationship characteristics in column (2) should be less than 1. The results show that the institution which issued the non-fixed-asset line of credit is more likely to be a small bank when the firm has a longer relationship and a checking account with the institution. This adds support to the prediction of the current paradigm that small banks have a greater comparative advantage in relationship lending relative to the other types of lending in the sample. However, there does not appear to be a significant effect of exclusivity on this comparative advantage.
results in Table 3, our results suggest that the advantage of small banks in relationship lending is primarily among larger firms. Only checking accounts appear to contribute significantly to a comparative advantage of small banks in relationship lending to small firms. The exclusive lender results appear to go the opposite direction as predicted. Large banks have a comparative advantage in lending to large firms when they are the exclusive lender. When looking across the three components of strong relationship, it appears that the comparative advantage of small banks in using strong relationships to lend to large firms is driven primarily by the length of the relationship. This confirms our prior finding that the comparative advantage of small banks in relationship lending is greater for large firms and also shows that the result is primarily driven by length of relationship.
Table 4 Panel B shows the effects of the strong relationship components on the predicted probability of borrowing from a large bank. For large firms, a one standard deviation increase in the log of relationship length leads to a reduction in the probability of borrowing from a large bank of 7.3 percentage points. In contrast, large firms with an exclusive lender are 11.6 percentage points more likely to borrow from a large bank than large firms without an exclusive lender. Among small firms, those firms with a checking account at their lender are 14 percentage points less likely to borrow from a large bank.
6. Conclusion
of distinct comparative advantages for large and small banks across lending technologies and firm sizes. We test the empirical predictions of the current paradigm using data on U.S. small businesses, the markets in which they operate, the banks that lend to them, the contract characteristics of these loans, and other information.
Under the current paradigm, large banks have comparative advantages in using lending technologies based on “hard” quantitative information – such as financial statements – to lend to relatively large, transparent firms. Small banks, by contrast, have the advantage in using relationship lending based on “soft” qualitative information to lend to the smallest, least transparent firms. We break these assumed linkages among bank size, lending technologies, and firm size in the current paradigm by allowing for other possibilities. We allow large banks’ comparative advantages to extend beyond lending to large, transparent firms because hard information is available in forms other than financial statements. For instance, large banks may be able to lend to small firms with limited transparency by using hard information about the firms’ collateral without using significant hard information about the firm themselves. We also allow for small banks’ comparative advantage in relationship lending to differ by firm size.
Our empirical analysis yields three main results which conflict with the predictions of the current paradigm. First, we find that the comparative advantages of large banks in using lending technologies based on hard information differs across hard technologies. Our results suggest that large banks have a clear superiority in leasing relative to other fixed-asset lending technologies. These results conflict with the current paradigm, under which it is implied that large banks have equal comparative advantages in all hard-information technologies.
technologies, the advantage in leasing relative to the other technologies differs by firm size. Specifically, large banks have a relatively strong comparative advantage in leasing to medium and large firms, but this does not exist for the smallest firms. These findings are based on our approach to lending technologies and firm size which allows for the possibility that banks can effectively use some forms of hard information, such as fixed-asset collateral, to lend to relatively small firms.
Finally, our results indicate that small banks have a comparative advantage in relationship lending, but this advantage is strongest for the largest firms. This suggests that the collection of soft information through relationships has less value for the smallest firms, perhaps due to the increased use of credit scoring. Another possibility is that small banks may be more likely to use judgment lending to lend to small firms with which they do not have a strong relationship. Judgment lending may be an important soft-information technology that is neglected by the current paradigm.
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Table 1
Descriptive Statistics
Variable Description Mean Std Dev Mean Std Dev
Bank Size
Large Bank Bank gross total assets (GTA) > $1B 0.497 0.697 0.460
Firm Characteristics
Small Firm Firm total assets (TA) ≤ $100K 0.380 0.207 0.406
Medium Firm Firm total assets (TA) > $100K and ≤ $1M 0.490 0.387 0.487
Large Firm Firm total assets (TA) > $1M 0.495 0.406 0.491
Return on Equity Firm profit / Firm equity 0.370 1.161 0.429 1.187
Fixed-Asset Lending Technologies
Leasing (LEASE) Contract in which the bank owns the asset 0.116 0.320
Commercial Real Estate (CRE) Commercial real estate pledged as collateral 0.214 0.410
Residential Real Estate (RRE) Residential real estate pledged as collateral 0.079 0.270
Motor Vehicle Loan (MVL) Motor vehicles pledged as collateral 0.363 0.481
Equipment Loan (EQ) Equipment pledged as collateral 0.228 0.420
Relationship Strength
Strong Relationship Dummy variable based on length, breadth, and exclusivity* 0.407 0.492
Relationship Characteristics
ln(Relate) Log of the length of relationship 4.202 1.011
Checking Firm has a checking account at the bank 0.877 0.329
Exclusive Bank is firm's exclusive lender 0.423 0.494
Mark et Characteristics
Large Bank Branch Market Share Share of large bank branches in market 0.564 0.267 0.621 0.242
Herfindahl Concentration of bank deposits in market 0.217 0.114 0.200 0.093
MSA Firm in MSA or NECMA 0.677 0.468 0.795 0.404
Number of Observations
Table 1 shows the mean and standard deviation of each variable for a sample of loans in the 1998 Survey of Small Business Finance (SSBF). The samples are separated into fixed-asset loans and lines of credit without fixed-asset collateral. Other data sources are the 1997 Call Reports and the 1997 Summary of Deposits. For the variables in dollar amounts, K, M, and B indicate thousands, millions, and billions, respectively. Return on Equity is winsorized at the 10% level and is missing for one observation in column (1) and two observations in column (2).
1000 811
(1) (2)
Fixed-Asset Loans
Lines of Credit without Fixed-Asset Collateral
0.554
0.175 0.399 0.426
Table 2
Tests of Leasing versus Other Fixed-Asset Lending Technologies (Panel A) Regression Results
(1) (2) (3) (4) Firm Characteristics Medium Firm 0.691 0.790 2.339 [1.741]* [1.078] [1.074] Large Firm 1.045 1.208 4.010 [0.203] [0.826] [1.936]* Return on Equity 1.130 1.134 1.116 [2.018]** [2.045]** [1.775]*
Fixed-Asset Lending Technology
Commercial Real Estate Lending (CRE) 0.238 0.238 [4.468]*** [4.354]*** Residential Real Estate Lending (RRE) 0.375 0.403 [2.557]** [2.301]**
Motor Vehicle Loan (MV) 0.306 0.312
[3.858]*** [3.672]***
Equipment Loan (EQ) 0.246 0.244
[4.405]*** [4.324]***
Interactions
Small Firm * CRE 0.888
[0.158]
Small Firm * RRE 0.962
[0.058]
Small Firm * MV 0.760
[0.474]
Small Firm * EQ 0.565
[0.853]
Medium Firm * CRE 0.141
[3.079]***
Medium Firm * RRE 0.361
[1.471]
Medium Firm * MV 0.288
[2.015]**
Medium Firm * EQ 0.157
[2.879]***
Large Firm * CRE 0.190
[3.167]***
Large Firm * RRE 0.175
[2.141]** Large Firm * MV 0.167 [3.449]*** Large Firm * EQ 0.196 [3.117]*** Mark et Characteristics
Large Bank Branch Market Share 29.805 28.556 27.736 28.267 [10.051]*** [10.021]*** [9.917]*** [9.912]*** Herfindahl 1.963 2.330 2.197 2.320 [0.893] [1.139] [1.054] [1.122] MSA 1.251 1.272 1.229 1.230 [1.206] [1.269] [1.084] [1.084] Pseudo R-Squared 0.147 0.159 0.168 0.175
Table 2
Tests of Leasing versus Other Fixed-Asset Lending Technologies
(Panel B) Tests of Predicted Probabilities of Large Bank by Firm Size and Lending Technology
(1) (2) (3)
Leasing Other Fixed-Asset Other Fixed-Asset − Leasing Lending Technologies Lending Technologies
Small Firm 0.637 CRE 0.609 -0.028
[0.158] 0.637 RRE 0.628 -0.009 [0.058] 0.637 MV 0.572 -0.065 [0.474] 0.637 EQ 0.498 -0.139 [0.853]
Medium Firm 0.804 CRE 0.366 -0.438
[3.079]*** 0.804 RRE 0.598 -0.206 [1.471] 0.804 MV 0.542 -0.262 [2.015]** 0.804 EQ 0.392 -0.412 [2.879]***
Large Firm 0.876 CRE 0.572 -0.304
