1 Journal of Banking & Finance 23 (1999) 427±458 How important are small banks to small business lending? New evidence from a survey of small rms Jith Jayaratne a, John Wolken b, * a The Tilden Group, 5335 College Avenue, Oakland, CA 94618, USA b Board of Governors of the Federal Reserve System, Financial Structure Section, Division of Research and Statistics, Washington, DC 20551, USA Abstract Typically, small banks lend a larger proportion of their assets to small businesses than do large banks. The recent wave of bank mergers has thinned the ranks of small banks, raising the concern that small rms may nd it di cult to access bank credit. However, bank consolidation will reduce small business credit only if small banks enjoy an advantage in lending to small businesses. We test the existence of a small bank cost advantage in small business lending by conducting the following simple test: If such advantages exist, then we should observe small businesses in areas with few small banks to have less bank credit. Using data on small business borrowers from the 1993 National Survey of Small Business Finance, we nd that the probability of a small rm having a line of credit from a bank does not decrease in the long run when there are fewer small banks in the area, although short-run disruptions may occur. Nor do we nd that rms in areas with few small banks are any more likely to repay trade credit late, suggesting that such rms are no more credit constrained than rms in areas with many small banks. Ó 1999 Elsevier Science B.V. All rights reserved. JEL classi cation: G21; G32 Keywords: Small business lending; Bank mergers * Corresponding author. Tel.: ; fax: ; /99/$ ± see front matter Ó 1999 Elsevier Science B.V. All rights reserved. PII: S ( 9 8 )
2 428 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427± Introduction The recent wave of bank mergers ± and the resulting decrease in the presence of small banks ± has raised the concern that bank consolidation may reduce the availability of credit to small businesses. 1 This concern is justi ed, however, only if small banks enjoy a cost advantage over large banks in lending to small businesses. Cost advantages for small business loans may arise in connection with loan origination and monitoring costs, and consequently smaller banks may be more likely to lend to small rms than large banks. In this paper, we test for the ``Small Bank Advantage'' hypothesis employing data on the use of credit obtained from small business owners. Until now, studies of the e ects of bank consolidation on small business lending have relied exclusively on loan data from banks, producing mixed results. In this study, we use data from the 1993 National Survey of Small Business Finances. These data are obtained from small businesses ± the borrowers. Focusing on borrower data has several advantages over data obtained from banks. In particular, the data set contains information on the rm and owner's credit quality and other characteristics as well as a complete inventory of nancial services used by that rm. If a ``Small Bank Advantage'' exists, then we should observe small businesses in areas with few small banks to have less bank credit. We test this implication in two ways. First, we examine the e ect of small bank presence (measured, for example, as the fraction of deposits in a Metropolitan Statistical Area (MSA) controlled by banks with assets under $300 million) on the probability of a small business having a line of credit (a form of credit with few non-bank providers). Second, we examine the e ect of small bank presence on whether a small business repaid trade credit after the due date (an indicator of credit constraints faced by rms since trade credit is potentially an expensive source of credit). We also test the Small Bank Advantage hypothesis by testing a second implication of that conjecture: If small banks enjoy a cost advantage in originating loans that require signi cant monitoring (such as small business loans), and if marginally creditworthy small rms require especially intensive scrutiny, then we should observe marginal rms borrowing from small banks more often than non-marginal small rms. The results generally do not support the ``Small Bank Advantage'' hypothesis. We nd that the probability of a small rm having a line of credit from a bank does not decrease in the long run when there are fewer small banks in the area, although short-run disruptions may occur. Nor do we nd that 1 Texas legislators cited this concern in 1995 when they voted to opt out of the provisions of the 1994 Riegle±Neal Act ± which allowed full interstate banking ± thereby limiting the bank consolidation that could have resulted from more out-of-state banks entering Texas.
3 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427± rms in areas with few small banks are any more likely to repay trade credit late, suggesting that such rms are no more credit constrained than rms in areas with many small banks. Even when we examine marginally creditworthy rms separately, we nd that small bank presence a ects neither their use of bank credit lines nor the probability of late repayment of trade credit. Finally, we nd that marginal rms ± small rms with poor or short credit histories ± are as likely to borrow from small banks as are non-marginal rms. The unimportance of small banks appears to be the result of large banksõ willingness to lend to small borrowers, suggesting that bank consolidation will have little long-run e ect on credit availability to these rms. 2. Background Large banks make proportionately fewer small business loans than small banks. This is the most common statistical evidence cited to support the claim that bank consolidation will reduce credit availability to small businesses. Banks with less than $100 million in assets have nearly 9 percent of their assets in small business loans; banks with more than $5 billion in assets have only 3 percent of their assets in loans to small rms (Table 1). Similar results are reported by Keeton (1995) who nds that banks a liated with out-of-state bank holding companies (BHCs) originate fewer small business loans than independent banks. 2 Based on these di erences in lending patterns between banks of di erent size and complexity, several observers have inferred that consolidation that reduces the number of independent small banks will also reduce small business lending. For example, Berger et al. (1995) predict that bank consolidation resulting from full nationwide banking would reduce small business lending by 32 percent in just ve years. This inference is plausible ± and bank consolidation will reduce small business lending ± if small banks are able to lend to small businesses at a lower cost than large banks (and this explains in part why small banks lend proportionately more to small businesses, as seen in Table 1). Why might this be true? Perhaps small business loans are mostly ``relationship loans'' that require substantial individual monitoring and nurturing by a bankõs loan o cers. Consequently, such lending may require individual loan o cers to have considerable autonomy to set underwriting standards and discretion to monitor and evaluate. But autonomy to loan o cers might be abused, and banks may have to pocket the cost of poor loan decisions by its loan o cers. Small banks 2 However, Whalen (1995) ± using a di erent sample of banks from Keeton (1995) ± nds that banks owned by out-of-state holding companies did not lend any less to small rms than other banks.
4 430 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427±458 Table 1 Portfolio shares of small C&I loans, by bank size Banks by asset size Small C&I loans/total C&I loans (percent) Small C&I loans/total assets (percent) Less than $100 million $100 million±$300 million $300 million±$1 billion $1 billion±$5 billion Greater than $5 billion Source: P. Strahan and J. Weston, 1996, Small Business Lending and Bank Consolidation: Is There Cause for Concern?, Federal Reserve Bank of New York, Current Issues in Economics and Finance 2. Based on the June 1995 Reports of Condition and Income. may contain such costs because their small size allows upper management to monitor their loan departments fairly easily. However, large banks, given their size and complexity, may nd it more costly to monitor their agents. If bigger and more complex banking organizations try to contain these costs by setting rigid underwriting standards, then large banks will nd it more di cult to originate and maintain the relationship loans required by small businesses. We shall call this conjectured disadvantage of large banks the ``Small Bank Advantage'' hypothesis. 3 Existing tests of this conjecture have used loan data from banksõ balance sheets and have produced mixed results. One test of the Small Bank Advantage hypothesis is to see what happens to small business lending by a bank after it is acquired by another bank or by a bank holding company. If larger banks su er from higher costs of making relationship loans (as the Small Bank Advantage hypothesis claims), then the new bank formed by the merger should originate fewer small business loans after the merger. Consistent with this prediction, Berger et al. (1998) nd that after a merger, the new bank originates fewer small business loans than the independent banks prior to the merger. Similarly, Peek and Rosengren (1997) conclude that mergers reduced small business lending except when small banks bought other small banks ± then small business lending actually increased. Keeton (1997) nds that small business lending by small urban banks decreased when they were acquired by out-of- 3 This conjectured cost disadvantage of large banks when making small business loans (and it is only a conjecture) is still not su cient to conclude that bank consolidation will reduce the supply of credit to small businesses. Even if large banks are at a disadvantage in this loan market, small banks (existing or new entrants) may pick up the excess demand and lend to small businesses denied credit because of bank consolidation. If the supply of loans by small banks is elastic, then consolidation will have little e ect on the supply of credit to small rms. However, small banks may nd it hard to raise the extra funds needed to nance additional lending or entry barriers (regulatory or otherwise) may limit the number of small banks that may enter a market to lend to small businesses cut out by large banks.
5 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427± state holding companies or when they were bought by in-state organizations that held these small banks as junior a liates; other types of acquisitions did not reduce lending to small rms. In contrast, Strahan and Weston (1998) conclude that bank mergers actually increased such lending ± at least for those mergers where both participants were small (other types of mergers appear to have no e ect). 3. Data and empirical model Data on small business nance are scarce. One of the few available sources is the National Survey of Small Business Finance (NSSBF), a nationally representative sample of non- nancial, non-farm small businesses sponsored by the Board of Governors of the Federal Reserve System and the US Small Business Administration. We use data from the December 1993 Survey, which was conducted in 1994/95 and which records nancial information for The sample was strati ed by census region, urban/rural location and employment size (500-employee rms being the largest rms sampled), and minority partition. 4 The sample for this paper includes 4630 rms, of which 3697 rms were located in MSAs and 933 were located in non-msa rural counties. The NSSBF is a rich source of information on the current nancial condition of small businesses as well as on the nancial history of these rms. Firms report their obligations by type of nancial institution as well as non-institutional funding sources. In this paper, we rely primarily on those parts of the survey that provide information on the lines of credit and on trade credit from suppliers. Using these data, we test two implications of the Small Bank Advantage conjecture: First, small rms in areas with few small banks should be more credit constrained, and second, marginally creditworthy small rms should be more likely to obtain credit from small banks than non-marginal rms. 5 To test these two implications, we conduct a total of three tests. The rst examines the use of bank lines of credit. 6 The second examines the repayment 4 For additional information on the survey methodology, see National Survey of Small Business Finances, ``Methodology Report'', mimeo., March (Available through the Federal Reserve BoardÕs public web site.) 5 Note that we do not argue that the Small Bank Advantage hypothesis implies that marginal rms rely on small banks more often than on large banks (i.e., more than 50 percent of marginal rms borrow from small banks). This may not follow from the Small Bank Advantage conjecture simply because marginal rms may not have enough small banks that they can turn to. If most bank funds in a market are controlled by large banks, most marginal rms may resort to large banks even if small banks have a cost advantage in lending to such rms. 6 Commercial banks and thrifts are included in our working de nition of ``bank'' in this paper.
6 432 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427±458 of trade credit after the due date, a proxy for excess demand for institutional credit. Next, we re ne these two tests and examine whether small bank presence has a stronger e ect on marginal (smaller, younger, poorer credit history) rms. And the third test examines whether marginal rms are more likely than non-marginal rms to obtain credit from small banks. Before describing these three tests in more detail, we discuss the measure of small bank presence. De ning small bank presence. In estimating the empirical model above, the measurement of small bank presence (``SMALL'') is debatable. The de nition of what constitutes a ``small bank,'' and the relative size of small banks vis-a-vis the market in which they operate are both subject to interpretation. We test the robustness of our results by using four de nitions of SMALL. In all four measures of SMALL, we assume that the MSA is the relevant geographic area for urban banking markets and the non-msa county for rural markets (an assumption routinely made by antitrust regulators). In our rst de nition, SMALL was measured as the fraction of banking market deposits held by banks that had fewer than $300 million in assets as of June To construct this measure, we rst identi ed all banks (and thrifts) in a banking market that had assets less than $300 million ± these are our ``small banks''. Next, we calculated the amount of deposits held at branches of such small banks in that banking market and divided that number by the total deposits held at all banks in that market to generate ``SMALL''. 7 We used the $300 million cuto because banks with more than $300 million in assets appear to lend to small businesses sharply less than smaller banks (Table 1). Our second de nition of SMALL allows for the possibility that even ``small'' banks a liated with bank holding companies may behave like large banks (as noted by Keeton, 1995), and we de ne ``small banks'' as only those banks that have less than $300 million in assets and that are una liated with a large holding company (de ned as holding companies with assets greater than $300 million as of June 1993). 8 The remainder of the construction of SMALL is as before. Our third de nition of SMALL relaxes an implicit assumption in our previous de nitions of this variable, i.e., that a bankõs deposits in an MSA proxies for the resources available to that bank to originate small business loans in that MSA (although this assumption is routinely made by bank regulators and the Department of Justice in their antitrust review of bank mergers). The third 7 Bank asset data are from the Quarterly Reports of Condition led by all banks, and branch deposit data are from the Summary of Deposits database created by the Federal Deposit Insurance Corporation. 8 Bank ownership information are from the Federal Reserve BoardÕs National Information Center database.
7 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427± measure of SMALL is simply the fraction of banks in a banking market that have assets less than $300 million. Our fourth de nition of SMALL is the fraction of banks in a banking market that have assets less than $300 million and that are una liated with a large BHC. 9 Test 1. Lines of credit and small bank presence. Our rst test measures access to bank credit in terms of bank lines of credit. We focus here on lines of credit (and not on other types of bank lending) partly because few non-banks provide a similar service. Only 1.5 percent of small businesses in the NSSBF obtain a line of credit from a source other than a bank, while 23.5 percent have credit lines from banks. In contrast, other types of bank lending ± such as mortgage loans, equipment loans and vehicle loans ± are also provided by non-banks, so that even if consolidation reduces mortgage lending by banks, borrowers may go to mortgage nance companies and others for such credit. Another reason to focus on credit lines is that rms in the NSSBF used credit lines more often than any other type of credit. Twenty- ve percent of rms in the sample had credit lines, while only 6 percent had mortgage loans and 14 percent had equipment loans. 10 We estimate the following Logit model using rm-level data from the NSSBF to see if small borrowers nd it more di cult to access bank lines of credit in areas with few small banks: Probability Firm j has a bank line of credit ˆ f SMALL; change in SMALL; firm j 0 s creditworthiness; demand for credit : 1 Variable de nitions are summarized in Appendix A. SMALL is a measure of small bank presence in the MSA or non-msa county where rm j is located. We use several measures of small bank presence discussed earlier including ± for example ± the fraction of total deposits in a MSA held by banks with assets under $300 million. CHANGE IN SMALL is measured as the change in SMALL over the ve years prior to 1993 (divided by the average value of SMALL over the period). As indicators of a rmõs creditworthiness, we use 9 As a further test of the robustness of our results, we also de ned SMALL using a $100 million asset cuto, and we found similar results as those reported here using the $300 million asset cuto. 10 These fractions do not agree with those in Table 2 because these are based on weighted data whereas Table 2 relies on unweighted data. The weights are used to adjust for the fact that the NSSBF over-sampled certain categories of rms (especially large rms and minority-owned rms). The weighting process attaches a lower weight to observations of rms that belong to over-sampled categories, and thereby recreates a sample that is representative of the population of small rms in the US (The regressions in this paper approximate the same procedure by including controls for sampling strata such as minority ownership, rm size and census region [although several of these are not included in the tables with regression results]).
8 434 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427±458 variables for personal delinquency, business delinquency, bankruptcy, and a rmõs age. 11 A rmõs demand for credit is proxied by its pro tability, rm size, by the growth rate of the stateõs economy in 1993 and the growth rate of total bank lending in that state in We also included the Her ndahl±hirschmann Index (HHI) of deposit concentration in a banking market in the above model. This controls for competitive conditions in the rmõs banking market. 13 We interpret SMALL to capture the long-run e ects of small bank presence on credit supply. We include the CHANGE IN SMALL to identify short-run e ects of a diminished small bank presence in an area. Changes in small bank presence may produce only short-run disruptions in credit supply when ± for example ± loan o cers are laid o or underwriting criteria are revised after bank mergers and current borrowers switch to alternative lenders; this adjustment process may take some time to be completed. 14 A positive coe cient on SMALL (or CHANGE IN SMALL) would con- rm the conjecture that small businesses in areas with few small banks nd it relatively di cult to access bank lines of credit. Bank consolidation is likely to reduce small business credit. A zero coe cient on SMALL suggests that a 11 The NSSBF records whether a rmõs owner has been delinquent in his/her personal or business obligations within the last three years or declared bankruptcy within the seven years prior to the end of We include rm age as a control variable because moral hazard problems such as risk shifting are likely to be greater for younger rms ± as such, younger rms will nd it more costly to borrow (Diamond, 1989). 12 Firms in the NSSBF report the city and state where they are located. Although this information is not available in the public access version of the NSSBF, location information was made available to us by the Board of Governors. 13 The standard argument for including the HHI is that the supply of credit is likely to be constrained in less competitive markets. Alternatively, Petersen and Rajan (1995b) argue that small businesses in more concentrated banking markets nd it easier to get bank credit because market power makes loan contracts easier to enforce. For a more complete explanation of this argument, see Petersen and Rajan (1995b) who nd evidence consistent with this result using the 1987 NSSBF. For our purposes here, omitting the HHI could produce biased estimates of the e ects of SMALL because the HHI and SMALL are inversely correlated. 14 Another reason to include CHANGE IN SMALL is more complicated. If the Small Bank Advantage hypothesis is correct, then small banks may follow demand and locate in areas with many small businesses (and small rms may follow supply and locate in areas with many small banks). In frictionless markets for small business lending, this process will equalize loan rates across geographic markets. Firms in areas with many small banks will not have lower funding costs than areas with few small banks. The coe cient of SMALL will be zero ± even though the Small Bank Advantage hypothesis is true. But this argument assumes that banks and rms can relocate quickly and at low cost. If such relocation costs are signi cant (or if regulatory and other entry barriers produce an inelastic supply of banks), then the adjustment process may slow and markets need not clear instantaneously, producing positive coe cients on CHANGE IN SMALL and (perhaps) SMALL.
9 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427± decline in the presence of small banks does not a ect the supply of bank lines of credit to small rms. 15 Test 2. Repaying trade credit after the due date and small bank presence. Lines of credit are only one type of bank credit. As a somewhat more general indicator of access to institutional nance, we look at the repayment of trade credit after the due date ± the focus of our second test. This test starts from the premise that trade credit is an expensive source of liquidity, used as a last resort by most rms. Trade credit is typically provided by suppliers as an interest-free loan up to a due date (often 30 days). Payment by that date will incur no interest. But payment after the due date incurs an extremely high interest rate and may constitute a very expensive loan. 16 Firms use trade credit to facilitate obtaining supplies and paying for these supplies at di erent points in time. However, rms may as a last resort ``borrow'' from their trade suppliers (i.e., pay after the due date) when they have trouble accessing institutional credit. 17 As such, repaying trade credit after the due date is a summary indicator of access to institutional credit. If small businesses in areas with few small banks are more likely to repay after the due date, we conclude that such borrowers nd it more di cult to access bank credit of all types. 18 We implement this second test by estimating the following model: Probability Firm j repays its suppliers after the due date ˆ f SMALL; CHANGE IN SMALL; firm j 0 s creditworthiness; demand for credit; late payment penalty : The dependent variable takes on multiple values since the NSSBF asks rms whether they paid (1) none, (2) less than half, (3) about half, (4) more than half, or (5) almost all or all of their trade credit after the due date. Consequently, we estimate Eq. (2) as an Ordered Logit Other possible reasons for why SMALL may have a non-signi cant coe cient are discussed in Section Although trade credit is generally granted for a period (e.g., 30 days) without an explicit interest charge, most trade credit accounts do o er cash discounts of one or two percent if the bill is paid within a short period, generally within 10 days. Hence, there is also an opportunity cost associated with the ``discount'' period. 17 This assumption is supported by Petersen and Rajan (1995a) who nd that rms use more trade credit when they do not have a bank line of credit. Petersen and Rajan (1994) nd that rms repay trade credit late more often when they have a relatively short relationship with their primary bank lender. Also supporting this assumption is the nding by Calomiris et al. (1995) that high credit-quality rms issue commercial paper to nance accounts receivables during downturns in the business cycle (when bank credit is tight) ± that is, they act as nancial intermediaries who lend via trade credit to rms of lesser credit quality when credit is tight. 18 Not all rms repaying trade credit after the due date face an explicit penalty. We include a variable to control for the cost of late payments.
10 436 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427±458 A negative coe cient on SMALL implies that small rms in areas with many small banks are less likely to pay their suppliers after the due date (i.e., they are less likely to use the high-interest loan component of trade credit). We interpret a negative coe cient on SMALL to mean that small rms in areas with many small banks demand less trade credit, supporting the conjecture that small businesses in such areas nd it easier to access bank credit. As with Eq. (1), a zero coe cient on SMALL suggests that a decline in the presence of small banks does not a ect the supply of bank credit to small rms. Similarly, we interpret a negative coe cient on CHANGE IN SMALL to re ect increased short-run demand for trade credit in areas with recent decreases in small banks, re ecting disruptions in credit supply. In the rst test we interpret the coe cients of SMALL and CHANGE IN SMALL to be ``supply'' parameters and in the second test, as ``demand'' parameters. So, for example, a negative coe cient of SMALL is taken to mean that the demand for trade credit decreases when there are more small banks in the area (because such banks are more willing to lend to small rms). 19 This is despite the fact that both empirical equations above are clearly reduced forms, and not structural demand or supply equations. We believe that such a structural interpretation of the coe cients of SMALL and CHANGE IN SMALL is reasonable here for the following reason: while the null hypothesis explains why SMALL (and CHANGE IN SMALL) a ects the supply of bank lines of credit (in Eq. (1)) and the demand for trade credit (in Eq. (2)), there is no obvious reason for why SMALL (and CHANGE IN SMALL) would a ect demand for bank credit lines in Eq. (1) or the supply of trade credit in Eq. (2). For all other variables in the two empirical models above, no such structural interpretation is possible since they probably in uence both demand for and supply of credit. Re nements of Tests 1 and 2: Marginal rms and small bank presence. Even if the average small rm is not credit constrained by a decrease in the number of small banks, marginally creditworthy small rms may nd it harder to access credit when there are fewer small banks. Perhaps large banks make cookiecutter loans because of in exible underwriting standards, making it di cult for rms without a long credit history or with a poor credit history to borrow because such borrowers require exible pricing and underwriting standards Consistent with this, we do not use accounts payable data as an indicator of trade credit use because most accounts payable on rmsõ balance sheets are driven by the supply of trade credit, not demand for trade credit (Petersen and Rajan, 1995a). The fraction of trade credit paid back after the due date (the high interest loan part of trade credit) is as close as we can get to estimating ``demand'' for trade credit (although even this captures supply to some extent in that a rm can use such credit conditional on it being o ered trade credit). 20 Cole et al. (1997) show that large banks rely more on nancial ratios based on balance sheets and income statements when making small business loans ± more so than small banks.
11 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427± Consequently, marginal rms may be more adversely a ected by changes in small bank presence than non-marginal rms. We test for this possibility by estimating separate coe cients on SMALL and CHANGE IN SMALL for the marginal rms (separate from non-marginal rms). To implement this test, we include dummy variables for young rms (5 or fewer years), small rms (5 or fewer employees), and rms with poor credit histories, and interact these terms with SMALL and CHANGE IN SMALL. In this test involving marginal rms, we assume that young rms, rms with poor credit histories and small rms are ``marginal borrowers'', requiring especially close monitoring and screening. Admittedly all three qualities of age, credit history and size are observable equally to large and small banks. However, we interpret these rm qualities to be proxies for information problems. For example, Diamond (1989) shows that older rms are less likely to choose relatively risky investments than young rms. This reduces the lenderõs cost of monitoring. Moreover, an older rm has a longer performance history, providing a potential lender with more initial information about the rmõs prospects, thereby reducing the need for ex post monitoring. Test 3. The propensity of marginal rms to use small banks. Our third test examines whether marginal rms ( rms that are small, young and have poor credit histories) have a greater propensity than non-marginal rms to use small banks for lines of credit. We use the following model: Probability Firm j obtains its line of credit from a large bank ˆ f firm j 0 s age; firm j 0 s credit history; firm j 0 s size; HHI; demand for credit : 3 4. The e ects of small bank presence on credit availability: Results In this section, we report results from estimating the models discussed in Section The NSSBF sample is described in Table 2. Because the sample was strati ed by size and minority partition, all models estimated include 21 Although we report here results from estimating the regressions using all rms, both rural and urban, we also estimated the models separately for urban and rural rms to nd similar results. We separated urban and rural rms because urban and rural credit markets are likely to have di erent characteristics. For example, urban areas may have more non-bank sources of credit, and consequently, the e ects of SMALL on credit access may be quite di erent in urban and rural areas. Another reason for splitting urban rms from rural rms is that our de nition of SMALL is di erent for urban areas: we use MSAs to de ne the geographic area for banking markets and individual counties to de ne rural banking markets.
12 438 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427±458 Table 2 The 1993 NSSBF survey: Selected descriptive statistics (all rms) Mean Median Standard deviation Mean change 1988±93 Market characteristics SMALL De nition 1: Proportion of deposits held by banks with less than $300 million in assets De nition 2: Proportion of deposits held by banks with assets less than $300 million and if part of holding company, holding company assets are less than $300 million De nition 3: Proportion of banks with less than $300 million in assets De nition 4: Proportion of banks with assets less than $300 million and if part of holding company, holding company assets are less than $300 million Her ndahl Index of deposit concentration Firm characteristics Number of rms 4637 No. of employees in rm Age of rm Return on assets Percentage of rms responding ``yes'' Does the rm have a line of credit from a 33 bank? Does the rm use trade credit? 68 If using trade credit, did the rm repay after 58 the due date? Has the owner declared bankruptcy over 3 the past seven years? Has the owner been delinquent in repaying 13 personal debt over past three years? Has the owner been delinquent in repaying 20 business debt over past three years?
13 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427± controls for rm size, census region of the rm, and the sampling partition. 22 Test 1. Lines of credit and small bank presence. Estimates for Eq. (1) ± which models the odds of having a bank line of credit ± are reported in Table 3. Thirty three percent of rms report having a bank line of credit. The key result in Table 3 is that SMALL has no signi cant e ect on the odds of a rm having a bank line of credit, while CHANGE IN SMALL has a signi cant positive e ect in three of four regressions (columns (1)±(3). The insigni cance of SMALL suggests that, in the long run, small businesses in areas with few small banks (or in areas where small banks have few resources) are no less likely to have a bank credit line than small rms in areas with many small banks. This result is robust to di erent measures of SMALL. In fact, two of the four de nitions of SMALL have the ``wrong sign'' (negative, but insigni cantly di erent from zero) on their coe cients, suggesting that a decrease in SMALL is associated with an increase in the odds of having a bank line of credit (columns (1) and (2)). 23 However, the positive e ect of CHANGE IN SMALL suggests that recent changes in small bank presence a ects the supply of bank credit in the short run. Nevertheless, that short run e ect is small ± a 12 percentage point increase in small bank presence (as measured by the fraction of deposits held by banks with less than $300 million in assets ± twice the mean increase observed over the 1988±1993 period) increases the probability of having a bank line of credit by only 0.6 percentage points. Most other variables have predictable e ects on the dependent variable. If a rmõs owner has a awed credit history, then that rm is less likely to have a credit line (but, puzzlingly, business delinquency actually increases the likelihood of having a credit line). More pro table businesses are less likely to have lines of credit, probably because internal funds (such as retained pro ts) are a cheaper source of funds than external funds (such as bank debt) because of capital market frictions. As such, rms resort to internal funds rst when looking for investment funds (Myers and Majluf, 1984). This ``demand'' e ect 22 Throughout, we present only unweighted regression results. The NSSBF sample does not accurately reproduce the population of small rms in the US because minority-owned rms and larger rms were over sampled in the survey. To account for the e ects of sample design, we included right-hand side variables for region, industry, minority partition and size (sales and employment). We also estimated the regression models weighted, which produced similar results. 23 Perhaps small rms in areas with an especially thin presence of small banks may nd it di cult to access bank credit even if rms in areas with a moderate presence face the same supply of credit as rms in cities with many small banks. A linear SMALL term need not detect this possibility. Allowing for this, we also estimated the model in Table 3 with SMALL and SMALL squared. Neither the linear nor the quadratic SMALL term is signi cant. Similarly, including a second order term for SMALL did not change the results reported in Table 4.
14 440 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427±458 Table 3 E ects of small bank presence on the probablility of having a bank line of credit (logit estimates; all rms) Independent variable Measure of small bank presence Market structure Small bank presence (SMALL) Change in SMALL, 1988±93 Her ndahl Index of deposit concentration Metropolitan statistical area (MSA) SMALL ˆ Fraction deposits held by banks with assets less than $300 million SMALL ˆ Fraction deposits held by banks with assets less than $300 million and if part of holding company, holding company assets less than $300 million SMALL ˆ Fraction of banks that have assets less than $300 million SMALL ˆ Fraction of banks with assets less than $300 million and if part of holding company, holding company assets less than $300 million (1) (2) (3) (4) ( 0.48) ( 0.92) (0.72) (0.19) (2.47) (1.96) (2.15) (1.3) (0.11) (0.12) (0.18) (0.06) ( 1.16) ( 1.6) ( 1.53) ( 1.51) Creditworthiness Bankruptcy ( 3.95) ( 3.97) ( 3.95) ( 3.99) Personal delinquency ( 3.64) ( 3.65) ( 3.63) ( 3.65) Business delinquency (4.14) (4.15) (4.19) (4.18) Firm age (0.28) (0.28) (0.27) (0.27) Firm age squared (0.17) (0.17) (0.20) (0.18)
15 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427± Credit demand Number of employees (13.3) (13.3) (13.3) (13.3) Number of employees squared ( 11.5) ( 11.5) ( 11.5) ( 11.5) Pro tability (return on assets) Growth rate of the state's economy Growth rate of bank lending statewide ( 2.81) ( 2.85) ( 2.81) ( 2.84) ( 0.45) ( 0.85) ( 0.46) ( 0.71) (1.77) (1.79) (1.70) (1.83) Sample size Pseudo R Notes: 1. Bankruptcy, personal delinquency and business delinquency are indicator variables that equal 1 if the owner of the rm has, respectively, declared bankruptcy in the last seven years, been late in paying personal debts or been late in paying business debt over the three years prior to The ``Change in Small, 1988±93'' was calculated by dividing the change in SMALL over the 1988±93 period by the average of SMALL over the 1988±1993 period. 3. Z-statistics are given within parentheses. 4. Although not shown, controls for sales and sampling partitions (census region of rm and minority partitions) were also included in the regression. * Indicates signi cance at a 5% level. ** Indicates signi cance at a 1% level.
16 442 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427±458 Table 4 E ects of small bank presence on the amount of trade credit repaid after the due date (ordered logit estimates; all rms that used trade credit) Independent variable Measure of small bank presence Market structure Small bank presence (SMALL) Change in SMALL, 1988±93 Her ndahl Index of deposit concentration Metropolitan statistical area (MSA) SMALL ˆ Fraction deposits held by banks with assets less than $300 million SMALL ˆ Fraction deposits held by banks with assets less than $300 million and if part of holding company, holding company assets less than $300 million SMALL ˆ Fraction of banks with assets less than $300 million SMALL ˆ Fraction of banks with assets less than $300 million and if part of holding company, holding company assets less than $300 million (1) (2) (3) (4) ( 1.02) ( 0.89) (0.25) (0.4) ( 1.17) ( 0.39) ( 0.81) (0.01) ( 0.14) ( 0.10) ( 0.06) (0.02) (0.76) (1.01) (1.75) (1.71) Creditworthiness Bankruptcy (1.61) (1.62) (1.61) (1.6) Personal delinquency (6.64) (6.64) (6.60) (6.63) Business delinquency (20.0) (20.0) (20.0) (19.9) Firm age ( 0.67) ( 0.65) ( 0.68) ( 0.68) Firm age squared 6: : ( 0.12) ( 0.13) ( 0.14) ( 0.13)
17 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427± Credit demand No penalty for paying late on trade credit (4.68) (4.7) (4.69) (4.69) Number. of employees (2.16) (2.18) (2.18) (2.16) Number of employees squared Pro tability (return on assets) Growth rate of the stateõs economy Growth rate of bank lending statewide ( 0.68) ( 0.72) ( 0.71) ( 0.69) ( 2.08) ( 2.06) ( 2.08) ( 2.05) ( 0.74) ( 0.55) ( 0.57) ( 0.56) (0.06) ( 0.07) ( 0.04) ( 0.08) Sample size Pseudo R Notes: 1. Only those rms that reported using trade credit are included. 2. Bankruptcy, personal delinquency and business delinquency are indicator variables that equal 1 if the owner of the rm has, respectively, declared bankruptcy in the last seven years, been late in paying personal debts or been late in paying business debt over the three years prior to The ``Change in Small, 1988±93'' was calculated by dividing the change in SMALL over the 1988±93 period by the average of SMALL over the 1988±1993 period. 4. Z-statistics are given within parentheses. 5. Although not shown, controls for sales and sampling partitions (census region of rm and minority partitions) were also included in the regression. ** Indicates signi cance at a 1% level. * Indicates signi cance at a 5% level.
18 444 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427±458 appears to dominate the opposite ``supply'' e ect produced by the fact that banks are likely to lend to more pro table rms, which are better credit risks. Finally, bigger rms are more likely to have lines of credit, probably because bigger rms need the liquidity provided by lines of credit. Interestingly, rm age has no e ect. Test 2. Repaying trade credit after the due date and small bank presence. Our second test of the e ects of small bank presence on credit access focuses on trade credit. Of the 4637 rms in the sample, 68 percent reported using trade credit in Of the rms using trade credit, 58 percent repaid their suppliers after the due date. Table 4 reports ordered Logit estimates for the model of determinants of late repayment of trade credit by small businesses. This model uses only those 3126 rms that reported using trade credit during Again, the key result is that neither SMALL nor CHANGE IN SMALL has a statistically signi cant impact, and ± in all but two cases ± the coe cient is positive, the opposite of that predicted by the null hypothesis. That is, small rms in areas with few small banks (or in areas where small banks have fewer resources) do not pay back their suppliers late any more than small rms in areas with many small banks. SMALL does not appear to a ect the demand for trade credit, implying that SMALL does not a ect access to bank credit, even in the short run. This result is robust against changes in the de nition of SMALL. 25 Other variables that are statistically signi cant have predictable e ects on late repayment. Poor credit histories by the owners is associated with more late repayments, while increased cash ows reduce late repayments. Interestingly, larger rms pay late more frequently; at the very least this suggests that small rms are not necessarily more liquidity constrained than larger rms once we control for pro tability and ownersõ credit history. Firm age has no e ect. Finally, we added an indicator variable that equals one if a rm reported that it did not have to pay a ne if it repaid trade credit late. This variable has a predictable e ect: those rms who do not face such a penalty are more likely to repay after the due date. Re nements of Tests 1 and 2: Marginal rms and small bank presence. The results thus far suggest that small rms in areas with a thin presence of small banks do not have greater di culty accessing bank credit. But this result is obtained only after we control for the creditworthiness of small rms. Perhaps 24 Because we are estimating the odds of repaying trade credit late conditional on using trade credit, our inferences here are limited to only those rms that actually use trade credit. 25 SMALL and HHI are inversely correlated, raising the possibility that the non-signi cance of SMALL in both Tables 3 and 4 is due to the collinearity between these two variables (despite the large sample size). However, dropping the HHI did not change the results.
19 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427± marginally creditworthy rms, those rms that require substantial monitoring by their creditors, may face greater di culty accessing bank credit when there are fewer small banks in the area because larger banks have more di culty in customizing loans to the needs of individual borrowers (although this may well be the e cient outcome if such rms are ine cient). Our results so far do not rule out this possibility because the average rm in our sample is probably not a marginal rm (only 3 percent of rms in our sample have owners who have declared bankruptcy, for example). To test whether marginal borrowers have greater di culty accessing bank credit in areas with few small banks, we begin by de ning marginal rms as those which satisfy one or more of the following conditions: (1) their owners have awed credit histories; (2) are ve years or younger; (3) ve or fewer employees. 26 We create three indicator variables to ag marginal rms as per these three criteria: POOR CREDIT equals one only if a rmõs owner has a awed credit history, YOUNG FIRM equals one only if the rm is ve years or younger and SMALL FIRM equals one only if the rm has ve or fewer employees. In Tables 5 and 6, we essentially replicate Tables 3 and 4 ± but now we interact the three indicator variables for marginal rms with SMALL and CHANGE IN SMALL. 27 The results in Table 5 are from estimating a model that is identical to that in Table 3 (with the probability of having a bank line of credit as the dependent variable), except that rm age and employees has been converted into discrete variables as described above (and the second order terms of rm age and employees dropped). Moreover, the three indicator variables for poor credit history used in Table 3 has been collapsed into one indicator variable in Table 5. Only the small bank presence variables and their interactions with marginal rm indicators are shown to save space. If small bank presence a ects credit access especially for marginal rms, then we would expect to see the following pattern in Table 5: the coe cients of SMALL and CHANGE IN SMALL (which capture the e ects of these variables on non-marginal rms) would be insigni cant (or positive and signi cant), but the interaction termsõ coe cients should be positive and 26 Twenty ve percent of rms report their owners as declaring bankruptcy or being delinquent in their business or personal loans over the 1990±1993 period. Fifteen percent of rms are ve years or younger and 68 percent have ve or fewer employees. 27 Using indicator variables to ag ``marginal rms'' and interacting them with SMALL and CHANGE IN SMALL allows easier interpretation of results ± easier than interacting continuous measures of rm age and size with small bank presence measures. Nevertheless, we also estimated the models that interacted continuous measures of rm age and size with small bank presence measures to nd similar results to those reported below.
20 446 J. Jayaratne, J. Wolken / Journal of Banking & Finance 23 (1999) 427±458 Table 5 E ects of small bank presence on the probability of having a bank line of credit: Marginal vs. non-marginal rms (logit estimates; all rms) Independent variable Measure of small bank presence SMALL ˆ Fraction deposits held by banks with assets less than $300 million SMALL ˆ Fraction deposits held by banks with assets less than $300 million and if part of holding company, holding company assets less than $300 million SMALL ˆ Fraction of banks that have assets less than $300 million SMALL ˆ Fraction of banks with assets less than $300 million and if part of holding company, holding company assets less than $300 million (1) (2) (3) (4) Small bank presence (SMALL) ( 1.65) ( 2.1) (0.35) ( 0.25) Change in SMALL, 1988 ± (2.45) (1.24) (1.51) (0.92) POOR CREDIT ( 0.25) (0.17) (0.57) (1.2) POOR CREDITSMALL ( 0.082) ( 0.35) ( 0.6) ( 1.3) POOR CREDITChange in SMALL ( 0.41) (1.04) (0.67) (1.6) YOUNG FIRM ( 3.7) ( 3.9) ( 0.64) ( 1.1) YOUNG FIRMSMALL (2.5) (2.7) ( 0.07) (0.25) YOUNG FIRMChange in SMALL ( 1.02) ( 0.48) (1.2) (0.29) SMALL FIRM ( 11.53) ( 12.81) ( 5.8) ( 7.2) SMALL FIRMSMALL (1.58) (1.76) (1.05) (1.89)
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