Availability and cost of credit for small businesses: Customer relationships and credit cooperatives

Transcription

1 Journal of Banking & Finance 22 (1998) 925±954 Availability and cost of credit for small businesses: Customer relationships and credit cooperatives P. Angelini a, R. Di Salvo b, G. Ferri a, * a Bank of Italy, Research Department, Via Nazionale 91, Rome, Italy b Institute for Research on Cooperative Credit and Local Economic Development (IRCEL), Via D'Azeglio 33, 0018 Rome, Italy Abstract The paper investigates the e ects of bank± rm relationships on the cost and the availability of credit for a sample of small Italian rms, focusing on possible di erential e ects related to the local and/or cooperative nature of lending banks. We nd that with banks other than cooperative banks, lending rates tend to increase with the length of the relationship for all customers, whereas with local cooperative banks (CCBs) this is the case for non-member customers only; by contrast, long-standing relationships have no signi cant e ect on lending rates for CCBs' own members. This evidence is in line with bank capture theories, which may not apply to CCB members. We also nd that CCB members enjoy easier access to credit, unlike non-member customers. Our results indicate that the main distinctive features of CCBs relative to commercial banks stem from their cooperative ownership rather than their local nature. Ó 1998 Elsevier Science B.V. All rights reserved. JEL classi cation: G21; G30; D82 Keywords: Small business; Customer relationships; Credit cooperatives; Rationing * Corresponding authors. Tel.: (2343); fax: ; i (i )@interbusiness.it /98/$19.00 Ó 1998 Elsevier Science B.V. All rights reserved. PII S ( 9 8 )

2 926 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925± Introduction According to a growing strand of literature, liquidity constraints stemming from asymmetric information and incentive (principal-agent) problems may hold back investment and production. Banks are often thought to play an important role in solving these problems. In particular, rms with a relatively short observable history, most often small in size, should prefer bank loans to other forms of external nance, because they are more likely to face asymmetric information problems. Various contributions suggest that the terms and availability of credit are in uenced by certain features of rms' relationships with the banks. More speci cally, it is widely agreed that close customer relationships should reduce liquidity constraints, but their e ect on the cost of credit is ambiguous. Closer customer relationships help overcome information asymmetries, producing a gain in allocative e ciency. Some authors argue that the bank will share this gain with the rm by way of lower lending rates (Diamond, 1991; Boot and Thakor, 1994), while others contend that the bank will appropriate most of the allocative gain as a result of the fact that customers may be ``informationally captured'' (Sharpe, 1990; Rajan, 1992). A related but distinct strand of literature addresses the speci c patterns of credit relationships between local and/or cooperative banks and small businesses. It holds that credit conditions for small rms are a ected not only by individual customer relationships, but also by group interactions within the local community, which may improve the bank's ability to screen and monitor borrowers and to enforce debt contracts. Banerjee et al. (1994) propose two distinct, though not necessarily alternative, hypotheses. The ``long-term interaction'' hypothesis emphasizes that quality information about local borrowers, costly to acquire for outside banks, is available at practically no cost to the local bank. The ``peer monitoring'' hypothesis focuses instead on the speci c features of debt contracts embodying group incentive schemes, such as those adopted in some borrower collectives, in which the availability of credit for each member depends on the performance of the loans granted to all the others. Our paper lies at the junction between these two strands of literature and addresses three broad sets of issues. First, we try to discriminate between the above-mentioned ``virtuous e ect'' and ``bank capture'' theories, focusing on how various proxies of customer relationships a ect the cost of credit for a rm. Special attention is devoted to the impact of the length of the relationship, a crucial variable that has been empirically analyzed by other authors with mixed results (Petersen and Rajan, 1994, 1995; Berger and Udell, 1995). Second, we test the e ects of relationship lending on credit availability. 1 For both 1 Other empirical studies analyze the e ectiveness of relationship lending by focusing on their impact on banks' pro t and loss accounts (e.g. Berlin and Mester, 1998).

3 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925± issues, our approach follows that of Petersen and Rajan (1994), who use reduced form regressions to investigate how the cost of credit and its availability are a ected by customer relationships. However, the particular dataset employed enables us in principle directly to detect liquidity constraints and thus allows us to avoid resorting to the proxies for credit rationing that are often used in the previous literature. Our third aim is to ascertain whether and how customer relationships (and their impact on credit conditions) depend on the local and/or cooperative nature of intermediaries; in particular, we seek to discriminate between the long-term interaction and peer monitoring hypotheses. To this end, we focus on Italy's numerous banche di credito cooperativo (hereinafter cooperative credit banks, or CCBs), that embrace most of the country's small cooperative credit institutions. In contrast with US credit cooperatives, which are usually government-sponsored institutions designed to provide nance to speci c industries, Italy's CCBs are private sector banks similar to credit unions but focusing on lending to small businesses rather than to consumers. A considerable part of CCBs' credit is granted to their own members, even though the share accruing to non-members often exceeds 50%. This feature makes CCBs suitable to discriminate between e ects stemming from the local nature of a credit intermediary and those related to its cooperative structure. Our tailor-made dataset derives from a sample survey of more than 1800 small Italian non- nancial businesses. The interview contains questions speci cally designed to gauge each rm's credit cost and needs and identify the main features of its relationships with the banking system. Additional rmspeci c information is taken from the Central Credit Register of the Bank of Italy. In Section 2 we review the main strands of theory providing the background for our empirical analysis. In Section 3 we discuss the methodology adopted to identify liquidity-constrained rms. Section 4 describes the dataset and Section 5 presents our regression analysis. We summarize and discuss the main results in Section Background literature The literature on asymmetric information and agency problems highlights that liquidity constraints can hinder rms' investment and production (see, among others, Fazzari et al., 1988; Hubbard, 1990; Bernanke and Gertler, 1995). Part of this literature considers the role played by banks and suggests that banks' screening and monitoring procedures allow them to overcome information and incentive problems (Leland and Pyle, 1977; Diamond, 1984; Bhattacharya and Thakor, 1993) and hence to reduce liquidity constraints for borrowers. Several recent papers have focused on the e ects of customer

4 928 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925±954 relationships. Whereas there is broad empirical and theoretical agreement on the view that closer relationships should increase credit availability, there is little consensus about their impact on the cost of bank loans. Two contrasting views have been set forth. In Diamond (1989) two mechanisms may generate a pattern of decreasing interest rates over time. First, some borrowers who choose risky projects default in each period and are denied credit thereafter; this improves the quality of the pool of borrowers and leads banks gradually to lower lending rates. Second, a reputation e ect may induce borrowers to prefer safe projects; in practice, those who are able to continuously repay their loans build a good reputation and enjoy lower rates. It is worth noting that this framework emphasizes the relevance of the borrower's credit history rather than the length of the customer relationship. Boot and Thakor (1994) show that, with an optimal credit contract, the borrower is initially charged an above-market interest rate and must post collateral, but after her rst successful project the bank will grant her unsecured loans inde nitely at a cost below the spot market rate. On the opposite front, various contributions emphasize the inertia e ect stemming from information capture problems (Sharpe, 1990; Rajan, 1992), or from the presence of xed costs associated with the search for a new bank (e.g. Blackwell and Santomero, 1982). Such inertia can in principle allow the incumbent bank to extract surplus from the rm. Building on Diamond's analysis, Petersen and Rajan (1995) develop a twoperiod model with adverse selection and moral hazard in which the information e ect yields lower rates in the second period; however, the interest rate decreases less in more concentrated credit markets, where bank capture e ects will tend to be more severe. Greenbaum et al. (1989) reach more extreme conclusions. Within a model in which rms bear search costs to nd a new bank, they show that the borrowing rate is a non-decreasing function of the duration of the credit relationship; that the probability that a rm will terminate a relationship increases with its duration; that the average lending rate o ered by banks competing with the incumbent bank is lower than their funding cost (i.e., in order to ``capture'' the rm they are prepared to bear a loss in the initial period). A separate strand of literature analyzes relationships at local and/or cooperative banks. Although most of these studies focus on developing or rural economies, one may argue that, in principle, analogous mechanisms may also be operating in local communities of industrialized countries, thus providing a link with our analysis. These contributions share the view that the local nature and/or cooperative ownership structure of these community banks can give them a comparative advantage in dealing with asymmetric information and agency problems. For simplicity, we can distinguish two points of view in this literature. Some authors suggest that agents who take part in the life of a community share relationships of various kinds, not only economic, through which they acquire information that would be available to an outsider only at a

5 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925± cost. A bank operating in a small community, owned and/or managed by community members, may take advantage of this information in its lending activity. An additional advantage could stem from the possibility of applying ``social sanctions'', which are generally not available to ordinary commercial banks. These theories have been called the ``long-term interaction hypothesis'' by Banerjee et al. (1994) and Besley and Coate (1995). A di erent e ect is at work when the bene t for each member of a given group depends in some well-de ned way (e.g. a contract) on the behavior of all the others. In the case analyzed by Stiglitz (1990), Varian (1990), and Ho and Stiglitz (1990), each member may continue to bene t from her loan only if all the others' projects are successful, so members have an incentive to control each other. Banerjee et al. (1994), following Stiglitz, call this the ``peer monitoring hypothesis''. Although focusing on a di erent mechanism, Smith and Stutzer (1990) also consider a contract that ties the return of each borrower to the behavior of the others. They argue that cooperative banks arise as a selfselection mechanism to assess borrowers' creditworthiness in the presence of systematic risk and adverse selection. In particular, less risky borrowers can credibly signal themselves as such by joining a cooperative bank, in which their payo depends on the overall pro tability of the bank. This enhances the quality of the group of borrowers, who therefore pay lower interest rates on average. Turning to the empirical evidence, various papers have analyzed small rm nancing in the United States, using data from the National Survey of Small Business Finance. Petersen and Rajan (1994) nd indications that, ceteris paribus, older rms, or rms dealing with a relatively small number of banks, bene t from easier access to bank credit and from lower interest rates; the length of the relationship has a positive and signi cant e ect on credit availability, whereas its impact on the cost of credit is positive but insigni cant. The results of Petersen and Rajan (1995) are in line with those of the previous paper as far as credit availability is concerned, whereas the length of the relationship is not included among the determinants of the borrowing rate; for younger businesses they also show the cost of credit to be lower in less competitive markets, where the bank can more easily internalize the value of the customer relationship. Berger and Udell (1995) nd that for rms maintaining longstanding relationships with banks the cost of borrowing on previously negotiated credit lines is smaller and collateral is less frequently required. Cole (1998) nds that a lender is less likely to grant credit to a rm if the customer relationship has lasted for one year or less, or if the rm deals with other - nancial counterparts. Several authors have analyzed the case of Japanese rms. Among them, Hoshi et al. (1990) show that the costs borne to overcome episodes of nancial distress are signi cantly lower for rms enjoying long-standing relationships with a main bank; Weinstein and Yafeh (1998) nd that close ties to a bank enable the rm to adopt capital-intensive production

6 930 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925±954 techniques, but that the main bank extracts signi cant rents from its client rms in the form of higher-than-average interest rates. Ongena and Smith (1997) report that in a sample of rms listed on the Oslo stock exchange the probability that a bank± rm relationship will be terminated does not signi cantly increase with its length, thus rejecting the above-mentioned prediction by Greenbaum et al. (1989). Regarding Italy, D'Auria and Foglia (1997), working on a panel of large rms, nd a positive e ect of their proxy for the length of the relationship on the cost of credit. Fewer empirical studies have addressed the peculiarities of local and/or cooperative banks. Banerjee et al. (1994) put forward the two hypotheses of long-term interaction and peer monitoring, and note that the failure rate of the 19th-century German cooperative banks of the Rai eisen type (characterized by unlimited liability) is lower than that of institutions of the Schultze±Delitsch type (limited liability). They interpret this as evidence in favor of the peer monitoring view, but argue that long-term interaction e ects cannot be ruled out. Using sectoral data Cannari and Signorini (1997) suggest that the availability of credit in Italy is larger for CCBs' customers than for comparable pools of borrowers. 3. De nition of liquidity constraints In principle, an economic agent is said to be liquidity constrained if at the interest rate applicable to her risk class she demands a larger volume of credit but cannot obtain it in the market. Credit rationing could therefore be measured by the gap between the rm's marginal revenue from capital investment and the market interest rate. In practice, however, direct measures of liquidity constraints cannot be observed, so that the empirical literature on credit rationing has adopted a wide array of proxies. Fazzari et al. (1988) group rms in their sample on the basis of dividend policy and argue that rms with a high ratio of retained earnings are more likely to be liquidity constrained. Hoshi et al. (1990) use customer relationships as their identi cation criterion and submit that rms with closer relationships are less likely to be liquidity constrained. Since loan commitments should bind the lender to grant credit at the borrower's request up to some predetermined amount, Berger and Udell (1992) use the share of new commitment loans as an indicator of liquidity constraints: this share should rise in periods of tight credit if rationing is signi cant. Petersen and Rajan (1994) note that liquidity-constrained rms should be willing to pay a higher cost to increase their borrowing and group in this category all rms resorting to non-institutional lenders at above-market interest rates. Gertler and Gilchrist (1994) hold that large rms normally have access to a broader range of alternative nancing sources and should therefore be subject to relatively less credit rationing than small rms.

7 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925± These indirect indicators are undoubtedly useful, but they all share a common drawback: the assumption that the selected proxy is a good indicator of credit availability cannot be veri ed. Furthermore, regardless of how good these proxies are, they may re ect other e ects that have little or nothing to do with liquidity constraints. In principle, the methodology used in this paper allows us to detect liquidity constraints directly and is therefore exempt from these objections. 2 Each rm was asked the following questions: (i) Did it desire more credit at the current market rate? If the answer was ``yes'', the next question was: (ii) What conditions were acceptable in order to obtain more credit?: (a) the same interest rate and the same collateral; (b) a higher interest rate but the same collateral; (c) the same interest rate but more collateral; (d) a higher interest rate and more collateral. If the answer was not (a), the next question was: (iii) Why had the rm not obtained additional credit?: (a) the bank had not yet been approached; (b) the rm thought that the bank would refuse it; (c) the bank had e ectively refused it. Based on the answers to these questions, each rm was assigned one out of four integer values (from zero to three) of a discrete variable, RAT. The variable partitions our dataset into the four groups presented in Table 1, ranked in increasing order of liquidity constraints. Thus, for instance, RAT is equal to zero for those rms not wishing additional bank credit, and equal to three for rms whose application for additional funds has actually been turned down (iii.c) or for rms of the ``discouraged borrower'' type (iii.b). Although it can be argued that only the latter group of rms should be labeled as credit rationed, the choice is not so clear in practice. In particular, the survey did not allow us to understand why a substantial share of rms wished additional credit even at worse conditions but had not yet applied for it (the group identi ed by RAT equal to two, or 7.4% of our sample). Thus, based on the information in Table 1 we build several proxies for credit rationing, ranging from the narrowest (labeling as credit rationed only the rms with RAT ˆ 3) to the broadest (extending the label to rms with RAT ˆ 1 and RAT ˆ 2). We then make sure that our results are robust to the choice of the de nition. 4. The data The data base is derived from a eld survey carried out in November 1995, addressed to a sample of Italian non- nancial rms. The sample was obtained as follows. First, from a total of some 600 CCBs existing in Italy we extracted a 2 Analogous measures have been widely used in the literature on consumer credit rationing (see e.g. Jappelli, 1990; Duca and Rosenthal, 1993).

8 932 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925±954 Table 1 Demand for loans by small rms

9 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925± sample of 90 banks, strati ed by size and location, thus selecting 90 municipalities (comuni) in each of which a local CCB is located. These are mainly relatively a uent townships: only 3 out of 90 have more than 100,000 inhabitants (48 have fewer than 10,000); in 53 per capita income is above the national average. We then extracted 6164 non- nancial rms from among businesses based in these municipalities, for which information was on le in the Central Credit Register. With a view to detecting possible di erences between CCBs and other banks, we had the following ex ante strati cation: (i) 23.9% of the rms borrowed only from CCBs; (ii) 28.0% borrowed from both CCBs and other banks; (iii) 48.1% only borrowed from other banks. The number of rms actually surveyed is 1858, including 34 for which the information was incomplete. 3 The composition of the sample of 1824 rms actually used in the regressions for the above groups (i) through (iii), in the order, is 408 rms (22.4% of the sample), 813 (44.6%) and 603 (33.0%). 4 Thus, 67% of the rms in our sample are CCBs' clients. 44% are members of CCBs; only 12% of this group do not borrow from CCBs. In interpreting our results, one must keep in mind that our sample substantially overrepresents CCBs, whose share of the Italian loan market is around 5% (see the Appendix). Firms in the sample are small and very small: the average (median) size is slightly above 10 (5) employees and gross sales are about 1 (0.7) billion lira, approximately US$600,000 ($400,000) at the exchange rate of November An additional source is the Central Credit Register, which allowed us to build indicators of the structure of rms' bank debt and to control for problem borrowers (not covered by the standard asymmetric information theory of credit rationing). The share of ``strictly'' credit rationed businesses in our sample is only 2.6% (Table 1; 2.1% if rms with bad loans are not considered). In the sample of large Italian businesses used by Angelini and Guiso (1994) (673 employees on average), this share was substantially similar (3.1%; Table 2, data for 1995). However, using broader indicators, small rms turn out to be more liquidity constrained. Firms desiring more credit constitute 26.1% of the sample of small rms could not be reached for various reasons (telephone number unavailable, or recent change of address, etc.), and 2495 declined to be interviewed or terminated the interview before completion. 4 Our dataset may su er from two possible selection problems. First, there is a large di erence between the response rates for groups (ii) and (iii). Second, although other characteristics do not di er signi cantly, the share of problem borrowers (as reported to the Central Credit Register, see below) is higher among the original 6164 rms than in the nal sample (4.6 versus 2.3%) this could lower average liquidity constraints and interest rates in the sample. The overall e ect of these biases on our results is unclear.

10 934 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925±954 Table 2 Credit rationing over the business cycle in a sample of large Italian manufacturing rms Share of rationed rms a (%) (A) (B) (C) Industrial production (% annual growth) ) ) ) Finance to nonstate sector (% annual growth) b a The shares are computed from an annual survey run by the Bank of Italy. Firms that answered the survey question on credit rationing had 673 employees on average in Column (A) reports the share of rms that desired additional credit and were willing to accept worse conditions in order to obtain it, but had seen their applications denied by the banks. Column (B) reports the share of rms that desired additional credit and were willing to accept worse conditions in order to obtain it, but either had not yet applied for it or had seen their applications denied by the banks. Column (C) reports the share of rms that desired additional credit at current or worse conditions. b Does not include nancing from abroad. rms and 10.6% of that of large ones; these shares drop to 10.0% and 5.2% if we only consider rms willing to pay a higher rate or to o er more collateral. 5 Table 3 presents a series of statistics for the four groups of rms described in Table 1. On average, liquidity constraints diminish for older rms, while they increase, though not uniformly, with various measures of indebtedness. The proxies for customer relationships also seem to follow our a priori: a long-term lending relationship with the main lending bank and a limited number of lenders are negatively correlated with liquidity constraints. To assess the statistical signi cance of these factors we estimate probit models of the probability that a given rm is liquidity constrained (Section 5.2), and evaluate the impact of customer relationships on the cost of credit (Section 5.1). 5. The empirical analysis This Section has three main aims. First, we test whether and how closer customer relationships a ect the determination of loan rates and, if so, how. Second, we ascertain whether they reduce the likelihood that rms are liquidity 5 Figures derive from surveys run in the same period, with broadly comparable wording of the questions. However, Angelini and Guiso's survey was conducted via direct (not telephone) interviews. Altogether, caution is necessary in interpreting a comparison of unconditional means (the best we could do owing to the di erences between the two datasets).

11 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925± Table 3 Selected statistics of a sample of 1858 small Italian rms in order of increasing liquidity constraints a RAT Total sample Characteristics of location area Average values Market concentration Her ndahl index computed on deposits Her ndahl index computed on loans Bank branches 1000/inhabitants Firm is located in the South (0,1) Firm characteristics Number of employees Age of rm (years) Gross sales (billions of lira) Performance Firm purchased equipment in last ve years (0,1) Firm underwent restructuring in last ve years (0,1) Retained earnings Ownership of rm changed in last ve years (0,1) Debt position Short-term bank loans/total bank loans Total bank loans (millions of lira)/no. of employees Short-term collateralized loans/total bank loans Interest rate on short-term lines of credit (%) At least some bank loans classi ed as bad (0,1) (%) Over 60% of bank loans classi ed as bad (0,1) (%) Bank± rm relationships Length of relationship with 1st bank (years) No. banks granting credit No. banks supplying non-credit services over last ve years Credit drawn/credit granted Firm is CCB member (0,1) Firm is CCB client (0,1) a See Section 3 and Table 1 for the de nition of RAT. The measures of market concentration refer to the municipalities where rms are located, the nest geographical breakdown level for which data are available (source: Bank of Italy). The binary variables are set to one in case of a rmative answer to the underlying survey question and to zero otherwise. For example, the geographical location variable is equal to one for businesses located in Southern Italy and zero otherwise. The dummy `` rm purchased equipment in last ve years'' is derived from a multiple choice survey question; rms could choose from four answers, ranging from `` rm made only moderate or no purchase of new equipment'' to `` rm heavily invested in new equipment''; the dummy was set to one if the latter answer was given. Firms were asked to rate the importance of six possible sources of nance; for each one they could choose from four answers, ranging from 1 (moderately or not important) to 4 (extremely important). The retained earnings variable was constructed by dividing the value assigned to retained earnings by the sum of the values assigned to all six sources of - nance. Among the variables under the heading ``debt position'', data on interest rates and numbers of employees derive from the survey; all other variables, as well as the ratio of credit drawn to credit granted, were computed from Central Credit Register data as average values over the period November 1994±June 1995.

12 936 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925±954 constrained. Third, we focus on possible di erential e ects related to the local and/or cooperative nature of banks Customer relationships and the cost of credit A total of 1095 rms in our sample provided information about the interest rates on overdraft loans charged by up to three banks. 6 Whereas in the dataset in Section 5.2, focusing on liquidity constraints, there is a one-to-one correspondence between observations and rms, in this section we stacked the interest rate data, so that each rm appears one, two or three times, yielding a dataset with 2232 observations. We chose to focus on drawings on credit lines since they are the most common type of loan in Italy and the one most likely to reveal the impact of customer relationships on the cost of credit (Berger and Udell, 1995). In the linear equations reported in Table 4, estimated by OLS, the dependent variable is the cost of credit. Column (4a) is the baseline speci cation, while the others can be read as robustness tests. We use 10 dummies to control for industry e ects (detailed in the footnote to Table 4) and 39 for the province of residence, to eliminate xed e ects associated with rms' geographical location. Although we do not report the related coe cients, several turn out to be signi cant. By contrast, ve dummies controlling for the legal form of the rm were never even weakly signi cant and were therefore omitted from the speci cations presented. Additional controls for a ner geographical partition include population of the municipality, a dummy for cities, and an indicator of competition in the local credit market (a dummy constructed on the basis of the Her ndahl index of bank deposit concentration at the municipal level). 7 The latter variable is never signi cant in our estimates, in contrast with Petersen and Rajan (1995), who nd that both the cost and availability of credit are signi cantly a ected by market concentration. The di erence between the two results could derive from the fact that the credit markets we consider are geographically smaller units than those used by Petersen and Rajan. 6 Firms dealing with more than three banks were asked to report only the rates charged by the rst three in order of importance. In order to check for self-selection bias we computed summary statistics for this sub-sample and compared them with corresponding statistics for the total sample. The only important di erence found concerned the percentage of rms with bad loans (1.3%, as opposed to 2.3% for the whole sample). 7 The credit market concentration dummy used in the regressions of Tables 4 and 5 is set to one for municipal markets with a Her ndahl index computed on bank deposits greater than the median value. We derived several alternative proxies of market competition from the Her ndahl index computed on loans and from the ratio of bank branches to population; we also tried various threshold values in addition to the median. In general these measures, including those presented in Tables 4 and 5, did not yield robust results. However, all the other regression results turned out to be fairly insensitive to the choice of this proxy.

13 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925± Table 4 Determinants of the cost of credit for a sample of small Italian rms a Variables Dependent variable: Interest rate on loans drawn on credit lines (a) (b) (c) (d) (e) (f) (g) Baseline CCB variables are omitted Firms with bad loans are dropped Ownership changes are controlled for Province dummies are omitted Only rst lending bank is considered CCB membership per se is considered Coe. t-stat. Coe. t-stat. Coe. t-stat. Coe. t-stat. Coe. t-stat. Coe. t-stat. Coe. t-stat. Location area Population ) Credit market concentration (0,1) ) Firm is located in the South (0,1) City with more than inhabitants Number of employees ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) Age of rm ) ) ) ) ) ) ) Performance Firm purchased equipment in last ve years (0,1) Firm underwent restructuring in last ve years (0,1) Retained earnings Ownership of rm changed in last ve years (0,1) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) )

14 938 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925±954 Table 4 (Continued) Variables Dependent variable: Interest rate on loans drawn on credit lines (a) (b) (c) (d) (e) (f) (g) Baseline CCB variables are omitted Firms with bad loans are dropped Ownership changes are controlled for Province dummies are omitted Only rst lending bank is considered CCB membership per se is considered Coe. t-stat. Coe. t-stat. Coe. t-stat. Coe. t-stat. Coe. t-stat. Coe. t-stat. Coe. t-stat. Debt position Short-term bank loans/ Total bank loans Total bank loans/no. of employees Some bank loans classi ed as bad (0,1) Over 60% of bank loans classi ed as bad (0,1) Bank± rm relationship Length of relationship (log) Length of relationship (log) if credit granted by CCB to CCB member ) ) ) ) ) ) ) ) ) ) ) ) )

15 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925± Table 4 (Continued) Variables Dependent variable: Interest rate on loans drawn on credit lines (a) (b) (c) (d) (e) (f) (g) Baseline CCB variables are omitted Firms with bad loans are dropped Ownership changes are controlled for Province dummies are omitted Only rst lending bank is considered CCB membership per se is considered Coe. t-stat. Coe. t-stat. Coe. t-stat. Coe. t-stat. Coe. t-stat. Coe. t-stat. Coe. t-stat. Credit granted by CCB (0,1) Credit granted by CCB to CCB member (0,1) Number of banks granting credit (log) Indicator of illiquidity (RAT) Number of observations ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) R a For details on the regressors see the footnote to Table 3. The t statistics are computed with Huber heteroskedasticity-robust standard errors. The table does not report the constant, the coe cients of 39 zero±one province dummies ± used in all regressions except (e) ± and those of the 10 dummies for the following industry characteristics, computed from two-digit Nace-Clio R25 codes (the EU general classi cation system for economic activities), used in all regressions: agricultural, forestry and shery products (4.5% of the sample used in speci cation (a) of Table 5); fuel and power products, ores and metals, non-metallic minerals and mineral products, chemical products, metal products, except machinery and transport equipment (13.4%); agricultural and industrial machinery, o ce and data processing machines, precision and optical instruments, electrical goods, transport equipment (8.3%); food, beverages, tobacco (3.1%); textiles and clothing, leather and footwear, paper and printing products, rubber and plastic products, other manufactured products (17.8%); building and construction (9.5%); recovery and repair services, wholesale and retail trade services (27.6%); lodging and catering services (3.2%); services for inland transport, maritime and air transport, auxiliary transport, communication (2.9%); other market services except credit and insurance (7.5%). For 39 rms the code was missing. Two rms declared that the length of the relationship with their rst lending bank was equal to zero; in this case the variable was set to 1 before taking logs. A few observations were lost in speci cation (d) because some information on ownership changes was missing., or denote levels of signi cance equal to 10%, 5% and 1%, respectively.

16 940 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925±954 We also introduce two dummies to control for the 42 rms in our sample that are likely to become insolvent, according to the reports of their lending banks to the Central Credit Register. The rst is equal to one if any share of the rm's total bank borrowing is reported as a bad loan; the second is set to one if such share exceeds 60%. We did not attempt to use rm dummies on account of the high number of businesses dealing with only one bank and reporting a single interest rate. Variables designed to control for xed e ects attributable to the rm's area of location are not signi cant in the speci cations with province dummies (4a± 4d and 4f±4g); rms located in the less developed provinces of the South pay substantially higher loan rates (speci cation 4e). As far as the characteristics of the business are concerned, size (measured by the number of employees) has a negative sign and is statistically signi cant. Age has the expected (negative) sign, but is not signi cant regardless of whether levels or logs are used. Overall, the evidence in favor of a negative e ect of age on the cost of credit is weaker than in either Berger and Udell (1995) or Petersen and Rajan (1994, 1995). On the basis of the estimated coe cient, interest rates paid by rms that wish additional bank credit (RAT > 0) are 68 basis points higher than those paid by the rest of the sample (RAT ˆ 0; estimates not reported); the premium rises to 1.14 percentage points for ``strictly'' rationed rms (RAT ˆ 3; speci cation 4e). This evidence is contrary to the thesis of Blackwell and Santomero (1982), who predict that credit rationed rms should pay below-average rates. 8 Firms with high retained earnings pay substantially lower rates, possibly as a result of their greater bargaining power. As additional indicators of performance we use two dummy variables, for businesses which underwent reorganization or undertook major investment in the last ve years. In none of the speci cations considered did these measures have any e ect on borrowing rates; however, this result could re ect their inadequacy, attributable to the practical impossibility of asking any direct question regarding pro tability. Total bank borrowing per employee has no e ect; this should be a good measure of leverage, since most rms in our sample do not issue debt on nancial markets. However, rms with 8 The variable RAT is used only in speci cation 4e because it may generate simultaneity problems, as both decisions (loan size and interest rate) are taken by the bank at the same time. However, if the lagged ratio between credit drawn and the credit limit ± a good proxy of liquidity constraints according to Cannari and Signorini (1997) ± is included in the regressions of Table 4, a positive association between credit rationing and interest rates is consistently detected.

17 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925± a higher share of short-term credit pay higher rates, and this could re ect their higher riskiness. 9 Two major elements characterize customer relationships: length and exclusiveness. A long relationship should grant the incumbent bank an information advantage vis-a-vis potential competitors. We have a direct measure of this variable, namely the length of the relationship between the rm and each of its lending banks. As a proxy for exclusiveness we use the number of these banks. Other things being equal, as this number increases each bank in the pool should have access to a smaller information ow. The most interesting results concern these proxies for customer relationships. First, the coe cient measuring the e ect of the duration of the relationship on the cost of credit is positive but not signi cant (speci cation 4b). However, if we allow for a differential e ect for loans granted by CCBs to their members, the coe cient increases and becomes signi cant, whereas it is negative and signi cant for CCB members. This result is robust across the alternative speci cations presented. In particular, it does not vanish even when only observations pertaining to the main lending bank are used (speci cation 4f). We interpret this as evidence that in our sample the bank capture e ect, according to which a longer bank± rm relationship should be associated with higher borrowing rates, seems to prevail over the information production e ect, which should work in the opposite direction (the exception represented by CCB members will be discussed below). 10 The overall picture is in line with the theoretical results of Greenbaum et al. (1989), and in contrast with the ndings of Petersen and Rajan (1995) and Berger and Udell (1995). However, in their empirical analysis Petersen and Rajan use the rm's age rather than the length of the relationship. Berger and Udell point out that their result vanishes for a sub-sample of smaller rms, whose size seems reasonably more comparable with that of our rms. 11 We also nd that, other conditions being equal, rms dealing with several banks tend to pay lower interest rates. Petersen and Rajan (1994) nd an e ect 9 According to Diamond (1991), two opposite types of businesses tend to resort more to shortterm credit: those with a high credit rating, which issue commercial paper at low cost, and those with no alternative but to use short-term bank credit at a relatively high cost. Since rms in our sample are not large enough to issue liabilities on the nancial market, a high proportion of shortterm bank borrowing might signal a lower-than-average credit rating. 10 Alternatively, a ``quiet life'' hypothesis could account for the fact that borrowing rates increase with the duration of the relationship. That is, rms in our sample may have chosen to forgo growth opportunities and new nancing options and the rise in their borrowing rates over time may simply signal their relatively poor performance. However, this hypothesis would not explain why borrowing rates do not increase with the length of the relationship for CCB member customers. 11 Total assets of the median rm in Berger and Udell's sample are slightly less than $500,000, close to the gross sales of our median rm. Considering that sales normally far exceed total assets, rms in our sample should be much smaller.

18 942 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925±954 of the opposite sign, and interpret it as an indication that rms may concentrate their borrowing in order to reduce monitoring costs and improve their customer relationships with the banks. As an alternative explanation they suggest that the number of banks may be a proxy for lower-quality- rms that are unable to borrow more from their bank and must therefore approach other banks for additional funds. However, they do not nd strong evidence in support of this hypothesis. In our sample, rms with three banks or more are of higher average quality: those with problem or bad loans are only 1.7% of this group, as opposed to 2.6% among rms with one or two banks. However, this does not explain our result, since we control for problem borrowers. The result could re ect a greater bargaining power on the part of these rms, which can credibly threaten to move to another bank. 12 We also analyze in some detail rms signaling ownership changes in the ve years prior to the survey (9.6% of our sample). For this group of rms the reported average length of the relationship with the main lending bank is 13 years, remarkably similar to the overall sample average of 14 years. 13 However, since an ownership change may in principle substantially a ect customer relationships, we performed some additional checks. In particular, a dummy for ownership changes was introduced in speci cations analogous to those of Table 4; the related coe cient was positive and signi cant, but the other results were not altered. Furthermore, we consider an extreme scenario in which the customer relationship completely vanishes with the ownership change. In practice, for these rms we replace the declared length of the relationship with the number of years elapsed since the ownership change (speci cation 4d), and fail to detect signi cant changes relative to the baseline; this is also the case if all the rms with ownership changes are discarded (result not reported) Customer relationships and liquidity constraints We assume that a bank's decision to grant or refuse credit to a business loan applicant depends on a set of observable characteristics of the rm. Logically, the bank will use these observations to infer the quality of the business under 12 Angelini and Guiso (1994) and D'Auria and Foglia (1997) obtain the same result with samples of Italian rms of much larger size. A wide number of factors may a ect the international comparison. In Italy the practice of resorting to several banks is quite common. Detragiache et al. (1997) report that on two comparable samples of large rms (50±500 employees) the median Italian rm has nine bank relationships, compared with only two for its US counterparts. 13 This is due to the fact that in the survey we explicitly asked for the duration of the relationship between the bank and the rm (not its owner). We have evidence for Italy that the new owner typically does not move to new banks (Capra et al., 1994, ch. 2). Furthermore, Cesari and Salvo (1996), in a sample of 300 manufacturing rms with between 20 and 499 employees, found that over 50% of ownership changes are intergenerational transfers within the family.

19 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925± scrutiny and decide whether or not to grant credit. Based on the partitioning of Table 1, we therefore constructed several proxies of credit rationing and regressed them on a set of rm characteristics. We estimated several multinomial probit models, using RAT itself as the dependent variable, or aggregating two adjacent classes of RAT at a time (i.e. reducing the total number of classes from four to three). We also experimented with various binary dependent variables, obtained by further aggregation. The regression results turned out to be remarkably invariant across all experiments, as long as the groups corresponding to RAT ˆ 0 and RAT ˆ 1 are kept separate. 14 Given these robustness tests, for simplicity we present the results from a standard binomial probit model in which the dependent variable is equal to zero for RAT ˆ 0 and to one for RAT > 0, thereby discriminating between rms that wish additional credit and those that do not. The estimates are presented in Table 5, where column (5a) is the baseline speci cation. As in Section 5.1, most of the province and sector dummies turn out to be signi cant, whereas those controlling for the legal form of the rm were insigni cant and were omitted; likewise, market concentration is not signi cant in our estimates, although the considerations made in Section 5.1 apply here as well. Regarding the two dummies for rms with bad loans, the rst, as expected, identi es more pervasive liquidity constraints, but the second (which is equal to one for rms with a share of bad loans in excess of 60%) has a negative and signi cant coe cient, although smaller in absolute value than that for the rst dummy. We interpret this result as a demand e ect: beyond a certain threshold, rms recognize that additional credit is not the solution to their problems. The results do not change appreciably if problem rms are dropped from the sample (speci cation 5c). Among rms' individual characteristics, only age has a signi cant (negative) impact on the probability of being liquidity constrained. Neither size (measured by the number of employees) nor performance seems to exert any in uence at all. As already mentioned, the fact that performance appears to be irrelevant could derive from the inadequacy of our indicators. Our proxy for retained earnings is also not signi cant and is therefore excluded from the regressions presented in the table. 15 Our proxy for leverage (total bank borrowing per employee) has a positive but weak link with liquidity 14 This seems to suggest that the relevant distinction is between rms that wish additional credit and those that do not. However, the estimation of the full four-class multinomial probit yields three parameters which separate the classes, and the null hypothesis that any adjacent two of them are equal can be rejected at any signi cance level, thereby signaling that the four-class partition is a meaningful one. 15 This nding is not in line with the a priori in Fazzari et al. (1988), who use cash ow as a proxy for liquidity constraints. On this issue, see also Kaplan and Zingales (1997).

20 944 P. Angelini et al. / Journal of Banking & Finance 22 (1998) 925±954 Table 5 Perceived access to credit for a sample of small Italian rms a Variables Probit estimates ± dependent variable: Firm says it would like more credit (a) (b) (c) (d) (e) (f 0 ) Baseline CCB variables are omitted Firms with bad loans are dropped Ownership changes are controlled for Province dummies are omitted Length of relationship is included Coe. z-stat. Coe. z-stat. Coe. z-stat. Coe. z-stat. Coe. z-stat. Coe. z-stat. Location area Population Credit market concentration (0,1) ) ) ) ) ) Firm is located in the South (0,1) Firm is in city with over inhabitants (0,1) Number of rm's employees ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) Age of rm ) ) ) ) ) ) Performance Firm purchased equipment in last ve years (0,1) Firm underwent restructuring in last ve years (0,1) Ownership of rm changed in last ve years (0,1) Debt position Total bank loans/no. of employees ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) )