Lending Relationships and Information Rents: Do Banks Exploit Their Information Advantages?

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1 Lending Relationships and Information Rents: Do Banks Exploit Their Information Advantages? Carola Schenone University of Virginia March 2007 Abstract This paper empirically examines whether banks exploit the information advantage they acquire as a result of long-standing lending relationships with firms. Once a lending relationship is established, the borrower s cost of switching lenders can allow relationship banks to exploit their privileged information. This paper identifies two distinct scenarios in which firms face substantially different switching costs: pre- and post- a firm s IPO. Prior to going public, firms lack a credible and established mechanism for disseminating information (e.g., they are not required to formally disclose information or report financial data with the SEC). This greatly restricts the information non-relationship banks have about the firm, leading to high costs of switching lenders. Following the firm s IPO, these costs are greatly reduced, as information about the firm becomes publicly available. The paper s results reveal that when the firm is private and switching costs are high, the interest rate the relationship bank demands falls initially but increase once the firm is locked in the relationship (rates follow a U-shape pattern as a function of relationship intensity). However, once the firm goes public, and switching costs fall, interest rates decrease in relationship intensity. This evidence is consistent with the notion that relationship banks exploit their information advantage while the client firm is locked in, but share the benefits of the bank s privileged information after the firm s IPO. The results are robust to controlling for firm fixed effects, relationship/loan year fixed effect, and several loan and firm characteristics. The results are robust to several loan and firm risk measures such as the loan s debt rating, and the firm s leverage ratio during the year the loan was taken. I am gratful to Allen Berger, Federico Ciliberto, Mark Flannery, Rick Green, Robert Hauswald, Joel Houston, Ravi Jagannathan, Wei Liu, Felicia Marston, Kieron Meagher, Mitchell Petersen, David Smith, Greg Nini, Jay Ritter, Claire Rosenfeld, Phil Strahan, Bill Wilhelm, and Andy Winton for helpful discussions and suggestions. I also thank seminar participants at the Federal Reserve Board, the University of Florida Gainesville, The Stockholm School of Econoomics, The Security and Exchange Commission, American University, the University of Ohio, and the Norweigian School of Management in Oslo for valuable comments. Evan Kwiatkowski and Katie Rohyans provided truly outstanding research assistance. All errors remain my own. Contact information: and schenone@virginia.edu 1 Electronic copy of this paper is available at:

2 1 Introduction In the process of lending to a firm, a bank acquires proprietary firm-specific information that is unavailable to other banks. Such asymmetric evolution of information between lending and non-lending banks can lock in the borrowing firm and grant the lender an information monopoly (see for instance, Sharpe (1990) and Rajan (1992)). In this paper, I investigate whether relationship banks exploit their information advantage by charging interest rates higher than those that would prevail if information among banks was symmetric. Petersen and Rajan (1994) and Berger and Udell (1995) use a sample of firms from the National Survey of Small Business Finances (NSSBF) to study whether relationships banks exploit their information monopoly. Petersen and Rajan (1994) show that as the lending relationship evolves in time, borrowers receive no pricing benefit from their relationship lender. This suggests that banks do not share any of the information surplus with their client firms. The authors also show that the benefits derived from lending relationships typically take the form of greater credit availability. Berger and Udell (1995) restrict the sample to those firms with lines of credit and showed that, as the number of years that a firm has conducted business with its current lender increases, the interest rates on these credit lines drop and the firm is less likely to pledge collateral. Thus, Berger and Udell conclude that relationship banks do, in fact, pass along to their clients some of the cost savings they realize from their privileged information. Itakeadifferent approach to studying whether relationship banks exploit their priviledged information. I study bank loan pricing around a significant information-releasing event in the life of a borrower; an event likely to change the nature of the banking relationship, and in particular, the relationship bank s ability to exploit its information advantage. I take as a starting point the notion that the firm s cost of switching lenders allows relationship banks to exploit their information advantage. Building on this, I identify an event that divides a firm s life into two distinct periods during which it faces substantially different switching costs, and study differences in the pattern of interest rates demanded by the relationship bank during each of these two informationally different periods in the firm s life. The firm s IPO is precisely such a significant information-releasing event that divides the firm s life into the two informationally distinct periods.. Prior to going public, firms lack a 2 Electronic copy of this paper is available at:

3 credible and established mechanism for disseminating information (e.g., they are not required to formally disclose information or report financial data with the SEC). This greatly restricts the information non-relationship market participants have about the firm. For this reason, prospective lenders face an adverse selection problem when evaluating a firm that already has an established relationship with another bank (Detragiache, Garella, Guiso, (2000)). Hence, the firm seeking new lenders would have to spend extra resources, as well as managerial time and effort, to convince new lenders that their firm is, in fact, creditworthy. These transaction costs elevate the firm s switching costs, thus solidifying the inside bank s information monopoly. During and following the IPO, this paradigm changes radically. During the IPO, a substantial amount of information about the firm is revealed in the course of the firm s road show, advertising efforts, and the certification of the investment banks handling the deal. Following the IPO, the firm must comply with the disclosure requirements mandated by the SEC and the stock exchange where the firm trades. Furthermore, once the firm starts trading in the public market, its stock price alone acts as a significant transmitter of relevant firm information. Such information disclosure acts to markedly lower the firm s switching cost, thereby greatly reducing the informational advantage enjoyed by the relationship bank. Consequently, the IPO acts as a significant information event that changes the costs associated with switching lenders, thereby hampering the extraction of rents by the relationship lender. To investigate this hypothesis, I build a new dataset that traces a firm s lending relationships before and after its IPO. Specifically, I hand-match IPO firms identified using the Security Data Company (SDC) database with loan information from the Dealscan database compiled by Loan Pricing Corporation. 1 Merging the SDC and Dealscan database, I construct a panel dataset where the unit of observation is a firm s loan before and after its IPO. I further add data from COMPUSTAT and the firm s prospectus to include a measure of the firm s leverage ratio on the year the loan was taken to control for changes in financial risk that may occur around the firm s IPO date. This is the first paper to exploit the panel structure of data on firm-loan observations to 1 The SDC database is compiled from regulatory filings, news sources, company press releases, and the firm s IPO prospectus. The Dealscan database is compiled by the Loan Pricing Corporation, and records loan information for loans over $100, 000, for private and public firms. 3

4 address whether relationship banks exploit their informational advantage by extracting higher interest rates than those that prevail when information among banks is symmetric. 2 Having a panel is essential for several reasons. First, it allows the firm-bank relationship to be tracked as it evolves. Specifically, the panel-data format allows for the construction of a measure of relationship strength that captures the degree to which a firm relies on each one of its lending banks. This intensity variable is defined as the number of loans that a firm has drawn, to date, from the current lead lender, as a fraction of the total number of loans the firm has taken to date. Second, the panel structure of the data allows for the estimation of a fixed effects model of the interest rate with respect to the intensity of the lending relationship. Using firm and loan year fixed effects represents an important contribution to the existing research for several reasons. First, it allows me to control for important unobservable firm characteristics that can affect the firm s cost of borrowing (for example, the ability of managers to negotiate new loans, the firm s corporate governance structure, the firm s credit risk both as a private and public enterprise, and the firm s optimal choice of relationship intensity with any given lender). Second, by estimating firm fixed effects regressions I am effectively running regressions on deviations from firm means, and therefore the coefficients are estimated from within firm variation. The results show that, as predicted, prior to the firm s IPO interest rates as a function of relationship intensity follow a U- shaped curve: When the intensity of the lending relationship is low and the bank has not yet securely locked in its client firm, spreads decrease in relationship intensity as the bank shares the benefits of its privileged information; but as the banking relationship intensifies, and the bank has securely locked in the firm, the relationship bank begins to extract rents, leading to increasing rates. Following the IPO, when switching costs are predicted to be lower, or vanish all together, the results show that as the firm draws a 2 While Ongena and Smith (2001) and Houston and James (1996) use a panel of firmstoaddressissues associated with relationship bank s information monopolies, they do not tackle the pricing issue. Elsas and Krahnen (1998) and Degryse and Cayseele (2000) make use of a panel of firm-loan data on German and Belgium firms but do not exploit the panel structure of the data in their empirical analysis. Pagano, Paneta and Zingales (1998) use a panel of Italian firms and track them both before and after their respective IPO. The purpose of their study, however, is not to examine the patterns of interest rates but to extract and disentangle the determinants of a firm s decision to go public. And they show that the cost of pre-ipo debt is not a significant determinant of the firm s decision to go public. 4

5 greater fraction of loans from the same lead lender, the spread that the firm pays decreases monotonically. These results are robust to controls for firm leverage both before and after the IPO, thus the results are robust to potential changes in financial risk that may occur after the firm goes public. Furthermore, the results show that the average spread paid by the firm significantly drops after the firm goes public, even after controlling for changes in firm characteristics following its IPO. The change in the pattern of spreads, as well as in the average spread, reveals that once the relationship bank s information monopoly is not naturally protected by the firm s high switching cost, the bank adjusts its behavior and begins to share with the firm the cost savings of continued lending generated by the bank s information advantage. These results are consistent with a simple framework (developed in Section7) where banks exploit their information rents when switching costs are high but share with the firm its lower lending costs when switching costs are low. The remainder of this paper is organized as follows. Section 2 relates this paper to previous work. Section 3 illustrates the empirical predictions of this paper. These predictions are derived from a simple framework which is presented later in the paper. Section 4 describes the sample and presents summary statistics. Section 5 presents the econometric specifications, empirical predictions, and estimation results. Section 6 explores whether issuing public and/or private debt prior to a firm s IPO affects the relationship banks information monopoly while the firm is private. Section 7 presents in detail the simple framework. Section 8 concludes. 2 Related Literature Petersen and Rajan (1994) and Berger and Udell (1995) were among the first to look at changes in loan pricing over the course of a firm s lending relationship. While Petersen and Rajan find that the duration of the lending relationship has no statistical, or economic, significant impact on the loan rate the bank offers the firm, Berger and Udell show that, when lines of credit are considered, the interest rate charged on these loans falls as relationship duration increases. Both of these papers use data from the NSSBF, hence neither of these papers have a panel of firms, and inferences are made by comparing firms at different points in time of their lending 5

6 relationship. 3 The results of this paper can be compared to those in Petersen and Rajan (1995). In the authors model, lenders in a competitive credit market cannot expect to share in the firm s future surplus and must break even on every loan; therefore young firms, whose prospects are uncertain, are charged high interest rates until the firm-specific uncertainty is resolved. In contrast, lenders in a concentrated credit market are able to share in the firm s anticipated future surplus, thus allowing them to subsidize young firms and extract rents once the firm has successfully established itself. The authors empirical finding shows that in concentrated credit markets younger firms pay lower interest rates than do similar firms in competitive credit markets. Likewise, more established firms pay higher interest rates in concentrated markets relative to those of comparable firms in competitive markets. The sample examined in Petersen and Rajan s (1995) study is a cross-section of firms from the NSSBF. Inferences about changes on a firm s borrowing pattern are based on across-firm comparisons. A parallel can be drawn between the current study and the paper described above: The pre-ipo scenario in this paper resembles a concentrated credit market, and the post-ipo scenario resembles a competitive credit market. Rather than working with a cross-section of firms, this paper works with a panel of firms as they lengthen and deepen their lending relationships, and as the relationships moves from operating within the context of a concentrated credit market scenario, to operating within the context of a competitive market. The empirical findings of the two papers differ: this paper finds that in the concentrated credit market scenario the pattern of interest rates as a function of relationship intensity follows a U-shaped curve, while in the competitive scenario, interest rates monotonically decrease in relationship strength. There are a number of theories on the effects of information monopolies derived from lending relationships. Boot and Thakor (1994) show that in a model without learning over time, the pattern of interest rates is expected to decline over time. This is, until the borrowing firm demonstrates success, it will face above market-spot borrowing costs, and further, will be 3 Elsas and Krahnen (1998) study rates charged on lines of credits for a randomly selected sample of firms borrowing from five leading German banks over a five year period and find no evidence that the duration of the lending relationship affects interest rates. Note that though the paper has a panel of firms, the panel structure of the data is not exploited in the estimation methodology. Degryse and Cayseele (2000) employ a large sample of loans granted by an important Belgian bank to small Belgian firms, and find that loan rates increase with the duration of the lending relationship, but decrease when the firm purchases other information sensitive products from the relationship bank. Again, the authors do not exploit the panel structure of the data. 6

7 required to provide loan collateral. Following its first success, however, the firm will pay below market-spot rates, and the collateral requirement will be dropped. In contrast, the theories of Sharpe (1990) as well as of Greenbaum, Kanatas, and Venezia (1989) predict precisely the opposite pattern of interest rates. Their studies show that ex-post monopoly rents disappear amidst lender competition (i.e., rents are competed away via lower interest rates on loans offered at the beginning of the lending relationship). In this way, the authors predict that relationship banks, behaving like monopolists, will subsidize borrowers during the early stages of a relationship, while extracting information rents at later stages. These papers, however, do not consider that the relationship bank s actions may transmit revealing signals to nonrelationship banks, signaling important, if only partial, pieces of information. As shown in the framework presented in Section 7 of this study, accounting for such possibilities helps to explain the empirical finding that in circumstances of high switching costs, the pattern of interest rates follows a U-shaped curve. There are several contributions in the literature studying the firm s choice of public versus private debt as an attempt to hinder the relationship bank from locking in the firm. See for instance, Rajan (1992), and Diamond (1991). These theory papers show that a mix of private and public debt can limit the inside bank s bargaining power, in turn improving a borrowing firm s investment decision and strengthening its overall value. Santos and Winton (2006) argue that since firms are typically in greater danger of failing during recessions, banks that have an exploitable information advantage should, in times of recession, be able to raise their rates by more than would be justified by borrower default risk alone, and show that during recessions banks raise their rates more for bank-dependent borrowers than for those with access to public bond markets. In this sense, the results of the current paper are consistent with those of Santos and Winton. Houston and James (1996) empirically show that potential hold-up problems arising from bank borrowing are particularly severe for firms characterized as having high growth opportunities. The authors also show that having multiple bank relationships, as well as borrowing from the public markets, can mitigate such problems. Ongena and Smith (2001) use a panel of publicly traded Norwegian firms and find that firms are more likely to leave a bank as their relationship with the bank matures. This evidence is also consistent with firms avoiding potential lock-in problems. 7

8 3 Predicting the Pattern of Interest Rates As the firm-bank lending relationship evolves, the firm faces higher costs of switching lenders, which can help to lock the firm in the relationship, and allow the lender to extract rents. On the other hand, as the relationship bank acquires information about its borrowing firm, some information spills over to non-relationship, or outside, banks. By observing the relationship bank s actions, the outside banks can infer some characteristics of the borrowing firm. This information spillover can lower the outside banks lending cost and might help to attract the borrowing firm s business. Spillover and switching cost effects exert opposite force on the equilibrium interest rate that the relationship bank can demand from the borrower. Thus, the equilibrium pattern of interest rates depends on which force dominates. Spillover effects are present when the firm faces high costs of switching lenders and also when it faces low switching costs. Thus, a key ingredient determining whether the relationship bank can extract rents from its information monopoly, is the presence or absence of switching costs. Therefore, I distinguish two distict scenarios where switching costs vary. The pre-ipo period is characterized by high switching costs since firms lack credible methods for disclosing and disseminating information. The post-ipo period is characterized by negligible switching costs, since the SEC requires publicly traded firms to disclose information, adding to the information revealed by analyst coverage, newspaper scrutiny, and the stock price. This public disclosure of information reduces the relationship bank s information advantage vis-a-vis other banks, consequently allowing firms to switch banks more easily. In this scenario: What is the predicted pattern of interest rates as the intensity of the firm s lending relationship increases? Here intensity is defined as the dependence of the firm on its relationship bank and is measured as the proportion of loans that the firm draws from the relationship bank. 3.1 Pre-IPO: The Presence of Switching Costs Prior to a firm s IPO, and as intensity with the inside bank increases, two opposing forces are at play. On the one hand, the firm s switching costs increase, thereby facilitating the inside bank s ability to lock-in the firm and increase the borrowing rates. On the other hand, as the relationship bank acquires information, some of this information spills over and is acquired 8

9 by outside banks. Such information spillovers lower the outside bank s cost of lending to the firm. Consequently, the firm s outside option improves, the relationship bank looses its grip on the borrowing firm. This reduces the inside bank s ability to increase rates. The question is whether switching costs or information spillover effects dominate? If switching costs dominate, the predicted pattern of interest rates will be increasing in relationship intensity as the relationship bank extracts rents from the borrowing firm. If instead information spillovers dominate, interest rates would decrease in intensity as the outside bank can learn about the firm and lower its lending costs thus lowering the outside option upon which the relationship bank can extract rents. Switching costs might dominate spillover effects over certain ranges of relationship intensity, and spillover effects might dominate over other ranges of intensity. For instance, spillover effects might dominate for low values of relationship intensity, since the relationship bank is beginning to build its information monopoly and the firm s cost of switching lenders is low. But for high values of intensity, switching costs could dominate spillover effects as the firm has developed relationship specific capital, and the marginal value of any information spillover is lower. If this is the case, the predicted pattern of interest rates as a function of relationship intensity would follow a U-shaped curve: spreads decrease for low values of intensity and increase for high intensity values. 3.2 Post-IPO: The Absence of Switching Costs Following the IPO, switching costs become negligible. Hence, the trade-off between switching costs and spillover effects vanishes, and the only effect left over is the spillover effect. As information spills over and the relationship bank looses its priviledged position, finding itself in competition for the firm s business, the bank might choose to share some of the benefits of its information advantage with the firm, by offering lower rates on subsequent loans. Thus, as relationship intensity increases, interest rates should decrease. Table 1 summarizes the predictions derived from this hypothesis. The next sections describe the data and the empirical pattern of interest rates as a function of relationship intensity; a simple framework that rationalizes the results follows after the empirical analysis. 9

10 4 Data 4.1 The sample The data used in this paper is obtained by hand matching, IPOs listed in SDC with loan data from Dealscan. Using the SDC database I identify all IPOs between 1998 and 2003 that satisfy the standard researcher s requirements (excluding ADRs, closed-end funds, REITS, financial institutions, private placement, rights and unit issues, and best efforts, non firm commitment, and auctioned offers), and for which Dealscan reports a loan. For loans greater than $100, 000, and granted in 1986 or later, Dealscan reports the structure of the lending syndicate and the identity of the syndicate members, as well as loan characteristics, such as the interest rates (the all-in-spread-drawn and the all-in-spread-undrawn), the loan amount, time to maturity, the S&P Senior debt ratings at the onset of the loan and at the time of the loan s cancellation, any fees the borrower must pay the lender, and the type and purpose of the loan. 4 Of the firms going public between 1998 and 2003, Dealscan shows that at least one loan was made inthecaseof411 firms, about 35 percent of the IPO firms in the sample period. For these 411 firms, there are 993 loans for which the all-in-drawn variable is available. Of these loans, 519 loans are pre-ipo loans, while the remaining 474 loans are post-ipo. Only 104 firms have exclusively pre-ipo loans, and only 28 firms have exclusively post-ipo loans. This leaves 250 firms with at least one loan before the IPO, and at least one loan after the IPO. For these 250 firms, IsearchinCOMPUSTAT andinthefirm s prospectus for the firm s debt, asset and equity values on the year the loan was taken, to build leverage ratios that can control for the firm s financial risk at the time the loan was taken. 5 Two notes on sample selection are warranted. First, only those firmsthathavegonethrough the IPO process have been selected for the sample. Indeed, the paper s experiment is precisely to focus on a significant information-releasing event in the firm s life that can eliminate the relationship bank s information monopoly and the IPO is such as event. Thus, by design, IPO firms were selected. 6 4 See Sufi (2005) for a detailed explanation of syndicate loan structures. 5 For pre-ipo data I use COMPUSTAT and the firm s IPO prospectus. COMPUSTAT back files balance sheet data from pre-ipo years using the data reported in the firm s IPO prospectus. When COMPUSTAT data is missing I check the IPO prospectus to confirm the missing data or complete the missing information. Post IPO data is comprehensively covered in COMPUSTAT. 6 The firm s decision to go public and the precise timing of the IPO are not the focus of this paper. Fur- 10

11 Second, among firms going through the IPO process, those firms with bank loans reported in Dealscan were further selected. Selecting firms with bank loans is a necessary condition for monitoring changes in interest rate patterns over the course of the banking relationship. It should be noted, however, that using Dealscan to identify bank loans might introduce a selection bias given that Dealscan only reports loans in excess of $100, 000 and it might be that firms garnering such loans share a specific setoffirm characteristics. For example, these firms could be "bigger and better" than the average IPO firm. They may be bigger because they have loans of over 100, 000 dollars, and they might be better because (as James and Weir (1990) show), only those firms seen as potentially high value typically apply for -and are granted- inside debt prior to issuing public stock. 7 This would imply that firms in my sample should be smarter at preventing the relationship bank from extracting information rents. The presence of such a bias, however, is contrary to my finding. In fact, this study finds strong evidence of relationship banks exploiting their information advantage prior to the firm s IPO. If those firms not included in Dealscan database were also to be included in this paper s sample,itismorethanlikelythatevenstrongerevidence of relationship banks exploiting their information privilege would emerge. To further address any possible sample selection related issues, and in an effort to capture any firm characteristic common to firms in Dealscan, the econometric specification includes firm fixed effects. 8 By using firm fixed effects, the estimated coefficients are within estimators, that is, coefficients are estimated from within firm variation, therefore controlling for any unobservable firm-specific characteristic. The Dealscan item all-in-spread-drawn (spread hereafter) is the interest rate that the borrower pays the lender on the amount drawn on the loan, and it is measured as a mark-up over LIBOR. When a loan has several facilities, the spread is the weighted average of the spreads for each facility, where the weights are the amount of the loan in that facility relative ther, note that Pagano, Paneta and Zingales (1998) show that pre-ipo borrowing costs are not a significant determinant of the firm s going public decision. The authors measure the relative cost of credit to a firm as the ratio between the interest factor charged to firm i at time t and the sample average interest factor. Table III in their paper shows that the cost and availability of credit do not have much of an explanatory power in the firm s decision to go public. For more on the firm s decision to go public see, for instance, Benveniste, Busaba, and Wilhelm (1997), Pagano, Paneta, and Zingales (1998), Chemmanur and Fulghieri (1999), Maksimovic and Pichler (2001). 7 See also Mikkelson and Partch (1986), James (1987), and Lummer and McConnell (1989). 8 On dealing with selectivity bias using a panel data set, see Veerbek, M. and T. Nijman (1992). 11

12 to the total amount of that loan. Dealscan also records the name of all the banks involved in the loan and specifies the role of each of the lending banks (e.g., lead lender, co-lead lender, and other loan participants). Other relevant loan characteristics reported include the amount of the loans, their time to maturity, the firm s debt rating at the time it took the loan, and whether or not the loan was collateralized. Information on the specific amountthateach lender contributed towards the loan is available for about half the sample. The loan s time to maturity is measured in months. For loans with multiple facilities, the maturity corresponds to the maximum between the maturities of the facilities. The amount of the loans is measured in millions of US dollars, and when a loan has several facilities, the loan amount is the sum of the amounts in each facility. The mean spread is basis points. The mean spread on the undrawn portion of the loan (called the all-in-spread-undrawn) is basis points. The average up-front-fee and commitment fee are and basis points respectively. On average, there are more than 5 lending banks per loan, and the lead lender contributes slightly over 60 percent of the total loan amount. The mean loan amount is million US dollars and the average loan length is about 49 months. 4.2 Lending relationship variables Prior research by Petersen and Rajan (1994), Berger and Udell (1995), Elsas (1998), Degryse and Cayseele (2000), and Ongena and Smith (2001) has measured the strength of the relationship by the length of time the lender and the firm have been doing business with one another. While this provides a meaningful measure of relationships, it might not fully capture how dependent the firm is on its current lead lender. A longer loan maturity or a longer lasting lending relationship does not preclude the firm from having many of such relationships, and, therefore, not becoming dependent on any one lender. To measure the degree to which a firm relies on its bank, this paper uses a measure of relationship intensity defined as the number of loans that a firm has drawn from its current lead lender as a proportion of the total number of loans the firm has drawn to date. 9 9 Note also that using loan maturity as the length of the relationship might be misleading since firms can cancel their loans before the loan matures, or can renegotiate the loan and extend the maturity of the existing loan. 12

13 For each firm i the firm s loans are ordered chronologically from l =1to l = L where L is the maximum number of loans observed for that firm. Prior_by_Lead i,l is defined as the total number of loans in which the lead lender for loan l has participated in, up to loan number l. For the first loan firm i takes, Prior_by_Lead i,l =0by definition. And for each firm i, and loan l, Loans_to_Date i,l = l. The relationship intensity variable is defined as, Intensity i,l = µ Prior_by_Lead Loans_to_Date i,l Higher values of Intensity i,l suggest that the borrower is more dependent on the current lender. When counting the number of prior loans a relationship firm has taken from the current lead lender, Prior_by_Lead i,l, it is important to account for two issues: bank mergers, and loans granted by a subsidiary of a parent bank and by the parent bank itself. Bank mergers can potentially disrupt a pre-existing lending relationship. See, for instance, Ongena and Smith (2001), Sapienza (2002), and Karceski, Ongena, and Smith (2005). For the purpose of this paper, the relevant question is: What happens to the acquirer s and the target s information about their clients after they merge? The information a bank has regarding its client is likely to be inherited by the merged entity. For instance, the information Chase Manhattan Bank had about its client is likely to have been transferred to JP Morgan Chase following the JP Morgan - Chase Manhattan Bank merger. Accordingly, loans that a firm took from a bank that subsequently entered into a merger are counted as prior loans from the merged entity. In the previous example, a loan granted by Chase Manhattan Bank would be counted as a prior loan to any loan subsequently granted by the merged entity, JP Morgan Chase. Loans granted by a parent bank, and loans granted by a subsidiary, or a branch of this bank, are treated as loans originating from the same lead lender. 10 This treatment is based on the assumption that information flows between a subsidiary and its parent bank: It is unlikely that different sections of the same financial holding company would not share information about common clients, as such information sharing could substantially reduce the cost of 10 For instance, when a firm receives a loan from a parent commercial bank, e.g. Citibank, and later receives a loan from one of its subsidiaries, e.g. Salomon Smith Barney, these loans are treated as originating from the same lead lender, Citibank. 13

14 doing business with the firm in several different departments. 11 This study makes use of the most recent data on subsidiaries of bank holding companies, obtained from the Federal Reserve Board website Empirical Analysis 5.1 Empirical Predictions The empirical predictions stated in Section 3 and displayed in Table 1 are summarized below: 1. Prior to going public, firms face high costs associated with switching lenders. As long as spillover effects dominate switching costs the lending bank cannot exploit its information advantage. As soon as switching costs become dominant, the relationship bank can extract rents from having locked in the client firm. The pattern of interest rates depends on the trade off between switching cost and spillover effects. 2. After firms have gone public,and as a result of the information disclosure that occurs during and after the IPO process, the borrowing firm s switching cost is significantly reduced. Absent switching costs, spillover effects dominate, and relationship banks are unable to exploit their information privileges. In this case, the predicted pattern of interest rates is monotonically decreasing in relationship intensity. 3. Since after going public switching costs are significantly reduced, the mean borrowing rate for these firms should be lower than when the firms where private, all else equal. 11 Such information transfers are even more likely following the Gramm-Leach-Bliley Act of 1999, when the firewalls that previously existed between commercial banks and their subsidiaries came down. 12 See: 14

15 5.2 Univariate Results Table 2 compares the interest rate paid by firms before and after their IPO, for low and high values of relationship intensity. The results are summarized here: 1. Prior to going public: (a) When relationship intensity is low, the mean interest rate is about 218 basis points. (b) This interest rate increases by basis points, to about 263 basis points, as relationship intensity increases. This increase is statistically significant at 1 percent. (c) Spillover effects dominate for low values of relationship intensity and switching costs dominate for high intensity values. This explains the difference in spreads for low and high relationship intensity values. 2. After firms have gone public: (a) As a firm moves from a low to a high relationship intensity, interest rates drop by about 14 percent, though this fall is statistically significant at 20 percent. (b) Firms with high values of relationship intensity experience a drop in interest rates of basis points. This drop is statistically significant at 1 percent. 3. Mean borrowing rates significantly fall by over 21 basis points once the firm goes public. This is statistically significant at 1 percent Pre-andpost-IPOloancharacteristics Table 3 reports summary statistics for loan characteristics. Column 1 reports characteristics for the sample, columns 2 and 3 report characteristics both before and after the IPO, respectively, and column 4 shows the difference in the mean values of these variables as the firm goes public. For loans taken prior to the firm s IPO, the average spread is basis points, and following the IPO this spread drops to basis points. The difference, basis points, is statistically significant at 1 percent. After a firm goes public the number of banks from which the firm borrows increases, and the percentage of the total loan amount contributed by 15

16 the lead lender decreases from percent to percent, a difference that is significant at the one percent level. Once the firm is public, it borrows from a larger set of banks and pays asignificantly lower interest rate. These summary statistics for loan characteristics suggest that the IPO event deteriorates the pre-ipo relationship s bank privileged position, making lending to the firm more accessible to other banks. These loan characteristics will be used, together with other firm characteristics including the firm s leverage ratio on the year the firm took the loan, in the firm fixed effects regressions reported in Section Multivariate Analysis To address the empirical predictions outlined above, the following sections trace the pattern of interest rates as the lending relationship matures Interest rate dynamics leading up to the IPO To focus on the pattern of interest rates before the firm s IPO, the sample is restricted to include only pre-ipo loans, and only firms having a minimum of two such loans. These restrictions result in a sample of 295 loans corresponding to 98 different firms, or an average of about three pre-ipo loans per firm. The predicted interest rate pattern for the pre-ipo, high switching cost, scenario is a non linear function of relationship intensity. The regression equation of interest is of the following type 13 : 13 This specification,aswellasthenextspecifications, is similar to Petersen and Rajan (1994). The authors estimate a cross section regression of the type: Interest rate i = β 0 + β Relationship Characteristics i + β 1 Economy wide rates i + β 2 Firm Characteristics i +β 3 Loan and Lender Characteristics i + β 4 Industry Dummies i + ε i My specification differsinthefollowingways: (1). I have a panel of firmssoicantrackonefirm over time and study how that firm s lending relationship affects that same firm s interest rate. I do this running firm fixed effects regressions. (2). By using firm fixed effects I can control for unobervable firm characteristics. Using time-fixed effects allows me to control for economy wide changes over time. Thus my specifications does not include industry dummies (this would be accounted for in the firm fixed effects), or economy wide interest rates (this would be accounted for in the year fixed effects). (3). I distinguish two distinct periods in the firm s life: before the IPO and after the IPO. These are informationally very different scenarios and the bank s information monopoly differes across the two. 16

17 Spread i,l = β 0 + β Intensity (Intensity) i,l + β Intensity_Sqrd (Intensity) 2 i,l (1) +β Loan_Characteristics (Loan Characteristics) i,l +β Firm_Characteristics (Firm_Characteristics) i,l +β Switched (Switched_Lenders) i,l + β First_Loan (First_Loan) i,l +β Loan_Year_FE (Loan_Year_FE) i,l +ε i + η i,l Where i indexes for the firm, and l for the loan number. ³ Spread i,l is the all-in-spread drawn Prior_by_Lead for firm i s loan l. Intensity i,l is as defined above, Loans_to_Date, and measures for each i,l loan l the number of prior loans the firm has drawn from the current lead lender as a fraction of the total number of loans drawn to date. The intensity variable captures how much the firm depends on its relationship bank. Loan_Characteristis i,l is a vector of firm i 0 s loan l 0 s characteristics, such as the number of lenders in the lending syndicate, the loan s maturity, the type of loan (e.g., whether the loan is a revolver line of credit, a term loan, etc), and purpose of the loan. Firm_Characteristics i,l is a vector of firm i 0 s characteristics at the time loan l was taken, such as the debt rating firm i received when taking loan l. These debt ratings are S&P senior debt ratings at the onset of the loan. Switched_Lenders i,l is a categorical variable equal to one when i 0 s lead lender for loan l has not participated in any of the prior l 1 loans. First_loan i,l is a categorical variable equal to one when firm i 0 s loan l is the firm s first loan. For these last two categorical variables the fraction of loans taken from the current loan s lead lender, Intensity i,l, equals zero as the number of prior loans by lead lender is, by definition, zero. Loan_Year_FE i,l represent loan year fixed effects; ε i represents firm-specific unobservable characteristics, and η i,l represents the idiosyncratic error term. Equation (1) is estimated for different specifications under both a fixed effects and random effects model, and the Hausman Specification test is used to evaluate which model better fits the data. The Hausman test reveals that the random effect estimates are biased, and hence the (4). The relationship variables differ again because I can exploit the panel structure of the data. Petersen and Rajan use the length of the relationship, the firm s age, the number of banks from which the firm borrows, and whether the firm has a deposit account with the lender. 17

18 fixed effects model fits the data better. By using the fixed effects specification, the coefficients are estimated from regressions run on deviations from means; therefore, the coefficients are within estimators. The importance of this result cannot be overlooked, as it shows that the identification comes from within-firm variation, in particular, the coefficients β Intensity and β Intensity_Sqrd measure how a firm s spread changes when that firm s relationship intensity changes. Results reported in Table 4 correspond to fixed effects estimates. The baseline regression, reported in column 1 of Table 4, reveals the predicted U-shaped pattern of interest rates on relationship intensity: β Intensity = and β Intensity_Sqrd = , significant at 1 and 5 percent levels respectively. For values of Intensity i,l less than 0.59 interest rates decrease as Intensity i,l rises, and for value of Intensity i,l greater than 0.59 the spread rises as Intensity i,l increases. This suggests that relationship banks can successfully lock-in their clients after providing about sixty percent of the loans the relationship firm takes. At this point, the bank s information monopoly is secured and the bank begins to extract rents. 14 The baseline regression reported in column 2 of Table 4, adds loan year fixed effects, and confirms the previous result. Hereafter, all reported regressions include these year fixed effects. Single banking relationships can further secure the inside bank s information monopoly. To avoid this, firms might attempt to borrow from multiple banks. Several papers have addressed the benefits and costs of multiple banking relationships (see for instance, Rajan (1992), Bolton and Scharfstein (1996), Houston and James (1996), Detragiache, Garella, and Guiso (2000) and Ongena and Smith (2001)). If multiple banking relationships do in fact mitigate a bank s acquisition of ex-post monopoly rents, firms that borrow from multiple lenders should face lower interest rates. Two ways to deal with this follow. First, consider the impact on interest rates of switching to a new lender. Column 3 of Table 4 includes a categorical variable Switched i,l equal to one when firm i 0 s loan l is granted by a lead lender who has not been a lead lender in any of the prior l 1 loans. When a relationship firm takes a loan from a lead lender from whom it has not previously borrowed, rates drop by about 90 basis points (β Switched = 89.98, significant at 5 percent). Firms that succeed at 14 The critical value of Intensity i,l after which relationship banks begin to exploit information rents, is recalculated for the different specifications and the results are consistent with those reported for the baseline regression (about 60 percent). 18

19 switching might be smarter at negotiating loans, and they might face lower switching costs in the first place. Note that, because the regressions include firm fixed effects, there is no endogeneity concern. The firm fixed effects control for the unobservable "firm smartness" and "low switching cost firm" characteristic. Second, the variable Nu_Lenders i,l further accounts for the effect of multiple lenders. Nu_Lenders i,l equals the number of banks in firm i 0 s loan l lending syndicate. A larger number of lenders in the syndicate means more banks are informed about the firm, thus reducing any one bank s monopoly power and, accordingly, increasing competition. The results reported in Column 3 of Table 4 confirm this by showing that interest rates are decreasing in the number of lending banks (β Nu_Lenders = 1.50, significant at 5 percent). Loans of longer maturity might be riskier than loans of shorter maturity. Columns 4 and 5 in Panel A of Table 4 shows that interest rates are increasing in the time to maturity. Relationship intensity does not change this result. This is, longer maturity loans taken from banks with which the firm has dealt with several times, do not exhibit any interest rate discount (the interaction term between maturity and intensity is insignificant). The type of loan taken, as well as the use of the loan s proceeds, may also have an impact on the interest rate demanded. Berger and Udell (1995) show that firms receiving lines of credit pay lower interest rates as the relationship evolves. Columns 1 and 2 in Panel B of Table 4 incorporates loan-type fixed effects. The most common types of loans drawn by pre-ipo firms in my sample are revolving lines of credit (a firm s credit card ) and term loans (loans with fixed maturities, and on which interest and principal are paid on a regular basis). While I find that firms taking lines of credit pay on average lower interest rates, I do not find that as the intensity of the relationship increases borrowers taking lines of credit pay lower rates (the coefficient on an interaction term between spread and lines of credit is not significant). Column 3 adds loan-purpose fixed effects. Loans drawn to fund riskier projects are expected to demand higher interest rates. I find that loans used for LBO/MBO are associated with an interest premium of more than 70 basis points, and loans applied to acquisitions involve a premium of about 30 basis points. The risk of the loan contributes to the interest rate charged on the loan. I control for loan risk in two ways. First, I use Dealscan to find the S&P senior debt rating the firm received 19

20 at the time the loan was taken. Column 4 in Panel B of Table 4 shows that including a categorical variable indicating whether the firm s loan rating is vulnerable (between CCC+i and BBBis) does not affect the main result on interest rate dynamics. Second, to control for the firm s financial risk at the time the loan was taken, I include the firm s age at the time the loan was taken in the regression equation, since age has been reported to impact firm risk. Thedatethefirm is founded is gathered from SDC, and missing observations are filled from data reported in the "History Overview" section of the firm s website. Column 5 shows that firm age at the time the loan is taken does not significantly affect the interest spread, nor does it alter the pattern of spreads as a function of relationship intensity. Loan pricing might also depend on whether the firm chooses to make use of the same relationship bank for loans immediately subsequent to the loan in question. That is, it is possible that banks might offer their clients a menu of interest rates, and the pricing of one loan might depend on whether the bank will be providing the firm s next loan as well. Columns 1 and 2 in Panel C of Table 4 include controls for whether the firm s lead lender in loan l is the same as the lead lender for loan l 1, as well as for whether any of the lenders in loan l are members of the syndicate of lenders for loan l 1, respectively. Neither factor appears to influence the spread paid by the relationship firm. Following the Gramm-Leach-Bliley Act of 1999, which allows commercial banks to underwrite securities issues, commercial banks have become actively involved in underwriting services, and investment banks have entered the commercial lending business. Information economies of scope allows banks to efficiently provide several services to a firm at a lower cost than that which would prevail if fewer services were provided. 15 For the purpose of this paper, it is important to see whether or not the interest rate demanded by a bank is affected by the bank s participation in the firm s IPO. In particular, banks might be selling a bundle of services, including underwriting and lending services, and the services contained in the bundle might not be priced independently. Since this paper s subject is the dynamics of interest rates as the lending relationship evolves, it is important to account for the possibility that the interest rate depend on the bank s participation in the firm s IPO. Two possibilities arise: Banks bundling their services might offer an interest rate discount on pre-ipo loans in exchange for 15 See for instance Bharath, Dahiya, Saunders, and Srinivasan (2005), and Narayanan, Rangan and Rangan (2004), Drucker and Puri (2005). 20