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- Copyright
- Copyright © 2021 Elsevier Inc.
- eISSN
- ARCH-4059
- DOI
- 10.2139/ssrn.3877687
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- See Article on Publisher Site
Abstract
In this paper, we examine the significance and uniqueness of the individual-pair relationship cultivated through repeated loan interactions by the firm’s borrowing manager and the bank’s loan officer. Using a hand-collected dataset of borrowing manager and loan officer information, we find that individual-pair relationship loans result in a reduction of the cost of debt of between 7-27 basis points. We also document that the economic impact of individual-pair relationships exists even when other types of lending relationships, e.g., institutional pairs and social ties, are taken into consideration. Lastly, we find evidence that individual-pair relationships are especially important when either the firm has a high level of information asymmetry, the bank is smaller, or loan officers have slimmer portfolios. Cumulatively, our results highlight the value of sustained professional engagement between two individuals in the lending process. Keywords: Individual-Pair Lending Relationships, Asymmetric information, Professional connections, Bank lending, Debt Contracting, Cost of Debt JEL Classifications: G21, G30, D23, D82, J24 Acknowledgments: We appreciate the helpful suggestions and comments from David Aboody, Bugra Ozel, Arthur Morris, Derrald Stice, and workshop participants at the University of Utah. Electronic copy available at: https://ssrn.com/abstract=3877687 1. Introduction Debt contracting theory suggests that relationships provide a meaningful mechanism to overcome information asymmetry. Early studies, which focus on the institutional-pair level (i.e., firms-banks), document mixed empirical evidence regarding relationship lending’s benefits and costs (Berger and Udell, 1995; Boot and Thakor, 1994; Bharath et al., 2011; Cole, 1998; Petersen and Rajan, 1994, 1995; Prilmeier, 2017; Rajan 1992; Schenone, 2010; Sharpe 1990). These varied findings may result from and attest to the fact that the underlying relationships involve individuals rather than just institutions. Acknowledging this issue, more recent research focuses on the importance of individuals (Bushman et al., 2021; Drexler and Schoar, 2014; Haselmann et al., 2013; Herpfer, 2021; Karolyi, 2018; Khan et al., 2019). These studies have enhanced our understanding of how the “human factor” affects debt contracting. In this paper, we extend the literature by focusing entirely on individual-pair relationships, and, more specifically, the relationships cultivated between the two key individuals directly involved in the loan process: the borrowing manager and the loan officer. We have adopted a rigorous and time-intensive approach to track the impact of the individual- pair lending relationship. Our sample begins with all syndicated loans secured from 1996 to 2016. Next, we follow Nini et al. (2009) and collect all disclosures that are likely to be credit agreements For example, Boot and Thakor (1994) document that borrowers gain an unsecured loan with a below spot market interest rate based on their durable relationship with banks. Berger and Udell (1995) state that among small firms, borrowers with a bank relationship enjoy lower interest rates and are more likely to be granted a collateral waiver. Bharath et al. (2011) show that repeated borrowing from the same lender results in lower loan spreads. Petersen and Rajan (1994) observe that relationships with institutional lenders increase financing availability. Lenders are also more inclined to extend credit to a firm with whom they have a pre-existing relationship (Bharath et al., 2007; Cole, 1998). Prilmeier (2017) finds that a covenant’s strictness is relaxed over the duration of a relationship. Petersen and Rajan (1995) further show that a relationship’s value depends on the extent of credit market competition. However, despite the shared benefits of soft information accumulation, other studies cite banks’ exploitation of their information advantage through a lock-in effect, e.g., imposing less favorable terms (Rajan, 1992) or offering higher interest rates (Sharpe, 1990). Schenone (2010) finds that after a borrower’s initial public offering (IPO), interest rates decrease significantly. Electronic copy available at: https://ssrn.com/abstract=3877687 and match the contracts to DealScan. We then hand collect both the borrowing manager and loan officer information from the signature page of each matched contract to identify the discrete individuals involved. Our focus on these two key actors aligns with Bushman et al. (2021) and Herpfer’s (2021) assertion that contract signatories are typically highly engaged in contracting negotiations and interact extensively throughout the process. We code the individual pairs as relational if they have been jointly involved in more than one credit agreement. This approach allows us to study direct and transparent relationships and thus to overcome the limitations of prior research. It also helps us to separate individual-pair lending interactions from institutional-pair and individual-institutional lending interactions. Our resulting sample consists of 3,883 loans with 3,496 unique individual borrower-lender pairs. To our knowledge, this is the first comprehensive large-scale sample of debt contracts across multiple individual-pairs over time. We begin our analysis by examining the impact of individual-pair lending relationships on the loan spread. Our results provide evidence that, after controlling for the observable borrower and contract characteristics, an individual-pair lending relationship generates an approximate 13 basis point (bps) reduction in loan spread, equal to a 7% reduction in the cost of debt as compared with the average spread. Given our focus, we investigate whether the individual-pair relationship matters or whether it is proxying for a higher relationship level (either the institutional-pair relationship or the individual-institutional relationship). There are two types of individual-institutional relationships. The first involves borrowing firms’ executives and lending banks. This relationship is studied by Khan et al. (2019) and Karolyi (2018), who show that an executive’s prior affiliations with bank institutions can play an important role in debt contracting outcomes, e.g., spread, collateral, amount, etc. The second relationship is that between the loan officer and the borrowing firm. In this setting, Drexler and Schoar (2014) Electronic copy available at: https://ssrn.com/abstract=3877687 and Herpfer (2021) provide evidence that loan officers and their relationships with borrowing firms exert significant influence over the debt contracting process. While these studies identify the impact of one individual, i.e., the borrowing executive or the loan officer, on contracting outcomes, they ignore the relationship built between the two individuals directly involved in the transaction, i.e., the borrowing manager and the loan officer. Examining those individuals is important, as this is the level where much of the soft, non-transferable information is gathered about the borrower (Campbell et al., 2019). While it is appealing to include all levels of relationship lending in one regression, due to the high correlation between the variables, this approach is not possible. To overcome this design limitation, we conduct two types of analysis. First, we test for the effect of individual lending relationship on loan spread by running multiple regressions where each regression controls for one other lending relationship type used in prior research. Our results from these analyses show that our new measure is significant and economically stronger than all other relationship measures. However, when we control for the loan officer-firm relationship level, we find that both variables are negative but insignificant. Given that this result is likely due to two variables’ high collinearity, we move to our second analysis. In this analysis, we follow Bharath et al. (2007), who measure the benefit of relationship lending by focusing on the probability of a future transaction between the borrowing firm and the lending bank. Specifically, we investigate instances where the individual-pair relationship is broken by one entity’s departure from their respective institution. In doing so, we assume that the exit of either borrowing manager or loan officer could disrupt the benefits of the individual-pair relationship. We also theorize that the relationship’s significance will be gauged by whether the non-exiting individual continues to Electronic copy available at: https://ssrn.com/abstract=3877687 transact with the other entity. Then, following Even-Tov and Ozel (2021), we consult LinkedIn to hand collect the employment history of the borrowing manager and loan officer. Using this reduced sample, we test whether institutional relationships persist after an individual with an established relationship is no longer associated with one of the institutions. We find that when both the borrowing manager and the loan officer are no longer employed by their respective institutions, the two firms are highly unlikely to engage in a new loan, as evidenced by a 69.6% reduction in the odds ratio compared to if both are still working for their respective companies (LinkedIn).Similarly, when the borrowing manager is no longer employed by the borrowing firm, but the loan officer is still employed by the bank, the odds ratio of future loan engagement is reduced by 73.8 percent. Lastly, when the borrowing manager remains, but the initial loan officer has departed, the odds decrease by 67.4 percent. These results demonstrate that individual relationships matter. Given that the individual-pair relationship is the only one that ruptures in all three scenarios and that the probability change is similar across all three specifications, our findings suggest that the individual-pair relationship is a primary driver in the reduction of future institutional engagement. While this test does not directly examine the change in loan spread, it highlights the primacy of the individual-pair relationship over the individual- institutional relationship in loan contracting decisions. Next, we test whether our findings are distinct from those of Engelberg et al.’s (2012) on the impact of common ties on lending market outcomes. Engelberg et al. focus on how the personal Ideally, we would have liked to isolate the effect of the individual-level relationship rupture on loan spread. However, this requires more restrictions on our sample than our current design of testing the individual-pair relationship rupture’s effect on future institutional level lending. Because our current design tests whether the second loan between the borrowing firm and the lender occurs, we are able to use both transactions that involve the same two parties and those that do not. In comparison, the ideal design would require us to limit our purview to only transactions between the same two parties and to then examine the effect of the individual-level relationship rupture on loan spread in a second loan with institutional-pair relationship lending. This additional restriction results in a sub-sample that is too small to conduct our test. Electronic copy available at: https://ssrn.com/abstract=3877687 networks established through alumni affiliation or previous employment in the same industry affect business and investment decisions. Our work is different in that we highlight extended and direct engagement, i.e., the cultivation of individual-pair relationships forged over the course of multiple loans, and study its impact on contracting outcomes. Controlling for individual-pair alumni affiliation and borrowing manager industry experience, we offer robust evidence that the individual-pair lending relationships established through repeated debt contracting arrangements are economically meaningful. We then turn to prior research that demonstrates the heightened value of relationship lending in situations where information asymmetry plays a significant role (Güntay and Hackbarth, 2010; Khan et al., 2019; Kraft, 2015; Mansi et al., 2011; Rajan, 1992; Sufi, 2007). Since information asymmetry is arguably more pronounced in borrowing firms with either lower analyst coverage or a lower credit rating, we follow prior studies and use two measures to proxy for a firm’s information asymmetry level: low analyst following or non-investment grade status. We find that individual-pair lending relationships among borrowers with high information asymmetry can reduce the loan spread by between 15 and 27 bps over our sample period relative to borrowers with a lower level of information asymmetry. To further discern individual-pair lending relationships’ effect on the loan spread, we examine the characteristics of both the lending institution and the loan officer. Due to their competitive market position and the volume of their lending activity (Presbitero and Zazzaro, 2011), larger institutions that perform more deals in the syndicated loan market are more likely to adopt a transactional-based approach to lending. Accordingly, these types of lenders theoretically value the relationship-based loan less than smaller institutions. Using proxies to capture the lender’s prominence, we confirm that individual-pair lending relationships bear more significance Electronic copy available at: https://ssrn.com/abstract=3877687 for lower-volume lenders than they do for larger lenders. We document similar results when we examine loan officer activity. Specifically, we find that the individual-pair relationship effect is stronger for officers who issue less loans. The aforementioned results highlight the economic importance of individual-pair lending relationships. Next, we perform a more granular analysis of individual-pair lending relationship dynamics. Specifically, we examine the relationship’s time-series behavior. Prior literature suggests that the cultivation of a lending relationship leads to either increased benefits for the borrower through reduced spread and/or a reduction in the lender’s willingness to share the benefits of reduced information asymmetry with the borrower (Berger and Udell, 1995; Bharath et al., 2011; Boot and Thakor, 2000; Duqi et al., 2018; Elyasiani and Goldberg, 2004; Kysucky and Norden, 2016; Petersen and Rajan, 1994; Rajan 1992; Sharpe 1990). Our results suggest that the benefits derived from individual-pair lending relationships endure over time and that there is little variation over subsequent loans. In our final analysis, we examine future borrower downgrades and defaults to assess whether loan outcomes differ based on the presence or absence of individual-pair lending relationships. We find that while loans that involve individual-pair relationships are less likely to obtain a future downgrade than those without such a relationship, they do not exhibit a difference in default rate. Thus, our results suggest that individual-pair lending relationships build over time through extended interaction and that they may enhance a loan officer’s ability to screen and/or monitor a loan. As a final exploratory analysis, we look at the effect of individual-pair relationships on other contracting terms to see if there is a substitution effect. The results of this analysis do not provide any evidence that the reduced spread from individual-pair relationships results in an increase in monitoring provisions. In contrast, the institutional relationships do show evidence of Electronic copy available at: https://ssrn.com/abstract=3877687 a substitution, further suggesting that individual-pair and institutional relationships are very different types of relationships. Our study makes a number of important contributions. Most importantly, we are the first to examine individual-pair lending relationships that stem from mutual engagement in the contracting process where much of the softer information is gathered about the borrower (Campbell et al., 2019). Previous research has primarily focused on institutional-pair relationships (e.g., Berger and Udell, 1995; Bharath et al., 2011; Petersen and Rajan, 1994; Schenone, 2010). More recent research focuses on the importance of the human factor. Khan et al. (2019) and Karolyi (2018) report that when a borrowing firm’s executive departs (e.g., a CEO or CFO), the firm loses that executive’s past lender relationships and subsequently contracts with new lenders, especially ones that share a relational bond with the incoming executive. In our sample, 62% of the borrowing managers who sign the loan agreement do not serve as either CEOs or CFOs. This is important as studies that focus on borrowing firms’ executives and lending banks do not necessarily investigate the person who is directly handling the loan. Bushman et al. (2021) and Herpfer (2021) show that loan officers can mitigate information asymmetry. By comparing the benefits of individual-pair lending relationships at the borrowing manager-loan officer level to those at the institutional level, we are able to demonstrate that individual-pair lending relationships are economically meaningful and are more impactful than individual-institutional pair relationships. Second, emerging literature on personal relationships based on social ties and prior affinity has also shown positive effects on debt contracting (Engelberg et al., 2012; Haselmann et al., 2013). Our contribution is distinct from these studies in two ways: (1) the relational pair we examine is not bound by past affiliations, i.e., their relationship is built entirely through Electronic copy available at: https://ssrn.com/abstract=3877687 professional interaction rather than based on external common ties; (2) the relationship’s impact on contracting outcomes is examined over the course of multiple loans; and (3) unlike prior literature that uses common ties as a lens and concentrates on top executives, we identify and track relationships between individuals directly involved in the loan process. These differences are significant because they yield new insights regarding the differing effects of personal lending relationships and social relationships. Through our analyses, we provide evidence that individual relationships built and sustained across firms are economically meaningful. Finally, we contribute to the stream of management literature that examines the cost of non-executive employee turnover (Allen et al., 2010; Hancock et al., 2013; O'Connell and Kung, 2007; Tziner and Birati, 1996). The majority of prior studies consider only the employer’s subsequent replacement costs, e.g., recruitment, training, etc. Our results suggest that the private information shared and the quality of relationship maintained between individuals does not easily transfer to other parties. More specifically, the individual-pair lending relationship does not shift readily from the individual to the institution. Thus, employee turnover may impose more substantive effects than replacement-related expenses. In our setting, a firm may also incur increased borrowing costs on subsequent loans due to the rupture of the individual-pair relationship. The remainder of the paper proceeds as follows. Section 2 explains our sample selection and data collection process. Section 3 presents the main results. Section 4 provides additional analyses, and section 5 concludes. 2. Sample Construction and Descriptive Statistics Our syndicated loan sample spans 1996 to 2016. We begin in 1996 because electronic filings are only sparsely available on EDGAR prior to that year. We end in 2016 because that is Electronic copy available at: https://ssrn.com/abstract=3877687 when the DealScan-Compustat Linking Database from Chava and Roberts (2008) concludes its updated comprehensive coverage. Following Nini et al. (2009), we use text-search programs to scan all of EDGAR’s available filings (8-K, 10-K, 10-Q, etc.) for loan contracts. We search for the following 10 terms: “credit agreement,” “loan agreement,” “credit facility,” “loan and security agreement,” “loan & security agreement,” “revolving credit,” “financing and security agreement,” “financing & security agreement,” “credit and guarantee agreement,” and “credit & guarantee agreement.” Second, in order to merge with syndicated loans in DealScan, we use the firm’s tax identification number (CIK) in EDGAR to match with their identifier in Compustat (GVKEY). We then use the GVKEY and the loan date to match each credit agreement with syndicated loans in DealScan using the DealScan-Compustat Linking Database. After manually checking the robustness of the matching procedure by borrower name and lender name, we end up with 8,109 credit agreements on EDGAR successfully matched to DealScan loans from 1996 to 2016. Nini et al. (2009) have 3,720 successful matches from 1996 to 2005; our procedure yields a similar matching rate. Third, we collect the names of the borrowing managers and the loan officers at lead banks from the signature pages attached at the end of the loan agreements. We retain all documents that contain at least one instance of the string “/s/,” which indicates the presence of an electronic signature. For each instance of this string, we extract the name of the signer, their institutional employer, and their title. Given the heterogeneity of loan contract forms, we also manually verify every signature to ensure its accuracy. Our final sample consists of 5,361 credit agreements with As a placebo test, we collect the names of loan officers at the participant level and test the effect of individual relationship of borrowing managers and loan officers at the participant level on loan spread. We fin d insignificant results, consistent with the notion that participant banks don’t directly engage in the due diligence and the monitoring process. Electronic copy available at: https://ssrn.com/abstract=3877687 signature information from both the borrower and the lender. We lose 2,748 credit agreements due to absence of signatures in the original documents, which may occur because the contract does not include a signature page or the signature page contains only the names of banking institutions but not those of their loan officers. We then drop the observations with missing control variables from CRSP and Compustat and retain only non-financial firms, resulting in 3,883 loans for our main analysis (Table 1, Panel A). Our sample size is comparable to other studies that collect the signature data on the loan officer side only (Bushman et al., 2021; Herpfer, 2021). Among the 3,883 loans, there are 2,798 unique borrowing managers, 2,128 unique loan officers, and 3,496 unique borrowing manager-loan officer pairs (Table 1, Panel B). Of those 3,496 entities, 3,124 pairs transacted only once, 310 transacted twice, 53 transacted three times, and nine transacted at least four times (Table 1, Panel C). Table 2 presents the summary statistics for our sample of 3,883 loans with 3,496 borrowing manager-loan officer pairs. Our individual-pair relationship lending measure is an indicator variable equal to one if a borrowing manager-loan officer pair have engaged in a prior loan transaction. As shown in the table, the variable’s mean value is 0.11, indicating that about 11% of our sample’s pairs have an established transactional relationship. As expected, the mean value of the institutional-pair relationship lending measure is significantly higher, at 0.4, showing that 40% of a borrowing firm’s loans are secured from a lending bank with whom they have previously transacted. Consistent with Herpfer (2021), who employed DealScan and the loan signature page to conduct his study, we find that the average loan is priced at 180 bps above LIBOR and that it matures in just over four years. Electronic copy available at: https://ssrn.com/abstract=3877687 3. Results 3.1 Individual-pair lending relationship and loan spread We begin our analysis by examining the impact of individual-pair lending relationships on loan spreads by estimating the following regression: ( ) 𝐿𝑜𝑎𝑛 𝑝𝑟𝑒𝑎𝑆𝑑 = 𝐼𝑛𝑖𝑑𝑖𝑑𝑢𝑣𝑎𝑙 − 𝑃𝑎𝑖𝑟 𝑅𝑒𝑙𝑎𝑠𝑡𝑖𝑜𝑛 ℎ𝑖𝑝 𝑑𝑖𝑛𝐿𝑔𝑒𝑛 + 𝐿𝑜𝑎𝑛 𝐿𝑒𝑒𝑙𝑣 𝐶𝑜𝑙𝑛𝑠𝑡𝑟𝑜 1 + 𝐹𝑖𝑟𝑚 𝐿𝑒𝑣 𝑒 𝑙 𝑟𝐶𝑜𝑜𝑙𝑛𝑠𝑡 + 𝑌𝑒𝑎𝑟 𝐹𝐸 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝐸 + 𝑃𝑢𝑟𝑝𝑜𝑠𝑒 𝐹𝐸 where Loan Spread is the all-in-drawn loan spread over LIBOR. Since individual-pair and institutional-pair relationship lending take place at the loan deal level, we retain the loan facility with the largest loan amount to represent the loan deal. The variable Individual-Pair Relationship Lending is an indicator variable equal to one if borrowing manager i has previously engaged with loan officer j. Of the 3,883 loan transactions in our sample, there are 3,496 unique individual borrowing manager-loan officer pairs (see Table 1). We also include a set of loan-level control variables commonly used in prior studies (Bharath et al., 2011; Herpfer, 2021). First, we include Maturity, which is the natural logarithm of loan maturity (in months). The mean number of months until a loan in our sample matures is 49 (slightly over four years). Second, we include Loan Size as the natural logarithm of loan amount. We use the largest facility amount per loan, with an average loan size of $569 million. Third, Collateral is an indicator variable equal to one if the loan has collateral, and zero otherwise. About 51% of the loans have collateral. Fourth, Term Loan is an indicator variable equal to one if the loan type is a term loan, and zero otherwise. 20% of the loans are term loans. We also include common firm-level controls, such as Firm Size, Leverage, Profitability, Tangibility, MTB, Interest Coverage, Current Ratio, Non-Investment Grade. All of our Electronic copy available at: https://ssrn.com/abstract=3877687 𝑖𝑗 regressions include year, industry, and loan purpose fixed effects and cluster standard errors at the firm level. We include all definitions of dependent and independent variables in Appendix A. We report the results of estimating regression (1) using different specifications in Table 3. Column (1) Panel A provides the results from regression (1) using only our main variable of interest and shows that the coefficient on Individual-Pair Relationship Lending is significantly negative. As expected, the loan spread is significantly lower when the loan officer-borrowing manager pair have engaged in a loan transaction. Specifically, the magnitude of the coefficient, - 12.635, translates to a 12.635 bps decrease in loan spread in the presence of a prior working relationship. To help contextualize the effect of the individual-pair relationship, a one standard deviation in profitability (leverage) results in a 24.5 (15) bps reduction (increase). Similarly, a non- investment grade (term loan) designation results in a 40 (72) bps increase. These results reflect the economic significance of the individual-pair relationship. While not as large as the hard information captured in observable fundamentals, the effect is meaningful, since it captures the soft information gathered by the negotiating parties. To compare the effect of individual-pair relationships with other relationship levels, we run separate regressions of Individual-Pair Relationship Lending on loan spread by adding Institutional-Pair Relationship Lending, CEO-Bank Relationship Lending, Borrowing Manager- Bank Relationship Lending, and Loan Officer-Firm Relationship Lending as additional control variables, one at a time. The results of these tests are reported in Columns (2) through (5) of Panel A. The coefficients on Individual-Pair Relationship Lending are significantly negative in Columns (2) through (4), suggesting that individual-pair relationship lending is also an important factor in determining loan spread. In addition, the magnitude and significance of these coefficients are larger, as compared with the additional relationship level control variables. This provides further Electronic copy available at: https://ssrn.com/abstract=3877687 evidence that the individual-pair relationship provides the basis for most soft relationship information gathering. For Column (5), the coefficient on Individual-Pair Relationship Lending and on Loan Officer-Firm Relationship Lending are both negative but insignificant, possibly due to the fact that the high correlation of these two variables (0.8266) contributes to an increase in the variance inflations factors (VIF) (3.24 compared with 1.07 to 1.76 in the other specifications). Due to high correlation among the lending relationship variables, we cannot include all levels of relationship lending in one regression. For example, the correlations between Individual- Pair Relationship Lending and Institutional-Pair Relationship Lending, CEO-Bank Relationship Lending, Borrowing Manager-Bank Relationship Lending, and Loan Officer-Firm Relationship Lending are 0.1470, 0.3974, 0.6465, and 0.8266, respectively. These are consistent with the endogenous nature of relationships (Bharath et al. 2011), which in our setting develop simultaneously, especially in Individual-Pair Relationship Lending and Individual-Institutional Relationship Lending scenarios. Given that we cannot control for all levels of lending relationships together with our Individual-Pair Relationship Lending, mainly due to the collinearity between them, in the remainder of the paper we only control for Institutional-Pair Relationship Lending, since it is the most established lending relationship represented in the literature (Berger and Udell, 1995; Boot, 2000; Duqi, Tomaselli and Torluccio, 2018; Elyasiani and Goldberg, 2004; Kysucky and Norden, 2015; Petersen and Rajan, 1994). In order to further confirm that our results are driven by individual-pair level relationships and not by higher-level relationships, we follow Bharath et al. (2007), who measure the benefit of relationship lending by focusing on the probability of a future interaction between the borrowing As Mansfield and Helms (1982) noted, if VIF is significantly larger than 1, then multicollinearity is a problem. Electronic copy available at: https://ssrn.com/abstract=3877687 firm and the lending bank. Specifically, we investigate instances where the individual-pair relationship is broken by one entity’s departure from their respective institution. We assess this specific relationship’s endurance and impact by determining whether the entities engage in future loan transactions. To isolate the significance of the individual-pair relationship, we take a deductive approach by removing various relationships from the sample and observing the change in probability of future transactions. Because isolating the individual-pair is problematic, we evaluate other relationships to infer its effect, as shown in Figure 1: (A) represents the institutional-pair relationship, (B) represents one type of individual-institutional relationship (i.e., borrowing manager-lending bank), (C) represents the other type of individual-institutional relationship (i.e., loan officer-borrowing firm), and (D) represents the individual-pair relationship. We begin by looking at instances where both the borrowing manager and loan officer leave their respective firms. This means that relationships (B), (C), and (D) are all broken, and only the institutional-pair relationship (A) remains. We then estimate the probability of the borrowing firm and lending bank entering into a future loan contract. Next, we rerun the analysis by focusing on instances where the borrowing manager leaves the firm, such that relationships (B) and (D) are ruptured. When we recalculate the probability of the borrowing firm and bank entering into a subsequent loan, the resulting change reflects the disappearance of both the borrowing manager-lending bank relationship (i.e., relationship (B)) and the individual-pair relationship (i.e., relationship (D)). We rerun the analysis a final time to explore instances where the loan officer leaves the bank. In this scenario, because relationships (C) and (D) are severed, any reduction in the probability of future loan engagement can be directly traced to that occurrence. To deduce the effect of the individual-pair relationship, we compare the probability changes reflected in all three analyses. The sole relationship that is Electronic copy available at: https://ssrn.com/abstract=3877687 broken across all of them is the individual-pair relationship. If the change in probability of future lending is similar across all three specifications, we can deduce that the effect is driven by the only common denominator: the absence of the individual-pair (i.e. relationship (D)). To test whether the individual-pair relationship is one of the main determinants of future loan transaction probability, we create a subsample of loans using each entity’s first loan as the benchmark. Then, following Even-Tov and Ozel (2021), we consult LinkedIn to hand collect the employment history of both the borrowing manager and loan officer; this process significantly reduces our sample size. We use this smaller sample to test whether the likelihood of future loan engagement is affected when an individual in an established relationship is no longer associated with the institution. Panel B of Table 3 reports the results of this analysis. Columns (1) through (3) report the probability of future loan engagement according to three scenarios: both individuals leave (Column 1), only the borrowing manager leaves (Column 2), or only the loan officer leaves (Column 3). In all of these specifications, we find that the borrower’s propensity to engage in a subsequent loan with the same lender is significantly lower. Specifically, when both the borrowing manager and loan officer leave, the odds ratio of future engagement is reduced by 69.6 percent. When the borrowing manager departs, but the loan officer remains constant, the odds ratio is reduced by 73.8 percent. Lastly, when the loan officer has left their bank, the odds ratio is reduced by 67.4 percent. The similarity in outcomes suggests that the individual-pair relationship is the primary driver in the decreased likelihood of future engagement. Prior research documents that lending relationships based on social ties also reduce loan spread. Specifically, Engelberg et al. (2012) show that affiliations through shared college or work experience reduce the cost of debt by 28 bps. In order to alleviate concern that the effects we document are attributable to social ties, we re-estimate regression (1) and add new control Electronic copy available at: https://ssrn.com/abstract=3877687 variables. In Column (1) Panel C of Table 3, we add the variable Same College, which is an indicator variable equal to one if the borrowing manager and the loan officer attended the same undergraduate institution. Similarly, in Column (2), we add the variable Borrower Manager Worked in Financial Industry, which is an indicator variable equal to one if the borrowing manager has previously worked in the banking industry, i.e., the same industry as the loan officer. To obtain the data that inform our additional variables, we consult LinkedIn to identify the borrowing manager’s and loan officer's past education and work experience. We find college affiliation for about 19% of our sample (750 observations) and professional background for just over half (2,027 observations). Our results using the reduced samples show that individual-pair lending relationships cultivated through repeated engagement are economically meaningful. Given the insignificant coefficients of the social ties variables and the lack of available data pertaining to them, the remainder of our analyses focus on the full sample. Whereas Khan et al. (2019) and Karolyi (2018) investigate the relationship between borrowing firm top executives and lending banks, our paper looks specifically at the relationship between borrowing managers and loan officers. In our sample, 62% of the borrowing managers who sign the loan agreement do not serve as CEOs or CFOs. In an untabulated analysis, we drop the 38% of managers who serve as CEOs or CFOs to confirm that our findings are not driven by top executives. Our results remain qualitatively similar for this reduced sample. Joint employment in the banking industry is the main prerequisite for same-company overlap between the borrowing manager and the lender. But we did not find enough observations that reflect such overlap in LinkedIn to generate any statistical analysis. We have also confirmed our results by conducting other untabulated robustness analyses where we control for the following: geographical distance between firm and bank, borrowing managers with chief titles, loan intensity, and bank FE. Electronic copy available at: https://ssrn.com/abstract=3877687 3.2 The cross-sectional effects of individual-pair relationship lending on loan spread based on borrowing firms’ characteristics Prior theoretical and empirical studies suggest that relationship lending is most valuable in situations where there is higher information asymmetry among borrowers (Rajan, 1992; Khan et al., 2019). Following the literature (e.g., Güntay and Hackbarth 2010; Mansi et al. 2011; Sufi 2007; Kraft 2015; Khan et al. 2019), we create two indicator variables that capture information asymmetry at the borrowing firms. The first, Low Analyst Following, is an indicator variable equal to one if the firm’s analyst coverage is lower than the sample median. The second, Non-Investment Grade, is an indicator variable equal to one if a firm’s S&P rating is below BBB. Table 4 reports the results of re-estimating regression (1) and adding either the Low Analyst Following variable (Column 1) or the Non-Investment Grade variable (Column 2) as an independent variable and interacting them with the individual-pair lending relationship variable. Focusing on low analyst following (Column 1), the coefficient on the interaction term Individual-Pair Relationship Lending*Low Analyst Following is significantly negative and economically large, as reflected by a -27.205 bps in loan spread. We find similar results when we turn to non-investment grade firms (Column 2). Specifically, the interaction term Individual-Pair Relationship Lending*Non- Investment Grade is significantly negative, at -15.352 bps. These results show substantive loan spread variation within the individual-pairs according to the borrower’s level of information asymmetry. Specifically, the borrowers with higher levels of information symmetry enjoy a significant reduction in loan spread: between 15 and 27 bps over our sample period. Our evidence indicates that the individual-pair relationship mitigates information asymmetry most notably when there is higher level of borrower information asymmetry. Electronic copy available at: https://ssrn.com/abstract=3877687 3.3 The cross-sectional effects of individual-pair relationship lending on loan spread based on lending bank characteristics In this sub-section, we examine how the lending institution’s market prominence and the officer’s loan volume shape the effect of individual-pair lending relationships on the loan spread. Due to their volume of lending activity and competitive market positioning (Presbitero and Zazzaro, 2011), larger institutions that perform more deals in the syndicated loan market are likely to adopt a transactional-based approach. Therefore, we expect this group to value the individual- pair relationship less than banks that are smaller players in the market. To test our predictions, we follow prior studies and create two indicator variables to capture the lender’s prominence in the syndicated loan market (e.g., Lee, Mullineaux, 2004; Sufi, 2007). The first, Non-Top10 Bank, is an indicator variable equal to one if the bank’s total number of loan packages during the sample period is lower than those of the top 10 banks. The second, Low Bank Market Share, is an indicator variable equal to one if the lead bank’s market share measured by the dollar value of all loans is below the sample median. Table 5 Panel A reports the results of re- estimating regression (1) and adding either the Non-Top10 Bank (Column 1) or the Low Bank Market Share variable (Column 2) as an independent variable and interacting it with the individual-pair lending relationship variable. Focusing on Column 1, the coefficient on the interaction term Individual-Pair Relationship Lending*Non-Top10 Bank, -18.249 bps in loan spread, is significantly negative and economically large. We find similar results when we focus on banks with low market share (Column 2). Specifically, the interaction term Individual-Pair Relationship Lending*Low Bank Market Share is significantly negative: -12.514 bps. The evidence from these analyses suggests that relative to The sample size of this analysis is slightly smaller than that in our main table because either several bank names in loan contracts cannot be matched with bank names in DealScan or some banks can’t be merged with Compustat to calculate market share and ranks on the bank holding company (BHC) level. Electronic copy available at: https://ssrn.com/abstract=3877687 the larger volume lenders, smaller volume lenders in individual-pair lending relationships enjoy a significant reduction of between 12 and 18 bps in the loan spread over our sample period. Institutional-pair relationship-based lenders do not observe any reduction in loan spread. Next we test whether the lending relationship confers greater value to less active loan officers. Similar to smaller lenders, officers who engage in fewer transactions are not as well- positioned to mitigate information asymmetry. We predict that this factor will lead to heavier reliance on individual relationships. To test our supposition, we follow the same logic as in our bank cross-section tests and create two indicator variables to capture the loan officer’s activity level. The first, Non-Top10 Loan Officer, is an indicator variable equal to one if the officer’s total number of loan packages during the sample period is not among the top 10 loan officers. The second, Low Loan Officer Market Share, is an indicator variable equal to one if the loan officer’s market share measured by the dollar value of all loans is below the sample median. Table 5 Panel B reports the results of re-estimating regression (1) and adding either the Non-Top10 Loan Officer (Column 1) or the Low Loan Officer Market Share variable (Column 2) as an independent variable and interacting it with the individual-pair lending relationship variable. Focusing on Column 1, the coefficient on the interaction term Individual-Pair Relationship Lending* Non-Top10 Loan Officer is significantly negative and economically large, at -20.248 bps in loan spread. We find similar results among loan officers with a low market share (Column 2). Specifically, the interaction term Individual-Pair Relationship Lending* Low Loan Officer Market Share is significantly negative, at -27.114 bps. These analyses show that less active loan officers in an individual-pair lending relationship enjoy a significant reduction in loan spread of between 20 and 27 bps. Our evidence suggests that lower-volume loan officers may be less equipped to screen and Electronic copy available at: https://ssrn.com/abstract=3877687 monitor loans and therefore may rely more heavily on individual-pair relationships to mitigate information asymmetry. 3.4 The impact of individual-pair relationship lending on loan spread based on the dynamics of the individual-pair lending relationship The results in sub-section 3.3 illuminate the economic importance of individual-pair lending relationships. Our analyses explore the evolution of the individual-pair lending relationship by looking at its time-series behavior. Prior literature (Berger and Udell, 1995; Bharath et al. 2011; Boot, 2000; Duqi, Tomaselli and Torluccio, 2018; Elyasiani and Goldberg, 2004; Kysucky and Norden, 2015; Petersen and Rajan, 1994) suggests that as a lending relationship develops, one of two contrasting scenarios emerges: either the lender is increasingly willing to offer benefits to the borrower or they are increasingly reluctant to share their information gains. To test this prediction, we re-create our individual-pair relationship lending variable by separating the second, third, or higher number of transactions between the borrowing manager- loan officer pair and develop three new measures. They are: Individual-Pair Relationship Lending First, an indicator variable equal to one if it is the first repeated (i.e., the second) loan transaction between a borrowing manager and a loan officer; Individual-Pair Relationship Lending Second, an indicator variable equal to one if it is the second repeated (i.e., the third) loan transaction between a borrowing manager and a loan officer; and Individual-Pair Relationship Lending Second or Higher (Individual-Pair Relationship Lending Third or Higher), an indicator variable equal to one if it is the second repeated (third repeated) or more transaction between a borrowing manager and a loan officer. Table 6 reports the results from re-estimating regression (1) and replacing our main variable of interest, Individual-Pair Relationship Lending, with our three new variables. In Column Electronic copy available at: https://ssrn.com/abstract=3877687 (1) we distinguish between the second (i.e., first repeated) loan transaction and future transactions, and in Column (2), we distinguish between the second, third, and future transactions. In both columns, we find a significantly negative and economically large reduction of around 13 bps in loan spread. In comparison, the coefficients on the future transaction variables are not significantly different from zero. Our results suggest that the benefits from individual-pair lending relationships are fairly sticky, given that there is little variation over subsequent lending. 3.5 The impact of individual-pair relationship lending on loan performance In this subsection, we investigate whether individual-pair relationship-based loans perform differently than other loans. To do so, we look at the borrower’s future downgrades and defaults. In Table 7, we re-estimate regression (1) by replacing our dependent variable, Loan Spread, with Downgrade (Column 1) and Default (Column 2). We define Downgrade (Default) as an indicator variable equal to one if the borrowing firm is downgraded (defaults) between the loan initiation date and maturity. Column 1 of Table 7 shows that the coefficient on Individual-Pair Relationship Lending is significantly negative (at the 5% level), at -0.322. This means that the odds of a borrowing firm downgrade is almost 28% lower in individual-pair relationship loans relative to those without this relationship. In Column 2 of Table 7, we report that the coefficient on Individual-Pair Relationship Lending, albeit negative, is insignificantly different from zero. Overall, our evidence suggests that the presence of individual-pair lending relationships decreases a borrower’s chance of future downgrade and amplifies the lender’s ability to screen and/or monitor the loan. 3.6 Impact of individual-pair relationships on other contracting terms and lead bank share. Thus far, our paper has primarily focused on the cost of debt. Next, we look at other contracting terms. Bradley and Roberts (2015) suggest that there is a tradeoff when contracting Electronic copy available at: https://ssrn.com/abstract=3877687 between the cost of the debt and other contracting features. Accordingly, we explore whether other terms adjust and act as a substitute for the decreased cost of debt for individual-pair relationship loans. We focus on seven different contracting features: (1) upfront fees, (2) loan size, (3) collateral, (4) maturity, (5) number of covenants, (6) covenant strictness, and (7) lead bank share. We collect all variables from DealScan and re-run our spread regressions, substituting each of the six other contract terms and using lead bank share as the dependent variable. The results in Table 8 show a marginal increase in loan size and a slight reduction in collateral for individual- pair relationship loans. For all other terms, there is no statistically significant association. The presence of an individual-pair relationship does not seem to affect lead bank share, suggesting that participating lenders may be either unaware of or not influenced by the individual-pair relationship. Taken together, these results provide evidence that the reduced spread secured through individual-pair lending relationships is not offset by other contracting terms. In fact, the results show marginal evidence of additional benefits from individual-pair lending relationships. In contrast, while there is a negative relationship between upfront fees and institutional-pair lending and a positive relationship between loan size and institutional-pair lending, there is a positive relationship between the number of covenants and the presence of an institutional lending relationship. The latter association suggests that while institutional-pair relationship lending reduces the cost of borrowing and increases loan size, it also elevates monitoring. Similar to Sufi (2007), there is a negative association between lead bank share and institutional lending relationships. Lastly, the fact that the number of covenants is only significantly associated with institutional lending relationships further supports our contention that these two relationships are distinct from one another. Electronic copy available at: https://ssrn.com/abstract=3877687 4. Conclusion Relationship lending has often been considered a mechanism to reduce information asymmetry in debt contracting. While prior research has explored many different types of lending relationships, we are the first to examine the individual-pair lending relationships developed through repeated interactions. We investigate this angle by using a hand-collected sample of loan contracts that identifies the individuals directly involved in the contracting process. This novel dataset allows us to show that the individual-pair lending relationship is both economically significant and distinct from institutional-pair and individual-institutional lending relationships. These results are important, as prior literature has focused on higher-level relationships and, accordingly, has overlooked the fundamental relationship cultivated between the individuals who are most highly involved in the loan process. We also provide evidence that the individual-pair relationship we study is different from those based on social ties. 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Electronic copy available at: https://ssrn.com/abstract=3877687 Appendix A: Variable Definitions Variable Name Variable Definition Source Relationship Lending Measures Individual-Pair An indicator variable equal to one if a borrowing manager-loan Raw loan Relationship Lending officer pair has engaged in a loan transaction before the current contracts transaction, and zero otherwise. Institutional-Pair An indicator variable equal to one if a borrowing firm-lending DealScan Relationship bank pair has engaged in a loan transaction within five years of the Lending current transaction, and zero otherwise. Major Loan and Borrower Characteristics Loan Spread All-in-drawn loan spread over LIBOR. DealScan Maturity A natural logarithm of loan maturity (in months). Unlogged value DealScan is reported in the descriptive statistics. Loan Size A natural logarithm of loan amount. We use the largest facility DealScan amount per loan. Unlogged value is reported in the descriptive statistics. Collateral An indicator variable equal to one if the loan has collateral, and DealScan zero otherwise. Term Loan An indicator variable equal to one if the loan type is term loan, and DealScan zero otherwise. Firm Size The natural logarithm of the origin firm’s total assets. Unlogged Compustat value is reported in the descriptive statistics. Leverage (Long-term debt + current debt )/total assets. Compustat Profitability Earnings before interest, taxes, and depreciation/total assets. Compustat Tangibility Property, plant, and equipment /total assets. Compustat MTB (Stock price*shares outstanding)/(stockholders' equity - preferred Compustat stock + deferred taxes and investment tax credit). Interest Coverage EBIT/interest expense. Compustat Current Ratio Current asset/current liability. Compustat Non-Investment Grade An indicator variable equal to one if a firm’s S&P rating is below Compustat BBB, and zero otherwise. Other Variables Same College An indicator variable equal to one if the borrowing manager and LinkedIn loan officer went to the same college, and zero otherwise. Borrower Manager An indicator variable equal to one if the borrowing manager LinkedIn Worked in Financial worked in the financial industry, and zero otherwise. Industry Low Analyst An indicator variable equal to one if the firm’s analyst coverage is I/B/E/S Following lower than the sample median, and zero otherwise. Non-Investment Grade An indicator variable equal to one if the firm’s credit rating is lower than BBB, and zero otherwise. Non-Top10 Bank An indicator variable equal to one if the bank’s total number of DealScan loan packages during the sample period is not among the top 10 banks, and zero otherwise. Low Bank Market An indicator variable equal to one if the lead bank’s market share DealScan Share measured by the dollar value of all loans during the entire sample period is below the sample median, and zero otherwise. Non-Top 10 Loan An indicator variable equal to one if the loan officer’s total number Raw loan Officer of loan packages during the sample period is not among the top 10 contracts & loan officers, and zero otherwise. DealScan Electronic copy available at: https://ssrn.com/abstract=3877687 Low Loan Officer An indicator variable equal to one if the loan officer’s market share Raw loan Market share measured by the dollar value of all loans during the sample period contracts & is below the sample median, and zero otherwise. DealScan Downgrade An indicator variable equal to one if a firm is downgraded between S&P ratings the loan initiation date and maturity date, and zero otherwise. Default An indicator variable equal to one if the loan receives a default S&P ratings rating between the loan initiation date and maturity date, and zero otherwise. Upfront Fee The upfront fee the borrower pays to the lender at loan initiation DealScan Number of Covenants Number of covenants in the loan contract DealScan Covenant Strictness Probability of covenant violation calculated following Demerjian Demerjian and and Owens (2016) Owens (2016) Lead Bank Share The percentage a lead lender has committed to the given facility. DealScan Electronic copy available at: https://ssrn.com/abstract=3877687 Figure 1: Lending Relationships This figure illustrates the different types of lending relationships. Relationship A represents the institutional-pair lending relationship between a borrowing firm and a lending bank. Relationship B shows the individual-institutional lending relationship between a firm’s borrowing manager and a lending bank. Relationship C depicts the individual-institutional lending relationship between a loan officer and a borrowing firm. Finally, Relationship D represents the individual-pair lending relationship between a borrowing manager and a loan officer. Borrowing Firm Lending Bank Borrowing Manager Loan Officer Electronic copy available at: https://ssrn.com/abstract=3877687 Table 1: Sample Construction Panel A provides details on our sample’s construction of credit agreements with information about the borrowing manager and loan officer between the years 1996–2016. Panel B shows the number of borrowing manager-loan officer pairs in our sample. Panel A: Sample Construction Number of Observations 8,109 Loans from EDGAR matched with DealScan 5,361 Loans with a signature page 4,296 Loans with non-missing control variables from CRSP and Compustat 3,883 Loans from non-financial firms Panel B: Number of matched borrowing managers and loan officers Number Unique borrowing managers 2,798 Unique loan officers 2,128 3,496 Unique borrowing manager-loan officer pairs Panel C: Number of loans among unique borrowing manager and loan officer pairs Number of loans among pairs Frequency One 3,124 Two Three Four and higher Total 3,496 Electronic copy available at: https://ssrn.com/abstract=3877687 Table 2: Summary Statistics This table provides descriptive statistics for key variables in our sample between the years 1996–2016. Panel A reports the summary statistics for the relationship lending measures. Panel B reports the summary statistics of major loan and firm characteristics. Panel C reports the summary statistics of other variables used in our cross-sectional and robustness analyses. All variables are defined in Appendix A. N Mean Sd P10 P25 P50 P75 P90 Panel A: Lending Relationship Measures Individual-Pair Relationship 3,883 0.11 0.31 0 0 0 0 1 Lending Institutional-Pair Relationship 3,883 0.4 0.49 0 0 0 1 1 Lending Panel B: Major Loan and Firm Characteristics Loan Spread 3,883 179.55 120.56 50 100 150 250 325 Maturity (in months) 3,883 49.13 19.29 12 36 60 60 61 Loan Size ( in millions) 3,883 569.51 1443.86 35 100 250 600 1250 Collateral 3,883 0.51 0.5 0 0 1 1 1 Term Loan 3,883 0.2 0.4 0 0 0 0 1 Firm Size (in millions) 3,883 5,277 17,320 131 393 1,233 4,050 12,050 Leverage 3,883 0.23 0.18 0 0.09 0.22 0.35 0.47 Profitability 3,883 0.15 0.1 0.06 0.1 0.14 0.19 0.25 Tangibility 3,883 0.33 0.26 0.06 0.12 0.26 0.52 0.75 MTB 3,883 3.13 4.91 0.84 1.29 2.09 3.31 5.36 Interest Coverage 3,883 39.55 564.42 0 1.75 4.41 11.62 33.99 Current Ratio 3,883 1.99 1.74 0.78 1.14 1.68 2.43 3.45 Non-Investment Grade 3,883 0.79 0.41 0 1 1 1 1 Panel C: Other Variables Same College 750 0.04 0.19 0 0 0 0 0 Borrower Manager Worked in 2,027 0.11 0.31 0 0 0 0 1 Financial Industry Analyst Following 3,883 8.79 7.76 0 3 7 13 20 Non_Top10 Bank 3,674 0.33 0.47 0 0 0 1 1 Bank Market Share 3,674 21.24 14.98 1.1 5.56 22.13 34.32 44.6 Non_Top10 Loan Officer 3,883 0.91 0.29 1 1 1 1 1 Loan Officer Market Share 3,883 0.16 0.37 0 0.01 0.03 0.13 0.32 Downgrade 3,883 0.2 0.4 0 0 0 0 1 Default 3,883 0.02 0.14 0 0 0 0 0 Upfront Fee 619 60.63 95.43 10 15 32.47 75 125 Number of Covenants 3,883 3.91 3.01 0 2 3 6 9 Covenant Strictness 2,708 0.31 0.4 0 0.01 0.07 0.77 0.99 Lead Bank Share 1,616 27.64 25.2 7.89 10.83 18.38 33.33 61.54 Electronic copy available at: https://ssrn.com/abstract=3877687 Table 3: The Impact of Individual-Pair Relationship Lending on Loan Spread This table reports the impact of individual-pair relationship lending relative to other relationship levels. Panel A shows the relative effects of Individual-Pair Relationship Lending and higher-level relationships on Loan Spread. Individual-Pair Relationship Lending is an indicator variable equal to one if a borrowing manager-loan officer pair has engaged in a loan transaction before the current transaction, and zero otherwise. Institutional-Pair Relationship Lending is an indicator variable equal to one if a borrowing firm- lending bank pair has engaged in a loan transaction within five years of the current transaction, and zero otherwise. CEO-Bank Relationship Lending is an indicator variable equal to one if the firm CEO-lending bank pair has engaged in a loan transaction before the current transaction, and zero otherwise. Borrowing Manager-Bank Relationship Lending is an indicator variable equal to one if the borrowing manager-lending bank pair has engaged in a loan transaction prior to the current transaction, and zero otherwise. Loan Officer-Firm Relationship Lending is an indicator variable equal to one if the loan officer-borrowing firm pair has engaged in a loan transaction before the current transaction, and zero otherwise. The dependent variable is Loan Spread, which is the all-in-drawn loan spread over LIBOR. All other variables are defined in Appendix A. We include year, industry (using Fama-French 48 industries classification), and loan purpose fixed effects and cluster standard errors at the firm level. ***, **, and * signify statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: The impact of individual-pair relationship lending compared with higher-level relationships (1) (2) (3) (4) (5) Loan Spread Loan Spread Loan Spread Loan Spread Loan Spread Individual-Pair -12.635*** -11.719*** -6.895* -10.667** -6.546 Relationship Lending (-3.31) (-3.10) (-1.73) (-2.28) (-0.93) Institutional-Pair -5.195* Relationship Lending (-1.94) CEO-Bank -5.009* Relationship Lending (-1.71) Borrowing Manager-Bank -2.513 Relationship Lending (-0.62) Loan Officer-Firm -6.595 Relationship Lending (-1.01) Maturity -17.659*** -17.709*** -12.321*** -17.623*** -17.682*** (-4.54) (-4.55) (-2.95) (-4.53) (-4.55) Loan Size -9.109*** -8.933*** -4.949* -9.107*** -9.071*** (-3.58) (-3.50) (-1.79) (-3.58) (-3.56) Collateral 52.612*** 52.650*** 44.578*** 52.576*** 52.590*** (15.25) (15.28) (10.92) (15.23) (15.25) Term Loan 72.269*** 71.974*** 62.019*** 72.205*** 72.192*** (12.28) (12.24) (9.35) (12.25) (12.28) Firm Size -8.962*** -8.882*** -4.797** -8.919*** -8.943*** (-3.92) (-3.89) (-1.98) (-3.89) (-3.91) Leverage 82.346*** 83.764*** 107.019*** 82.340*** 82.477*** (6.84) (6.95) (7.68) (6.84) (6.86) Profitability -245.821*** -244.625*** -195.549*** -245.847*** -245.556*** (-9.89) (-9.84) (-7.59) (-9.89) (-9.87) Tangibility -5.781 -5.645 -19.869* -5.804 -5.854 (-0.51) (-0.49) (-1.93) (-0.51) (-0.51) MTB 0.582 0.575 0.500 0.582 0.577 Electronic copy available at: https://ssrn.com/abstract=3877687 (1.33) (1.32) (0.88) (1.33) (1.32) Interest Coverage 0.001 0.001 0.001 0.001 0.001 (0.63) (0.56) (0.93) (0.62) (0.63) Current Ratio -3.206** -3.276** -3.729** -3.213** -3.214** (-2.45) (-2.48) (-2.08) (-2.45) (-2.45) Non-Investment Grade 40.381*** 40.227*** 47.329*** 40.386*** 40.469*** (9.30) (9.30) (10.76) (9.30) (9.30) Year FE Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Purpose FE Yes Yes Yes Yes Yes VIF 1.03 1.07 1.23 1.76 3.24 Adj. R-squared 0.524 0.523 0.549 0.523 0.524 Observations 3,883 3,883 2,547 3,883 3,883 Electronic copy available at: https://ssrn.com/abstract=3877687 Panel B: The Effect of Relationship Rupture on Subsequent Loan Transactions This panel reports Logit regression results for the effect of individual employee departure on the likelihood of a firm’s future same-bank borrowing. Borrow from the Same Bank is an indicator variable equal to one if the loan is secured from the same bank as the previous loan, and zero otherwise (DealScan). Both Left is an indicator variable equal to one if both the borrowing manager and the loan officer left the company, and zero if both are still employed by their respective companies (LinkedIn). Only Borrowing Manager Left is an indicator variable equal to one if only the borrowing manager departed, and zero if both are still working for their respective companies (LinkedIn). Only Loan Officer Left is an indicator variable equal to one if only the loan officer left the company, and zero if both are still working for their respective companies (LinkedIn). (1) (2) (3) Dependent variable: Borrow from the Same Bank Both Left -1.192** (-2.49) Only Borrowing Manager Left -1.341*** (-3.64) Only Loan Officer Left -1.121*** (-3.33) Firm Size 0.131 0.193* 0.201 -1.1 -1.74 -1.64 Leverage -0.783 -1.143 -1.717* (-0.76) (-1.21) (-1.71) Profitability 0.811 -0.173 -0.169 -0.34 (-0.08) (-0.07) Tangibility -1.234 -0.73 -0.398 (-1.21) (-0.81) (-0.45) MTB 0.058 0.07 0.096 -0.91 -1.03 -1.11 Interest Coverage 0.008 0.011 0.012* -1.26 -1.58 -1.71 Current Ratio 0.129 0.05 0.026 -0.74 -0.34 -0.18 Non-Investment Grade 0.166 0.484 0.647 -0.41 -1.33 -1.57 Year FE Yes Yes Yes Industry FE Yes Yes Yes Pseudo R-squared 0.204 0.209 0.201 Observations 345 368 378 Electronic copy available at: https://ssrn.com/abstract=3877687 Panel C: Controlling for Social Ties This panel reports the regression results of individual-pair relationship lending’s effect on loan spread controlling for social ties. Column (1) examines the effect of individual-pair relationship controlling for Same College, which is an indicator variable equal to one if the borrowing manager and the loan officer attended the same undergraduate institution. Similarly, in Column (2), we examine the effect of the individual-pair relationship controlling for Borrower Manager Worked in Financial Industry, which is an indicator variable equal to one if the borrowing manager has previously worked in the banking industry, i.e., the same industry as the loan officer. All variables are defined in Appendix A. We include year, industry (using Fama-French 48 industries classification), and loan purpose fixed effects and cluster standard errors at the firm level. ***, **, and * signify statistical significance at the 1%, 5%, and 10% levels, respectively. (1) (2) Loan Spread Loan Spread Individual-Pair Relationship Lending -17.288* -13.411** (-1.77) (-2.49) Institutional-Pair Relationship Lending -5.430 -9.220** (-0.87) (-2.39) Same College 13.135 (0.77) Borrower Manager Worked in Financial Industry 1.414 (0.21) Maturity 8.203 -15.198** (1.26) (-2.39) Loan Size -12.136** -11.135*** (-2.03) (-2.82) Collateral 47.087*** 47.618*** (7.27) (9.92) Term Loan 60.459*** 69.630*** (5.31) (8.81) Firm Size -7.128 -5.915* (-1.29) (-1.73) Leverage 49.994** 84.604*** (2.12) (4.74) Profitability -215.948*** -217.367*** (-4.40) (-5.35) Tangibility -36.102* -12.675 (-1.81) (-0.76) MTB 0.344 1.213* (0.40) (1.73) Interest Coverage -0.001 -0.001 (-0.28) (-0.79) Current Ratio -7.187** -5.863** (-2.26) (-2.58) Non-Investment Grade 29.848*** 45.139*** (3.48) (7.15) Yes Yes Year FE Yes Yes Industry FE Electronic copy available at: https://ssrn.com/abstract=3877687 Yes Yes Purpose FE Adj. R-squared 0.536 0.507 Observations 748 2,027 Electronic copy available at: https://ssrn.com/abstract=3877687 Table 4: The Cross-Sectional Effects of Individual-Pair Relationship Lending based on Borrowing Firms’ Characteristics This table reports the regression results of individual-pair relationship lending’s effect on loan spread based on different borrowing firms’ characteristics. Column (1) examines the effect based on whether analyst coverage is above or below the sample median number. Column (2) examines the effect based on whether the borrowing firm’s rating is non-investment grade. All variables are defined in Appendix A. We include year, industry (using Fama-French 48 industries classification), and loan purpose fixed effects and cluster standard errors at the firm level. ***, **, and * signify statistical significance at the 1%, 5%, and 10% levels, respectively. (1) (2) Loan Spread Loan Spread Individual-Pair Relationship Lending -1.299 -0.466 (-0.31) (-0.08) Low Analyst Following 16.196*** (4.37) Individual-Pair Relationship Lending -27.205*** *Low Analyst Following (-3.39) Individual-Pair Relationship Lending -15.352** *Non-Investment Grade (-2.05) Institutional-Pair Relationship Lending -5.115* -5.038* (-1.92) (-1.88) Maturity -17.370*** -17.656*** (-4.50) (-4.54) Loan Size 52.236*** 52.594*** (15.26) (15.27) Collateral -8.755*** -8.887*** (-3.46) (-3.48) Term Loan 71.802*** 72.056*** (12.26) (12.26) Firm Size -6.302*** -8.872*** (-2.60) (-3.89) Leverage 79.867*** 84.065*** (6.67) (6.98) Profitability -237.801*** -244.009*** (-9.49) (-9.82) Tangibility -5.223 -5.421 (-0.46) (-0.48) MTB 0.724 0.585 (1.64) (1.34) Interest Coverage 0.001 0.001 (0.54) (0.57) Electronic copy available at: https://ssrn.com/abstract=3877687 Current Ratio -3.030** -3.267** (-2.35) (-2.47) Non-Investment Grade 40.578*** 42.325*** (9.38) (9.51) Year FE Yes Yes Industry FE Yes Yes Purpose FE Yes Yes Adj. R-squared 0.527 0.524 Observations 3,883 3,883 Electronic copy available at: https://ssrn.com/abstract=3877687 Table 5: The Cross-Sectional Effects of Individual-Pair Relationship Lending based on Lending Bank and Loan Officer Characteristics This table reports the regression results of individual-pair relationship lending on loan spread based on different lending bank characteristics. Panel A examines the effect based on whether a bank’s total number of loan packages during the sample period is amongst the top 10 banks or whether the lead bank’s market share measured by the dollar value of all loans during the sample period is below the sample median. Panel B examines the effect based on whether the loan officer’s total number of loan packages during the sample period is among the top 10 loan officers and whether the loan officer’s market share measured by the dollar value of all loans during the sample period is below the sample median. All variables are defined in Appendix A. We include year, industry (using Fama-French 48 industries classification), and loan purpose fixed effects and cluster standard errors at the firm level. ***, **, and * signify statistical significance at the 1%, 5%, and 10% levels, respectively. Panel A: Cross-Section Variation on the Effect of Individual-Pair Relationship Lending based on Lending Bank Size and Market Share (1) (2) Loan Spread Loan Spread Individual-Pair Relationship Lending -5.279 -4.381 (-1.37) (-0.99) Non_top10 Bank 15.053*** (4.12) Individual-Pair Relationship Lending*Non-Top10 Bank -18.249** (-1.99) Low Bank Market Share 12.931*** (4.42) Individual-Pair Relationship Lending*Low Bank Market Share -12.514* (-1.72) Institutional-Pair Relationship Lending -3.961 -4.095 (-1.50) (-1.55) Maturity -14.404*** -14.632*** (-3.84) (-3.90) Loan Size 51.935*** 51.363*** (15.05) (14.84) Collateral -9.689*** -9.637*** (-3.81) (-3.79) Term Loan 66.303*** 66.514*** (11.76) (11.71) Firm Size -6.078*** -6.097*** (-2.74) (-2.73) Leverage 90.401*** 89.250*** (7.65) (7.57) Profitability -210.586*** -210.503*** (-9.83) (-9.77) Tangibility -5.552 -4.967 (-0.58) (-0.52) MTB 0.538 0.538 (1.20) (1.21) Interest Coverage 0.001 0.000 (0.39) (0.28) Electronic copy available at: https://ssrn.com/abstract=3877687 Current Ratio -4.388*** -4.448*** (-3.08) (-3.13) Non-Investment Grade 41.738*** 41.718*** (10.02) (9.91) Year FE Yes Yes Industry FE Yes Yes Purpose FE Yes Yes Adj. R-squared 0.532 0.531 Observations 3,673 3,673 Electronic copy available at: https://ssrn.com/abstract=3877687 Panel B: Cross-Section Variation on the Effect of Individual-Pair Relationship Lending based on Loan Officer Lending Activity (1) (2) Loan Spread Loan Spread Individual-Pair Relationship Lending 4.903 -2.590 (0.67) (-0.59) Non-Top10 Loan Officer 3.251 (0.77) Individual-Pair Relationship Lending*Non-Top10 Loan Officer -20.248** (-2.32) Low Loan Officer Market share 8.269** (2.52) Individual-Pair Relationship Lending*Low Loan Officer Market share -27.114*** (-3.46) Institutional-Pair Relationship Lending -5.198* -4.960* (-1.94) (-1.85) Maturity -17.511*** -17.153*** (-4.50) (-4.42) Loan Size 52.671*** 52.409*** (15.26) (15.20) Collateral -8.889*** -7.852*** (-3.49) (-3.04) Term Loan 72.105*** 72.059*** (12.26) (12.27) Firm Size -8.964*** -8.779*** (-3.90) (-3.83) Leverage 83.387*** 83.244*** (6.91) (6.94) Profitability -244.626*** -244.642*** (-9.85) (-9.88) Tangibility -5.718 -6.030 (-0.50) (-0.54) MTB 0.571 0.592 (1.30) (1.36) Interest Coverage 0.001 0.001 (0.57) (0.62) Current Ratio -3.294** -3.318** (-2.49) (-2.51) Non-Investment Grade 40.046*** 40.119*** (9.23) (9.30) Year FE Yes Yes Industry FE Yes Yes Purpose FE Yes Yes Adj. R-squared 0.524 0.525 Observations 3,883 3,883 Electronic copy available at: https://ssrn.com/abstract=3877687 Table 6: The Impact of Individual-Pair Relationship Lending on Loan Spread based on Previous Loan Transactions This table reports the regression results of individual-pair relationship lending on loan spread based on the number of loan transactions the pair has conducted. Individual-Pair Relationship Lending_First is an indicator variable equal to one if it is the second loan transaction between a borrowing manager-loan officer pair. Individual-Pair Relationship Lending_Second or higher is an indicator variable equal to one if it is the third or more loan transaction between the pair. All other variables are defined in Appendix A. We include year, industry (using Fama-French 48 industries classification), and loan purpose fixed effects and cluster standard errors at the firm level. ***, **, and * signify statistical significance at the 1%, 5%, and 10% levels, respectively. (1) (2) Loan Spread Loan Spread Individual-Pair Relationship Lending_First -12.598*** -12.597*** (-3.33) (-3.33) Individual-Pair Relationship Lending_Second or Higher -6.953 (-0.75) Individual-Pair Relationship Lending_Second -7.052 (-0.73) Individual-Pair Relationship Lending _Third or Higher -6.343 (-0.22) Institutional-Pair Relationship Lending -5.190* -5.190* (-1.94) (-1.94) Maturity -17.667*** -17.666*** (-4.54) (-4.54) Loan Size -8.926*** -8.926*** (-3.50) (-3.50) Collateral 52.684*** 52.685*** (15.31) (15.31) Term Loan 71.999*** 72.000*** (12.24) (12.24) Firm Size -8.903*** -8.903*** (-3.90) (-3.90) Leverage 83.828*** 83.831*** (6.95) (6.95) Profitability -244.491*** -244.486*** (-9.84) (-9.84) Tangibility -5.654 -5.655 (-0.49) (-0.49) MTB 0.575 0.575 (1.32) (1.32) Interest Coverage 0.001 0.001 (0.57) (0.57) Current Ratio -3.278** -3.278** Electronic copy available at: https://ssrn.com/abstract=3877687 (-2.48) (-2.48) Non-Investment Grade 40.198*** 40.197*** (9.30) (9.29) Year FE Yes Yes Industry FE Yes Yes Purpose FE Yes Yes Adj. R-squared 0.524 0.524 Observations 3,883 3,883 Electronic copy available at: https://ssrn.com/abstract=3877687 Table 7: The Impact of Individual-Pair Relationship Lending on Loan Performance This table reports the regression results of individual-pair relationship lending on loan performance: Downgrade (an indicator for whether a firm is downgraded between the loan initiation date and maturity date) and Default (an indicator variable equal to one if a firm receives a default rating from S&P prior to maturity). All other variables are defined in Appendix A. We include year, industry (using Fama-French 48 industries classification), and loan purpose fixed effects and cluster standard errors at the firm level. ***, **, and * signify statistical significance at the 1%, 5%, and 10% levels, respectively (1) (2) Downgrade Default Individual-Pair Relationship Lending -0.322** -0.233 (-2.26) (-0.51) Institutional-Pair Relationship Lending -0.109 0.274 (-1.11) (0.93) Maturity 0.930*** 1.204** (8.39) (2.47) Loan Size 0.305** 0.694 (2.40) (1.63) Collateral 0.157* 0.075 (1.96) (0.37) Term Loan 0.016 -0.942** (0.12) (-2.40) Loan Spread -0.010 1.263*** (-0.09) (3.69) Firm Size 0.535*** 0.203 (7.26) (1.35) Leverage 1.856*** 3.084*** (5.02) (2.96) Profitability 0.966 -0.507 (1.34) (-0.27) Tangibility 0.602* 0.576 (1.72) (0.69) MTB 0.006 -0.008 (0.64) (-0.35) Interest Coverage -0.001* -0.004* (-1.73) (-1.81) Current Ratio 0.062 -0.040 (1.30) (-0.32) Year FE Yes Yes Industry FE Yes Yes Purpose FE Yes Yes Pseudo R-squared 0.187 0.320 Observations 3,883 3,883 Electronic copy available at: https://ssrn.com/abstract=3877687 Table 8: The Impact of Individual-Pair Relationship Lending on Different Lending Terms and lead bank share This table reports the regression results of individual-pair relationship lending on the following lending contract terms: Upfront Fee, Loan Size, Collateral, Maturity, Number of Covenants, Covenant Strictness, and Lead Bank Share. All variables are defined in Appendix A. We include year, industry (using Fama- French 48 industries classification), and loan purpose fixed effects and cluster standard errors at the firm level. ***, **, and * signify statistical significance at the 1%, 5%, and 10% levels, respectively. Column (1) is based on the reduced sample where upfront fee is available. Column (6) is based on the reduced sample where covenant is available. Column (7) is based on the reduced sample where there is lead bank share information available. We report the Pseudo R-squared for the Logit model of the indicator variable Collateral and for the Tobit model of the covenant strictness measure that ranges from 0 to 1. All other models are OLS models with Adj. R-squared. (1) (2) (3) (4) (5) (6) (7) Number of Covenant Lead Bank Upfront Fee Loan Size Collateral Maturity Covenants Strictness Share Individual-Pair Relationship -17.675 0.080** -0.275** -0.029 0.014 -0.032 0.429 Lending (-1.61) (2.27) (-2.07) (-1.15) (0.13) (-1.25) (0.32) Institutional-Pair -20.312** 0.068*** 0.038 -0.010 0.211*** -0.025 -2.488*** Relationship Lending (-2.43) (2.98) (0.43) (-0.61) (2.72) (-1.42) (-2.86) Maturity -8.389 0.387*** 0.553*** 0.206** -0.011 -7.292*** (-1.06) (14.40) (4.86) (2.55) (-0.59) (-6.75) Loan Size 7.860 0.004 0.204*** 0.412*** -0.039*** -10.836*** (1.20) (0.06) (14.47) (6.70) (-2.66) (-12.51) Collateral 20.691** 0.007 0.108*** 1.587*** 0.131*** 0.172 (2.28) (0.25) (5.23) (16.26) (5.73) (0.15) Term Loan 24.356*** -0.279*** 0.624*** 0.247*** 0.905*** -0.015 2.830 (2.65) (-7.92) (4.96) (10.22) (7.33) (-0.59) (1.49) Firm Size -5.055 0.663*** -0.607*** -0.132*** -0.300*** -0.029** -2.581*** (-0.82) (54.83) (-9.60) (-10.71) (-5.59) (-2.03) (-3.64) Leverage 24.664 0.353*** 2.907*** 0.037 1.044*** 0.702*** -8.572** (0.83) (3.81) (9.22) (0.69) (3.48) (10.55) (-2.17) Profitability -134.173*** 1.052*** -4.377*** 0.428*** 2.044*** -1.509*** -20.456*** (-3.07) (7.04) (-7.58) (4.36) (4.50) (-11.92) (-3.47) Tangibility -20.381 -0.125 -0.315 0.054 -0.726*** -0.108* 0.150 (-0.83) (-1.34) (-1.04) (1.02) (-2.60) (-1.70) (0.04) MTB 0.045 0.008*** -0.015 -0.004* -0.020*** 0.002 0.092 (0.10) (3.46) (-1.34) (-1.87) (-2.72) (0.81) (1.38) Interest Coverage 0.007* -0.000*** 0.000*** -0.000** 0.000* 0.000*** 0.001 (1.72) (-2.70) (2.89) (-2.46) (1.81) (2.81) (1.50) Current Ratio -6.090** -0.012 -0.049 0.014*** 0.007 -0.042*** 0.366 (-2.02) (-1.28) (-1.32) (2.81) (0.27) (-5.38) (0.72) Non-Investment Grade 12.444 0.036 2.095*** 0.116*** 0.815*** 0.177*** -3.390*** (1.36) (0.86) (10.17) (3.65) (7.51) (4.97) (-2.71) Year FE Yes Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Yes Purpose FE Yes Yes Yes Yes Yes Yes Yes Adj. R-squared / Pseudo R-squared 0.218 0.770 0.317 0.300 0.443 0.271 0.548 Observations 614 3883 3883 3883 3883 2708 1614 Electronic copy available at: https://ssrn.com/abstract=3877687
Journal
ARN Conferences & Meetings – SSRN
Published: Jun 30, 2021
Keywords: Individual-Pair Lending Relationships, Asymmetric information, Professional connections, Bank lending, Debt Contracting, Cost of Debt