co-migration and the benefits of relationships in bank ...€¦ · co-migration and the benefits of...
TRANSCRIPT
Electronic copy available at: http://ssrn.com/abstract=2700514
Co-Migration and the Benefits of Relationships in Bank Lending*
Urooj Khan
Columbia Business School Columbia University
Email: [email protected]
Xinlei Li Columbia Business School
Columbia University Email: [email protected]
Chris Williams Stephen M. Ross School of Business
University of Michigan Email: [email protected]
Regina Wittenberg-Moerman Marshall School of Business
University of Southern California Email: [email protected]
Current version: December, 2015
* We appreciate the helpful comments of conference participants at the University of North Carolina Accounting Alumni Conference and workshop participants at Columbia University. We are grateful to Vincent Pham for his excellent research assistance. We thank the Thomson Reuters Loan Pricing Corporation for providing loan data. We gratefully acknowledge the financial support of Columbia Business School, Ross School of Business, University of Michigan, the University of Chicago Booth School of Business and the Marshall School of Business, the University of Southern California.
Electronic copy available at: http://ssrn.com/abstract=2700514
Co-Migration and the Benefits of Relationships in Bank Lending
Abstract
Established relationships with a borrower is one of the primary channels through which lenders reduce agency problems and information asymmetry in debt contracting. Given the critical role individual managers play in shaping corporate policies and performance, we investigate the importance of lenders’ relationships with individual managers and their impact on lending practices. Utilizing a setting of executive turnovers, we find that a lender is 1.6 times more likely to start a lending relationship with a firm when a manager with whom it has a prior lending relationship is hired by the firm as a top executive. We also find that the likelihood of a lender’s co-migration with a manager is higher when the firm is more opaque, consistent with the higher importance of prior relationships in poor information environments. We next show that co-migration benefits the borrowing firm through greater access to credit and a lower cost of financing. Further, a stronger lender-manager relationship increases the likelihood of a lender’s co-migration with the manager, especially when the lender is under pressure to expand her lending portfolio and seek new borrowers. Overall, our paper provides novel evidence on the importance of lender-manager relationships in credit markets.
Electronic copy available at: http://ssrn.com/abstract=2700514
1
1. Introduction
Economic theories suggest that market frictions such as information asymmetry and agency
costs can hinder the allocation of capital to positive NPV projects (e.g., Stiglitz and Weiss, 1981).
By ex-ante screening and ex-post monitoring of a borrower, banks reduce information asymmetry
and allow for the efficient allocation of capital to these projects (Diamond, 1984, and Fama, 1985).
One of the primary channels through which lenders reduce agency problems is by forging a
relationship with their borrowers. By developing a strong relationship through repeated
transactions and continuous interaction with the borrowing firm, lenders are able to gather relevant
information not only about the borrowing firm’s prospects, but also about the competence and
trustworthiness of its management (e.g., Rajan, 1992, and Schenone, 2010).1 However, prior
studies that examine relationship lending focus primarily on the relationship between lenders and
the borrowing firm, without addressing the role played by the relationship between lenders and
individual managers (e.g., Petersen and Rajan, 1994 and 1995, Degryse and Van Cayseele, 2000,
Bharath et al., 2011, Santos and Winton, 2009, and Schenone, 2010). In this paper, we investigate
the importance of the lender-manager relationship and its impact on lending practices.
Individual managers play an important role in shaping corporate behavior and performance
(Hambrick and Mason, 1984, and Hambrick, 2007). A manager’s characteristics and style
significantly affect a variety of firm practices and policies, including investment and financial
strategies (Bertrand and Schoar, 2003), disclosure and communication choices (Bamber, Jiang and
Wang, 2010, Ge, Matsumoto and Zhang, 2011, DeJong and Ling, 2013) and the extent of tax
aggressiveness (Dyreng, Hanlon and Maydew, 2010). Prior research also documents that
uncertainty about manager-specific characteristics increases investors’ assessment of a firm’s
1 See Boot (2000) and Elyasiani and Goldberg (2004) for a comprehensive review of the literature on relationship lending.
2
riskiness and increases its cost of debt (e.g., Pan et al., 2015a and 2015b). Institutional evidence
also suggests that credit rating agencies and loan officers consider a manager’s “character” as one
of the key factors in assessing a firm’s credit risk (Plath et al., 2008, and Koch and MacDonald,
2014). Because, through prior interactions with a manager, lenders can assess her talent, honesty,
strategic vision, motivation and risk attitude, we expect the lender-manager relationship to reduce
lenders’ information asymmetry about a borrowing firm’s creditworthiness and future
performance and thus significantly affect lending practices.
As our primary focus in this paper is lenders’ relationship with a manager, the key empirical
design challenge we face is how to disentangle this relationship from the one between lenders and
the borrowing firm. To accurately identify the lender-manager relationship and its consequences
on lending practices, we utilize a setting of executive turnover. Specifically, we examine how the
relationship a lender develops with a manager at the company the manager leaves (origin firm)
affects its lending to the company that the manager joins (destination firm). We identify 648 origin-
destination firm pairs that experience CEO or CFO turnover over the 1992-2014 period. To
accurately assess the effects of the lender-manager relationship, we also construct a matched
sample of pseudo origin-destination firm pairs (Brochet et al., 2014). We analyze these firms’
syndicated lending and thus focus on the lead arrangers of syndication, which take on the primary
information collection and monitoring responsibilities. 2 Based on this empirical setting, we
investigate three broad research questions: 1) do an origin firm’s lead arrangers follow the manager
to the destination firm (i.e., co-migrate with the manager)? 2) what factors increase the likelihood
of co-migration? and 3) are there benefits to co-migration for the destination firm?
We expect that lenders’ established relationship with a manager will decrease their
2We use lenders and lead arrangers interchangeably throughout the rest of the paper.
3
uncertainty about the destination firm’s risk taking, investment strategies and future financial
performance, thus reducing the risks involved in providing it credit funding. In addition, because
managers often provide private lenders with important non‐public information about their
financial position (e.g., Dennis and Mullineaux, 2000, Standard and Poor’s, 2007), lender-manager
relationship will enhance the reliability of such information and thus reduce lenders’ efforts to
verify its quality and accuracy. We therefore predict that lenders will have incentives to continue
utilizing their valuable relationships with the manager and will be likely to co-migrate with the
manager to the destination firm. We find evidence consistent with this prediction. A lead arranger
is 1.6 times more likely to start a lending relationship with a firm when a manager with whom it
has a prior lending relationship moves to the firm as a top executive. This finding emphasizes that
lenders create important relationships with the managers of a borrowing firm.
We supplement this analysis by testing whether the probability of a lead arranger co-
migration with the manager is higher for more informationally opaque borrowers. Lending to
opaque borrowers is associated with substantial agency costs and costly monitoring (e.g.,
Diamond, 1991, Petersen and Rajan, 1994 and 1995, Berger and Udell, 1995), suggesting that the
lenders’ relationship with a manager should be particularly valuable when the destination firm
operates in a poor information environment. We consider a destination firm to be more
informationally opaque when it is smaller (e.g., Bharath et al., 2007, and Wittenberg-Moerman,
2008), not rated by credit rating agencies (e.g., Sufi, 2007, and Kraft, 2014) or when it has low
analyst coverage (Güntay and Hackbarth, 2010, and Mansi et al., 2010). Across all information
opaqueness measures, we find that the likelihood of co-migration is significantly higher when the
destination firm is more opaque.
We next investigate whether the lenders’ co-migration benefits the destination firm. Building
4
on the prior relationship lending literature that documents that borrowers with close ties to lenders
have greater availability of credit (Petersen and Rajan, 1994, and Rajan and Zingales, 1998), we
predict that lender co-migration endows the destination firms with higher access to debt capital.
We assess our prediction in two ways. First, we examine whether a lender is more likely to co-
migrate with the manager when a destination firm has limited access to capital, which would
suggest that co-migration at least partially alleviates the firm’s capital constraints. Using the
number of the destination firm’s prior lead arranger relationships as a measure of its access to
credit (e.g., Houston and James, 1996, and Murfin, 2012), we find that the probability of co-
migration is significantly higher for firms with weak access. We supplement this evidence by
examining whether the probability of co-migration is higher when the lending standards in credit
markets are tight. Using changes in bank lending standards, as reported by the Federal Reserve
Board’s Senior Loan Officer Opinion Survey (e.g., Bassett et al., 2012), we find that lead arrangers
are more likely to co-migrate with a manager when credit standards are tight and firms in the
economy face greater credit constraints.
Second, we perform a more direct test of credit availability by examining whether co-
migration increases the extent of the destination firm’s syndicated loan financing. For the sample
of firms that experience executive turnover, we find that a lead arranger’s co-migration is
associated with an 18 percent higher syndicated loan borrowing for destination firms following
turnover relative to destination firms that do not experience lender co-migration. Overall, our
findings suggest that managers’ relationships with lenders benefit borrowers by enabling greater
access to credit.
We extend these analyses by exploring whether co-migration also has a favorable effect on
the cost of debt. It is possible that through their relationship with the manager, lenders will share
5
potential cost savings due to reduced information asymmetry with the destination firms. However,
prior literature provides mixed evidence with respect to whether relationship lending decreases the
cost of borrowing, sometimes even finding an increase in these costs due to the hold-up problem
in relationship lending (e.g., Petersen and Rajan, 1994, Degryse and Van Cayseele, 2000, Santos
and Winton, 2009, and Schenone, 2010). We find that the destination firms’ cost of borrowing is,
on average, 21.7 basis points lower when lenders co-migrate with managers, which represents 11.8
percent of the average interest spread for the sample destination firms.
Although our analyses so far focus on borrowers’ characteristics and financing needs, in our
last set of tests we examine what factors affect a lender’s decision to co-migrate with a manager.
We predict that the strength of the lender-manager relationship increases the probability of co-
migration. Because a stronger lender-manager relationship is likely to be associated with lenders’
greater trust in the manager’s integrity, talent and information that she provides, it should therefore
lead to a higher reduction in information asymmetry about the borrowing firm. We perform lender
level analyses for a sample of all lead arrangers that had syndicated loans to the origin firm over a
manager’s tenure. We designate lead arrangers that syndicated more than 75 percent of all loans
to the origin firm as having a strong lending relationship with the manager. We find that these lead
arrangers are 3.4 times more likely to follow a manager to the destination firm relative to other
lead arrangers of the origin firm.
We add to this analysis by examining whether the strength of the lender-manager relationship
has a more significant effect on the co-migration probability when lenders are under pressure to
expand their loan portfolio and seek new borrowers. We show that when lead arrangers with a
strong relationship with a manager experience relatively low loan growth, they are more likely to
expand their lending portfolio by extending credit to the destination firm. We also find that the
6
strength of the lending relationship has a higher impact on the probability of co-migration when a
lead arranger experiences low profitability, consistent with new lending relationships having the
potential to increase a lender’s future profitability via additional interest revenues, loan origination
fees or fees from other services provided to the destination firm.
Our paper contributes to the literature across several dimensions. First, we add to the
literature on relationship lending. Prior studies have documented that the relationship between
lenders and a borrowing firm plays a critical role in reducing information asymmetry and agency
problems in private lending (e.g., Rajan, 2002, Berger and Udell, 1995, Bharath et al., 2011, Santos
and Winton, 2009, and Schenone, 2010). Petersen and Rajan (1994, 1995), Boot and Thakor
(1994), Berger and Udell (1995) and Bharath et al. (2011), among others, also demonstrate the
benefits to borrowers of an established lending relationship. We extend these studies by showing
that lenders establish a valuable relationship not only with the borrowing firm, but also with its
managers. We also document that lender-manager relationships benefit the firm the manager joins
via enhanced access to credit and lower cost of debt financing.
Our findings also add to the emerging literature on the role of relationships in capital markets
in general, and in bank lending in particular. Engleberg et al. (2012) show that firms benefit from
lower interest spreads on their loans when firm and bank executives attended the same college or
previously worked together. Haselmann et al. (2013) report that German banks provide
significantly more credit to firms whose executives belong to the same social club as the bank’s
executives. Karolyi (2014) reports that following executive turnover, borrowers lose as much as
74% of the benefits from lenders with personal relationships with the former executive and
therefore tend to borrow from new lenders. We supplement these studies by documenting that
personal relationships between executives and lenders survive managerial turnover and carry over
7
to the firm the manager joins. We also show that a borrower’s information opacity and capital
needs influence the likelihood of lender co-migration.
Third, our study is also relevant to the literature on the impact of personal networks and
relationships on business and investment decisions. Brochet et al. (2014) find that executive
turnover triggers analysts following the company that the manager leaves to initiate coverage of
the company the executive joins. Cohen et al. (2008) show that personal relationships influence
the flow of information into asset prices. They find that mutual fund managers are more likely to
place larger bets on firms run by managers who graduated from the same institution and that this
practice allows them to earn higher average returns on their investments. Fracassi (2014) and
Schmidt (2015) show that social networks influence capital investments and mergers and
acquisition outcomes, respectively. We add to these studies by documenting that lenders’ personal
relationship with a manager significantly affects their decision to initiate a lending relationship
with the new firm. We also show that the strength of the lender’s prior relationship with a manager
increases the probability that the lender provides credit to the new firm the manager joins.
The rest of the paper is organized as follows. Section 2 discusses the related literature and
develop the hypotheses. Section 3 describes the sample selection and empirical design. Section 4
reports the empirical results and Section 5 concludes.
2. Related Literature and Hypotheses Development
2.1. Manager and Lender Co-Migration
Prior literature has extensively examined the importance of relationship lending.
Relationship lenders have deep knowledge of a borrower’s operations and well developed channels
of communication with the borrower’s managers (e.g., Rajan, 1992, Petersen and Rajan, 1994,
Bharath et al., 2007, and Schenone, 2010). Prior studies also suggest that borrowers are inclined
8
to reveal more information to lenders with whom they have an established lending relationship
and that relationship lenders have stronger incentives to invest in information production
(Greenbaum and Thakor, 1995, Boot, 2000, and Bharath et al., 2008). Relationship lenders thus
play an important role in reducing the agency problems associated with debt financing. There is
also ample evidence that relationship lending has important consequences for lending practices,
such as a higher availability of credit to borrowers with a strong relationship with their lenders
(e.g., Hoshi, Kashyap and Scharfstein, 1990a, 1990b and 1991, and Petersen and Rajan, 1994 and
1995).
For the most part, previous studies focus on the relationship at the institutional level between
lenders and the borrowing firm (e.g., Petersen and Rajan, 1994 and 1995, Degryse and Van
Cayseele, 2000, Bharath et al., 2011, Santos and Winton, 2009, and Schenone, 2010). For example,
Petersen and Rajan (1994) and Berger and Udell (1995) focus on lenders’ relationship with small
firms, for which data is available under the National Survey of Small Business Finances. Degryse
and Van Cayseele (2000) examine relationship lending in the context of small businesses in
Belgium. Bharath et al. (2011), Santos and Winton (2009) and Schenone (2010) utilize the
relationship between lenders and borrowing firms in the syndicated loan market.
However, individual managers play a crucial role in shaping firm strategies and performance
(Hambrick and Mason, 1984, and Hambrick, 2007). Manager characteristics, such as confidence,
risk attitude, intrinsic motivation and underlying talent are often collectively referred to as
“management style.” Prior research documents that management style impacts a wide spectrum
of organizational outcomes. Bertrand and Schoar (2003) report that management style explains a
significant amount of the heterogeneity in the investment, financial and organizational practices
of firms. The individual-specific characteristics of managers have also been shown to affect other
9
firm policies and practices, such as tax avoidance strategies (Dyreng et al., 2010), accounting
choices (Ge et al., 2011, and DeJong and Ling, 2013) and voluntary financial disclosure policies
(Bamber et al., 2010). We therefore explore whether lenders’ relationships with individual
managers significantly affect lending practices. 3
The lender-manager relationship is likely to reduce lenders’ uncertainty about the manager’s
underlying talent, personality, risk attitude and strategic vision, consequently reducing the risks
involved in funding the borrowing firm. Pan et al. (2015a) report that a firm’s stock return volatility
increases significantly around CEO turnover and then declines over the tenure of the CEO. They
interpret this finding as suggesting that over time, the market learns about a CEO’s individual-
specific characteristics and that this reduction in uncertainty results in a decline in a firm’s
riskiness. Similarly, Pan et al. (2015b) show that uncertainty about a firm’s management also
affects the cost of borrowing. They find that a firm’s CDS spreads, loan spreads and bond yield
spreads decline over the first three years of CEO tenure, holding other macroeconomic, firm and
security level factors constant. Further, evidence from practice also highlights the importance of
manager “character” in credit decisions. Koch and MacDonald (2014) report that it is standard
practice for banks to assess managers’ abilities and talents when evaluating credit requests. Credit
rating agencies also carefully analyze a manager’s impact on firm value when evaluating a firm’s
credit riskiness (e.g., Plath et al., 2008).
We expect the lender-manager relationship to be particularly important when lenders extend
credit to a new borrower, as adverse selection problems are the most severe in this case. Therefore,
to explore the effects of the lender-manager relationship on lending practices, we utilize an
empirical setting where a lender extends credit to a new borrower whose manager has an
3 It would also be interesting to examine the importance of a manager’s relationship with the loan officers assigned to its loans, but we do not possess data on manager-loan officer interactions.
10
established relationship with the lender. Specifically, we focus on a sample of firms that experience
executive turnover, where a lender has developed a relationship with a manager at the company
the manager leaves (origin firm) and may start a lending relationship with the new company a
manager joins (destination firm). Through prior transactions with the manager at the origin firm,
the lender has had an opportunity to assess her talent, trustworthiness, strategic vision, motivation
and risk taking behavior. This assessment will help the lender to decrease its uncertainty about the
destination firm’s investment and financial policies, future strategies and financial performance,
thus allowing a more accurate evaluation of its credit risk. In addition, because in private lending
managers often provide lenders with confidential information about their financial position and
projections (e.g., Dennis and Mullineaux, 2000, Standard and Poor’s, 2007), prior lender-manager
relationship will enhance the reliability of such information. This in turn will decrease the efforts
lenders need to exert to verify the quality and accuracy of this non-public information and will
further enhance lenders’ ability to correctly asses the risks involved in funding the borrower.
We therefore predict that lenders will have incentives to continue utilizing their valuable
information about the manager and well established channels of communication with her and will
be likely to follow the manager to the destination firm. Because we conduct our analyses in the
syndicated loan market setting, we focus our hypotheses on the lead arrangers of the syndication.
Lead arrangers establish a relationship with the borrower, negotiate the terms of the loan contract,
perform the primary screening and monitoring of the borrower, and recruit participants to join the
syndicate (e.g., Sufi, 2007). Accordingly, our first research hypothesis is as follows:
H1 – A lead arranger is more likely to start a lending relationship with a firm when a
manager with whom it has a prior relationship moves as a top executive in this firm.
11
2.2. The Effect of a Borrower’s Information Opacity on the Probability of a Lender’s Co-Migration
with the Manager
We expect the lender-manager relationship to be especially valuable in lending to
informationally opaque borrowers. These borrowers rely primarily on relationship lending because
information asymmetry about their performance and creditworthiness deters prospective lenders
from extending them credit (e.g., Diamond, 1991, Petersen and Rajan, 1994 and 1995, and Berger
and Udell, 1995). Providing credit to informationally opaque borrowers requires lenders to exert
substantial screening and monitoring efforts. Further, assessing the reliability and accuracy of non-
public information provided by managers at loan initiation and within the course of the loan is
particularly important in lending transactions with these borrowers (Bharath et al, 2007 and
Bushman et al., 2010). In the syndicated loan market, loans to non-transparent borrowers are also
more costly for the lead arranger to syndicate as the arranger has to commit to holding a larger
proportion of the loans to attract syndicate participants (e.g., Sufi, 2007, and Ivashina, 2009). Thus,
when extending credit to borrowers that operate in a poor information environment, having an
established relationship with a manager should endow lenders with meaningful cost savings.
From the borrower’s perspective, opaque borrowers are also more likely to seek financing
from lenders that have established relationships with the new manager. Information asymmetry
increases loan pricing and has a detrimental effect on other contractual terms, such as maturity and
collateral requirements (e.g., Francis et al., 2005, Zhang, 2007, Bharath et al., 2008, and Costello
and Wittenberg-Moerman, 2011). Therefore, non-transparent borrowers are likely to benefit from
more lenient loan terms when information asymmetry is mitigated due to lender-manager
relationships. Combined, lender- and borrower-based arguments lead to our second hypothesis:
12
H2 – The likelihood of the lead arranger’s co-migration with a manager is higher when the
destination firm is more informationally opaque.
2.3. The Consequences of Lender-Manager Co-Migration: Benefits to the Destination Firm
Prior literature on relationship lending documents that a borrower’s close ties with its lenders
allow for greater access to credit (Petersen and Rajan, 1994, and Rajan and Zingales, 1998).
Petersen and Rajan (1995) extend this evidence and show that relationship lending is especially
beneficial to young, more credit rationed firms. Bolton et al. (2013) further highlight that
relationship lending is valuable when firms have limited access to credit. They show that banks
endowed their borrowers with higher credit availability during the recent financial crisis, although
they charge higher interest spreads in normal times.
We extend these arguments to the lender-manager relationship setting and predict that when
the lender co-migrates with the manager, the destination firm benefits from higher access to debt
capital. However, empirically assessing the change in credit availability to the borrower is always
challenging. Therefore, we assess our prediction in two ways. First, we examine whether a lender
is more likely to co-migrate with the manager when the destination firm has more limited access
to debt capital, thus relieving, at least partially, its capital constraints. Our third hypothesis is thus
stated as following:
H3a – The likelihood of the lead arranger’s co-migration with a manager is higher when the
destination firm is more capital constrained.
Second, we examine the association between a lender’s co-migration and the extent of the
destination firm’s syndicated loan borrowing following an executive turnover. Stated as a formal
hypothesis:
H3b – The extent of a destination firm’s syndicated loan financing following executive
13
turnover is higher when a lead arranger co-migrates with a manager relative to when the lead
arranger does not follow the manager to the destination firm.
The benefits of lender-manager relationship can also extend to less expensive credit. Lenders
may pass on some of the potential cost savings due to the reduced information asymmetry from its
established relationship with a manager to the destination firm. If this is the case, a lender’s co-
migration is likely to be associated with a lower interest spread. However, prior literature provides
mixed evidence with respect to whether relationship lending results in cheaper credit. Berger and
Udell (1995) show that borrowers with longer lending relationships experience lower credit costs
than do borrowers with shorter relationships. Similarly, Bharath et al. (2011) show that repeated
borrowing from the same lender results in lower spreads. In contrast, Degryse and Van Cayseele
(2000) find that interest rates increase with the length of the lending relationship, implying that
relationship banks exploit their information advantage. Consistent with the hold-up problem
associated with relationship lending (Rajan, 1992), Santos and Winton (2009) find that bank-
dependent borrowers pay higher spreads during a recession. Similarly, Schenone (2010) shows
that relationship loans become more expensive at high levels of relationship lending intensity.
Despite this conflicting prior evidence, we formulate the following hypothesis:
H4 – The cost of borrowing following executive turnover is lower for destination firms that
experience a lead arranger’s co-migration with the manager relative to destination firms where a
lead arranger does not follow the manager.
2.4 The Importance of the Strength of the Lender-Manager Relationship in Determining Lenders’
Co-Migration
Next, we focus on a lender’s incentives to co-migrate. We have assumed so far that all
relationships are equal; however, the strength of the lender-manager relationship can be an
14
important determinant of co-migration. Specifically, we expect a stronger lender-manager
relationship to be associated with the lender placing greater trust in a manager’s integrity, talent
and information that she provides. Consequently, this stronger relationship is likely to reduce
information asymmetry about the future performance and creditworthiness of the destination firm
to a greater extent. Building on these arguments, we state our next hypothesis:
H5a – The likelihood of the lead arranger’s co-migration with a manager to the destination
firm increases with the strength of the lender-manager relationship.
Although we predict that a strong relationship between the lender and the manager should
substantially reduce information asymmetry about the destination firm, adverse selection still
remains a significant concern when lenders extend credit to new borrowers. We therefore expect
the strength of the lender-manager relationship to have a more significant effect on the co-
migration probability when lenders are under pressure to expand their loan portfolio and thus seek
new borrowers. Lenders are more likely to extend their borrowing base when they experience slow
loan growth. Low profitability may also incentivize lenders to follow the manager to the
destination firm. New lending relationships can increase lenders profitability via additional interest
revenues and loan origination fees. Establishing a lending relationship with the destination firm
may also help a lender gain future underwriting of its public bonds and equity (e.g., Drucker and
Puri, 2005, Yasuda, 2005, and Brarath, 2007), thus increasing fee revenues. To reflect these
arguments, we formulate the following hypothesis:
H5b – The strength of the lead-arranger-manager relationship has a stronger effect on the
co-migration probability when a lead arranger experiences lower loan growth or lower
profitability relative to other lenders.
15
3. Sample Selection and Matching Procedure
3.1. Sample Selection
Because our study explores the co-migration of managers and lenders, we start our sample
selection process by identifying executives that switch companies. We collect executive turnovers
from the Execucomp database for the period from 1992 to 2014. We next search the names of the
executives joining and leaving Execucomp firms in the BoardEx database to construct their
comprehensive employment history. For each manager switching firms, we identify her origin and
destination firms. We require a manager to hold a senior executive position for a period of at least
three years in each firm. We focus on the turnovers of CEOs and CFOs because a firm’s top
management designs its policies and strategic goals and thus significantly affects its performance
and creditworthiness.
Specifically, we identify executives that held the position of a CEO or CFO at the origin firm
and migrated to the destination firm as a CEO or a CFO. As reported in Panel A of Table 1, we
are able to identify 723 instances of CEOs’ or CFOs’ migrations in Execucomp over our sample
period. We next obtain firm characteristics from Compustat and syndicated loan data from
DealScan. We require each origin and destination firm to have at least one syndicated loan during
the executive’s tenure at each firm. We also require that sample firms have sufficient data for our
analyses. These requirements reduce our sample to 648 executive turnovers.
In Panel B of Table 1, we present the comparison of origin and destination firms across a
number of characteristics, including size, profitability and leverage. Origin Firm Size is the natural
logarithm of total assets of the origin firm. Origin Firm Profitability is the ratio of the origin firm’s
net income before extraordinary items to total assets. Origin Firm Leverage is the ratio of the
origin firm’s long-term debt to total assets. Destination Firm Size, Destination Firm Profitability
16
and Destination Firm Leverage are defined analogously. All firm characteristics are measured at
the end of the most recent fiscal year preceding the executive’s turnover. We find that origin firms
are on average, larger, have higher leverage and are more profitable than destination firms. These
differences between the origin and destination firms’ characteristics are statistically significant,
although the difference between the two set of firms’ leverage is only significant at the 10% level.
3.2. Control Sample
To accurately assess the effects of lenders’ co-migration with the manager to the destination
firm, we construct a control sample of origin-destination firm pairs. The matched pairs are created
to mirror the treatment origin-destination firm pairs that experience executive turnover. Our
matching procedure follows Brochet et al. (2014) and employs the Propensity Score Methodology
(PSM), as in Rosenbaum and Rubin (1983). Specifically, we estimate a propensity score for the
origin as well as the destination firm and identify a matched pair of origin-destination firms. To
do so, we estimate the following Logit Model:
.
(1)
As described in Figure 1, the matching procedure involves a three step process. In the first
step (i.e., Step 1 in Figure 1), we pair the origin firm with all firms in the destination firm’s two-
digit SIC group and estimate Equation (1). The dependent variable, Turnover, equals one when
the executive’s origin firm is paired with its actual destination firm, zero otherwise. Therefore, in
this step, Equation (1) is modeling the executive’s decision to migrate from the origin to a
particular destination firm. Next, we use the estimated coefficients to find a matched “pseudo”
17
destination firm with the closest propensity score to the actual destination firm.
In Step 2, we re-estimate Equation (1) for a sample consisting of the executive’s actual
destination firm paired with all firms in its origin firm’s two-digit SIC group. For this sample, the
dependent variable, Turnover, equals one if the executive’s destination firm is paired with its actual
origin firm, zero otherwise. In this step, Equation (1) is modeling a destination firm’s decision to
hire an executive from a certain origin firm. Next, as in Step 1, we use the estimated coefficients
to find a matched “pseudo” origin firm with the closest propensity score to the actual origin firm.
In Step 3, the matched pseudo origin and pseudo destination firms from Steps 1 and 2,
respectively, are paired together for each actual origin-destination firm pair to create a matched
control pair. Essentially, using propensity score matching we are able to identify matched origin-
destination control firm pairs that are similar to the actual origin-destination firm pairs across a
variety of firm characteristics.
We perform the matching based on the following origin and destination firm characteristics.
Origin Firm Size, Origin Firm Profitability, Origin Firm Leverage, Destination Firm Size,
Destination Firm Profitability and Destination Firm Leverage are as previously defined. All firm
characteristics are measured at the end of the most recent fiscal year preceding the executive’s
turnover. Whether origin and destination firms operate in the same industry is another important
component of the matching model. Same Industry is an indicator variable that equals one if the
origin and destination firms are in the same four-digit SIC group, zero otherwise. We also include
variables reflecting the extent of the origin and destination firms’ lending relationships in the
model. Banks Lending to Origin Firm is the number of lead arrangers that syndicated loans to the
origin firm during the executive’s tenure. Banks Lending to Destination Firm is the number of lead
arrangers that syndicated loans to the destination firm during the three years prior to the executive’s
18
appointment. Finally, we address the possibility that the same lead arrangers may be syndicating
loans to both the origin and destination firms prior to the executive’s turnover. Bank Overlap is an
indicator variable that equals one if at least one lead arranger lending to the origin firm during the
executive’s tenure also syndicated loans to the destination firm during the three years prior to the
executive’s appointment to the destination firm, zero otherwise. We measure our lead arranger
lending-based variables for the destination firm over the three-year period prior to the executive’s
turnover. However, our primary findings do not change if we extend this estimation period to five
years. Detailed variable definitions are provided in Appendix A.
Based on our three step matching procedure, we identify 648 control firm pairs for our
CEO/CFO turnover sample. Table 2 reports the summary statistics for the comparison between the
turnover and propensity-score-based matched samples. The differences in variable means between
these samples are insignificant for all variables, except for Bank Overlap, which is significant at
the 10% level. This evidence mitigates the concern that origin and destination firm-specific
characteristics may affect our findings.
4. Empirical Analyses
4.1 Test of Co-Migration
We begin our analysis by investigating the determinants of the co-migration of lenders and
managers between origin and destination firms using the following logistic regression –
.
(2)
Migrate is an indicator variable that equals one if at least one lead arranger that syndicated
loans to the origin firm during the executive’s tenure starts syndicating loans to the destination
19
firm within three years of the executive’s appointment, zero otherwise. We consider the lead
arranger as initiating a lending relationship with the destination firm if it did not arrange its
syndicated loans over the three-year period prior to the executive’s appointment at the destination
firm. We obtain similar results if we require the lead arranger to not have a lending relationship
with the destination firm over the five years prior to the executive’s appointment. Executive
Turnover is an indicator variable that equals one if the CEO or CFO of the origin firm migrates as
a CEO or CFO to the destination firm, zero otherwise (i.e., Executive Turnover equals one for the
actual origin-destination firm pairs and zero for the pseudo pairs).
We expect that lenders are more likely to migrate with a manager if the origin and destination
firms are more similar. Therefore, we control for differences in size, profitability and leverage
between the origin and destination firms. We measure Size Difference as the difference between
Destination Firm Size and Origin Firm Size. Profitability Difference and Leverage Difference are
defined analogously. If lenders have industry expertise, they are more likely to migrate with the
manager if she switches to another company in the same industry. Therefore, we include the Same
Industry indicator variable in the model (defined as in Model 1).
We also control for the number of the origin firm’s lead arrangers during the executive’s
tenure (Banks Lending to Origin Firm), as a larger number of lead arrangers with established
relationships with an executive should be associated with a higher probability of at least one of
them migrating to the destination firm. In contrast, a greater number of lead arrangers with
established relationships with the destination firm (Banks Lending to Destination Firm) is likely
to increase competition on syndicating its loans (Rajan, 1992), thus decreasing the likelihood of
the origin firm lead arrangers’ migrating with the executive. We also account for the possibility
that lead arrangers lending to the origin firms have a lending relationship with the destination firm
20
prior to the executive’s turnover (Bank Overlap). Banks Lending to Origin Firm, Banks Lending
to Destination Firm and Bank Overlap are defined as in Model 1.
We present our findings about the determinants of the co-migration of managers and lenders
in Table 3. We find a significant and positive coefficient on Executive Turnover, consistent with
our predictions that lenders are likely to follow a manager to the destination firm. The coefficient
of 0.451 on the Executive Turnover variable indicates that a lender is 1.6 times more likely to start
a lending relationship with a firm when a manager with whom it has a prior lending relationship
moves to the firm. The evidence of lender-manager co-migration suggests that personal
relationships between lenders and managers survive and carry over to the new firm following
managerial turnover.
In terms of control variables, as expected, we find a significant and positive coefficient on
the Banks Lending to Origin Firm variable. Bank Overlap also significantly increases the
probability of co-migration. This result is intuitive as the overlap in lead arrangers between the
origin and destination firms suggests that these borrowers are likely to have similar credit profiles,
increasing the probability that the origin firm’s lenders consider the destination firm to be an
attractive borrower.
We supplement these tests with untabulated analyses using a sample of firms that experience
turnover of non-CEO-CFO top officers who are disclosed in SEC filings as senior executives (e.g.,
Chief Risk Officer, Chief Operating Officer, Executive Vice President, Corporate Marketing and
Communications, Executive Vice President and General Counsel, or Executive Vice President,
Administration).4 Although relative to CEOs and CFOs these executives play a lesser role in
4The firms are required to provide information about their top 5 executive in SEC filings. Therefore, we are able to identify the turnover of non-CEO-CFO executives based on the Execucomp and BoardEx databases.
21
establishing a firm’s policies and strategies, lenders’ familiarity with their managerial style and
trust in their integrity may increase the likelihood of the lenders initiating lending relationships
with the destination firm. We identify 2,008 non-CEO-CFO executive moves and follow the same
procedure as with the CEO-CFO sample to create a control sample of origin-destination firm pairs.
We re-estimate Model 2 for the non-CEO-CFO sample and continue to find a positive and
significant coefficient on the Turnover variable. This evidence further supports our hypothesis that
lenders’ prior relationship with the origin firm’s top executives reduces information asymmetry
about the destination firm, increasing the probability that they will provide debt capital to the
destination firm.
4.2 Co-Migration and the Destination Firm’s Information Opacity
We next turn to testing and exploring cross-sectional differences in lender-manager co-
migration. As discussed in Section 2, we expect the likelihood of co-migration to be higher when
the destination firm is characterized by a poor information environment. We examine three
different proxies for the destination firm’s information environment. First, consistent with prior
research, we consider smaller firms to be more informationally opaque (e.g., Bharath et al., 2007,
and Wittenberg-Moerman, 2008). We assign a borrower to the small (large) firm subsample if its
long-term assets, measured at the end of the most recent fiscal year preceding the executive’s
turnover, are below (above) the sample median. Because credit rating agencies provide
assessments of a borrower’s creditworthiness that are valuable to lenders (e.g., Sufi, 2007, and
Kraft, 2014), the second proxy we use for a firm’s information environment is whether the firm is
rated by a credit rating agency. We obtain information on the credit ratings of the sample borrowers
from the S&P and Moody’s historical credit ratings databases. We assign a borrower to the non-
rated subsample if it is not rated by either S&P or Moody’s when a firm experiences the executive’s
22
turnover. Otherwise, the borrower is included in the rated subsample. We further assess a
borrower’s information environment by the extent of its equity analysts’ coverage (Güntay and
Hackbarth, 2010, and Mansi et al., 2010). We obtain analysts’ coverage data from the I/B/E/S
database. We assign a borrower to the low (high) analyst coverage subsample if the number of
analysts covering the firm in the year preceding the year of the manager’s move to the destination
firm is below (above) the sample median.
We report the results of these analyses in Table 4. As evidenced from Panel A, while the
coefficient on Executive Turnover is not statistically significant for the larger firm partition, a lead
arranger is 1.95 times more likely to start a lending relationship with a smaller borrower when a
manager with whom it has a prior lending relationship moves to this firm. The magnitude of the
Executive Turnover coefficient is also significantly larger for the small size partition (the
difference in coefficients on Executive Turnover across partitions is significant at the 10% level).
For non-rated borrowers, when an executive moves to the destination firm, the probability that one
of the origin firm’s lenders will start lending to the destination firm is higher by 2.3 times relative
to control firms. The effect of executive turnover is not significant for the rated partition, with the
difference in the coefficients on Executive Turnover between the two partitions being highly
significant. These findings are in line with Bharath (2007) findings that small and non-rated
borrowers are significantly more likely to use their relationship banks for future loans.
We obtain similar results for our last information opacity measure. Relative to more
transparent borrowers, when a borrower has low analyst coverage, lenders are significantly more
likely to co-migrate. For the low analyst coverage partition, the probability that one of the origin
firm’s lenders starts lending to the destination firm is 2.0 times higher when a manager with whom
it has a prior lending relationship moves to the firm. Overall, the results in Table 4 provide strong
23
support for our prediction that the occurrence of co-migration substantially increases when the
destination firm is more informationally opaque.
4.3. Benefits of Co-Migration to the Destination Firm
In this section, we investigate the benefits of co-migration to destination firms. An important
potential benefit to destination firms of hiring a manager with an established lender relationship is
access to financing through the relationship lender. We expect this access to relieve the capital
constraints that a destination firm may be facing. We investigate this conjecture by two sets of
tests. We start by examining whether the probability of co-migration is associated with a
destination firm’s access to debt capital. We predict that the likelihood of co-migration is higher
when the destination firm has limited access to capital. Next, we perform more direct analyses of
the benefits of co-migration by examining co-migration’s effects on the destination firm’s amount
of syndicated borrowing and its pricing.
4.3.1 Co-migration and access to debt capital
We employ two proxies for the destination firm’s access to debt capital. First, we assume
that a higher number of lead arrangers syndicating the destination firm’s loans (Banks Lending to
Destination Firm) indicates its better access to syndicated loan financing (Houston and James,
1996, and Murfin, 2012). We assign the destination firm to the weak access to credit category if it
had no lending relationships in the syndicated loan market in the three years prior to the executive
turnover. Otherwise, the destination firm is assigned to the strong access to credit category. In
untabulated analyses, we assign destination firms to the access to credit categories based on the
median number of lead arrangers and find similar results. Second, we assess the strength of the
destination firm’s access to debt capital based on the overall state of the debt markets. We assume
that the destination firm’s access to credit is weaker when the overall lending standards in credit
24
markets are tight. We use changes in bank lending standards, as reported by the Federal Reserve
Board’s Senior Loan Officer Opinion Survey on Bank Lending Practices (SLOOS) and which is
available from the Federal Reserve Bank of St. Louis, to identify changes in banks’ lending
standards (e.g., Bassett et al., 2012). We assign the destination firm to the weak access to credit
category when lending standards are tightening in the quarter of the loan’s origination, and to the
strong access to credit category otherwise.
Panel A of Table 5 reports the results from the estimation of Model 2 for subsamples based
on the destination firm’s number of lead arrangers. We find that the coefficient on Executive
Turnover is only significant in the weak access to credit subsample. Also, the coefficient is
significantly larger for destination firms with weak access relative to destination firms that have
better access to credit (the difference in coefficients between the weak and strong access to credit
partitions is significant at the 1 percent level). These findings suggest that a lead arranger’s co-
migration with the manager likely alleviates the destination firm’s credit constraints.
The results reported in Panel B further support these inferences. We find that the coefficient
on Executive Turnover is significant and positive only in periods of tight credit supply. Further,
the probability of a lead arranger starting a lending relationship with the destination firm is
significantly higher in periods of tight credit supply, compared to periods in which the credit supply
is relatively loose (the difference in coefficients between the weak and strong access to credit
partitions is significant at the 1 percent level). Thus, the destination firm benefits from access to
capital through the manager’s lending relationships when credit is tight and firms in the economy
are more likely to face credit constraints. Overall, the evidence presented in Table 5 is also
consistent with the relationship lending literature that demonstrates that the benefits of relationship
lending are particularly important when a borrower has limited access to debt capital (e.g., Rajan,
25
1992, Petersen and Rajan, 1994 and 1995) or during periods when bank credit is relatively scarce
(e.g., Bolton et al., 2013).
4.3.2 Co-migration and the extent and pricing of syndicated lending
In this section, to supplement our findings of lender-manager co-migration when the
destination firm is in need of debt capital, we more directly investigate the benefits of lenders’ co-
migration to the destination firm by focusing on the extent of the destination firm’s loan financing.
For the sample of firms that experienced executive turnover, we examine whether the destination
firm’s syndicated loan borrowing is more extensive when the lender co-migrates with the executive
relative to other destination firms where the lender does not co-migrate. We estimate the following
model using ordinary least square regressions:
. (3)
Amount is the destination firm’s syndicated loan borrowing, estimated as the average of the annual
syndication loan issuance divided by total assets over the first three years following the executive’s
turnover. Untabulated analysis indicates that the destination firms rely heavily on syndicated
lending. On average, these firms’ syndicated borrowing represents 34% of their long-term assets.
Migrate is defined as in previous analyses. A positive coefficient on Migrate will be consistent
with our expectation that destination firms have greater access to debt capital when the lead
arranger with an established relationship with the new manager co-migrates. We control for a
number of variables that are likely to be related to a destination firm’s financing needs. We capture
firm growth opportunities by the market-to-book ratio (Destination Firm Market-to-Book),
measured as the sum of market value of equity and total debt divided by the book value of assets.
26
We also include growth in the destination firm’s sales relative to the prior year (Destination Firm
Sales Growth) as another growth measure. Destination Firm Size, Destination Firm Profitability
and Destination Firm Leverage are defined as in previous analyses. All firm level characteristics
are measured in the year prior to the executive’s turnover.
We report the results of these tests in Panel A of Table 6. Consistent with our expectation,
we find a positive and significant (at the 10 percent level) effect of a lender’s co-migration on the
destination firm’s syndicated loan borrowing. The coefficient of 0.176 on the Migrate variable
suggests that lender co-migration results in an 18 percent higher syndicated loan borrowing for
destination firms relative to destination firms that do not experience lender co-migration. This
evidence of higher syndicated loan borrowing for destination firms when lead arrangers follow the
manager complements our findings in Table 5 that lead arrangers are more likely to co-migrate
when the destination firm is in need of relatively more debt capital.
We further extend the benefits of our co-migration analyses by exploring whether co-
migration is also associated with less expensive credit. As we discuss in Section 2, while lenders
may pass on some of the potential cost savings associated with lender-manager relationships to the
destination firm, the prior relationship lending literature provides mixed evidence with respect to
whether relationship lending decreases the cost of borrowing (e.g., Petersen and Rajan, 1994,
Degryse and Van Cayseele, 2000, Schenone, 2010, Bharath et al., 2011). We employ Model 4
above to examine whether loan pricing is associated with lender-manager co-migration, with
Spread as the dependent variable. Spread is the average interest rate spread on all syndicated loans
issued by the destination firm over the three-year period following the executive’s turnover. Note
that we focus on the average interest spread on all syndicated loans instead of the interest spread
on loans issued by the migrating banks only. Availability of credit from the migrating bank and
27
potentially more favorable pricing terms that it offers are likely to put competitive pressure on the
pricing of loans provided by other lenders (e.g., Bushman et al., 2015), thus making the average
loan pricing comparison more appropriate. Because loan pricing analyses focus on a sample of
treatment firms that experience executive turnover, this approach also allows us to assess the effect
of co-migration on loan pricing by comparing the cost of syndicated loan financing for firms where
the lender co-migrates with the manager to the cost of syndicated loan financing for firms that do
not experience co-migration. Lastly, we augment the model with the Amount variable to control
for the extent of syndicated lending.
Panel B of Table 6 presents the results of this analysis. We find that the coefficient on
Migrate is negative and significant (at the 10 percent level). This suggests that destination firms’
cost of borrowing is, on average, 21.7 basis points lower when lenders co-migrate with the
managers. Economically, this effect represents 11.8 percent of the average interest spread for the
sample destination firms. The coefficients on the control variables are also consistent with
expectations: larger and more profitable firms as well as firms with higher growth options
experience lower interest spreads, while firms with higher leverage and a higher extent of
syndicated lending pay higher spreads. Overall, interest spread analyses further support our
inference that lender co-migration brings important benefits to the destination firm.
4.4. The Strength of Lender-Manager Relationship and Co-Migration
Our analyses so far have focused primarily on the characteristics and financing needs of the
destination firm. In this section, we shed light on what affects a lender’s decision to co-migrate
with a manager. We predict that a stronger lender-manager relationship reduces information
asymmetry about the destination firm to a greater extent and thus is associated with a higher
probability of lenders’ co-migration. To investigate this prediction, we introduce a new
28
methodology where our level of observation is the lender and we consider each of the lead
arrangers that syndicated loans to the origin firm over the manager’s tenure in this analysis. The
sample comprises only those observations where Executive Turnover is equal to 1 (i.e., our
treatment sample from the analyses above where the manager moves from the origin firm to the
destination firm). We estimate the following lender level logistic regression:
.
(4)
Migrant is an indicator variable that equals one if the lead arranger migrates from the origin to the
destination firm, zero otherwise. We find that ten percent of the lead arrangers of the origin firms
follow the executive to the destination firm (untabulated). Primary Lender is our main variable of
interest and it reflects the strength of the lender-manager relationship. It is an indicator variable
that equals one if the lead arranger syndicated more than 75 percent of all loans to the origin firm
during the executive’s tenure, zero otherwise. We predict a positive coefficient on this variable.
We control for a number of variables that may affect the lead arranger’s likelihood of co-
migration. Banks Lending to Destination Firm is defined as in previous tests. A higher number of
lead arrangers with relationships with the destination firm is likely to increase the competition for
its loans (e.g., Rajan, 1992), reducing the probability of co-migration. Lending to Origin Firm
reflects the importance of the origin firm’s business to the lead arranger. It is defined as the ratio
of the number of the lead arranger’s syndicated loan deals to the origin firm divided by the total
number of deals syndicated by the lead arranger, estimated over the three-year period prior to the
executive turnover. The higher importance of the origin firm for the lead arranger’s syndication
activity may reduce its probability of co-migrating with the manager. We also control for a number
29
of the lead arranger’s additional characteristics that may affect its likelihood of initiating a new
lending relationship. In particular, we control for the lead arranger’s size (Lender Size), measured
as the natural logarithm of its total assets, its profitability (Lender Profitability), measured as the
ratio of the lender’s net income to shareholder equity, and capital ratio (Lender Capital), measured
as shareholder’s equity divided by total assets. These variables are estimated in the year preceding
the manager’s turnover. Finally, we add a control for the lead arranger’s loan growth (Lender Loan
Growth), measured as the annual percentage change in the lender’s loan portfolio as of the end of
the most recent fiscal year preceding the executive’s move to the destination firm.
We present our findings in Table 7. Consistent with our prediction about the importance of
the strength of the lender-manager relationship, we find a positive and significant coefficient on
the Primary Lender variable. Economically, the coefficient on Primary Lender indicates that
primary lead arrangers are 3.4 times more likely to co-migrate with the manager relative to the
origin firm’s other lead arrangers. In terms of control variables, a negative coefficient on Banks
Lending to Destination Firm is consistent with competition over the destination firm’s loans
decreasing the probability of co-migration. We also find that larger and more profitable lead
arrangers are more likely to co-migrate, potentially due to their stronger financial position, which
allows them to extend lending.
In the last set of analyses, we examine whether the strength of the lender-manager
relationship has a more significant effect on the probability of co-migration when lenders are under
pressure to expand their loan portfolio and seek new borrowers. In Panel A of Table 8, we present
the results of estimating Model 5 for the subsamples of low and high loan growth, based on the
sample median value of the Lender Loan Growth variable. We find that although the coefficient
on Primary Lender is significant for both subsamples, it is significantly higher for the low loan
30
growth lenders (the difference in coefficients across the low and high growth subsamples is
significant at the five percent level). This finding supports our prediction that relatively low loan
growth induces lead arrangers that have strong relationships with executives to expand their
lending portfolio by extending credit to destination firms.
The analyses based on the lender’s profitability-based classification are presented in Panel
B. We assign lenders to the low versus high profitability partition based on the sample median
value of Lender Profitability. The coefficient on Primary Lender is positive and significant only
in the low profitability partition, with the difference in the coefficients on this variable being
significantly different at the one percent level between the two partitions. This finding suggests
that low profitability incentivizes primary lead arrangers to follow managers to destination firms,
as this new lending relationship may increase their future profitability via additional interest
revenues, loan origination fees or fees from other services provided to the destination firm.
5. Conclusion
An extensive literature examines the nature and consequences of relationship lending. The
majority of prior studies focus on the relationship at the institutional level between lenders and the
borrowing firm (e.g., Petersen and Rajan, 1994 and 1995, Degryse and Van Cayseele, 2000,
Bharath et al., 2011, and Schenone, 2010). Because managers significantly affect a firm’s
corporate policies, strategies and performance, in this study we pose the question of whether
lenders develop relationships not only with a borrowing firm, but also with its managers, and
examine how these lender-manager relationships affect private lending. We posit that lenders’
relationships with managers reduce information asymmetry about the borrowing firm’s
creditworthiness and future performance and thus significantly affect lending practices.
Consistent with this prediction, using a setting of executive turnovers to identify lender-
31
manager relationships, we find that a lender is 1.6 times more likely to commence a lending
relationship with a firm when a manager with whom it has a prior relationship joins the firm as a
top executive. Lenders’ relationships with managers are particularly valuable when borrowers are
informationally opaque - the likelihood of lenders co-migrating is significantly higher when the
destination firm is smaller, not rated by a credit rating agency or when it has low analyst following.
Further, co-migration benefits destination firms by providing them with greater access to credit
and at lower costs. We find that lenders are more likely to follow managers when the firms they
join have weaker access to debt capital. We also show that lenders’ co-migration is associated with
an 18 percent higher syndicated loan borrowing for destination firms following executive turnover
relative to destination firms that do not experience lender co-migration. On average, the destination
firms’ cost of borrowing is 21.7 basis points lower when lenders co-migrate with managers. With
respect to the factors that affects lenders’ decision to co-migrate, we find that co-migration is more
likely when lenders have a stronger prior relationship with the manager. Finally, the strength of
the lender-manager relationship has a significantly stronger influence on the decision to co-migrate
when lenders are under pressure to expand their loan portfolios and are seeking new clients.
We contribute to the relationship lending literature by highlighting the importance of the
relationship between lenders and managers. We also show that this relationship significantly
benefits borrowers via enhanced access to credit and a lower cost of debt financing. We extend the
developing literature on the role of personal relationships in capital markets. Our findings suggest
that the lender-manager relationship carries over to the new firm that hires the manager as a top
executive. We further contribute to the literature that examines how personal networks and
relationships affect business and investment decisions. We show that lenders’ relationship with a
manager incentivizes lenders to initiate a lending relationship with the firm that the manager joins.
32
References
Bamber, L., J. Jiang, and I. Wang. 2010. What’s my style? The influence of top managers and their personal backgrounds on voluntary corporate financial disclosure. The Accounting Review 85 (4): 1131-1162.
Bassett, W., M. Chosak, J. Driscoll, and E. Zakrajsek, 2012. Changes in bank lending standards and the macroeconomy. Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C.
Berger, A. N., and G. F. Udell. 1995. Relationship lending and lines of credit in small firm finance. Journal of Business 68: 351-382.
Bertrand, M., and A. Schoar. 2003. Managing with style: The effect of managers on firm policies. The Quarterly Journal of Economics 118 (4): 1169-1208.
Bharath, S., S. Dahiya, A. Saunders, and A. Srinivasan. 2007. So what do I get? The bank's view of lending relationships. Journal of Financial Economics 85 (2): 368-419.
Bharath, S., S. Dahiya, A. Saunders, and A. Srinivasan. 2011. Lending relationships and loan contract terms. Review of Financial Studies 24: 1141-1203.
Bharath, S., J. Sunder, and S. V. Sunder. 2008. Accounting quality and debt contracting. The Accounting Review 83 (1) :1-28.
Bolton, P., X. Freixas, L. Gambacorta, and P. Mistrulli. 2013. Relationship and transaction lending in a crisis. National Bureau of Economic Research Working Paper.
Boot, A. 2000. Relationship banking: what do we know? Journal of Financial Intermediation 9 (1): 7-25.
Boot, A., and A. Thakor. 1994. Moral hazard and secured lending in an infinitely repeated credit market game. International Economic Review 35 (4): 899-920.
Brochet, F., G. Miller, and S. Srinivasan. 2014. Do analysts follow managers who switch companies? An analysis of relationships in the capital markets. The Accounting Review 89 (2): 451-482.
Bushman, R., A. Smith, and R. Wittenberg-Moerman. 2010. Price discovery and dissemination of private information by loan syndicate participants. Journal of Accounting Research 48: 921- 972.
Bushman, R., B. Hendricks, and C. Williams. 2015. Bank competition: measurement, decision-making and risk-taking, Journal of Accounting Research, forthcoming.
Cohen, L., A. Frazzini, and C. Malloy. 2008. The small world investing: Board connections and mutual fund returns. Journal of Political Economy 116 (5): 951-979.
Costello, A. M., and R. Wittenberg-Moerman. 2011. The impact of financial reporting quality on debt contracting: Evidence from internal control weakness reports. Journal of Accounting Research 49 (1): 97-136.
33
Degryse, H., and P. Van Cayseele. 2000. Relationship lending within a bank-based system: Evidence from European small business data. Journal of Financial Intermediation 9 (1): 90-109.
DeJong, D., and Z. Ling. 2013. Managers: Their effects on accruals and firm policies. Journal of Business, Finance and Accounting 40 (1-2): 82-114.
Dennis, S., and D. Mullineaux. 2000. Syndicated loans. Journal of Financial Intermediation 9: 404-426.
Diamond, D. 1984. Financial intermediation and delegated monitoring. Review of Economic Studies 51: 393-414.
Diamond, D. 1991. Monitoring and reputation: The choice between bank loans and directly placed debt. Journal of Political Economy 99: 688-721.
Drucker, S., and M. Puri. 2005. On the benefits of concurrent lending and underwriting. The Journal of Finance 60 (6): 2763-2799.
Dyreng, S. D., M. Hanlon, and E. L. Maydew. 2010. The effects of managers on corporate tax
avoidance. The Accounting Review 85 (4): 1163-1189.
Elyasiani, E., and L. G. Goldberg. 2004. Relationship lending: A survey of the literature. Journal of Economics and Business 56 (4):315-330.
Engleberg, J., P. Gao, and C. A. Parsons. 2012. Friends with money. Journal of Financial Economics 103: 169-188.
Fracassi, C. 2014. Corporate finance policies and social networks. Working Paper.
Ge, W., D. Matsumoto, and J. Zhang. 2011. Do CFOs have styles of their own? An empirical investigation of the effect of individual CFOs on financial reporting practices. Contemporary Accounting Research 28 (4): 1141-1179.
Greenbaum, S., and Thakor, A. V. 1995. Contemporary Financial Intermediation. Dryden Press, New York.
Güntay, L., and Hackbarth, D. 2010. Corporate bond credit spreads and forecast dispersion. Journal of Banking & Finance, 34(10): 2328-2345.
Hambrick, D., 2007. Upper echelons theory: An update. Academy of Management Review 32 (2): 334-343.
Hambrick, D. C., and P. A. Mason. 1984. Upper echelons: The organization as a reflection of its top managers. Academy of Management Review 9 (2): 193-206.
Haselmann, R., D. Schoenherr, and V. Vig. 2013. Lending in social networks. Working Paper.
Hoshi, T., A. Kashyap, and D. Scharfstein. 1990a. Bank monitoring and investment: Evidence from the changing structure of Japanese corporate banking relationships. In R. Glenn Hubbard (ed.): Asymmetric Information, Corporate Finance and Investment. University of Chicago Press, Chicago.
34
Hoshi, T., A. Kashyap, and D. Scharfstein. 1990b. The role of banks in reducing the costs of financial distress. Journal of Financial Economics 27: 67-88.
Hoshi, T., A. Kashyap, and D. Scharfstein. 1991. Corporate structure, liquidity and investment: Evidence from Japanese industrial groups. The Quarterly Journal of Economics 106: 33-60.
Houston, J., and C. James. 1996. Bank information monopolies and the mix of private and public debt claims. Journal of Finance: 1863-1889.
Ivashina, V. 2009. Asymmetric information effects on loan spreads. Journal of financial Economics 92 (2): 300-319.
Karolyi, S. A. 2014. Personal lending relationships. Working Paper, Carnegie Mellon University.
Koch, T., and S. MacDonald. 2014. Bank management. Cengage Learning.
Kraft, P. 2015. Rating agency adjustments to GAAP financial statements and their effect on ratings and credit spreads. The Accounting Review 90 (2): 641-674.
Mansi, S., W. Maxwell, and D. Miller. 2011. Analyst forecast characteristics and the cost of debt, Review of Accounting Studies 16: 116-142.
Murfin, J. 2012. The supply‐side determinants of loan contract strictness. The Journal of Finance 67 (5): 1565-1601.
Pan, Y., T. Y. Wang, and M. S. Weisbach. 2015a. Learning about CEO ability and stock return volatility. Review of Financial Studies 28: 1623-1666.
Pan, Y., T. Y. Wang, and M. S. Weisbach. 2015b. Management risk and the cost of borrowing. Working Paper, Fisher College of Business.
Petersen, M. A. 2004. Information: Hard and soft. Working Paper, Northwestern University.
Petersen, M., and R. Rajan. 1994. The benefits of relationships: Evidence from small business data. The Journal of Finance 49: 1367-1400.
Petersen, M., and R. Rajan. 1995. The effect of credit market competition on lending relationships. The Quarterly Journal of Economics 110 (2): 407-443.
Plath, C. 2008. Analyzing credit and governance implications of management succession planning. Working Paper.
Rajan, R. 1992. Insiders and outsiders: The choice between informed and arm’s length debt. Journal of Finance 47: 1367-1400.
Rajan, R. G., and L. Zingales. 1998. Which capitalism? Lessons form the East Asian crisis. Journal of Applied Corporate Finance 11 (3): 40-48.
Rosenbaum, P., and D. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70 (1):41-55.
Santos, J., and A. Winton. 2013. Bank capital, borrower power, and loan rates. EFA 2009 Bergen Meetings Paper
Schenone, C. 2010. Lending relationships and information rents: Do banks exploit their information advantages? The Review of Financial Studies:1149-1199.
35
Schmidt, B. 2015. Costs and benefits of friendly boards during mergers and acquisitions. Journal of Financial Economics 117 (2): 424-447.
Sharpe, S. 1990. Asymmetric information, bank lending and implicit contracts: A stylized model of customer relationships. Journal of Finance 45: 1069-1087.
Standard & Poor’s. 2007. A Guide to the Loan Market (Standard & Poor’s, New York).
Stiglitz, J., and A. Weiss. 1981. Credit rationing in markets with imperfect information. American Economic Review 71: 393-410.
Sufi, A. 2007. Information asymmetry and financing arrangements: Evidence from syndicated loans. The Journal of Finance 62 (2): 629-668.
Wittenberg-Moerman, R. 2008. The role of information asymmetry and financial reporting quality in debt trading: Evidence from the secondary loan market. Journal of Accounting and Economics 46 (2): 240-260.
Yasuda, A. 2005. Do bank relationships affect the firm's underwriter choice in the corporate‐bond underwriting market? The Journal of Finance 60 (3): 1259-1292.
36
Appendix A: Variable Definitions Variable Definition Amount
Banks Lending to Origin Firm
The destination firm’s syndicated loan borrowing, estimated as the average of the annual syndication loan issuance divided by total assets over the first three years following the executive’s turnover (DealScan and Compustat).
The number of lead arrangers that syndicated loans to the origin firm during the executive’s tenure (DealScan).
Banks Lending to Destination Firm
The number of lead arrangers that syndicated loans to the destination firm during the three years prior to the executive’s appointment (DealScan).
Bank Overlap
An indicator variable equal to one if at least one lead arranger lending to the origin firm during the executive’s tenure also syndicated loans to the destination firm during the three years prior to the executive’s appointment at the destination firm, zero otherwise (DealScan).
Destination Firm Leverage
Destination Firm Market-to-Book
Destination Firm Profitability
Destination Firm Sales Growth
Destination Firm Size
The ratio of the destination firm’s long-term debt to total assets, measured at the end of the most recent fiscal year preceding the executive’s turnover (Compustat).
The sum of the destination firm’s market value of equity and total debt divided by the book value of assets, measured at the end of the most recent fiscal year preceding the executive’s turnover (Compustat).
The ratio of the destination firm’s net income before extraordinary items to total assets, measured at the end of the most recent fiscal year preceding the executive’s turnover (Compustat).
The destination firm’s sales growth relative to the prior year, measured at the end of the most recent fiscal year preceding the executive’s turnover (Compustat).
A natural logarithm of the destination firm’s total assets, measured at the end of the most recent fiscal year preceding the executive’s turnover (Compustat).
37
Executive Turnover
Lender Capital
Lender Loan Growth
Lender Profitability
Lender Size
Lending to Origin Firm
Leverage Difference
Migrant
Migrate
Origin Firm Leverage
An indicator variable that takes the value of one if the CEO or CFO of the origin firm migrates to the destination firm as a CEO or CFO, zero otherwise (Execucomp and Bordex).
Lead arranger’s shareholder equity divided by total assets, measured at the end of the most recent fiscal year preceding the executive’s turnover (Bank Compustat).
The annual percentage change in the size of the lead arranger’s loan portfolio, measured at the end of the most recent fiscal year preceding the executive’s turnover (Bank Compustat).
The ratio of a lead arranger’s net income to shareholders’ equity (Bank Compustat), measured at the end of the most recent fiscal year preceding the executive’s turnover (Bank Compustat).
The natural logarithm of the lead arranger’s total assets, measured at the end of the most recent fiscal year preceding the executive’s turnover (Bank Compustat).
The ratio of the number of syndicated loan deals of the lead arranger to the origin firm divided by the total number of deals syndicated by the lead arranger, estimated over the three-year period prior to the executives’ turnover (DealScan).
The difference between Destination Firm Leverage and Origin Firm Leverage.
An indicator variable that takes the value of one if the lead arranger migrates with the manager to the destination firm, zero otherwise (DealScan).
An indicator variable that takes the value of one if at least one lead arranger that syndicated loans to the origin firm during the executive’s tenure starts syndicating loans to the destination firm within three years of the executive’s appointment, zero otherwise. We consider the lead arranger as starting the relationship with the destination firms if it had not arranged its syndicated loans over the three-year period prior to the executive turnover (DealScan).
The ratio of the origin firm’s long-term debt to total assets, measured at the end of the most recent fiscal
38
Origin Firm Profitability
Origin Firm Size
year preceding the executive’s turnover (Compustat).
The ratio of the origin firm’s net income before extraordinary items to total assets, measured at the end of the most recent fiscal year preceding the executive’s turnover (Compustat).
A natural logarithm of the origin firm’s total assets, measured at the end of the most recent fiscal year preceding the executive’s turnover (Compustat).
Primary Lender
Profitability Difference
Same Industry
Size Difference
Spread
An indicator variable that takes the value of one if the lead arranger syndicated more than 75 percent of all loans to the origin firm during the executive’s tenure, zero otherwise (DealScan).
The difference between Destination Firm Profitability and Origin Firm Profitability.
An indicator variable equal to 1 if the origin and destination firms are in the same four-digit SIC group, and 0 otherwise (Compustat).
The difference between Destination Firm Size and Origin Firm Size.
An average interest rate spread on all syndicated loans issued by the destination firm over the three-year period following the executive’s turnover (DealScan).
Turnover
For Step 1 of the matching procedure, an indicator variable that takes the value of one when the executive’s origin firm is paired with its actual destination firm, zero otherwise. For Step 2 of the matching procedure, an indicator variable that takes the value of one when the executive’s destination firm is paired with its actual origin firm, zero otherwise (Execucomp and Boardex).
39
Figure 1: The Illustration of the Matching procedure Step 1 X= every pseudo destination firm that has the same 2-digit SIC code as destination firm B. b = the pseudo destination firm that has the closest propensity score to the actual destination firm. Step 2 X=every pseudo origin firm that has the same 2-digit SIC code as origin firm A. a= the pseudo origin firm that has the closest propensity score to the actual origin firm. Step 3
Treatment sample
Turnover pair Origin firm A &
Destination firm B
1 to N matching Pseudo turnover pairs
Origin firm A & Pseudo destination firm X
1 to 1 Matched sample
Pseudo turnover pair Origin firm A &
Pseudo Destination firm b
Treatment sample
Turnover pair Origin firm A &
Destination firm B
1 to N matching Pseudo turnover pairs Pseudo Origin firm X &
Destination firm B
1 to1 Matched sample
Pseudo turnover pair
Pseudo Origin firm a & Destination firm B
Treatment sample Turnover pair Origin firm A &
Destination firm B
1 to 1 Matched sample Pseudo turnover pair Pseudo origin firm a &
Pseudo destination firm b
40
Table 1:Sample Selection and Origin and Destination Firm Characteristics This table presents the sample selection process (Panel A) and descriptive statistics for origin and destination firms (Panel B). Variables are defined in Appendix A. Panel A: Sample Selection
Sample selection step CEO-CFO moves Unique executive moves in Execucomp & Boardex firms 723 Merged with Compustat, Dealscan and I/B/E/S 648
Panel B: Firm Characteristics of Origin and Destination Firms Variable Mean P-value for the difference Origin Firm Size 7.5463 Destination Firm Size 7.3554 Difference 0.1909 0.0409
Origin Firm Profitability 0.0170 Destination Firm Profitability -0.0011 Difference 0.0181 0.0285
Origin Firm Leverage 0.2228 Destination Firm Leverage 0.2053 Difference 0.0175 0.0916
41
Table 2: Treatment and Control Samples and Descriptive Statistics This table provides descriptive statistics for the treatment and control samples of origin and destination firms. There are 648 observations in each sample. Variables are defined in Appendix A.
Variable Treatment Sample
Mean Control Sample
Mean P-Value for Differences
Size Difference -0.1173 -0.1456 0.7794 Profitability Difference
-0.0220 -0.0120 0.4883
Leverage Difference -0.0087 -0.0116 0.8706 Same Industry 0.2047 0.1767 0.2024 Banks lending to original firm
2.4605 2.5256 0.7446
Banks lending to destination firm
1.2264 1.3984 0.1538
Bank overlap 0.1209 0.0915 0.0861
42
Table 3: Do Lenders Follow the Manager to the Destination Firm?The table presents the analysis of the effect of the executive turnover on the probability that at least one of the lead arrangers of the origin firm initiates a lending relationship with the destination firm. The dependent variable is Migrate, which takes the value of one if at least one lead arranger that syndicated loans to the origin firm during the executive’s tenure starts syndicating loans to the destination firm within 3 years of the executive’s appointment, zero otherwise. All other variables are defined in Appendix A. The t-statistics are reported in parentheses. ***, ** and * indicates significance at the 0.01, 0.05, 0.10 level, respectively.
Variables Dependent Variable: Migrate Executive Turnover 0.451** (2.38)Size Difference 0.072 (1.24) Profitability Difference 0.385 (0.87) Leverage Difference 0.043 (0.15) Same Industry 0.253 (1.13) Banks Lending to Origin Firm 0.100*** (4.43) Bank Lending to Destination Firm 0.032 (0.85) Bank Overlap 1.832*** (8.20) Model Logit Pseudo R2 N
0.1423 1,296
43
Table 4: Lenders’ Co-migration and the Destination Firm’s Information Opacity This table presents the analyses of the effect of the executive turnover on the probability that at least one of the lead arrangers of the origin firm initiates a lending relationship with the destination firm, conditional on the destination firm’s information opacity. Panel A presents the analyses based on a destination firm’s size, Panel B presents the analyses based on whether a borrower is rated by a credit rating agency and Panel C presents the analyses based on the extent of the destination firm’s analyst coverage. The dependent variable is Migrate, which takes the value of one if at least one lead arranger that syndicated loans to the origin firm during the executive’s tenure starts syndicating loans to the destination firm within three years of the executive’s appointment, zero otherwise. All other variables are defined in Appendix A. The t-statistics are reported in parentheses. ***, **, * indicates significance at the 0.01, 0.05, 0.10 level respectively. ###, ##, # indicates that the difference across destination firm size, rating status or analyst coverage partitions is significant at the 0.01, 0.05 and 0.10 level respectively. Panel A: Lenders’ co-migration with the executive, conditional on a destination firm’s size Dependent Variable: Migrate Variable Small Large Executive Turnover 0.667* 0.358 (1.83) (1.58)# Controls Included Included N 648 648
Panel B: Lenders’ co-migration with the executive, conditional on whether a destination firm is rated Dependent Variable: Migrate Variable Not Rated Rated Executive Turnover 0.840*** 0.027 (3.05) (0.09)### Controls Included Included N 853 443
Panel C: Lenders’ co-migration with the executive, conditional on the intensity of a destination firm’s analyst coverage Dependent Variable: Migrate Variable Low High Executive Turnover 0.690** 0.230 (2.42) (0.87)## Controls Included Included N 652 644
44
Table 5: Lenders’ Co-migration and a Destination Firm’s Access to Debt Capital This table presents the analyses of the effects of the executive turnover on the probability that at least one of the lead arrangers of the origin firm initiates a lending relationship with the destination firm, conditional on the destination firm’s access to debt capital. In Panel A, we measure a destination firm’s access to credit based on the number of lead arrangers that syndicated its loans over the three-year period prior to the executive’s turnover. In Panel B, we measure access to credit based on the tightness of the lending standards in credit markets in the quarter of a loan’s origination. The dependent variable is Migrate, which takes the value of one if at least one lead arranger that syndicated loans to the origin firm during the executive’s tenure starts syndicating loans to the destination firm within three years of the executive’s appointment, zero otherwise. All other variables are defined in Appendix A. The t-statistics are reported in parentheses. ***, **, * indicates significance at the 0.01, 0.05, 0.10 level respectively. ###, ##, # indicates that the difference across access to credit partitions is significant at the 0.01, 0.05 and 0.10 level respectively. Panel A: Measuring a destination firm’s access to credit based on the number of its prior banking relationships Dependent Variable: Migrate Variable Weak access Strong access Executive Turnover 1.990*** -0.037 (4.04) (-0.17)### Controls Included Included N 498 798
Panel B: Measuring a destination firm’s access to credit based on the tightness of lending standards in credit markets Dependent Variable: Migrate Variable Weak access Strong access Executive Turnover 0.694*** 0.317 (2.20) (1.31)### Controls Included Included N 525 771
45
Table 6: Co-Migration and the Extent and Pricing of Syndicated Lending This table presents the analyses of the effect of lenders’ co-migration with the manager on the syndicated lending amount (Panel A) and its pricing (Panel B). In Panel A, the dependent variable is Amount, which is the destination firm’s syndicated loan borrowing, estimated as the average of the annual syndication loan issuance divided by total assets over the first three years following the executive’s appointment. In Panel B, the dependent variable is Spread, estimated as the average interest rate spread on all syndicated loans issued by the destination firm over the three-year period following the executive’s turnover. All other variables are defined in Appendix A. The t-statistics are reported in parentheses. ***, **, * indicates significance at the 0.01, 0.05 and 0.10 levels, respectively. Panel A: Syndicated loan amount Variables Dependent Variable: Amount Migrate 0.176* (1.73)Destination Firm Market-to-Book -0.013 (-0.70) Destination Firm Sales Growth 0.005 (0.04) Destination Firm Size -0.057*** (-3.07) Destination Firm Profitability 0.285* (1.96) Destination Firm Leverage 0.459*** (3.42) Fixed Effects Industry, Year Model OLS R2 0.093 N 560
46
Table 6: Co-migration and the extent and pricing of syndicated lending (continued) Panel B: Syndicated loan pricing Variables Dependent Variable: Spread Migrate -21.700* (-1.68) Destination Firm Market-to-Book -21.526*** (-3.17) Destination Firm Sales Growth 7.163 (0.22) Destination Firm Size -17.919*** (-4.90) Destination Firm Profitability -269.673*** (-5.26) Destination Firm Leverage 81.114*** (2.97) Destination Firm Amount 47.396*** (6.28) Fixed Effects Industry, Year Model OLS R2 0.448 N 407
47
Table 7: Co-migration and Lending Intensity This table presents the analysis of the effect of the strength of lender-manager relationship on the lender’s probability of co-migrating with the manager. The dependent variable is Migrant, which is an indicator variable that takes the value of one if the lead arranger migrates with the manager to the destination firm, zero otherwise. All other variables are defined in Appendix A. The t-statistics are reported in parentheses. ***, **, * indicates significance at the 0.01, 0.05, 0.10 level respectively.
Variables Dependent Variable: Migrant Primary Lender 1.233*** (3.95) Bank Lending to Destination Firm -0.066*** (-3.30) Lending to Origin Firm -0.712 (1.40) Lender Size 0.356*** (4.16) Lender Loan Growth -0.507 (0.62) Lender Profitability 8.602*** (1.31) Lender Capital 4.484 (3.89) Model Logit N 1,895
48
Table 8: A Lender’s Incentives to Co-Migrate – Loan Growth and Profitability This table presents the analyses of the effects of a primary lender’s loan growth and profitability on its probability of co-migrating with the executive. Panel A presents the analyses based on the growth of the lead arranger’s loan portfolio, while Panel B presents the analyses based on a lead arranger’s profitability. The dependent variable is Migrant, which is an indicator variable that takes the value of one if the lead arranger migrates with the manager to the destination firm, zero otherwise. All other variables are defined in Appendix A. The t-statistics are reported in parentheses. ***, **, * indicates significance at the 0.01, 0.05, 0.10 levels respectively. ###, ##, # indicates that the difference across the loan growth or profitability partitions is significant at the 0.01, 0.05 and 0.10 level respectively. Panel A: Loan Growth Dependent Variable: Migrant Variable Low High Primary Lender 1.648*** 0.818** (2.95) (2.01)## Controls Included Included N 959 936
Panel B: Profitability Dependent Variable: Migrant Variable Low High Primary Lender 1.959*** 0.257 (4.06) (0.58)### Controls Included Included N 1,002 893