Customer Concentration and Loan Contract Terms*

Murillo Campello Janet GaoCornell University & NBER Cornell University

campello@cornell.edu rg449@cornell.edu

This Draft: April 1, 2015

Abstract

Recent research argues that firms enjoy operating efficiencies when dealing with fewer, larger

customers. It ignores, however, how firms’ creditworthiness is affected by the exposure to those

customers. We study pricing and non-pricing features of bank loan contracts to gauge how the

credit market evaluates firms’ customer-base profile. We show that higher customer concen-

tration leads to increases in interest rate spreads and in the number of restrictive covenants

featured in newly-initiated as well as renegotiated bank loans. Concentration also reduces the

maturity of those loans. The duration and depth of the relationship between firms and their

banks are also negatively affected by customer concentration.

Key words: Customer Concentration, Bank Loans, Contract Terms, Financial Distress, Instru-

mental Variables.

JEL classification: G21, G30, G32.

*We are thankful to Ted Fee, Erasmo Giambona, and Sudheer Chava for sharing their data. We also

thank Kenneth Ahern, Kevin Aretz, Jean-Noel Barrot, Martijn Cremers, Sudipto Dasgupta, Jerry

Hoberg, Andrew Karolyi, Tomislav Ladika, Rafael Matta, Pamela Moulton, Justin Murfin, Maureen

O’Hara, and Felipe Silva for their valuable input. Comments from seminar participants at Cornell Uni-

versity, McGill–HEC Montreal, University of Notre Dame, and USC Finance, Organizations and Markets

conference are also appreciated.

Customer Concentration and Loan Contract Terms

Abstract

Recent research argues that firms enjoy operating efficiencies when dealing with fewer, larger

customers. It ignores, however, how firms’ creditworthiness is affected by the exposure to those

customers. We study pricing and non-pricing features of bank loan contracts to gauge how the

credit market evaluates firms’ customer-base profile. We show that higher customer concen-

tration leads to increases in interest rate spreads and in the number of restrictive covenants

featured in newly-initiated as well as renegotiated bank loans. Concentration also reduces the

maturity of those loans. The duration and depth of the relationship between firms and their

banks are also negatively affected by customer concentration.

Key words: Customer Concentration, Bank Loans, Contract Terms, Financial Distress, Instru-

mental Variables.

JEL classification: G21, G30, G32.

1 Introduction

U.S. manufacturers attribute over one-third of their sales to “large customers.” A con-

centrated customer base is often cited as a positive factor in analyst reports, management

forecasts, and even IPO prospectuses, as it is believed to enhance firms’ profitability by

reducing overhead costs. Notably, these arguments find support in recent academic re-

search (e.g., Patatoukas (2012) and Irvine et al. (2013)). Relying on major customers has

shortcomings, nonetheless. Major customers demand lower prices, purchase irregularly,

and may delay payments (Fee and Thomas (2004), Kelly et al. (2013), and Murfin and

Njoroge (2013)).1 Shocks to major customers are also known to reverberate through the

supply chain, affecting suppliers (Cohen and Frazzini (2008) and Kolay et al. (2012)).

While these problems are important, the literature has not examined whether a close

association with fewer, larger customers exposes firms to costs and risks that may ul-

timately affect their access to credit. This problem becomes pressing as the level of

customer concentration has increased in recent years.

This paper examines how the credit market evaluates a firm’s customer-base profile,

showing how customer concentration and customer financial status affect a firm’s cred-

itworthiness. It does so considering both pricing and non-pricing features of bank loan

contracts, as well as firm–bank relationships. Our contract-level analysis allows us to

assess how informed lenders modify the terms of their credit offerings in response to the

evolving nature of firms’ customer-base profile. We first model the interplay between

customer concentration, firm investment choices, and loan contract terms in a simple

theoretical framework. The model is useful in providing predictions that can be taken to

the data. We then empirically examine the impact of customer concentration on several

features of bank loan contracts, including interest rate spreads, maturity, and the number

of restrictive covenants. We also examine the impact of customer concentration on the

length and depth of the relationships between firms and their banks. Our results are new

in revealing significant costs associated with firms’ reliance on large customers. We show

that these costs are manifested along various credit dimensions, pointing to limitations

to deeper integration among firms along their supply chain.

Our model characterizes firms’ incentives to invest in projects that enhance relation-

ships with their customers, showing how this affects the firm’s credit access. Relationship-

specific investments have long been described in the existing literature (see Bolton and

1These behaviors have attracted the attention of the financial press, with reports that large, powerfulfirms such as Walmart and P&G “abuse” their suppliers when paying for products. See Wall StreetJournal article: “Small Firms’ Big Customers Are Slow to Pay” (June 6, 2012).

1

Scharfstein (1998) for a theoretical framework and Allen and Phillips (2000) for early

empirical work). These projects involve investments in R&D, unique fixed assets, and

modifications to standard production processes. Critically, relationship-specific projects

may be less desirable from lenders’ perspective because their uniqueness engenders higher

risks and lower resale values in liquidation. Our analysis shows that the higher the im-

portance of major customers, the greater the operational gains from relationship-specific

investments, but the lower the expected credit quality of firms undertaking those invest-

ments. The model implies that higher customer concentration prompts banks to impose

costlier, stricter loan contract terms.

To test our model’s predictions, we gather information on bank loan terms from LPC–

Dealscan and merge that information with data on corporate customers from Compustat’s

Segment Database. Our data collection produces a comprehensive sample of 3,375 loans

granted to 1,110 individual firms in the manufacturing sector over 25 years. We add to

these data information on corporate failures, product differentiation, and CDS spreads.

Our baseline results can be summarized as follows. A more concentrated customer

base increases both the interest rate spreads and the number of restrictive covenants fea-

tured in new (or renewed) bank loans. Customer concentration also reduces the maturity

of those loans. These effects are statistically and economically significant. Controlling for

bank identity, industry effects, macroeconomic conditions, and firm characteristics, a one-

standard-deviation increase in customer concentration leads to 10 basis points higher in-

terest spreads on bank loans; or a 6% higher loan markups compared to an average spread

of 179 basis points. The same shift leads to 0.2 additional restrictive loan covenants; com-

pared to the sample mean of 1.8 covenants. It also leads to a reduction in loan maturity by

2 months; compared to average maturity of 46 months. These magnitudes are significant

given the high level of competition in the market for manufacturing lending.

We also examine whether customer concentration affects the length and depth of a

firm’s banking relationships. Banks lend less to firms with concentrated customer bases

over time. They also abbreviate the duration of their relationships with those firms.

Estimates of the relation between customer concentration and borrowing terms may be

subject to empirical biases. In particular, one could argue that unobserved characteristics

might lead a firm’s customer concentration to increase and its credit terms to deterio-

rate. This is a tall order in light of the documented positive relation between customer

concentration and firm profitability — a relation that we verify in our data. To alleviate

those concerns, however, we experiment with testing approach that exploits M&A waves

in customers’ industries (downstream mergers) as an instrument for customer concen-

2

tration. Downstream M&A activity is a plausible instrument for two reasons. First, it

is related to customers’ own growth prospects (see Fee and Thomas (2004) and Erel et

al. (2014)) and following mergers in customer industries, suppliers face higher customer

concentration (inclusion restriction). Second, downstream M&A activity is not a policy

variable for suppliers and is unlikely to affect their borrowing terms through channels

other than customer–supplier linkages (exclusion restriction). Bearing in mind concerns

that suppliers’ industry-level, time-varying dynamics could influence customers’ M&A

activity and credit terms, we further account for industry-year-fixed effects in our tests.

We go further and incorporate in our test strategy downstream M&A activity that is

triggered by acts by the Federal Government that change entry, prices, and other ele-

ments of the competitive environment of the industries affected.2 Our IV estimations

confirm the prior that following high levels of M&A activity in downstream industries,

firms observe higher customer-base concentration, which then leads to costlier, stricter

borrowing terms, as well as shorter banking relationships.

Our theoretical analysis predicts that deeper integration along the supply chain can

fundamentally change firms’ operations, especially the types of investments they make.

When firms choose to conduct relationship-specific investments, loan terms will be more

adversely affected by customer concentration, since those investments embed higher risk

exposure for creditors. We test this prediction empirically using a proxy for the specificity

of the investment firms make that is based on the uniqueness of the inputs they use in their

production processes (cf. Giannetti et al. (2011)). Our results point to a strong effect of

customer concentration on loan contract terms in industries where relationship-specific

investments are more prevalent. In industries with low levels of relationship-specific in-

vestments, in contrast, the relation between customer concentration and loan terms is

quite weak. Aside from changing the types of investments, a deeper supply-chain inte-

gration may also change firms’ financing. In particular, as firms trade with concentrated

customers and deliver those customers’ preferred products, they may receive payments

at a faster rate so as to finance their customized production cycle. Indeed, we find a

negative relation between customer concentration and the ratio of account receivables to

sales, indicating that firms facing more concentrated customer bases gain by operating

with shorter receivables cycles.

We dig deeper into the meaning of our results by examining whether and how the

2Examples of regulatory interventions that we use include the Natural Gas Wellhead Decontrol Actof 1989, the Energy Policy Act of 1992, the Trucking Industry and Regulatory Reform Act of 1992, andthe Telecommunications Act of 1996.

3

financial conditions of a firm’s large customers affect the firm’s credit terms. Customers

in worse financial shape may, for example, face difficulties in maintaining purchase agree-

ments and paying on time, eventually burdening their suppliers. Confirming the logic of

this argument, we find that loan spreads increase even more and the number of restrictive

covenants is even higher when a firm’s large customers are likely to be distressed. Large

customers’ financial distress further reduces a firm’s loan maturity, and the length and

depth of its banking relationships.

Our empirical investigation further characterizes the channels through which customer

concentration affects the credit terms offered by banks. As highly-regulated intermedi-

aries, banks are particularly concerned about loan underperformance. If higher customer

concentration is associated with higher loan failure rates, banks should naturally impose

stricter loan terms. To examine this link, we identify loan failures by matching our sam-

ple with a corporate bankruptcy database used in Chava and Jarrow (2004) and Chava

et al. (2011). We find a positive, significant relation between customer concentration

and loan failure rates. To wit, a one-standard-deviation increase in a firm’s customer

concentration is associated with a 2-percentage point increase in the likelihood that the

firm files for bankruptcy before paying its loan. This impact is sizable when compared

to the sample average loan failure rate of 6.1%. These results confirm that customer

concentration is an important risk concern for banks’ lending decisions.

Our paper is related to several strands of literature. First, it speaks to a growing

literature on the relation between customer concentration and profitability. Patatoukas

(2012) argues that having large customers helps firms achieve economies of scale by lower-

ing overhead costs. Irvine et al. (2013) further show that the beneficial effects of customer

concentration vary according to suppliers’ size and age. Fee and Thomas (2004) report

that customers gain additional bargaining power over suppliers after horizontal mergers.

Greene et al. (2013) show that when customers become more powerful they demand

better trade terms. Like previous papers, we show that customer concentration is indeed

associated with higher profitability. Using creditors’ perspective, however, we show that

customer concentration ultimately bears negative implications for firm creditworthiness,

leading banks to impose costlier, stricter loan terms.

Our study is also related to existing work on how supply-chain relationships affect

firms’ financial policies. Titman and Wessel (1988) and Allen and Phillips (2000) show

that firms tend to procure unique assets when they rely on major customers. These

firms have lower leverage because customer liquidation imposes high redeployment costs

for their relationship-specific assets (see also Kale and Shahrur (2007) and Banerjee et

4

al. (2008)). We push this research forward by showing how several different features of

debt contracting — e.g., interest rates, maturity, and covenants — relate to the firm’s

customer base.

Finally, our study is related to the recent literature on the determinants of bank loan

terms (examples are Graham et al. (2008), Roberts and Sufi (2009), Hertzel and Officer

(2012), Valta (2012), and Cen et al. (2014)). Closer to our study, Valta finds that firms

in competitive industries face higher loan spreads because competition increases cash flow

risk. Hertzel and Officer report that firms face higher spreads following industry-rivals’

bankruptcies, especially in competitive industries. Cen et al. find that supply-chain rela-

tions may reduce informational asymmetries between firms and creditors in the long run.

None of these papers consider the effect of customer concentration or customer distress

on loan terms.

The paper proceeds as follows. Section 2 contains our theoretical motivation. Sec-

tion 3 describes our data and methodology. Section 4 reports univariate analyses. Sec-

tion 5 contains our baseline regression results. Section 6 reports our instrumental variable

analysis. Section 7 shows how relationship-specific investments alter the relation between

customer concentration and loan terms. Section 8 explores cross-sectional differences in

the effect of customers’ financial health on suppliers’ borrowing terms. Section 9 re-

ports tests of the effect of customer concentration on bank loan failure rates. Section 10

concludes.

2 Theoretical Framework

We analyze the relation between customer concentration and bank credit using a

simple theoretical framework. In it, we model the interplay between the customer, the

supplier firm, and the supplier’s bank, keeping the focus on the relations we want to study

empirically. We do not model industry dynamics. In turn, we take customer concentra-

tion as given, capturing the crux of our supply-chain story of a deep association between

a firm and its major customer. The model delivers several testable implications.

2.1 Setting

The base model contains a firm, a major customer, and a bank (we allow for multiple

firms and firm heterogeneity below). The setting has two time periods. At t = 0, the

firm faces two mutually exclusive projects. Both projects require initial investment I and

5

have a payoff at t = 1. The firm has no funds, so it needs to borrow from the bank.

Project A produces output αI with probability p, and 0 with probability 1− p. Project

B is safe and produces βI with probability 1.

Project B is designed as the optimal investment choice from an atomistic perspective

in which a firm’s relationships along its supply chain are ignored; that project gives the

highest expected payoff (β > pα). Our analysis allows project choice to have different

impacts on the firm’s relationship with its major customer and bank. Project B can be

thought of as a standard technology with ex-ante known ability to generate stable cash

flows and with high resale value; this is the project that is preferred by the bank. Project

A, in contrast, engenders relationship-specific investments the firm makes to fulfill the

needs of its major customer. These relationship-specific investments can involve expendi-

tures with R&D, unique assets, customization, and modifications to standard production

processes. In conducting such investments, firms often develop innovative production pro-

cesses and face higher uncertainty of success. The output from these investments may be

also difficult to transfer to alternative users. Accordingly, project A is riskier for the firm,

with lower success rate and lower resale value. At the same time, we allow for it to create

synergistic gains for the major customer that are ultimately shared by the firm. Assume

that the major customer derives per-unit value VA from project A and VB from project B.

We allow for the major customer to prefer the relationship-specific project: pαVA > βVB.

At t = 1, the firm sells a proportion µ of its output to the major customer; the re-

mainder, 1− µ, is sold to a set of small customers. We assume the non-major customers

pay a price 1 per unit of output from project B, but only η ≤ 1 per unit of output

from project A.3 In order to incentivize the firm to take project A, the major customer

will offer different prices for different project outputs. The major customer offers δA per

unit for project A and δB per unit for project B; where δA > δB. This price schedule

is the outcome of bargaining between the firm and the major customer. It reflects the

terms in the sales contract agreed upon by the parties and it is binding. It can include

future transaction prices, speed of payment, and can also reflect variations in overhead

costs during the production process. In other words, in our analysis, prices are a simple

representation of the monetary and non-monetary exchanges executed between the firm

and its customers. As such, the analysis does not specify the price-setting process.

3Non-major customers apply a discount to the output derived from project A because it is tailoredfor the need of the major customer (less standard output).

6

2.2 Base Analysis

To ease the exposition, we momentarily assume the bank can observe firm’s choice of

project and its customer concentration µ. The bank also knows that the firm will default

on its loan payment at t = 1 with probability 1− p if it chooses project A. Accordingly,

the bank imposes the rate R = rp

if the firm chooses the risky project, and the risk-free

rate r (1 ≤ r ≤ β) if the firm chooses the safe project. The firm chooses between the two

projects to maximize its total value at t = 1 given bank rates R and r as follows:

max{pα(µδA + (1− µ)η)I − pRI, β(µδB + 1− µ)I − rI}. (1)

The firm will choose to invest in project A if pα(µδA+(1−µ)η)−pR > β(µδB+1−µ)−r.Simplifying this condition, the firm chooses project A if the customer’s offer satisfies the

following:

pαδA − βδB >(1− µ)(β − pαη) + pR− r

µ. (2)

The major customer will also benefit from project A if the following holds:

pα(VA − δA) > β(VB − δB). (3)

Therefore, the firm and its major customer will be in agreement and choose project

A if:(1− µ)(β − pαη) + pR− r

µ< pαδA − βδB < pαVA − βVB. (4)

Conversely, project B will be chosen if:

(1− µ)(β − pαη) + pR− rµ

> pαδA − βδB > pαVA − βVB. (5)

The conditions above imply that for high levels of customer concentration µ (that is,

µ > β−pαη+pR−rβ−pαη−βVB+pαVA

), the firm and the customer will agree on the relationship-specific

investment, project A. For low levels of µ, the standard project B will be selected.

2.3 Firm Heterogeneity

To make the model realistic and deliver testable predictions, we allow for many firms

in the economy. Moreover, we allow firms to be heterogeneous in their ability to suc-

cessfully invest in the relationship-specific project. Finally, we relax the assumption that

7

banks have perfect information about firms. Instead, we only assume that banks can

observe (ex-post) firms’ project choices, and that they know the general distribution of

“firm quality” (ability to succeed in the relationship-specific project). We cut clutter

in the model’s notation by associating the distribution of firm quality with the success

probability parameter p, denoting F (p) as a function of p ⊂ [p, p]; where p (p) is the

lowest (highest) probability of success in the distribution F (p).

In this economy, banks set loan prices based on the expected probability of default.

This captures the fact that commercial banks are subject to regulatory capital require-

ments and cannot diversify away the default risk of their loans. We assume a competitive

banking market. Without loss of generality, we model the lending decision of only one

bank.

We take that major customers have private information about their needs for cus-

tomization and know their suppliers’ ability to successfully deliver the relationship-specific

project A. This reflects the common assumption that major customers have information

advantage over banks regarding projects their suppliers undertake, as customers can bet-

ter understand input transactions and trade credit works as a monitoring tool (see, e.g.,

Biais and Gollier (1997) and Burkart and Ellingsen (2004)). Finally, we assume that

major customers always prefer project A, which is equivalent to setting pαVA > βVB.

To derive the equilibrium of this setting, we start by examining the incentive-compatible

agreement between a firm and its major customer. A firm will agree to take project A if

its customer offers a sufficiently high price (as in Eq. (2)). As firms differ in their qual-

ity, major customers of better firms can offer a higher price, leading firms to undertake

project A. Major customers of worse firms, on the other hand, cannot provide such a

price given the limited expected payoff from production. It follows that for every level

of customer concentration µ, a separating equilibrium exists where there is a threshold

value p∗ such that the “better firms” (those whose p > p∗) will choose the risky project

and the “worse firms” (whose p < p∗) will choose the safe project.

In this environment, the bank charges a break-even rate R = 1E[p|p≥p∗] if the firm takes

the risky project, and the risk-free rate r if the firm takes the safe project. The only

condition needed for the equilibrium is that the better firms may prefer to take the risky

project and the worse firms prefer to take the safe project:

pα(µδA + (1− µ)η)− pR > β(µδB + 1− µ)− r, ∀p > p∗ (6)

pα(µδA + (1− µ)η)− pR < β(µδB + 1− µ)− r, ∀p < p∗. (7)

8

Note that R decreases with p∗ ( ∂R∂p∗

< 0), suggesting that when the bank knows only

better firms undertake the risky project, it is less worried about default and will thus

charge a lower rate.

The equilibrium threshold p∗ will satisfy the following break-even condition:

(1− µ)(β − p∗αη) + p∗R− rµ

= p∗αVA − βVB. (8)

This expression describes the trade-off between projects for the marginal firm. The left-

hand side presents the cost of undertaking project A. The first term captures the loss of

sales to the non-major customers, while the other terms capture the additional cost (mark

up) of the bank loan. The right-hand side presents the benefit of undertaking project A,

which is also the maximum level of “inducement” the major customer could offer. The

threshold value p∗ is determined by equating the costs to the benefits of project A for

the firm.

Eq. (8) can be written as F = (p∗αVA−βVB)µ+(p∗αη−β)(1−µ)− (p∗R−r). Given∂R∂p∗

< 0, it follows that ∂F∂p∗

> 0 and ∂F∂µ

> 0. Using the Implicit Function Theorem, we

have ∂p∗

∂µ< 0, which implies that the quality of firms taking the risky project declines with

customer concentration. The end result is that a higher level of customer concentration,

µ, prompts more marginal firms to invest in the relationship-specific project A (lower p∗),

prompting the bank to charge a higher mark up interest rate (R− r).

2.4 Empirical Predictions

The model delivers several empirical implications and it is worth collecting them in a

subsection. To wit, as µ increases, the firm’s payoff depends more on its large customer.

That large customer can then more easily “induce” the firm to undertake the relationship-

specific, risky project. For larger µ, lower quality firms will choose the risky project. It

follows that the quality threshold p∗ declines with customer concentration. A lower p∗

indicates higher overall failure rates for firms that choose to conduct relationship-specific

projects. Anticipating the higher default rates that are associated with those projects, the

bank will require costlier, stricter terms for its loans. We write these central predictions

as follows:

Hypothesis 1 Banks will impose costlier, stricter loan contract terms on firms with

higher customer-base concentration.

9

Hypothesis 2 Firms with higher customer-base concentration will experience higher loan

failure rates.

The logic of our model implies that once a firm conducts relationship-specific invest-

ments for the major customer, the firm’s ability to pay back its bank loan will be more

closely tied to the financial conditions of the customer. In case its large customer enters

into financial distress, for example, the firm may face a cash flow shortage and default

on its loans. On seeing worsening financial conditions of major customers, banks should

further impose costlier and stricter loan contract terms. This natural extension of our

model can be stated as follows:4

Hypothesis 3 Banks will impose costlier, stricter loan contract terms on firms that face

customers in worse financial conditions.

The model predicts that large customers make firms conduct relationship-specific

investments, which leads banks to impose costlier loan terms. The empirical data will

present heterogeneity in firm investment and the intensity of relationship-specific projects

will depend on the industry in which firms operate. In taking our theory to the data,

one can think of industries with more intensive relationship-specific investments as hav-

ing lower levels of p (a downward shift in the distribution of p), as firms face riskier

investments and lower resale values for those investments. In these industries, banks will

impose even higher interest rates and stricter contract terms as customer concentration

increases. In industries with lower requirements for relationship-specific investments, in

contrast, loan contract terms will be relatively less sensitive to customer concentration.

This hypothesis can be stated as follows:

Hypothesis 4 The relation between customer concentration and loan contract terms will

be stronger in industries that involve more relationship-specific investments.

We note that the model focuses on a general notion of firms’ “borrowing costs” for

simplicity. The intuition easily extends to various features of standard loan terms, in-

cluding interest rate spread, maturity, and the presence of restrictive covenants. Making

these contract features costlier and stricter for the firm is meant to deter risk-taking. Our

empirical tests will revolve around each of these observable outcomes: loan markups, loan

maturity, loan covenants, and loan failures. We will also examine how customer concen-

tration affects derivative measures of the economic links between supplier firms and their

banks: depth and duration of lending relationships.

4In an omitted appendix, we formalize an extension of our model containing large customers’ finances.

10

3 Sample Construction and Empirical Methodology

We identify firms’ major customers using Compustat’s Segment Customer database.

Statement of Financial Accounting Standard (SFAS) No.14 requires firms to report all

customers that represent 10% of a firm’s total sales. The Segment database collects

information including the names of the customers and their assigned sales figures. In

identifying customer relations, we focus on recurring customers and exclude those that

appear for fewer than three times for a firm in the sample period. We focus on manufac-

turers (SIC 2000–3999) to ease the data collection process, to facilitate comparisons across

firms (reduce uncontrolled heterogeneity), and because firms operating in this sector re-

late more naturally to our supply-chain story. Information from the U.S. input/output

matrix suggests that supplier–customer links in the manufacturing sector feature firms

on both ends of the relationship.5

We extract bank loan contract information from LPC–Dealscan and link loan-level

data to Compustat firm data following Chava and Roberts (2008). We treat each loan

facility as an independent contract. We examine revolvers and term loans since these

types of loans contain more detailed information on the pricing and the restrictiveness of

bank credit.

We construct our final sample by combining the customer and bank loan databases

from 1985 through 2010. For a firm to be included in the sample, we require it to have

available customer information, basic loan features, and information on standard vari-

ables such as size, leverage, and market-to-book. We glean into how banks update loan

pricing and other contracting features by focusing on newly initiated (or renegotiated)

loans during the year when the firms report customer information. Following the existing

literature (e.g., Lin et al. (2011), Valta (2012), and Hertzel and Officer (2012)), we do

not repeatedly account for the same loans for the years after initiation. As a result, our

dataset has a panel structure in which individual firms appear sparsely (more on this

shortly). Our sample consists of 3,375 loans granted to 1,110 individual manufacturers.

3.1 Customer Concentration

The literature does not provide a consistent way to measure customer concentration.

Previous studies use a variety of measures, most of which are computations based on

the percentage of firm sales to major customers (examples are Banerjee et al. (2008)

5Over two-thirds of output in those industries is sold as intermediary goods to other manufacturers,the remainder largely goes to bulk retailers.

11

and Patatoukas (2012)). These approaches fit conveniently our theoretical analysis of

customer concentration, which is based on the parameter µ. We experiment with several

measures of concentration meant to capture the importance of a firm’s large customers.

Our first measure of concentration is based on the percentage of sales that a firm as-

signs to its major customers (similar to Banerjee et al. (2008)). In particular, we define

CustomerSales as the sum of the percentage sales coming from the set of customers the

firm reports as “major customers” (i.e., those with at least 10% of total sales). Custom-

erSales is computed as:

CustomerSalesi =

ni∑j=1

%Salesij,

where ni is the number of firm i ’s major customers, and %Salesij = Sales of i to jTotal Sales of i

, is the

percentage sales from firm i to customer j over i ’s total sales. A high level of Customer-

Sales means that a large proportion of a firm’s sales goes to its major customers.

Our second measure is the sales-weighted size of a firm’s major customers. This mea-

sure is more nuanced than the first in that it gives more importance to major customers

that also happen to be larger firms; likely more significant supply-chain partners. We

define CustomerSize as the firm’s percentage sales to major customers weighted by the

size of those customers. CustomerSize is computed as follows:

CustomerSizei =

ni∑j=1

%Salesij × Sizej,

where Sizej is the size (defined by log of total assets) of customer j. A high level of

CustomerSize means that a firm relies more heavily on fewer, large-sized customers.

Our third measure follows Patatoukas (2012), who defines concentration based on the

notion of a Herfindahl index of sales to large customers:

CustomerHHIi =

ni∑j=1

%Sales2ij.

Expanding on this approach, we also compute the Gini coefficient of a firm’s sales to

its large customers, a measure we call CustomerGini. Conversely, a simpler approach is

based on the percentage sales a firm assigns to its single largest customer, CustomerMax

= maxj=1,...,ni

%Salesij. Alternatively, the simple count of the number of large customers

12

provide an indication of the extent to which a firm deals with few, major customers,

CustomerCount = ni.

These different measures provide unique insights about the extent to which a firm is

engaged in trades with major customers in its supply-chain. While we conduct the bulk

of our analysis based on the first two measures, we often report results for other measures

as well.6

3.2 Borrowing Terms

Chava and Roberts (2008), Roberts and Sufi (2009), and Campello et al. (2011) de-

scribe the elements of the LPC–Dealscan dataset that are relevant for our analysis. We

follow the methodology in Campello et al. and measure three contract features of bank

loan terms. The first is loan spread (LoanSpread). LoanSpread is the “All-in-Drawn”

spread (in basis-points) over LIBOR. “All-in-Drawn” spread is computed as the sum of

coupon and annual fees on the loan in excess of six-month LIBOR. The second feature

is loan maturity (LoanMaturity). LoanMaturity is the number of months until maturity.

Finally, we count the total number of restrictive covenants present in the loan facility

(LoanCovenants).

3.3 Banking Relationships

In addition to adjusting the terms of new loan facilities, banks can also react to a

firm’s customer concentration by abbreviating their relationships with the firm. If cus-

tomer concentration is related to excessive credit risk or undesirable investment choices,

banks can reduce the amount of funds lent to firms or even terminate their relations. We

design empirical measures of firm–bank relationships to capture these dynamics.7

Each time a firm discloses its customer concentration, we look forward in the sam-

ple window searching for subsequent loan arrangements (renewed relations in the future)

with its current banks. We measure these future relations using two methods. First,

we measure the additional amount of lending extended by the bank to the firm given

the information of customer concentration (FutureLoans). FutureLoans is defined as the

total dollar amount of loans issued by the same bank following the currently observed

6In Appendix B, for example, we report the central tests of the paper (on loan markups) for all sixmeasures.

7A related paper is Bharath et al. (2011), who focus on the relative importance of a particular bankcompared to other banks. We get qualitatively similar results using the measure employed by thoseauthors.

13

agreement, scaled by the dollar amount of current loans. Higher values of FutureLoans

suggest that the bank maintains a stronger relationship with the firm after the disclosure

of information about customer concentration.

Our second measure of banking relationship is the length of the future banking re-

lationship, defined as the number of months in which the bank continues to lend to the

firm in the future (FutureDuration). For each year a firm receives a bank loan, Future-

Duration counts the number of months until the last occurrence of the firm receiving a

loan from the current bank. Similar to FutureLoans, FutureDuration also reflects a bank’s

commitment to the lending relationship. However, it emphasizes the length rather than

the intensity of the relationship.

Naturally, both measures of banking relationships suffer from attrition bias, as we

observe shorter future duration and less future lending as we approach the end of the

sample. We thus restrict our banking relationship tests to fiscal years prior to 2007.8

3.4 Loan Failures

To corroborate our argument that a more concentrated customer base is associated

with worse creditworthiness, we also examine the relation between loan failure rates and

customer concentration. If customer concentration is associated with a higher likelihood

of loan failure, banks will naturally impose stricter loan terms ex-ante.

We examine this conjecture by using bankruptcy data from Sudheer Chava’s collec-

tion of corporate bankruptcies.9 This database provides a comprehensive coverage of

bankruptcy filings over the 1962–2010 window. We match these bankruptcy data with

bank loan information, and identify a loan failure event if the borrower files for bankruptcy

prior to an existing loan maturity date. For this case, we assign an indicator variable

LoanFailure to 1. If there is no bankruptcy before the maturity of the loan, then Loan-

Failure is set to 0. We match the loan failure variable with firms’ customer information

and identify 241 loan failures in our sample.

3.5 Empirical Methodology

We estimate panel regression models for our baseline tests that build on a large litera-

ture on bank loans (see, among others, Graham et al. (2008), Campello et al. (2011), and

Lin et al. (2011)). Our models regress loan term variables on customer concentration

8Nonetheless, our results are unaffected if we do not impose this time window constraint.9We thank Sudheer Chava for kindly providing these updated data.

14

measures together with standard firm-level, loan-level, and macro-level controls. The

specifications also feature bank effects, capturing firm–bank pairings. The model can be

written as follows:

LoanTermi,k,t = β0 + β1CustomerConcentrationi,t + β2FirmCharacteristici,t

+ β3MacroV ariablet + β4LoanCharacteristick,t +∑g

Industryg +∑h

Bankh + εi,k,t,

(9)

where i indicates the supplier, k indicates newly initiated loans, t indicates the year of

the loan initiation; LoanTerm ∈ {LoanSpread, LoanMaturity, LoanCovenants}, and Cus-

tomerConcentration ∈ {CustomerSales, CustomerSize}. Borrowing terms and customer

concentration may vary significantly across industries. We thus include industry-fixed

effects (Industryg). Differences of borrowing terms can also arise from banks’ screening

technology. Some banks may be able to better detect firms’ credit quality or to more

closely monitor the firms. These banks may select firms with lower customer concentra-

tion and impose looser borrowing terms. We thus include bank-fixed effects (Bankh).

We report heteroskedasticity-robust errors clustered by industry.

Firm characteristics include proxies for profitability, size, age, tangibility, market-to-

book, leverage, and credit ratings. Macroeconomic conditions are measured by the credit

spread, the term spread, and the GDP growth rate. Loan characteristics include loan

maturity, loan size, and loan spread. We also include a dummy variable for loan type

(term loans or revolvers). A detailed definition of the variables is provided in Appendix A.

Our model predicts that customer concentration has negative implications for firm

borrowing terms. Therefore, we expect the coefficient on customer concentration, β1, to

be positive in the regressions for loan spreads and for the number of covenants. In the

regression for loan maturity, we expect that coefficient to be negative.

We estimate analogous models for the link between customer concentration and future

firm–bank relationships as follows:

BankingRelationshipi,h,t = β0+β1CustomerConcentrationi,t+β2FirmCharacteristici,t

+ β3MacroV ariablet +∑g

Industryg +∑h

Bankh + ui,h,t, (10)

where h indicates the lending bank and BankingRelationship ∈ {FutureLoans, FutureDu-

ration}. We expect customer concentration to hamper firms’ future relationships with

15

Figure 1. The frequency of firms’ distinct appearances in the sample. This figure shows thenumber of firms who appear in the sample for a certain number of distinct observations. The horizontalaxis shows the number of distinct observations. The vertical axis shows the number (frequency) of firms.

their banks. Therefore, we expect the coefficient β1 to be negative in both banking

relationship regressions.

3.6 Data Structure and Summary Statistics

Similar to prior studies on contracts features (Graham et al. (2008), Campello et

al. (2011), and Lin et al. (2011)), the unit of observation in our baseline tests is a loan

contract. As such, we only observe variations in a firm’s customer concentration if the

firm signs new contracts in different years. This results in few recurrences for each firm.

Figure 1 plots the histogram of firms’ distinct observations (entries) in the sample. The

distribution is highly skewed, with few firms appearing in the sample more than two or

three times. Indeed, nearly 50% of the firms appear in the sample only once. Given this

data structure, similar to prior studies in the loan literature, we are unable to include

firm-fixed effects in our regressions.

Table 1 reports the summary statistics of the suppliers’ characteristics, customer

concentration, loan terms, and banking relationship measures in our sample. The firms

sampled attribute about 30% of their sales to major customers. These firms, on average,

have total assets of 590 million, asset tangibility of 26%, and leverage of 29%. These

figures are similar to those in Campello et al. (2011), among others, who report total asset

of 680 million, tangibility of 33%, and leverage of 29%. The average loan contracts in our

16

0.135

0.140

0.145

0.150

0.155

0.160

0.165

1 2 3 4 5 6 7 8 9 10

Rank of Customer Concentration

Profitability

0.085

0.095

0.105

0.115

0.125

0.135

0.145

0.155

0.165

1 2 3 4 5 6 7 8 9 10

Rank of Customer Concentration

Sales Growth

Figure 2. The relation between customer concentration and firm operating performance.The left panel shows the relation between customer concentration and profitability; the right panel showsthe relation between concentration and sales growth. Customer concentration is measured by the totalpercentage sales to all major customers, CustomerSales. The decile ranking of CustomerSales is shownon the horizontal axes.

sample have spreads of 179 bps over LIBOR, maturity of 46 months, and 1.8 covenants.

Table 1 About Here

4 Univariate Analysis

We start our investigation by characterizing the very phenomenon of customer con-

centration, which is still understudied. Prior research points to significant gains in con-

centrating sales to a small group of buyers. These benefits arise from the argument that

firms can achieve economies of scale and superior operating efficiency (cf. Patatoukas

(2012) and Irvine et al. (2013)). It is important that we verify these benefits in our data.

Otherwise, one could attribute the worsening of borrowing terms that we document to

potentially negative effects of customer concentration on operating performance. Along

similar lines, we conjecture that customer concentration may be associated with other

firm characteristics that influence their credit terms. Although our multivariate analyses

are designed to address concerns about confounding heterogeneity effects, it is impor-

tant that we have a basic understanding of these relations. As we show next, customer

concentration is related to firm fundamentals such as size, age, and technology.

4.1 Customer Concentration and Firm Operating Performance

We depict the relation between customer concentration and operating performance in

Figure 2. Following Patatoukas (2012), we rank firms into deciles according to their cus-

17

0.25

0.27

0.29

0.31

0.33

0.35

0.37

0.39

0.41

0.43

0.45

1 2 3 4 5 6 7 8 9 10

Rank of Firm Size

Customer Concentration

0.25

0.27

0.29

0.31

0.33

0.35

0.37

0.39

0.41

0.43

0.45

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Firm Age (in Years)

Customer Concentration

Figure 3. The relation between customer concentration with firm size and age. The leftpanel shows the relation of size with customer concentration, where the horizontal axis shows the decileranking of firm size. The right panel shows the relation of firm age with customer concentration, wherethe horizontal axis shows firm age. Customer concentration is measured by the total percentage sales toall major customers, CustomerSales.

tomer concentration measure CustomerSales and plot the average operating performance

of firms in each decile. The left (right) panel shows the average sales growth (profitabil-

ity) of firms in each customer concentration level. Sales performance and profitability

both increase with customer concentration. Firms in the lowest deciles of customer con-

centration, for example, observe average annual sales growth of about 9%, while those in

the highest deciles observe 13% or higher growth (nearly 50% higher growth rates). The

patterns we document in Figure 2 are consistent Patatoukas’s argument that firms with

concentrated customer bases enjoy improved performance (see also Irvine et al. (2013)).

Important for our purposes, those patterns show that firms with high customer concen-

tration are not “worse firms” who observe low profits and should naturally face costlier,

stricter loan terms.

4.2 Customer Concentration and Firm Characteristics

Customer concentration can be correlated with important firm characteristics such as

size, age, leverage, and market-to-book. We explore the relation of customer concentra-

tion with these firm characteristics, since they may also affect credit terms.

Figure 3 shows that customer concentration is negatively related to firm size and

age, indicating that smaller, younger firms tend to deal more frequently with major

customers.10 This association can lead to spurious correlation between customer concen-

10To depict the process of customer-base concentration across firms’ “life cycle,” we only include inthe figure firms whose life spans exceed 10 years. Yet, including firms who exist in the sample for fewer

18

0.20

0.21

0.22

0.23

0.24

0.25

0.26

0.27

0.28

1 2 3 4 5 6 7 8 9 10

Rank of Customer Concentration

Leverage

1.5

1.6

1.7

1.8

1.9

2.0

2.1

2.2

2.3

2.4

2.5

1 2 3 4 5 6 7 8 9 10

Rank of Customer Concentration

Market-to-Book

Figure 4. The relation between customer concentration with firm leverage and market-to-book. The left panel shows the relation between customer concentration and firm leverage. Theright panel shows the relation between concentration and market-to-book. Customer concentration ismeasured by the total percentage sales to all major customers, CustomerSales. The decile ranking ofCustomerSales is shown on the horizontal axes.

tration and loan terms, since smaller, younger firms also tend to face more informational

problems, hence higher borrowing costs. It is thus important to control for firm size and

age effects in our tests.

Figure 4 provides further insights into firms that operate with higher levels of cus-

tomer concentration. Concentration is associated with lower leverage ratios. It is also

associated with higher market-to-book ratios. Notably, research shows that firms with

lower leverage and higher market-to-book are able to command lower interest rate spreads

in their bank loans (e.g., Graham et al. (2008), Campello et al. (2011), and Lin et al.

(2011)). These findings corroborate the argument that firms with major customers are

not underperforming businesses that are naturally prone to receive costlier, stricter loan

terms from their banks.

The concentration of firms’ customer bases may well vary across industries. Figure 5

shows the distribution of customer concentration in each manufacturing industry. It

shows that firms in most manufacturing industries have customer concentration around

20% to 30%. However, in industries such as furniture and fixtures, customer bases reach

nearly 50%, while in the food industry the average is only about 17%. These patterns

suggest that it is necessary for account for industry effects in our analysis.

than 10 years does not change our inferences.

19

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Distribution of Customer Concentration by Industry

(p25) Customer Concentration (Median) Customer Concentration (p75) Customer Concentration

Figure 5. The dispersion of customer concentration across industries. This figure shows the 25percentile, median, and 75 percentile levels of customer concentration for each manufacturing industry.Industry is classified using 2-digit SIC code, and customer concentration is measured by CustomerSales,total percentage sales to major customers.

4.3 Customer Concentration and Relationship-Specific Invest-

ment

Our model implies that higher customer concentration may prompt a higher level of

relationship-specific investment. To wit, Eq. (8) shows that a higher µ induces a lower

p∗, meaning that higher customer concentration will induce more firms to undertake the

relationship-specific project. This result seems sensible: an important customer can more

easily contract with the firm to invest in customized projects that are suitable to its par-

ticular needs. Firms who do not have to cater to a major customer, on the other hand,

only need to manufacture standardized products. We study this implication by exam-

ining the relation between customer concentration and relationship-specific investment.

While it is difficult to measure relationship-specific investment, we gauge the uniqueness

of firms’ investment and production following the existing literature.

Our first measure focuses on the level of differentiated inputs firms use. In particular,

we examine how specific are the inputs they employ in their production process. Firms

that use more differentiated inputs have been shown to offer more differentiated products

to their customers, and we use this metric to gauge of the depth of their relationship.

20

Figure 6. The relation between customer concentration and relationship-specific invest-ments. The left panel shows the relation of customer concentration with firms’ uniqueness of inputs,measured by the percentage of firms’ inputs from industries producing differentiated goods; the middlepanel shows the relation of concentration with the originality of patents; and the right panel shows therelation of concentration with R&D intensity. Customer concentration is measured by the total percent-age sales to all major customers, CustomerSales. The decile ranking of CustomerSales is shown on thehorizontal axes.

Giannetti et al. (2011) provide detailed information on industries’ use of differentiated

inputs. We follow Giannetti et al.’s approach and assign a firm to a given level of differ-

entiated inputs usage according to the industry in which it operates. Our second measure

is based on the uniqueness of firms’ patents. We gather information on firms’ patents

from the NBER database and focus on “patent uniqueness,” measured by the width of

different patents cited in the creation of firms’ granted patents (see Hall et al. (2001) for

details). Firms with more unique patents are thought to develop and sell more unique,

customized products to their customers. Finally, following prior literature (e.g., Allen

and Phillips (2000) and Kale and Shahrur (2007)), we also measure relationship-specific

investments using firms’ R&D intensity, defined as the ratio of firms’ R&D expenditure

scaled by total assets.

Figure 6 points to a positive relation between firms’ relationship-specific investments

and customer concentration. The plots in the figure suggest that firms that have higher

customer concentration use more specific inputs for production, produce more original

patents, and invest more in R&D. These patterns are consistent with the economics of

our model. They are also consistent with theories of optimal stakeholder investment and

the limits of the firm, which predict that a firm’s relations with its important stakeholders

will involve relationship-specific investments (e.g., Titman (1984) and Hart (1995)).

21

5 Regression Analysis

We estimate panel regressions of loan spreads, the number of restrictive covenants,

loan maturity, and future banking relationship variables (length and depth) on each of

our measures of customer concentration. In each estimation run, we first control for firm-

level characteristics, industry-fixed effects, and bank-fixed effects. We then augment the

model with macro variables as controls. In the last round, we further include loan-level

characteristics in the set of controls. Tables 2 through 5 present results for 22 such models.

Table 2 shows the results for regressions of loan spreads on alternative measures of

customer concentration, CustomerSales and CustomerSize. Both measures attract signif-

icant and positive coefficients across all estimations, suggesting that firms with a higher

customer concentration are asked to pay higher spreads in their next bank loan facilities.

The most conservative set of estimates in the table (column (6), featuring the full set

of controls) suggests that a one-standard-deviation increase in customer concentration

is associated with a 10 basis points increase in the loan spread. This amounts to a 6%

increase relative to the average loan spread of 179 basis points. Our results suggest that

banks consider customer concentration as a negative factor affecting firms’ credit quality;

a factor that is priced into loan mark-ups.

Table 2 About Here

Table 3 shows results for the number of restrictive covenants. Both measures of cus-

tomer concentration attract positive and statistically significant coefficients across all

regressions, suggesting that firms with high customer concentration have more restrictive

covenants written in their new loan contracts. The estimates from column (6) indicate

that a one-standard-deviation increase in customer concentration is associated with a 0.2

increase in the number of restrictive covenants, which accounts for a 12% increase relative

to the average number of covenants (1.8 covenants) for the loans in the sample.

Table 3 About Here

Table 4 shows results for loan maturity. In this set of regressions, only term loans

are used, since revolvers do not set fixed loan maturities. Both measures of customer

concentration attract negative coefficients, suggesting that firms with higher customer

concentration receive loans with shorter maturity. The statistical significance of these

estimates is less strong. In economic terms, however, a one-standard-deviation increase

22

in customer concentration is associated a 2-month reduction in loan maturity; compared

to the average maturity of 46 months.

Table 4 About Here

We also study the impact of customer concentration on length and depth of the

relationship of the supplier firm and its bank. The results are reported in Table 5. In

economic terms, the most conservative estimates in the table suggest that a one-standard-

deviation increase in customer concentration is associated with a decline in the amount

of future lending extended by the firm’s bank equivalent to 10% of the sample mean.

A one-standard-deviation increase in concentration is associated with 8 fewer months of

future relations with the bank; a 15% drop from the mean.

Table 5 About Here

The results in Tables 2 through 5 are internally coherent and consistent with the pre-

dictions of our simple theoretical model. They show that a more concentrated customer

base is associated with costlier, stricter loan terms for the firm’s loans, including higher

interest rate spreads, more restrictive covenants, and shorter maturities. Customer con-

centration is also associated with the deterioration of firm–bank associations, represented

by shorter relations and less lending by the bank in the future.

6 An Instrumental Variables Approach

Although common in the loan contracting literature, regressions such as those per-

formed in the previous section are subject to several concerns about estimation biases.

Concerns about endogeneity biases, in particular, may arise from the fact the model

lacks an explicit source of exogenous variation in concentration. To allay those concerns,

this section conducts instrumental variables (IV) tests that exploit sharp shifts in the

concentration of a firm’s customer base.

We use aggregate merger and acquisition activity in customers’ industries (down-

stream M&A) as an instrument in assessing the impact of customer concentration on

suppliers’ loan terms and banking relationships. Our instrumental approach implies that

suppliers will face a more concentrated customer base following M&A waves in their cus-

tomers’ industries (inclusion restriction). Existing research supports such prior (e.g., Fee

23

and Thomas (2004), and Bhattacharya and Nain (2011)) and we verify that this is indeed

the case in the tests below. The approach also assumes that downstream M&A affects

suppliers’ borrowing terms through customer–supplier links (exclusion restriction). This

is a plausible assumption since downstream M&A activity (among customers) is not a

policy variable for the supplier. Yet, that activity may be influenced by suppliers’ indus-

try dynamics affecting both suppliers’ customer base and their financial conditions (e.g.,

industry profit declines can be correlated with both a firm’s customer concentration and

credit condition). To control for these effects, as we discuss below, we further introduce

suppliers’ industry-level, time-varying effects in our tests.

6.1 Measuring M&A Activity in the Customer Industry

We gather information on M&A deals from SDC database and apply the following

data filters following Ahern and Harford (2014): 1) only include completed deals; 2) both

the acquirer and target are U.S. firms; 3) the acquirer can be matched with a Compustat

identifier; 4) the acquirer purchases at least 20% of the target during the transaction, and

owns more than 51% after the transaction; and 5) the acquirer does not buy its suppliers.

Finally, we exclude suppliers who are in the same 2-digit SIC industry as their customers.

We use the transaction values of M&As scaled by the acquirers’ total sales as a proxy

for acquisition activity. An industry-level 5-year mean acquisition activity is measured

as the average acquisition of firms in the industry over the past five years. Each of the

firms in our sample supplies products to a portfolio of customers, and those customers

may be in different industries. For each sample firm, we gauge the potential impact of

downstream M&A activity on customer concentration by taking the average of the 5-year

acquisition activity across the industries to which the firm’s customers belong. We refer

to this variable as CustumerM&A. The variable is defined as follows:

CustomerM&Ai =1

ni

ni∑j=1

Industry Average(Acquisitionj

Salesj).

6.2 IV Specification and Results

We use two-stage least square regressions to reassess the impact of customer concen-

tration on loan terms. In the first stage, we regress customer concentration measures

(CustomerSales and CustomerSize) on CustumerM&A, together with a full set of con-

trols. In the second stage, we regress borrowing terms and banking relationship variables

24

on the projected customer concentration measures, together with controls. The two-stage

system for loan terms can be written as follows:

CustomerConcentrationi,k,t = β0 + β1CustomerM&Ai,t + Controls+ εi,k,t [First stage] (15a)

LoanTermsi,k,t = β2 + β3 ̂CustomerConcentrationi,k,t + Controls+ νi,k,t, [Second stage] (15b)

where i indicates the supplier, k indicates loans, t indicates the fiscal year. LoanTerms in-

cludes the loan term variables LoanSpread, LoanMaturity, and LoanCovenants. Customer-

Concentration indicates customer concentration measures. ̂CustomerConcentration de-

notes the predicted value of customer concentration from the first-stage regression (Eq.

(15a)), reflecting the variation of customer concentration induced by the variation M&A

activity in customers’ industries. Controls contains the set of control variables used in

our baseline regressions, including firm-level controls, macro variables, loan-level controls,

and bank-fixed effects.

Our IV tests further account for unobserved industry dynamics that can drive both

downstream merger waves and firms’ credit terms. Economic and technological shocks to

an industry, for example, can lead to merger waves (Harford (2005)). These industry-wide

shocks may further propagate along the supply chain and affect all firms in the system. To

control for industry-level, time-varying dynamics that may confound our results, we fur-

ther incorporate supplier industry-year-fixed effects in our specifications. The fixed effects

approach removes any unobservable shock that is common to an industry in a given time

period, thus preventing it from contaminating the exclusion restriction of our instrument.

The two-stage system for banking relationships can be written as:

CustomerConcentrationi,h,t = β0 + β1CustomerM&Ai,t + Controls+ εi,h,t [First stage] (16a)

BankingRelationshipi,h,t = β2 + β3 ̂CustomerConcentrationi,h,t + Controls+ ηi,h,t, [Second stage] (16b)

where h indicates the lending bank. BankingRelationship is the measures for bank rela-

tionships: FutureLoans and FutureDuration.

In the first stage (Eqs. (15a) and (16a)), we expect β1 to be positive, indicating that

suppliers experience increases in customer concentration following high levels of M&A

activity in the customers’ industries. In the second stage, we expect β3 to be positive for

the loan spreads and covenants regressions, and negative for the loan maturity and bank

relationship regressions.

Table 6 shows the first-stage regression results for customer concentration on cus-

tomer industry acquisition activity (CustumerM&A). The instrument loads significantly

25

positively in all models. For brevity, we only present results for our first customer con-

centration measure, CustomerSales (the results are similar for CustomerSize). Results

in Table 6 are consistent with the prior of our identification strategy implying that firms

face more concentrated customer bases following high levels of M&A activity in their cus-

tomers’ industries. In economic terms, a one-standard-deviation increase in downstream

M&A contributes to a 3.6-percentage points increase in customer concentration, which is

a 11% increase relative to the average customer concentration. Notably, the F -statistics

from the first-stage regressions pass the weak identification tests at the 1% level. The

Kleinberg-Paap statistics pass the under-identification tests.

Table 6 About Here

Table 7 shows the second-stage regression results of loan terms and banking relation-

ships on the instrumented customer concentration. Consistent with our OLS results, the

instrumented customer concentration is positive and statistically significant in the loan

spread and loan covenants regressions. Also consistent with the OLS results, the coeffi-

cients associated with customer concentration are negative and statistically significant in

the loan maturity and future banking relationship regressions.

Table 7 About Here

Our IV results confirm our baseline findings that increases in customer concentration

lead to higher loan spreads and more loan covenants for supplier firms. Higher customer

concentration also leads to lower loan maturity, shorter banking relationships, and less

lending extended by the same bank in the future. The economic and statistical signif-

icance of the impact of customer concentration on all the outcome variables we study

are magnified under the IV approach. In all, the evidence we gather suggests that while

customer concentration may make supply chain relations more efficient and increase firm

profits, a deeper exposure to a small set of customers also has negative consequences for

a firm’s relations with its creditors.

6.3 Strategy Verification: Focusing on Regulatory-Induced M&A

Activity

The merger activity we exploit in our IV tests are spontaneous and one could be

concerned that unobserved developments or trends affecting the supplier firms we study

26

could: 1) lead to changes in the contracting terms those firms receive from their banks,

and simultaneously 2) trigger M&A activity among their customers in downstream indus-

tries. It is ultimately hard to completely rule in or rule out such a claim, but we take one

extra step at ensuring that this is not a concern for the results derived from our IV tests.

We do so designing a test strategy that relies solely on downstream M&A activity that

is associated with acts by the Federal Government that change entry, prices, and other

elements of the competitive environment of the downstream industry affected. Ample

literature shows that these regulatory actions have led to spikes in the M&A activity

in the industries affected (e.g., Schoenberg and Reeves (1999), Andrade et al. (2001),

Bruner (2004), and Ovtchinnikov (2010)). We use these induced spikes in M&A in a

difference-in-differences framework.

There were eight major nation-wide regulatory actions applicable to our analysis in

the sample period we examine: the Natural Gas Wellhead Decontrol Act (1989), the

Cable Television Consumer Protection and Competition Act (1992), the Energy Pol-

icy Act (1992), the Federal Energy Regulatory Commission (FERC) Order 636 (1992),

the Negotiated Rates Act (1992), the Trucking Industry and Regulatory Reform Act

(1992), the Telecommunications Act (1996), and the FERC Order 888 (1996).11 The af-

fected (“deregulated”) industries include natural gas, cable TV, trucking, telephone, and

wholesale, defined at the 4-digit SIC level. We verify claims by previous researchers that

these Federally-mandated Acts and Orders affecting the competitive environment had

the end result of triggering M&A activity. We do so by comparing the M&A activity in

the industries affected by regulation with the M&A activity in other industries that share

the same 3-digit SIC codes. As shown in Figure 7, the average level of M&A activity

(acquisition deal value scaled by total sales of acquirers) in the deregulated industries

increased 3- fold following a regulatory innovation. By comparison, the M&A activity in

other industries under the same 3-digit SIC code did not exhibit any change.

The next step of our analysis is to establish supplier–customer links for the industries

affected by the eight regulatory events listed above. Our specific goal is to identify the

suppliers of these deregulated industries. We do so using the input–output (I–O) ma-

trix provided by the Bureau of Economic Analysis.12 To closely capture input–output

relations amongst industries in our sample period, we use the I–O matrix of 1987 for

the regulatory event taking place in 1989, and the I–O matrix of 1992 for the events of

1992 and 1996. We first identify the suppliers of the deregulated industries, and examine

11See Asker and Ljungqvist (2010) for a detailed summary of these deregulation events.12The matching of 4-digit SIC industries with I-O industries comes from Joseph Fan’s website.

27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

-3 -2 -1 0 1 2 3

Dea

l V

alu

e/S

ales

Years around Deregulation

Merger Acitivity around Deregulation

Control Industries Deregulated Industries

Figure 7. M&A activity around deregulation. This figure shows the industry average level ofM&A activity around deregulation events. The horizontal axis shows the number of years past thederegulation event, and the vertical axis shows the industry average level of M&A activity, measuredby acquisition value scaled by sales. Deregulated industries are classified at the 4-digit SIC level, whilecontrol industries include other firms in the same 3-digit SIC industries as the deregulated firms, butwith different 4-digit SIC codes.

the changes in firm-level customer concentration and loan contract terms for firms in the

supplier industries.13 In other words, we consider the suppliers of deregulated industries

as our treated group. We define our control group as the other firms in the same 3-digit

SIC industries as these suppliers, but with different 4-digit SIC codes (non-deregulated

industries). In this way, we are able to keep similarity between treated and control groups

both economically and in terms of sample size. We further restrict the event period to

be from 3 years before to 3 years after each regulatory event.

Using a differences-in-differences panel setup, we examine how regulatory-induced

downstream mergers affect a firm’s customer concentration and the loan contract terms

it receives. Formally, we estimate the following regressions:

Yi,k,t = β0 + β1Treatedi + β2Postt + β3Treatedi × Postt + Controls+ εi,k,t, (17)

where i indicates the borrower, k indicates loans, t indicates the fiscal year. Outcome

variable Y includes CustomerSales and LoanSpread. Treated is a dummy variable indicat-

13We restrict suppliers to be in upstream industries that attribute over 3% sales to the deregulatedindustries.

28

ing whether a firm’s customer industry experienced a Federally-mandated Act or Order

affecting its competitive environment. Post is a dummy variable covering the period fol-

lowing the regulatory event. Finally, Controls contains the set of control variables used

in our baseline regressions, including firm-level controls, macro-level variables, loan-level

controls, industry-fixed effects, and bank-fixed effects.

Table 8 reports the results from Eq. (17). Columns (1) and (2) show the results for

customer concentration. The interaction term Treated×Post loads positively, indicating

that the mergers driven by deregulation events increase the customer-base concentration

for suppliers. Columns (3) and (4) show the results for loan markups, where the in-

teraction term Treated × Post also loads positively. This result suggests that creditors

become concerned about suppliers whose customers concentrate following regulatory acts,

imposing higher interest rates on their loans.

Table 8 About Here

The evidence we gather from tests around regulatory events are consistent with our

prior results on the relation between customer concentration and credit terms. In particu-

lar, the study of downstream M&A activity triggered by Federally-mandated Acts and Or-

ders supports the results of our larger IV-based tests, which suggest that firms’ customer

concentration can lead to costlier, stricter loan contract terms imposed by their banks.

7 Deepening Relations Along the Supply Chain

7.1 Relationship-Specific Investments

According to our model, the relationship-specific nature of the investments firms make

may be a function of the importance of its major customers. In Section 4, we reported a

robust association between customer concentration and relationship-specific investments

(RSI). In this section, we examine whether the relation between customer concentration

and bank loan terms is modulated by the nature (or specificity) of the investments made

by firms.

Recent research by Giannetti et al. (2011) suggests that firms that use more differen-

tiated inputs also supply more differentiated products to their customers. We follow their

analysis in using input differentiation as a proxy for the prevalence of RSI across different

industries. We start by classifying firms into high and low RSI categories. To do so,

29

we rank the industries in our sample and partition our data so that we have a balanced

number of firms across high, middle, and low levels of RSI (tirciles). We then conduct

subsample analysis using our baseline regression model (Eq. (9)) for firms in high-RSI

industries and low-RSI industries. We expect customer concentration to be a particularly

important determinant of loan contract terms in high-RSI industries, and relatively less

so in low-RSI industries.

The results are reported in Table 9. Column (1) shows that customer concentration

has a strong positive impact on loan spreads across firms in high-RSI industries (consid-

erably larger than the baseline estimates of column (3) of Table 2). For the subsample

of low-RSI industries, however, the coefficient on customer concentration is economi-

cally negligible and statistically insignificant (see column (4)). This is consistent with

the conjecture that customer concentration affects firms’ credit profile as a function of

relationship-specific investments in supply-chain relations. Additionally, we also find that

customer concentration leads to shorter loan maturities in high-RSI industries. Our tests,

however, do not show a measurable impact of RSI on the association between customer

concentration and covenants.

Table 9 About Here

7.2 Account Receivables Cycle

As firms operate in a more integrated fashion along their supply chain, they may not

only conduct different types of investments, but may also present different financing pat-

terns. In our setting, one would argue that firms dealing with a concentrated customer

base (delivering their preferred products) may receive payments in a more expeditious

basis as a way to finance their production cycle. This follows from our model’s hypothesis

that suppliers and large customer share mutual benefits from deepening their relations.

One way to empirically examine this conjecture is to study the relation between re-

ceivables and customer concentration. We do so using our baseline model and report

the results in Table 10. The dependent variables are account receivables, which we scale,

alternatively, by the cost of goods sold (COGS ) and total sales. The proxies we examined

provide a gauge for the firm’s “account receivables cycle.”

We find results indicating that customer concentration is negatively related to ac-

count receivables cycle. The estimates in Table 10 suggest that a one-standard-deviation

increase in customer concentration is associated with a 1.3% drop in receivables relative

30

to cost of goods sold (compared to the mean of 27%), and a 0.7% drop in receivables

relative to total sales (compared to the mean level of 16%). These results are interesting

in suggesting that firms with concentrated customer bases partially offset their costlier,

stricter bank facilities with more favorable receivables cycle from their large customers.

Table 10 About Here

8 Customer Financial Condition

We study the financial conditions of major customers to better understand how cus-

tomer concentration may trigger worse credit terms for supplier firms. Prior evidence

suggests that when facing financially-distressed customers, firms tend to grant deep con-

cessions to maintain product market relationships (Wilner (2000)). One could expect

a firm whose large customers are in worse financial shape to receive worse loan terms,

including higher spreads, more covenants, and shorter maturity.

8.1 Measuring Large Customers’ Financial Conditions

We design two measures of large customers’ financial conditions. One measure is sim-

ply based on customers’ leverage, the other is based on customers’ probability of default.

When a customer has a higher level of indebtedness, it may have difficulty maintaining

existing purchase schedules or paying suppliers on time. These problems will be more

relevant for suppliers when that customer is large. Accordingly, we construct a measure

of customer financial condition that aggregates customers’ leverage for each supplier.

CustomerLeverage is defined as:

CustomerLeveragei =

ni∑j=1

%Salesij × Leveragej,

where Leveragej is the leverage of customer j. Higher values of CustomerLeverage indi-

cate that the firm’s large customers are more indebted.

Beyond assessing leverage, we directly gauge customers’ financial distress using their

probability of default based on Merton’s (1974) model. We follow Bharath and Shumway

(2008) and employ a reduced form model to calculate customers’ distance to default (DD).

Higher distance to default indicates that a firm is less likely to default. For each supplier,

we measure the average default likelihood of its major customers using its percentage

31

sales to these customers and refer to this variable as CustomerDefault. The variable is

defined as follows:

CustomerDefaulti =

ni∑j=1

%Salesij × (1−DDj),

where DDj is the predicted distance to default for customer j, scaled by 100 so it is within

the range of 0 to 1. Similar to CustomerLeverage, higher values of CustomerDefault in-

dicate that the firm faces a more financially-distressed customer base.

8.2 Results

We report the impact of customers’ finances on firms’ borrowing terms in Table 11,

where we estimate models that resemble those of our baseline tests.14 Columns (1) and

(2) of Table 11 show the relation between loan spreads and customer financial condition

measures CustomerLeverage and CustomerDefault. Both measures attract significant,

positive coefficients, suggesting that firms facing large, financially-distressed customers

experience higher interest spreads on their new loans. Columns (3) and (4) show the

relation between the number of restrictive covenants and customers’ financial condition.

The results suggest that banks impose more restrictive covenants for firms with large,

more financially-troubled customers. Finally, despite weaker statistical significance, re-

sults in columns (5) and (6) suggest that firms with more distressed customers are offered

shorter maturities on their loans. Notably, the economic magnitudes of the coefficients

in Table 11 imply that the negative effects of customer concentration on loan terms are

even larger than those of our baseline tests in Tables 2 through 4.

Table 11 About Here

The results of this section are important in confirming the logic behind our base find-

ings. A deeper relation with a small set of large customers has negative consequences for

a firm’s relations with its creditors. And more so the more financially unhealthy those

customers are. In the next two sections we discuss how fears associated with exposure

and contagion may explain banks’ decision to offer worse credit terms when firms sell

their output to larger customers.

14Results on the duration and depth of bank relationships are omitted for brevity, but are readilyavailable.

32

9 Customer Concentration and Bank Exposure

As a last step of our study, we examine the relation between firm credit risk and

customer concentration. We do this to substantiate the argument that firms with higher

customer concentration face more adverse credit terms because of their deteriorated cred-

itworthiness. Banks are highly regulated intermediaries and the exposure to loan risk

bears tremendous costs for them. If customer concentration increases the likelihood of

loan failure, it should be associated with costlier, stricter bank loan terms. Yet, these

results could be ameliorated if insurance contracts (such as CDS) are available.

In this section, we match our customer and loan datasets with the Sudheer Chava’s

bankruptcy database (updated from Chava and Jarrow (2004)) to test the idea that cus-

tomer concentration is related to loan failures. We also match our sample with data

from CDS market to assess if the costs banks impose on firms with higher customer

concentration are affected by the degree to which creditors can resort to CDS insur-

ance. We also examine the syndication structure of our sample. Finally, we provide a

back-of-the-envelope calculation of the aggregate credit losses associated with customer

concentration, showing how they evolve over time.

9.1 The Likelihood of Loan Failures

The variable LoanFailure indicates whether the company files for bankruptcy before a

loan matures in our dataset. To test the idea that a more concentrated customer base is

associated with higher likelihood of supplier loan failure, we estimate the following logit

regression model:

LoanFailurei,k,t = β0 + β1CustomerConcentrationi,t + β2FirmCharacteristici,t

+ β3MacroV ariablet + β4LoanCharacteristick,t +∑g

Industryg +∑h

Bankh + εi,k,t,

(18)

where CustomerConcentration includes CustomerSales, CustomerSize, CustomerLever-

age, and CustomerDefault. The model features the same sets of controls used in our

baseline regressions.

Table 12 reports results for CustomerSales and CustomerSize. Both customer con-

centration variables have positive and significant coefficients, suggesting that firms with

more concentrated customer bases are more likely to fail during the existence of a loan

33

60

80

100

120

140

160

180

200

220

1 2 3 4 5 6 7 8 9 10

Rank for Customer Concentration

Loan Spreads with Customer Concentration

Spreads for firms without CDS Spreads for firms with CDS

Figure 8. Loan spreads for firms with and without CDS trading. This figure shows therelation between loan spreads and customer concentration for firms that have and have no CDS tradingduring the sample period. The horizontal axis shows the ranks for customer concentration, measured byCustomerSales.

contract, exposing their banks to higher loan default risk. To help interpret our estimates,

note that the coefficient for CustomerSize in column (6) suggests that a one-standard-

deviation increase in customer concentration is associated with a 2-percentage points

increase in loan failure rates. This is a sizable effect, especially in comparison to the av-

erage loan failure rate of 6.1%. In unreported tables, we further show that firms are more

likely to fail on their loans as their customers’ financial condition deteriorates (proxied

by CustomerLeverage and CustomerDefault).

Table 12 About Here

9.2 The Impact of CDS Insurance on Loan Markups

The results from Table 12 show that firms with more concentrated customer bases

are more likely to default on their loans. This begs the question: With the emergence

of CDS markets, can banks hedge away firm default risk using CDSs? To answer this

question, we collect CDS information from Datastream and classify firms into two groups

depending on whether they have CDS traded on their debt. We then examine whether

banks are less concerned about default risk caused by customer concentration when they

can hedge their credit exposures using CDSs. Figure 8 provides evidence supporting this

34

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8 9 10

Rank of Customer Concentration

Share of Lead Arranger (%)

Figure 9. The relation of customer concentration with lead arranger shares. This figureshows the relation between customer concentration and the percentage shares of the syndicated loanstaken by lead arrangers. The horizontal axis shows the ranks for customer concentration, measured byCustomerSales.

conjecture. For firms that have traded CDS contracts, loan spreads do not vary much

with customer concentration. In contrast, for firms that do not have CDSs, loan spreads

increase as firms face higher levels of customer concentration.

9.3 Loan Syndication Structure and Customer Concentration

A potential concern for focusing on the syndicated loan market is that firms facing

concentrated customer bases may self-select into this market in order to avoid intense bank

monitoring. This could potentially cause biases in our estimation due to the following

scenario: firms with higher customer concentration could prefer a syndicated structure

where the lead bank takes a smaller percentage of the facility. In such a scenario, the lead

bank may impose higher spreads simply as a way to compensate for lower monitoring

effort. However, we do not observe any correlation between customer concentration and

lead bank shares; the correlation coefficient is less than 1%. Figure 9 corroborates this

claim. The lack of a meaningful relation between customer concentration and lead bank

shares casts doubt on a self-selection story.15

15We further control for lead bank shares in our analyses. Our results are robust to this additionalcontrol.

35

0

5

10

15

20

25

30

35

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Estimated Loss from Default

Driven by Customer Concentration (Billion $)

(Mean) Loss from Default (p5) Loss from Default (p95) Loss from Default

Figure 10. Loss from default driven by customer concentration over time. This figure showsthe estimated loss from loan failures over time in billion dollars. The solid line shows the estimated lossdriven by the average concentration of borrowers’ customer bases. The dotted lines show the 5% and95% confidence interval of such losses. Customer concentration is measured by CustomerSales.

9.4 The Impact of Customer Concentration on Aggregate Loan

Losses

Finally, we investigate if the additional default hazard driven by customer concentra-

tion generates significant monetary losses. To provide a rough estimate of such losses,

we take the product of four pieces of information: 1) the average loan face value, 2) the

average loss given default (see Altman and Suggitt (2000)), 3) the additional default rate

driven by customer concentration (from in Table 12), and 4) the average customer concen-

tration for our sample firms. Our back-of-the-envelope calculation suggests that customer

concentration leads to around $2 million loss per loan.16 Figure 10 depicts the evolution

of such losses over time. As customer concentration increases over the past two decades, it

leads to increasing losses from loan failures, ranging from around $10 billion per year in the

early-1990s to $20 billion per year in the mid-2000s. Our loan failure analysis helps fur-

ther explain why lenders impose costlier, stricter terms on loans offered to suppliers with

concentrated customer bases, even though those suppliers themselves are more profitable.

16To be precise, we arrive at this number by multiplying average customer concentration 30%, themarginal effect from the loan failure estimate 0.09 (generated by estimates in column (3) in Table 12),the loss given default 20% (from Altman and Suggitt (2000)), and the average loan face value $312 million.

36

10 Concluding Remarks

Recent literature argues that customer concentration can increase firms’ operational

efficiency, leading to significant increases in profitability. While recognizing those gains,

we examine the potential costs associated with deeper relationships between suppliers

and customers.

Our study looks at how credit markets respond to customer-base concentration. We

do so examining the credit terms offered by banks. Specifically, using detailed informa-

tion on bank loans, we study how customer-base concentration affects a firm’s borrowing

terms, including loan interest spread, maturity, and the number of restrictive covenants.

We further gauge the impact of customer profile on the length and intensity of suppliers’

relations with their banks. Our tests show that higher levels of customer concentra-

tion increase the interest rates and the number of restrictive covenants on a firm’s bank

loans. Customer concentration also reduces loan maturities and the duration and depth

of banking relationships.

Exploring the cross-sectional variation in customer characteristics, we further show

that when firms have larger, more financially-distressed customers, they face even worse

loan terms. We also show direct evidence that a more concentrated customer base af-

fects a firm’s credit risk. In particular, suppliers with higher customer concentration and

more financially-troubled customers are more likely to default on their loans. This result

corroborates the argument that while customer concentration may make supply chain re-

lations more efficient and profitable, a deeper exposure to a small set of large customers

has negative consequences for the firm’s relations with its creditors.

Understanding the trade-offs associated with customer concentration is important

as the modern business-to-business economy experiences increasing levels of customer

concentration. Integration along the supply chain is fraught with contracting prob-

lems between customers and suppliers. These problems allow powerful customers to

set trade terms to their own advantage, while leaving suppliers “held-up” due to costly,

relationship-specific investments they may be forced to make. Our paper sheds light on

one additional cost induced by this contracting problem. This phenomenon is reflected

in the increased borrowing costs and tightened debt contract terms required by banks.

Future research would benefit from understanding how bank contracting can complement

or substitute for supply chain contracting. Overall, our analyses suggest that the cost re-

lated to customer concentration deserves better understanding and may give new insights

into important research topics, such as the issue of optimal concentration and integration

along the productive supply chain and the limits of the modern corporation.

37

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40

Appendix A Variable Definitions

LoanSpread : All-in-Drawn loan spread over LIBOR

LoanCovenants : Total number of covenants on the loan package

LoanMaturity : Loan maturity in months

FutureLoans : The total dollar amount of loans issued by the same bank in the future,scaled by the current loan amount

FutureDuration: The number of months until the last loan extended by the same bank

CustomerSales : Total percentage sales to all major customers

CustomerSize: Total size of all major customers, weighted by the firm’s percentage salesto customer

CustomerLeverage: Leverage of major customers, weighted by the firm’s percentagesales to customer

CustomerDefault : The inverse distance to default (1-distance to default) of major cus-tomers predicted by Bharath and Shumway (2008)’s distance-to-default model,weighted by the firm’s percentage sales to customer

Size: Log of total assets (AT)

Age: Years after a firm’s first appearance in Compustat database

Profitability : Operating income (OIBDP)/total assets

Tangibility : Property, plant, and equipment (PPENT)/total assets

M/B : (Stock price (PRCC)×shares outstanding (CSHO) + total assets – book equity(CEQ))/total assets

Leverage: (Long-term debt (DLTT) + current debt (DLC))/total assets

Ratings : A dummy variable that equals 1 if the firm has a bond rating, 0 otherwise

CreditSpread : Yield spread between average AAA-rated corporate bonds and averageBBB-rated corporate bonds

TermSpread : Yield spread between 10-year Treasury bond and 3-month Treasury bills

GDPGrowth: Quarterly average of GDP growth rate of the year

LoanSize: Log of total loan amount (in dollars)

LoanType: A dummy variable that equals 1 if the loan is a term loan, 0 if loan is arevolver

41

Appendix B

Table A1Loan Spreads and Alternative Measures of Customer ConcentrationThis table shows the relation between loan spreads and alternative measures of customer concentration.The dependent variable is All-in-Drawn loan spread (LoanSpread). All regressions use industry-fixedeffects and bank-fixed effects. Industry is classified as 2-digit SIC industry and the lending banks areclassified by their ultimate parents. Robust-clustered t-statistics are shown in parentheses. Customer-Sales: the sum of the percentage sales to the set of customers a firm reports as “major customers”.Columns (4) through (6) show the regression results for CustomerSize: the total size of all major cus-tomers, weighted by the firm’s percentage sales to these customers. CustomerHHI is measured as thesum of squared percentage sales to major customers; CustomerMax is the highest percentage sales tomajor customers; CustomerCount is the total number of a firm’s major customers; and CustomerGiniis the Gini coefficient of a firm’s sales to its customers.

CustomerConcentration is:

Dep. Var.: LoanSpread (1) (2) (3) (4) (5) (6)Measure of Concentration: CustomerSales CustomerSize CustomerHHI CustomerGini CustomerMax CustomerCount

CustomerConcentration 40.89*** 5.28*** 72.99*** 7.38** 53.10* 6.55*(3.32) (3.72) (5.12) (2.14) (1.83) (1.96)

Size -17.69*** -17.97*** -17.60*** -17.42*** -18.18*** -17.61***(-5.40) (-5.43) (-6.68) (-6.01) (-6.93) (-6.39)

Age -0.60*** -0.61*** -0.78*** -0.82*** -0.85*** -0.82***(-2.88) (-3.07) (-3.77) (-4.04) (-4.33) (-4.05)

Profitability -246.94*** -273.44*** -257.76*** -260.61*** -263.00*** -261.58***(-3.89) (-4.23) (-3.96) (-3.99) (-3.71) (-3.87)

Tangibility -27.66 -22.23 -8.96 -3.84 -0.02 -3.34(-0.77) (-0.62) (-0.42) (-0.17) (-0.00) (-0.14)

M/B -3.75 -1.83 -4.99 -4.96 -4.93 -4.96(-0.90) (-0.40) (-1.55) (-1.56) (-1.44) (-1.53)

Leverage 128.86*** 131.47*** 122.48*** 120.42*** 117.93*** 120.01***(8.49) (8.21) (10.84) (10.68) (9.90) (10.27)

Ratings 35.74*** 35.24*** 41.52*** 41.93*** 42.92*** 42.36***(4.68) (4.62) (6.96) (6.95) (7.77) (7.17)

CreditSpread 81.53*** 76.32*** 88.35*** 89.42*** 88.03*** 89.36***(5.49) (4.97) (6.43) (6.49) (6.35) (6.45)

TermSpread 3.40 4.42* 2.49 2.66 2.65 2.64(1.31) (1.69) (1.27) (1.36) (1.33) (1.33)

GDPGrowth -4.79* -5.18** -3.96 -3.67 -4.14 -3.74(-1.96) (-2.08) (-1.65) (-1.55) (-1.68) (-1.57)

LoanMaturity 5.91 6.48 8.18* 7.99* 8.01* 8.02*(1.50) (1.61) (1.88) (1.83) (1.78) (1.83)

LoanSize -15.77*** -16.02*** -16.04*** -15.67*** -15.74*** -15.71***(-5.66) (-5.69) (-6.65) (-6.45) (-6.41) (-6.45)

LoanType 45.78*** 44.87*** 48.80*** 49.04*** 48.93*** 49.05***(6.86) (6.76) (7.07) (7.06) (7.05) (7.04)

Observations 2,983 2,983 2,983 2,983 2,983 2,983R-squared 0.61 0.62 0.57 0.57 0.57 0.57

*** p-value<0.01, ** p-value<0.05, * p-value<0.10

42

Table 1Summary StatisticsThis table shows the summary statistics of the firm characteristics variables, customer variables, and loanterm variables. The sample spans the 1985–2010 window, featuring manufacturing firms (SIC 2000–3999)in Compustat that have available firm characteristics and loan term variables. All continuous variablesexcept leverage and the number of covenants (count) are winsorized within 5 and 95 percentile. Leverageis restricted to the 0–1 range.

Variables Mean Std. Dev. p25 p50 p75 #Obs

LoanSpread 179.16 109.33 75 175 275 3,375

LoanMaturity 46 22 29 48 60 3,384

LoanCovenants 1.80 1.65 0 2 3 3,454

FutureLoans 4.43 5.41 1.08 2.39 5.67 1,410

FutureDuration 57.22 46.29 24 44 74 1,410

CustomerSales 0.30 0.20 0.15 0.23 0.39 3,454

CustomerSize 2.72 1.86 1.29 2.05 3.48 3,358

CustomerLeverage 0.09 0.08 0.03 0.06 0.11 3,358

CustomerDefault 0.30 0.26 0.12 0.20 0.38 3,358

Size 6.38 1.62 5.13 6.41 7.61 3,454

Age 18.41 16.45 5 12 31 3,454

Profitability 0.12 0.11 0.08 0.13 0.18 3,436

Tangibility 0.26 0.15 0.15 0.24 0.37 3,451

M/B 1.71 1.06 1.12 1.38 1.91 3,454

Leverage 0.33 0.21 0.18 0.31 0.45 3,454

Ratings 0.48 0.50 0 0 1 3,454

LoanType 0.28 0.45 0 0 1 3,454

43

Table 2Loan Spreads and Customer ConcentrationThis table shows the relation between loan spreads and customer concentration. The dependent variableis All-in-Drawn loan spread (LoanSpread). All regressions use industry-fixed effects and bank-fixedeffects. Industry is classified as 2-digit SIC industry and the lending banks are classified by their ultimateparents. Robust-clustered t-statistics are shown in parentheses. Columns (1) through (3) show theregression results for CustomerSales: the sum of the percentage sales to the set of customers a firmreports as “major customers”. Columns (4) through (6) show the regression results for CustomerSize:the total size of all major customers, weighted by the firm’s percentage sales to these customers.

Dep. Var.: LoanSpread CustomerConcentration is CustomerSales CustomerConcentration is CustomerSize

(1) (2) (3) (4) (5) (6)

CustomerConcentration 52.56*** 47.14** 40.89*** 7.05*** 5.90** 5.28***(3.15) (2.67) (3.32) (3.78) (2.84) (3.72)

Size -27.58*** -28.28*** -17.69*** -28.11*** -28.64*** -17.97***(-10.44) (-9.60) (-5.40) (-10.36) (-9.99) (-5.43)

Age -0.78*** -0.96*** -0.60*** -0.80*** -0.98*** -0.61***(-3.07) (-4.30) (-2.88) (-3.29) (-4.56) (-3.07)

Profitability -272.89*** -260.23*** -246.94*** -292.40*** -284.38*** -273.44***(-3.20) (-3.62) (-3.89) (-3.53) (-4.12) (-4.23)

Tangibility -20.65 -4.89 -27.66 -16.13 -1.90 -22.23(-0.79) (-0.24) (-0.77) (-0.63) (-0.09) (-0.62)

M/B -11.07*** -6.58* -3.75 -9.23** -4.60 -1.83(-3.09) (-1.88) (-0.90) (-2.20) (-1.07) (-0.40)

Leverage 135.13*** 146.97*** 128.86*** 138.60*** 149.30*** 131.47***(7.92) (10.58) (8.49) (7.53) (10.01) (8.21)

Ratings 38.08*** 37.98*** 35.74*** 37.94*** 38.06*** 35.24***(5.60) (5.80) (4.68) (5.55) (5.83) (4.62)

CreditSpread 87.21*** 81.53*** 82.11*** 76.32***(5.37) (5.49) (5.17) (4.97)

TermSpread 1.74 3.40 2.98 4.42*(0.75) (1.31) (1.33) (1.69)

GDPGrowth -3.96 -4.79* -4.18 -5.18**(-1.47) (-1.96) (-1.60) (-2.08)

LoanMaturity 5.91 6.48(1.50) (1.61)

LoanSize -15.77*** -16.02***(-5.66) (-5.69)

LoanType 45.78*** 44.87***(6.86) (6.76)

Observations 3,045 3,045 2,983 2,965 2,965 2,903R-squared 0.45 0.52 0.61 0.45 0.52 0.62

*** p-value<0.01, ** p-value<0.05, * p-value<0.10

44

Table 3Number of Restrictive Covenants and Customer ConcentrationThis table shows the relation between the number of restrictive covenants written on bank loans andcustomer concentration. The dependent variable is the number of loan covenants (LoanCovenants). Allregressions use industry-fixed effects and bank-fixed effects. Industry is classified as 2-digit SIC industryand the lending banks are classified by their ultimate parents. Robust-clustered t-statistics are shownin parentheses. Columns (1) through (3) show the regression results for CustomerSales. Columns (4)through (6) show the regression results for CustomerSize.

Dep. Var.: LoanCovenants CustomerConcentration is CustomerSales CustomerConcentration is CustomerSize

(1) (2) (3) (4) (5) (6)

CustomerConcentration 1.04*** 1.01*** 0.95*** 0.12*** 0.11*** 0.09***(4.85) (4.81) (2.75) (4.98) (4.89) (3.31)

Size -0.24*** -0.23*** -0.22*** -0.24*** -0.24*** -0.18***(-4.20) (-4.38) (-4.08) (-4.17) (-4.35) (-6.31)

Age -0.00 -0.00 0.00 -0.00 -0.00 0.01**(-1.02) (-1.20) (1.13) (-1.14) (-1.31) (2.58)

Profitability 0.79 0.83 1.59*** 0.66 0.73 1.57**(1.15) (1.28) (3.24) (1.07) (1.18) (2.54)

Tangibility -0.53 -0.39 -0.45 -0.58 -0.46 -0.18(-1.11) (-0.82) (-0.71) (-1.15) (-0.94) (-0.37)

M/B -0.15*** -0.13*** -0.06* -0.13** -0.12** -0.04(-3.04) (-3.06) (-1.66) (-2.41) (-2.59) (-0.72)

Leverage 0.73** 0.72** -0.20 0.74** 0.73** -0.17(2.47) (2.70) (-0.79) (2.66) (2.84) (-0.88)

Ratings 0.20** 0.19** 0.16 0.20** 0.19** 0.08(2.32) (2.18) (1.37) (2.23) (2.14) (1.09)

CreditSpread 1.46*** 1.01*** 1.38*** 1.05***(4.03) (4.09) (3.89) (3.55)

TermSpread -0.11* -0.11* -0.10* -0.11*(-1.90) (-1.86) (-1.75) (-2.06)

GDPGrowth 0.21*** 0.23*** 0.21*** 0.23***(4.56) (5.25) (4.76) (5.38)

LoanSpread 0.68*** 0.71***(11.81) (25.99)

LoanMaturity 0.21*** 0.27***(3.82) (5.49)

LoanSize 0.13*** 0.12**(3.54) (2.17)

LoanType 0.06 0.06(1.52) (1.28)

Observations 3,055 3,055 2,983 2,975 2,975 2,903R-squared 0.17 0.19 0.33 0.17 0.19 0.28

*** p-value<0.01, ** p-value<0.05, * p-value<0.10

45

Table 4Loan Maturity and Customer ConcentrationThis table shows the relation between the bank loan maturity (in months) and customer concentration.The dependent variable is loan maturity (LoanMaturity). Only term loans are considered for this test.All regressions use industry-fixed effects and bank-fixed effects. Industry is classified as 2-digit SICindustry and the lending banks are classified by their ultimate parents. Robust-clustered t-statistics areshown in parentheses. Columns (1) through (3) show the regression results for CustomerSales. Columns(4) through (6) show the regression results for CustomerSize.

Dep. Var.: LoanMaturity CustomerConcentration is CustomerSales CustomerConcentration is CustomerSize

(1) (2) (3) (4) (5) (6)

CustomerConcentration -5.83* -5.70* -8.33** -0.77 -0.68 -1.04**

(-1.83) (-2.03) (-2.27) (-1.71) (-1.71) (-2.12)

Size -0.33 -0.28 -2.30 -0.38 -0.34 -2.35

(-0.26) (-0.22) (-1.73) (-0.29) (-0.25) (-1.74)

Age -0.24*** -0.20** -0.16*** -0.23** -0.19** -0.14***

(-3.16) (-2.86) (-3.10) (-2.91) (-2.61) (-2.93)

Profitability 44.20*** 45.07*** 49.95*** 42.79*** 44.72*** 49.80***

(4.63) (4.27) (4.40) (4.05) (3.90) (4.06)

Tangibility -18.46** -18.27** -19.26** -18.61** -18.24** -19.82**

(-2.44) (-2.48) (-2.61) (-2.37) (-2.37) (-2.57)

M/B -2.29 -2.91* -3.19** -2.49* -3.17** -3.43**

(-1.61) (-2.01) (-2.34) (-1.76) (-2.23) (-2.53)

Leverage 12.07 9.95 4.04 12.40 10.46 4.07

(1.68) (1.38) (0.55) (1.60) (1.34) (0.52)

Ratings 5.09 4.50 2.78 4.79 4.14 2.20

(1.57) (1.43) (0.90) (1.49) (1.30) (0.75)

CreditSpread -2.63 -5.57 -1.77 -4.75

(-0.49) (-0.94) (-0.31) (-0.76)

TermSpread -1.40 -0.91 -1.59* -1.10

(-1.71) (-1.17) (-1.85) (-1.37)

GDPGrowth 2.06** 2.26** 2.14** 2.38**

(2.21) (2.46) (2.22) (2.55)

LoanSpread 4.01 4.70**

(1.74) (2.28)

LoanSize 4.25*** 4.49***

(3.21) (3.43)

Observations 903 903 894 879 879 870

R-squared 0.23 0.27 0.29 0.23 0.27 0.30

*** p-value<0.01, ** p-value<0.05, * p-value<0.10

46

Table 5Future Bank Relationships and Customer ConcentrationThis table shows the relation between future bank relationships to a firm and the firm’s customer concen-tration. The dependent variable is future bank relationships, as measured by the total amount of lendingextended by the same bank scaled by the current amount of lending (FutureLoans) and the durationof banking relationships in the future (FutureDuration). All regressions use industry-fixed effects andbank-fixed effects. Industry is classified as 2-digit SIC industry and lending banks are classified by theirultimate parents. Robust-clustered t-statistics are shown in parentheses. Columns (1) and (2) show theregression results for CustomerSales. Columns (3) and (4) show the regression results for CustomerSize.

CustomerConcentration is CustomerSales CustomerConcentration is CustomerSize

(1) (2) (3) (4)Dep. Var.: FutureLoans FutureDuration FutureLoans FutureDuration

CustomerConcentration -1.81** -38.88*** -0.23** -4.90***(-2.44) (-4.96) (-2.63) (-5.63)

Size 0.10 -0.76 0.05 -0.88(0.34) (-0.33) (0.18) (-0.37)

Age -0.02 -0.27** -0.02 -0.26**(-1.13) (-2.67) (-0.90) (-2.54)

Profitability 6.43*** 66.46*** 6.02*** 63.63***(3.20) (3.79) (2.94) (3.62)

Tangibility 2.72 26.68*** 2.85 28.42***(1.57) (3.32) (1.59) (3.36)

M/B 0.87*** 2.29 0.84*** 2.18(3.61) (1.33) (3.58) (1.41)

Leverage -1.25 -1.72 -1.49 -2.81(-1.25) (-0.19) (-1.47) (-0.31)

Ratings 0.32 11.67** 0.27 11.79**(0.60) (2.81) (0.49) (2.79)

CreditSpread -0.52 -24.60* -0.19 -21.48(-0.33) (-1.90) (-0.12) (-1.57)

TermSpread 0.39** 3.22** 0.35* 3.18**(2.15) (2.34) (2.03) (2.31)

GDPGrowth -0.17 -1.06 -0.17 -0.98(-0.94) (-1.24) (-0.99) (-1.12)

Observations 1,271 1,271 1,239 1,239R-squared 0.16 0.13 0.15 0.13

*** p-value<0.01, ** p-value<0.05, * p-value<0.10

47

Table 6First-Stage Results for IV EstimationsThis table shows the first-stage regression results of a firm’s customer concentration (CustomerSales) onthe average customers’ industry acquisition activity (CustomerM&A). All regressions use industry-year-fixed effects and bank-fixed effects. Robust-clustered t-statistics are shown in parentheses.

(1) (2) (3) (4) (5)Dep. Var. in the Second Stage: LoanSpread LoanCovenants LoanMaturity FutureLoans FutureDuration

CustomerM&A 5.82*** 5.63*** 6.61*** 4.30*** 4.30***(3.62) (3.61) (3.04) (4.62) (4.62)

Size -0.01 -0.01 -0.01 -0.02 -0.02(-0.97) (-0.68) (-0.37) (-1.67) (-1.67)

Age -0.00* -0.00 -0.00 -0.00** -0.00**(-1.77) (-1.60) (-1.19) (-2.45) (-2.45)

Profitability 0.06 0.09 0.04 -0.13 -0.13(1.08) (1.53) (0.21) (-0.86) (-0.86)

Tangibility -0.05 -0.04 -0.08 0.00 0.00(-0.54) (-0.45) (-0.73) (0.04) (0.04)

M/B -0.01 -0.01 -0.01 -0.00 -0.00(-1.71) (-1.54) (-0.38) (-0.22) (-0.22)

Leverage -0.07*** -0.09*** -0.15* -0.10*** -0.10***(-3.78) (-3.36) (-2.07) (-2.45) (-2.45)

Ratings 0.01 0.01 0.02 0.03 0.03(0.39) (0.27) (0.46) (1.72) (1.72)

LoanSpread 0.02 0.03(1.28) (1.10)

LoanMaturity 0.01 0.01(0.99) (0.78)

LoanSize 0.01 0.01 0.01(1.05) (1.52) (0.36)

LoanType -0.01 -0.01(-0.94) (-1.70)

Observations 1,957 1,957 570 871 871R-squared 0.40 0.40 0.59 0.21 0.21

First Stage F -test 13.09 13.05 9.25 21.34 21.34p-value 0.00 0.00 0.01 0.00 0.00Kleibergen-Paap LM Stat 5.97 5.67 6.7 7.33 7.33p-value 0.01 0.02 0.01 0.00 0.00

*** p-value<0.01, ** p-value<0.05, * p-value<0.10

48

Table 7Second-Stage Results for Customer ConcentrationThis table shows the second-stage results of the relation between a firm’s customer concentration and bothits borrowing terms and future bank relationships. Customer concentration measures are instrumentedwith the acquisition levels in the customer’s industries (CustomerM&A). The dependent variables includeloan spreads (LoanSpread), number of loan covenants (LoanCovenants), loan maturity (LoanMaturity),and future bank relationships as measured by the future amount of loans extended by the same bank,scaled by the current loan amount (FutureLoans) and the duration of banking relationships in the future(FutureDuration). CustomerConcentration is the projected customer concentration from the first-stageregressions. Customer concentration is measured by CustomerSales. All regressions use industry-year-fixed effects and bank-fixed effects. Industry is classified as 2-digit SIC industry and lending banks areclassified by their ultimate parents. Robust-clustered z -statistics are shown in parentheses.

(1) (2) (3) (4) (5)Dep. Var.: LoanSpread LoanCovenants LoanMaturity FutureLoans FutureDuration

CustomerConcentration 235.82*** 4.21** -57.08*** -20.63** -83.92***(2.77) (2.41) (-3.05) (-2.18) (-3.64)

Size -17.13*** -0.06 -2.71* -0.04 2.60(-3.74) (-0.99) (-1.81) (-0.18) (1.42)

Age -0.59** 0.01** -0.24** -0.03 -0.17(-2.05) (2.15) (-1.97) (-0.79) (-1.61)

Profitability -259.51*** 1.22* 41.66*** 4.36 19.49(-8.28) (1.77) (3.59) (0.92) (1.27)

Tangibility -21.64 -0.13 -8.35 -0.35 -3.51(-0.89) (-0.39) (-0.89) (-0.14) (-0.29)

M/B -4.28* -0.04 -2.49** 0.52** -0.45(-1.94) (-0.87) (-2.16) (1.99) (-0.24)

Leverage 154.93*** -0.09 -13.16* -1.84 -6.30(12.68) (-0.25) (-1.66) (-0.80) (-0.61)

Ratings 36.25*** -0.05 14.49*** 0.56 8.95(3.64) (-0.38) (2.58) (0.68) (1.57)

LoanSpread 0.55*** 3.16(8.50) (1.11)

LoanMaturity 6.40 0.25***(1.31) (3.75)

LoanSize -15.12*** -0.00 3.06**(-4.80) (-0.07) (1.99)

LoanType 34.92*** 0.06(9.47) (1.00)

Observations 1,957 1,957 570 871 871R-squared 0.64 0.40 0.41 0.25 0.61

*** p-value<0.01, ** p-value<0.05, * p-value<0.10

49

Table 8Customer Concentration and Loan Spreads following Deregulation EventsThis table shows how customer concentration and loan spreads change following customer industries’deregulation events. The dependent variable in columns (1) and (2) is customer concentration, measuredby CustomerSales. The dependent variable in columns (3) and (4) is loan spreads (LoanSpread). Treatedis a dummy variable indicating whether a firm’s customer industry experienced a Federally-mandatedAct or Order affecting its competitive environment. Post is a dummy variable covering the periodfollowing the regulatory event. All regressions use industry-fixed effects and bank-fixed effects. Industryis classified as 3-digit SIC industry and lending banks are classified by their ultimate parents. Robust-clustered z -statistics are shown in parentheses.

Dep. Var.: CustomerConcentration LoanSpread

(1) (2) (3) (4)

Treated 0.00 0.00 -9.98 -7.10(0.17) (0.20) (-1.97) (-1.37)

Post -0.03*** -0.03*** 5.08 6.68**(-6.17) (-4.35) (1.14) (3.01)

Treated×Post 0.03*** 0.04*** 21.39** 12.55***(4.09) (6.01) (3.72) (4.60)

Size -0.05** -0.07*** -6.48 -16.57(-2.97) (-6.19) (-0.43) (-0.75)

Age -0.00 0.00 -0.54 -0.99(-0.29) (0.04) (-0.81) (-1.74)

Profitability -0.14*** -0.11* 16.45 22.12(-5.12) (-2.48) (0.22) (0.26)

Tangibility 0.30** 0.26*** -112.16 -136.75(3.76) (4.29) (-0.84) (-1.06)

M/B 0.02 0.01 -17.01** -9.57***(0.87) (0.52) (-3.79) (-4.36)

Leverage -0.10* -0.06 173.40** 136.35(-2.06) (-1.80) (2.60) (1.86)

Ratings -0.03 -0.04 -31.72 -13.84(-0.41) (-0.57) (-1.32) (-0.66)

CreditSpread -0.31*** -0.29*** 83.59 93.50*(-34.78) (-10.16) (1.68) (2.07)

TermSpread -0.02 -0.03 -8.49 -7.08(-0.92) (-1.64) (-0.86) (-1.22)

GDPGrowth -0.01 -0.01 -2.66 -4.76(-0.99) (-1.07) (-0.28) (-0.46)

LoanMaturity -0.00*** -0.05(-5.28) (-0.07)

LoanSize 0.04 -0.55(1.94) (-0.05)

LoanType 0.02 55.59**(1.01) (3.80)

Observations 216 211 212 207R-squared 0.77 0.78 0.69 0.75

*** p-value<0.01, ** p-value<0.05, * p-value<0.10

50

Table 9Loan Terms and Relationship-Specific InvestmentsThis table shows subsample results of the relation between loan terms and customer concentration, todemonstrate how this relation can be affected by firms’ relationship-specific investments. The dependentvariable are loan spread, covenants, and maturity. Customer concentration is measured by Customer-Sales. All regressions use industry-fixed effects and bank-fixed effects. Industry is classified as 2-digit SICindustry and the lending banks are classified by their ultimate parents. Robust-clustered t-statistics areshown in parentheses. Columns (1) through (3) show the regression results for the subsample contain-ing industries with high relationship-specific investments. Columns (4) through (6) show the regressionresults for for the subsample containing industries with low relationship-specific investments.

High-RSI Industries Low-RSI Industries

(1) (2) (3) (4) (5) (6)Dep. Var.: LoanSpread LoanCovenants LoanMaturity LoanSpread LoanCovenants LoanMaturity

CustomerConcentration 65.55*** 0.56 -9.56** 12.83 0.72 -4.01(5.46) (1.53) (-3.48) (0.72) (1.84) (-0.93)

Size -19.40*** -0.11 -0.05 -17.23*** -0.22* -2.27(-5.20) (-2.06) (-0.05) (-7.86) (-2.02) (-0.84)

Age -0.37 0.00 -0.14** -1.02** 0.01*** -0.04(-1.69) (1.06) (-3.67) (-2.86) (4.06) (-0.31)

Profitability -335.01 1.72 38.06* -174.97*** 1.74** 59.55(-1.79) (1.85) (2.26) (-6.42) (3.12) (2.12)

Tangibility 58.13** -0.51 -5.08 -24.89 0.29 -35.77**(3.03) (-0.52) (-1.28) (-0.86) (0.46) (-3.47)

M/B -6.40 -0.03 -1.59 -1.64 -0.03 -3.51(-1.15) (-0.73) (-1.38) (-0.53) (-0.37) (-1.73)

Leverage 134.51*** -0.28 6.19 132.03*** -0.26 7.35(6.86) (-1.15) (0.37) (15.50) (-0.70) (1.02)

Ratings 40.19*** -0.03 3.47 49.55*** 0.28* 2.55(9.38) (-0.24) (0.75) (5.27) (2.01) (1.25)

CreditSpread 59.61** 0.44 -1.45 96.52** 1.13** -0.84(3.95) (1.46) (-0.30) (3.55) (2.86) (-0.10)

TermSpread 3.30 -0.05 0.21 0.61 -0.26*** -1.61(1.67) (-0.96) (0.68) (0.43) (-7.30) (-1.64)

GDPGrowth -6.15* 0.13* 3.38** -0.20 0.22** 2.48(-2.62) (2.57) (3.18) (-0.07) (3.56) (1.90)

LoanSpread 0.69*** 1.58 0.74*** 1.10(12.36) (0.35) (11.80) (0.42)

LoanMaturity 1.62 0.33** 4.59 0.22***(0.26) (3.13) (0.87) (4.78)

LoanSize -17.24** 0.12 3.33* -15.61*** 0.05 2.81(-4.49) (1.67) (2.50) (-5.67) (0.54) (1.59)

LoanType 56.30*** 0.08 46.98*** 0.08(5.43) (1.32) (8.87) (1.53)

Observations 1,141 1,141 390 1,101 1,101 315R-squared 0.55 0.28 0.25 0.60 0.35 0.29

*** p-value<0.01, ** p-value<0.05, * p-value<0.10

51

Table 10Customer Concentration and Account ReceivablesThis table shows the relation between a firm’s customer concentration and its receivables. Customerconcentration is measured by CustomerSales. The dependent variables are account receivables, scaledby cost of goods sold (COGS ), and total sales. All regressions use industry-fixed effects. The sampleincludes all manufacturing firms with available customer information and firm fundamental information.Industry is classified as 2-digit SIC industry. Robust-clustered t-statistics are shown in parentheses.

Dep. Var.: Receivables/COGS Receivables/Sales

(1) (2) (3) (4) (5) (6)

CustomerConcentration -0.05*** -0.06*** -0.06*** -0.02*** -0.03*** -0.03***(-4.18) (-5.61) (-5.70) (-3.71) (-5.51) (-5.30)

Size 0.01 0.01 -0.00 -0.00(1.52) (1.52) (-1.13) (-0.79)

Age -0.00 -0.00 -0.00* -0.00*(-1.46) (-1.71) (-2.00) (-2.02)

Profitability 0.05 0.05 -0.05*** -0.05***(0.64) (0.67) (-3.33) (-3.38)

Tangibility -0.22*** -0.22*** -0.06*** -0.06***(-7.74) (-7.99) (-6.06) (-6.50)

M/B 0.01** 0.01** -0.00*** -0.00***(2.55) (2.52) (-5.43) (-5.14)

Leverage -0.01 -0.00 0.01* 0.01*(-0.12) (-0.12) (1.84) (1.83)

Ratings -0.01 -0.01 0.00 0.00(-0.68) (-0.64) (0.01) (0.03)

CreditSpread 0.02 0.01***(1.61) (2.90)

TermSpread 0.00*** 0.00(4.53) (0.75)

GDPGrowth -0.00 0.00***(-1.12) (3.58)

Observations 17,225 15,858 15,858 17,225 15,858 15,858R-squared 0.12 0.16 0.16 0.12 0.16 0.16

*** p-value<0.01, ** p-value<0.05, * p-value<0.10

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Table 11Loan Terms and Customer Financial ConditionThis table shows the relation between loan terms and customer financial condition. The dependentvariable are loan spread, covenants, and maturity. All regressions use industry-fixed effects and bank-fixed effects. Industry is classified as 2-digit SIC industry and the lending banks are classified by theirultimate parents. Robust-clustered t-statistics are shown in parentheses. Columns (1), (3), and (5)show the regression results for CustomerLeverage: the leverage of major customers, weighted by thefirm’s percentage sales to these customers. Columns (2), (4), and (6) show the regression results forCustomerDefault : the inverse of the distance to default of major customers predicted by the KMV-Merton’s model, weighted by the firm’s percentage sales to these customers.

Dep. Var.: LoanSpread LoanCovenants LoanMaturity

(1) (2) (3) (4) (5) (6)

CustomerLeverage 151.43*** 1.43*** -23.55*(3.07) (3.45) (-1.73)

CustomerDefault 47.90*** 0.70*** -6.56(4.15) (3.71) (-1.29)

Size -17.45*** -18.39*** -0.17*** -0.16*** -2.44* -2.59*(-7.68) (-8.63) (-5.58) (-3.93) (-1.77) (-2.04)

Age -0.80*** -0.84*** 0.00** 0.01** -0.14** -0.15***(-3.63) (-3.94) (2.34) (2.30) (-2.72) (-3.17)

Profitability -279.22*** -276.11*** 1.53** 1.31** 50.05*** 52.04***(-4.11) (-3.83) (2.41) (2.16) (4.05) (4.32)

Tangibility -7.60 -2.07 -0.18 -0.27 -19.44** -18.48**(-0.40) (-0.10) (-0.37) (-0.55) (-2.46) (-2.39)

M/B -2.49 -4.00 -0.03 -0.04 -3.47** -3.49**(-0.58) (-1.01) (-0.58) (-0.74) (-2.51) (-2.51)

Leverage 122.86*** 121.88*** -0.25 -0.08 4.65 4.64(10.58) (9.76) (-1.23) (-0.40) (0.58) (0.56)

Ratings 40.61*** 41.33*** 0.08 0.04 2.32 2.46(7.04) (6.97) (1.14) (0.55) (0.74) (0.90)

CreditSpread 83.57*** 81.27*** 1.07*** 0.99*** -4.72 -6.68(5.77) (5.30) (3.61) (3.26) (-0.77) (-1.12)

TermSpread 3.39* 3.95* -0.11* -0.10* -1.14 -1.11(1.77) (2.00) (-2.04) (-1.79) (-1.41) (-1.27)

GDPGrowth -4.30 -4.81* 0.23*** 0.23*** 2.38** 2.07**(-1.73) (-1.98) (5.36) (5.14) (2.60) (2.25)

LoanMaturity 9.35** 10.56** 0.28*** 0.31***(2.13) (2.30) (5.52) (6.67)

LoanSize -16.62*** -15.81*** 0.12** 0.09* 4.45*** 4.58***(-7.20) (-6.06) (2.34) (1.93) (3.35) (2.97)

LoanType 47.53*** 48.69*** 0.06 0.04(7.50) (7.35) (1.30) (0.83)

LoanSpread 0.72*** 0.71*** 4.82** 4.64*(23.97) (18.49) (2.30) (2.01)

Observations 2,903 2,770 2,903 2,770 870 833R-squared 0.58 0.59 0.28 0.29 0.30 0.31

*** p-value<0.01, ** p-value<0.05, * p-value<0.10

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Table 12Loan Failure Rate and Customer ConcentrationThis table shows the relation between bank loan failure rates and customer concentration. The dependentvariable is loan failure (LoanFailure), a dummy variable that equals 1 if a firm files for bankruptcy beforeloan maturity, 0 otherwise. All regressions use industry-fixed effects and bank-fixed effects. Industry isclassified as 2-digit SIC industry and the banks are classified by their ultimate parents. Robust-clusteredz -statistics are shown in parentheses. Columns (1) through (3) show the logit regression results forCustomerSales. Columns (4) through (6) show the logit regression results for CustomerSize.

Dep. Var.: LoanFailure CustomerConcentration is CustomerSales CustomerConcentration is CustomerSize

(1) (2) (3) (4) (5) (6)

CustomerConcentration 1.79*** 1.73*** 1.59** 0.21*** 0.21*** 0.19**(2.79) (2.70) (2.28) (3.25) (3.30) (2.53)

Size -0.08 -0.07 -0.18 -0.12 -0.12 -0.25(-0.35) (-0.32) (-0.67) (-0.62) (-0.58) (-1.11)

Age -0.00 -0.00 0.01 0.00 0.00 0.01(-0.07) (-0.06) (0.66) (0.17) (0.18) (0.73)

Profitability -7.87*** -8.34*** -10.85*** -5.70*** -6.02*** -8.21***(-5.38) (-5.42) (-6.17) (-3.06) (-2.66) (-4.47)

Tangibility 1.65 1.73 2.11 1.94 1.95 2.21(1.05) (1.13) (1.40) (1.35) (1.43) (1.57)

M/B -1.60*** -1.67*** -1.42*** -2.40*** -2.49*** -2.33***(-4.97) (-4.83) (-2.99) (-5.72) (-5.35) (-5.02)

Leverage 5.69*** 5.56*** 4.66*** 6.11*** 5.95*** 5.02***(6.35) (6.42) (7.97) (5.55) (5.55) (6.27)

Ratings 0.63* 0.78** 0.61 0.58* 0.73** 0.57(1.70) (2.10) (1.51) (1.68) (2.10) (1.48)

CreditSpread -0.84 -1.31 -0.96 -1.51*(-1.28) (-1.46) (-1.50) (-1.74)

TermSpread 0.04 0.15** 0.04 0.12*(0.51) (2.20) (0.49) (1.73)

GDPGrowth 0.15 0.01 0.14 -0.01(1.51) (0.06) (1.39) (-0.08)

LoanSpread 0.79*** 0.81***(3.39) (3.22)

LoanMaturity 2.43*** 2.35***(4.02) (4.62)

LoanSize 0.18** 0.20**(2.02) (2.30)

LoanType -0.02 -0.02(-0.14) (-0.10)

Observations 2,166 2,166 2,118 2,104 2,104 2,056

*** p-value<0.01, ** p-value<0.05, * p-value<0.10

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