customer concentration and managerial agency costs · 2019. 4. 24. · customer concentration and...
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Customer Concentration and Managerial Agency Costs*
Taeyeon Kim
Korea Advanced Institute of Science and Technology (KAIST)
Hyun-Dong Kim†
Sogang University
Kwangwoo Park
Korea Advanced Institute of Science and Technology (KAIST)
This version: February 15, 2019
* This research was supported by the Sogang University Research Grant of 2018 (201810007.01).
† Corresponding author: Professor of International Finance, Graduate School of International Studies, Sogang
University; 35 Baekbeom-ro, Mapo-gu, Seoul 04107, South Korea; Tel: +82-2-705-8682; Email:
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Customer Concentration and Managerial Agency Costs
Abstract
Using a sample of U.S. firms over the 1977-2016 period, we find that a higher level of
customer concentration is related to lower market value of suppliers’ excess cash. We
conjecture that managerial agency problems are aggravated in dependent suppliers with a few
large customers, resulting in investors’ lower assessment value to suppliers’ excess cash
reserves. Furthermore, we show that managers in supplier firms with higher customer
concentration receive greater compensation, engage in more value-destroying mergers, and
experience less forced turnover. Our results add an agency view to the prevailing risk-based
view of customer concentration in the existing literature.
JEL classifications: G30; G32; G34
Keywords: Customer concentration; managerial agency costs; value of cash; suppliers;
relationship-specific investments
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1. Introduction
The finance literature on a firm’s relationship with non-shareholder stakeholders—
including employees, suppliers, and customers—has grown dramatically over the past few
decades. Many existing studies on this area focus on the interaction between a firm and its
customers and suppliers, and examine how a firm’s supplier–customer relationship affects
corporate policy decisions.1 For example, Kale and Shahrur (2007) and Banerjee, Dasgupta
and Kim (2008) show that suppliers tend to maintain lower leverage when they depend on
major customers. Dhaliwal, Judd, Serfling and Shaikh (2016) and Campello and Gao (2017)
provide evidence that firms with a more concentrated customer base have a higher cost of
capital. Wang (2012) further find a negative relation between a supplier’s dependence on
supplier-customer relationships and its dividend payments.2
The existing studies, however, relate customer concentration to its business risks between
suppliers and stakeholders. Although the current literature improves our understanding on the
risk-based view on customer concentration, relativity little is known about agency issues that
stem from a firm’s supplier–customer relationship. In this paper, we fill this gap by
examining the relationship between customer concentration and the managerial agency
problem. Specifically, we focus on how a closer relationship with fewer and larger customers
induces and aggravates a supplier’s managerial agency problem.
When a firm’s business depends on a few major customers, the relationship with its
customers becomes more important as it affects the firm’s daily operation. These
1 “Customer concentration” is a commonly used concept in the supplier–customer relationship literature. A high
level of a supplier’s customer concentration indicates that the supplier depends on a small set of major
customers for a large portion of its sales. 2 Itzkowitz (2013) documents two recent trends in U.S. firms that relate to the increasing importance of
supplier–customer relationships. First, a decrease in diversification across industries and vertical integration has
been observed, creating new relationships between suppliers and customers. Second, shifting from selecting
customers based on price, many firms focus on a few major customers (partners) to create enduring business
relationships for better innovation, enhanced product quality, and more valuable teamwork.
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relationships include supply contracts and interactions between related people for managing
the transaction of goods and services (Crawford, Huang, Li, and Yang 2016). The extant
literature suggests that firms with concentrated customers are more beneficial because of
increased operational efficiencies (Patatoukas, 2011; Cen, Maydew, Zhang, and Zuo, 2017).
Moreover, by having a few key customers, suppliers may signal the market that their products
are of high quality (Cen, Dasgupta, Elkamhi, and Pungaliya, 2015).
There exists, however, a dark side of concentrated supplier-customer relationships.
Suppliers with a few major customers are less diversified, and are required to engage in
relationship-specific investments, which may have a very limited value outside the supplier-
customer relationship (Titman and Wessels, 1988; Allen and Phillips, 2000). Thus, customer-
dependent suppliers face a potential hold-up risk. The shocks threatening relationships with
concentrated customers can drive suppliers into a significant risk of financial distress,
reverberating throughout their supply chains (Kolay, Lemmon, and Tashjian, 2016; Intintoli,
Serfling, and Shaikh, 2017). Furthermore, given the long-lasting relationships with a few key
customers (Emshwiller, 1991), the suppliers have strong incentives to maintain their
relationships with such customers in the course of business (Raman and Shahrur, 2008).
Existing studies (e.g., Shleifer and Summers, 1988; Coates, 2001; Stout, 2002) suggest
that a firm needs to make a commitment not to act opportunistically to exploit its
counterparties’ quasi-rents.3 This idea is consistent with the view of Johnson, Karpoff, and Yi
(2015) that a supplier’s commitment is important for maintaining a supplier–customer
relationship. In the relationship between customer-dependent suppliers and major customers,
implicit contracts to engage in this type of firm commitment appear to be valuable in
3 Quasi-rents occur when a counterparty engages in a relationship-specific investment that may lose value if the
firm adjusts its policies and decisions (Johnson et al., 2015).
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resolving hold-up problems (Gillan, Hartzell, and Parrino, 2009; Johnson et al., 2015).4 Such
implicit contracts are informally implemented by managers’ personal connections and
reputations with their major counterparties (Klein and Leffler, 1981). Johnson et al. (2015)
note that CEOs’ personal connections and reputations at customer-dependent suppliers are
essential in retaining long-term relationships with customers.5
In line with this thought, managers of customer-dependent suppliers are hired for their
personal commitments to their customers (Shleifer and Summers, 1988; Intintoli et al., 2017).
Such connections and reputations make it possible that suppliers do not behave themselves
like opportunists to exploit their customers’ quasi-rents (Johnson et al., 2015). Accordingly,
personal commitments encourage customers to consistently engage in relationship-specific
investment. However, if the managers are removed, existing personal commitments are no
longer effective, and suppliers fail to retain their stable relationship with customers (Shleifer
and Summers, 1988). In a similar vein, Johnson et al. (2015) suggest that takeover defenses
become important in maintaining implicit commitments because they decrease the likelihood
of managerial replacement. Intintoli et al. (2017) further show that the replacement of
managers disrupting supplier–customer relationships has a negative effect on the financial
performance of suppliers. Taken together, incumbent CEOs of customer-dependent suppliers
who have personal connections and reputations are most likely to continue close relationships
with major customers. This implies that replacing these CEOs is costly to suppliers with
concentrated customers.
Since the seminal work by Jensen and Meckling (1976), extensive literature argues that
4 In the classical Fisher Body–General Motors example described in Klein, Crawford and Alchian (1978),
managers of Fisher Body (supplier) could promise not to increase prices to appropriate General Motors’
(customer) quasi-rents. See Klein et al. (1978), Johnson et al. (2015), and Gillan et al. (2009) for more detailed
descriptions of the Fisher Body–General Motors example. 5 Johnson et al. (2015) provide an example of the Pemstar–International Business Machines case in their study.
See Johnson et al. (2015) for more detailed descriptions on the case.
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an agency conflict between a principal (a shareholder) and an agency (a manager) occurs due
to an irreversible investment from their unique relationship (Williamson, 1975; Klein et al.,
1978; Grossman and Hart, 1986). In particular, Shleifer and Vishny (1989) model how
managers can entrench themselves by engaging in manager-specific investments. Managers
have an incentive to engage in businesses associated with their skills and expertise. Specially,
managers focus on investing in assets specific to their skills, and the value of such assets
would be higher than if they were controlled by alternative managers without the required set
of skills. Such investments will make themselves valuable to shareholders and they are less
likely to be replaced even after a poor performance. As a result, managers can be entrenched
and pursue perquisites by wasting free cash flow.
Returning to the issue of dependent suppliers on concentrated customers, we conjecture
that CEOs having implicit commitments with concentrated customers are more likely to make
manager-specific investments associated with supplier–customer relationships. So, replacing
CEOs of customer-dependent suppliers imposes a substantial cost to these firms. Hence,
CEOs with major customers can entrench themselves by engaging in excessive investments
in their own specific assets. In this regard, managerial agency problems resulting from
manager-specific investments appear to be significantly prevalent in suppliers with highly
concentrated customers.
As an example of agency costs in practice, extensive studies discuss that managers waste
corporate resources by using the resources to their own devices. To capture the inefficient
uses of corporate resources and the possible value destruction resulting from agency
problems, we focus on cash reserves.6 Firms should hold a certain amount of cash to pay out
6 Three reasons are suggested by extant literature for using cash reserves as a proxy for agency costs (e.g.,
Dittmar and Mahrt-Smith, 2007; Frésard and Salva, 2010; Faulkender and Wang, 2006). First, managers can
easily access cash reserves with little monitoring, and also have much discretion on their use. Thus, cash may
provide managers with resources to invest in non-positive net present value (NPV) projects, destroying
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funds for day-by-day operation, and to provide a buffer against unexpected events or the cost
of external financing for their investments. However, if cash reserves are held in excess of the
amount committed for operations and investments, they can be exploited as resources for
managers’ private benefits (Myers and Rajan, 1998). In this case, holding excessive cash may
harm firm value, suggesting that the market value of one dollar cash reserves may not be
equivalent to the value of a dollar. To justify this thought, Dittmar and Mahrt-Smith (2007)
and Frésard and Salva (2010) examine how investors value excess cash holdings of a firm
with weak governance. When investors recognize that managers may use cash inefficiently,
the market value of those resources is discounted. We thus hypothesize that excess cash
holdings of suppliers with more concentrated customers will be valued lower by investors
because managerial agency problems are expected to be more severe for such suppliers.
Using a comprehensive sample of U.S. firms spanning 1977 to 2016, we examine the
relationship between suppliers’ customer concentration and their managerial agency costs.
We find that a higher level of customer concentration is related to a lower market value of
suppliers’ excess cash, implying that managerial agency problems are expected to be more
severe in suppliers with concentrated customers. Our baseline results may be subject to
endogeneity concerns related to measurement errors, omitted variable bias, and reverse
causality. Hence, we conduct various tests designed to mitigate potential endogeneity issues.
Our results are qualitatively similar when we employ alternative measures for the market
value of excess cash and also control for time-invariant omitted CEO and firm characteristics,
as well as corporate governance. Moreover, the results still hold when a propensity score
matching procedure and two-stage least square regressions are run.
shareholders’ wealth. Second, firms reserve substantial amounts of cash, and the value of cash holdings accounts
for a significant proportion of their wealth. Third, while a supplier–customer level is quite sticky, the degree of
cash holdings substantially varies over time. This variation in cash provides us with an optimal setting to test the
effect of customer concentration on the value of cash in suppliers.
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In addition, we investigate how customer concentration affects CEO compensation and
acquisition decisions to confirm the existence of agency problems. Our results show that
managers in suppliers with higher customer concentration receive greater compensation than
vice versa. Such suppliers also experience lower abnormal stock returns after a mergers and
acquisitions (M&A) announcement upon acquiring a target firm. Thus, when a business
relationship with customers is more important, managers extract higher benefits from
shareholders, in the form of receiving a higher compensation and investing in value-
destroying deals.
Next, we examine the possible channel through which higher customer concentration
leads to managerial agency costs of suppliers. Because supplier–customer relationships
become more important for suppliers who highly depend on a few major customers, these
suppliers are likely to hire managers who are better able to retain customer relationships.
Thus, supplier–customer relationships are most valuable under current managers and
replacing managers is very costly to suppliers with a highly concentrated customer base. As
expected, we find that customer-related CEOs are more prevalent in suppliers with higher,
than lower, customer concentration. Our finding also shows that such managers are less
forced out, allowing them greater job security.
We further identify potential circumstances wherein customer concentration is more
closely associated with managerial agency problems. We find that the negative relationship
between customer concentration and the market value of excess cash is more pronounced in
suppliers that are dependent on major customers who can easily switch their suppliers. This
result shows that the negative market value of excess cash from customer concentration
occurs particularly under circumstances wherein the management of supplier–customer
relationships and the role of the supplier’s manager are more important.
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Our study contributes to the extant literature in numerous ways. First, to the best of our
knowledge, we are the first to directly examine managerial agency costs of suppliers that
result from their concentrated customer base. Prior studies investigate the governance role of
customers in suppliers, and postulate that major customers have incentives to monitor the
managers of suppliers (Wang, 2012; Johnson et al., 2015; Kang, Liu, Yi, and Zhang, 2015).
Shifting our attention from the literature on customers’ role in monitoring supplier managers,
we focus on managerial agency conflicts with shareholders in customer-dependent suppliers
and suggest a mechanism by which managerial agency problems occur.7 Second, our study
adds to the literature that investigates management entrenchment due to manager-specific
investments (e.g., Shleifer and Vishny, 1989). Our findings show that managers for suppliers
with major customers can entrench themselves by engaging in specific investments
associated with supplier–customer relationships. Third, our findings also contribute to the
existing literature on the value of cash holdings (e.g., Dittmar and Mahrt-Smith, 2007;
Faulkender and Wang, 2006). These extant studies mainly address how corporate governance
and corporate financial policy affect the value of cash. We further show that the marginal
value of excess cash declines with higher customer concentration.
The rest of the paper is organized as follows. Section 2 describes the sample and variables
used in this study, and provides the descriptive statistics. Next, section 3 discusses the
empirical results of our paper. Finally, section 4 presents our conclusions.
7 The monitoring roles of customers can be different from those of general shareholders. Since customers are
concerned about their suppliers’ daily operations rather than growth or performance, their incentives to monitor
managers of suppliers depend largely on how effectively their implicit contracts are enforced (Wang, 2012;
Kang et al., 2015). On the other hand, shareholder incentives to monitor their managers vary with the size of
their financial claims (Shleifer and Vishny, 1986), implying that the interests of customers are different from
those of shareholders. These arguments suggest that agency conflicts between shareholders and managers still
exist in suppliers with a highly concentrated customer base, even though customers appropriately monitor their
suppliers’ managers.
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2. Data Description and Research Design
2.1 Sample Construction
We obtain supplier–customer data from the segment customer files provided by
Compustat. We also collect accounting and financial information from Compustat, and then
match it with supplier–customer data. We exclude firms with missing data and firms in
utilities (SIC 4900-4999) and financial industries (SIC 6000-6999) because their financial
decisions are heavily regulated by the government. Moreover, firms with negative excess
cash holdings are excluded since financial constraints are likely to be more important to
determine the market value of excess cash rather than managerial agency cost. Our final
sample contains 67,016 U.S. supplier-year observations from 1977 to 2016.
To conduct additional tests, we collect diverse information from several data sources. The
analyst forecasting and CEO-level variables are obtained from the Institutional Brokers’
Estimate System (I/B/E/S) and ExecuComp dataset, respectively. We retrieve a sample of
M&A deals from the Securities Data Company (SDC) Platinum database, and obtain stock
information from Center for Research in Securities Prices (CRSP). Moreover, information on
CEO forced turnover is hand-collected using Factiva, and corporate governance variables are
obtained from the Institutional Shareholder Services (ISS) database.
2.2 Customer Concentration Measures
We identify suppliers and their major customers from Compustat. Since 1976, the
Statement of Financial Accounting Standards No. 14 (SFAS 14) has required suppliers to
report their major customers that account for 10% or more of their total sales. Segment
customer files of Compustat provide the information on the SFAS 14 regulation, including
suppliers’ and names of their major customers, as well as suppliers’ sales to each major
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customer. Although suppliers are required to identify customers who represent at least 10% of
their sales, some suppliers voluntarily report accounting information of customers that
constitute less than 10% of their total sales. To reduce a potential selection bias, we exclude
this customer data when measuring relevant variables.
Following Dhaliwal et al. (2016), we construct three different variables to measure a
customer concentration level. First, we construct an indicator variable that is equal to one if a
supplier reports at least one corporate customer that accounts for 10% or more of its sales and
zero otherwise. This variable is denoted as Major Customer for the remainder of this paper.
Second, based on Banerjee et al. (2008), we define Major Customer Sales as the fraction of
the supplier’s total sales generated by major customers. Major Customer Sales is calculated
as follows:
𝑀𝑎𝑗𝑜𝑟 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑆𝑎𝑙𝑒𝑠𝑖,𝑡 = ∑ 𝑆𝑎𝑙𝑒𝑠 𝑅𝑎𝑡𝑖𝑜𝑗,𝑡𝜂𝑖,𝑡
𝑗=1 (1)
where 𝜂𝑖,𝑡 is the number of supplier 𝑖’s major customers in year 𝑡, and sales ratio is the
ratio of supplier 𝑖’s sales to major customer 𝑗 over supplier 𝑖’s total sales in year 𝑡. A high
level of Major Customer Sales indicates that a large fraction of a supplier’s sales is captured
by its major customers.
Following Patatoukas (2011), we also define the third measure of customer concentration,
Customer HHI, as a Herfindahl index of supplier’s sales to major customers, and calculate
this variable as below:
𝐶𝑢𝑠𝑡𝑚𝑒𝑟 𝐻𝐻𝐼𝑖,𝑡 = ∑ 𝑆𝑎𝑙𝑒𝑠 𝑅𝑎𝑡𝑖𝑜𝑗,𝑡2𝜂𝑖,𝑡
𝑗=1 (2)
This measure considers both the number of major customers and the volume of a
supplier’s sales to each major customer. Higher Customer HHI means that a large proportion
of a supplier’s sales comes from a small number of major customers.
In addition, Major Customer Sales and Customer HHI take the value of zero if a supplier
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does not report any major customer, otherwise they take the value of one if a supplier
depends on only one major customer.
2.3 Market Value of Excess Cash Holdings
We use the market value of suppliers’ excess cash holdings to measure their managerial
agency cost. Excess cash, defined as the cash that is not required for firm operations or
investments, is measured as the cash reserved above a predicted normal level of cash by
following Dittmar and Mahrt-Smith (2007). We estimate the amount of normal cash by
regressing suppliers’ cash holdings on variables that capture their various motives of cash
reserves as identified by the extant literature (Fama and French, 1998; Dittmar and Mahrt-
Smith, 2007; Frésard and Salva, 2010).8 In addition, we control for customer concentration
level in those regressions because suppliers with more concentrated customers tend to hold
more cash than those with less concentrated customers (Itzkowitz, 2013; Bae and Wang,
2015). Then, excess cash, denoted as Xcash, is calculated as the residuals from regressions on
normal cash. We only consider positive excess cash because negative excess cash concerns
financial constraints rather than agency issues.
To measure the market value of excess cash, we regress the market value of the firm on
excess cash holdings and other control variables based on Dittmar and Mahrt-Smith (2007).
In this regression, the interaction term between customer concentration and excess cash, our
main variables of interest, is included to capture the effect of customer concentration on the
market value of excess cash. We also run the regression with a customer concentration
variable to control for customer concentration’s direct impacts on the market value of the
8 Firms’ motive to hold cash includes hedging needs, growth options, and financing restrictions. Our regression
model of excess cash estimation is fully described in Appendix B.
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firm. Our regression model is shown below:
𝑀𝑎𝑟𝑘𝑒𝑡 𝑉𝑎𝑙𝑢𝑒/𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡 = 𝛼 + 𝛽1𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 + 𝛽2𝑋𝑐𝑎𝑠ℎ𝑖,𝑡 +
𝛽3𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 ∗ 𝑋𝑐𝑎𝑠ℎ𝑖,𝑡 +
𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 + 𝑌𝑒𝑎𝑟 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 +
𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 + 𝜀𝑖,𝑡 (3)
where Market Value/Assetsi,t is the market value of supplier i scaled by total assets at time t,
which is calculated as the sum of market value of equity and book value of short- and long-
term debt divided by total assets. Customer Concentrationi,t and Xcashi,t are supplier i’s
customer concentration and excess cash holdings in period t, respectively.
Following Fama and French (1998), we control for suppliers’ earnings, research and
development (R&D) expenditures, interest expenses, and dividend payouts. To control for
investors’ expectation, we also include two-year lagged (from year t-2 to year t) and forward
(from year t to year t+2) changes of these control variables, as well as two-year forward
changes in the market value of the firm. All controls are normalized by total assets, and year
and industry fixed effects are included in the regressions.
2.4 Descriptive Statistics
Panel A of Table 1 presents the descriptive statistics for our variables. The mean value of
Major Customer is 0.345, which indicates that 34.5% of suppliers in our sample have at least
one major customer. The average supplier’s sales to major customers account for 13.9% of
total sales, and the mean value of Customer HHI is 0.047. Suppliers with at least one major
customer sell 40.2% of their total sales to those customers on average, and the mean value of
their Customer HHI is 0.136. The mean excess cash held by suppliers is 0.867, and cash
reserves account for 19.8% of total assets on average. The mean total assets and net assets of
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suppliers are 2,261 and 1,975 million dollars, respectively. On average, the market value of
the firm to total assets is 1.159, and earnings ratio to total assets is 0.087. The average R&D
expenditure and interest expense amount to 3.8% and 2.7% of total assets, respectively.
Moreover, the mean value of dividend ratio is 0.010. To mitigate the outlier effect, all
continuous variables are winsorized at the 1% level.
[Insert Table 1 here]
We conduct univariate tests to obtain preliminary insights on the relationship between
customer concentration and excess cash. In Panel B of Table 1, we split the sample into two
groups according to whether or not suppliers disclose at least one major customer. We
perform the difference-in-mean and difference-in-median tests between these two groups.
Suppliers with at least one major customer hold more excess cash than those without a major
customer. Specifically, the mean value of excess cash holdings is 0.899 for suppliers that
have at least one major customer, whereas it is 0.849 for those with no major customer. The
difference is statistically significant at the 1% confidence level. The results of the univariate
tests suggest that agency problems are more severe in suppliers with more concentrated
customers.9 In addition, suppliers with at least one major customer have smaller total assets
and net assets; higher cash, market value, and R&D expenditure; and lower interest expense
and dividend payouts.10 These results are consistent with the extant literature.11
9 Since suppliers with more concentrated customers are likely to face high risks (Itzkowitz, 2013; Bae and
Wang, 2015; Dhaliwal et al., 2016; Campello and Gao, 2017), they may hold more cash reserves. To focus on
the agency problem, rather than the precautionary purpose of cash holdings, we investigate the market value of
excess cash holdings using positive excess cash, as explained in section 2.3. 10 Campello and Gao (2017) find that higher customer concentration increases interest rate spreads since the
risk of firms with concentrated customers is higher than the risk for other firms. Such firms suffer from higher
costs of debt and may face difficulties in raising capital from debt financing, thus yielding lower leverage and
interest expense. 11 See Wang (2012), Itzkowitz (2013), Dhaliwal et al. (2016), Campello and Gao (2017), and Krolikowski and
Yuan (2017).
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3. Baseline Regression Analysis
To investigate the effect of customer concentration on the market value of excess cash
holdings, we estimate equation (3) by using ordinary least square (OLS) regressions. Table 2
presents our baseline results. In column (1), we use Major Customer as a customer
concentration measure, and the coefficient on excess cash (Xcash), -0.253, is significantly
negative at the 1% level. Given suppliers with positive excess cash are only used in our
analysis, negative coefficient on excess cash appears to be reasonable because investors may
be concerned about excessive cash reserves that can potentially result in an agency problem.
The coefficient on interaction term between customer concentration and excess cash (Major
Customer*Xcash here), our main variable of interest, is -0.116, with statistical significance at
the 1% level. This result suggests that the market value of excess cash is lower in suppliers
with at least one major customer than in those with no major customer. In the perspective of
economic significance, the market value of suppliers with at least one major customer
decreases by $ 0.369 (=0.253+0.116) as they hold an additional one dollar of excess cash,
whereas the market value of those with no major customer reduces by $0.253 with an
additional one dollar of excess cash.
[Insert Table 2 here]
Columns (2) and (3) of Table 2 present the regression results using Major Customer Sales
and Customer HHI as the measures of customer concentration, respectively. The coefficients
on Xcash and Customer Concentration*Xcash are significantly negative at the 1% level.
Given that the means of Major Customer Sales and Customer HHI are 0.139 and 0.047 in
Panel A of Table 1, respectively, the market value of an additional one dollar on excess cash
is -$ 0.298 (=-0.249-0.354*0.139) and -$0.300 (=-0.259-0.875*0.047), respectively. If Major
Customer Sales and Customer HHI increase by one standard deviation (0.379 for Major
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Customer Sales and 0.160 for Customer HHI in Panel A of Table 1), the market value of an
additional one dollar of excess cash is -$ 0.383 (=-0.249-0.354*0.379) and -$ 0.399 (=-0.259-
0.875*0.160), respectively. Accordingly, the results of columns (2) and (3) show that the one-
standard-deviation increases in Major Customer Sales and Customer HHI lead to around 30%
drop in the market value of excess cash. These results are qualitatively similar when we re-
estimate equation (3) using a subset of suppliers that report at least one major customer in
columns (4) and (5). Overall, our results of Table 2 show that the negative impact of customer
concentration on the market value of excess cash holdings is economically significant,
thereby providing empirical evidence on the presence of managerial agency problems in
suppliers with a concentrated customer base.
4. Endogeneity Issue
In section 3, we have shown a negative relationship between customer concentration and
the market value of excess cash holdings. However, our results might be subject to various
types of endogeneity problems, such as measurement error, omitted variable bias, and reverse
causality. Although we measure our main variables and control for other determinants by
following prior literature, a measurement error and an omitted variables bias can still
influence both customer concentration and the market value of excess cash holdings. Such
issues would make our observed relationship suspicious. Because exogenous variations in
customer concentration measures appear to be insufficient, reverse causality may also arise in
the baseline results. Thus, we perform additional tests to mitigate endogeneity concerns.
4.1 Alternative Measures for the Market Value of Excess Cash Holdings
In Table 3, we address issues on measurement errors by employing the market value of
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cash ratio and the forecasted market value of excess cash holdings, instead of the market
value of excess cash holdings. We re-estimate our baseline specification in Panel A of Table 3
using cash ratio. In columns (1)–(3), the coefficients on Cash Ratio are significantly positive.
Given that cash holdings can be used as buffers for new investments or against financial
constraints, the positive sign seems to be reasonable. Furthermore, the coefficients on the
interaction term between customer concentration and cash ratio are negative, with statistical
significance at the 1% level. These results are consistent with our baseline results in Table 2,
indicating that the market value of cash ratio is lower for suppliers with more concentrated
customers.
[Insert Table 3 here]
In our normal cash regressions used to calculate excess cash, we control for the market
value of assets normalized by total assets, but this variable is included again as a dependent
variable in equation (3). This measurement process may lead to endogeneity related to the
market value of assets. To address endogeneity, we use the forecasted market value of assets.
The market value of assets forecasted by analysts is measured as the sum of the forecasted
market value of equity and the book value of short- and long-term debt. In addition,
forecasted market value of equity is defined as the product of the common shares outstanding
and the average target price predicted by analysts. To calculate the average target stock price,
we use analysts’ prediction, whose forecast horizon is 12 months; the prediction is announced
within three months before and after the fiscal year end. Because analysts are more informed
than other types of investors, their predicted market value of excess cash would better reflect
governance issues, including agency problems.
In Panel B of Table 3, we re-run the regressions with the forecasted market value
normalized by total assets. The coefficients on the interaction term between customer
17
concentration and excess cash remain significantly negative in all columns, suggesting that
analysts poorly evaluate excess cash holdings of suppliers that depend on a few major
customers. Taken together, our main results hold unaffected even when we use alternative
measures for the market value of excess cash holdings.
4.2 Propensity Score Matched Sample Analysis
We have so far controlled for various firm-level characteristics, but our estimation may
still suffer from omitted variables that correlate with both customer concentration and the
market value of excess cash holdings. To address any omitted variable bias, we employ a
propensity score matching procedure to enable a closer comparison between firms sharing
similar characteristics in all respects, with the only exception of customer concentration.
Using a logit regression model, we first regress Major Customer on our control variables
used in equation (3), and estimate the propensity score—that is, the probability that a supplier
has at least one major customer. Based on a nearest-neighbor propensity score matching
procedure, we match each supplier with at least one major customer to a supplier with no
major customer, but with the closest propensity score. Finally, we construct a smaller
subsample of suppliers with at least one major customer and matched suppliers with no major
customer. We then run our main regression again for this subsample in Table 4.
[Insert Table 4 here]
The results using a subsample obtained from propensity score matching are comparable to
our baseline result in Table 2. The coefficients on Xcash’s interaction terms with customer
concentration are significantly negative in columns (1)–(3). This is clear indication that our
earlier finding is not likely to be a by-product of omitted variable issues.
18
4.3 CEO and Firm Fixed Effects Regressions
To additionally alleviate any omitted variable bias, we re-estimate baseline regressions
with CEO and fixed effects in Table 5. Columns (1)–(3) report the results including the CEO
fixed effects, and the other columns present those with firm fixed effects regression.12 All
coefficients on interaction terms between customer concentration and excess cash are
negative, with statistical significance at the 1% level. These results suggest that omitted
variables related to CEO or firm characteristics are not likely to drive our baseline finding.
[Insert Table 5 here]
4.4 Two-stage Least Squares Regressions
Furthermore, we run two-stage least squares (2SLS) regressions with instrumental
variables in order to mitigate primary endogeneity issues, including omitted variable bias and
reverse causality. Particularly, we address endogeneity concerns that may arise because the
exogenous variations in our customer concentration variables are not sufficient. An
instrumental variable used in our 2SLS regressions should capture a variation in customer
concentration (i.e., inclusion restriction), but be exogenous to the market value of assets (i.e.,
exclusion restriction). As in Campello and Gao (2017), we employ M&A activities in major
customers’ industries (horizontal M&A), denoted as Customer M&A, as an instrumental
variable. Extant studies (e.g., Fee and Thomas, 2004; Bhattacharyya and Nain, 2011;
Campello and Gao, 2017) show that suppliers tend to engage in business with more
concentrated customers after M&A waves in their customers’ industries because the number
of customers decreases by their horizontal M&A. Therefore, M&A activities in major
12 We use ExecuComp database to retrieve information on CEOs. Because ExecuComp database starts from
1993, our sample substantially decreases in the regressions with CEO fixed effects.
19
customers’ industries are positively associated with the level of customer concentration,
which satisfies the inclusion restriction. On the other hand, Customer M&A appears to affect
suppliers’ market value of assets only through the supplier–customer link—that is, a shock of
customers’ industries is unlikely to directly influence supplier characteristics, which satisfies
the exclusion restriction.
For the M&A data, we focus on observations with the deals that are initiated and
completed by acquirers in the same two-digit SIC code as their targets. We use the deal value
of M&A normalized by the acquirer’s total sales as a proxy for acquisition activity. Then, the
extent of industry-level acquisition is defined as the average acquisition activities of firms in
a given industry over the past five years. The five-year window is used to prevent a few big
deals from driving the total deal value. Next, we manually match the reported major
customers’ name with the historical company name listed on the Compustat database.13,14
Finally, after matching major customers’ industries with industry-level acquisition activities,
we measure Customer M&A as the weighted sum of acquisition activities across industries in
which the supplier’s major customers operate.15 To meet the exclusion condition, we exclude
the observations that a supplier operates in the same industries with its customers.
Our main regressions include two potential endogenous variables, Customer
Concentration and Customer Concentration*Xcash. Thus, we run two first-stage regressions
with two instrumental variables, Customer M&A and Customer M&A*Xcash, following the
methodology of Benmelech and Frydman (2015). Our two instrumental variables are
included in each first-stage regression. In addition, suppliers that report their major customers
13 While the Compustat segment customer file provides suppliers’ identification number, customers’
identification numbers are not available. 14 Our sample significantly reduces because we use only suppliers whose major customers are matched with
Compustat’s historical company data. 15 Since a supplier may have major customers operating in different industries, we use the weight that is defined
as the supplier’s percentage sales to each major customer.
20
are only useful because we should know the major customers’ names to measure Customer
M&A. Under this construction rule, a sample of Major Customer equal to one only remains,
making Major Customer useless in the 2SLS regression. We therefore employ Major
Customer Sales and Customer HHI as customer concentration variables. Table 6 reports the
results on 2SLS regressions.
[Insert Table 6 here]
Columns (1)–(4) present the results of first-stage regressions, and columns (5) and (6)
report those of the second-stage regressions. Major Customer Sales is used in columns (1),
(2), and (5), while Customer HHI is used in columns (3), (4), and (6). Major Customer Sales
and Customer HHI are included in the first-stage regression as dependent variables in
columns (1) and (3), respectively. In addition, Major Customer Sales*Xcash and Customer
HHI*Xcash are used as dependent variables in columns (2) and (4), respectively. On the other
hand, the market value of assets normalized by total assets is included in the second-stage
regressions as a dependent variable. The coefficients on Customer M&A (Customer
M&A*Xcash) in columns (1) and (3) (columns (2) and (4)) are significantly positive. The F-
statistics of the first-stage regressions are also sizable, exceeding the rule-of-thumb value of
10 for the weak instrument test. In the second-stage regressions, the coefficients on the
interaction terms between customer concentration and excess cash (Major Customer
Sales*Xcash and Customer HHI*Xcash) are negative, with statistical significance at the 1%
level. The results on the F-statistics obtained from the Wu–Hausman test also confirm that
our customer concentration measures are exogenous by themselves. In short, our main
finding, namely, the negative impact of customer concentration on managerial agency
problems, remains significant, suggesting that the result is not likely to be driven by
endogeneity issues
21
5. Further Robustness Tests
5.1 Alternative Measures of Managerial Agency Costs
We have far argued that excess cash held by suppliers with more customer concentration
is poorly valued by the capital market because of the potential agency problem. In this
section, we perform additional robustness tests to confirm the presence of an agency problem
for suppliers with a concentrated customer base.
Following Masulis, Wang and Xie (2009) and Chen, Harford and Lin (2015), we first
focus on CEO compensation. Since CEO compensation can be the most direct way to shift
shareholders’ wealth to managers, a rich body of literature on corporate governance considers
a higher level of CEO compensation, relative to comparable firms, as a by-product of a
managerial agency problem (e.g., Bertrand and Mullainathan, 1999; Core, Holthausen, and
Larcker, 1999; Masulis et al., 2009; Chen et al., 2015). Thus, we expect that managers of
suppliers with a highly concentrated customer base will receive a higher level of
compensation customer concentration.
Panel A of Table 7 shows the regression results for the effect of customer concentration
on CEO compensation. We employ two different CEO compensation variables, ln(CEO Total
Compensation) and CEO Excess Compensation, as dependent variables. ln(CEO Total
Compensation) is the natural logarithm of total annual compensation of CEO, while CEO
Excess Compensation is the residuals from the regression of the natural logarithm of CEO
total compensation on the natural logarithm of total market value of the firm. Following
Masulis et al. (2009), firm size (ln(Assets)), Tobin’s q (Tobin’s Q), return on assets (ROA),
leverage(Leverage), excess stock return (Excess Stock Return), stock return volatility (Stock
Return Volatility), R&D expenses (R&D Expenses/Assets), capital expenditure
22
(CAPEX/Assets), firm ages (ln(Firm Age)), and CEO tenure (ln(CEO Tenure)) are included as
control variables in the regressions.
[Insert Table 7 here]
In general, ln(CEO Total Compensation) and CEO Excess Compensation are positively
related to Customer Concentration, with statistical significance. Those results show that CEO
total and excess compensation increase with a more concentrated customer base, supporting
our conjecture that customer concentration appears to induce a managerial agency problem
for suppliers.
Furthermore, we explore managerial decisions on mergers in suppliers with a
concentrated customer base to identify the positive relationship between customer
concentration and the agency problem in suppliers. The literature suggests that an acquisition
can be used to pursue managers’ private benefit at the expense of shareholders (e.g., Jensen
and Ruback, 1983; Masulis, Wang, and Xie, 2007). Since agency-driven managers want to
construct their own empire building by increasing firm size and scope, they have incentive to
engage in value-destroying merges. Masulis et al. (2009) and Chen et al. (2015) show that
firms that suffer from an agency problem experience negative announcement abnormal
returns on their M&A decisions. In a similar vein, we investigate whether there are negative
announcement returns on the acquisitions initiated by suppliers with a high customer
concentration.
For our M&A data, we require that acquisitions are completed, and acquirers have stock
return during 210 trading days before the announcement data. Then, we compute the five-day
cumulative abnormal returns (CARs) over the (-2, +2) window around the announcement
date of mergers, which is denoted as CAR(-2, +2).16 We additionally construct Negative
16 We calculate abnormal returns using the residuals from a market model. Its parameters are estimated over the
23
CAR(-2, +2) Dummy, which is an indicator variable that is equal to one if CAR(-2, +2) is
negative and zero otherwise. Based on M&A literature (e.g., Masulis et al., 2007 and Masulis
et al., 2009), we control for various firm and deal characteristics that can affect
announcement stock returns on mergers. For firm controls, ln(Assets), Tobin’s Q, ROA, and
Leverage are used in our analysis; the deal characteristics include Relative Deal Size, High-
tech, Diversifying Acquisition, Public Target, Private Target, Subsidiary Target, Stock Deal,
and All Cash Deal. Detailed definitions for each variable are available in Appendix A.
Panel B of Table 7 presents the results for the regressions of announcement abnormal
stock returns on customer concentration. CAR(-2, +2) and Negative CAR(-2, +2) Dummy are
used as dependent variables in columns (1)–(3) and (4)–(6), respectively. The coefficients on
Customer Concentration are significantly negative in columns (1)–(3), showing that suppliers
with more concentrated customers experience lower abnormal returns after their acquisition
announcement. Moreover, Customer Concentration is positively and significantly related to
Negative CAR(-2, +2) Dummy in columns (4)–(6). This result indicates that, as suppliers
become more dependent on a few major concentrated customers, they tend to engage more in
value-destroying deals. Overall, our findings from Table 7 suggest that managerial agency
problems are aggravated in suppliers with more concentrated customers.
5.2 Possible Channel
We have argued that the role of CEOs in managing customers is more important in
suppliers with a highly concentrated customer base. Thus, replacing the managers is very
costly to such suppliers, and these managers can be entrenched. In this section, we examine
the importance of a supplier’s CEO for maintaining customer relationships in order to
(-210, -11) window and market returns are measured as the value-weighted return of CRSP.
24
identify a potential channel through which customer concentration may affect managerial
agency costs of suppliers.
First, we investigate whether CEOs who are better able to manage supplier–customer
relationships are more likely to be appointed by suppliers with a higher customer
concentration. We consider a CEO who formerly served major customers as a competent
CEO in managing customer relations. To construct this variable, information on the CEO’s
work experience is obtained from the ISS database. The variable Customer-related CEO is
defined as an indicator variable equal to one if the supplier’s CEO previously served as a
senior manager or board member for one or more major customers, and zero otherwise. Then,
we analyze whether suppliers with more concentrated customers more frequently hire CEOs
who previously worked for major customers.
Table 8 reports the results of univariate comparisons and multivariate regressions to
examine the relationship between customer concentration and customer-related CEO. In
Panel A, we split the sample into two groups according to whether or not the supplier has a
higher level of customer concentration than the median of the total sample’s customer
concentration level. We then test the difference in mean between these two groups. We find
that a customer-related CEO is more likely present in the sample with a higher, than lower,
customer concentration, suggesting that CEOs who better manage customer relations are
more likely to serve suppliers with more concentrated customers. Panel B of Table 8 presents
the results from logit regressions of the customer-related CEO on customer concentration. All
coefficients on Customer Concentration are significantly positive, indicating that CEOs who
have former work experience with major customers tend to serve suppliers with a highly
concentrated customer base. Thus, the results in Table 8 imply that CEOs who are better able
to manage supplier–customer relationships are more frequently appointed by suppliers for
25
whom retaining relationships with customers is more important.
[Insert Table 8 here]
Second, we examine the relationship between CEO forced turnover and customer
concentration. It would be more difficult for suppliers with higher customer concentration to
dismiss their CEOs because the role of CEOs in managing relationships with customers may
be more valuable in such suppliers.
To measure the forced CEO turnover, we initially collect information on CEO departure
from the ExecuComp database. Next, we manually identify reasons for CEO departure using
the Factiva search engine, and classify CEO departure into forced turnover and voluntary
leave. Following Parrino (1997) and Campbell, Gallmeyer, Johnson, Rutherford and Stanley
(2011), we define CEO departure as forced turnover if related news satisfies the following
criteria: (1) The CEO turnover is not announced over at least six months prior to this event.
(2) The departing CEO does not leave due to reasons of health problem, death, or the
acceptance of another position elsewhere. (3) The departing CEO is under the age of 60 - that
is, unlikely to be retired. (4) The departing CEO does not sit on the board of directors after
leaving the CEO position. Finally, we construct Forced CEO Turnover, which is equal to one
if the CEO is forced out and zero otherwise.
Panel A of Table 9 presents the results from the logit and Cox regressions of forced CEO
turnover on customer concentration. Following Campbell et al. (2011), we control for various
firm- and CEO-level characteristics, including firm size (ln(Assets)), industry adjusted ROA
(Industry Adjusted ROA), excess stock return (Excess Stock Return), stock return volatility
(Stock Return Volatility), firm ages (ln(Firm Age)), CEO tenure (ln(CEO Tenure)), CEO total
compensation (ln(CEO Total Compensation)), and CEO ownership (CEO Ownership). The
detailed definitions for all variables are provided in Appendix A. All coefficients on Customer
26
Concentration are significantly negative, indicating that the likelihood of CEOs being forced
out decreases for suppliers with higher customer concentration.
[Insert Table 9 here]
We next explore the effect of CEO tenure on the relationship between CEO forced
turnover and customer concentration. We expect that a negative relationship between CEO
forced turnover and customer concentration strengthens in suppliers with longer CEO tenure
because long-tenured CEOs may build closer personal connections with primary customers.
In Panel B of Table 9, we decompose the sample into two groups depending on whether or
not the supplier’s CEO tenure is longer than the median of the total sample firms’ CEO
tenure. We then re-run the logit regressions of Panel A for each subsample to compare the
effect of customer concentration on CEO forced turnover between the two groups. We find
that the coefficients on Customer Concentration are more significant and smaller for
suppliers with a longer-tenured CEO than those with a shorter-tenured CEO.
5.3 Customer Switching Costs
We now turn our attention to a potential circumstance wherein customer concentration
can induce managerial agency problems manifested within suppliers. Particularly, we focus
on customer switching costs. Managing supplier–customer relationships is more valuable to
suppliers when their customers can easily replace them. Under this corporate environment,
the role of managers in keeping this relationship increases. Thus, we expect customer
concentration’s negative influence on agency problems to be more pronounced for suppliers
whose customers have lower switching costs, which are fixed costs incurred by customers
when changing suppliers. Following the extant literature (e.g., Inderst and Wey, 2007; Hui,
Klasa, and Yeung, 2012; Dhaliwal et al., 2016), we construct two customer switching cost
27
variables, namely Supplier Market Share and Fraction of Customer COGS. When a supplier’s
market share is low, its customers may readily find alternative suppliers in the market. Based
on this thought, the suppliers’ market share is used to measure customer switching costs.
Supplier Market Share is a supplier’s sales divided by total sales of the supplier’s industry. As
an additional measurement of customer switching costs, we employ Fraction of Customer
COGS, which is the weighted sum of each major customer’s purchases from the supplier
divided by each customer’s cost of goods sold (COGS). The weight is the supplier’s
percentage sales to each major customer. As a fraction of the supplier’s sales over a
customer’s COGS is smaller, the customer becomes less dependent on the supplier, which
lowers the cost of changing the supplier. Using these two variables on customer switching
costs, we split the supplier sample into two subsamples according to whether or not major
customers have lower switching costs than the median of the total sample’s switching costs.
We conjecture that the negative relationship between customer concentration and the market
value of excess cash is likely to strengthen when the market share of the supplier is low, and
when the supplier’s sales account for a lower proportion of the customer’s COGS.
Table 10 presents the results of the subsample analysis based on customer switching
costs. In Panel A, we use Supplier Market Share to measure customer switching costs. The
coefficients on Customer Concentration*Excess Cash are more significant and smaller for
suppliers with low market shares than for suppliers with high market shares. Those results
suggest that the negative association between customer concentration and market value of
excess cash is prominent when customers can switch their suppliers at low costs.
[Insert Table 10 here]
In Panel B, we conduct the subsample analysis using Fraction of Customer COGS.17 We
17 Since customer information is required to measure Fraction of Customer COGS, suppliers whose major
28
find that the negative effect of customer concentration on the market value of excess cash is
magnified for suppliers with low Fraction of Customer COGS, compared with those who
have high Fraction of Customer COGS. This result is consistent with our prediction on
customer switching costs.
5.4 Corporate Governance
For robustness, we investigate whether our observed relationship between customer
concentration and the market value of excess cash holdings is influenced by corporate
governance. Because corporate governance is closely associated with the market value of
excess cash holdings (Dittmar and Mahrt-Smith, 2007), the suppliers’ governance may affect
our results. To alleviate this concern, we try to control for corporate governance in our
regressions. To proxy corporate governance, we use the entrenchment index (E-index)
constructed by Bebchuk, Cohen, and Ferrell (2009), which is based on six antitakeover
provisions.18 In general, high E-index indicates weak governance in that anti-takeover
provisions enable managers to be more entrenched, thus aggravating agency problems. Table
11 presents the results on regressions that include E-index. These results indicate that our
main findings still remain unchanged when we control for corporate governance.
[Insert Table 11 here]
4. Conclusion
In this paper, we focus on analyzing the effect of customer concentration on managerial
agency costs in suppliers. To test this, we investigate how the extent of suppliers’ customer
customers are available in Compustat are only included in our analysis. Thus, our sample in Panel B of Table 10
substantially reduces. 18 The E-index is constructed using the ISS database. See Appendix A for a detailed definition of the E-index.
29
concentration influences the market value of their excess cash holdings. We conjecture that
excess cash holdings in suppliers with higher concentrated customers will be valued lower in
the capital market because managerial agency problems are expected to be more severe in
such suppliers. Consistent with this conjecture, our empirical results show that customer
concentration is negatively related to the market value of suppliers’ excess cash reserves. The
results persist when we address endogeneity issues and conduct additional robustness tests.
To the best of our knowledge, this study is the first to directly explore managerial agency
costs of suppliers that may stem from their concentrated customer base. The extant literature
largely examines the role of customers in suppliers’ governance, finding that major customers
have incentives to monitor managers of suppliers. However, monitoring incentives of
customers are different from those of shareholders, and thus agency conflicts between
managers and shareholders still exist within customer-dependent suppliers in spite of
monitoring of suppliers’ managers by customers. Along this line of thought, we turn our
attention to managerial agency costs of suppliers with higher customer concentration. We
suggest a mechanism through which managerial agency problems arise for such suppliers.
Moreover, our study adds to the literature that examines management entrenchment related to
investments in manager-specific assets. We also contribute to the existing studies on the value
of cash holdings by providing evidence on how the extent of customer concentration affects
the market value of suppliers’ excess cash.
30
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Appendix A. Variable Definitions
Variable Definition
Customer Concentration Measures
Major Customer An indicator variable equal to one if the supplier reports at least one
corporate customer that accounts for more than 10% of its sales and zero
otherwise
Major Customer Sales Fraction of the supplier’s total sales generated by major customers
Customer HHI Sum of squares for ratios of the supplier’s sales to each major customer
over its total sales
Cash Measures
Xcash Excess cash holdings, calculated as the residuals from the normal cash
regression in column (6) of Table A1
Cash Ratio Ratio of cash holdings to total assets
Firm Characteristics
Assets Total assets
Net Assets Total assets minus cash holdings
Market Value Sum of the market value of equity and the book value of short- and
long-term debt
Forecasted Market Value Sum of the forecasted market value of equity and the book value of
short- and long-term debt; the forecasted market value of equity is
defined as the product of the common shares outstanding and the
average target price predicted by analysts
Earnings Net income before interests and extraordinary items
R&D Expense Research and development expenses, which are set to zero if missing
Interest Expense Interest expenses
Dividends Amount of dividend paid to common shares
ROA Ratio of operating income before depreciation to total assets
Leverage Sum of short- and long-term debt divided by total assets
Industry Adjusted ROA ROA minus industry median ROA
Excess Stock Return Buy-and-hold stock returns minus value-weighted market returns over
the given year
Stock Return Volatility Standard deviation of monthly stock returns over the given year
Firm Age Ages of the firm based on the years listed on Compustat
Tobin’s Q Ratio of the market value of assets to the book value of assets
CAPEX Capital expenditures
Advertising Expense Advertising expenses
CF Cash flow, calculated as earnings before interests and taxes
Std. Industry CF Industry median of cash flow’s standard deviation over the past ten
years
NWC Net working capital, calculated as current assets minus current liabilities
and cash holdings
Market-to-Book Ratio Ratio of the market value of equity to the book value of equity
Instrumental Variables
Customer M&A Weighted sum of acquisition activities across industries in which the
supplier’s major customers operate; the weight is the supplier’s
percentage sales to each major customer; industry-level acquisition
activities are calculated as the average acquisitions of firms in the
industry over the past five years; deal values scaled by acquirer’s total
sales are used as a proxy for each firm’s acquisition level
3-year Lagged Sales Growth Three-year lagged compound sales growth
CEO Characteristics
ustomer-related CEO An indicator variable equal to one if the supplier’s CEO previously
34
served as a senior manager or board member at one or more major
customers and zero otherwise
Forced CEO Turnover An indicator variable equal to one if the CEO is forced out and zero
otherwise
CEO Tenure CEO tenure in the given year
CEO Total Compensation Total annual compensation of CEO
CEO Excess Compensation Residuals from the regression of the natural logarithm of CEO total
compensation on the natural logarithm of total market value of firm
CEO Ownership Percentage of the firm’s equity owned by the CEO
Deal Characteristics
CAR(-2, +2) Five-day cumulative abnormal returns over the (-2, +2) window around
the announcement date of mergers
Negative CAR(-2, +2) Dummy An indicator variable equal to one if CAR(-2, +2) is negative and zero
otherwise
Relative Deal Size A deal value scaled by acquirer’s market value of assets
High-tech An indicator variable equal to one if both acquirer and target operate in
the high-tech industries defined by Loughran and Ritter (2004) and zero
otherwise
Diversifying Merger An indicator variable equal to one if the acquirer and target have
different first two-digit SIC code and zero otherwise
Public Target An indicator variable equal to one if the target is a public target and zero
otherwise
Private Target An indicator variable equal to one if the target is a private target and
zero otherwise
Subsidiary Target An indicator variable equal to one if the target is a subsidiary target and
zero otherwise
Stock Deal An indicator variable equal to one if the deal is at least partially financed
by stocks and zero otherwise
All Cash Deal An indicator variable equal to one if the deal is only financed by cash
and zero otherwise
Switching Cost Measures
Supplier Market Share Supplier’s sales divided by total sales of its industry
Fraction of Customer COGS The weighted sum of each major customer’s purchases from the supplier
divided by each customer’s cost of goods sold (COGS); the weight is the
supplier’s percentage sales to each major customer
Corporate Governance Measure
E-index Bebchuk et al.’s (2009) entrenchment index; this index is calculated as
the number of antitakeover provisions held by the firm among its six
provisions: staggered boards, limits to shareholder by-law amendments,
supermajority requirements for mergers, supermajority requirements for
charter amendments, poison pills, and golden parachutes.
35
Appendix B. Measuring Excess Cash
In this appendix, we explain our methodology to measure excess cash. Following Dittmar
and Mahrt-Smith (2007), excess cash is defined as the difference between actual cash and the
predicted normal level of cash. The regression model for predicting the normal level of cash
is shown below
ln (𝐶𝑎𝑠ℎ 𝑅𝑎𝑡𝑖𝑜)𝑖,𝑡 = 𝛼 + 𝛽1 𝑀𝑎𝑟𝑘𝑒𝑡 𝑉𝑙𝑎𝑢𝑒/𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡 + 𝛽2ln (𝐴𝑠𝑠𝑒𝑡𝑠)𝑖,𝑡 +
𝛽3𝐶𝐹/𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡 + 𝛽4𝑆𝑡𝑑. 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐶𝐹/𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡 +
𝛽5𝑁𝑊𝐶/𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡 + 𝛽6𝑅&𝐷 𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑠/𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡 +
𝛽7𝐶𝐴𝑃𝐸𝑋/𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡 + 𝛽8𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖,𝑡 + 𝛽9𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑/𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡 +
𝛽10𝑀𝑎𝑗𝑜𝑟 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑖,𝑡 + 𝑌𝑒𝑎𝑟 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 +
𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 + 𝜀𝑖,𝑡 (B.1)
where Cash Ratio is the ratio of cash holdings to total assets; Market Value is the sum of the
market value of equity and the book value of short-term and long-term debt; Assets is total
assets; CF is cash flow, calculated as earnings before interests and taxes. Moreover, Std.
Industry CF is the industry median of cash flow’s standard deviation over the past 10 years;
NWC is net working capital, which is calculated as current assets minus current liabilities and
cash holdings; R&D Expense is research and development expenses, which are set to zero if
missing. Finally, CAPEX is capital expenditures; Leverage is the sum of short-term and long-
term debt divided by total assets; Dividend is the amount of dividend paid to common shares;
Major Customer is an indicator variable equal to one if the supplier reports at least one
corporate customer that accounts for more than 10% of its sales and zero otherwise.
In our normal cash model, we include Major Customer to control for customer
concentration because recent studies suggest a positive relationship between customer
36
concentration and cash holdings.19 We also use 2SLS regressions with three-year lagged
sales growth (3-year Lagged Sales Growth) as an instrumental variable for Market
Value/Assets by following Dittmar and Mahrt-Smith (2007) and Frésard and Salva (2010).
Since the proxy for investment opportunities (Market Value/Assets) in equation (B.1) is
simultaneously used as the dependent variable for market value in our main model (equation
(3)), we introduce a 2SLS estimation to mitigate endogeneity issues. 3-year Lagged Sales
Growth appears to satisfy two requirements to make the instrumental variable valid. First, a
positive relationship between past sales growth and market-to-book ratio is likely to exist
because these variables are generally used to proxy a firm’s investment opportunities.
Second, 3-year Lagged Sales Growth may be exogenous to corporate decisions on cash
holdings—that is, current cash holdings are not able to affect past sales growth.
Table A1 reports the results on regressions that estimate equation (4). OLS regression and
2SLS regression results are present in columns (1)–(2) and columns (3)–(6), respectively. The
coefficients on Major Customer Sales in columns (2) and (6) are significantly positive,
confirming that we need to control for a customer concentration. Furthermore, the
coefficients on 3-year Lagged Sales Growth are significantly positive in columns (3) and (4).
The F-statistics obtained from the Wu–Hausman test indicate that we should treat the
endogeneity concern. The F-statistics in the first-stage regressions also reject that the
instrument is weak. Finally, excess cash holdings used in our main regressions are the
residual obtained from the regression in column (6).20
[Insert Table B1 here]
19 See Itzkowitz (2013) and Bae and Wang (2015). 20 Our main results remain unchanged even when we measure excess cash holdings as the residuals of
regressions in columns (2) and (5).
37
Table 1. Summary Statistics and Univariate Test
This table presents summary statistics and the results on univariate tests. Panel A reports the number of observations,
25th percentile, mean, median, 75th percentile and standard deviations for customer concentration measures, cash holding
measures, and various firm characteristics. All continuous variables are winsorized at the 1% level. Variable definitions
are provided in Appendix A. Panel B presents mean and median comparison test results of cash holding measures and
firm characteristics between two groups. The sample is split into two subsamples according to whether the supplier
reports at least one major customer. t-tests and Willcoxon-Mann-Whitney tests are conducted for the comparison test in
the means and medians, respectively. Significance at the 10%, 5% and 1% is indicated by *, ** and ***, respectively.
Panel A. Summary Statistics
Obs. 25% Mean Median 75% Std.
Customer concentration measures
Major Customer 67,016 0.000 0.345 0.000 1.000 0.475
Major Customer Sales 67,016 0.000 0.139 0.000 0.202 0.240
Customer HHI 67,016 0.000 0.047 0.000 0.033 0.113
Customer concentration measures for suppliers with at least one major customer
Major Customer Sales 23,121 0.187 0.402 0.346 0.571 0.247
Customer HHI 23,121 0.029 0.136 0.073 0.176 0.157
Cash holding measures
Xcash 67,016 0.396 0.867 0.776 1.233 0.588
Cash Ratio 67,016 0.070 0.198 0.135 0.259 0.182
Firm characteristics
Assets (in millions) 67,016 24.148 2,260.833 133.896 816.207 7,437.480
Net Assets (in millions) 67,016 17.543 1,974.522 102.595 683.874 6,614.770
Market Value/Assets 67,016 0.653 1.159 0.941 1.426 0.855
Earnings/Assets 67,016 0.050 0.087 0.080 0.113 0.054
R&D Expense/Assets 67,016 0.000 0.038 0.000 0.037 0.083
Interest Expense/Assets 67,016 0.005 0.027 0.017 0.035 0.035
Dividends/Assets 67,016 0.000 0.010 0.000 0.013 0.019
Panel B. Univariate Tests
Suppliers with at least
one major customer
(Obs. = 23,122)
Suppliers with no
major customers
(Obs. = 43,894) Test of difference
Mean Median Mean Median Mean Median
Xcash 0.899 0.824 0.849 0.752 0.050*** 0.072***
Cash Ratio 0.234 0.169 0.178 0.122 0.056*** 0.047***
Assets (in millions) 1010.119 77.566 2919.671 181.154 -1909.552*** -103.588***
Net Assets (in millions) 841.884 53.477 2571.161 146.904 -1729.277*** -93.427***
Market Value/Assets 1.266 1.013 1.103 0.907 0.163*** 0.106***
Earnings/Assets 0.087 0.077 0.087 0.080 0.000 -0.003
R&D Expense/Assets 0.057 0.007 0.028 0.000 0.029*** 0.007***
Interest Expense/Assets 0.025 0.014 0.028 0.019 -0.003*** -0.005***
Dividends/Assets 0.007 0.000 0.011 0.000 -0.004*** -0.000***
38
Table 2. Customer Concentration and Market Value of Excess Cash Holdings
This table presents the results of baseline regressions of market value of excess cash on customer concentration. Market
Value/Assets is calculated as the sum of the market value of equity and the book value of short-term and long-term debt,
divided by total assets. Xcash is excess cash holdings, which are defined as the residuals from the normal cash regression
in column (6) of Table A1. Three customer concentration variables are used: (1) Major Customer is an indicator variable
that is equal to one if the supplier reports at least one corporate customer that accounts for more than 10% of its sales,
and zero otherwise. (2) Major Customer Sales is the fraction of the supplier’s total sales generated by major customers.
(3) Customer HHI is the squared sum of ratios of the supplier’s sales to each major customer over its total sales. ∆L2
and ∆2 indicate the change in a relevant variable from year t-2 to year t and from year t and t+2, respectively. Variable
definitions are provided in Appendix A. All regressions include year and industry fixed effects, based on two-digit SIC
codes. All continuous variables are winsorized at the 1% level. Standard errors are corrected for clustering at the firm
level. t-statistics are in parentheses. Significance at the 10%, 5% and 1% is indicated by *, ** and ***, respectively.
Dependent variable = Market Value/Assets
All suppliers
Suppliers with at least
one major customer
Major
Customer
Major
Customer Sales Customer HHI
Major
Customer Sales Customer HHI
(1) (2) (3) (4) (5)
Customer Concentration 0.149*** 0.468*** 1.050*** 0.582*** 0.931***
(7.76) (10.34) (10.41) (8.44) (7.86)
Xcash -0.253*** -0.249*** -0.259*** -0.213*** -0.288***
(-24.79) (-25.44) (-27.62) (-8.82) (-16.25)
Customer Concentration*Xcash -0.116*** -0.354*** -0.875*** -0.442*** -0.761***
(-7.28) (-9.26) (-9.02) (-7.26) (-6.50)
Earnings/Assets -0.914*** -0.909*** -0.901*** -0.937*** -0.929***
(-15.88) (-15.84) (-15.72) (-11.43) (-11.35)
∆L2 Earnings/Assets 0.315*** 0.312*** 0.311*** 0.275*** 0.273***
(10.17) (10.10) (10.05) (7.24) (7.20)
∆2 Earnings/Assets -0.185*** -0.180*** -0.175*** -0.181*** -0.176***
(-5.19) (-5.07) (-4.95) (-3.72) (-3.60)
∆L2 Net Assets/Assets 0.234*** 0.235*** 0.235*** 0.271*** 0.269***
(15.56) (15.66) (15.64) (11.25) (11.19)
∆2 Net Assets/Assets 0.242*** 0.241*** 0.241*** 0.239*** 0.240***
(17.51) (17.47) (17.49) (12.19) (12.22)
R&D Expense/Assets 2.181*** 2.146*** 2.144*** 1.942*** 1.943***
(18.85) (18.60) (18.65) (12.95) (12.99)
∆L2 R&D Expense/Assets 1.003*** 1.004*** 1.003*** 0.910*** 0.910***
(6.90) (6.94) (6.95) (4.97) (4.99)
∆2 R&D Expense/Assets 2.271*** 2.245*** 2.254*** 2.268*** 2.287***
(14.79) (14.74) (14.82) (11.60) (11.71)
Interest Expense/Assets 5.690*** 5.703*** 5.700*** 5.883*** 5.887***
(19.68) (19.77) (19.82) (13.92) (13.97)
∆L2 Interest Expense/Assets -0.958*** -0.950*** -0.943*** -0.914** -0.894**
(-3.74) (-3.72) (-3.70) (-2.49) (-2.44)
∆2 Interest Expense/Assets 0.643*** 0.637*** 0.643*** 0.631 0.647
39
(2.69) (2.66) (2.69) (1.54) (1.58)
Dividends/Assets 8.191*** 8.247*** 8.187*** 8.686*** 8.626***
(19.69) (19.88) (19.76) (13.49) (13.45)
∆L2 Dividends/Assets 1.986*** 1.952*** 1.985*** 1.153* 1.180*
(5.08) (5.01) (5.10) (1.75) (1.78)
∆2 Dividends/Assets 6.032*** 6.043*** 6.011*** 5.402*** 5.365***
(16.96) (17.00) (16.97) (9.50) (9.45)
∆2 Market Value/Assets -0.039*** -0.039*** -0.039*** -0.048*** -0.048***
(-8.34) (-8.30) (-8.33) (-7.61) (-7.64)
Observations 67,016 67,016 67,016 23,121 23,121
Adjusted R-squared 0.350 0.352 0.352 0.385 0.385
Year Fixed Effects Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes
40
Table 3. Customer Concentration and Alternative Measures of Market Value of Excess Cash Holdings: Market
Value of Cash Ratio and Forecasted Market Value of Cash Holdings
This table presents the results of regressions using alternative measures of the market value of excess cash holdings to
examine the effect of customer concentration on market value of excess cash holdings. Market Value/Assets is defined
as the sum of the market value of equity and the book value of short- and long-term debt, divided by total assets.
Forecasted Market Value/Assets is defined as the sum of the forecasted market value of equity and the book value of
short- and long-term debt. Cash Ratio is the ratio of cash holdings to total assets. Xcash is excess cash holdings, which
are defined as the residuals from the normal cash regression in column (6) of Table A1. To measure a level of customer
concentration, three different variables are used: (1) Major Customer is an indicator variable that is equal to one if the
supplier reports at least one corporate customer that accounts for more than 10% of its sales and zero otherwise. (2)
Major Customer Sales is the fraction of the supplier’s total sales generated by major customers. (3) Customer HHI is
the sum of squares for ratios of the supplier’s sales to each major customer over its total sales. Variable definitions are
provided in Appendix A. The estimation results of other controls are omitted for brevity. All regressions include year
and industry fixed effects, based on two-digit SIC codes. Continuous variables are winsorized at the 1% level. Standard
errors are corrected for clustering at the firm level. t-statistics are in parentheses. Significance at the 10%, 5% and 1%
is indicated by *, ** and ***, respectively.
Panel A. Customer Concentration and the Market Value of Cash Ratio
Dependent variable = Market Value/Assets
Major Customer Major Customer Sales Customer HHI
(1) (2) (3)
Customer Concentration 0.008 0.078* 0.387***
(0.42) (1.69) (3.47)
Cash Ratio 1.904*** 1.941*** 1.919***
(19.24) (20.77) (21.47)
Customer Concentration*Cash Ratio -0.269** -0.808*** -2.167***
(-2.31) (-3.75) (-4.24)
Observations 123,639 123,639 123,639
Adjusted R-squared 0.453 0.453 0.453
Control Variables Included Included Included
Year Fixed Effects Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
Panel B. Customer Concentration and Forecasted Market Value of Excess Cash Holdings
Dependent variable = Forecasted Market Value/Assets
Major Customer Major Customer Sales Customer HHI
(1) (2) (3)
Customer Concentration 0.523 0.913 3.127
(0.93) (0.65) (0.91)
Xcash -0.139 -0.307 -0.468
(-0.29) (-0.68) (-1.20)
Customer Concentration*Xcash -2.132*** -3.808** -7.410**
(-2.83) (-2.30) (-2.16)
Observations 15,843 15,843 15,843
Adjusted R-squared 0.210 0.210 0.210
Control Variables Included Included Included
Year Fixed Effects Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
41
Table 4. Customer Concentration and Market Value of Excess Cash Holdings: Propensity Score Matching
Analysis
This table presents the results of regressions using the propensity score matched sample to examine the effect of
customer concentration on market value of excess cash holdings. Dependent variable is Market Value/Assets, which is
calculated as the sum of the market value of equity and the book value of short- and long-term debt divided by total
assets. Xcash is excess cash holdings, which are defined as the residuals from the normal cash regression in column (6)
of Table A1. To measure a level of customer concentration, three different variables are used: (1) Major Customer is an
indicator variable that is equal to one if the supplier reports at least one corporate customer that accounts for more than
10% of its sales and zero otherwise. (2) Major Customer Sales is the fraction of the supplier’s total sales generated by
major customers. (3) Customer HHI is the sum of squares for ratios of the supplier’s sales to each major customer over
its total sales. Variable definitions are provided in Appendix A. Control variables are identical to controls in Table 2,
whose estimates are omitted for brevity. All regressions include year and industry fixed effects, based on two-digit SIC
codes. Continuous variables are winsorized at the 1% level. Standard errors are corrected for clustering at the firm level.
t-statistics are in parentheses. Significance at the 10%, 5% and 1% is indicated by *, ** and ***, respectively.
Dependent variable = Market Value/Assets
Major Customer Major Customer Sales Customer HHI
(1) (2) (3)
Customer Concentration 0.064** 0.341*** 0.815***
(2.55) (6.56) (7.56)
Xcash -0.338*** -0.317*** -0.325***
(-18.07) (-19.15) (-21.76)
Customer Concentration*Xcash -0.042* -0.238*** -0.642***
(-1.94) (-5.25) (-6.07)
Observations 46,244 46,244 46,244
Adjusted R-squared 0.378 0.380 0.380
Control Variables Included Included Included
Year Fixed Effects Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
42
Table 5. Customer Concentration and Market Value of Excess Cash Holdings: CEO and Firm Fixed Effects
This table presents the results of regressions with CEO and firm fixed effects to examine the effect of customer
concentration on market value of excess cash holdings. Dependent variable is Market Value/Assets, which is calculated
as the sum of the market value of equity and the book value of short- and long-term debt divided by total assets. Xcash
is excess cash holdings, which are defined as the residuals from the normal cash regression in column (6) of Table A1.
To measure a level of customer concentration, three different variables are used: (1) Major Customer is an indicator
variable that is equal to one if the supplier reports at least one corporate customer that accounts for more than 10% of
its sales and zero otherwise. (2) Major Customer Sales is the fraction of the supplier’s total sales generated by major
customers. (3) Customer HHI is the sum of squares for ratios of the supplier’s sales to each major customer over its total
sales. Variable definitions are provided in Appendix A. Control variables are identical to controls in Table 2, whose
estimates are omitted for brevity. All regressions include year fixed effects. Continuous variables are winsorized at the
1% level. Standard errors are corrected for clustering at the firm level. t-statistics are in parentheses. Significance at the
10%, 5% and 1% is indicated by *, ** and ***, respectively.
Dependent variable = Market Value/Assets
CEO fixed effects Firm fixed effects
Major
Customer
Major Customer
Sales
Customer
HHI
Major
Customer
Major Customer
Sales
Customer
HHI
(1) (2) (3) (4) (5) (6)
Customer Concentration 0.136*** 0.325*** 0.664*** 0.104*** 0.327*** 0.695***
(4.54) (4.46) (3.88) (5.90) (7.33) (7.00)
Xcash -0.335*** -0.337*** -0.349*** -0.324*** -0.320*** -0.331***
(-12.76) (-13.28) (-14.22) (-26.07) (-26.99) (-29.30)
Customer Concentration*Xcash -0.140*** -0.348*** -0.855*** -0.108*** -0.307*** -0.722***
(-4.37) (-4.40) (-4.49) (-6.85) (-7.94) (-7.39)
Observations 10,683 10,683 10,683 65,245 65,245 65,245
Adjusted R-squared 0.789 0.790 0.789 0.639 0.640 0.640
Control Variables Included Included Included Included Included Included
Year Fixed Effects Yes Yes Yes Yes Yes Yes
CEO Fixed Effects Yes Yes Yes No No No
Firm Fixed Effects No No No Yes Yes Yes
43
Table 6. Customer Concentration and Market Value of Excess Cash Holdings: 2SLS Regressions
This table presents the results of two stage least squares (2SLS) regressions of market value of excess cash on customer
concentration. Customer M&A, defined as the weighted sum of acquisition activities across industries in which the
supplier’s major customers operate, is employed as an instrument variable for customer concentration. To measure a
level of customer concentration, two different variables are used: (1) Major Customer Sales is the fraction of the
supplier’s total sales generated by major customers. (2) Customer HHI is the sum of squares for ratios of the supplier’s
sales to each major customer over its total sales. Market Value/Assets is calculated as the sum of the market value of
equity and the book value of short- and long-term debt divided by total assets. Xcash is excess cash holdings, which are
defined as the residuals from the normal cash regression in column (6) of Table A1. Variable definitions are provided in
Appendix A. Control variables are identical to controls in Table 2, whose estimates are omitted for brevity. All
regressions include year and industry fixed effects, defined based on two-digit SIC codes. Continuous variables are
winsorized at the 1% level. Standard errors are corrected for clustering at the firm level. t-statistics are in parentheses.
Significance at the 10%, 5% and 1% is indicated by *, ** and ***, respectively.
1st stage 2nd stage
Dependent Variable Dependent Variable
Major
Customer Sales
Major Customer
Sales*Xcash
Customer
HHI
Customer
HHI*Xcash
Market Value
/Assets
Market Value
/Assets
(1) (2) (3) (4) (5) (6)
Major Customer Sales 1.795***
(3.09)
Major Customer Sales*Xcash -1.418***
(-3.79)
Customer HHI 3.441***
(3.26)
Customer HHI*Xcash -3.130***
(-3.71)
Customer M&A 0.039*** -0.051*** 0.025*** -0.019***
(4.05) (-4.65) (3.36) (-2.82)
Customer M&A*Xcash 0.018 0.140*** 0.017* 0.072***
(1.47) (7.77) (1.90) (6.59)
Xcash 0.047*** 0.519*** 0.020*** 0.153*** 0.268 0.028
(4.54) (35.24) (3.54) (21.11) (1.46) (0.22)
First Stage F-statistics 38.923*** 38.731*** 34.506*** 33.151*** N/A N/A
Wu-Hausman F-statistics N/A N/A N/A N/A 5.961*** 6.395***
Observations 7,152 7,152 7,152 7,152 7,152 7,152
Adjusted R-squared 0.138 0.550 0.118 0.327 0.320 0.320
Control Variables Included Included Included Included Included Included
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
44
Table 7. Customer Concentration and Alternative Measures of Agency Costs: Suppliers’ CEO Compensation and
Acquisition Decisions
This table presents the results of regressions of suppliers’ CEO compensation and acquisition decisions on customer
concentration. ln(CEO Total Compensation) is the natural logarithm of total annual compensation of CEO. CEO Excess
Compensation is the residuals from the regression of the natural logarithm of CEO total compensation on the natural
logarithm of total market value of firm. CAR(-2, +2) is defined as five-day cumulative abnormal returns over the (-2,
+2) window around the announcement date of mergers. Negative CAR(-2, +2) Dummy is an indicator variable that is
equal to one if CAR(-2, +2) is negative and zero otherwise. To measure a level of customer concentration, three different
variables are used: (1) Major Customer is an indicator variable that is equal to one if the supplier reports at least one
corporate customer that accounts for more than 10% of its sales and zero otherwise. (2) Major Customer Sales is the
fraction of the supplier’s total sales generated by major customers. (3) Customer HHI is the sum of squares for ratios of
the supplier’s sales to each major customer over its total sales. Variable definitions are provided in Appendix A. All
independent variables are lagged by one year. All regressions include year and industry fixed effects, based on two-digit
SIC codes. Continuous variables are winsorized at the 1% level. Standard errors are corrected for clustering at the firm
level. t-statistics are in parentheses. Significance at the 10%, 5% and 1% is indicated by *, ** and ***, respectively.
Panel A. Suppliers’ CEO Compensation
Dependent variable =
ln(CEO Total Compensation)
Dependent variable =
CEO Excess Compensation
Major
Customer
Major Customer
Sales
Customer
HHI
Major
Customer
Major Customer
Sales
Customer
HHI
(1) (2) (3) (4) (5) (6)
Customer Concentration 0.095** 0.171* 0.493** 0.079** 0.146 0.457**
(2.49) (1.91) (2.15) (2.11) (1.64) (2.03)
ln(Assets) 0.453*** 0.453*** 0.453*** 0.045*** 0.045*** 0.045***
(24.26) (24.30) (24.26) (2.73) (2.75) (2.75)
Tobin’s Q 0.045** 0.045** 0.045** -0.086*** -0.086*** -0.086***
(2.28) (2.27) (2.26) (-4.46) (-4.46) (-4.45)
ROA 0.839*** 0.843*** 0.847*** 0.315 0.318 0.321
(4.04) (4.06) (4.07) (1.58) (1.59) (1.61)
Leverage -0.086 -0.083 -0.081 0.024 0.027 0.028
(-0.97) (-0.93) (-0.91) (0.28) (0.31) (0.32)
Excess Stock Return 0.215*** 0.214*** 0.214*** 0.161*** 0.160*** 0.160***
(9.11) (9.05) (9.04) (7.10) (7.05) (7.04)
Stock Return Volatility -0.149 -0.149 -0.154 0.395* 0.396* 0.389*
(-0.65) (-0.64) (-0.67) (1.74) (1.74) (1.71)
R&D Expense/Assets 1.216** 1.221** 1.249** 0.882* 0.884* 0.902*
(2.31) (2.30) (2.37) (1.70) (1.68) (1.73)
CAPEX/Assets -0.453 -0.459 -0.469 -0.567 -0.571 -0.576*
(-1.25) (-1.26) (-1.30) (-1.63) (-1.64) (-1.65)
Advertising Expense/Assets 0.180 0.188 0.192 0.269 0.275 0.280
(0.45) (0.46) (0.47) (0.69) (0.70) (0.71)
ln(Firm Age) 0.016 0.014 0.013 0.038 0.037 0.036
(0.59) (0.53) (0.50) (1.52) (1.46) (1.45)
ln(CEO Tenure) -0.054*** -0.054*** -0.054*** -0.063*** -0.063*** -0.063***
(-2.60) (-2.59) (-2.58) (-3.02) (-3.01) (-3.00)
Observations 10,144 10,144 10,144 10,144 10,144 10,144
Adjusted R-squared 0.533 0.532 0.532 0.062 0.062 0.062
45
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Panel B. Suppliers’ Acquisition Decisions
Dependent variable = CAR(-2, +2) Dependent variable =
Negative CAR(-2, +2) Dummy
Major
Customer
Major Customer
Sales
Customer
HHI
Major
Customer
Major Customer
Sales
Customer
HHI
(1) (2) (3) (4) (5) (6)
Customer Concentration -0.005* -0.012* -0.044* 0.114* 0.296** 1.119**
(-1.80) (-1.87) (-1.69) (1.84) (2.21) (2.13)
ln(Assets) -0.005*** -0.005*** -0.005*** 0.071*** 0.072*** 0.071***
(-7.46) (-7.55) (-7.52) (4.49) (4.57) (4.54)
Tobin’s Q -0.004*** -0.004*** -0.004*** 0.088*** 0.087*** 0.087***
(-4.36) (-4.32) (-4.33) (4.00) (3.96) (3.96)
ROA 0.006 0.005 0.005 -0.501** -0.478** -0.475**
(0.49) (0.41) (0.41) (-2.25) (-2.14) (-2.13)
Leverage 0.013* 0.013* 0.013* -0.185 -0.187 -0.187
(1.78) (1.79) (1.79) (-1.27) (-1.28) (-1.28)
Relative Deal Size 0.005 0.005 0.005 0.052 0.050 0.050
(1.32) (1.34) (1.34) (0.89) (0.86) (0.85)
High-tech 0.002 0.002 0.002 0.089 0.087 0.088
(0.49) (0.50) (0.49) (1.02) (1.01) (1.01)
High-tech*Relative Deal Size -0.044*** -0.044*** -0.044*** 0.329* 0.334* 0.335*
(-3.89) (-3.90) (-3.90) (1.74) (1.77) (1.77)
Diversifying Merger -0.010*** -0.010*** -0.010*** 0.274*** 0.277*** 0.277***
(-4.31) (-4.36) (-4.35) (4.69) (4.74) (4.73)
Public Target*Stock Deal -0.031*** -0.031*** -0.031*** 0.482*** 0.480*** 0.478***
(-8.99) (-8.97) (-8.95) (6.12) (6.10) (6.08)
Public Target*All Cash Deal 0.002 0.002 0.002 -0.193*** -0.194*** -0.195***
(0.78) (0.80) (0.81) (-2.60) (-2.62) (-2.63)
Private Target*Stock Deal 0.005 0.005 0.005 -0.062 -0.061 -0.059
(0.50) (0.50) (0.49) (-0.30) (-0.29) (-0.29)
Private Target*All Cash Deal 0.012 0.012 0.012 -0.133 -0.135 -0.136
(1.21) (1.22) (1.23) (-0.51) (-0.52) (-0.53)
Subsidiary Target*All Cash Deal 0.002 0.002 0.002 -0.348* -0.352* -0.354*
(0.28) (0.31) (0.32) (-1.75) (-1.77) (-1.79)
Observations 7,029 7,029 7,029 7,025 7,025 7,025
Adjusted R-squared 0.069 0.069 0.069 N/A N/A N/A
Pseudo R-squared N/A N/A N/A 0.046 0.046 0.046
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
46
Table 8. Customer Concentration and Customer-related CEO
This table presents the results of univariate comparisons and multivariate regressions to examine the relation between
customer concentration and customer-related CEO. Customer-related CEO is an indicator variable equal to one if the
supplier’s CEO previously served as a senior manager or board member at one or more major customers and zero
otherwise. To measure a level of customer concentration, two different variables are used: (1) Major Customer Sales is
the fraction of the supplier’s total sales generated by major customers. (2) Customer HHI is the sum of squares for ratios
of the supplier’s sales to each major customer over its total sales. Variable definitions are provided in Appendix A. All
regressions include year and industry fixed effects, based on two-digit SIC codes. Continuous variables are winsorized
at the 1% level. Standard errors are corrected for clustering at the firm level. t-statistics are in parentheses. Significance
at the 10%, 5% and 1% is indicated by *, ** and ***, respectively.
Panel A: Univariate Comparisons
High Major Customer Sales
(N = 3,315)
Low Major Customer Sales
(N = 3,317)
Mean Difference
Customer-related CEO 0.044 0.035 0.009**
High Customer HHI
(N = 407)
Low Customer HHI
(N = 6,225)
Mean Difference
Customer-related CEO 0.081cu 0.037 0.044***
Panel B: Multivariate Tests
Dependent variable = Customer-related CEO
Major Customer Sales Customer HHI
(1) (2)
Customer Concentration 0.785* 1.725***
(1.72) (2.80)
ln(Assets) 0.180*** 0.203***
(2.72) (2.93)
Market-to-book Ratio -0.062 -0.071
(-0.72) (-0.82)
Observations 6,016 6,016
Pseudo R-squared 0.109 0.115
Year Fixed Effects Yes Yes
Industry Fixed Effects Yes Yes
47
Table 9. Customer Concentration and Forced CEO Turnover
This table presents the results of logit and Cox hazard regressions of forced CEO turnover on customer concentration.
Forced CEO Turnover is an indicator variable equal to one if the CEO is forced out and zero otherwise. To measure a
level of customer concentration, three different variables are used: (1) Major Customer is an indicator variable that is
equal to one if the supplier reports at least one corporate customer that accounts for more than 10% of its sales and zero
otherwise. (2) Major Customer Sales is the fraction of the supplier’s total sales generated by major customers. (3)
Customer HHI is the sum of squares for ratios of the supplier’s sales to each major customer over its total sales. Variable
definitions are provided in Appendix A. All independent variables are lagged by one year. All regressions include year
and industry fixed effects, based on two-digit SIC codes. Continuous variables are winsorized at the 1% level. Standard
errors are corrected for clustering at the firm level. t-statistics are in parentheses. Significance at the 10%, 5% and 1%
is indicated by *, ** and ***, respectively.
Panel A. Full Sample Analysis
Dependent variable = Forced CEO Turnover
Logit Regressions Cox Regressions
Major
Customer
Major Customer
Sales
Customer
HHI
Major
Customer
Major Customer
Sales
Customer
HHI
(1) (2) (3) (4) (5) (6)
Customer Concentration -0.424* -1.692*** -6.363*** -0.426** -1.674*** -6.282**
(-1.96) (-2.72) (-2.58) (-2.02) (-2.74) (-2.57)
ln(Assets) 0.203** 0.195** 0.193** 0.185** 0.177* 0.175*
(2.19) (2.10) (2.09) (2.04) (1.96) (1.94)
Industry Adjusted ROA -2.146** -2.253** -2.315** -2.057** -2.162** -2.220**
(-2.17) (-2.26) (-2.32) (-2.15) (-2.25) (-2.30)
Excess Stock Return -0.823*** -0.822*** -0.817*** -0.794*** -0.791*** -0.784***
(-3.48) (-3.47) (-3.46) (-3.48) (-3.47) (-3.46)
Stock Return Volatility 1.316 1.391 1.325 1.381 1.454 1.390
(0.80) (0.85) (0.81) (0.87) (0.92) (0.88)
ln(Firm Age) -0.018 -0.033 -0.030 -0.028 -0.042 -0.038
(-0.13) (-0.24) (-0.21) (-0.20) (-0.31) (-0.29)
ln(CEO Tenure) -0.295*** -0.291*** -0.291*** -0.331** -0.330** -0.332**
(-3.15) (-3.12) (-3.12) (-2.55) (-2.53) (-2.55)
ln(CEO Total Compensation) 0.077 0.080 0.079 0.088 0.090 0.088
(0.65) (0.67) (0.66) (0.76) (0.79) (0.77)
CEO Ownership -0.006* -0.006* -0.006* -0.006 -0.006 -0.006
(-1.69) (-1.70) (-1.70) (-1.62) (-1.63) (-1.63)
Observations 10,029 10,029 10,029 10,840 10,840 10,840
Pseudo R-squared 0.0946 0.0972 0.0981 0.0728 0.0746 0.0751
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Panel B. Subsample Analysis
Dependent variable = Forced CEO Turnover
Long CEO Tenure Short CEO Tenure
48
Major
Customer
Major Customer
Sales
Customer
HHI
Major
Customer
Major Customer
Sales
Customer
HHI
(1) (2) (3) (4) (5) (6)
Customer Concentration -0.569* -1.985** -6.427** -0.272 -1.285 -5.738*
(-1.65) (-2.16) (-1.98) (-0.94) (-1.50) (-1.67)
Observations 5,223 5,223 5,223 3,580 3,580 3,580
Pseudo R-squared 0.104 0.107 0.107 0.122 0.124 0.126
Control Variables Included Included Included Included Included Included
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
49
Table 10. Customer Concentration and Market Value of Excess Cash Holdings: Low vs. High Customer
Switching Costs
This table reports the results of regressions of market value of excess cash on customer concentration according to
subsample analysis of customer switching costs. We use two customers switching cost variables, Supplier Market Share
and Fraction of Customer COGS. Supplier Market Share is defined as a supplier’s sales divided by total sales of the
supplier’s industry. Fraction of Customer COGS is defined as the weighted sum of each major customer’s purchases
from the supplier divided by each customer’s cost of goods sold (COGS). Market Value/Assets is calculated as the sum
of the market value of equity and the book value of short- and long-term debt divided by total assets. Xcash is excess
cash holdings, which are defined as the residuals from the normal cash regression in column (6) of Table A1. To measure
a level of customer concentration, three different variables are used: (1) Major Customer is an indicator variable that is
equal to one if the supplier reports at least one corporate customer that accounts for more than 10% of its sales and zero
otherwise. (2) Major Customer Sales is the fraction of the supplier’s total sales generated by major customers. (3)
Customer HHI is the sum of squares for ratios of the supplier’s sales to each major customer over its total sales. Variable
definitions are provided in Appendix A. Chi-squared test statistics test for the equality of coefficient estimates on
Customer Concentration*Xcash between low and high Supplier Market Share subsamples and also between low and
high Fraction of Customer COGS subsamples. Control variables are identical to controls in Table 2, whose estimates
are omitted for brevity. All regressions include year and industry fixed effects, based on two-digit SIC codes. Continuous
variables are winsorized at the 1% level. Standard errors are corrected for clustering at the firm level. t-statistics are in
parentheses. Significance at the 10%, 5% and 1% is indicated by *, ** and ***, respectively.
Panel A. Supplier Market Share
Dependent variable = Market Value/Assets
Low Supplier Market Share High Supplier Market Share
(1) (2) (3) (4) (5) (6)
Major
Customer
Major Customer
Sales
Customer
HHI
Major
Customer
Major Customer
Sales
Customer
HHI
Customer Concentration 0.116*** 0.466*** 0.978*** 0.066*** 0.139** 0.431**
(4.28) (8.03) (8.29) (2.82) (2.32) (2.34)
Xcash -0.346*** -0.332*** -0.348*** -0.155*** -0.155*** -0.153***
(-21.57) (-22.13) (-24.73) (-13.48) (-13.97) (-14.26)
Customer Concentration*Xcash -0.108*** -0.365*** -0.831*** -0.029 -0.093* -0.419**
(-5.00) (-7.63) (-7.37) (-1.37) (-1.73) (-2.40)
Chi-squared Test Statistics N/A N/A N/A 7.03*** 14.76*** 3.96**
Observations 35,445 35,445 35,445 31,569 31,569 31,569
Adjusted R-squared 0.386 0.388 0.389 0.348 0.347 0.347
Control Variables Included Included Included Included Included Included
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes
Panel B. Fraction of Customer Cost of Goods Sold
Dependent variable = Market Value/Assets
Low Fraction of Customer COGS High Fraction of Customer COGS
(1) (2) (3) (4)
Major Customer Sales Customer
HHI
Major Customer Sales Customer HHI
Customer Concentration 0.547*** 1.303*** 0.219 0.553**
(4.09) (3.86) (1.36) (2.48)
Xcash -0.176*** -0.242*** -0.355*** -0.352***
(-3.96) (-6.72) (-4.50) (-7.55)
50
Customer Concentration*Xcash -0.408*** -0.926*** -0.128 -0.367*
(-3.41) (-3.21) (-0.92) (-1.81)
Chi-squared Test Statistics N/A N/A 2.51 2.71*
Observations 4,466 4,466 4,465 4,465
Adjusted R-squared 0.403 0.404 0.392 0.394
Control Variables Included Included Included Included
Year Fixed Effects Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes
51
Table 11. Customer Concentration and Market Value of Excess Cash Holdings: Controlling for Corporate
Governance
This table reports the results of regressions of market value of excess cash on customer concentration controlling for the
supplier’s governance. E-index is Bebchuk, Cohen, and Ferrell (2009) index of 6-antitakeover provisions with higher
values indicating weaker governance. Dependent variable is Market Value/Assets, which is calculated as the sum of the
market value of equity and the book value of short-term and long-term debt divided by total assets. Xcash is excess cash
holdings, which are defined as the residuals from the normal cash regression in column (6) of Table A1. To measure a
level of customer concentration, three different variables are used: (1) Major Customer is an indicator variable that is
equal to one if the supplier reports at least one corporate customer that accounts for more than 10% of its sales, and zero
otherwise. (2) Major Customer Sales is the fraction of the supplier’s total sales generated by major customers. (3)
Customer HHI is the sum of squares for ratios of the supplier’s sales to each major customer over its total sales. Variable
definitions are provided in appendix A. Control variables are identical to controls in Table 2, whose estimates are omitted
for brevity. All regressions include year and industry fixed effects, defined based on two-digit SIC codes. Continuous
variables are winsorized at the 1% level. Standard errors are corrected for clustering at the firm level. t-statistics are in
parentheses. Significance at the 10%, 5% and 1% is indicated by *, ** and ***, respectively.
Dependent variable = Market Value/Assets
(1) (2) (3)
Major Customer Major Customer Sales Customer HHI
Customer Concentration 0.072** 0.191** 0.479**
(1.99) (2.32) (2.25)
Xcash -0.277*** -0.265*** -0.271***
(-11.38) (-11.24) (-11.97)
Customer Concentration*Xcash -0.105* -0.241** -0.682**
(-1.81) (-2.27) (-2.51)
E-index -0.045*** -0.045*** -0.045***
(-4.10) (-4.06) (-4.06)
Observations 13,910 13,910 13,910
Adjusted R-squared 0.495 0.496 0.495
Control Variables Included Included Included
Year Fixed Effects Yes Yes Yes
Industry Fixed Effects Yes Yes Yes
52
Table B1. The Estimation of the Normal Cash Level
This table presents the normal cash regression results to measure excess cash holdings. Ordinary least squares (OLS)
regressions are used in columns (1) and (2), while two stage least squares (2SLS) regression is employed columns (3)
to (6). We employ 3-year Lagged Sales Growth, which is 3-year lagged compounding sales growth of suppliers, as an
instrument for the market-to-book ratio. Major Customer, which is an indicator variable equal to one if the supplier
reports at least one corporate customer that accounts for more than 10% of its sales and zero otherwise, is included as a
control variable in columns (2), (4) and (6). Cash Ratio is the ratio of cash holdings to total assets. Market Value/Assets
is defined as the market value divided by total assets. Variable definitions are provided in Appendix A. All regressions
include year and industry fixed effects, based on two-digit SIC codes. Continuous variables are winsorized at the 1%
level. Standard errors are corrected for clustering at the firm level. t-statistics are in parentheses. Significance at the 10%,
5% and 1% is indicated by *, ** and ***, respectively.
OLS 2SLS – 1st stage 2SLS – 2nd stage
(1) (2) (3) (4) (5) (6)
ln(Cash
Ratio)
ln(Cash
Ratio)
Market Value
/Assets
Market Value
/Assets
ln(Cash
Ratio)
ln(Cash
Ratio)
Market Value/Assets 0.089*** 0.089*** 0.491*** 0.489***
(23.56) (23.60) (9.78) (9.78)
3-year Lagged Sales Growth 0.032*** 0.032***
(13.89) (13.92)
ln(Assets) 0.033*** 0.034*** -0.015*** -0.017*** 0.039*** 0.041***
(7.96) (7.96) (-3.10) (-3.43) (8.54) (8.66)
CF/Assets 0.482*** 0.480*** -1.482*** -1.475*** 1.084*** 1.078***
(17.11) (17.02) (-24.62) (-24.53) (12.75) (12.74)
Std. Industry CF/Assets 0.017*** 0.017*** -0.008 -0.008 0.020*** 0.020***
(3.77) (3.79) (-1.38) (-1.41) (3.89) (3.92)
NWC/Assets 1.314*** 1.314*** 0.682*** 0.682*** 1.039*** 1.040***
(67.19) (67.20) (27.31) (27.33) (26.02) (26.08)
R&D Expense/Assets 1.500*** 1.496*** 2.341*** 2.357*** 0.542*** 0.536***
(18.73) (18.69) (13.03) (13.11) (3.34) (3.30)
CAPEX/Assets -0.410*** -0.413*** 3.452*** 3.464*** -1.841*** -1.844***
(-3.84) (-3.88) (28.08) (28.24) (-8.67) (-8.69)
Leverage -1.647*** -1.647*** 0.449*** 0.449*** -1.825*** -1.824***
(-56.46) (-56.45) (10.87) (10.88) (-46.48) (-46.54)
Dividends/Assets -0.321 -0.312 11.740*** 11.701*** -4.895*** -4.857***
(-0.79) (-0.76) (20.56) (20.56) (-6.62) (-6.59)
Major Customer 0.010* -0.045*** 0.028*
(1.69) (-2.75) (1.72)
First Stage F-statistics N/A N/A 192.955*** 193.804*** N/A N/A
Wu-Hausman F-statistics N/A N/A N/A N/A 103.412*** 103.023***
Observations 161,183 161,183 161,183 161,183 161,183 161,183
Adjusted R-squared 0.392 0.392 0.344 0.345 0.238 0.239
Year Fixed Effects Yes Yes Yes Yes Yes Yes
Industry Fixed Effects Yes Yes Yes Yes Yes Yes