Customer-Supplier Relationships and Strategic
Disclosures of Litigation Loss Contingencies∗
Ling Cen
Rotman School of Management
University of Toronto
Ling.Cen@Rotman.Utoronto.Ca
Feng Chen
Rotman School of Management
University of Toronto
Feng.Chen@Rotman.Utoronto.Ca
Yu Hou
Queen’s School of Business
Queen’s University
Yu.Hou@Queensu.Ca
Gordon Richardson
Rotman School of Management
University of Toronto
Gordon.Richardson@Rotman.Utoronto.Ca
This Draft: November 2014
∗
We appreciate the helpful comments from Francesco Bova, Jeffrey Callen, Ole-Kristian Hope, Wenli Huang
(JCAE discussant), Kai Wai Hui, Hai Lu (CAAA discussant), Partha Mohanram, Donald Monk (FARS
discussant), Don Pagach (AAA discussant), Atul Rai, Peter G. Szilagyi (CICF discussant), Zheng Wang, Terry
Warfield, Luo Zuo and participants at the 2013 American Accounting Association annual meeting, the 2013
Canadian Academic Accounting Association annual meeting, the 2013 MIT Asia Conference in Accounting,
the 2014 China International Conference in Finance, the 2014 JCAE Symposium, the 2014 AAA Financial
Accounting and Reporting Section Midyear Meeting, The Chinese University of Hong Kong, The University
of Nebraska-Lincoln, and The University of Toronto research workshops. We thank Rafay Aman, Arif
Amjad, Shiyuan Li, Sarah Richardson, and Yasir Shaikh for their able research assistance. We gratefully
acknowledge the financial support from the Social Sciences and Humanities Research Council of Canada.
Gordon Richardson would like to thank KPMG for their generous support. Corresponding author and
mailing address: Gordon Richardson, Rotman School of Management, University of Toronto, 105 St. George
Street, Toronto, ON M5S 3E6, Canada.
Customer-Supplier Relationships and Strategic Disclosures
of Litigation Loss Contingencies
Abstract: In a customer-supplier relationship, when a third party sues the supplier, the resulting
litigation loss contingency may trigger the principal customer’s concerns regarding supply chain risks.
We find that customers are likely to hedge against such supply chain risks by weakening or
terminating relationships with dependent suppliers who are being sued. Having established the
existence and quantified the level of potential proprietary costs in this setting, we next show that
dependent suppliers being sued make strategic disclosures regarding loss contingencies in their
financial statements in order to avoid relationship disruption. Relative to firms with no principal
customers, dependent suppliers tend to promptly reveal their good news and strategically withhold
their bad news. This pattern is stronger when customers face lower switching costs. Our findings are
useful to the SEC, which would like to see clearer disclosures of potential losses from litigation in
financial statements.
Keywords: Customer-Supplier Relationships; Litigation Loss Contingency Disclosures; Proprietary
Costs
JEL Classification: M41; M48; K22
2
Customer-Supplier Relationships and Strategic Disclosures
of Litigation Loss Contingencies
I. INTRODUCTION
Good corporate disclosure policies are essential for investors and creditors to make efficient
financing decisions, and for regulators to maintain the fairness and stability of financial markets.
Previous studies have extensively examined how shareholder governance and government
regulations (e.g., Regulation Fair Disclosure), as well as the demand from creditors, affect corporate
disclosure policies. However, few studies in accounting have focused on how a firm’s financial
reporting disclosures are affected by other important stakeholders such as customers and suppliers
(Healy and Palepu 2001; Beyer, Cohen, Lys, and Walther 2010). Our paper answers the call for more
research in the literature.
Concurrent research examines the transparency commitment of suppliers and customers
within supply chains in a repeated game setting (see Hui, Klasa, and Yeung 2012; Cao, Hsieh, and
Kohlbeck 2013; Dou, Hope, and Thomas 2013; Radhakrishnan, Wang, and Zhang 2013). Adopting
an implicit contracting perspective in a repeated game setting, their predictions and results rely on
the assumption that the bonding/reputation arising from a long-term relationship is the
commitment device that makes transparent accrual or disclosure choices self-enforcing. Our setting
is different from theirs, as are our conclusions regarding transparent disclosures. We examine a
potential end-game setting in which a supplier is sued by a third party and makes strategic loss
contingency disclosures in order to avoid relationship termination within a supply chain.
Differing from strategic disclosures for routine (i.e., repeated) corporate activities, our setting
of disclosures for corporate litigation crises is particularly interesting for the following reasons. First,
the lawsuit as well as the loss contingency may negatively affect a customer’s inventory management,
1
product quality and delivery, production efficiency, and ultimately the customer’s operating and
financial performance (Bhagat, Brickley and Coles 1994). The benefits to customers from
relationship termination relate to hedging against supplier litigation risk, and acting before disruption
sets in, thus ensuring continuity and stability of supply. The corresponding costs to customers vary
with switching costs, which in turn vary by industry and depend on the uniqueness of products, the
requirement of relationship-specific investments as well as the availability of replacements. As a
result, the customer will weigh the costs and benefits of relationship termination, conditional upon
information about the litigation outcome available to the customer. Termination will occur when the
tipping point is reached somewhere along the time line of litigation. Consequently, customer firms
have high demand for information related to supplier litigation risk. In one recent field survey, 43%
of customer firms surveyed indicated that “incomplete information regarding corporate supplier
relationships is a top business pressure” affecting supply chain risk management (Aberdeen Group
2012). In response to the information demand, there are two information services to customer firms:
Supplier & Risk Monitor from Dow Jones and SmartWatch from LexisNexis that aim to help customer
firms to “anticipate and manage supplier risk, and minimize supply chain disruptions”. 1 Both
services list “legal/litigation risks” from suppliers as one of the top supply chain risks. 2
Second, litigation by its nature involves a high level of ambiguity and uncertainty regarding
expected litigation losses (e.g., Nelson and Kinney 1997). The ambiguity and uncertainty trigger a
high level of information asymmetry between insiders (e.g., managers) and outsiders (e.g., investors
According to Brigitte Ricou-Bellan, vice president and managing director of Enterprise Solutions, Dow Jones &
Company, “the Dow Jones Supplier & Risk Monitor helps manage the often overwhelming and fragmented flow of
information related to key suppliers, providing supply chain managers with critical information that can help them better
manage risk and identify potentially disruptive events before they lead to full-blown supply chain disasters such as
product recalls or loss of revenue”.
2 Other common supply chain risks include bankruptcy, mergers and acquisitions, and regulatory compliance, etc. Our
litigation setting differs sharply from the bankruptcy setting, since a dependent supplier faces going concern risk only if
the supplier loses the case and the settlement amounts are material. Thus, our setting is conditional on expected
settlement losses, a very different setting from the one in which either a supplier or a customer exhibits signs of distress.
Furthermore, expected litigation losses need not be severe in order to result in relationship discontinuation. Any
disruption that potentially affects supply chain stability may lead to discontinuation.
1
2
and customers). Due to this ambiguity, it is costly for the customers to differentiate between
informed managers with bad news and truly uninformed managers, especially at the early stage of
litigation. Further, the tension between the high demand for litigation-related information and the
ambiguous nature of litigations makes suppliers’ disclosure of litigation risks particularly important
for shareholders and stakeholders.
Third, our setting permits us to quantify potential proprietary costs. Termination of the
customer-supplier relationship is extremely costly to suppliers because sales to the principal
customers typically account for a significant proportion (e.g., 42.8% on average in our sample) in the
suppliers’ total sales. Together, high proprietary costs and the opportunity to withhold bad news
given the above-documented ambiguity give rise to strategic disclosure decisions by suppliers with
principal customers (hereafter, “dependent suppliers”).
We explore the following three research questions in this study. First, we examine whether
litigation risk faced by supplier firms adversely affects the likelihood of continuation of the
customer-supplier relationship. Second, having established the existence and quantified the level of
potential proprietary costs in this setting, we next turn to causes of firm disclosure choices and
explore whether dependent suppliers being sued adopt strategic disclosure strategies in their
financial statements. Finally, we examine the consequences of litigation disclosure decisions and
explore whether litigation disclosures affect the likelihood of continuation of the customer-supplier
relationship.
Regarding the first research question, we predict and find that suppliers’ litigation risks,
proxied by the incidence and number of lawsuits involved, reduce the likelihood of the customersupplier relationship continuation. In particular, the likelihood of customer-supplier relationship
continuation in year t+1 decreases by 7.6% if the supplier is sued in year t. This effect is stronger
when suppliers are involved in material loss cases, i.e., the likelihood of relationship continuation is
3
further reduced by 22% for material cases, in addition to that for immaterial cases. Furthermore, the
effect is stronger when customers face lower costs of switching to other suppliers.
After quantifying the potential proprietary costs related to supply chain discontinuation, we
next turn to our second research question. Given the ambiguity and uncertainty inherent in litigation,
public disclosures provide a credible source that allows outsiders to learn about legal cases before
their final settlement. We thus predict that dependent suppliers being sued have a strong economic
incentive to choose disclosure policies carefully to avoid or delay relationship termination with
principal customers. Researchers in the voluntary disclosure literature have shown that firms
consider both costs and benefits given their endowment of news when making disclosure decisions
(e.g., Skinner 1994; Healy and Palepu 2001; Dye 2001; Verrecchia 2001; Wang 2007; Kothari, Shu,
and Wysocki 2009). More recently, researchers have examined compliance with mandatory
disclosure requirements, and have concluded that, to the extent that compliance is discretionary,
firms consider disclosure costs and benefits given their incentives (e.g., Heitzman, Wasley, and
Zimmerman 2010; Dye 2013; Peters and Romi 2013). In the U.S., SFAS No. 5 Accounting for
Contingencies (FASB 1975; now Topic 450) is the primary standard governing the reporting of
potential losses from pending litigations. Critics argue that SFAS 5 allows for considerable discretion
by managers in the application of the accounting standard. 3 Our setting aligns with the voluntary
disclosure literature if compliance with SFAS 5 GAAP disclosure requirements is weak, as alleged by
the SEC and FASB, 4 leading to a de facto discretionary disclosure decision by the dependent supplier
Specifically, SFAS 5 divides loss contingencies into three groups based on the likelihood that the loss will be realized:
probable, reasonably possible, and remote. Material loss contingencies that are both probable and reasonably estimable
must be accrued on the balance sheet. Loss contingencies that are deemed probable or reasonably possible must also be
disclosed, even if an inability to estimate the potential losses prevents accrual. Further, this disclosure should indicate the
nature of the loss contingency and provide a point or range estimate of the amount of loss. If the potential loss cannot
be estimated, a statement to that effect is required. Contingencies in which the chance of loss is judged to be remote are
generally not required to be disclosed or accrued. However, SFAS 5 offers limited guidance on how the terms
“probable”, “reasonably possible”, and “remote” should be interpreted.
4 The SEC issued a “Dear CFO” letter to companies in October 2010 asking for improved disclosures about loss
contingencies under existing rules (SEC 2010). The FASB had attempted to modify the disclosure requirements of SFAS
3
4
being sued (Chen, Hou, Richardson, and Ye 2013; Hennes 2014). Assuming that ex post realizations
will on average be unbiased estimates of ex ante expectations, we exploit litigation outcome
knowledge ex post to infer, ex ante, the direction of private news (good vs. bad) possessed by the
dependent supplier being sued. 5
To explore causes of litigation disclosure decisions, we examine how customer-supplier
relationships affect litigation loss contingency disclosures made by dependent suppliers. We develop
and test two causal predictions about litigation disclosures. When endowed with unfavorable private
information regarding litigation outcomes, dependent suppliers are less likely to provide timely prewarnings regarding legal suits compared with non-dependent suppliers. On the other hand, when
endowed with favorable private information regarding litigation outcomes, dependent suppliers are
more likely to provide optimistic disclosures on a timely basis, compared with non-dependent
suppliers. Our results on the likelihood and timeliness tests are consistent with both predictions. In
addition, the results are stronger when customers face lower switching costs.
Finally, we examine how litigation disclosures affect the likelihood of continuation of the
customer-supplier relationship. We develop and test two predictions about the consequences of
dependent suppliers’ litigation disclosures. When dependent supplier firms are endowed with
unfavorable private information regarding litigation outcomes, providing pre-warning disclosures
increases the likelihood of relationship termination with principal customers. This establishes an
additional incentive for the delay of bad news by informed managers and assumes the existence of a
pooling equilibrium (i.e., when customers do not observe pre-warnings from their suppliers,
customers cannot distinguish whether dependent suppliers have no private information or
5 (FASB Exposure Drafts 2008, 2010), but it has met stiff resistance from financial statement preparers and attorneys. In
July 2012, the FASB removed this project from its technical agenda because it now believes that robust compliance with
existing requirements is more important than additional standard setting (Chassan 2012).
5 This assumption, which is made in many disclosure studies, is only reasonable on average. Some suppliers will simply
be uninformed prior to the litigation outcome, while other suppliers will have prior beliefs inconsistent with the actual
outcome, as we shall later demonstrate.
5
dependent suppliers deliberately withhold private information). Alternatively, when dependent
supplier firms are endowed with favorable private information regarding litigation outcomes, making
public optimistic disclosures provides credible signals of this private information and increases the
likelihood of relationship continuation with principal customers. This assumes the existence of a
separating equilibrium (i.e., it is costly for dependent supplier firms that are not confident about
immaterial outcomes to mimic optimistic claims before the cases are fully resolved). Once again, our
results are consistent with both predictions.
As a caveat, our consequences evidence establishes the costs to dependent suppliers from
disclosing bad news but does not establish the benefits to withholding bad news because it is not
empirically tractable to do so. As researchers, we are in the same position as customers when
confronted with nondisclosure from their suppliers. As such, our evidence regarding the
consequences of pre-warnings is best viewed as a consistency check of our above hypotheses with
respect to accelerating good news and withholding bad news.
Our evidence on litigation disclosures by dependent suppliers is related to the supply chain
information sharing literature. According to that literature, sharing certain private information with
its contracting party enables a supply chain to better coordinate inventory and sales flow to meet
changing demand (e.g., Cachon and Fisher 2000; Kulp, Lee, and Ofek 2004; Li and Zhang 2008;
Schloetzer 2012). While information sharing among supply chain partners is usually beneficial, there
are several obstacles for supply chain partners to share private information in a crisis setting. For
example, Baiman and Rajan (2002) show that investment hold-up concerns and strategic
appropriation by suppliers may undermine information sharing. The potential for information
leakage to competitors further undercuts incentives for information sharing (Li 2002).
While we do not exclude the possibility that principal customers obtain information directly
from suppliers through private communications, we argue that, when the manager of a dependent
6
supplier is endowed with private information regarding litigation loss contingencies, private
communication with principal customers is not a rational choice in equilibrium. In particular, when
the manager is endowed with favorable private information, an optimistic disclosure made privately
may not be credible to customers. Moreover, when the manager is endowed with unfavorable
private information that significantly increases customers’ estimated likelihood of supply chain
disruption, private communication of bad news would not help retain the customers. In an
untabulated robustness check, we use relationship duration to proxy for the likelihood of private
communications between customers and suppliers. We do not find that the effect of customersupplier relationships on the public disclosure of litigation loss contingencies becomes weaker when
supply chain relationships last longer. This result suggests that, for dependent suppliers being sued,
the private communication channel cannot substitute for the public disclosure channel through
SFAS 5 footnotes. Thus, we explore whether dependent suppliers disclose strategically in their SFAS
5 footnotes when sharing private information with their principal customers. 6
Our paper contributes to the accounting and finance literature in the following ways. First,
we are the first to provide evidence on how the litigation risk of suppliers affects the continuation
and strength of customer-supplier relationships. Supply chains involve bilateral relations in which
customers and suppliers are concerned about various counterparty risks causing supply chain
disruptions. This has been established with regard to bankruptcy risk (Hertzel, Li, Officer and
Rodgers 2008), managerial turnover risk (Intintoli, Serfling, and Shaikh 2013) and takeover risk (Cen,
Dasgupta, and Sen 2012). We view our study as a natural extension of this literature, in that it
focuses on supply chain disruptions caused by suppliers’ litigation risk.
Second, we identify an end-game setting for disclosure strategies after empirically quantifying
proprietary costs involving supply chain discontinuation. Our study complements the existing
In fact, the practitioner literature on supply chain risk management advises customer firms to scan suppliers’ SEC
filings for material litigation information (e.g., Toby 2005; Trentacosta 2010).
6
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literature focusing on the repeated game setting while examining the role of corporate disclosure
between customers and suppliers. Our results show that, under an end-game setting, such
proprietary costs lead to strategic disclosure decisions by dependent suppliers who tend to promptly
reveal good news and strategically withhold bad news. Our findings are potentially useful to the SEC,
which has recently put registrants on notice that it would like to see clearer disclosures of potential
losses from litigation in financial statements. The findings are also potentially useful to the auditors
of dependent suppliers being sued, since audit risk increases with undetected noncompliance with
GAAP disclosure requirements.
Our paper is organized as follows. Section II presents our theoretical framework and testable
hypotheses, and Section III describes the sample characteristics and presents descriptive statistics.
Section IV presents our empirical tests and results, and Section V concludes the paper.
II. RELATED LITERATURE AND HYPOTHESIS DEVELOPMENT
The Effect of Litigation on Customer-Supplier Relationships
While a close economic link between customers and suppliers is a necessary condition to
generate synergies in vertical integrations, it also triggers supply chain risks for both parties. When
supply chain risks increase to the extent that suppliers are not able to fulfill contracts or
commitments in the relationships, the operating and stock performance of their counterparts will be
adversely affected (e.g., Hendricks and Singhal 2003, 2005; Hertzel et al. 2008). As the practitioner
literature (e.g., Aberdeen Group 2012) points out, litigation risk from suppliers is considered to be
one of the top supply chain risks leading to supply chain disruptions.
When a dependent supplier is sued by a third party, the litigation loss contingency could
partially hurt the supplier’s financial stability and lead to deteriorating product quality and
insufficient relationship-specific investment, which in turn could adversely affect the customer’s
8
operating and financial performance ex post. To avoid or mitigate the negative impacts mentioned
above, the customers will weigh the costs and benefits of terminating the supply chain relationship
prematurely, conditional on information about the likely litigation outcome available to the
customers. The costs include switching costs mentioned above. The benefits relate to hedging
against supplier litigation risk, and acting before disruption sets in, thus ensuring the continuity and
stability of supply. The tipping point along the time line of a particular litigation episode occurs
when the benefits of discontinuation exceed the costs. Therefore, our first hypothesis, stated in an
alternative form, is as follows:
H1: Litigation risk of supplier firms is negatively associated with the likelihood of customer-supplier
relationship continuation.
Litigation Loss Contingency Disclosures and Customer-Supplier Relationships
The accounting literature has explored disclosure choices, in the supply chain setting. Several
studies address the contracting demand for accounting information and adopt an implicit
contracting perspective. Bowen, Ducharme, and Shores (1995) argue that both suppliers and
customers have an incentive to choose accounting methods which influence assessments of the
firm’s future ability to fulfill its implicit claims within supply chains. In our setting, litigation brings
the supplier closer to what Bowen et al. (1995) refer to as the “reneging” state, i.e., the state where
the dependent supplier can no longer honor its implicit contracts with the customer. Termination is
being considered by the customer, as discussed above, and reputational consequences with the
customer under a repeated game setting no longer guide accrual or disclosure choices made by the
supplier regarding pending litigation. Instead, opportunism sets in, especially if the dependent
supplier is endowed with negative information about the litigation outcome and it is costly for the
9
customer to verify the delay of disclosing negative information. In this setting, the dependent
supplier will not honor any past commitments to use timely loss recognition and thus will not
disclose or accrue for expected litigation losses in a timely manner, when doing so will result in
termination. As such, the use of strategic disclosures places our paper closest to Raman and Sharur
(2008), who argue that firms will engage in earnings management and inflate reported earnings in
order to favorably influence stakeholder perceptions within supply chains.
Because the cases for which managers of dependent suppliers being sued are endowed with
negative information (i.e., the legal cases are likely to generate material loss outcomes) and positive
information (i.e., the legal cases are likely to generate immaterial loss outcomes) produce distinctive
information equilibria, we discuss them separately below.
When Managers of Suppliers Are Endowed with Negative Information
When dependent suppliers are endowed with bad news (i.e., a high probability about an
upcoming material litigation loss), they face three disclosure choices: honest disclosure (e.g., make a
pre-warning), cheating (make an optimistic claim), or postponing disclosure. Although cheating can
strengthen customer-supplier relationships in the short run, it will generate potential regulatory costs
and follow-on litigation when the disclosure is asserted to be untruthful by customers or regulators
ex post. Therefore, cheating is not likely to be an optimal strategy. Another choice, honest disclosure,
will not lead to ex post penalties from customers and regulators. However, dependent suppliers have
to consider the substantial proprietary costs since honest disclosure may trigger immediate
relationship termination with principal customers. Under such circumstances, customers are unlikely
to place a lot of weight on the fact that the suppliers make honest disclosures when they decide to
terminate the relationships because of the “updated and verified” supply chain risks.
10
The last choice—postponing bad news disclosure—has several benefits. First, it is much
more costly for customers and regulators to verify intentional disclosure delay than cheating in
public disclosure. Specifically, if it is costly for customers and regulators to verify a dependent
supplier’s endowment of negative private information ex ante and penalize intentional delay, a
pooling equilibrium arises because the dependent supplier can successfully pool with other sued
firms with no private information. Second, there is an option value arising from delaying disclosure
of unfavorable news regarding litigation outcomes because it is always possible that certain future
new events could come to light and have an impact on the settlement favorably. 7 Third, if disclosure
of bad news about litigation outcomes leads to immediate termination of customer-supplier
relationships, the cash flows of dependent suppliers being sued would be significantly reduced,
which leads the affected suppliers to a higher level of financial distress. This could potentially
compound litigation-related losses because, with such a high level of financial distress, dependent
suppliers may not even have sufficient financial resources to continue their legal battle to win the
case. On the other hand, postponing bad news is not completely costless. The costs of verification
are decreasing over the life cycle of litigations because public information regarding litigations
accumulates over time. As cases approach their outcome announcements, it is easier for regulators
(and customers) to verify disclosure of dependent suppliers, including claims to be uninformed,
compared with the ability to do so when cases are initially launched against the dependent suppliers
(See Beyer et al. 2010, page 302 for theoretical support of this point).
[Insert Figure 1 Here]
We illustrate our arguments above in Figure 1. Given our earlier arguments that compliance
with SFAS 5 disclosure requirements involves a de facto discretionary disclosure decision, we discuss
7
According to the survey evidence in Graham, Harvey, and Rajgopal (2005), some CFOs claim that they delay bad news
disclosures in the hope that they may never have to release the bad news if the firm’s status improves before the required
information release.
11
costs and benefits related to compliance facing both dependent and non-dependent suppliers. Other
than supply chain considerations, we assume that both supplier types face similar disclosure costs
and benefits and we further assume that the costs and benefits of bad news disclosure are functions
of the time distance to the expected date of outcome announcement. From the cost side, as the
expected date of outcome announcement approaches, more private information will be leaked into
the public domain. It would be difficult for the suppliers being sued to argue that their private
information is not sufficient to issue pre-warnings while the public information has already shown a
high likelihood of litigation loss. Thus, the costs of postponing bad news disclosure increase over
time (see also Skinner 1994). For both supplier types, the benefits from postponing bad news
disclosure are mainly associated with the option value of delaying, such as propping up stock price,
enhancing management tenure, and so on. Given this real-option feature, one can argue based on
standard option pricing theory that the benefits of postponing decrease over time as cases gradually
approach outcome announcement (i.e., the maturity date of the real option). As shown in Figure 1,
the decreasing benefit function (Line B) and the increasing cost function (Line C) will jointly
determine the optimal timing of bad news disclosure, which is denoted as T* for firms with no
principal customers in the figure. Before a case reaches T*, the non-dependent suppliers being sued
with negative private information will choose to delay disclosure and pool with sued firms with no
private information; however, after a case reaches T*, it is optimal for such firms to make honest
disclosure of material outcomes since further delaying is likely to be caught and penalized by
regulators.
Compared with firms with no principal customers, dependent suppliers have an important
additional benefit of postponing bad news disclosure—i.e., to avoid the immediate relationship
break-up with their principal customers that may lead to a high level of financial distress risk and a
likely bankruptcy. This additional benefit is shown by the gap between the benefit function for non12
dependent suppliers (i.e., Line B) and the benefit function for dependent suppliers (i.e., Line B’). As
a consequence of this difference, we show in Figure 1 that dependent suppliers’ optimal disclosure
of bad news (i.e., T’ in Figure 1) would be less timely than the optimal disclosure of non-dependent
suppliers (i.e., T* in Figure 1).
The above pooling equilibrium discussion leads to the following testable hypothesis: 8
H2: Ceteris paribus, when endowed with unfavorable private information regarding litigation
outcomes,
(a) dependent supplier firms are less likely to provide timely pre-warnings regarding legal suits
compared with their peers with no principal customers;
(b) providing pre-warnings disclosures increases the likelihood of relationship termination with
principal customers.
When Managers of Suppliers are Endowed with Positive Information
When managers are endowed with positive private information (i.e., a high probability of
case resolution in favor of the defendant), our predictions are natural extensions of the above
arguments. We begin with a discussion of disclosure costs and benefits other than supply chain
considerations and once again our predictions invoke the ceteris paribus assumption that these are
the same across supplier types.
The trade-off between the benefit and the cost of optimistic disclosure relies on the
separating equilibrium between firms that have private information suggesting likely immaterial
losses and firms that are either uninformed or possess bad news, since it is potentially costly for
these managers to mimic the disclosures of informed managers with favorable news. Optimistic
Ceteris paribus assumes that all disclosure costs and benefits other than supply-chain proprietary costs are the same
across dependent and non-dependent suppliers.
8
13
claims by managers who are uninformed or possess bad news may be inconsistent with litigation
outcomes, potentially resulting in follow-on litigation and regulatory investigations (e.g., Rogers, Van
Buskirk, and Zechman 2011). 9
[Insert Figure 2 Here]
We illustrate our arguments above in Figure 2. From the cost side, the accumulated
information, as time elapses, will allow managers to reduce the likelihood of mistaken disclosure.
Therefore, as shown in Figure 2, the cost of optimistic disclosure (i.e., Line C) is a decreasing
function of the time T. From the benefit side, the separating equilibrium establishes a credible
signaling mechanism and earlier disclosure will generate a higher level of benefit given that the
adverse effect of information uncertainty is mitigated earlier. Therefore, as shown in Figure 2, the
benefit of optimistic disclosure (i.e., Line B) is also a decreasing function of time T. 10 A firm will
make an optimistic disclosure once the benefit exceeds the cost, which for non-dependent suppliers
is described by T* (i.e., the intersection of Line B and Line C) in Figure 2. For such firms, the
benefits of timely good news disclosure include a reduction of uncertainty regarding the case and the
consequent advantages in terms of a higher stock price, a lower cost of capital, and so on.
We now focus on the crucial difference between supplier types with respect to the disclosure
of good news. Compared with firms with no principal customers, dependent suppliers enjoy an
important additional benefit from disclosing favorable private information early, namely, an increase
in the likelihood of customer-supplier relationship continuation. This intuition is captured by an
upward shift from Line B to Line B’ in Figure 2. As a result of the aforementioned difference, the
The costs of mimicking good news disclosures are greater than the costs of withholding bad news, in a litigation
context, for the reason as follows. A firm that mimics in effect claims to be informed of favorable information. If the
claim is inconsistent with litigation outcomes, the firm is open to challenge by regulators, i.e., it must furnish the
information upon which the false claim is made. In contrast, a firm that is informed of bad news can withhold such
news and claim to be uninformed. It is very difficult for regulators to successfully challenge that claim.
10
It is worth mentioning that, at Time 0 (when the litigation starts), the cost is usually larger than the benefit of
optimistic disclosure. If this condition was violated, all firms would make optimistic disclosures at Time 0, which is not
the case.
9
14
optimal timing of optimistic disclosure moves from T* to T’. This suggests that, ceteris paribus,
dependent suppliers are likely to make optimistic disclosures on a more timely basis compared to
non-dependent suppliers, with respect to contingent litigation losses.
The above discussion leads to the following testable hypothesis:
H3: Ceteris paribus, when endowed with favorable private information regarding litigation
outcomes,
(a) dependent supplier firms are more likely to provide optimistic disclosures on a more timely basis
regarding legal suits as compared with their peers with no principal customers;
(b) making optimistic disclosures increases the likelihood of relationship continuation with principal
customers.
The Role of Switching Costs
It is worth mentioning that dependent suppliers being sued choose strategic disclosure, as
predicted in H2 and H3, in order to minimize the proprietary costs related to supply chain
discontinuation. These strategic disclosure behaviors are only economically meaningful to dependent
suppliers when litigations likely trigger supply chain discontinuation. Therefore, we expect that
dependent suppliers are more likely to adopt strategic disclosure of litigation risks when their
customers face a low level of switching costs, e.g., when dependent suppliers are specialized in
competitive industries and/or in non-durable industries. We control for switching costs in our tests
of H2 and H3.
III. DATA SOURCE AND SAMPLE CONSTRUCTION
15
Our initial sample is the intersection of two data sources: the customer-supplier relationship
dataset from the Compustat Segment Customer File and the litigation dataset from the Audit
Analytics Legal Files. Since Audit Analytics Legal Files starts a comprehensive coverage of legal
cases from 2000, our main sample covers a period from 2000 to 2008.
SFAS No. 131 (FASB 1997) requires firms to disclose the existence and sales to individual
external customers representing more than 10% of total firm revenues. In practice, a firm can
voluntarily identify principal customers who account for less than 10% of total revenues. We define
a customer to be a principal customer if the customer’s existence is reported by a supplier in the
Compustat Segment Customer File. We define a dependent supplier if the firm reports the existence
of one or more principal customers in the Compustat Segment Customer File.
The datasets for our relationship termination tests (Tables 1-5) are organized at the
customer-supplier pair level. The sampling filters used to obtain the observations employed in these
tests are as follows. We follow the classification procedure in the prior literature (e.g., Banerjee,
Dasgupta, and Kim 2008; Cohen and Frazzini 2008), and classify all customers into government
(mainly domestic or foreign government agencies), non-government (mainly public or private firms), or
unidentified. To test the relationship termination, we must be able to identify the exact identities of
principal customers. For the years from 2000 to 2008, we exclude government and unidentified
customers and obtain 19,110 customer-supplier-year pairs for the sample where the nongovernment customer firms are clearly identifiable. For those identifiable customers, we manually
match corporate customer names with the Compustat identifiers (i.e., GVKEYs) whenever possible.
This process allows us to identify 12,306 customer-supplier-year pairs where the customer firms are
16
listed companies with GVKEYs. The change from 19,110 to 12,306 arises from excluding private
customer firms. 11
We carefully address two known data issues when we test how suppliers’ litigation risks
affect the continuation and strength of customer-supplier relationships. . The first issue is that SFAS
131 only requires the disclosure of the existence of major customers. Therefore, after 1998, suppliers
can choose whether they disclose the names of their major customers or not (Ellis, Fee, and Thomas
2012). Fortunately, this dataset allows us to detect whether suppliers are reluctant to report the
names of some major customers. 12 While we report the results based on the full sample in the paper,
our results are robust for a subsample of relationship pairs where dependent suppliers report names
of all their major customers in a specific year t.
The other issue is the arbitrary cut-off level, 10%, for the mandatory disclosure of the
existence of major customers. This concern has already been significantly mitigated by dependent
suppliers’ voluntary disclosure of major customers whose sales account for less than 10% of
suppliers’ total sales. For example, in our initial sample of 19,110 observations for Table 2, 35.85%
of identified principal customers have sales that account for the less than 10% of total revenues. For
the relationship termination tests in Tables 1-5, we avoid the use of a 10% threshold for identifying
principal customers since that could lead to mechanical “relationship termination” when the
percentage sales to one customer drops from 10.1% to 9.9%. As such, our definition does not
depend on a particular threshold. Moreover, we supplement the tests in Table 2 that indicate the
increased probability of relationship termination with tests in Table 5 that indicate falling sales
Ellis et al. (2012) document that some dependent suppliers do not reveal the identity of a given principal customer, in
contravention of SEC requirements. For the results reported in Table 2, we impose the sampling constraint that the
customer name must be identified. This introduces a conservative bias for our tests. Dependent suppliers who withhold
customer names come from more competitive industries, according to Ellis et al. (2012). In our tests underlying Table 2,
we assume that switching costs are lower in more competitive supplier industries. Thus, litigation-related proprietary
costs would be higher and the effects in Table 2 would be more pronounced if we were able to include observations for
which customers are not identified.
12 For example, the sales to major customers are still reported, but the names of major customers are presented as “Not
Reported”.
11
17
following supplier litigation. This approach again does not depend on a particular threshold. In
another robustness check, we also use an alternative cut-off threshold (i.e., 20%) to identify principal
customers and find similar results.
[Insert Table 1 Here]
Table 1 compares a few firm characteristics of dependent suppliers and principal customers.
Our comparison suggests that principal customers tend to be firms of larger size, higher profitability,
and higher leverage relative to their dependent suppliers. Further, dependent suppliers and principal
customers are of mutual importance to each other. Specifically, for an average dependent supplier
firm, the mean sales to principal customers account for 45.8% of its total sales; for an average
principal customer firm, the purchases from dependent suppliers account for 4.8% of its costs of
goods sold (COGS). 13 Turning to dependent supplier firms being sued, Column (3) of Table 1
indicates that such firms are also smaller relative to principal customer firms. The mean sales to
principal customers account for 42.8% of their total sales. Therefore, dependent supplier firms being
sued have a strong incentive to disclose strategically to maintain relationships with principal
customers.
The datasets for our disclosure tests (i.e., Tables 6-11) are organized at the case level. The
sampling filters used to obtain the observations employed in these tests are as follows. The Audit
Analytics Legal File provides litigation data under 94 categories. We require that legal cases must
satisfy the following criteria to be included in our sample: (1) a defendant firm must have nonmissing information for its corporate identifier (i.e., CIK) so that the litigation dataset and the
customer-supplier relationship dataset can be merged, and (2) for each selected legal case, the
13
The importance of a dependent supplier to its principal customers may not be fully reflected by the percentage of
COGS from that supplier. The importance often lies in the fact that the supplied components are uniquely designed and
are often patented. Therefore, it is also costly for customers to replace their suppliers.
18
information regarding case type must be available. As indicated in Appendix I, this screening process
yields a large sample of 12,301 legal cases.
As sales to principal customers are reported yearly, we are unable to empirically discern the
impact of legal contingency disclosures on supply chain relationships if cases are resolved quickly.
The median duration of legal cases in the Audit Analytics Legal File is only 276 days (untabulated).
Therefore, for the disclosure tests, we require that the duration of cases must be longer than 365
days. As indicated in Appendix I, this screening procedure reduces our sample to 4,944 cases.
Next, we require that the outcomes of legal cases must be disclosed in firms’ 10-Qs and 10Ks when the cases are fully resolved. 14 For our disclosure tests, it is important to know the fiscal
quarter when the outcome is disclosed in the SFAS 5 footnotes. This disclosure timing information
is not available in the Audit Analytics Legal File. This procedure further reduces our sample for the
disclosure-related tests to 1,663 cases.
We partition our cases into material and immaterial cases based on their actual losses.
Material (immaterial) cases refer to those cases with actual losses that exceed (are less than) 0.5% of
the defendant firm’s total assets. The materiality threshold accords with the International Standard
on Auditing (ISA) 320 from the IASB and Statements on Auditing Standards (SAS) 107 from the
AICPA. In order to enhance the use of ex post outcomes as a proxy for en ante favorable information
known to the manager, we exclude immaterial cases with non-zero-loss outcomes and instead focus
on zero-loss immaterial cases only in our disclosure tests. This procedure further reduces our sample
for the disclosure-related tests to 990 cases, as indicated in Appendix I.
To ensure that our results are not dominated by cases with immaterial outcomes, we match
the material and immaterial cases by industry classification (i.e., the two-digit Standard Industrial
Prior to the case settlement, SFAS 5 disclosure requirements apply only to potentially material outcomes. We require
that the zero loss outcomes for our “immaterial” cases be mentioned in 10-Qs and 10-Ks, which likely biases such cases
to ones that, ex ante, could have resulted in a material loss. While true, we are unaware of any systematic differences in
this possibility across treatment and control groups.
14
19
Classification (SIC) codes) and firm size. We end up with 377 pairs of material and immaterial cases
(754 cases in total, see Appendix I) in which defendant firms in the two groups are of similar
industry classifications and firm sizes. For the 377 immaterial cases, 133 and 234 observations relate
to dependent and non-dependent suppliers, respectively. For the 377 material cases, 104 and 273
observations relate to dependent and non-dependent suppliers, respectively. For each of 754 cases
identified above, we manually search the defendant firm’s 10-Qs and 10-Ks for the litigation-related
disclosures (in the sections titled “Legal Proceedings” and “Commitments and Contingencies”)
within a period between the initiation of the case and the resolution of the case. Our dataset is by
far the largest one in the literature where the litigation-related disclosure is identified for dependent
suppliers and non-dependent suppliers.
For the entire life cycle of each lawsuit, we identify two major types of disclosures—prewarnings and optimistic claims. These disclosures contain managers’ private information that is
expected to be useful to shareholders and stakeholders in predicting case outcomes. We define prewarnings and optimistic claims in the following way. Pre-warnings, in particular, are those
disclosures in which firms do one or more of the following: warn investors of potentially significant
adverse economic consequences from lawsuits, provide a material loss estimate, and/or accrue a loss.
Optimistic claims are those disclosures in which firms express optimistic views that the cases are not
likely to have material impacts, and/or the cases have no merit. For illustration purposes, we present
several examples of pre-warnings and optimistic claims made by defendant firms in Appendix II.
IV. EMPIRICAL TESTS AND RESULTS
The Effect of Suppliers’ Legal Cases on Customer-Supplier Relationships
H1 predicts a negative impact of legal cases on the continuation of customer-supplier
relationships. To examine H1, we run Probit regression models on the data at the customer-supplier
20
relationship level. The dependent variable, Cont, is an indicator variable that equals one if customersupplier relationships continue in the next year (i.e., year t+1), and zero otherwise. We choose to
measure a firm’s litigation risk (Legal Case) in two ways: First, we examine whether a supplier is the
defendant in at least one case in year t as indicated by a dummy variable Dummy Legal Case; and
second, we count the total number of legal cases in which the supplier gets involved as a defendant
(Num Legal Case). We also include year fixed effects and cluster standard errors at the customersupplier relationship level.
Columns (1) – (4) of Table 2 show the results of Probit regressions for the full sample. In
addition to our variables of interest, Dummy Legal Case and Num Legal Case, we control for a set of
firm characteristics for both customers and suppliers in Columns (3) and (4). As discussed in Section
III, these controls require that customer firms must be public companies with available accounting
information. In particular, we include the length of the past relationship, the dependent supplier’s
percentage sales to the principal customer, the customer’s percentage of cost of goods sold from the
dependent supplier, both the customer’s and supplier’s returns on assets (ROA), and the sizes of
both parties. These variables mainly capture the bargaining powers of customers vis-à-vis dependent
suppliers, which have been documented to affect the likelihood of supply chain termination in the
prior literature (e.g., Cen et al. 2012). Moreover, we control for Altman’s (1968) Z-score, which is
used to isolate the potential confounding effect from distress risks of both customers and suppliers.
As reported in Columns (1) – (4) of Table 2, the coefficients of both litigation risk proxies
are negative and statistically significant at the 1% levels, showing that being sued in litigation will
reduce the likelihood of the customer-supplier relationship continuation. In particular, as suggested
by the results in Column (3), the likelihood that the principal customer will stay in year t+1 decreases
by 7.6% if the dependent supplier is sued in at least one case in year t. In addition, in Column (4),
the coefficient on Num Legal Case suggests that getting involved in one more case will decrease the
21
likelihood of the relationship continuation by 4.0%. These results are consistent with H1, which
suggests that the litigation risk of the dependent supplier, as indicated by being a defendant in legal
cases, has a significant adverse effect on customer-supplier relationships. It is important to point out
that the Audit Analytics Legal File includes many trivial legal cases with zero claims and zero
settlement payments. These trivial legal cases would generate results biased against finding the
expected results (i.e., our coefficient estimates tend to be understated). We will further address this
issue by examining the impact of material loss cases on customer-supplier relationships in tests
reported in Table 4.
[Insert Table 2 Here]
Turning to the effect of other control variables on the relationship continuation, consistent
with prior studies, we find that the likelihood of relationship continuation increases with increased
length of the past relationship, the dependent supplier’s sales to principal customers, the size and
profitability of both suppliers and customers, and the dependent supplier’s Z-score. However, these
additional control variables do not weaken the impact of litigations on customer-supplier
relationship continuation.
Furthermore, in Columns (5) – (8), we partition our sample based on the level of switching
costs as proxied by the competiveness and product durability of the dependent supplier’s industry.
An industry is defined to be competitive if the Herfindahl–Hirschman Index of the SIC 3-digit
industry is lower than 0.1, and monopolistic otherwise. Durable goods industries are defined in the
same way as Gomes, Kogan, and Yogo (2009) and all other industries are defined as non-durable
goods industries. Consistent with our theoretical predictions, the results show that the supplier’s
litigation risk, as captured by Dummy Legal Case, only affects the customer-supplier relationship when
the customer’s switching cost is low, i.e., when the supplier is specialized in a competitive or nondurable goods industry. This is indicated by the coefficients of Dummy Legal Case, which are negative
22
and statistically significant at the 1% levels in Columns (5) and (7) but insignificant in Columns (6)
and (8).
[Insert Table 3 Here]
Different types of legal cases are expected to have differential impacts on customer-supplier
relationships. We repeat a test similar to that reported in Column (4) of Table 2, where the variable
Num Legal Case is replaced by 94 variables, each carrying the numbers of cases for each case category
and zero otherwise. Their coefficients, therefore, reflect the impacts of legal cases of various
categories on the likelihood of relationship continuation.
In Table 3, we present the coefficients and marginal effects of Num Legal Case for the top 10
categories based on the incidence of legal cases. In a total of 94 categories defined by Audit
Analytics, these top 10 categories account for more than 57% of all legal cases in our data set. We
find a large dispersion in terms of the effects of different types of legal cases on the continuation of
the customer-supplier relationship. We find that legal cases related to accounting malpractice,
financial reporting, multidistrict litigation, and patent law have the most damaging impacts on
customer-supplier relationships. Consistent with Hui et al. (2012), we find that customers care about
the financial reporting malpractice of their suppliers. Specifically, if a supplier is sued for alleged
accounting malpractice (or financial misrepresentation), the likelihood of customer-supplier
relationship continuation decreases by 17% (11.4%).
[Insert Table 4 Here]
If litigation risk affects customer-supplier relationships, cases leading to material losses are
expected to have a greater impact than those leading to immaterial outcomes. In this test, we use the
ex post losses of defendants to proxy for the ex ante potential losses. The results with regard to this
prediction are shown in Table 4. Building upon the analysis in Table 2, we interact Dummy Legal Case
with the variables that capture the materiality of legal cases. Specifically, Dummy Material Legal Case
23
equals one if the supplier is involved in a material legal case as the defendant at year t, and zero
otherwise. In Column (1), the coefficient of the interaction term Dummy Legal Case × Dummy Material
Legal Case is negative and statistically significant at the 1% levels. The marginal effect suggests that,
when the case is material, the likelihood of relationship continuation will be further reduced by 22%,
in addition to that for immaterial cases. In addition to Dummy Legal Case, which is a binary partition
based on the 0.5% cut-off, we also measure materiality by the litigation losses scaled by the
defendant firm’s total assets, i.e., Settlement as a Pct of Total Assets. This variable is not vulnerable to
the concerns triggered by arbitrary cut-offs. The interaction term, Dummy Legal Case × Settlement as a
Pct of Total Assets, in Column (2) demonstrates the effect of case materiality on the continuation of
customer-supplier relationships. The result indicates that a 1% increase in Settlement as a Pct of Total
Assets reduces the likelihood of relationship continuation by 1.3%. Overall, our results are consistent
with the notion that a higher potential litigation loss leads to a higher likelihood of relationship
termination.
[Insert Table 5 Here]
When customers learn about their dependent suppliers becoming mired in legal troubles,
they might not terminate the relationships immediately because switching to other suppliers is costly
in the short term. However, they might choose to weaken the relationship by reducing their
purchases from the troubled suppliers. This effect is expected to be different depending on the
materiality of the cases. Panel A of Table 5 illustrates the abnormal growth rate of sales to principal
customers (i.e., the difference between the growth rate of sales to principal customers between
dependent suppliers with legal cases and dependent suppliers with no legal cases in the benchmark
group, constructed based on the lagged size, book-to-market ratio, and sales growth rate). We
calculate the abnormal growth rates under three time horizons (i.e., one, two, and three years,
24
respectively). 15 We further split our sample cases into material and immaterial groups. In the material
group, the abnormal sales growth rates are significantly negative for all three time horizons. For
example, the three-year abnormal sales growth rate is -0.63, meaning that suppliers involved in
material legal cases underperform their peers in the benchmark groups by 63% in terms of the
growth rate of sales to principal customers. In contrast, for immaterial cases, the adverse effect of
legal cases only lasts for one year. Abnormal sales growth rates in both two- and three-year horizons
are not statistically different from zero. These results suggest that principal customers indeed hedge
against litigation risk in their supply chain by weakening the relationship strength ex ante.
Furthermore, although the uncertainty in the initial stage of any litigation weakens the relationship,
such an adverse effect on customer-supplier relationships is not permanent when the uncertainty in
immaterial cases resolves in the long term.
Likewise, we indicate in Panel B of Table 5 the abnormal stock returns for dependent
suppliers with legal cases and dependent suppliers with no legal cases. We compute the abnormal
stock returns following Daniel, Grinblatt, Titman and Wermers (1997) in one-, two-, and three-year
periods after the cases are launched. Consistent with our observations in abnormal sales growth
rates, we show that the abnormal stock returns of dependent suppliers being sued are significantly
negative for all three time horizons in the material group, but insignificant for the immaterial group.
These results are consistent with the notion that investors, at least in the long run, are aware of the
impact of material and immaterial legal cases on customer-supplier relationships. Consistent with
Hertzel et al. (2008), we show that the adverse impact of suppliers’ legal cases on the customersupplier relationships is indeed reflected in their stock prices.
The Effect of Customer-Supplier Relationships on Suppliers’ Litigation Disclosures
Therefore, to be included in our sample in this test, we require the availability of three years of data for sales growth
and stock returns of sued firms.
15
25
In this section, we test two causal predictions about litigation disclosures—H2a and H3a. In
particular, we examine whether existing relationships with principal customers affect the likelihood
and timeliness of litigation-related disclosures by suppliers when managers are endowed with
unfavorable and favorable private information, respectively. In subsequent tests, we assume that
managers are endowed with favorable private information when litigations result in immaterial loss
outcomes; on the other hand, we consider managers to be endowed with unfavorable private
information when litigations result in material loss outcomes. To carry out these tests, we identify
754 cases (377 material and 377 immaterial cases, respectively) in which the disclosure decisions
made by firms are identifiable in their quarterly and yearly SEC filings (we have explained the datascreening and data-collection processes in Section III and Appendices I & II).
[Insert Table 6 Here]
In the following tests, we focus on two types of disclosures, namely optimistic claims and
pre-warnings, because they contain the most important private information from managers that is
useful for stakeholders to predict case outcomes. Table 6 presents the descriptive results of both the
incidence and the timing of making these disclosures. The incidence of disclosure is measured by the
percentage of cases in which firms issue optimistic claims or pre-warnings before the case is fully
resolved. The timing of disclosure is measured by the number of quarters between the date of case
initiation and the date of litigation-related disclosure scaled by the number of total quarters between
the date of case initiation and the date when the case is fully resolved. For immaterial cases, if there
are no optimistic claims before the cases are fully resolved, the announcements of the immaterial
outcomes (e.g., when a case is fully resolved) can be regarded as the latest optimistic claims.
Therefore, the disclosure timing ratios of optimistic claims are equal to one for these cases. For the
same reason, in material cases, if there are no pre-warnings before the cases are fully resolved, the
disclosure timing ratios of pre-warnings are equal to one. On the other hand, it is impossible to infer
26
the timing of pre-warnings in immaterial cases or optimistic claims in material cases unless they are
actually observed in the data, and so our sample size is reduced accordingly in these two tests.
In Panel A of Table 6, we focus on the incidence and timing of optimistic claims for
immaterial cases because the optimistic disclosure is consistent with the immaterial loss outcome
and is more likely to reflect suppliers’ private information. Among immaterial loss outcome cases, 49%
of non-dependent suppliers make optimistic claims, while 62% of dependent suppliers make
optimistic claims. The difference is significant at the 5% level, which suggests that dependent
suppliers are more likely to make optimistic claims for immaterial loss outcome cases. This result is
consistent with H3a. Specifically, in immaterial cases, dependent suppliers are more likely to make
optimistic public disclosures regarding litigation outcomes. With respect to the timing, we find that
dependent suppliers make optimistic claims sooner as compared with non-dependent suppliers. In
particular, non-dependent suppliers make the claims when 81% of the total case duration has
elapsed, on average, compared with 63% for dependent suppliers. The difference is also significant
at the 1% level.
Although we have no hypothesis regarding inconsistent disclosures, it is interesting to
examine whether and when managers issue pre-warnings for the immaterial group. Note that prewarnings can occur for immaterial cases because managers may not know the exact outcome prior
to case settlement. Our results show that dependent suppliers are less likely to issue pre-warnings.
One possible explanation is that managers of dependent suppliers are more careful than those of
non-dependent suppliers about issuing pre-warnings because they are aware of the adverse impacts
of pre-warnings on their relationships with principal customers. Further, our results in Panel A
suggest no significant difference in the timeliness of pre-warnings between the two groups of
suppliers.
27
The univariate results for material loss outcome cases are reported in Panel B of Table 6. We
find that 55% of non-dependent suppliers issue pre-warnings for material loss outcome cases, while
only 29% of dependent suppliers make such pre-warnings. The difference is significant at the 1%
level for these groups. In terms of the timing of pre-warnings, we also find that dependent suppliers
issue pre-warnings much later than non-dependent suppliers. In particular, non-dependent suppliers
provide pre-warnings when 70% of the total case duration has elapsed, on average, compared with
94% for dependent suppliers. These results are consistent with our H2a, which states that dependent
suppliers are more reluctant to issue timely pre-warnings about material cases in their public
financial reports.
For material loss outcome cases, optimistic claims are inconsistent disclosures. We find that
dependent suppliers are less likely to make optimistic claims. While we have no hypothesis regarding
the inconsistent disclosures, one possible explanation is that false optimistic claims constitute
especially risky behaviors for dependent suppliers since the regulator is aware of their incentive to
avoid relationship termination. Further, we find no significant difference in the timeliness of
optimistic claims between the two groups of suppliers.
[Insert Table 7 Here]
Recall that H2a and H3a are based on the ceteris paribus assumption that firm characteristics
associated with voluntary disclosures in the disclosure literature are the same across dependent and
non-dependent suppliers. We know from the supply chain literature that dependent suppliers differ
from non-dependent suppliers on several dimensions that have also been associated with voluntary
disclosures. Any important differences across the two supplier types must be controlled for
empirically in order to enhance causal assertions. The internal validity of our inferences is threatened
by the two sources of endogeneity concerns. First, the selection factors determining the supplier
status must be controlled for as the factors are otherwise potentially important omitted variables for
28
both the supplier status and disclosure behavior of suppliers (hereafter the selection issue). Second,
given the strong incentive to stay out of legal troubles, dependent suppliers might have a lower
litigation rate in certain types of litigations compared to non-dependent suppliers ex ante (hereafter
the litigation propensity issue).
Turning first to the selection issue, we describe in Table 7 firm characteristics shown by the
disclosure literature to be related to the costs and benefits of transparency as well as governance
characteristics that impact on the opportunity for lack of transparency, including auditor quality (see
Chen et al. 2013). Table 7 reveals that dependent suppliers are smaller in firm size, lower in leverage,
and less profitable. This pattern is consistent with that documented in the supply chain literature
(e.g., Banerjee et al. 2008). Therefore, in order to mitigate the selection issue, in the results reported
in Tables 8-10 we control for firm size (Log Size), profitability (ROA) and firm leverage (Leverage),
which may affect both the selection of dependent suppliers and corporate disclosure policies. We
also observe in Table 7 that the two supplier types differ in a number of dimensions that are
associated with disclosure propensity in the voluntary disclosure literature (e.g., Lang and Lundholm
1993; Johnson et al. 2001). For example, compared with non-dependent suppliers, dependent
suppliers have smaller board sizes, lower institutional holdings, a higher likelihood of equity issuance
and are covered by fewer financial analysts. Thus, we add other determinants for corporate
disclosure policies in the full specification for Tables 8-10. 16
Turning next to the litigation propensity issue, we explore whether dependent suppliers are
less likely to be sued, by types of litigation. First, based on Logit models with year fixed effects, we
examine the impact of supplier status in year t on the likelihood of being sued by at least one third
party in year t+1, by types of litigations. Column (4) of Appendix III shows that dependent suppliers
We do not include firm size, institutional ownership and analyst coverage in control variables simultaneously, due to
the multicollinearity concern. However, in our robustness checks of results reported in Tables 8 and 9, we replace firm
size by analyst coverage or institutional ownerships and find similar results.
16
29
are less likely to be sued for most types of litigations. This is consistent with the conjecture that
suppliers are more careful than their peers in avoiding litigations ex ante in order to reduce the costs
related to relationship termination. Second, Table 7 indicates that the amount of litigation losses as a
percentage of total assets is similar across the dependent and non-dependent suppliers (0.049 vs.
0.048). 17 We formalize a test of such differences in Column (5) of Appendix III, where we examine
the impact of supplier status in year t on the settlement loss in year t+1. For all types of cases, none
of the differences in litigation losses between dependent and non-dependent suppliers is statistically
significant. This is an important result, since disclosure predictions in H2a and H3a assume all else
being equal between dependent and non-dependent suppliers, including the amounts of litigation
loss. Nevertheless, we still control for types of cases and ex post litigation losses in the tests reported
in Tables 8-10.
[Insert Table 8 Here]
In Tables 8 and 9, we conduct multivariate analyses to control for additional variables that
have known effects on corporate disclosures. In Table 8, we examine the likelihood of providing
optimistic claims and pre-warnings. The dependent variable for tests of optimistic claims (prewarnings) is an indicator variable that equals one if the defendant firm makes optimistic claims (prewarnings) before the case is fully resolved, and zero otherwise. Given the nature of this test, we
carry out Probit regressions to estimate the impact of independent variables on the likelihood of
litigation-related disclosure. In addition to the key variable of interest—i.e., an indicator variable on
whether the supplier has at least one principal customer (Dependent Supplier)—we also include in our
specification the natural logarithm of total assets (Log Size), return on total assets (ROA), the
leverage in book value (Leverage), the book-to-market ratio of equity (Book-to-Market), future equity
issuance (Equity Issue), future debt issuance (Debt Issue), the percentage of independent directors
Conditional on the lawsuits being material, this difference of litigation losses between dependent and non-dependent
suppliers (i.e., 0.087 vs. 0.091) is also statistically insignificant from zero.
17
30
(%Ind Directors), the natural logarithm of the number of all directors in the board (Log(Board Size)), an
indicator of being audited by local specialist auditors (Specialist Auditor), the percentage of litigation
loss amount in total assets for material outcome cases (Settlement as a Pct of Total Assets), and case-type
fixed effects. As mentioned above, the first three variables have been shown in the supply chain
literature to be known determinants of dependent supplier status and the other variables are known
determinants of corporate disclosure policies. Moreover, both Settlement as a Pct of Total Assets and
case-type fixed effects are included in our specifications to address the litigation propensity issue.
Except for Settlement as a Pct of Total Assets, all other control variables are measured at the most recent
fiscal year ends before firms are sued. Here, our focus is the sign for our treatment variable,
Dependent Supplier. According to H3a, the estimated coefficient on Dependent Supplier is expected to be
positive in Columns (1) and (2) of Table 8; further, according to H2a, the estimated coefficient on
Dependent Supplier is expected to be negative in Columns (7) and (8).
For immaterial cases, our results in Table 8 suggest that dependent suppliers are more likely
to make optimistic claims and less likely to issue pre-warnings than non-dependent suppliers. They
are consistent with the results shown in Table 6, and they support H3a. The estimated coefficients
on Dependent Supplier in Columns (1) and (2) are positive and statistically significantly at the 5% level
or higher. The estimated marginal probability in Column (2) suggests that the likelihood of making
optimistic claims for immaterial cases is 19.1% higher for dependent suppliers than that for nondependent suppliers. Given the audit review by public auditors as well as the oversight by regulators,
it would be potentially costly for defendant firms to provide optimistic claims in audited financial
statements that turn out to be inconsistent with litigation outcomes. In this case, our results in
Columns (1) and (2) suggest that managers of dependent suppliers are more likely to reveal their
favorable beliefs about case outcomes in financial statements to increase their credibility with
customers. Turning to tests of H2a, our results in Columns (7) and (8) are consistent with H2a and
31
imply that dependent suppliers are less likely to issue pre-warnings than non-dependent suppliers,
given their expectation of material loss outcomes. The estimated coefficients of -0.757 and -0.807,
respectively, are both significant at the 1% levels. The estimated coefficient on Dependent Supplier in
Column (8) suggests that the likelihood of making pre-warning disclosures for material cases is 26.8%
lower for dependent suppliers than that for non-dependent suppliers, confirming dependent
suppliers’ incentive to mitigate proprietary costs. In other words, managers of dependent suppliers
tend to withhold their unfavorable beliefs by pooling with uninformed managers, as we argue in
Section II. Combined, the results of Table 8 support our causal hypotheses, H2a and H3a.
Columns (3)-(6) of Table 8 present the results when the disclosure is inconsistent with the
case outcome. The estimated coefficients on Dependent Supplier in these columns are all significantly
negative, implying that dependent suppliers are less likely to make inconsistent disclosures.
Consistent with what we find in Table 6, these results suggest that managers of dependent suppliers
are more careful than those of non-dependent suppliers because they are aware of the adverse
impacts of inconsistent disclosures on their relationships with principal customers.
Regarding other control variables, our results in Column (8) show that the coefficient of Log
Size is positive and statistically significant at the 1% level. This result remains robust when firm size
is replaced by analyst coverage or institutional ownership. Consistent with previous disclosure
literature, these results suggest that firms of larger size, covered by more analysts and held by more
institutional investors are more likely to have transparent disclosures.
One may argue that controlling for the known differences between suppliers and nonsuppliers, such as total assets (Log Size), return on total assets (ROA), and the leverage in book value
(Leverage), as independent variables in regressions may not be sufficient to address the selection
concern in disclosure tests. To mitigate this concern, we follow DeFond, Erkens and Zhang (2014)
and restrict our sample by applying the Coarsened Exact Matching (CEM) technique based on these
32
three distinct differences between suppliers and non-suppliers. Our results (untabulated) remain
robust for the matched sample.
[Insert Table 9 Here]
In addition to the likelihood of litigation disclosures, the timeliness is another important
dimension of corporate disclosure, and our predictions involving timeliness are stated in H2a and
H3a. We report our timeliness results in Table 9. We find that, as predicted by H3a for immaterial
loss outcome cases, dependent suppliers make optimistic claims sooner than non-dependent
suppliers, as indicated by the significantly negative coefficients on Dependent Supplier in Columns (1)
and (2) of -0.340 and -0.485, respectively. This is consistent with the notion captured in Figure 2 —
i.e., in immaterial cases, managers of dependent suppliers (T’ in Figure 2) are more eager to reveal
positive private information than those of non-dependent suppliers (T* in Figure 2), to mitigate
information uncertainty that could weaken relationships ex ante. In addition, as predicted by H2a for
material loss outcome cases, dependent suppliers issue pre-warnings later than do non-dependent
suppliers, as indicated by the significantly positive coefficients on Dependent Supplier in Columns (7)
and (8) of 0.252 and 0.257, respectively. This is consistent with the notion captured in Figure 1—i.e.,
in material cases, the benefits of postponing pre-warnings are larger for dependent suppliers than for
non-dependent suppliers, and therefore the optimal timing for bad news disclosure is later for
dependent suppliers (T’ in Figure 1) than for non-dependent suppliers (T* in Figure 1). Consistent
with our previous finding in Table 8, results in Column (8) of Table 9 also suggest that, relative to
smaller firms, firms with larger size (covered by more analysts or held by more institutional investors)
are also likely to make more timely disclosure of bad news.
[Insert Table 10 Here]
Furthermore, we predict in Section II that the strategic disclosure behaviors are more likely
to be adopted by dependent suppliers when their customers face lower switching costs. In Table 10
33
we formally test this theoretical prediction where switching costs are proxied by the competiveness
or the product durability of the supplier’s industry. Specifically, as in Table 2, Competitive Industry
Dummy equals one if the Herfindahl–Hirschman Index of the SIC 3-digit industry that a firm
belongs to is lower than 0.1, and zero otherwise. Non-durable Goods Industry Dummy equals one if the
firm does not belong to the durable goods industry as defined in Gomes et al. (2009), and zero
otherwise. We incorporate Competitive Industry Dummy (Non-durable Goods Industry) and the interactive
term Dependent Supplier × Competitive Industry Dummy (Non-durable Goods Industry Dummy) into our
specifications, together with all other control variables reported in Tables 8 and 9. Panel A shows
the results concerning industry competiveness and Panel B shows the results concerning industry
product durability. For the sake of parsimony, we only report the coefficients on Dependent Supplier
and Dependent Supplier × Competitive Industry Dummy (Non-durable Goods Industry Dummy) in Table 10.
For each panel, the upper panel shows the results with respect to the likelihood and the lower panel
shows the results with respect to the timing. Because the results are very similar across industry
competiveness (Panel A) and product durability (Panel B), we only discuss the results related to
industry competiveness in Panel A. For immaterial cases, dependent suppliers are shown to be more
likely to disclose optimistic claims, and disclose optimal claims earlier when they are specialized in
competitive industries. These results are indicated by a significantly positive coefficient (0.374) on
the interaction term in the Optimistic Claims (OC) test and by a significantly negative coefficient (0.680) on the interaction term in the OC Timing Ratio test, both under Column (1). Similarly, for
material cases, dependent suppliers are shown to be less likely to issue pre-warnings, and issue prewarnings later if they are in competitive industries. These results are indicated by a significantly
negative coefficient (-0.536) on the interaction term in the Pre-warning (PW) test and by a
significantly positive coefficient (0.231) on the interaction term in the PW Timing Ratio test, both
under Column (4). On the other hand, we find that the supplier status does not lead to strategic
34
disclosure of litigation loss contingencies when customers are faced with high switching costs, e.g.,
when suppliers reside in monopolistic industries or in durable goods industries. Specifically, the
coefficients of Dependent Suppliers are statistically insignificant in Columns (1) and (4). Further, we do
not find that switching costs affect dependent suppliers’ disclosure behavior when they make
inconsistent disclosures in Column (2) and (3). In sum, these results show that switching costs of
customers do play a significant role when their suppliers are faced with litigation risks. They
corroborate our analysis on costs and benefits when dependent suppliers make their litigation
disclosure choices.
The Consequences of Litigation Disclosures
In this section, we test our prediction regarding the consequences of litigation disclosures—
i.e., H2b and H3b. In particular, we examine how optimistic claims and pre-warnings affect supply
chain relationships. We adopt discrete-time logistic hazard models to estimate the odds of
relationship termination if the dependent suppliers make optimistic claims in immaterial cases or
pre-warnings in material cases. To ensure that potential relationship termination is indeed an
outcome of litigation, our tests are carried out in a relationship-year panel data set that is right
censored at five years after cases are fully resolved. The dependent variable of the logistic hazard
model is a dummy variable that equals one if the relationship terminates at year t. Three key
independent variables are defined as follows: After Optimistic Claims is a dummy variable that equals
one if the dependent supplier being sued has made optimistic claims in immaterial cases by year t,
and zero otherwise; After Pre-warnings is a dummy variable that equals one if the dependent supplier
being sued has issued pre-warnings in material cases by year t, and zero otherwise; After Announcement
of Outcomes is a dummy variable that equals one if the case has been fully resolved and the outcome
has been announced by year t, and zero otherwise. Since it is clear that disclosure does not matter
35
after cases are fully resolved, we identify the consequences of disclosure before outcome
announcements in the following way. When both After Optimistic Claims and After Announcement of
Outcomes are controlled in the logistic hazard models for immaterial cases, the coefficient of After
Optimistic Claims will suggest the impact of optimistic claims on the odds of relationship termination
before the (immaterial) outcomes are announced. Similarly, when both After Pre-warnings and After
Announcement of Outcomes are controlled for material cases, the coefficient of After Pre-warnings will
suggest the impact of pre-warnings on the odds of relationship termination before the (material)
outcomes are announced.
[Insert Table 11 Here]
Our results are reported in Table 11. Columns (1) and (2) report the estimates of the logistic
hazard models for immaterial cases. Consistent with H3b, our results suggest that optimistic claims
indeed reduce the odds of relationship termination in immaterial cases, and this effect is robust after
controlling for relationship-specific characteristics and firm-level characteristics of customers and
suppliers. For example, the coefficient of After Optimistic Claims in Column (2), -1.810, is negative
and statistically significant at the 5% level. This result suggests that, all else being equal, the odds of
relationship termination at year t (before outcomes are announced) for dependent suppliers that
make optimistic claims are only 16% (=e-1.810) of those for firms that do not make optimistic claims.
This establishes an incentive to make optimistic claims for managers of dependent suppliers when
endowed with favorable private information. We do find in Section IV that these managers are more
likely to make optimistic claims in their financial statements and in a timelier manner.
Similarly, Columns (3) and (4) in Table 11 report the estimates of the logistic hazard models
for material litigation loss outcomes. Our results are consistent with H2b and imply that prewarnings of dependent suppliers being sued do increase the odds of relationship termination before
the outcomes of cases are announced. For example, the coefficient of After Pre-warnings in Column
36
(4), 2.527, is positive and statistically significant at the 1% level. This result suggests that, all else
being equal, the odds of relationship termination at year t (before outcomes are announced) for
dependent suppliers being sued that issue pre-warnings are 11.5 times (=e2.527) larger than those for
suppliers that do not issue pre-warnings. This result is not surprising. First, while pre-warnings
indicate that dependent suppliers are honest, they also credibly update customers’ expected
probability of financial distresses in their supply chain. Second, customers would realize that the prewarnings from their suppliers indicate a likely and imminent material loss, which the dependent
supplier can no longer hide by pooling with uninformed firms. Third, consistent with the results in
Section IV, the result also reinforces suppliers’ incentives to delay bad news when their managers are
endowed with unfavorable private information.
Other Robustness Checks
We carry out the following tests as robustness checks for our main results. For the sake of
parsimony, results are not tabulated but are available upon request.
First, in our definition of principal customers, we do not impose a 10% threshold. In order
to ensure that our results are robust, for the purposes of the tests reported in Tables 2 to 4, we
redefine a customer to be a principal customer if the customer accounts for more than 10% of the
supplier’s total revenue. Similarly, for the tests reported in Tables 8 to 10, we redefine a firm to be a
dependent supplier if the firm reports at least one customer who consists of 10% or more of its sales.
Our results are qualitatively similar to those in our main tests and the inferences remain unchanged.
Second, we address a possible private communication channel between customers and
suppliers by incorporating the relationship duration (i.e., an interaction term between Dependent
Supplier and Relationship Duration) into specifications reported in Tables 8 to 10. Here, we argue that
the likelihood of private communication increases as the customer-supplier relationship lasts. The
37
coefficients of this interaction term in all of our specifications are statistically insignificant from zero,
suggesting that the effect of customer-supplier relationships on litigation-related public disclosure
does not get weaker as relationship duration increases. This result confirms our conjecture that
SFAS 5 footnotes, as an important public disclosure channel, cannot be substituted by the private
communication channel between dependent suppliers and their principal customers.
Third, since our disclosure predictions (i.e., H2a and H3a) assume all else being equal, it is
important to ensure that litigation information arrival patterns are the same across dependent and
non-dependent suppliers. Since differences in the arrival rates of litigation “news” will confound our
inferences, while the manager’s private news is inherently unobservable to customers, regulators and
researchers, we use the prior news articles in the Factiva about the litigation prior to the settlement
to proxy for unobservable news arrival. For the 237 cases involving dependent suppliers (see Table
7), there is a prior news article in 8.0% of cases. For the 517 cases involving non-dependent
suppliers (see Table 7), there is a prior news article in 18.4% of cases. We repeat the disclosure tests
in Tables 8 and 9 after excluding all observations with prior news, in order to enhance the “all else
being equal” assumption. Our inferences remain the same.
V. CONCLUSION
Our study presents a number of interesting findings regarding how dependent suppliers’
litigation risks affect the customer-supplier relationships and how dependent suppliers make
strategic disclosures on litigation loss contingencies to minimize the costs generated by potential
relationship terminations. First, we establish that dependent suppliers’ litigation risks, as indicated by
being defendants in legal cases, have a significantly adverse effect on customer-supplier relationships.
This finding nicely complements the evidence in the previous literature with respect to the impact of
increasing business risks (e.g., bankruptcy and acquisition risks) on supply chain disruptions. In
38
addition, we quantify the proprietary costs for disclosing private bad news about litigation outcomes
and establish a plausible motive for truthfully disclosing private good news on a timely basis.
We further demonstrate that, compared with defendant firms not involved in supply chains,
dependent suppliers are more likely to make optimistic claims for immaterial loss outcome cases and
tend to make these optimistic claims sooner. This finding is consistent with our predicted disclosure
behavior with respect to good news. In addition, we find that, compared with defendant firms not
involved in supply chains, dependent suppliers are far less likely to provide pre-warnings for case
outcomes involving material losses, and they disclose pre-warnings much later than non-dependent
suppliers. These patterns are much stronger when customers face a lower level of switching costs.
This finding is consistent with our predicted disclosure behavior with respect to bad news.
With respect to disclosure consequences, we show that optimistic claims for immaterial loss
outcome cases improve the likelihood of relationship continuation. This is consistent with a
separating equilibrium whereby it is costly for dependent suppliers who do not possess favorable
private information to mimic the claims of dependent suppliers with favorable private information
about the legal case. In contrast, providing honest and timely pre-warning disclosures increases the
likelihood of relationship termination with principal customers. All of these results are based on the
mild assumption that ex post outcomes indicate, at least on average, ex ante news endowment.
Overall, in the setting where compliance with SFAS 5 requires a de facto discretionary
disclosure decision, our results point to dependent suppliers revealing private good news and
strategically withholding private bad news about ultimate litigation loss outcomes. The results should
be useful to the SEC, which is on record as being concerned about compliance with SFAS 5 and is
seeking to monitor such disclosures by registrants. Our results imply a very plausible motive for the
delay of private bad news about litigation outcomes, namely the impact of disclosed bad news on
possible supply chain disruptions. The findings should also be potentially useful to the auditors of
39
dependent suppliers being sued, since audit risk increases with undetected noncompliance with
GAAP disclosure requirements.
40
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43
Table 1
Summary Statistics: Firms in Customer-Supplier Relationships
The mean and median values of several firm characteristics of three samples for the period 2000–2008 are reported in this table. The sample reported in
Column (1) consists of dependent suppliers that report at least one identifiable principal customer. The sample reported in Column (2) includes all
identified public principal customer firms that have dependent suppliers. In Column (3), the sample includes supplier firms that are sued by third parties.
Total Assets is the book value of total assets; ROA is defined as EBITDA scaled by the book value of total assets; Book-to-market is the book-to-market
ratio of equity; Leverage is the book leverage ratio; and Z-score is Altman’s (1968) Z-score. We also report the mean and median values of the proportional
sales to principal customers for dependent suppliers and proportional inputs from dependent suppliers for principal customers. We require that all firms
in these samples have positive book values of total assets.
(1) Dependent Supplier
Firms
(2) Identified Public
Principal Customer Firms
(3) Dependent Supplier Firms being
Sued
Mean
Median
Mean
Median
Mean
Median
1831.681
214.871
45752.980
7085.237
3728.817
555.721
ROA
0.010
0.086
0.115
0.119
0.036
0.088
Book-to-Market
0.581
0.477
0.617
0.428
0.570
0.425
Leverage
0.259
0.157
0.283
0.234
0.219
0.147
Z-score
3.173
2.834
3.715
2.793
3.512
2.943
Sup Pct Sales
0.458
0.430
0.428
0.396
Total Assets (mil)
Cus Pct COGS
0.048
0.011
Number of Firm-Years
10,371
5,757
2,776
Number of Relationship-Years
19,110
12,306
5,510
44
Table 2
The Impact of Dependent Suppliers’ Legal Cases on the Continuation of Customer-Supplier Relationships
This table reports the estimates from Probit regressions about the impact of legal cases of dependent suppliers on the continuation of customersupplier relationships. The sample covers the time series of customer-supplier pairs identified by the COMPUSTAT Segment Customer file between
2000 and 2008. We require that both customer firms and supplier firms must be covered by the Audit Analytics database. The dependent variable is an
indicator variable that equals 1 if the customer-supplier relationship continues in the next year (i.e., year t+1), and zero otherwise. There are two
alternative proxies for litigation status: Dummy Legal Case is an indicator variable that equals 1 if the supplier is the defendant in at least one legal case in
year t and 0 otherwise; Num Legal Case is the total number of legal cases in which the supplier gets involved as a defendant in year t. Other independent
variables are defined as follows: Log (1+ Past Relation) is the natural logarithm of one plus the duration of past relationship between the customer and
the supplier at year t; Sup Pct Sales to Prin. Customers is the supplier’s percentage sales to the principal customer at year t; Cus Pct COGS from Dep. Suppliers
is the customer’s percentage of cost of goods sold from the dependent supplier at year t; Sup Log Size (Cus Log Size) is the natural logarithm of the
supplier’s (customer’s) book value of total assets at year t; Sup ROA (Cus ROA) is the supplier’s (customer’s) return on assets at year t; and Sup Z-score
(Cus Z-score) is supplier’s (customer’s) Altman’s Z-score at year t. An industry is considered to be competitive if the Herfindahl–Hirschman Index of the
SIC 3-digit industry is lower than 0.1 and monopolistic otherwise. Durable goods industries are defined following Gomes et al. (2009) and all other
industries are defined as non-durable goods industries. Standard errors, reported in parentheses, have been adjusted for the clustering at the customersupplier relationship level. The marginal effects of Dummy Legal Case and Num Legal Case evaluated at the means of other independent variables are
reported in the square brackets. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
45
(1)
Dummy Legal Case
(2)
(4)
(5)
(6)
Competitive
Monopolistic
(7)
NonDurable
(8)
Durable
-0.174***
-0.203***
-0.266***
-0.061
-0.218***
0.010
(-0.026)
(0.032)
(0.041)
(0.033)
[-0.076]
[-0.101]
(0.050)
[-0.022]
(0.109)
[0.004]
[-0.066]
-0.089***
Num Legal Case
Log (1 + Past Relation)
(3)
[-0.082]
-0.110***
(0.004)
(0.006)
[-0.033]
[-0.040]
0.371***
0.375***
0.239***
0.230***
0.195***
0.249***
0.224***
0.385***
(0.019)
(0.018)
(0.026)
(0.026)
(0.036)
(0.040)
(0.027)
(0.080)
Sup Pct Sales to Prin. Customers
Cus Pct COGS from Dep. Suppliers
Sup Log Size
Cus Log Size
Sup ROA
Cus ROA
Sup Z-score
Cus Z-score
1.945***
2.022***
1.661***
2.907***
1.988***
1.772*
(0.257)
(0.260)
(0.294)
(0.533)
(0.267)
(0.914)
0.062**
0.068**
0.067**
-0.198
0.061**
0.845
(0.029)
(0.033)
(0.033)
(0.339)
(0.027)
(0.595)
0.018*
0.066***
-0.006
0.064***
0.010
0.108***
(0.011)
(0.010)
(0.014)
(0.017)
(0.011)
(0.037)
0.031***
0.032***
0.024**
0.028*
0.024**
0.096***
(0.010)
(0.010)
(0.012)
(0.016)
(0.010)
(0.036)
0.477***
0.407***
0.454***
0.520***
0.493***
0.507*
(0.061)
(0.060)
(0.067)
(0.134)
(0.063)
(0.303)
0.529***
0.383**
0.493**
0.524*
0.625***
-0.249
(0.177)
(0.170)
(0.204)
(0.303)
(0.193)
(0.515)
0.072***
0.115***
0.078***
0.069***
0.080***
0.014
(0.015)
(0.016)
(0.018)
(0.027)
(0.016)
(0.060)
-0.006
-0.012
0.014
-0.058*
-0.012
0.119
(0.028)
Yes
(0.030)
Yes
(0.020)
Yes
(0.080)
Yes
Year Dummy
Yes
Yes
(0.020)
Yes
(0.020)
Yes
SE Cluster (relationship)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Number of Observations
19,110
19,110
12,306
12,306
7,356
4,950
10,091
2,215
0.025
0.049
0.086
0.125
0.078
0.116
0.087
0.107
Pseudo R-square
46
Table 3
Legal Case Categories and the Continuation of Customer-Supplier Relationships
This table reports the estimates from Probit regressions about the impact of legal cases on the continuation of customer-supplier relationships. The
sample covers the time series of customer-supplier pairs identified by the COMPUSTAT Segment Customer file between 2000 and 2008. We require
that both customer firms and supplier firms must be covered by the Audit Analytics database. The Probit model specification is the same as that in
Column (6) of Table 2 except that the Num Legal Case is replaced by Num Legal Case Type Xs (X = 1-99), which are the numbers of cases in each type as
defined by Audit Analytics. In Columns (6) and (7), we report the coefficients and marginal effects of Num Legal Case Type X for the top 10 legal case
types according to their frequency in our sample. In addition, we report the numbers of cases in each category in the full sample as well as in the subsample in which the dependent suppliers are the defendants in the legal cases.
(1)
(2)
(3)
(4)
Legal Case Category
in Audit Analytics
Category Description
in Audit Analytics
Num. of Cases
from Audit Analytics
(5)
1
35
Patent Law
1,365
Num. of Cases
where suppliers
are defendants
481
2
1
Class Action
1,593
Securities Law
1,241
Rank
3
41
(6)
Coefficient
Num Legal Case
Type X
(7)
Marginal Effect
Num Legal Case
Type X
-0.253***
-0.080
358
-0.066
-0.021
282
-0.208***
-0.066
182
4
48
Financial Reporting
693
-0.360***
-0.114
5
2
Accounting Malpractice
723
181
-0.535**
-0.170
6
59
Contract
469
116
-0.107*
-0.034
Antitrust & Trade Regulation
324
67
-0.146*
-0.046
58
7
6
8
36
Product Liability
226
-0.204**
-0.065
9
58
Other Statutory Actions
184
50
-0.203**
-0.064
10
56
Multi District Litigation (MDL)
204
39
-0.265***
-0.084
47
Table 4
The Materiality of Dependent Suppliers’ Legal Cases on the Continuation of
Customer-Supplier Relationships
This table reports the estimates from Probit regressions about the impact of legal cases on the continuation
of customer-supplier relationships. The sample covers the time series of customer-supplier pairs identified by
the COMPUSTAT Segment Customer file between 2000 and 2008. We require that both customer firms and
supplier firms must be covered by the Audit Analytics database. The dependent variable is an indicator
variable that equals 1 if the customer-supplier relationship continues in the next year (i.e., year t+1), and 0
otherwise. Material cases are defined as the cases in which losses exceed 0.5% of total assets. Dummy Material
Legal Case is a dummy variable that equals 1 if the supplier is involved in a material legal case as the defendant
at year t, and 0 otherwise; Pct Settlement in Total Assets is the percentage of settlement payments made by the
defendants scaled by their book values of total assets; and Dummy Legal Case is a dummy variable that equals 1
if the supplier is the defendant in at least one legal case in year t, and 0 otherwise. Other independent
variables are defined as follows: Log (1+ Past Relation) is the natural logarithm of 1 plus the duration of the
past relationship between the customer and the supplier at year t; Sup Pct Sales to Prin. Customers is the
supplier’s percentage sales to the principal customer at year t; Cus Pct COGS from Dep. Suppliers is the
customer’s percentage of cost of goods sold from the dependent supplier at year t; Sup Log Size (Cus Log Size)
is the natural logarithm of the supplier’s (customer’s) book value of total assets at year t; Sup ROA (Cus ROA)
is the supplier’s (customer’s) return on assets at year t; and Sup Z-score (Cus Z-score) is supplier’s (customer’s)
Altman’s Z-score at year t. Standard errors, reported in parentheses, have been adjusted for the clustering at
the customer-supplier relationship level. The marginal effects of Dummy Material Legal Case, Pct Settlement in
Total Assets, and Dummy Legal Case, evaluated at the means of other independent variables, are reported in the
square brackets. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
48
(1)
Dummy Legal Case × Dummy Material Legal Case
(2)
-0.577***
(0.074)
[-0.217]
Dummy Legal Case × Settlement as a Pct of Total Assets (%)
-0.036***
(0.007)
[-0.013]
Dummy Legal Case
Log (1 + Past Relation)
Sup Pct Sales to Prin. Customers
Cus Pct COGS from Dep. Suppliers
Sup Log Size
Cus Log Size
Sup ROA
Cus ROA
Sup Z-score
-0.117***
-0.145***
(0.034)
(0.033)
[-0.036]
[-0.055]
0.230***
0.235***
(0.026)
(0.026)
1.939***
1.938***
(0.257)
(0.257)
0.055**
0.060**
(0.027)
(0.028)
0.009
0.010
(0.011)
(0.011)
0.030***
0.031***
(0.010)
(0.010)
0.462***
0.468***
(0.060)
(0.060)
0.524***
0.536***
(0.176)
(0.176)
0.064***
0.068***
(0.015)
(0.015)
-0.019
-0.012
(0.019)
(0.020)
Year Dummy
Yes
Yes
SE Cluster (relationship)
Yes
Yes
Number of Observations
12,306
12,306
Pseudo R-square
0.093
0.089
Cus Z-score
49
Table 5
The Impact of Dependent Suppliers’ Legal Cases on Their Sales to
Principal Customers and Their Stock Returns
This table reports the abnormal growth rate of sales to principal customers (Panel A) and abnormal stock
returns (Panel B) of dependent suppliers who are defendants in legal cases. Year t is the first year when the
litigation against a dependent supplier is launched. In Panel A, Ab_Sale_Growth(t, t+kY) is the difference in
sales growth rate to principal customers from year t to year t+k between the dependent suppliers with legal
cases and dependent suppliers with no legal cases in the benchmark group. The benchmark group is
constructed based on lagged size, book-to-market ratio and sales growth rate. Specifically, the suppliers in the
benchmark group are required to (1) be involved in no legal cases from year t-3 to year t+3; and (2) have the
same size quintile, book-to-market quintile, and past sales growth quintile as the suppliers involved in legal
cases. In Panel B, Ab_Ret(t, t+kY) is the difference in stock returns from year t to year t+k between the
dependent suppliers with legal cases and dependent suppliers with no legal cases in the benchmark group.
Similarly, the benchmark group is constructed based on lagged size, book-to-market ratio, and past one-year
stock returns. We divide the full sample into two groups based on whether the cases are material or not,
which is defined in Table 4. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,
respectively.
Panel A: Abnormal Sale Growth Rate
Ab_Sale_Growth(t, t+1Y)
Ab_Sale_Growth(t, t+2Y)
Ab_Sale_Growth(t, t+3Y)
Immaterial Cases
Material Cases
-0.154***
-0.255***
(-3.74)
(-4.41)
-0.021
-0.468***
(-0.33)
(-5.65)
0.033
(0.39)
-0.630***
(-10.05)
Panel B: Abnormal Stock Returns (%)
Ab_Ret(t, t+1Y)
Ab_Ret(t, t +2Y)
Ab_Ret(t, t+3Y)
Number of Cases
-5.701
-9.493**
(-1.35)
(-2.02)
0.785
-15.947**
(0.25)
(-2.18)
0.482
-25.833***
(0.21)
(3.23)
825
253
50
Table 6
Summary Statistics: The Incidence and Timing of Litigation Disclosures
This table reports the summary statistics of the incidence and timing of litigation disclosures for our matched
samples of material and immaterial cases. We focus on two types of disclosures, namely pre-warnings and
optimistic claims. Pre-warnings are those disclosures in which firms warn investors of potential material
losses or mention that there are significant adverse economic consequences if the company losses a suit;
optimistic claims are those disclosures in which firms mention that the cases are not likely to have material
impacts and/or the cases have no merit. The incidence of disclosure is measured by the percentage of firms
that make pre-warning disclosures or optimistic claims before the case is fully resolved. The timing of
disclosure is defined as the ratio of the number of quarters between the date of case initiation and the date of
the disclosures, scaled by the number of total quarters for the case. For immaterial cases, if there are no
optimistic claims before the cases are fully resolved, we assume that the optimistic claims are made when the
cases are fully resolved, and therefore the disclosure timing ratios of optimistic claims are equal to one;
similarly, for material cases, if there are no pre-warnings before the cases are fully resolved, we assume that
the warnings are made when the cases are fully resolved, and therefore the disclosure timing ratios of prewarnings are equal to one. We report the statistics of immaterial cases in Panel A and those of material cases
in Panel B. In each panel, we also partition the sample according to whether the firm has at least one principal
customer when the case is launched, and we report the number of cases in each sub-group in squared
brackets. In addition, we compute the differences of the likelihood and timing of litigation disclosure between
the non-dependent-supplier group and the dependent-supplier group, and the t-statistics corresponding to the
differences are reported in parentheses.
Panel A: Immaterial Cases
Incidence
Non-Dependent Suppliers
Dependent Suppliers
Difference
0.492
[244]
0.241
[244]
0.624
[133]
0.150
[133]
-0.132**
(-2.47)
0.091**
(2.09)
0.806
[244]
0.403
[59]
0.633
[133]
0.285
[20]
0.173***
(3.65)
0.118
(1.04)
Non-Dependent Suppliers
Dependent Suppliers
Difference
0.480
[273]
0.546
[273]
0.337
[104]
0.288
[104]
0.143**
(2.51)
0.257***
(4.58)
0.263
[131]
0.702
[273]
0.310
[35]
0.937
[104]
-0.047
(-0.95)
-0.235***
(-6.59)
Optimistic Claim
Pre-warning
Timing
Optimistic Claim
Pre-warning
Panel B: Material Cases
Incidence
Optimistic Claim
Pre-warning
Timing
Optimistic Claim
Pre-warning
51
Table 7
Summary Statistics: Dependent Suppliers Vs. Non-Dependent Suppliers in the Matched Samples
This table reports the means of firm characteristics for dependent and non-dependent suppliers in the
matched sample based on case materiality. Total Assets is the book value of total assets (in millions); Num
Analysts is the number of analysts covering this firm; %Inst Holdings is the percentage of shares owned by
institutional investors; ROA is the return on assets; Book-to-Market is the book-to-market ratio of equity;
Leverage is the total leverage ratio based on the book values of debt and equity; Equity Issue is a dummy
variable that equals 1 if the defendant firm will make significant equity issuance (i.e., more than 10% of total
assets in aggregate) in a three-year period after the litigation starts, and 0 otherwise; Debt Issue is a dummy
variable that equals 1 if the defendant firm will make significant debt issuance (i.e., more than 10% of total
assets in aggregate) in a three-year period after the litigation starts, and 0 otherwise; %Ind Directors is the
percentage of independent directors among all directors in the board; Board Size is the number of all directors
in the board; Specialist Auditor is a dummy variable that equals 1 if the auditor of this firm has the highest
market share, as measured by total auditing fees, within a two-digit SIC category for the city where the firm
locates; and Settlement as a Pct of Total Assets (%) is the percentage of settlement payment in total assets in
material outcome cases. Except for Settlement as a Pct of Total Assets (%), all variables in this table reflect
information at the fiscal year end prior to the case initiation year. In addition, we provide the statistical
significances for the differences of these variables between the two groups in the last column.
Variables
Total Assets(mil)
(1)
(2)
(2)-(1)
Dependent Suppliers
Non-Dependent Suppliers
Difference
1431.090
8382.471
6951.381**
Num Analysts
7.865
9.811
1.946**
%Inst Holdings
34.54%
39.98%
5.44%**
ROA
-0.026
0.032
0.058***
Book-to-Market
0.468
0.540
0.072*
Leverage
0.255
0.292
0.037*
Equity Issue
0.382
0.291
-0.091***
Debt Issue
0.473
0.488
0.015
%Ind Directors
0.530
0.508
-0.022
Board Size
4.434
5.338
Specialist Auditor
0.208
0.188
-0.020
Settlement as a Pct of Total Assets (%)
0.049
0.048
-0.001
237
517
Number of Observations
52
0.904***
Table 8
Relationship with Principal Customers and the Likelihood of Litigation Disclosures
This table reports the estimates of Probit regressions that investigate the impact of the existing relationship with principal customers on the likelihood
of defendant firms’ litigation disclosure before the cases are fully resolved. We focus on two types of disclosures, namely pre-warnings and optimistic
claims. Pre-warnings are those disclosures in which firms warn investors of potential material losses or mention that there are significant adverse
economic consequences if the company losses a suit; optimistic claims are those disclosures in which firms mention that the cases are not likely to have
material impacts or/and the cases have no merit. The dependent variable for the first column (Optimistic Claims or OC) is a dummy variable that equals 1
if a firm makes an optimistic claim before the case is fully resolved, and 0 otherwise; similarly, the dependent variable for the second column (Prewarnings or PW) is a dummy variable that equals 1 if a firm makes a pre-warning before the case is fully resolved, and 0 otherwise. The key independent
variable, Dependent Supplier, is a dummy variable that equals to one if the sued firm has at least one principal customer when the case is launched. In
addition, we include the following independent variables in our specification: Log Size is the natural logarithm of the book value of total assets; ROA is
the return on assets; Leverage is the total leverage ratio based on the book values of debt and equity; Book-to-Market is the book-to-market ratio of equity;
Equity Issue is a dummy variable that equals 1 if the defendant firm will make significant equity issuance (i.e., more than 10% of total assets in aggregate)
in a three-year period after the litigation starts, and 0 otherwise; Debt Issue is a dummy variable that equals 1 if the defendant firm will make significant
debt issuance (i.e., more than 10% of total assets in aggregate) in a three-year period after the litigation starts, and 0 otherwise; %Ind Directors is the
percentage of independent directors among all directors in the board; Log(Board Size) is the natural logarithm of the number of all directors in the board;
Specialist Auditor is a dummy variable that equals 1 if the auditor of this firm has the highest market share, as measured by total auditing fees, within a
two-digit SIC category for the city where the firm locates; and Settlement as a Pct of Total Assets (%) is the percentage of settlement payment in total assets
in material outcome cases. Except for Settlement as a Pct of Total Assets (%), all other independent variables reflect information at the fiscal year end prior
to the year when the cases are launched against the defendant firms. We control for the year fixed effect and case-type fixed effect in our specifications,
and the standard errors reported in parentheses have been adjusted for clustering at the firm level. The marginal effects of Dependent Supplier, evaluated
at the means of other independent variables, are reported in the square brackets. ***, **, and * denote statistical significance at the 1%, 5%, and 10%
levels, respectively.
53
Immaterial Cases
Optimistic Claims (OC)
(1)
(2)
Dependent Supplier
Material Cases
Pre-warnings (PW)
(3)
(4)
Optimistic Claims (OC)
(5)
(6)
Pre-warning (PW)
(7)
(8)
0.410**
0.534***
-0.476***
-0.829***
-0.467***
-0.729***
-0.757***
-0.807***
(0.160)
[0.151]
(0.197)
[0.191]
(0.163)
[-0.141]
(0.231)
[-0.238]
(0.175)
[-0.176]
(0.204)
[-0.254]
(0.181)
[-0.262]
(0.214)
[-0.268]
Log Size
-0.038
-0.145***
-0.067
-0.015
-0.049
-0.084
0.106**
0.141**
ROA
(0.037)
0.959**
(0.056)
0.245
(0.045)
-0.027
(0.069)
-0.567
(0.041)
0.465
(0.060)
0.796*
(0.046)
-1.316***
(0.063)
-1.224***
Leverage
(0.438)
-0.038
(0.672)
-0.083
(0.291)
-0.574
(0.827)
-1.064*
(0.312)
-0.326
(0.424)
-0.296
(0.313)
-0.123
(0.407)
-0.602*
(0.303)
(0.439)
-0.131
(0.415)
(0.575)
-0.327**
(0.292)
(0.439)
0.095
(0.271)
(0.361)
-0.115
Book-to-Market
(0.139)
(0.161)
(0.104)
(0.103)
Equity Issue
0.087
(0.182)
0.252
(0.212)
-0.224
(0.180)
0.589***
(0.189)
Debt Issue
-0.397
(0.261)
0.067
(0.305)
0.207
(0.229)
0.227
(0.225)
%Ind Directors
0.155
1.147
-0.339
-0.264
Log(Board Size)
(0.501)
0.431*
(0.805)
0.261
(0.421)
-0.190
(0.428)
0.008
Specialist Auditor
(0.246)
0.156
(0.325)
-0.062
(0.251)
0.115
(0.271)
-0.254
(0.212)
(0.237)
(0.231)
-1.375
(0.220)
1.701
(1.312)
0.158
(1.277)
0.190
Settlement as a Pct of Total Assets (%)
Pseudo R-square
Year Dummy
Case Type Dummy
SE Cluster (Firm)
Number of Observations
0.099
0.126
0.090
0.141
0.076
0.141
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
377
377
377
377
54
Table 9
Relationship with Principal Customers and the Timing of Litigation Disclosures
This table reports the estimates of Tobit regressions that investigate the impact of the existing relationship with principal customers on the timing of
defendant firms’ litigation disclosure before the cases are fully resolved. We focus on two types of disclosures, namely pre-warnings and optimistic
claims. Pre-warnings are those disclosures in which firms warn investors of potential material losses or mention that there are significant adverse
economic consequences if the company losses a suit; optimistic claims are those disclosures in which firms mention that the cases are not likely to have
material impacts or/and the cases have no merit. The dependent variables, Optimistic Claim (OC) Timing Ratio and Pre-warning (PW) Timing Ratio, are
defined as the ratio of the number of quarters between the date of case initiation and the date of the disclosures, scaled by the number of total quarters
for the case. For immaterial cases, if there are no optimistic claims before the cases are fully resolved, we assume that the optimistic claims are made
when the cases are fully resolved, and therefore the OC Timing Ratios are equal to one; similarly, for material cases, if there are no pre-warnings before
the cases are fully resolved, we assume that the warnings are made when the cases are fully resolved, and therefore the Pre-warning (PW) Timing Ratios are
equal to one. The key independent variable, Dependent Supplier, is a dummy variable that equals to one if the sued firm has at least one principal customer
when the case is launched. In addition, we also include the following independent variables in our specification: Log Size is the natural logarithm of the
book value of total assets; ROA is the return on assets; Leverage is the total leverage ratio based on the book values of debt and equity; Book-to-Market is
the book-to-market ratio of equity; Equity Issue is a dummy variable that equals 1 if the defendant firm will make significant equity issuance (i.e., more
than 10% of total assets in aggregate) in a three-year period after the litigation starts, and 0 otherwise; Debt Issue is a dummy variable that equals 1 if the
defendant firm will make significant debt issuance (i.e., more than 10% of total assets in aggregate) in a three-year period after the litigation starts, and 0
otherwise; %Ind Directors is the the percentage of independent directors among all directors in the board; Log(Board Size) is the natural logarithm of the
number of all directors in the board; Specialist Auditor is a dummy variable that equals 1 if the auditor of this firm has the highest market share, as
measured by total auditing fees, within a two-digit SIC category for the city where the firm locates; and Settlement as a Pct of Total Assets (%) is the
percentage of settlement payment in total assets in material outcome cases. Except for Settlement as a Pct of Total Assets (%), all other independent
variables reflect information at the fiscal year end prior to the year when the cases are launched against defendant firms. We control for the year fixed
effect and case-type fixed effect in our specifications, and the standard errors reported in parentheses have been adjusted for clustering at the firm level.
***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
55
Immaterial Cases
Material Cases
OC Timing Ratio
PW Timing Ratio
OC Timing Ratio
PW Timing Ratio
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Dependent Supplier
-0.340**
-0.485***
0.002
-0.019
0.058
0.093
Log Size
(0.137)
0.240***
(0.042)
0.369***
(0.079)
-0.013
(0.105)
-0.007
(0.064)
-0.016
(0.068)
-0.017
(0.032)
-0.021*
(0.035)
-0.038**
(0.040)
(0.009)
(0.024)
(0.034)
(0.017)
(0.018)
(0.012)
(0.015)
ROA
-0.730***
(0.276)
-0.074
(0.116)
0.314*
(0.172)
0.167
(0.183)
0.200
(0.163)
0.190
(0.172)
0.239***
(0.075)
0.280***
(0.093)
Leverage
-0.091
(0.303)
-0.287***
(0.110)
-0.083
(0.167)
-0.599*
(0.316)
0.062
(0.137)
0.306*
(0.156)
0.018
(0.094)
0.169**
(0.083)
Book-to-Market Ratio
0.087***
(0.030)
-0.217
(0.226)
(8)
0.252***
0.257***
0.115**
(0.056)
0.005
(0.020)
Equity Issue
0.001
0.116
-0.083
-0.077*
Debt Issue
(0.046)
0.0450
(0.114)
-0.037
(0.062)
0.061
(0.044)
-0.057
%Ind Directors
(0.040)
-0.108
(0.137)
0.006
(0.074)
0.129
(0.053)
0.079
(0.070)
(0.310)
(0.154)
(0.100)
Log(Board Size)
-0.159***
(0.029)
0.060
(0.179)
0.011
(0.093)
0.048
(0.065)
Specialist Auditor
0.160***
(0.041)
-0.071
(0.117)
0.033
(0.088)
0.062
(0.053)
-0.559
(0.542)
-0.058
(0.272)
Settlement as a Pct of Total Assets (%)
Pseudo R-square
0.132
0.201
0.298
0.428
0.182
0.371
0.241
0.293
Year Dummy
Case Type Dummy
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
SE Cluster (Firm)
Yes
Yes
Yes
Yes
Number of Observations
377
79
166
377
56
Table 10
Industry Competitiveness, Product Durability and the Impact of Principal Customers on Litigation Disclosures
Tests reported in Tables 8 and 9 are repeated after incorporating the interactive effect between industry competitiveness (product durability) and the
existence of principal customers on litigation disclosure. Competitive Industry Dummy is a dummy variable that equals one if the Herfindahl–Hirschman
Index of the SIC 3-digit industry that a firm belongs to is lower than 0.1, and zero otherwise; Durable goods industry is defined following Gomes et al.
(2009) and all other industries are defined as non-durable goods industries. Non-durable Goods Industry Dummy is a dummy variable if the firm belongs to
non-durable goods industries, and zero if it belongs to durable goods industries. In addition to control variables reported in Tables 8 and 9, we also
incorporate Competitive Industry Dummy (Non-durable Goods Industry Dummy) and the interactive term Dependent Supplier × Competitive Industry Dummy (Nondurable Goods Industry Dummy) into our specifications. The coefficients of Dependent Supplier and Dependent Supplier × Competitive Industry Dummy (Non-durable
Goods Industry Dummy) are reported in Panel A (Panel B). For the sake of parsimony, we do not present the coefficient estimates of control variables.
Panel A: Industry Competitiveness
Incidence
Dependent Supplier
Dependent Supplier × Competitive Industry Dummy
Timing
Dependent Supplier
Dependent Supplier × Competitive Industry Dummy
Panel B: Industry Product Durability
Incidence
Dependent Supplier
Dependent Supplier × Non-durable Goods Industry Dummy
Timing
Dependent Supplier
Dependent Supplier × Non-durable Goods Industry Dummy
Immaterial Cases
Optimistic Claims (OC) Pre-warnings (PW)
(1)
(2)
-0.022
-0.675**
(0.337)
(0.339)
0.374**
0.359
(0.159)
(0.405)
OC Timing Ratio
PW Timing Ratio
0.423
-0.139
(0.291)
(0.154)
-0.680**
0.240
(0.242)
(0.217)
Material Cases
Optimistic Claims (OC)
Pre-warning (PW)
(3)
(4)
0.143
-0.363
(0.368)
(0.393)
-0.431*
-0.536**
(0.245)
(0.266)
OC Timing Ratio
PW Timing Ratio
0.129
0.024
(0.148)
(0.067)
-0.024
0.231***
(0.096)
(0.047)
Immaterial
Optimistic Claims (OC) Pre-warnings (PW)
(1)
(2)
-0.052
-0.396*
(0.491)
(0.207)
0.429**
-0.576
(0.182)
(0.655)
OC Timing Ratio
PW Timing Ratio
0.231
0.194
(0.436)
(0.391)
-0.477***
-0.020
(0.140)
(0.082)
Material
Optimistic Claims (OC)
Pre-warning (PW)
(3)
(4)
0.409
0.330
(0.432)
(0.426)
-0.584***
-0.752***
(0.215)
(0.235)
OC Timing Ratio
PW Timing Ratio
0.017
-0.106
(0.159)
(0.077)
0.049
0.261***
(0.091)
(0.039)
57
Table 11
The Impact of Litigation Disclosures on Relationship Termination with Principal Customers
This table reports the estimates of discrete-time logistic hazard models that investigate the impact of litigation
disclosures on future relationships with principal customers. To ensure that the relationship termination is
indeed an outcome of litigations, this test is carried out at the relationship-year level and the sample is rightcensored at five years after the cases are fully resolved. We require that the customer-supplier relationships
still exist when legal cases are launched against the suppliers (i.e., at Time A in the figure shown below).
After Announcement of Outcome =1
A: Case starts
B: Pre-warning or
optimistic claim is made
C: Case ends;
outcome is announced.
D: If relationship
continues, data are
censored at the year C+5.
After Optimistic Claims or After Pre-warnings =1
The dependent variable, Break, is a dummy variable that equals one if the customer-supplier relationships
break up. As shown in the figure above, After Optimistic Claims (After Pre-warnings) is a dummy variable that
equals one if the supplier in the relationship has issued an optimistic claim (pre-warnings) for the case in
which the supplier is involved as a defendant, and zero otherwise; After Announcement of Outcome is a dummy
variable that equals one if the litigation against the supplier has announced the outcome; Log (1+ Past Relation)
is the natural logarithm of one plus the duration of the past relationship between the customer and the
supplier at year t; Sup Pct Sales to Prin. Customers is the supplier’s percentage of sales to the principal customer
at year t; Cus Pct COGS from Dep. Suppliers is the customer’s percentage of cost of goods sold from the
dependent supplier at year t; Sup Log Size (Cus Log Size) is the natural logarithm of the supplier’s (customer’s)
book value of total assets at year t; Sup ROA (Cus ROA) is the supplier’s (customer’s) return on assets at year t;
and Sup Z-score (Cus Z-score) is supplier’s (customer’s) Altman’s (1968) Z-score. We include year dummies in
our specification. Standard errors, reported in parentheses, have been adjusted for the clustering at the
customer-supplier relationship level. ***, **, and * denote statistical significance at the 1%, 5%, and 10%
levels, respectively.
58
Immaterial Cases
(1)
After Optimistic Claims
(2)
-0.900***
-1.810**
(0.256)
(0.872)
After Pre-warnings
-1.852*
Material Cases
(3)
(4)
1.282***
2.527***
(0.223)
(0.956)
1.951***
2.223**
After Announcement of Outcome
-0.862*
(0.519)
(0.977)
(0.273)
(0.888)
Log (1 + Past Relation)
-0.457**
-0.087
-2.478***
-1.986***
(0.201)
(0.694)
(0.286)
(0.678)
Sup Pct Sales to Prin. Customers
-19.295***
-18.217***
(6.423)
(6.756)
-0.037*
-0.029
(0.022)
(0.051)
Sup Log Size
-0.566***
-0.115
(0.211)
(0.231)
Cus Log Size
-0.208*
-0.151
(0.111)
(0.103)
Sup ROA
-6.714**
-2.496
(2.906)
(2.255)
Cus ROA
-1.991
0.749
(2.670)
(5.468)
-0.091**
-0.096**
Cus Pct COGS from Dep. Suppliers
Sup Z-score
Cus Z-score
(0.043)
(0.042)
0.005
-0.553
(0.048)
(0.386)
Year Dummy
Yes
Yes
Yes
Yes
SE Cluster (Relationship)
Yes
Yes
Yes
Yes
Number of Observations
Pseudo R-square
717
717
675
675
0.145
0.422
0.250
0.446
59
Costs and Benefits
Line C: Costs of Postponing
Bad News Disclosure
Line B’: Benefits of Postponing
Bad News Disclosure for
Dependent Suppliers
Line B: Benefits of Postponing
Bad News Disclosure for Nondependent Suppliers
Case Starts
T*
Time
T’
Figure 1 Costs and Benefits of Postponing Bad News Disclosure and Optimal Disclosure Timing: Dependent
Suppliers vs. Non-Dependent Suppliers.
This figure presents the equilibrium of optimal timing of disclosing bad news for dependent and non-dependent
suppliers. Line C describes the cost function of postponing bad news disclosure; Line B (Line B’) describes the benefit
functions of postponing bad news disclosure for non-dependent suppliers (dependent suppliers). T* and T’ describe the
optimal timing of bad news disclosure for non-dependent and dependent suppliers, respectively.
60
Costs and Benefits
Line C: Costs of Disclosing
Good News
Line B’: Benefits of Disclosing
Good News for Dependent
Suppliers
Line B: Benefits of Disclosing
Good News for Non-dependent
Suppliers
Case Starts
T’
Time
T*
Figure 2 Costs and Benefits of Disclosing Good News and Optimal Disclosure Timing: Dependent Suppliers
vs. Non-Dependent Suppliers.
This figure presents the equilibrium of optimal timing of disclosing good news for dependent and non-dependent
suppliers. Line C describes the cost function of disclosing good news; Line B (Line B’) describes the benefit functions of
disclosing good news for non-dependent suppliers (dependent suppliers). T* and T’ describe the optimal timing of good
news disclosure for non-dependent and dependent suppliers, respectively.
61
Appendix I
Detailed Sample Selection Filters for the Disclosure Tests
Filters
Number of Cases
All legal cases from Audit Analytics during the period between 2000 and 2008
conditioned on that (1) a defendant firm must have non-missing information of its
corporate identifier, and that (2) the information regarding case type must be available
12,301
After requiring that the duration of cases must be longer than 365 days
4,944
After requiring that the outcomes of legal cases must be disclosed in firms’ 10-Qs and
10-Ks when the cases are fully resolved
1,663
After deleting immaterial cases with non-zero loss outcomes
990
After requiring that material and immaterial cases can be matched by industry
classification (i.e., two-digit SIC codes) and firm size
754
62
(377 pairs)
Appendix II
Coding Examples of Litigation Loss Contingency Disclosure Variables
Pre-warnings: Disclosures in which firms warn investors of significant adverse economic consequences
from lawsuits, and/or provide a material loss estimate, and/or accrue a loss.
• “Penalties for violating competition laws can be severe, involving both criminal and civil liability. … a
finding that we violated either U.S. antitrust laws or the competition laws of some other jurisdiction
could have a material adverse impact on our results of operations or financial condition.” (from the 10K
of UAL Corporation filed on February 29, 2008)
• “An adverse outcome to the proceeding could materially affect the Company’s financial position and
results of operations. In the event the Company is unsuccessful, it could be liable to Mr. Parker for
approximately $5.4 million under the Parker Agreement plus accrued interest and legal expenses.” (from
the 10Q of Pizza Inn, Inc. filed on November 9, 2005)
• “The Company has recorded a special litigation charge of $54.0 million pretax ($46.7 million after tax) in
its financial results for the three and six months ended June 30, 2006 to establish a reserve relating to this
matter based on discussions between the Attorney General’s Office, the Company and its legal counsel.”
(from the 10Q of Omnicare, Inc. filed on August 9, 2006)
Optimistic claims: Disclosures in which firms express optimistic views that the cases are not likely to have
material impacts, and/or the cases have no merit.
• “In the opinion of management and counsel, none of these lawsuits are material and they are all
adequately reserved for or covered by insurance or, if not covered, are without any or have little merit or
involve such amounts that if disposed of unfavorably would not have a material adverse effect on the
Company.” (from the 10Q of Columbia Laboratories, Inc. filed on May 13, 2002)
• “While the Company cannot make any assurance regarding the eventual resolution of this matter, the
Company does not believe it will have a material adverse effect on the consolidated results of operations
or financial condition.” (from the 10K of LSI Logic Corp. filed on March 15, 2004)
• “We do not believe the ultimate outcome of this litigation will have a material adverse impact on our
financial condition, results of operations, or cash flows.” (from the 10Q of Conexant Systems, Inc.
filed on August 6, 2007)
63
Appendix III
The Likelihood of being Sued and the Extent of Litigation Loss between Dependent and Non-Dependent Suppliers
Our sample includes all COMPUSTAT firms covered by Audit Analytics between 2000 and 2008. Column (4) reports the coefficient b1 of the following
Logit regression: Logit (dummy=1 if the firm is sued in a case under category X in year t)= constant + b1×Dependent Suppliert-1 + Σbi × other controls
t-1 + year fixed effect + ε. Column (5) reports the coefficient b1 of the following OLS regression: Settlement Payment in Certain Case Type/Total Assets
= constant + b1×Dependent Suppliert-1 + Σbi × other controls t-1 + year fixed effect. The case categories, including the corresponding codes in Audit
Analytics and short descriptions, are presented in the second and third columns, respectively. Dependent Supplier is a dummy variable that equals 1 if the
firm has at least one principal customer in that year. Other control variables, including Log_size, ROA, Book-to-market, and Leverage, are defined as in
Table 7. All independent variables are measured at year t-1, i.e., one year before the litigation starts.
(1)
Rank
(2)
(3)
Legal Case Category
in Audit Analytics
Category Description
in Audit Analytics
(4)
Dependent Variable=
Dummy (=1 if Being Sued by Certain
Case Type)
(5)
Dependent Variable=
(Settlement Payment in Certain Case
Type/Total Assets)
1
35
Patent Law
-0.381***
0.000
2
1
Class Action
-0.270***
0.008
3
41
Securities Law
-0.371***
0.009
4
48
Financial Reporting
-0.376***
0.009
5
2
Accounting Malpractice
-0.398***
0.007
6
59
Contract
-0.230**
0.000
7
6
Antitrust & Trade Regulation
-0.489***
0.000
8
36
Product Liability
-0.638***
0.000
9
58
Other Statutory Actions
-0.102
-0.001
10
56
Multi-District Litigation (MDL)
-0.640***
-0.000
64