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
University of Toronto
& University of Missouri
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: September 12, 2015

We appreciate the helpful comments from two anonymous reviewers, Francesco Bova, Jeffrey Callen, Sudipto
Dasgupta, Luminita Enache, Ole-Kristian Hope, Wenli Huang (JCAE discussant), Kai Wai Hui, Hai Lu (CAAA
discussant), Partha Mohanram, Donald Monk (FARS discussant), Don Pagach (AAA discussant), Antonio
Parbonetti (EAA discussant), Atul Rai, Peter G. Szilagyi (CICF discussant), Zheng Wang, Terry Warfield, T. J.
Wong (the Editor), 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 2015 European Accounting Association
Annual Congress, The Chinese University of Hong Kong, University of Nebraska-Lincoln, University of
Queensland, and University of Toronto research workshops. We thank Mahrukh Abid, 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
(SSHRC). 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: This paper establishes the existence of a proprietary cost by showing that the litigation risk
of supplier firms is positively associated with the likelihood of customer-supplier relationship
termination. Given the proprietary cost, we next show that dependent suppliers being sued make
strategic disclosures regarding litigation loss contingencies in their financial statements in order to
avoid relationship disruption. Relative to firms with no principal customers, dependent suppliers tend
to strategically withhold their bad news and promptly reveal their good news. This pattern is stronger
when customer switching costs are lower, resulting in higher termination risk for the dependent
supplier. Such strategic disclosure choices are evident only for case types involving relationship
termination risk, suggesting that proprietary costs vary across case types. Our findings regarding the
delay of bad news are useful to the SEC, which would like to see more timely 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
Information sharing is an important element in improving supply chain efficiency for both
suppliers and customers. Concurrent research examines the transparency commitment of suppliers
and customers within supply chains (see Hui, Klasa, and Yeung 2012; Dou, Hope, and Thomas 2013;
Cao, Hsieh, and Kohlbeck 2013; Krishnan, Lee, and Patatoukas 2014). 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. 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.
Our disclosure setting is particularly interesting for the following reasons. First, the lawsuit as
well as the loss contingency may negatively affect a customer’s inventory management, 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 their switching costs, which
in turn vary by industry and depend on the uniqueness of products, the requirement of relationshipspecific 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 a high demand for
1
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 for customer firms, Supplier & Risk
Monitor from Dow Jones and SmartWatch from LexisNexis, which allow 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.
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 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. The
tension between the high demand for litigation-related information and the ambiguous nature of
litigations makes disclosure of litigation risks by suppliers particularly important to customers.
Third, our setting permits us to identify potential proprietary costs affecting litigation
disclosure decisions. One of the most important differences between suppliers with principal
customers (hereafter, “dependent suppliers”) and non-dependent suppliers is customer concentration
(Patatoukas 2012). Because the sales to principal customers constitute a significant proportion of their
total sales (e.g., 19.5% on average in our sample), litigation risk leads to cash flow risks of a much
larger magnitude for dependent suppliers relative to non-dependent suppliers, under the assumption
that customer decisions are not perfectly correlated and non-dependent suppliers enjoy a higher level
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”.
1
2
of customer diversification. Together, high proprietary costs and the opportunity to withhold bad
news given the above-documented ambiguity give rise to strategic disclosure decisions by dependent
suppliers.
We start with an examination of whether litigation risk faced by dependent suppliers adversely
affects the likelihood of relationship termination. We predict and find 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. However, when customers face a higher level of switching costs
(proxied by the competiveness and product durability of the dependent supplier’s industry), the
relationship is less likely to be terminated when their suppliers are involved in litigations.
Having established the existence of potential proprietary costs related to supply chain
discontinuation, we next turn to firm disclosure and explore whether dependent suppliers being sued
make strategic disclosure choices. 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.
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. 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.2
We examine how customer-supplier relationships affect litigation loss contingency disclosures
made by dependent suppliers. When endowed with unfavorable private information regarding
This assumption, which is made in many disclosure studies, is only reasonable on average. We acknowledge that some
suppliers may simply be uninformed prior to the litigation outcome, while other suppliers may have prior beliefs
inconsistent with the actual outcome.
2
3
litigation outcomes, dependent suppliers are less likely to provide timely pre-warnings 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. For example, we find that the
likelihood of making pre-warning disclosures for material cases is 23.0% lower for dependent suppliers
than that for non-dependent suppliers. Conversely, the dependent supplier’s likelihood of making
optimistic claims for immaterial cases is 28.1% higher relative to non-dependent suppliers. We further
argue that, if customer switching costs are relatively low, the likelihood of exit by the customer is
higher, compared to when customer switching costs are relatively high. Given this enhanced concern
about termination, when customer switching costs are relatively low (i.e., when suppliers reside in nondurable or competitive industries), strategic disclosure choices (i.e., delaying bad news and accelerating
good news) are more likely to be observed.
In an attempt to provide a closer linkage between the termination and disclosure tests in our
study, we further explore how different case types affect supplier disclosure choices through economic
mechanisms that are specific to supply-chain relationships. Specifically, we show that the two case
types involving securities law violations (i.e., “Accounting Malpractice” and “Securities Laws, Other”)
are associated with relationship termination risk primarily through the immediate financial distress
channel. Four other case types, including “Breach of Contract”, “Product and Service Liability”,
“Social Responsibility Related Litigation”, and “Operational Malpractice” are also associated with
relationship termination risk, presumably due to reputation damage concerns. The remaining two case
types, consisting of “Patent and Copyright Related” and “Antitrust Violation”, are not associated with
relationship termination risk. We show that the first six case types affect differences in strategic
disclosure choices between the two supplier types, while the latter two case types do not. This
4
establishes an explicit link between a variation in proprietary costs across case types and a resulting
difference in supplier disclosure patterns. With case type partitions specific to economic mechanisms
through supply chains, this result also mitigates the endogeneity concern that our disclosure results
are driven by omitted variables unrelated to supply chains.
Our paper contributes to the accounting and finance literature in the following ways. First, we
add to a growing body of literature that addresses how parties other than the investor community
affect dependent suppliers’ disclosure decisions. In particular, we show that the existence of customerrelated proprietary costs involving supply chain termination affects the disclosure behavior of
dependent suppliers relative to non-dependent suppliers. Our results show that, under an end-game
setting, such proprietary costs lead to disclosure decisions by dependent suppliers who tend to
strategically withhold bad news and promptly reveal good news.
Second, our study complements and extends the current supply chain literature by addressing
an interesting tension between a commitment to share information within supply chains and
opportunistic disclosure by dependent suppliers. Some authors base their disclosure predictions on
notions of opportunism rather than efficient contracting. We argue that both phenomena (efficient
contracting, opportunism) manifest themselves in supply chains, depending on the level of switching
costs. We argue and show that the above commitment is less credible when customer switching costs
are lower.
Third, our evidence shows that customer-related proprietary costs vary by case type and we
establish an explicit link between a variation in proprietary costs across case types and a resulting
difference in supplier disclosure patterns. Strategic disclosure choices are evident only for case types
involving relationship termination risk.
Our paper is organized as follows. Section II provides the institutional background and Section
III presents our theoretical framework and testable hypotheses. Section IV describes the sample and
5
presents descriptive statistics. Section V presents our empirical tests and results. Section VI discusses
other robustness checks, and Section VII concludes the paper.
II. INSTUTITIONAL BACKGROUND
In the U.S., SFAS 5 divides loss contingencies into three groups based on the likelihood of
the event confirming a material loss (i.e., losing the case or receiving an unfavourable settlement):
remote, reasonably possible, and probable. Contingencies where the chance of loss is judged to be
remote are generally not required to be disclosed or accrued. Loss contingencies that are reasonably
possible or probable must be disclosed. SFAS 5 specifies that this disclosure should indicate the nature
of the loss contingency and provide a point or range estimate of the amount of loss. Loss
contingencies which are probable and estimable must be accrued. SFAS 5 offers limited guidance on
how the terms “remote”, “reasonably possible”, and “probable” should be interpreted. The lack of
quantitative thresholds allows for considerable discretion in the application of SFAS 5 and creates the
potential for expectation gaps among shareholders and auditors (Kinney and Nelson 1996).
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). Critics argue that SFAS 5 allows for considerable discretion by managers in the
application of the accounting standard. 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,
leading to a de facto discretionary disclosure decision by the firm being sued (Hennes 2014; Chen, Hou,
6
Richardson, and Ye 2015). Letters to the FASB regarding SFAS 5 criticize firms for failing to provide
advance warning of material losses from litigation. Some financial statement users continue to express
concerns that the disclosure criteria in SFAS 5 are “inadequate or ineffective” (FASB 2008, p. 1).
Consistent with investors’ criticisms, academic studies (e.g. Fesler and Hagler 1989; Desir,
Fanning, and Pfeiffer 2010; Hennes, 2014; and Chen et al. 2015) in the SFAS 5 literature have
established the delay of bad news about litigation outcomes as a stylized fact. The supply chain setting
is a fertile one to continue the exploration of the delay of bad news concerning litigation outcomes.
In the context of SFAS 5, we identify two major types of disclosures—pre-warnings for
material cases and optimistic claims for immaterial cases. These disclosures contain managers’ private
information that is expected to be useful to principal customers in predicting case outcomes. We
define pre-warnings and optimistic claims in the following way. Pre-warnings are those disclosures in
which firms warn investors of a potential material loss outcome for a particular lawsuit by doing one
or more of the following: warn investors of potentially significant adverse economic consequences
from the 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.3
Given our arguments that compliance with SFAS 5 disclosure requirements involves a de facto
discretionary disclosure decision, we discuss common costs and benefits related to compliance facing
both dependent and non-dependent suppliers. For both supplier types, the benefits of postponing
bad news relate to propping up stock price, enhancing management tenure, and so on. Further, for
both supplier types, postponing bad news is not completely costless. The costs of verification are
For the three types of pre-warnings, 92% consist of narrative warnings of potentially significant adverse economic
consequences. We repeat our Table 4 tests for this subset and the results are qualitatively the same. For optimistic claims,
the frequency is roughly evenly distributed across the two types. We repeat our Table 5 test for each type of optimistic
claims, and the results are qualitatively the same for the “no material impacts” subset but weaker for the “no merit” subset.
3
7
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 to verify SFAS
5 disclosures, including claims to be uninformed, compared with the ability to do so when cases are
initially launched against the dependent suppliers.
Turning to the endowment of good news about litigation outcomes, the benefits of timely
good news disclosures for both supplier types 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.
From the cost side, potential follow-on litigation and regulatory investigations arise from mistaken
disclosures (e.g., Rogers, Van Buskirk, and Zechman 2011) by either supplier types. Such costs are a
decreasing function of elapsed time.
Given the presence of common costs and benefits, as explained in the next section, dependent
suppliers face greater customer-related proprietary costs, compared to non-dependent suppliers. This
is the focus of our paper.
III. RELATED LITERATURE AND HYPOTHESIS DEVELOPMENT
The Effect of Litigation on Customer-Supplier Relationships
When a supplier firm is sued by a third party, the litigation loss contingency could potentially
hurt the supplier’s financial stability and lead to deteriorating product quality and insufficient
relationship-specific investment in the short term (Titman and Wessels 1988). Further, as suggested
by Johnson, Xie, and Yi (2014), reputation loss from the litigation may lead to a damaged public image,
which will eventually affect the long-term operating performance of the supplier. Given the close
economic link between customers and suppliers, the operating and stock performance of customer
firms is also potentially adversely affected (e.g., Hendricks and Singhal 2003, 2005; Hertzel et al. 2008).
To avoid or mitigate the negative impacts from suppliers being sued, the customers will weigh the
8
costs and benefits of terminating the supply chain relationship prematurely, conditional on
information about the likely litigation outcome available to the customers. The tipping point along the
time line of a particular litigation episode occurs when the benefits of termination exceed the costs.
Therefore, our first hypothesis, stated in an alternative form, is as follows:
H1: The litigation risk of supplier firms is positively associated with the likelihood of customersupplier relationship termination.
Litigation Loss Contingency Disclosures and Customer-Supplier Relationships
Difference between dependent suppliers and non-dependent suppliers
One of the most important differences between dependent suppliers and non-dependent
suppliers is customer concentration (Patatoukas 2012), i.e., dependent suppliers have a few principal
customers that take a significant proportion of their total sales. If customers’ relationship decisions
are not perfectly correlated, the difference in customer concentration makes dependent suppliers bear
higher proprietary costs than non-dependent suppliers since disclosing bad news or postponing good
news leads to an increasing likelihood of relationship termination with customers. Further, since the
sales to principal customers constitute a significant proportion of their total sales, dependent suppliers
face cash flow risks of a much larger magnitude relative to non-dependent suppliers, who enjoy more
customer diversification.4 Therefore, we expect that such additional proprietary costs would make
dependent suppliers behave differently from non-dependent suppliers in disclosing litigation loss
contingencies.
Economic incentives and disclosure policy
This intuition can be illustrated in a simple numerical example. Let us assume all customers terminate a supplier
relationship with a 50% probability when they receive bad news involving the supplier and their decisions are uncorrelated.
When a supplier firm discloses bad news, for the supplier that has only one (major) customer, the probability of losing
100% of its cash flow is 50%; for the supplier that has equal sales to 10 customers, the probability of losing 100% of its
cash flow is less than 0.1%.
4
9
As discussed in the previous section, firms consider disclosure costs and benefits based on
their economic incentives. Customers and suppliers are no exception. With superior bargaining power,
principal customers demand timely and truthful information disclosure from dependent suppliers in
order to maintain supply chain stability, especially when relationship-specific investments (“RSI”)
from both parties are important in supply-chain relationships. As suggested by Bowen, Ducharme,
and Shores (1995), in order to decide their own level of commitment, principal customers need
information to assess a supplier’s ability to maintain product quality and honor implicit claims related
to its RSI’s (e.g., the ability to provide spare parts or otherwise service unique assets). Therefore, it is
not surprising that efficient and truthful information sharing is often explicitly or implicitly required
in supply chain agreements. Specifically, customers will demand a commitment from the suppliers to
share news about future earnings in a timely and unbiased fashion.5
A question arises as to what would make the disclosure commitment as described above a
credible one. The primary economic incentive of dependent suppliers is to attract and retain principal
customers. As a consequence, dependent suppliers will adopt disclosure policies in order to avoid
relationship termination. Since principal customers demand efficient information sharing, dependent
suppliers will cater to this demand if truthful disclosure does not lead to relationship termination. In
the setting of Cao et al. (2013), when RSI’s are high, the commitment on the part of the supplier is to
provide management forecasts in a timely and unbiased fashion. In Hui et al. (2012), when RSI’s by
the supplier are high, the supplier’s commitment is to incorporate bad news about future earnings into
accruals in a timely fashion, referred to as timely loss recognition, a form of accounting conservatism.
Krishnan el al. (2014) observe that dependent suppliers are less likely to be associated with accounting
As another example of disclosure commitments, Dou et al. (2013) argue and show that principal customers will honor
their transparency commitment by using income smoothing to signal future cash flows, in order to induce suppliers to
make high RSIs.
5
10
restatements. Krishnan et al. (2014) invoke the notion of high RSI’s in order to explain this result, one
that can be understood within the implicit contracting setting described above.
On the other hand, when truthful disclosure may lead to relationship termination, the
dependent supplier’s commitment to truthful and efficient information sharing is no longer a rational
choice compatible with its primary economic incentive, especially when the news is bad. In this case,
opportunism sets in and dependent suppliers may deliberately distort truthful and timely disclosure to
minimize the likelihood of relationship termination. For example, when RSI’s are low, Cao et al. (2013)
observe optimistic management forecast bias, which they interpret to represent opportunistic
disclosure behavior adopted with an aim to influence the perceptions of principal customers. In
addition, Raman and Sharur (2008) suggest that dependent suppliers engage in earnings management
and inflate reported earnings in order to attract and retain principal customers when principal
customers may switch to other competitors.
The above discussions also apply to litigation loss contingency disclosures by dependent
suppliers. This is intuitive since private information about litigation outcomes is a form of news about
future earnings. When managers of supplier firms are endowed with positive private information (i.e.,
a high probability of case resolution in favor of the defendant), truthful and timely disclosure fulfills
both the demand by principal customers for efficient information sharing and the supplier’s own
economic incentive to retain such customers. Given that postponing optimistic disclosures involves
greater proprietary costs for dependent suppliers, they are more likely to provide timely optimistic
disclosures regarding litigation loss contingencies, compared to non-dependent suppliers. 6 This
conjecture is based on the assumption that private communication of favorable private news is not
The ceteris paribus assumption is that other capital market disclosure stimuli are the same across the two supplier types
or, alternatively, that such differences are controlled for.
6
11
credible.7 Optimistic disclosure in the public domain establishes a separating equilibrium between
firms that have favorable private information and firms that do not. Ceteris paribus, dependent
suppliers endowed with favorable private information are more eager to provide optimistic public
disclosure about legal cases, given that they face higher proprietary costs related to losing customers,
compared to non-dependent suppliers.
When dependent suppliers are endowed with bad news (i.e., a high probability about an
upcoming material litigation loss), truthful disclosure is no longer compatible with the economic
incentive of retaining principal customers. Dependent suppliers may choose to postpone pre-warnings
in order to avoid immediate relationship termination with principal customers. Ceteris paribus, given
higher proprietary costs, dependent suppliers endowed with bad news about litigation outcomes are
more likely to postpone or even avoid pre-warnings of upcoming litigation losses, compared to nondependent suppliers.
Litigation disclosure predictions depend on the extent of switching costs facing the principal
customer. As asserted in Titman and Wessels (1988) and Banerjee, Dasgupta, and Kim (2008), the
principal customer faces high switching costs if RSI’s by the supplier are high for two reasons. First,
if the supplier fails, another supplier will have to be convinced to make the necessary high RSI’s, and
this could take time, resulting in supply chain disruption. Second, as asserted by Banerjee et al., if the
supplier’s products are unique, this likely leads to specific investments by the customer. Further,
customer switching costs are also high when the supplier is hard to replace, which is the case when
supplier industry concentration is high. The risk of termination by a principal customer declines as
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 supplier is endowed with private information
regarding litigation loss contingencies, private communication with principal customers is not sustainable 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 and, therefore, it is not a rational choice.
7
12
switching costs rise. Thus the difference in proprietary disclosure costs between dependent and nondependent suppliers also declines. Ceteris paribus, we expect that the differences between the two
supplier types, in terms of delaying bad news and accelerating good news about litigation outcomes,
will diminish.
The above discussion leads to the following two testable hypotheses:
H2: Ceteris paribus, when endowed with unfavorable private information regarding litigation
outcomes,
(a) dependent suppliers are less likely to provide timely pre-warnings regarding legal suits compared
with non-dependent suppliers;
(b) the effect above is stronger when customers face lower switching costs.
H3: Ceteris paribus, when endowed with favorable private information regarding litigation outcomes,
(a) dependent suppliers are more likely to provide optimistic disclosures on a more timely basis
regarding legal suits as compared with non-dependent suppliers;
(b) the effect above is stronger when customers face lower switching costs.
In order to integrate H1–H3, we repeat the analyses implied by H1 to establish a variation in
proprietary costs across case types. Although not formally stated as a hypothesis, we expect that
strategic disclosure choices are evident only for case types involving relationship termination risk.
IV. DATA SOURCE AND SAMPLE CONSTRUCTION
For our relationship termination tests, our customer-supplier relationship sample comes from
the Compustat Segment Customer File, covering a period from 1994 to 2012. 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. For both our relationship termination and disclosure
13
tests, 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; and, we define a firm to be a dependent supplier
if the firm reports the existence of one or more principal customers in the Compustat Segment
Customer File.
Our relationship termination tests (Table 2) involve dependent suppliers only and the datasets
are organized at the customer-supplier relationship 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 et al. 2008; Cohen and Frazzini 2008), and classify all principal customers into
government (mainly domestic or foreign government agencies), non-government (mainly public or private
firms), or unidentified. To test for relationship termination, we must be able to identify the exact
identities of principal customers. For the years from 1994 to 2012, we first exclude government,
private and unidentified customers. For customer-supplier-year pairs where the customer firms are
clearly identifiable, we manually match corporate customer names with the Compustat identifiers (i.e.,
GVKEYs) whenever possible. We delete customer-supplier-year pairs where the plaintiff is a principal
customer. This process allows us to identify 24,590 customer-supplier-year pairs, consisting of 3,649
distinct supplier firms, and 1,638 distinct customer firms in 6,012 relationships.
[Insert Table 1 Here]
In Panel A of Table 1, we compare a few firm characteristics of dependent suppliers and
principal customers at the customer-supplier pair level. Consistent with the supply chain literature,
our comparison suggests that principal customers tend to be firms of larger size, higher profitability,
and better financial health relative to their dependent suppliers (e.g., Banerjee et al. 2008, Cen,
Dasgupta, and Sen 2015). Further, for an average dependent supplier firm, the mean sales to one of
its principal customers account for 19.5% of its total sales; for an average principal customer firm, the
purchases from one dependent supplier account for 1.8% of the customer’s costs of goods sold
14
(COGS).8 Both statistics are close to what are presented by Patatoukas (2012). Finally, the dependent
supplier firms are on average involved in 0.382 legal cases in a given year.
The litigation datasets for our disclosure tests (i.e., Tables 3-8) come from the Audit Analytics
Legal File, organized at the legal case level.9 We partition the legal 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 measured at the year of case
resolution. The materiality threshold accords with Statements on Auditing Standards (SAS) 107 from
the AICPA.
The sampling filters used to obtain material cases employed in the disclosure tests are listed in
Appendix I. First, we require that legal cases must satisfy the following criteria: (1) a defendant firm
must have non-missing information for its corporate identifier (i.e., CIK) so as to merge with
Compustat data, (2) the information regarding case settlement amount must be available, and (3) the
cases where principal customers sue their dependent suppliers are excluded in our sample. As indicated
in Appendix I, this screening process yields a sample of 1,710 material cases. Second, we require that
the duration of cases must be longer than 365 days, which reduces our sample to 892 cases.10 Next,
we require that the outcomes of legal cases must be disclosed in the SFAS 5 footnotes of firms’ 10Qs and 10-Ks when the cases are fully resolved. This procedure further reduces our sample for the
disclosure-related tests to 630 material cases, as indicated in Appendix I.11 Among 630 cases, 333 cases
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.
9 The Audit Analytics legal file mainly covers federal securities class action claims, SEC actions and federal civil litigation.
10 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 604 days (untabulated).
11
The attrition from 892 to 630 material cases results from some specific cases not being mentioned in SFAS 5 footnotes
since a firm may aggregates its discussion of multiple cases.
8
15
involve defendants who are dependent suppliers, while the remaining 297 cases involve defendants
being non-dependent suppliers.
The sampling filters used to obtain immaterial cases for the disclosure tests are similar to those
used to obtain material cases. The only additional filter that we impose on immaterial cases is that the
defendant firm must be a lead defendant since co-defendants have much less at stake (Greer 2010).
As indicated in Appendix I, the final sample for immaterial cases consists of 1,567 observations,
including 682 cases involving dependent suppliers and 885 cases involving non-dependent suppliers.
For each of 630 material cases and 1,567 immaterial cases identified above, we manually search
the defendant firm’s SFAS 5 footnotes in 10-Qs and 10-Ks within the period between the initiation
and resolution of the case. This content analysis enables us to identify disclosures which we
characterize as pre-warnings and optimistic claims.
Table 1, Panel B presents firm characteristics for material and immaterial cases, separately, in
the disclosure sample. For both material and immaterial cases, dependent suppliers are smaller in firm
size, lower in leverage, and less profitable compared to non-dependent suppliers. This pattern is
consistent with that documented in the supply chain literature (e.g., Banerjee et al. 2008). Furthermore,
for both material and immaterial cases, 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, Kasznik, and Nelson 2001). Compared with non-dependent suppliers,
dependent suppliers have smaller board sizes and a higher likelihood of equity issuance. However, we
do not observe significant differences for either the book-to-market ratio or debt issuance between
the two supplier types. We discuss our approach to mitigate selection issues in the next section of the
paper.
16
Based on the case information provided in the Audit Analytics Legal File and firms’ 10-Qs
and 10-Ks, we manually classify legal cases in our sample into eight case types.12 These case types
include: “Accounting Malpractice”; “Patent and Copyright Infringement”; “Breach of Contract”;
“Product and Service Liability”; “Social Responsibility Related Litigation”; “Operational Malpractice”;
“Securities Laws Other than Financial Misrepresentation” (“Securities Laws, Other” hereafter);
“Antitrust Violation”.13 Table 1, Panel C lists the case type distribution in our disclosure sample, first
by case materiality and then by supplier type. Across both groups of material and immaterial cases,
“Accounting Malpractice”, “Patent and Copyright Related”, and “Breach of Contract” are the three
case types that are most frequently represented in the disclosure sample. We conduct analyses
exploring the role of case type in the next section.
V. EMPIRICAL TESTS AND RESULTS
The Effect of Suppliers’ Litigation Risk on Customer-Supplier Relationships
H1 predicts a positive impact of dependent suppliers’ litigation risk on the termination of
customer-supplier relationships. For each year t, we measure a dependent supplier’s litigation risk as
the total number of legal cases in which the supplier gets involved as a defendant (NCase) in year t.
[Insert Table 2 Here]
To test H1, we run the Cox proportional hazards model and the linear probability model14
separately on the data at the customer-supplier relationship level. For the Cox proportional hazards
We use our judgement in creating the eight case type aggregations, since there are 94 case types indicated in the Audit
Analytics legal file.
13 The “Accounting Malpractice” case type consists primarily of securities law violations involving financial
misrepresentation; the “Social Responsibility” case type involves litigation cases related to labor and environmental laws;
the “Operational Malpractice” case type consists of litigation cases involving allegations of criminal behaviors such as
racketeering, corruption and tax evasion.
14 Angrist and Pischke (2010) argue that the asymptotic properties and flexibility of linear models often produce more
robust results than nonlinear models. In addition, Greene (2004) suggests that linear models can accommodate a large
number of industry and year fixed effects with fewer estimation biases than nonlinear models. More importantly, as
12
17
model with time-varying covariates, “failure” is defined by relationship termination at t + 1. For the
linear probability model, the dependent variable is an indicator variable that equals one if the customersupplier relationships are terminated in the next year (i.e., year t+1), and zero otherwise. In addition,
we control for a set of firm characteristics for both customers and suppliers measured at time t. As
discussed in Section III, these controls require that customer firms must be public companies with
available accounting information. In particular, we include the dependent supplier’s percentage of 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 existing relationship strength and bargaining power 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. 2015). Moreover, we control for Altman’s (1968) Zscore, which is used to isolate the potential confounding effect from distress risks of both customers
and suppliers. We also include year fixed effects in the linear probability models. Standard errors in
both specifications are clustered at the customer-supplier relationship level.
As reported in Columns (1) – (2) of Table 2, the coefficients for NCase are positive and
statistically significant at the 1% levels, confirming that being sued in litigation will increase the
likelihood of customer-supplier relationship termination. The coefficient for NCase in Column (1) (i.e.,
0.049) corresponds to a hazard ratio of 1.050. This result suggests that the hazard of relationship
termination for the dependent suppliers involved in one litigation is 1.05 times the rate for the
dependent suppliers with no litigations and increases as the number of cases increases. Further, as
indicated by the results in Column (2), the probability of relationship termination increases by 0.8
percentage point for each additional case. Both results are consistent with H1, which suggests that the
suggested by Ai and Norton (2003), the coefficients for interaction terms in nonlinear models do not equal the marginal
effects and the linear probability models allow us to have a clear and easy evaluation of marginal effects based on the
coefficients for interaction terms. Nevertheless, as we have shown, our results are robust to nonlinear models.
18
litigation risk of the dependent supplier, as indicated by being a defendant in legal cases, has a
significant adverse effect on customer-supplier relationships. Turning to the effect of other control
variables on relationship termination, consistent with prior studies, we find that the likelihood of
relationship termination decreases with the dependent supplier’s sales to principal customers, the size
and profitability of both suppliers and customers, and the dependent supplier’s Z-score.
As discussed in Section II, switching costs are an important consideration in H2 and H3. Their
role in H2 and H3 relies on the intuition that, when customers face a higher level of switching costs,
the relationship is less likely to be terminated when their suppliers are involved in litigations. We
confirm this intuition in tests reported in Columns (3) and (4) of Table 2. In these tests, switching
costs are proxied for by the competiveness and product durability of the dependent supplier’s industry.
Durable goods industries (Durability) are defined in the same way as Gomes, Kogan, and Yogo (2009)
and all other industries are defined as non-durable goods industries. Industry competitiveness is
defined by the Herfindahl–Hirschman Index of the SIC 3-digit industry. Following the definition of
highly concentrated industries from the U. S. Department of Justice, an industry is defined to be
monopolistic (Sup Mono Ind) if the Herfindahl–Hirschman Index of the SIC 3-digit industry is higher
than 0.25. 15 Column 3 (Column 4) shows that the coefficient for the interaction term NCase ×
Durability (NCase × Sup Mono Ind) of -0.010 (-0.012) is significantly negative. These results confirm our
intuition that the effect of a supplier’s litigation risk on relationship termination is substantially
mitigated when their customers face a high level of switching costs.
The Effect of Customer-Supplier Relationships on Suppliers’ Litigation Disclosures
In this section, we examine whether existing relationships with principal customers affect the
timeliness and likelihood of litigation-related disclosures by dependent suppliers when managers are
endowed with unfavorable and favorable private information. We consider managers to be endowed
15
See the interpretation from the U.S. Department of Justice (http://www.justice.gov/atr/herfindahl-hirschman-index).
19
with unfavorable private information when litigations result in material loss outcomes. Similarly, we
assume that managers are endowed with favorable private information when litigations result in
immaterial loss outcomes.
[Insert Table 3 Here]
Table 3 presents univariate evidence in support of H2a and H3a. The timing of disclosure is
measured by the number of quarters between the date of case initiation and the date of litigationrelated disclosure scaled by the number of total quarters between the date of case initiation and case
resolution. For immaterial cases, if there is no optimistic claim before the case is fully resolved, the
announcement of the immaterial outcome (e.g., when a case is fully resolved) is considered as the
latest optimistic claim. The disclosure timing ratio for optimistic claims is equal to one for such cases.
For the same reason, in material cases, if there is no pre-warning before the case is fully resolved, the
disclosure timing ratio for pre-warnings is equal to one. The incidence of disclosure is measured by
the percentage of cases in which the firm issues an optimistic claim or a pre-warning before the case
is fully resolved. The univariate results for material cases are reported in Panel A of Table 3. In terms
of the timing of pre-warnings, we find that dependent suppliers issue pre-warnings much later than
non-dependent suppliers. In particular, non-dependent suppliers provide pre-warnings when 75.2%
of total case duration has elapsed, on average, compared with 90.7% for dependent suppliers. In terms
of the likelihood of making pre-warnings, we also find that 37.7% of non-dependent suppliers issue
pre-warnings for material cases, while only 15.0% of dependent suppliers issue such pre-warnings
before case outcomes are announced. The difference is significant at the 1% level for these groups.
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.
In Panel B of Table 3, we focus on the timing and the incidence of optimistic claims. Among
immaterial loss outcome cases, we find that dependent suppliers make optimistic claims sooner than
20
non-dependent suppliers. In particular, non-dependent suppliers make the claims when 83.4% of the
total case duration has elapsed, on average, compared with 68.3% for dependent suppliers. The
difference is significant at the 1% level. Further, 25.5% of non-dependent suppliers make optimistic
claims, while 56.5% of dependent suppliers make optimistic claims. The difference is also significant
at the 1% level. Consistent with H3a, this result suggests that in immaterial cases, dependent suppliers
are more likely to make optimistic public disclosures regarding litigation outcomes.
[Insert Table 4 Here]
In Tables 4 and 5, we conduct multivariate analyses to control for additional variables that
have known effects on corporate disclosures. In Table 4, we examine the timing and the likelihood of
issuing pre-warnings by firms involved in material cases. Our key variable of interest is an indicator
variable reflecting 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 (%Ind
Directors), the natural logarithm of the number of all directors in the board (Log(Board Size)), case-type
fixed effects, and year 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, we include case-type
fixed effects in our specifications to address heterogeneity across case types. All independent variables
reflect information at the fiscal year end prior to the year when the cases are launched against the
defendant firms.
We report our tests for the timeliness of issuing pre-warnings in Columns (1)-(4) of Table 4.
Since the dependent variable, the disclosure timing ratio, is bounded between 0 and 1, we adopt a
Tobit model in Column (1). In other tests, we also use OLS models so that we can incorporate a large
21
number of fixed effects without biasing our coefficients and interpret the interactive terms without
difficulty. We find that, as predicted by H2a for material loss outcome cases, dependent suppliers take
more time in making pre-warning disclosures than non-dependent suppliers, as indicated by the
significantly positive coefficients for Dependent Supplier in Columns (1) and (2) of 0.737 and 0.158,
respectively. The results are consistent with the notion that in material cases, the benefits of
postponing pre-warnings are larger for dependent suppliers than those for non-dependent suppliers.
Further, we test whether the effect documented in Columns (1) and (2) is stronger when principal
customers face lower switching costs. As in Table 2, switching costs are proxied by whether the
suppliers reside in the durable goods or monopolistic industries. Accordingly, we add Durability (Sup
Mono Ind) and the interaction term Dependent Supplier × Durability (Sup Mono Ind) into our specifications
under Column (3) (Column (4)). In Column (3), the coefficient for Dependent Supplier is 0.233 and the
coefficient for the interaction term Dependent Supplier × Durability is -0.305. Both are statistically
significant at the 1% level. These results suggest that, relative to non-dependent suppliers, dependent
suppliers in non-durable industries tend to postpone pre-warnings (as predicted by H2a) and such a
difference between the two supplier types is much smaller when suppliers are specialized in durable
goods industries (as predicted by H2b). Similar inferences can be made when we use the monopolistic
industry indicator to proxy for the level of switching costs under Column (4). Consistent with H2b,
results based on both proxies for switching costs suggest that, relative to non-dependent suppliers,
the dependent suppliers are more likely to postpone pre-warnings when their principal customers face
lower switching costs.
We report tests for the likelihood of issuing pre-warnings in Columns (5) to (8) in Table 4.
The dependent variable in these tests is defined as an indicator variable that equals one if the defendant
firm issues pre-warnings before the case is fully resolved, and zero otherwise. We carry out Logit and
linear probability models to estimate the impact of independent variables on the likelihood of
22
litigation-related disclosure. The estimated coefficients for Dependent Supplier under these two columns
are -1.278 and -0.230, respectively, both significant at the 1% levels. The estimated coefficient for
Dependent Supplier in Column (6) suggests that the likelihood of making pre-warning disclosures for
material cases is 23.0% lower for dependent suppliers than for non-dependent suppliers. The results
in Columns (5) and (6) are consistent with H2a and imply that dependent suppliers are less likely to
issue pre-warnings than non-dependent suppliers, given that they face higher proprietary costs
resulting from disclosing material loss outcomes.
Furthermore, the coefficients for Dependent suppliers in both Columns (7) and (8) are negative
and statistically significant at the 1% level. The coefficients for the interaction terms Dependent Supplier
× Durability and Dependent Supplier × Sup Mono Ind of 0.456 and 0.204 are both positive and significant
at the 1% and 5% levels, respectively. These results are consistent with H2b, i.e., the negative
association between pre-warning disclosures and dependent supplier status becomes less pronounced
when customers face high switching costs.
Regarding other control variables, our timeliness tests generally show that firms with higher
ROA and leverage wait longer, and that firms with higher expected equity issuance take less time to
issue pre-warnings. Furthermore, our disclosure likelihood tests in Table 4 show that firms with higher
leverage are less likely to disclose bad news.
[Insert Table 5 Here]
Table 5 presents the multivariate test results as to whether being a dependent supplier affects
the timeliness and the likelihood of making optimistic claims. Columns (1) – (4) of Table 5 present
the results for the timeliness tests. First, the significantly negative coefficients on Dependent Supplier in
Columns (1) and (2) (-0.428 and -0.132, respectively) are consistent with H3a. Specifically, for
immaterial loss outcome cases, dependent suppliers make optimistic claims sooner than nondependent suppliers. The results suggest that, for immaterial cases, managers of dependent suppliers
23
are more eager to reveal positive private information than those of non-dependent suppliers. As we
discuss in Section III, making a public optimistic claim can credibly separate announcing firms from
those without favorable private information, which mitigates information uncertainty that could
weaken relationships ex ante. Furthermore, the significantly positive coefficients (i.e., 0.143 and 0.218)
for the interaction terms Dependent Supplier × Durability and Dependent Supplier × Sup Mono Ind in
Columns (3) and (4) indicates that, when customers face higher switching costs, the difference in
making optimistic claims between the two supplier types is much smaller (consistent with H3b).
Turning to the likelihood of making optimistic claims, our results under Columns (5) and (6)
show that dependent suppliers are more likely to make optimistic claims than non-dependent suppliers,
which is consistent with H3a. The estimated coefficients for Dependent Supplier in Columns (5) and (6)
of 1.237 and 0.281, respectively, are positive and statistically significantly at the 1% levels. The
coefficient for Dependent Supplier from the linear probability model suggests that, relatively to nondependent suppliers, the dependent supplier’s likelihood of making optimistic claims is higher by 28.1
percentage points. Moreover, the significantly negative coefficients for the interaction terms in
Columns (7) and (8), -0.304 for Dependent Supplier × Durability and -0.462 for Dependent Supplier × Sup
Mono Ind, indicate that the effect documented in Columns (5) and (6) is weaker when customers face
higher switching costs. These results are consistent with H3b that the difference in optimistic
disclosures between two supplier types diminishes when relationship termination is unlikely.16
In sum, our results in Tables 4 and 5 confirm that, relative to non-dependent suppliers,
dependent suppliers are more likely to postpone pre-warnings and accelerate optimistic claims. In
addition to the timeliness of disclosure, we observe a similar pattern based on the likelihood of
disclosure. Our results further show that the difference in disclosure policies between two supplier
In terms of the control variables, the results from Table 5 consistently show that larger firms are less likely to disclose
good litigation news and tend to disclose good news later.
16
24
types described above is particularly strong when principal customers face lower switching costs, i.e.,
when relationship termination is more likely to be triggered by making pre-warnings or postponing
optimistic claims.
Robustness Checks with the Coarsened Exact Matched Sample
It is documented in the supply chain literature (e.g., Banerjee et al. 2008) that dependent
suppliers are smaller in firm size, lower in leverage and less profitable relative to non-dependent
suppliers. Since these selection attributes are also likely to be associated with disclosure decisions,
endogeneity concerns arise. To mitigate this concern, we follow Iacus, King, and Porro (2011, 2012)
and DeFond, Erkens, and Zhang (2014) and restrict our sample by applying the Coarsened Exact
Matching (CEM) technique based on these three firm characteristics and year. Specifically, we regard
dependent suppliers as the treated units and the non-dependent suppliers as the control units. For the
entire Compustat cross section in year t, we create strata based on Log Size, ROA and Leverage separately
by quintile sorts. We drop any observation whose stratum does not contain at least one treated (i.e.,
dependent suppliers) and one control (i.e., non-dependent supplier) unit in our disclosure sample. As
a result, the CEM method can significantly improve the estimation of causal effects by reducing the
imbalance in covariates between the treated and control groups, especially for those that play a
significant role in the selection process.
The CEM technique results in 360 material cases and 559 immaterial cases. As shown in
Appendix II, among the material cases, 188 dependent suppliers are matched with 172 non-dependent
suppliers and there is no statistically significant difference in a number of dimensions that are
associated with disclosure propensity in the voluntary disclose literature (e.g., Lang and Lundholm
1993; Johnson et al. 2001): total assets, ROA, financial leverage, book-to-market ratio, equity and debt
issuance, the percentage of independent directors and board size. Similarly, no significant differences
in firm characteristics are observed across the matched groups for the 559 immaterial cases. These
25
results confirm that the CEM method has successfully removed imbalances in covariates in our sample,
which significantly mitigates selection concerns, i.e., the concern that imbalance in covariates, rather
than a difference in proprietary costs, is the actual driver leading to difference in disclosure across the
two supplier types.
[Insert Table 6 Here]
Table 6 suggests that our results reported in Tables 4 and 5 remain robust after CEM. Focusing
on the material cases, Panel A shows that the coefficients for Dependent Supplier in Columns (5) and (6)
are negative and significant at the 1% level, while the coefficients for Dependent Supplier in Columns (1)
and (2) are positive and significant at the 1% level. Combined, these results are consistent with H2a.
Furthermore, the significantly positive interaction terms in Columns (7) and (8) and the significantly
negative interaction terms in Columns (3) and (4) confirm that in the face of material loss outcomes,
dependent suppliers are less likely to delay pre-warnings when their customers face higher switching
costs.
Table 6, Panel B focuses on immaterial cases. Our results under Columns (5) and (6) show
that dependent suppliers are more likely to make optimistic claims than non-dependent suppliers,
which is consistent with H3a. Moreover, the significantly negative interaction terms, Dependent Supplier
× Durability and Dependent Supplier × Sup Mono Ind under Columns (7) and (8) indicate that given
immaterial expected outcomes, dependent suppliers are less likely to issue optimistic claims when
customers’ switching costs are higher. Panel B, Columns (1) – (4) present the results for the timeliness
tests. The results suggest that for immaterial cases, managers of dependent suppliers make optimistic
claims in a more timely fashion than those of non-dependent suppliers. Furthermore, higher customer
switching costs mitigate the dependent supplier’s incentive to disclose good news earlier.
Underlying Drivers of Relationship Termination and Their Impact on Litigation Disclosure
26
In this section, we explore how different drivers of supply chain relationship termination affect
supplier disclosure choices. This test has two goals in mind. First, the test establishes an explicit link
between a variation in proprietary costs across case types and a resulting difference in supplier
disclosure strategies, thus unifying the termination and disclosure tests in our study under one
economic framework. Second, since our case type partitions are based on economic mechanisms that
are specific to supply chains, the test mitigates the endogeneity concern that our disclosure results are
driven by omitted variables unrelated to supply chains.
[Insert Table 7 Here]
Panel A of Table 7 lists the case type distribution for all Compustat firms with available data
between 1994 and 2012 (i.e., 1,710 material cases and 8,882 immaterial cases as indicated in Appendix
I). For both material and immaterial cases, Columns (2) and (3) indicate that “Accounting Malpractice”,
“Patent and Copyright Related” and “Breach of Contract” are the most frequent types. In terms of
average percentage of settlement loss, the largest losses relate to “Accounting Malpractice” and
“Securities Laws, Other”.
Column (1) of Table 7, Panel B reports coefficient estimates from the Cox Proportional
Hazards regressions about the impact of various types of litigations on the likelihood of financial
failures. We use all firms with available Audit Analytics and Compustat data between 1994 and 2012,
resulting in 105,267 firm-year observations. Financial failures are defined by Chapter 7 or Chapter 11
bankruptcy filings or stock market delisting because of firm liquidation at year t + 1 given survival in
year t. The key independent variables are the number of legal cases in which the supplier gets involved
as a defendant each year by case type. Following Beaver, McNichols and Rhie (2005) and Beaver,
Correia, and McNichols (2012), we include control variables, such as LRSIZE to capture firm size,
LERET to capture prior year’s stock return, LSIGMA to capture stock return volatility, ROA to
capture profitability, ETL to capture the ability of cash flow from operations pre-interest and pre-tax
27
to cover principal and interest payments, and LTA to capture leverage.17 The result shows that two
types of legal cases, i.e., cases related to “Accounting Malpractice” and “Securities Laws, Other”, are
positively and significantly associated with the financial failure. This result is consistent with our
observation that, on average, these two types of legal cases are associated with the highest percentage
of litigation losses in total assets, as shown in Panel A of Table 7.
Column (2) of Table 7, Panel B reports the coefficient estimates from the Cox Proportional
Hazards regressions about the impact of various types of litigations on the termination of customersupplier relationships. We explore the same relationship termination sample as in Table 2 and we use
all of the control variables as in Column (1) of Table 2. Our key variables of interest are NCase for
each case type. The results indicate that the numbers of litigations for the following case types are
significant predictors of relationship termination: “Accounting Malpractice”, “Breach of Contract”,
“Product and Service Liability”, “Social Responsibility Related”, “Operational Malpractice”, and
“Securities Laws, Other”.
Based on the results from the multivariate tests in Panel B of Table 7, we consider two major
drivers of relationship termination. Specifically, when dependent suppliers are sued in litigations that
belong to the case types “Accounting Malpractice” or “Securities Laws, Other”, principal customers
may terminate the supply chain relationship due to the possible short-term bankruptcy risk of suppliers,
as indicated by significant coefficients (0.092 and 0.070, significant at the 1% and 3% level, respectively)
in Column (2) of Panel B, taken in conjunction with the results in Column (1). Therefore, our first
case type group consists of case types (“Accounting Malpractice” and “Securities Laws, Other”) with
immediate financial distress risk. Four other case types, including “Breach of Contract”, “Product and
Service Liability”, “Social Responsibility Related”, and “Operational Malpractice”, load significantly
in Column (2) of Panel B. While they do not predict immediate financial failures, these four case types
17
Panel A of Table 11 in Beaver et al. (2012) provides a detailed definition of these variables.
28
still predict relationship termination. We conjecture that these four case types, which together
represent our second case type group, may affect relationship termination primarily due to reputation
damage concerns, i.e., customers are worried that litigations may adversely affect the public image of
suppliers and thus affect their long-term operation stability (Johnson et al. 2014). In summary, because
the above six case types are associated with relationship termination risk, we expect them to be more
likely to affect differences across supplier types in strategic disclosure choices.
The third case type group consists of two case types (“Patent and Copyright Related” and
“Antitrust Violation”), which are not shown to be associated with either bankruptcy or relationship
termination risk. Therefore, we do not expect them to affect differences across supplier types in
strategic disclosure choices.
[Insert Table 8 Here]
In Table 8, we explore whether differences across supplier types in strategic disclosure choices
vary across different case types. Based on the results in Table 7, we repeat our main analysis reported
in Tables 4 and 5 separately for each of the three case type groups discussed above. In particular, Panel
A of Table 8 reports results for case types that may lead to relationship termination primarily due to
a customer’s concern pertaining to the short-term bankruptcy risk faced by a supplier being sued;
Panel B reports results for case types related to a supplier’s long-term reputation damage; and Panel
C reports case types that have no clear associations with relationship termination. As discussed above,
suppliers face potential proprietary costs related to their principal customers in Panels A and B but
not in Panel C, sine the latter case types have no clear association with relationship termination. Given
that H2 and H3 assume differences in proprietary costs between dependent and non-dependent
suppliers, we expect the main effects documented in Tables 4 and 5 to be more evident for case types
that are associated with short-term bankruptcy risk and long-term reputation damage.
29
Consistent with our predictions, the results reveal that dependent suppliers behave more
strategically than non-dependent suppliers when facing the first two groups of cases. For example,
Panel A shows that dependent suppliers are more likely to delay pre-warnings in material cases and
speed up optimistic claims in immaterial cases. The results are more pronounced when switching costs
are low. However, in Panel C, when cases are not associated with relationship terminations, we do not
observe any difference in litigation loss contingency disclosures between dependent and nondependent suppliers as indicated by insignificant coefficients for the variable Dependent Supplier. Overall,
the results in this section further corroborate our argument that dependent suppliers behave
strategically to minimize potential proprietary costs related to potential relationship termination.
VI. OTHER UNTABULATED ROBUSTNESS CHECKS
Robustness Checks for Our Termination Tests
Evidence that customer decisions are less than fully correlated when non-dependent suppliers
are sued
We compute the difference in the growth rate of sales between dependent and non-dependent
suppliers, when both types face litigations. If customer decisions are less than fully correlated, nondependent suppliers should experience a lower decline in sales relative to dependent suppliers. Our
untabulated results confirm this conjecture. In particular, we find that the total sales of dependent
suppliers drop more dramatically than those of matched non-dependent suppliers when they are being
sued.
Disclosure of the name of principal customers
SFAS 131 only requires the disclosure of the existence of principal customers. Therefore, after
1998, suppliers can choose whether they disclose the names of their principal customers or not (Ellis,
Fee, and Thomas 2012). Our relationship termination tests in Table 2 do require the identities of
30
principal customers. This dataset allows us to detect whether suppliers are reluctant to report the
names of some principal customers. 18 Consistent with Ellis et al. (2012), we find that dependent
suppliers that do not disclose principal customer names have smaller firm size, lower analyst coverage,
lower institutional holdings, and come from more competitive industries than dependent suppliers
that disclose some principal customer names. This suggests that the firms excluded in our sample have
poorer information environments, making it harder for customers to find out about litigation concerns.
In addition, because switching costs are lower in more competitive supplier industries, litigationrelated proprietary costs would be higher. For both of these reasons, the effects in Table 2 would be
more pronounced if we were able to include observations for which customers are not identified.
Arbitrary cutoff level for the existence of principal customers
The other issue is the arbitrary cut-off level, 10%, for the mandatory disclosure of the existence
of principal customers. This concern is mitigated by the voluntary disclosure of principal customers
whose sales account for less than 10% of the dependent supplier’s total sales. For example, in our
relationship termination sample of 24,590 observations, 26.1% of identified principal customers have
sales that account for the less than 10% of total revenues. For our relationship termination and
disclosure tests, we avoid the use of a 10% threshold for identifying principal customers. As such, the
concern that our definition depends on a particular threshold has been largely mitigated. Our next
robustness tests relate to the sensitivity of our relationship termination tests reported in Table 2 to the
arbitrary 10% threshold issue. We supplement the tests in Table 2 with tests that indicate falling sales
following supplier litigation, suggesting that principal customers choose to weaken the relationship by
reducing purchases from troubled suppliers. This approach does not depend on a particular threshold.
Specifically, we compute the difference in the growth rate of sales to principal customers between
For example, the sales to principal customers are still reported, but the names of principal customers are presented as
“Not Reported”.
18
31
dependent suppliers with legal cases and dependent suppliers with no legal cases under different time
horizons (i.e., one, two, and three years, respectively). For the material loss cases, during all three time
horizons, sales growth rates are significantly lower for dependent suppliers being sued. For immaterial
loss cases, similar adverse effects are observed but such effects last for only one year. In another
robustness check, we use an alternative cut-off threshold (i.e., 20%) to identify principal customers
and find similar results for our relationship termination tests. Such an approach avoids a mechanical
“relationship termination” when the percentage sales to one customer drops from 10.1% to 9.9%.
Robustness Checks for Our Disclosure Tests
Disclosure of the names of principal customers
This issue is not of concern for our disclosure tests, which do not require the identities of
principal customers. Our disclosure tests are conducted at the case level, not the relationship-pair level,
and for such tests we only need to know about the existence of principal customers.
Arbitrary cutoff level for the existence of principal customers
This issue is of concern for our disclosure tests because the existence of a principal customer
may be exaggerated if sales account for less than 10% of suppliers’ total sales, thus blurring the
distinction between dependent and non-dependent suppliers. Our robustness tests confirm that our
disclosure results are robust to an alternative cut-off threshold (i.e., 20%) to identify principal
customers.
The role of prior news coverage
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. Differences in the arrival rates of litigation “news” will confound our inferences, since we
would expect weaker disclosure effects if supplier disclosures are not the only source of information
for customers. We use litigation-related news articles in Factiva, prior to the settlement, to proxy for
32
unobservable news arrival.19 We find that our disclosure tests are stronger when there is no prior news
coverage. This result is consistent with the notion that, when principal customers have more
alternative information sources (e.g., public media), dependent suppliers are less likely to adopt
strategic public disclosure to maintain supply chain relationships.
Why would any supplier issue a pre-warning?
As discussed in Section II, for both supplier types, firms will issue a pre-warning when the
costs of postponing bad news (due to sanctions by regulators) exceed the benefits. To support this
conjecture, for both supplier types combined, we estimate a linear probability model where the
dependent variable equals one if the supplier issues a pre-warning. Our results indicate that both
supplier types are more likely to issue pre-warnings as the settlement loss scaled by total assets
increases and as the number of prior news articles about the case in Factiva increases. Thus, the costs
of postponing bad news increase as regulatory risk increases, as proxied by the materiality of the loss
and the existence of alternative information sources.
Are dependent suppliers that did not provide pre-warning more likely to be dropped after the
actual announcement?
We estimate a hazards model for all dependent suppliers not dropped prior to the case
settlement announcement. Our results indicate that there is no difference in the likelihood of being
discontinued between dependent suppliers who issued a pre-warning and those who did not, an
inference that is robust to controlling for the number of prior news articles in Factiva before
settlement. We conjecture that the principal customer is unable to ascertain whether or not the supplier
was informed prior to settlement. The absence of a settling-up role for a failure to provide a prewarning explains the opportunistic delay of bad news by dependent suppliers.
We find that 21% of material cases and 14% of immaterial cases in our sample have been covered by at least one
newspaper article before case settlements.
19
33
VII. CONCLUSION
Our study presents a number of interesting findings regarding how dependent suppliers’
litigation risks affect customer-supplier relationships and how dependent suppliers make strategic
disclosures involving litigation loss contingencies in order to minimize the costs generated by potential
relationship termination. 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 establishes a plausible motive for dependent suppliers to make strategic disclosure choices.
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. In addition, we 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.
Both disclosure patterns are stronger when customer switching costs are lower, resulting in higher
termination risk for the dependent supplier.
We address an interesting tension in the supply chain literature between a commitment to
share information within supply chains and opportunistic disclosure by dependent suppliers. We argue
and show that both phenomena (efficient contracting, opportunism) manifest themselves in supply
chains, depending on the level of switching costs. Our synthesis involving these two conflicting views
is that the commitment to share information is not sustained in equilibrium when customer switching
costs are low and the dependent supplier is concerned about termination risk.
Moreover, our evidence shows that customer-related proprietary costs vary by case type and
we establish an explicit link between a variation in proprietary costs across case types and a resulting
difference in supplier disclosure patterns. Strategic disclosure choices are evident only for case types
involving relationship termination risk.
34
Overall, in the setting where compliance with SFAS 5 requires a de facto discretionary disclosure
decision, our results point to settings where dependent suppliers are more likely to strategically
withhold bad news about litigation loss outcomes, relative to non-dependent suppliers. 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 bad news about litigation outcomes, namely, the impact of disclosed bad news on possible
supply chain disruption.
35
References
Aberdeen Group. 2012. Supplier lifecycle management: measuring performance while mitigating risk. Research
Report, Aberdeen Group. Available at: http://aberdeen.com/Aberdeen-Library/7491/RA-supplierlifecycle-management.aspx.
Ai, C., and E. Norton. 2003. Interaction terms in logit and probit models. Economics Letter 80: 123-129.
Altman, E. I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of
Finance 23 (4): 589-609.
Angrist, J. D., and J. S. Pischke. 2010. The credibility revolution in empirical economics: How better research
design is taking the con out of econometrics. Journal of Economic Perspectives 24 (1): 3-30.
Banerjee, S., S. Dasgupta, and Y. Kim. 2008. Buyer-supplier relationships and the stakeholder theory of capital
structure. Journal of Finance 63 (5): 2507–2552.
Beaver, W. H., M. Correia, and M. F. McNichols. 2012. Do differences in financial reporting attributes impair
the predictive ability of financial ratios for bankruptcy? Review of Accounting Studies 17 (4): 969-1010.
Beaver, W. H., M. F. McNichols, and J. W. Rhie. 2005. Have financial statements become less informative?
Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies 10 (1):
93-122.
Bhagat, S., J. A. Brickley, and J. L. Coles. 1994. The costs of inefficient bargaining and financial distress:
Evidence from corporate lawsuits. Journal of Financial Economics 35 (2): 221-247.
Bowen, R., L. Ducharme, and D. Shores. 1995. Stakeholders implicit claims and accounting method choice.
Journal of Accounting and Economics 20 (2): 255–295.
Cao, J., S. Hsieh, and M. Kohlbeck. 2013. Do major customers influence voluntary corporate disclosure?
Working Paper, Florida Atlantic University.
Cen, L., S. Dasgupta, and R. Sen. 2015. Discipline or disruption? Stakeholder relationships and the effect of
takeover threat. Management Science, Forthcoming
Chen, F., Y. Hou, G. Richardson, and M. Ye. 2015. Auditor quality and litigation loss contingency disclosures.
Working Paper, University of Toronto.
Cohen, L., and A. Frazzini. 2008. Economic links and predictable returns. Journal of Finance 63 (4): 1977–2011.
DeFond, M., D. H. Erkens, and J. Zhang. 2014. Do client characteristics really drive Big N quality
differentiations? Working Paper, University of Southern California.
Desir, R., K. Fanning, and R. J. Pfeiffer. 2010. Are revisions to SFAS No. 5 needed? Accounting Horizons 24 (4):
525-545.
Dou, Y., O.-K. Hope, and W. B. Thomas. 2013. Relationship-specificity, contract enforceability, and income
smoothing. The Accounting Review 88 (5): 1629-1656.
Dye, R. A. 2001. An evaluation of ‘essays on disclosure’ and the disclosure literature in accounting. Journal of
Accounting and Economics 32 (1–3): 181–235.
Dye, R. A. 2013. Voluntary disclosure and the duty to disclose. Working Paper, Northwestern University.
Ellis, J. A., C. E. Fee, and S. E. Thomas. 2012. Proprietary costs and the disclosure of information about
customers. Journal of Accounting Research 50 (3): 685-727.
Fesler, R. D., and J. L. Hagler. 1989. Litigation disclosures under SFAS No. 5: A study of actual cases. Accounting
Horizons 3 (1): 10-20.
36
Financial Accounting Standards Board (FASB). 1975. Statement of Financial Accounting Standards No. 5: Accounting
for Contingencies. Norwalk, CT: FASB.
Financial Accounting Standards Board (FASB). 1997. Statement of Financial Accounting Standards No. 131:
Disclosures about Segments of an Enterprise and Related Information. Norwalk, CT: FASB.
Financial Accounting Standards Board (FASB). 2008. Exposure Draft: Disclosure of Certain Loss Contingencies.
Norwalk, CT: FASB.
Gomes, J. F., L. Kogan, and M. Yogo. 2009. Durability of output and expected stock returns. Journal of Political
Economy 117 (5): 941-986.
Greene, W. 2004. The behaviour of the fixed effects estimator in nonlinear models. The Econometric Journal 7:
98-119.
Greer, M. H. 2010. A Practitioner's Guide to Class Actions. American Bar Association.
Healy, P. M., and K. G. Palepu. 2001. Information asymmetry, corporate disclosure, and the capital markets: A
review of the empirical disclosure literature. Journal of Accounting and Economics 31 (2–3): 405–440.
Heitzman, S., C. Wasley, and J. Zimmerman. 2010. The joint effects of materiality thresholds and voluntary
disclosure incentives on firms’ disclosure decisions. Journal of Accounting and Economics 49 (1–2): 109–
132.
Hendricks, K. B., and V. R. Singhal. 2003. The effect of supply chain glitches on shareholder value. Journal of
Operations Management 21 (5): 501–522.
Hendricks, K. B., and V. R. Singhal. 2005. Association between supply chain glitches and operating
performance. Management Science 51 (5): 695–711.
Hennes, K. M. 2014. Disclosure of contingent legal liabilities. Journal of Accounting and Public Policy 33 (1): 32-50.
Hertzel, M. G., Z. Li, M. S. Officer, and K. J. Rodgers. 2008. Inter–firm linkages and the wealth effects of
financial distress along the supply chain. Journal of Financial Economics 87 (2): 374–387.
Hui, K., S. Klasa, and E. Yeung. 2012. Corporate suppliers and customers and accounting conservatism. Journal
of Accounting and Economics 53 (1): 115–135.
Iacus, S. M., G. King, and G. Porro. 2011. Multivariate matching methods that are monotonic imbalance
bounding. Journal of the American Statistical Association 106: 345-361.
Iacus, S. M., G. King, and G. Porro. 2012. Causal inference without balance checking: Coarsened exact
matching. Political Analysis 20: 1-24.
Johnson, M. F., R. Kasznik, and K. K. Nelson. 2001. The impact of securities litigation reform on the disclosure
of forward-looking information by high technology firms. Journal of Accounting Research 39 (2): 297–327.
Johnson, W. C., W. Xie, and S. Yi. 2014. Corporate fraud and the value of reputations in the product market.
Journal of Corporate Finance 25 (1): 16-39.
Kinney, W. R., and M. W. Nelson. 1996. Outcome information and the “expectation gap”: The case of loss
contingencies. Journal of Accounting Research 34 (2): 281-299.
Kothari, S. P., S. Shu, and P. Wysocki. 2009. Do managers withhold bad news? Journal of Accounting Research 47
(1): 241–276.
Krishnan, G. V., H. S. Lee, and P. N. Patatoukas. 2014. Is customer dependency associated with supplier’s
misstatement risk and audit fees? Working Paper, Michigan State University.
Lang, M., and R. Lundholm. 1993. Cross–sectional determinants of analyst ratings of corporate disclosures.
Journal of Accounting Research 31(2): 246–271.
37
Nelson, M. W., and W. R. Kinney. 1997. The effect of ambiguity on loss contingency reporting judgments. The
Accounting Review 72 (2): 257-274.
Patatoukas, P. N. 2012. Customer-base concentration: Implications for firm performance and capital markets.
The Accounting Review 87 (2): 363-392.
Peters, G. F., and A. M. Romi. 2013. Discretionary compliance with mandatory environmental disclosures:
Evidence from SEC filings. Journal of Accounting and Public Policy 32 (4): 213-236.
Raman, K., and H. Sharur. 2008. Relationship-specific investments and earnings management: Evidence on
corporate suppliers and customers. The Accounting Review 83 (4): 1041-1081.
Rogers, J. L., A. Van Buskirk, and S. L. C. Zechman. 2011. Disclosure tone and shareholder litigation. The
Accounting Review 86 (6): 2155-2183.
Skinner, D. J. 1994. Why firms voluntarily disclose bad news. Journal of Accounting Research 32 (1): 38-60.
Titman, S., and R. Wessels. 1988. The determinants of capital structure choice. Journal of Finance 43 (1): 1-19.
Verrecchia, R. 2001. Essays on Disclosure. Journal of Accounting and Economics 32 (2–3): 97–180.
Wang, I. 2007. Private earnings guidance and its implications for disclosure regulation. The Accounting Review 82
(5): 1299–1332.
38
Table 1
Summary Statistics
The material (immaterial) loss cases are defined as the cases whose litigation loss is over (under) half a percent
of defendant firm’s total assets. Variables are defined in Appendix III. Panels A and B report averages for the
characteristics used in our tests involving two samples employed in this paper. Panel C reports distributions
across case types for the disclosure sample.
Panel A: Firm Characteristics for Relationship Termination Sample
Dependent Suppliers
Principal Customers
(1)
(2)
Log Size
5.578
9.890
ROA
Z-score
Sup Pct Sales to Prin. Customers
NCase
Cus Pct COGS from Dep. Suppliers
-0.049
2.063
0.045
2.412
0.195
0.382
-----
---
0.018
Num. Relationship-Years
24,590
Num. Relationship
Num. Unique Customers
6,012
1,638
Num. Unique Suppliers
3,649
Panel B: Firm Characteristics for Disclosure Sample
Material Cases
Dependent
Non-Dependent
Dependent
Difference
Variables
Suppliers
Suppliers
Suppliers
(2)-(1)
(1)
(2)
(1)
Log Size
5.535
5.953
0.418**
6.887
(2.55)
ROA
-0.115
-0.039
0.076***
-0.034
(3.56)
Leverage
0.204
0.232
0.028**
0.190
(2.14)
Book-to0.723
0.719
-0.004
0.702
Market
(-0.06)
Equity Issue
0.399
0.306
-0.093**
0.253
(-2.44)
Debt Issue
0.426
0.414
0.012
0.422
(0.31)
%Ind
0.773
0.736
0.037***
0.762
Directors
(2.86)
Log(Board
1.825
1.953
0.128***
1.970
Size)
(4.53)
Num. Obs.
333
297
--682
Panel C: Distribution of case types over disclosure sample
Disclosure Sample
Material Cases
Dependent
Non-Dependent
All
All
Type
Suppliers
Suppliers
Accounting Malpractice
170
85
85
350
Patent & Copyright Related
128
79
49
387
Breach of Contract
147
87
60
217
Product & Service Liability
32
16
16
84
Social Responsibility
56
15
41
213
Operational Malpractice
21
7
14
85
Securities Laws. Other
59
35
24
154
Antitrust Violation
17
9
8
77
Total
630
333
297
1567
39
Difference
(2)-(1)
T-test
4.312***
(271.42)
0.094***
0.349***
-----
(58.58)
(43.45)
-----
---
Immaterial Cases
Non-Dependent
Suppliers
(2)
7.849
0.065
0.258
0.705
0.193
0.394
0.767
2.097
885
---
Difference
(2)-(1)
0.962***
(7.96)
0.099***
(3.23)
0.068***
(5.48)
0.003
(0.43)
-0.060***
(-2.87)
-0.028
(-1.11)
0.005
(0.59)
0.127***
(6.59)
---
Immaterial Cases
Dependent
Non-Dependent
Suppliers
Suppliers
114
236
230
157
93
124
51
33
69
144
29
56
68
86
28
49
682
885
Table 2
The Impact of Dependent Suppliers’ Litigation on the Supply-Chain Relationship Termination
This table reports the estimates from regressions about the impact of legal cases involving dependent suppliers
on the termination of customer-supplier relationships. The sample covers the time series of customer-supplier
pairs identified by the Compustat Segment Customer file between 1994 and 2012. The dependent variable is
an indicator variable that equals 1 if the customer-supplier relationship terminates in the next year (i.e., year
t+1), and zero otherwise. Independent variables are defined in Appendix III. Standard errors, reported in
parentheses, have been adjusted for the clustering at the customer-supplier relationship level. Column (1)
reports estimates from the Cox proportional hazard model, and Columns (2) – (4) report estimates from the
linear probability model (LPM). ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,
respectively.
VARIABLES
NCase
(1)
Hazards
0.049***
(0.012)
(2)
LPM
0.008***
(0.002)
Durability
NCase * Durability
(3)
LPM
0.015***
(0.004)
0.001
(0.008)
-0.010**
(0.004)
Sup Mono Ind
NCase * Sup Mono Ind
Sup Pct Sales to Prin. Customers
Sup Log Size
Sup ROA
Sup Z-score
Cus Pct COGS from Dep. Suppliers
Cus Log Size
Cus ROA
Cus Z-score
Year Fixed Effect
SE Cluster (relationship)
Observations
Adj. R-squared
-1.525***
(0.107)
-0.137***
(0.011)
-0.602***
(0.054)
-0.110***
(0.017)
-0.222
(0.320)
-0.035***
(0.009)
-0.619***
(0.134)
-0.031
(0.024)
No
Yes
24,590
---
40
-0.312***
(0.019)
-0.032***
(0.003)
-0.181***
(0.017)
-0.028***
(0.004)
-0.031
(0.077)
-0.012***
(0.002)
-0.156***
(0.037)
-0.004
(0.006)
Yes
Yes
24,590
0.066
-0.313***
(0.019)
-0.032***
(0.003)
-0.180***
(0.017)
-0.028***
(0.004)
-0.028
(0.077)
-0.012***
(0.002)
-0.156***
(0.037)
-0.004
(0.006)
Yes
Yes
24,590
0.066
(4)
LPM
0.009***
(0.003)
-0.012
(0.009)
-0.012**
(0.006)
-0.313***
(0.019)
-0.031***
(0.003)
-0.180***
(0.017)
-0.028***
(0.004)
-0.020
(0.078)
-0.012***
(0.003)
-0.154***
(0.037)
-0.001
(0.006)
Yes
Yes
24,590
0.066
Table 3
Summary Statistics: The Timing and Incidence of Litigation Disclosures
This table reports summary statistics (i.e., averages) for the timing and likelihood of litigation disclosures for both dependent and non-dependent suppliers.
We focus on two types of disclosures, namely pre-warnings in material cases and optimistic claims in immaterial cases. Pre-warnings are those disclosures
in which firms warn investors of a potential material loss outcome for a particular lawsuit by doing one or more of the following: warn investors of
potentially significant adverse economic consequences from the 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. 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. The incidence of disclosure is measured by the percentage of firms that issue pre-warning disclosures or optimistic claims
before the case is fully resolved. We partition the sample by supplier type according to whether the firm has at least one principal customer when the case
is launched. In addition, we compute differences in the likelihood and timing of litigation disclosures across supplier types and the t-statistics corresponding
to the differences are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Panel A: Material Cases
Timing: Pre-Warnings
Dependent Suppliers
(1)
0.907
Non-Dependent Suppliers
(2)
0.752
Likelihood: Pre-Warnings
0.150
0.377
Num. Obs.
Panel B: Immaterial Cases
Timing: Optimistic Claims
333
297
0.683
0.834
Likelihood: Optimistic Claims
0.565
0.255
682
885
Num. Obs.
41
Difference
(2)-(1)
-0.155***
(-6.23)
0.227***
(6.73)
--0.151***
(9.02)
-0.309***
(13.09)
---
Table 4
Pre-warnings for Material Cases
This table reports the estimates of regressions that investigate the impact of the existing relationship with principal customers on the likelihood of
defendant firms’ pre-warnings before the cases are fully resolved. The dependent variable for Column (1) – (4), the timing of pre-warning, 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.
The dependent variable for Column (5) – (8), the incidence of pre-warning, is a dummy variable that equals 1 if a firm issue a pre-warning before the case
is fully resolved, and 0 otherwise. Independent variables are defined in Appendix III. We control for the year fixed effects and case-type fixed effects in
linear models, 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.
Variable
Dependent Supplier
Durability
(1)
Tobit
0.737***
(0.131)
Dependent Supplier * Durability
Sup Mono Ind
Timing of Pre-warnings
(2)
(3)
OLS
OLS
0.158***
0.233***
(0.030)
(0.032)
0.065
(0.057)
-0.305***
(0.075)
Dependent Supplier * Sup Mono Ind
Log Size
ROA
Leverage
Book-to-Market
Equity Issue
Debt Issue
%Ind Directors
Log(Board Size)
Case Type FE
Year FE
SE Cluster (Firm)
Observations
Pseudo/Adj. R-squared
-0.044
(0.037)
0.154*
(0.092)
0.967***
(0.295)
-0.073
(0.080)
-0.168
(0.141)
-0.005
(0.127)
-0.130
(0.382)
-0.119
(0.211)
No
No
Yes
630
0.068
-0.005
(0.008)
0.036
(0.022)
0.143***
(0.034)
-0.026
(0.019)
-0.054*
(0.031)
0.011
(0.028)
-0.010
(0.084)
-0.029
(0.044)
Yes
Yes
Yes
630
0.099
-0.005
(0.008)
0.038*
(0.020)
0.124***
(0.033)
-0.026
(0.016)
-0.063**
(0.030)
0.016
(0.027)
0.004
(0.081)
-0.022
(0.041)
Yes
Yes
Yes
630
0.156
42
(4)
OLS
0.184***
(0.034)
0.014
(0.058)
-0.150**
(0.072)
-0.005
(0.008)
0.038*
(0.022)
0.144***
(0.034)
-0.028
(0.019)
-0.060*
(0.032)
0.015
(0.028)
0.003
(0.083)
-0.024
(0.043)
Yes
Yes
Yes
630
0.111
(5)
Logit
-1.278***
(0.225)
0.064
(0.063)
-0.173
(0.126)
-1.368***
(0.458)
0.065
(0.134)
0.215
(0.234)
0.124
(0.212)
0.615
(0.684)
0.264
(0.375)
No
No
Yes
630
0.081
Likelihood of Pre-warnings
(6)
(7)
LPM
LPM
-0.230***
-0.341***
(0.040)
(0.040)
-0.088
(0.077)
0.456***
(0.100)
0.007
(0.011)
-0.029
(0.024)
-0.169***
(0.047)
0.010
(0.022)
0.042
(0.040)
0.017
(0.037)
0.104
(0.118)
0.047
(0.064)
Yes
Yes
Yes
630
0.096
0.006
(0.011)
-0.031
(0.021)
-0.140***
(0.047)
0.010
(0.019)
0.055
(0.038)
0.010
(0.035)
0.081
(0.112)
0.036
(0.059)
Yes
Yes
Yes
630
0.168
(8)
LPM
-0.262***
(0.042)
0.048
(0.079)
0.204**
(0.103)
0.007
(0.011)
-0.036
(0.023)
-0.172***
(0.046)
0.013
(0.022)
0.057
(0.040)
0.008
(0.037)
0.087
(0.116)
0.044
(0.063)
Yes
Yes
Yes
630
0.118
Table 5
Optimistic Claims for Immaterial Cases
This table reports the estimates of regressions that investigate the impact of the existing relationship with principal customers on the likelihood of
defendant firms’ optimistic claims before the cases are fully resolved. The dependent variable for Column (1) – (4), the timing of optimistic claim, 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. The dependent variable for Column (5) – (8), the incidence of optimistic claim, is a dummy variable that equals 1 if a firm makes an optimistic
claim before the case is fully resolved, and 0 otherwise. Independent variables are defined in Appendix III. We control for the year fixed effects and casetype fixed effects in linear models, 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.
Variable
Dependent Supplier
Durability
(1)
Tobit
-0.428***
(0.055)
Dependent Supplier * Durability
Sup Mono Ind
Timing of Optimistic Claims
(2)
(3)
OLS
OLS
-0.132***
-0.162***
(0.020)
(0.022)
0.039
(0.035)
0.143***
(0.050)
Dependent Supplier * Sup Mono Ind
Log Size
ROA
Leverage
Book-to-Market
Equity Issue
Debt Issue
%Ind Directors
Log(Board Size)
Case Type FE
Year FE
SE Cluster (Firm)
Observations
Pseudo/Adj. R-squared
0.081***
(0.023)
-0.039
(0.055)
-0.105
(0.118)
0.002
(0.018)
-0.061
(0.060)
-0.064
(0.055)
0.245
(0.171)
-0.111
(0.095)
No
No
Yes
1,567
0.065
0.026***
(0.007)
-0.015
(0.019)
-0.025
(0.040)
-0.000
(0.005)
-0.017
(0.022)
-0.014
(0.018)
0.098
(0.061)
-0.040
(0.031)
Yes
Yes
Yes
1,567
0.095
0.027***
(0.006)
-0.016
(0.019)
-0.019
(0.039)
0.000
(0.005)
-0.016
(0.022)
-0.011
(0.018)
0.091
(0.061)
-0.043
(0.031)
Yes
Yes
Yes
1,567
0.117
43
(4)
OLS
-0.175***
(0.023)
-0.001
(0.027)
0.218***
(0.045)
0.027***
(0.007)
-0.020
(0.019)
-0.040
(0.039)
0.001
(0.005)
-0.011
(0.022)
-0.017
(0.018)
0.088
(0.059)
-0.038
(0.031)
Yes
Yes
Yes
1,567
0.122
(5)
Logit
1.237***
(0.143)
-0.202***
(0.052)
0.081
(0.127)
0.278
(0.310)
0.024
(0.029)
0.208
(0.145)
0.088
(0.132)
-0.668
(0.445)
0.206
(0.230)
No
No
Yes
1,567
0.106
Likelihood of Optimistic Claims
(6)
(7)
LPM
LPM
0.281***
0.347***
(0.032)
(0.035)
-0.095**
(0.046)
-0.304***
(0.068)
-0.040***
(0.010)
0.015
(0.026)
0.052
(0.063)
0.006
(0.006)
0.043
(0.031)
0.010
(0.027)
-0.130
(0.093)
0.043
(0.048)
Yes
Yes
Yes
1,567
0.142
-0.041***
(0.009)
0.018
(0.025)
0.037
(0.061)
0.005
(0.006)
0.042
(0.029)
0.006
(0.027)
-0.114
(0.093)
0.050
(0.046)
Yes
Yes
Yes
1,567
0.192
(8)
LPM
0.373***
(0.036)
0.021
(0.040)
-0.462***
(0.063)
-0.042***
(0.010)
0.025
(0.027)
0.080
(0.059)
0.004
(0.006)
0.032
(0.030)
0.017
(0.026)
-0.108
(0.087)
0.041
(0.047)
Yes
Yes
Yes
1,567
0.195
Table 6
Robustness Check – CEM Matching based on Time, Size, ROA, and Leverage
This table reports the estimates of regressions that investigate the impact of the existing relationship with principal customers on the time and likelihood
of defendant firms’ litigation disclosures in CEM matched samples. 360 material and 559 immaterial cases are left after CEM matching for our tests on
pre-warnings and optimistic claims, as reported in Panels A and B, respectively. Our test specifications are identical to those used in Tables 4 and 5. We
include all control variables as those reported in Tables 4 and 5. For the sake of brevity, we only report the coefficients for the variable of interest (Dependent
Supplier), switching cost proxies (Durability and Sup Mono Ind), and their interactions. We control for the year fixed effects and case-type fixed effects in
linear models, 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.
Variable
Panel A: Pre-warnings in Material Cases
Dependent Supplier
Durability
(1)
Tobit
0.746***
(0.173)
Timing of Disclosure
(2)
(3)
OLS
OLS
0.161***
(0.039)
Dependent Supplier * Durability
Sup Mono Ind
0.232***
(0.041)
0.041
(0.078)
-0.274***
(0.098)
Dependent Supplier * Sup Mono Ind
Case Type FE
No
Year FE
No
SE Cluster (Firm)
Yes
Observations
360
Pseudo/Adj. R-squared
0.077
Panel B: Optimistic Claims in Immaterial Cases
Dependent Supplier
-0.533***
(0.086)
Durability
Yes
Yes
Yes
360
0.114
Yes
Yes
Yes
360
0.167
-0.183***
(0.033)
-0.244***
(0.036)
0.050
(0.061)
0.229***
(0.075)
Dependent Supplier * Durability
Sup Mono Ind
Dependent Supplier * Sup Mono Ind
Case Type FE
Year FE
SE Cluster (Firm)
Observations
Pseudo/Adj. R-squared
No
No
Yes
559
0.090
Yes
Yes
Yes
559
0.136
Yes
Yes
Yes
559
0.192
44
(4)
OLS
(5)
Logit
0.184***
(0.044)
-1.267***
(0.300)
Likelihood of Disclosure
(6)
(7)
LPM
LPM
-0.215***
(0.052)
-0.329***
(0.053)
-0.090
(0.100)
0.445***
(0.127)
0.022
(0.075)
-0.162**
(0.078)
Yes
Yes
Yes
360
0.124
No
No
Yes
360
0.086
Yes
Yes
Yes
360
0.097
Yes
Yes
Yes
360
0.165
-0.214***
(0.037)
1.258***
(0.242)
0.266***
(0.050)
0.351***
(0.056)
-0.119
(0.080)
-0.297***
(0.102)
0.017
(0.046)
0.162**
(0.082)
Yes
Yes
Yes
559
0.153
No
No
Yes
559
0.112
Yes
Yes
Yes
559
0.147
Yes
Yes
Yes
559
0.215
(8)
LPM
-0.248***
(0.056)
-0.022
(0.100)
0.232**
(0.102)
Yes
Yes
Yes
360
0.109
0.330***
(0.058)
0.028
(0.068)
-0.325***
(0.111)
Yes
Yes
Yes
559
0.172
Table 7
The Impact of Litigations on the Likelihood of Financial Failures
and Termination of Customer-Supplier Relationships
Panel A reports the case type distribution for all Compustat firms with available data between 1994 and 2012. Column (1) of Panel B reports estimates
from Cox Proportional Hazards regressions about the impact of all litigations on the likelihood of financial failures. Financial failures are defined by
Chapter 7 or Chapter 11 bankruptcy filings or stock market delisting because of liquidation. The sample covers all Compustat firms between 1994 and
2012. Column (2) of Panel B reports the estimates from Cox Proportional Hazards regressions about the impact of legal cases involving dependent
suppliers on the continuation of customer-supplier relationships. The sample covers the customer-supplier pairs employed in Table 2. For Column (1),
we include the control variables employed in Beaver et al. (2012), augmented by the number of legal cases for each of the eight case types. For Column
(2), we include the control variables employed in Table 2, augmented by the number of legal cases for each of the eight case types. For the sake of brevity,
we only report the coefficients for NCase for each case type in both columns. Standard errors, reported in parentheses, have been adjusted for the
clustering at the firm level and the customer-supplier relationship level, respectively. Marginal effects are reported in the square brackets. ***, **, and *
denote statistical significance at the 1%, 5%, and 10% levels, respectively.
Panel A: Case Type Distribution
(1)
% Settlement Loss
(2)
Material Cases
(3)
Immaterial Cases
Accounting Malpractice
3.29%
931
1801
Patent & Copyright Related
1.04%
249
2232
Breach of Contract
1.54%
249
1328
Product & Service Liability
0.43%
43
455
Social Responsibility Related
0.19%
78
1174
Operational Malpractice
1.31%
27
371
Securities Laws, Other
4.75%
94
1079
Antitrust Violation
0.34%
1.96%
39
1,710
442
8,882
Type
Total
45
Panel B: The Impact of Litigations on the Likelihood of Financial Failures and Termination of
Customer-Supplier Relationships
NCase: Accounting Malpractice
NCases: Patent & Copyright Related
NCases: Breach of Contract
NCases: Product & Service Liability
NCases: Social Responsibility Related
NCases: Operational Malpractice
NCases: Securities Laws, Other
NCases: Antitrust Violation
SE Clustered
Number of Observations
(1)
Financial Failure
0.202***
(0.039)
[1.224]
-0.066
(0.084)
[0.936]
-0.093
(0.167)
[0.911]
0.138
(0.152)
[1.148]
-0.141
(0.200)
[0.868]
-0.750
(0.599)
[0.599]
0.161***
(0.048)
[1.175]
0.092
(0.321)
[1.096]
Firm Level
105,267
46
(2)
Relationship Termination
0.092***
(0.032)
[1.097]
-0.029
(0.064)
[0.971]
0.051**
(0.025)
[1.052]
0.094***
(0.030)
[1.099]
0.045**
(0.022)
[1.046]
0.033*
(0.018)
[1.034]
0.070**
(0.031)
[1.073]
-0.156
(0.163)
[0.856]
Relationship Level
24,590
Table 8
Tests of H2 and H3 by Different Groups of Cases Types
This table reports the estimates of regressions that investigate the impact of the existing relationship with principal customers on the timing and likelihood
of defendant firms’ litigation disclosure by three groups of case types, separately. Panel A reports results for case types with immediate bankruptcy risk
(i.e., Accounting Malpractice/Securities Laws, Other); Panel B reports results for case types with no immediate bankruptcy risk but with relationship
termination risk (i.e., Breach of Contract/Product and Service Liability/Operational Malpractice/Social Responsibility Related); and Panel C reports case
types with neither immediate bankruptcy risk nor relationship termination risk (i.e., Patent and Copyright Related/Antitrust Violation). We include all
control variables as those reported in Tables 4 and 5. For the sake of brevity, we only report coefficients for the variables of interest: Dependent Supplier,
switching cost proxies (Durability and Sup Mono Ind), and their interactions. The dependent variable for Column (1) – (4), the timing of litigation 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. The dependent variable for Column (5) – (8), the incidence of litigation disclosure, is a dummy variable that equals 1 if a firm issues a prewarning or optimistic claim before the case is fully resolved, and 0 otherwise. Independent variables are defined in Appendix III. We control for the year
fixed effects and case-type fixed effects in linear models, 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.
47
Panel A: Case Types with Immediate Bankruptcy Risk (Accounting Malpractice/Securities Laws, Other)
Timing
Variable
Pre-warnings in Material Cases
Dependent Supplier
(1)
Tobit
(2)
OLS
(3)
OLS
(4)
OLS
(5)
Logit
0.966***
(0.255)
0.225***
(0.054)
0.316***
(0.054)
0.224**
(0.105)
-0.501***
(0.135)
0.280***
(0.063)
-1.464***
(0.401)
Durability
Dependent Supplier * Durability
Sup Mono Ind
Dependent Supplier * Sup Mono Ind
SE Cluster (Firm)
Yes
229
Observations
0.108
Pseudo/Adj. R-squared
Optimistic Claims in Immaterial Cases
-0.643***
Dependent Supplier
(0.091)
Durability
Yes
229
0.166
Yes
229
0.238
-0.184***
(0.033)
-0.242***
(0.036)
-0.038
(0.067)
0.270***
(0.087)
Dependent Supplier * Durability
Sup Mono Ind
Dependent Supplier * Sup Mono Ind
SE Cluster (Firm)
Observations
Pseudo/Adj. R-squared
Yes
504
0.093
Yes
504
0.145
Yes
504
0.176
48
Likelihood of Disclosure
(6)
(7)
LPM
LPM
-0.266***
(0.069)
-0.388***
(0.066)
-0.244*
(0.138)
0.650***
(0.184)
0.151*
(0.085)
-0.336***
(0.117)
Yes
229
0.192
Yes
229
0.108
Yes
229
0.121
Yes
229
0.206
-0.218***
(0.036)
1.817***
(0.231)
0.399***
(0.049)
0.516***
(0.050)
0.022
(0.087)
-0.507***
(0.119)
-0.002
(0.042)
0.235***
(0.082)
Yes
504
0.165
Yes
504
0.160
Yes
504
0.216
Yes
504
0.280
(8)
LPM
-0.326***
(0.076)
-0.093
(0.119)
0.454**
(0.195)
Yes
229
0.154
0.472***
(0.053)
0.001
(0.059)
-0.509***
(0.109)
Yes
504
0.262
Panel B: Case Types with No Immediate Bankruptcy Risk but with Relationship Termination Risk (Breach of Contract/Product and Service
Liability/Operational Malpractice/ Social Responsibility Related)
Timing
Variable
Pre-warnings in Material Cases
Dependent Supplier
(1)
Tobit
(2)
OLS
(3)
OLS
(4)
OLS
(5)
Logit
0.480***
(0.176)
0.092**
(0.040)
0.147***
(0.046)
0.111*
(0.067)
-0.236**
(0.091)
0.097**
(0.040)
-0.969***
(0.334)
Durability
Dependent Supplier * Durability
Sup Mono Ind
Dependent Supplier * Sup Mono Ind
SE Cluster (Firm)
Yes
256
Observations
0.063
Pseudo/Adj. R-squared
Optimistic Claims in Immaterial Cases
-0.307***
Dependent Supplier
(0.084)
Durability
Yes
256
0.069
Yes
256
0.096
-0.106***
(0.032)
-0.114***
(0.035)
0.055
(0.061)
0.047
(0.090)
Dependent Supplier * Durability
Sup Mono Ind
Dependent Supplier * Sup Mono Ind
SE Cluster (Firm)
Observations
Pseudo/Adj. R-squared
Yes
599
0.059
Yes
599
0.079
Yes
599
0.085
49
Likelihood of Disclosure
(6)
(7)
LPM
LPM
-0.168***
(0.058)
-0.285***
(0.062)
-0.226**
(0.095)
0.498***
(0.132)
-0.101
(0.083)
-0.130*
(0.070)
Yes
256
0.091
Yes
256
0.074
Yes
256
0.070
Yes
256
0.126
-0.183***
(0.036)
0.873***
(0.206)
0.199***
(0.047)
0.226***
(0.052)
-0.099
(0.083)
-0.167
(0.121)
-0.006
(0.041)
0.264***
(0.064)
Yes
599
0.126
Yes
599
0.088
Yes
599
0.114
Yes
599
0.131
(8)
LPM
-0.199***
(0.060)
0.124
(0.112)
0.193*
(0.106)
Yes
256
0.100
0.351***
(0.053)
0.028
(0.059)
-0.522***
(0.088)
Yes
599
0.197
Panel C: Case Types with Neither Immediate Bankruptcy Risk Nor Relationship Termination Risk (Patent and Copyright Related/Antitrust Violation)
Timing
Variable
Pre-warnings in Material Cases
Dependent Supplier
(1)
Tobit
(2)
OLS
(3)
OLS
(4)
OLS
(5)
Logit
0.259
(0.186)
0.080
(0.067)
0.108
(0.080)
-0.109
(0.111)
0.073
(0.153)
0.093
(0.069)
0.235
(0.373)
Durability
Dependent Supplier * Durability
Sup Mono Ind
Dependent Supplier * Sup Mono Ind
SE Cluster (Firm)
Yes
145
Observations
0.061
Pseudo/Adj. R-squared
Optimistic Claims in Immaterial Cases
0.089
Dependent Supplier
(0.172)
Durability
Yes
145
0.102
Yes
145
0.109
0.019
(0.032)
0.041
(0.037)
0.091*
(0.050)
-0.077
(0.064)
Dependent Supplier * Durability
Sup Mono Ind
Dependent Supplier * Sup Mono Ind
SE Cluster (Firm)
Observations
Pseudo/Adj. R-squared
Yes
464
0.028
Yes
464
0.033
Yes
464
0.041
50
Likelihood of Disclosure
(6)
(7)
LPM
LPM
0.044
(0.081)
0.075
(0.100)
0.174
(0.137)
-0.064
(0.184)
0.018
(0.136)
0.270
(0.199)
Yes
145
0.115
Yes
145
0.098
Yes
145
0.125
Yes
145
0.141
0.019
(0.035)
-0.114
(0.258)
-0.023
(0.043)
-0.066
(0.052)
-0.177***
(0.064)
0.151
(0.184)
-0.006
(0.055)
-0.008
(0.094)
Yes
464
0.033
Yes
464
0.026
Yes
464
0.027
Yes
464
0.043
(8)
LPM
0.081
(0.083)
0.221
(0.201)
-0.542
(0.331)
Yes
145
0.143
-0.027
(0.048)
0.032
(0.076)
0.038
(0.122)
Yes
464
0.029
Appendix I
Detailed Sample Selection Filters for the Disclosure Tests
This table presents sample selection procedures. The material (immaterial) loss cases are defined as cases for
which the litigation loss is over (under) half a percent of defendant firm’s total assets.
Filters for material cases
Number of Cases
All material loss legal cases from Audit Analytics during the period between 1994 and 2012
conditioned on the requirements that (1) the defendant firm must have non-missing
information of its corporate identifier and can be merged with Compustat, (2) the
information regarding settlement amount must be available, and (3) the cases where the
principal customers sue dependent suppliers are excluded
1,710
After requiring that the duration of cases must be longer than 365 days
892
After requiring that the outcomes of legal cases must be disclosed in firms’ SFAS 5
Footnotes in 10-Qs and 10-Ks when the cases are fully resolved
630
Number of cases whose defendants are dependent suppliers
333
Number of cases whose defendants are non-dependent suppliers
297
Filters for immaterial cases
Number of Cases
All immaterial loss legal cases from Audit Analytics during the period between 1994 and
2012 conditioned on the requirements that (1) the defendant firm must have non-missing
information of its corporate identifier and can be merged with Compustat, (2) the
information regarding settlement amount must be available, (3) the firm is a lead
defendant, and (4) the cases where the principal customers sue dependent suppliers are
excluded
8,882
After requiring that the duration of cases must be longer than 365 days
3,664
After requiring that the outcomes of legal cases must be disclosed in firms’ SFAS 5
Footnotes in 10-Qs and 10-Ks when the cases are fully resolved
1,567
Number of cases whose defendants are dependent suppliers
682
Number of cases whose defendants are non-dependent suppliers
885
51
Appendix II
Firm Characteristics after CEM Matching
Variables
Dependent
Suppliers
(1)
Log Size
5.529
Material Cases
NonDependent
Suppliers
(2)
5.768
-0.069
-0.054
Leverage
0.175
0.197
Book-toMarket
Equity Issue
0.687
0.715
0.457
0.431
Debt Issue
0.414
0.384
%Ind
Directors
Log(Board
Size)
Number of
Observations
0.786
0.750
1.808
1.826
188
172
ROA
Difference
(2)-(1)
0.239
(1.09)
0.015
(0.39)
0.022
(0.70)
0.028
(0.37)
-0.026
(-0.45)
-0.030
(-0.60)
-0.036
(-0.26)
0.018
(1.10)
Dependent
Suppliers
(1)
7.809
0.013
0.011
0.193
0.206
0.699
0.782
0.174
0.152
0.388
0.375
0.762
0.751
2.079
2.162
258
52
Immaterial Cases
NonDependent
Suppliers
(2)
8.147
301
Difference
(2)-(1)
0.338
(0.81)
-0.002
(-0.12)
0.013
(0.97)
0.083
(1.50)
-0.022
(-0.69)
-0.013
(-0.30)
-0.011
(-0.81)
0.083
(1.56)
Appendix III
Definitions of Independent Variables Used in the Analyses
Termination Tests (H1)
NCase
= the total number of legal cases in which a supplier gets involved as a defendant
Durability
= an indicator variable that equals 1 if the supplier is in an industry producing
durable goods as defined in Gomes et al. (2009), and 0 otherwise
Sup Mono Ind
= an indicator variable that equals 1 if the supplier is in an industry in which
Herfindahl–Hirschman Index of the SIC 3-digit industry is higher than 0.25, and
0 otherwise
Sup Pct Sales to Prin. = the supplier’s percentage sales to one of its principal customer
Customers
Log Size
= the logarithm of the book value of total assets
ROA
= earnings before interest, taxes, depreciation, and amortization (EBITDA) scaled
by the book value of total assets
Z-score
= Altman’s (1968) Z-score
Cus Pct COGS from = the customer’s percentage of cost of goods sold from the dependent supplier
Dep. Suppliers
For the termination tests, which predict termination in fiscal year t + 1, all independent variables are
measured at the end of fiscal year t.
Disclosure Tests (H2 & H3)
Dependent Supplier
= an indicator variable that equals to 1 if the sued firm has at least one principal
customer when the case is launched, and 0 otherwise
Durability
= as defined above
Sup Mono Ind
= as defined above
Log Size
= as defined above
ROA
= as defined above
Leverage
= the total leverage ratio based on the book values of debt and equity
Book-to-market
= the book-to-market ratio of equity
Equity Issue
= an indicator that equals 1 if the firm issues equity during the litigation period,
and 0 otherwise
Debt Issue
= an indicator that equals 1 if the firm issues debt during the litigation period, and
0 otherwise
%Ind Directors
= the percentage of independent directors to the number of all directors on the
board
Board Size
= the number of all directors on the board
For the disclosure tests, all independent variables except Equity Issue and Debt Issue are measured at the fiscal
year end prior to the year when the cases are launched against the defendant firms.
53