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Proprietary Costs and Sealing Documents in Patent Litigation
Richard Frankel
Olin Business School Washington University in St. Louis
Joshua Lee Terry College of Business
University of Georgia [email protected]
Zawadi Lemayian Olin Business School
Washington University in St. Louis [email protected]
Revised: April 2017
Abstract We study whether the sealing of a defendant’s judicial records during a patent lawsuit filing correlates with the defendant’s level of competition and disclosure. Courts permit sealing of judicial records when competitive damage outweighs the public interest in access to documents and records. We find that defendants with sealed judicial records have higher R&D, lower industry sales concentration, and more references to competition in their annual reports than defendants without sealed judicial records. We observe faster mean reversion of return on net operating assets when courts seal defendant records. The results suggest that sealing is related to archival measures of competition. Finally, consistent with proprietary costs restraining disclosure, we find that defendants with sealed judicial records are less likely to issue management forecasts, file fewer 8-Ks, and have longer and less readable 10-Ks. We thank Kevin Collins (Washington University in St. Louis Law School), David Deal (Washington University in St. Louis Law School), Pauline Kim (Washington University in St. Louis Law School), Navroop Pandher (LexMachina), and David Schwarz (Northwestern University Pritzker School of Law) for helpful discussions. We are grateful to Olin Business School and Terry College of Business for financial support.
Proprietary Costs and Sealing Documents in Patent Litigation
Abstract
We study whether the sealing of a defendant’s judicial records during a patent lawsuit filing correlates with the defendant’s level of competition and disclosure. Courts permit sealing of judicial records when competitive damage outweighs the public interest in access to documents and records. We find that defendants with sealed judicial records have higher R&D, lower industry sales concentration, and more references to competition in their annual reports than defendants without sealed judicial records. We observe faster mean reversion of return on net operating assets when courts seal defendant records. The results suggest that sealing is related to archival measures of competition. Finally, consistent with proprietary costs restraining disclosure, we find that defendants with sealed judicial records are less likely to issue management forecasts, file fewer 8-Ks, and have longer and less readable 10-Ks.
INTRODUCTION
We examine the relation between the sealing of a defendant’s judicial records in a patent
lawsuit and the defendant’s level of competition and disclosure. We find that defendants who
request and are granted the sealing of judicial records have higher R&D, lower industry-sales
concentration, and a greater number of references to competition in their 10-Ks. We also find
that their returns on added net operating investments mean revert more than those of defendants
without sealed records. We further find that defendants with sealed records are less likely to
provide earnings guidance, issue fewer 8-Ks, have higher 10-K Fog and 10-K length, and report
a smaller range of segment profitability but do not report a smaller number of segments. Our
evidence supports the theory that proprietary costs reduce voluntary disclosure. Our evidence
also suggests that defendants with greater proprietary costs are more likely to request and obtain
sealed judicial records, countering claims that judges lack grounds for sealing judicial records
(Moskowitz 2007; Chao 2011; Chao and Silver 2014).
Research on the relation between proprietary costs and disclosure propensity requires a
proxy for proprietary costs. We study a novel proxy and compare its power to that of previously
used measures (e.g., R&D spending; net property, plant, and equipment; book-to-market ratio;
Herfindahl-Hirschman Index [industry sales concentration]; and number of ‘competition’ words
in the 10-K). While the list of previously used measures is long, their ability to capture
competition, especially at the firm level, is questionable. Moreover, the significance of
proprietary costs arising from various disclosure forms (e.g., conference calls, 8-Ks, 10-K
readability, 10-K length, and segment disclosures) remains an open question.1 Our approach is
similar to that of Dedman and Lennox (2009). They test the relation between voluntary
disclosure and proprietary costs by surveying private firms’ perceptions of their proprietary
1 Lang and Sul (2014) provide an excellent discussion of these unresolved issues.
1
costs. We test the relation between our proxy for proprietary costs and firm disclosures, but do so
for public firms whose agency and external financing concerns can have an overriding influence
on the disclosure decision. The use of public firms also permits us to assess the significance of
proprietary costs on various voluntary-disclosure formats, because we can observe disclosure
choices.
We identify whether defendants successfully seal trial records in 339 patent litigation
cases with publicly traded defendants filed at the U.S. Court of Appeals for the federal circuit
(CAFC). Sealing requests correlate with several firm- and industry-level measures of proprietary
costs (e.g. the Herfindahl-Hirschman Index, the word count of competition-related terms in the
10-K, and R&D spending). While our results confirm that sealing is related to other measures in
the literature, these factors only weakly predict sealing, which implies variation unique to our
measure. Further, defendants who seal display greater mean reversion of accounting rates of
return, which suggests that they operate in highly competitive environments (Li, Lundholm, and
Minnis 2013). We find no evidence of faster reversion for defendants whose sealing requests are
denied. Although academics and the media often argue that courts rashly seal records in patent
cases, limiting access to information in high-profile cases (Chao and Silver 2014),2 our evidence
suggests that judges base their sealing decisions on the competitive costs of the disclosures.
We then examine the relation between the sealing of court documents and various
attributes of the defendant’s disclosure, based on the idea that proprietary costs affect firm
disclosure. We find that publicly traded defendants who seal filings have fewer management
forecasts and fewer 8-K filings but do not hold fewer conference calls. In addition, we find that
2http://patentlyo.com/patent/2012/08/non-public-litigation-the-hidden-story-of-monsanto-v-dupont.html See also Raymond Baldino, Federal Judge Orders Unsealing of Documents in Ongoing Apple-Samsung Patent Litigation, Reporters Committee for Freedom of the Press (July 20, 2012), available at http://www.rcfp.org/browse-media-law-resources/news/federal-judge-orders-unsealing-documents-ongoing-apple-samsung-paten (Last accessed 08/30/2016).
2
defendants who seal have longer annual reports, which are less readable than those of defendants
who do not seal. They also report a smaller range of segment profitability. These results suggest
that firms with proprietary costs are less likely to provide voluntary disclosure and are consistent
with the theoretical models from studies such as Verrecchia (1983), Wagenhofer (1990) and
Gigler (1994).
Our paper contributes to the disclosure literature as follows: We develop a firm-specific
measure of proprietary costs useful for validating existing measures and checking the
competitive implications of various disclosure channels. Several papers in this area rely on
industry-level measures, such as concentration ratios, which lack precision at the firm level.
More recent studies develop direct measures of firm competition based on managers’ references
to competition in their annual reports (Li et al. 2013) and survey responses that describe their
perception of competition (Dedman and Lennox 2009). We offer a firm-level measure of
proprietary costs that is externally validated by an impartial third party (i.e., a judge) who
directly assesses the level of proprietary information contained in the court records when making
a decision. Successful sealing requests must demonstrate to the judge that competitive harm will
result if the record is not sealed. We assess the validity of our construct, confirm its relation to
both industry- and firm-level proxies, and use it to test the relation between disclosure and
proprietary costs.
Prior studies examine the association between disclosure and proprietary costs and find
conflicting evidence (Bamber and Cheon 1998; Harris 1998; Ajinkya, Bhojraj, and Sengupta
2005; Ettredge, Kwon, Smith, and Stone 2006; Verrechia and Weber 2006; Berger and Hann
2007; Wang 2007; Li 2010; Ellis, Fee, and Thomas 2012, Ali, Klasa, and Yeung 2010). In
addition to managerial guidance and segment disclosures examined by these studies, we explore
3
other forms of disclosure such as annual reports, 8-Ks, conference calls, and the number of
geographical areas in which the defendants operate, and provide evidence that defendants with
sealed judicial records file fewer 8-Ks, have longer and less readable 10-Ks, and operate in more
geographical areas. We find no evidence of less frequent conference calls when defendants seal
records. To our knowledge, no other study has demonstrated this.
Our results are subject to caveats. First, our proxy may not correctly capture competition
and proprietary costs if judges grant sealing requests for reasons that correlate with other
disclosure costs/benefits. Second, our results are informative for a sample of firms that have been
sued for patent infringement, and they may not generalize to firms that are not engaged in a
lawsuit.
The rest of the paper is organized as follows. In Section 2, we discuss the prior literature
and the patent litigation setting. In Section 3, we develop our hypotheses. In Section 4, we
describe our sample selection process and research design. In Section 5, we discuss our results
and robustness tests. We conclude in Section 6.
PATENT LITIGATION: BACKGROUND AND PRIOR RESEARCH
Existing Literature on Proprietary Costs and Disclosure
The link between proprietary costs and disclosure is a central question in accounting
research. In spite of the widespread interest in this topic, neither theoretical nor empirical studies
have converged on a robust link for several reasons. First, analytical models of firms’ disclosure
offer different predictions depending on the assumptions used. In particular, the models are
sensitive to assumptions about the existence and significance of proprietary costs, and the
competitive environment in which the business operates (threats from existing and potential
competition). Second, a challenge to empirical papers is that there are no widely accepted
4
proxies of proprietary costs. As the ensuing discussion highlights, most proxies are measured at
the industry level, and their ability to capture competition, especially at the firm level, remains
questionable. Third, as Lang and Sul (2014) point out, empirical researchers have difficulty
identifying disclosures that are likely to carry significant costs. We discuss each of these issues
and conclude this section by showing how our paper complements existing literature and offers
several new contributions that address some of the challenges.
Theoretical Research on Proprietary Costs and Disclosure
Firms benefit from disclosing information. Early theoretical models (e.g. Grossman 1981;
Milgrom 1981) predict that managers disclose all information, because investors view failure to
disclose negatively, and discount firm value. However, in the presence of disclosure costs, a
range of disclosure outcomes is possible. Janovich (1982) and Verrecchia (1983) show that an
‘unraveling’ equilibrium will not occur when firms face disclosure costs. Dye (1986) shows that
firms can also withhold information when investors are uncertain about what the firm knows.
Though Verrecchia labels his disclosure cost “proprietary,” it lacks a key characteristic of
a competition-related cost. The cost does not vary with the sign of the news. For example, to
deter competition from incumbents and potential entrants, a firm might limit the disclosure of
good news while increasing the disclosure of bad news. On the other hand, pricing pressures
imply the disclosure of good news. Thus, the link between ‘overall’ disclosure and competition is
unclear.
Both Wagenhofer (1990) and Darrough and Stoughton (1990) take into account the
possibility of competitive damage from positive disclosure and show the potential for partial
disclosure (nondisclosure in the case of Darrough and Stoughton (1990)) equilibrium.3 Hayes
3 See also Feltham and Xie (1992) for a model of how the trade-off between the effects of disclosure of good news on product and capital market completion.
5
and Lundholm (1996) model the fineness of disclosure through the use of segments and show
that firms will be reluctant to report segments with differing performance in more competitive
environments. Overall, the models from this first stream of research predict less disclosure for
firms facing high proprietary costs.
Empirical Research on Proprietary Costs and Disclosure
The main challenge for empirical studies examining the link between disclosure and
proprietary costs is the lack of universally accepted constructs for proprietary costs. We discuss
these proxies below.
Industry-based Measures
The most commonly used measures of proprietary costs are industry concentration ratios
such as the Herfindahl-Hirschmann Index and four-firm concentration ratios (e.g., Haskel and
Martin 1994; Harris 1998; Botosan and Harris 2000; Botosan and Stanford 2005; Ettredge et al.
2006; Verrecchia and Weber 2006; Li 2010; Ellis et al. 2012; Ali et al. 2014). The Herfindahl-
Hirschmann Index measures market concentration and is computed by estimating each industry
participant’s market share of sales relative to other participants in the same market. Lower values
of the index are consistent with lower market concentration, which indicates higher competition.
The four-firm concentration ratio estimates the percentage of shares traded by the top four firms
in an industry. Similar to lower values of the Herfindahl-Hirschmann Index, lower values of this
ratio imply lower market concentration and greater competition.
Another common measure is the speed of profit adjustments proposed by Harris (1998)
and used in several studies (Haskel and Martin 1994; Ettredge et al. 2006; Ellis et al. 2012; Ali et
al. 2014). This measure estimates the persistence of a firm’s ROA above industry mean, with
higher persistence implying lower competition and lower proprietary costs, because price
6
competition reduces a firm’s ability to generate abnormal rents over multiple periods. Other less
common measures of proprietary costs include industry property, plant and equipment, and
industry research and development expenditures (Ettredge et al. 2006; Li 2010; Thompson
2013). The more property, plant and equipment-intensive or research and development-intensive
an industry is, the higher the amount of investment a competitor needs to make. In turn, higher
investment requirements imply greater barriers to entry, which suggests lower competition and
lower proprietary costs. Bens, Berger and Monahan (2011) use the ratio of total private firms to
total firms in an industry as a proprietary cost proxy. Finally, Thompson (2013) also uses the
number of firms in an industry as a proxy for proprietary costs, since competition is likely
greater in industries with more participants. In summary, many industry-level measures have
been proposed in empirical research. We discuss the mixed evidence from studies that have used
these measures below.
Several papers investigating the impact of proprietary costs examine segment reporting as
the measure of disclosure. One stream of research finds evidence of less disclosure by firms in
more concentrated (less competitive) industries. Using industry concentration as a measure of
proprietary costs, Harris (1998) finds that firms in more concentrated industries are less likely to
disclose voluntary segment information prior to the passage of SFAS 131, which increased
segment reporting requirements. Similarly, Botosan and Stanford (2005) show that firms use the
discretion available prior to the passage of SFAS 131 to conceal information about profitable
segments operating in more concentrated industries. In contrast, Botosan and Harris (2000) use
the same measure and find no evidence that firms that initiate disclosure after segment reporting
becomes mandatory operate in more competitive environments. Further, Ettredge et al. (2006)
use abnormal profit adjustments as a proxy for proprietary costs and find that firms are more
7
likely to disclose less (conceal segment profits) when competition is higher. Similarly, Bens et
al. (2011) use a concentration ratio based on the total number of private firms in an industry and
find that firms are less likely to disclose a segment externally when proprietary costs are high.
The authors conjecture that the exclusion of private firms results in a poor proxy of actual
industry concentration.
Other studies have focused on voluntary disclosures such as management forecasts.
Similar to research on segment reporting, studies using industry-based measures have produced
mixed results. Li (2010) finds evidence of lower frequency and shorter horizon of management
forecasts when threat of competition from incumbents is high (lower concentration). Ali et al.
(2014) find an opposite result: firms in more highly concentrated industries (lower competition)
issue fewer forecasts and forecasts of a shorter horizon. Li (2010) uses Compustat data that
includes mainly publicly traded firms, while Ali et al. (2014) use data from the U.S. census. Ali
et al. (2014) attribute the difference in their results to the differences between the two datasets
and conjecture that the use of Compustat-based measures of industry concentration is responsible
for the mixed evidence in this area of research.
Verrecchia and Weber (2006) find that firms are less likely to redact information in
material contracts when they operate in less competitive industries. The authors’ finding
indicates that competition reduces firms’ propensity to make voluntary disclosures. Ellis et al.
(2012) examine firm disclosures about customers. Using two industry-based measures, the
Herfindahl-Hirschmann Index and abnormal (above industry average) ROA, the authors find no
link between customer disclosures and the Herfindahl-Hirschmann Index but find that firms with
higher abnormal ROA are more likely to withhold customer information.
8
A key assumption in these studies is that less concentrated industries are more perfectly
competitive, which in turn implies that proprietary costs are higher. However, even this
assumption is problematic. Stiglitz (1987) uses a price search model to examine market
equilibrium and the consequences from an increase in the number of firms. He shows that in a
setting with a finite number of firms, a duopoly (two firms, high industry concentration) can be
more competitive than atomistic competition (more than two firms, low industry concentration).
The reason for this result is that if a firm changes the price of a product, consumers are more
likely to compare prices when there are fewer sellers, which leads to competitive pricing. In
contrast, as the number of sellers increases, it becomes more costly for a consumer to compare
prices, so sellers are less likely to be responsive and adjust prices accordingly. As a result, the
author posits that increasing the number of firms (lowering industry concentration) can actually
decrease price competition. Sutton (1991) argues that higher competition can be associated with
higher concentration, because when the market is competitive, profits per firm fall.
Consequently, the number of firms able to survive in the market decreases, which leads to higher
concentration. Raith (2003) corroborates this prediction by showing that high competition due to
product substitutability causes prices to fall and profits to shrink. In turn, firms exit the industry.
In sum, there does not appear to be a unified theoretical framework backing the use of
industry-based concentration measures as proxies of proprietary costs. Moreover, these proxies
are noisy and imperfectly capture competition, making it difficult to develop a robust link to
disclosure. Industry-based measures also implicitly assume that all firms face the same level of
competition (Dedman and Lennox 2009). Industry-based proxies are also sensitive to the
inclusion of private firms. These conflicting stories may explain why studies that use industry-
based concentration measures find mixed results for the relation between proprietary costs and
9
disclosure. In fact, Guo, Lev and Zhou (2004) argue that industry-based concentration measures
are reasonable only for industries with homogenous products, which is not the case for many of
the empirical studies discussed. In their review article, Beyer, Cohen, Lys and Walther (2010)
note that “A major challenge still remains in measuring and quantifying proprietary costs,
especially when focusing on the level of competition in an industry (as proxied by its level of
concentration).” In response to these criticisms, several recent studies have constructed firm-
level measures of competition. We discuss firm-specific measures that have been proposed in the
next section.
Firm-based Measures
Previous papers have used several firm-specific proxies of proprietary costs including
market-to-book ratio (Bamber and Cheon 1998; Nagar, Nanda, and Wysocki 2003; Ajinkya et al.
2005), research and development expenditures, intangible assets (e.g., patents), advertising
expenses (Guo et al. 2004; Jones 2007; Wang 2007; Ellis et al. 2012), managers’ responses to
surveys on their perceptions of competition (Carlin, Fries, Schaffer, and Seabright 2001;
Aucremanne and Druant 2005; Jones 2007; Dedman and Lennox 2009), the length of redaction
period for confidential treatment orders approved by the Securities and Exchange Commission
(Thompson 2013) and managers’ references to competition in annual reports (Li et al. 2013).
Market-to-book ratio compares a firm’s stock price to the book value of its tangible
assets. Higher market-to-book ratios are consistent with higher proprietary costs to the extent that
wider differences between book and market values reduce managers’ incentives to disclose
(Bamber and Cheon 1998). Bamber and Cheon (1998) find a negative relation between earnings
forecast specificity and market-to-book ratio, suggesting that firms withhold disclosure when the
proprietary costs are high. They do not find a relation between the proxy and the managers’
10
choice to use a special venue to release the forecast (e.g., a special press release). Nagar et al.
(2003) and Ajinkya et al. (2005) find no relation between the market-to-book ratio and
management forecast frequency. However, in both papers the proxy is a control variable, not the
main variable of interest. Thus, evidence using market-to-book ratios is mixed. Moreover,
market-to-book ratios may be biased proxies because they also measure growth opportunities and
firm size.
Proxies related to a firm’s intangible assets use mainly patent-based measures. Guo et al.
(2004) use the availability of patent protection, stage of product development, percentage of
ownership retained by pre-IPO owners and the presence of venture capital as indicators of
proprietary costs. Guo et al. (2004) find that biotech firms with IPOs provide more information
about products in IPO prospectuses when patent protection exists, suggesting that competitive
concerns affect disclosure strategy. Jones (2007) estimates the ratio of a firm’s number of patents
to research and development expenditure. Patents are awarded for innovation, and proprietary
costs are likely higher for firms with many patents. Jones (2007) finds no relation between a
firm’s research and development disclosure ranking based on a survey by investment
professionals and the proprietary cost proxies. The results from both studies are not comparable,
as the disclosure measures are different. The sample selection criteria exclude most firms,
including only those with high research and development expenditures (Jones 2007) and IPOs
(Guo et al. 2004).
Managers’ responses to their perception of competition in surveys have been used to
measure competition (e.g., Carlin et al. 2001; Aucremanne and Druant 2005; Dedman and
Lennox 2009), which is advantageous as managers are better informed about the firm’s
proprietary costs and its competitive environment. In practice, managers assert that competitive
11
concerns limit how much information they disclose. For example, Graham, Harvey and Rajgopal
(2005) find that more than three-fifths of survey participants agree that the cost of revealing
proprietary information to competitors limits how much information they disclose. Evidence on
the link between managers’ responses to surveys and disclosure is limited. An exception is a
study by Dedman and Lennox (2009), which finds that greater perception of competition (higher
proprietary costs) leads managers to file abbreviated accounts in which they disclose less, for a
sample of private firms in the U.K. The authors also demonstrate the bias in industry-based
measures. They check the relation between managers’ perceptions of competition and archival
measures, and they find a weak or no relation between them. The authors’ approach offers a
firm-specific measure of proprietary costs. However, this measure of competition is available
only for the private firms that participated in the survey, and any capital market considerations,
which are important to public firms, are not studied. More generally, survey-based measures are
based on individual views and may not accurately capture competition if managers have
incentives to distort their responses.
Thompson (2013) uses the length of redaction period for confidential treatment orders
filed with the Securities and Exchange Commission. Longer redaction periods suggest a greater
possibility of harm and higher proprietary costs. Li et al. (2013) develop a measure of
competition based on the ratio of the number of times words about competition appear in the
firm’s annual reports to the total number of words used in the report. They find that their
measure is related to existing industry-level measures, but it also captures something distinct
from them. Neither paper examines the link between the proxy for proprietary costs and
disclosure.
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In sum, recent papers offer improvements in capturing firm-specific proprietary costs but
provide limited evidence on the association between these proxies and disclosure, which is the
focus of our paper. To this end, Lang and Sul (2014) point out that the relation between
proprietary costs and disclosure is unresolved and suggest that future research examine firm-
level measures, as competition likely varies between firms within an industry. The mixed
evidence reported in this extensive line of work could be due to the challenge of measuring and
quantifying proprietary costs (Beyer et al. 2010). Our patent litigation setting allows us to
provide novel insights on firm-level proprietary costs. We discuss the institutional details in the
following section.
Institutional Background of Patent Litigation in the U.S.
Our setting focuses on patent cases at the U.S Court of Appeals for the Federal Circuit
(CAFC). This court has jurisdiction over nationwide patents and a variety of other areas.4
According to the CAFC, the court’s jurisdiction comprises administrative law cases (55%),
intellectual property cases (31%), and cases involving money damages against the United States.
Nearly all of the intellectual property cases involve patents. The patent litigation process begins
in a lower U.S. district court when a plaintiff files a complaint alleging patent infringement on
one or more U.S. patents. Once a verdict is reached, the losing party can appeal the decision to
the CAFC.
The First Amendment of the Bill of Rights grants presumptive public access to judicial
records of lawsuit filings. Transparency during litigation allows the public to understand the
judicial process and ensures that judges perform their assigned duties fairly. However, during
4 The court also has nationwide jurisdiction in international trade, trademarks, government contracts, certain money claims against the U.S. government, and veterans’ benefits. Appeals can also come from the U.S. Court of Appeals for Veteran Claims, the U.S. Court of Federal Claims, the U.S. Court of International Trade, and the U.S. Court of Federal Claims.
13
litigation, the parties can request limited public access of court filings. In patent litigation cases,
the most commonly cited reason is the use of protected information by rivals for competitive
purposes. For example, if this information is made public, competitors could use it to determine
litigant production costs and underprice the litigant. Suppliers can also use information about a
litigant’s profits and costs to negotiate higher prices. Consequently, when a court grants a request
to seal/redact information during trial, confidentiality concerns outweigh public interest. The
court maintains a comprehensive docket with information on whether a defendant’s sealing
request was granted. We use the sealing of a defendant’s judicial records to capture firm-specific
proprietary costs and examine the link between proprietary costs and voluntary and mandatory
disclosure.
Our approach captures proprietary costs directly because sealing requests are only
granted to parties that can demonstrate the harm that would result from the disclosure of the
information. Sealed filings typically contain confidential business information, whose revelation
can result in competitive harm. Moreover, a request to seal a filing is not a strategic choice to
maximize success in court because the sealed information is available to the responsible parties
involved in patent litigation. Sealing limits access only to third parties who may use this
information to harm the litigants. Unlike self-reported measures of managers’ perception of their
competitive environment, our measure is independently verified by an independent judge who
determines whether to approve or deny sealing requests. Finally, to encourage transparency of
the judicial process, the court’s guiding principles express preference for less aggressive
approaches to withhold information. Sealing is only permitted as a measure of last resort in the
absence of narrower and feasible alternatives such as redaction of documents. Thus, use of this
measure increases our confidence that the disclosures truly contain sensitive information,
14
addressing the challenge empirical researchers face in identifying disclosures that carry
significant proprietary costs (Lang and Sul, 2014).
Several caveats are necessary. First, our proxy may not correctly capture competition and
proprietary costs if judges grant sealing requests for reasons that correlate with other disclosure
costs/benefits. However, this would bias against finding results in our tests that examine the
relation between sealing and the defendant’s competitive environment. Second, our results are
informative for a sample of firms that have been sued for patent infringement, and they may not
generalize to firms that are not engaged in a lawsuit. In summary, our setting cleanly identifies
defendants’ proprietary costs.
HYPOTHESES DEVELOPMENT
Sealing Judicial Records and Competition
We first assess the construct validity of sealing as a measure of proprietary costs. We
posit that the likelihood of defendants sealing judicial filings depends on the competitive
environments in which they operate. To seal a judicial record, the requesting party must
articulate compelling reasons supported by specific factual findings that outweigh the general
history of access and public policies favoring disclosure.5 Once permission to file under seal is
granted, a court order follows.6 The sealed filings typically contain information outside the
public domain, such as trade secrets, source code, pricing terms, royalty rates, third-party market
5 According to guidance from the U.S. Supreme Court, courts recognize a general right to inspect and copy public records and documents, including judicial records and documents (Nixon v. Warner Communications, Inc., 435 U.S. 589, 597 (1978)). While this right is not absolute, the Supreme Court clarifies that a district court must base its decision to seal documents on a compelling reason and articulate the factual basis for that ruling, without relying on hypothesis or conjecture. In particular, in deciding whether to file documents under seal, many trial courts grant sealing of filings used in trial if the court expressly finds facts that establish (1) there is an overriding interest that overcomes the right of public access to the records, (2) the overriding interest supports sealing the records, (3) a substantial probability exists that the overriding interest will be prejudiced if the record is not sealed, (4) the proposed sealing is narrowly tailored, and (5) there is no less restrictive means to achieve the overriding interest (CRC, Rules 2.550- 2.551). We note that some trial courts have additional requirements for sealing requests. 6 Depending on which lower court the case is filed in, the types of disclosures required vary. Some courts require parties to file a redacted publicly available version of every sealed document, while others do not, creating variation in how much information related to the sealed filing is available.
15
research reports, and minimum payment terms of licensing agreements. If a judge’s decision to
permit defendants to seal records during litigation is indicative of potential competitive harm
outweighing presumptive public access, we expect defendants to display characteristics
consistent with operating in a competitive environment.
Our first hypothesis, stated in alternative form, is:
H1: Defendants with sealed judicial filings operate in highly competitive environments.
Sealing Judicial Records and Disclosure
Next, we examine the corporate disclosure policy of defendants involved in patent
litigation. Several theories suggest that firm disclosure choices are driven by proprietary costs. A
large body of work examines managers’ disclosure decisions. Verrecchia (1983) argues for a
partial disclosure equilibrium, which can result from proprietary cost concerns or managers’
strategically hiding bad news from capital markets. Clinch and Verrechia (1997) also show that a
well-informed firm can limit disclosure of information that indicates high demand for its
products to exploit competitors’ underproduction. We similarly hypothesize that firms with
greater proprietary costs provide less disclosure. Thus, if firms with sealed judicial records have
greater proprietary costs of disclosure, we expect these firms to provide less disclosure. Our
second hypothesis, stated in alternative form, is:
H2: Defendants with sealed judicial records provide less disclosure.
METHOD
Sample Description and Firm-level Measure of Proprietary Costs
We construct our sample based on patent litigation cases published on Justia. Justia
republishes lists of cases from PACER (Public Access to Court Electronic Records), the public
access repository of US federal court system documents. Justia lists case and docket information
16
related to US District Courts, US Courts of Appeals, and US Bankruptcy Courts. We restrict our
search to patent cases at the US Court of Appeals for the Federal Circuit (CAFC) because the
court has nationwide jurisdiction in patents, so all appeals for patent cases are tried in this court.
This initial effort yields 601 appeals spanning the years 2012 through 2016. We then match these
cases to the US CAFC’s archive dockets, which contain all materials filed by the court and/or
parties in the appeals court as well as the lower court, which allows us to determine whether the
defendant sealed judicial filings during trial.7,8 Next, we restrict our sample to cases with
publicly traded defendants, leaving 352 cases. Finally, we drop 13 observations for which
Compustat data is unavailable. The final sample used in our primary analysis has 339 cases
involving 218 firms.
We code an indicator variable, SEAL, which takes a value of one if the defendant’s
request to seal is granted.9 We note that the defendant’s request to seal is not affected by a
strategy to maximize success in court because the sealed information is available to the
responsible parties from the plaintiff’s side. The request to seal limits access only to third parties
that may use it to competitively harm the defendant. Thus, we argue that defendants’ sealing
during litigation is driven by proprietary cost concerns.
Test of H1: Sealing Judicial Records and Competition
Our first hypothesis, H1, predicts a positive relation between sealing, SEAL, and
competition. We follow Li et al. (2013) to construct a measure of the defendant’s competitive
7 Sometimes the CAFC dockets are incomplete or missing information. We supplement this missing information using LexMachina, a proprietary database that supports intellectual property litigation research and offers legal analytical data and software products. The firm collects information on cases, judges, lawyers, and litigants. We also verify that the filings for cases with information on the CAFC archives correspond to the information available on LexMachina. 8 Our analysis is limited to defendants because both plaintiffs and defendants constitute a single case observation. 9 We use an indicator variable rather than the proportion of documents sealed relative to the number of total filings in the case because the latter measure would be confounded with the total number of filings, which can be significantly affected by the visibility of the case and the difficulty of understanding what boundaries the case covers.
17
environment by counting the number of references to competition in the firm’s 10-K filing, and
then scaling the count by the length of the annual report. We label this variable PCOMPETE.
Similar to Li et al. (2013), we eliminate phrases such as “less competitive” from our search. This
measure relies on the assumption that firms disclose details related to their perception of the
competitive environment in surveys and annual reports (Dedman and Lennox 2009; Li et al.
2013). To test our first hypothesis, we estimate the following equation:
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = 𝛼𝛼 + 𝛽𝛽1𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑆𝑆𝑃𝑃𝑆𝑆 + ∑ 𝛽𝛽𝑗𝑗𝑗𝑗 𝑃𝑃𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑗𝑗 +𝜀𝜀𝑖𝑖,𝐶𝐶 (1)
We expect that defendants are more likely to seal when they perceive greater
competition, so we predict a positive coefficient for β1. Li et al. (2013) note that in addition to
picking up broadly defined constructs of competition that have been used in the literature, their
measure also measures distinct aspects of competition. Hence, we supplement PCOMPETE with
other measures in prior research to provide evidence on whether other additional firm- and
industry-level proxies are incrementally useful in explaining the sealing of defendants’ judicial
records. We use the book-to-market ratio, BTM; R&D expenditures, R&D; the Herfindahl-
Hirschman index measured as the sum of squared market shares of all firms in the same industry,
Herfindahl; the weighted average R&D expenditures in the industry, Ind R&D; and the weighted
average property, plant and equipment in the industry, Ind PPE (Bamber and Cheon 1998; Harris
1998; Ajinkya et al. 2005; Ettredge et al. 2006; Verrecchia and Weber 2006; Wang 2007; Li
2010; Dedman and Lennox 2009; Ellis et al. 2012; Ali et al. 2014). We also control for firm size
(SIZE) and performance (ROA and Loss_Dummy).
We also test whether sealing is related to mean reversion of accounting rates of return.
Several studies find that mean reversion in accounting rates of profitability and growth are
industry- and economy-wide phenomena (see, e.g., Fairfield, Sweeney, and Yohn 1996; Fama
18
and French 2000; Fairfield, Ramnath, and Yohn 2009). According to Fama and French (2000),
the reversion occurs because entrepreneurs leave unprofitable industries for profitable ones.
Consequently, in a competitive environment, mean reversion exists both within and across
industries. Building on this notion, Li et al. (2013) find greater mean reversion of accounting
rates for firms that make more references to competition in their annual reports, which implies
that firms in competitive environments experience quicker mean reversion. We conduct a similar
analysis to provide evidence on the rates of mean reversion for defendants with sealed judicial
filings, based on our premise that defendants with sealed judicial records operate in more highly
competitive environments (H1). We run the following regression:
Δ RNOA 𝑡𝑡+1 = 𝛼𝛼 + 𝛽𝛽1Δ NOA 𝑡𝑡 + 𝛽𝛽2RNOA 𝑡𝑡 + 𝛽𝛽3𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + 𝛽𝛽4𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 ∗ Δ NOA 𝑡𝑡
+ 𝛽𝛽5𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 ∗ RNOA 𝑡𝑡+𝜀𝜀𝑖𝑖,𝐶𝐶
(2)
where ΔRNOA is the one-year-ahead change in RNOA, and RNOA is defined as operating income
after depreciation divided by average NOA. Following Fairfield, Whisenant and Yohn (2003), we
define NOA as the sum of net accounts receivable; inventories; all other current assets; net
property, plant and equipment; intangibles; and all other assets, less accounts payable, all other
current liabilities, and all other liabilities.
Marginal returns accrue from both new and existing investments. In equation (2), β1
measures the marginal rate of return on changes in net operating assets (new investments), while
β2 measures the rate of mean reversion in RNOA (existing investments). Both coefficients are
hypothesized to be negative, consistent with diminishing marginal returns. Our coefficients of
interest are β4 and β5, which capture the rate of returns, conditional on defendants’ withholding
information. If SEAL correctly captures proprietary costs, then the rate of returns on new and
19
existing investments should revert faster for defendants with sealed judicial records. Thus, we
predict both β4 and β5 to be negative.
Test of H2: Sealing Judicial Records and Disclosure
Our next tests provide evidence on the relation between proprietary costs and various
methods of corporate voluntary and mandatory disclosure. We use the following equation to test
the relation between sealing and voluntary disclosure:
𝑉𝑉𝑉𝑉𝑖𝑖𝐶𝐶𝑉𝑉 = 𝛼𝛼 + 𝛽𝛽1𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + ∑ 𝛽𝛽𝑗𝑗𝑗𝑗 𝑃𝑃𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑗𝑗 + 𝜀𝜀 (3)
VDisc represents four alternative measures of firms’ voluntary disclosures that have been
used in the literature: #Mgmt forecasts, ConfCall Dummy, #8-Ks, and VDisc8-Ks. Our first
measure of voluntary disclosure, #Mgmt forecasts, is the frequency of voluntary management
earnings forecasts. Li (2010) argues that proprietary costs of disclosure are likely to be more
significant for annual forecasts than for quarterly forecasts, because the longer, annual horizon
gives competitors more time to respond to new information in the forecast. Thus, we focus on
managerial guidance of annual earnings. Our second proxy of voluntary disclosure is ConfCall
Dummy, a dummy variable equal to 1 if the defendant holds a conference call in the year the case
is appealed, and zero otherwise. Frankel, Johnson and Skinner (1999) find that firms that host
conference calls display characteristics consistent with more forthcoming disclosure policies.
Our third proxy, #8-Ks, the count of 8Ks per firm, is used in studies as a proxy for voluntary
disclosure (Leuz and Schrand 2009; Li 2013; Balakrishnan, Core, and Verdi 2014). Finally,
VDisc8-Ks offers a refinement of #8-Ks, isolating the number of voluntary items disclosed in an
8-K, because a single filing might include multiple transactions or events and information related
to mandatory or voluntary items (Cooper, He, and Plumlee 2016). In the four alternative
20
specifications, we expect firms with high proprietary costs to be less forthcoming with
disclosure, consistent with our second hypothesis; hence, we predict a negative coefficient on β1.
Following prior work, we add controls for other factors affecting the frequency of
voluntary disclosure. The controls vary according to the specification. We include size (SIZE),
performance (ROA), an indicator for loss firm-years (Loss_Dummy), institutional ownership
(InstOwn), analyst following (NumAnalysts), CEO equity-based compensation (EqComp),
average bid-ask spreads (Avg Sprd), current-period-return volatility (Ret Vol), book-to-market
ratio (BTM), external financing (EqIss and DebtIss), and mandatory items in 8K filings (Mand8-
Ks) (Dye 1985; Waymire 1985; Jung and Kwon 1988; Abarbanell, Lanen, and Verrecchia 1995;
Kasznik and Lev 1995; Ajinkya et al. 2005; Kothari, Shu, and Wysocki 2009; Li 2010; Ali et al.
2014; Cooper et al. 2016).
Our second set of tests on disclosure investigate the relation between sealing judicial
records and aspects of mandatory disclosures, given that firms can alter the informativeness of
their mandatory disclosures. For example, managers can reduce the readability of a report to
obfuscate. We run the following regression:
𝑃𝑃𝑉𝑉𝑖𝑖𝐶𝐶𝑉𝑉 = 𝛼𝛼 + 𝛽𝛽1𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + ∑ 𝛽𝛽𝑗𝑗𝑗𝑗 𝑃𝑃𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑗𝑗 + 𝜀𝜀 (4)
MDisc represents several characteristics of mandatory disclosure measures following the
motivation of prior work. Our first measure, Fog_10K, follows Li (2008) and is a measure of the
readability of the defendant’s annual report. Fog is calculated as:
Fog = (words per sentence + percentage of words with three or more syllables) × 0.4.
A higher fog index indicates a less readable disclosure. We also use the length of the annual
report, Log(Wordcount_10K), to measure readability. Li (2008) suggests that longer documents
increase the information-processing cost of reading them, so all else equal, longer documents are
21
more difficult to read. To the extent that managers can use the discretion available in mandatory
reporting to obfuscate, we expect that firms with higher proprietary costs provide less readable
mandatory disclosures. Hence, we predict a positive coefficient on β1.
Our final analysis explores defendants’ segment reporting choices, which prior research
argues are affected by proprietary costs.
𝑆𝑆𝑆𝑆𝑆𝑆 𝑅𝑅𝑆𝑆𝑅𝑅𝐶𝐶𝐶𝐶𝐶𝐶 = 𝛼𝛼 + 𝛽𝛽1𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + ∑ 𝛽𝛽𝑗𝑗𝑗𝑗 𝑃𝑃𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑗𝑗 + 𝜀𝜀 (5)
We examine three segment reporting outcomes: cross-segment profitability (Range
(ROS)), number of reported segments (numSeg), and the number of geographic segments
(numGeo). Range (ROS) is the cross-segment variability of reported profits of multi-segment
firms, measured as the range of a firm’s segment return on sales. We subtract the smallest return
on sales of the firm’s segments from the largest. The return on sales is the ratio of a segment’s
operating profit to its revenues. Finer reporting of segment profitability increases this range if
managers tend to group segments together to disguise profitable segments. numSeg is the number
of reported segments disclosed, and numGeo is the number of geographic areas in which the firm
operates. In addition, we control for various firm-specific factors that have been documented to
affect segment reporting in previous work, such as size (Size), complexity (Complexity), stock
return volatility (Ret Vol), firm age (Age), performance (ROA), and sales concentration
(Herfindahl) (Harris 1998; Botosan and Harris 2000; Botosan and Stanford 2005; Ettredge et al.
2006; Berger and Hann 2007).
RESULTS
Summary Statistics
In Table 2, we report the descriptive statistics for the test and control variables for the
defendants during the year of appeal. To minimize outlier effects, we winsorize all continuous
22
variables at the 1st and 99th percentiles. As indicated in the table, approximately 30% of our
sample firms are granted permission to seal judicial records, which suggests variation in judges’
determination of potential competitive harm. Approximately 87% of the firms hold a conference
call in the year of the appeal, which is consistent with widespread use of this voluntary
disclosure channel. On average, defendants file 28 8-Ks in the year of the appeal, and 11 of these
are voluntary 8-Ks, VDisc8Ks. The mean of mandatory 8-Ks, MDisc8Ks, is 17, suggesting that
the average firm discloses mandatory items more frequently than voluntary items in the 8-K. On
average, the firms reference competition about 0.1% of the time in their annual disclosures. We
also find these disclosures to be highly unreadable (mean FOG index = 20.64), since a FOG
index greater than 18 suggests complex and unreadable prose.
Our firms are also fairly large, with a mean of $4.4 billion in assets. In terms of
profitability, on average the defendants report a 5% return on assets (ROA), while approximately
18% of the firms in our sample suffer losses (Loss_Dummy). Roughly 23 analysts (Num_Analyst)
issue earnings forecasts in the year of the appeal on average, and institutional investors
(InstOwn) own about 74% of the shares. In addition, about 4% of the defendants obtain external
financing (Ext_Fin) in the form of debt or stock issuances in the year of the appeal. The average
age of the firm is 43 years (Age), and the firms report 3 segments (NumSeg) on average. Finally,
because our sample comprises only firms involved in patent litigation, we caution that these
figures may not be generalizable to other types of lawsuits or even to firms without lawsuits.
In Table 3, we provide Spearman and Pearson correlations between SEAL and other
variables that proxy for proprietary costs in prior studies. The evidence suggests that SEAL is
weakly related to these measures. Firms that seal are more likely to reference competition in their
10-Ks, which suggests that they face competitive challenges. We also observe a positive relation
23
between sealing and R&D expenditure, which is consistent with Ellis et al. (2012), who find that
firms with high R&D expenditure are less likely to disclose customer information in the annual
report. We also observe a negative relation between industry-level PP&E and sealing. Li et al.
(2013) posit that the required investment in PP&E-intensive industries is high; this creates high
barriers to entry, which in turn reduce competition. Finally, SEAL is negatively correlated with
Herfindahl, which suggests that defendants seal more when they operate in less concentrated
(more competitive) industries.
In sum, SEAL appears to capture proprietary costs, given the correlation we observe with
existing measures. However, these correlations are fairly low and even insignificant in a few
instances, implying that our measure captures some unique firm-level variation. In the next
section, we investigate the significance of our proxy in regression specifications that include the
existing proxies. This exercise is intended to ensure that our proxy is not a noisy version of
another measure, and it also gives further insight into the unique firm-level competition that we
capture.
Empirical Findings: Determinants of Sealing Judicial Records
In Table 4, we report the results of our probit analysis examining the determinants of
defendants’ sealing judicial records. First, we find that defendants are more likely to seal when
they reference competition (PCOMPETE) more in their annual reports. This result confirms the
univariate correlation and suggests that judges permit sealing when they determine that
competitive costs outweigh the benefits of public access. This result also suggests that
defendants communicate their competitive concerns through other channels (e.g., in their annual
reports) in addition to the formal sealing requests made during the judicial process. We also find
that firms with high R&D expenditure (R&D) are more likely to seal. Our results support those
24
of Jones (2007), who examines 119 firms in R&D-intensive industries and observes less
disclosure for high proprietary cost firms. Also, Wang (2007) confirms that firms with high R&D
expenditure face greater proprietary costs, which may reflect the cost of continuously innovating.
Our results also indicate that firms operating in less concentrated industries are more likely to
seal. This finding is in line with the evidence in prior studies that companies in less concentrated
(i.e., more competitive) industries are more likely to withhold information (Verrecchia and
Weber 2006; Dedman and Lennox 2009).
In contrast, the book-to-market ratio (BTM), also a proxy for proprietary costs, is not a
significant predictor of the decision to seal records. Similarly, size (Size) and profitability (ROA
and Loss_Dummy) do not explain sealing. Overall, our results suggest that our measure of
proprietary costs is consistent with other proxies. In light of recent debate about courts
permitting the sealing of judicial records, these results provide initial evidence supporting the
competitive concerns that defendants cite. However, the overall low goodness of fit (R-squared
of 8%) suggests that these factors do not fully explain the courts’ decision—either judges use
soft, qualitative measures of competition, or factors aside from competition influence their
decisions.
Empirical Findings: Competition and Sealing Judicial Records
In Table 5, we present the results of our analysis on diminishing marginal returns, which
is our second test for defendants’ competitive environment. In Column 1, the negative and
significant coefficient (5% level) on ΔNOA of -0.1456 suggests that all else equal, the return on
net operating assets (RNOA) decreases by approximately 15% of the increase in net operating
assets, consistent with diminishing marginal returns from new investments. Similarly, the
negative and significant coefficient (5% level) of -0.2189 on RNOAt suggests that all else equal,
25
the return on net operating assets decreases by 22% of the current return on net operating assets,
consistent with diminishing marginal returns from existing investments. These estimates are
comparable to those of prior studies that have examined mean reversion of accounting rates
(Fairfield et al. 1996; Fama and French 2000; Fairfield et al. 2009; Li et al. 2013). More
importantly, we find a significantly negative coefficient on SEAL*ΔNOA (coefficient = -0.0386;
t-stat = 1.87), which implies faster mean reversion from new investments for defendants with
sealed judicial records, consistent with high competitive costs for firms with sealed judicial
records. In contrast, the coefficient on SEAL*RNOA is not significant, providing no evidence of
greater reversion of existing assets for firms with sealed judicial records.
Empirical Findings: Disclosure and Sealing Judicial Records
We next investigate the relation between sealing and defendants’ corporate disclosure
policies. As discussed previously, prior research frequently cites proprietary costs as a
determinant of firm disclosure decisions. In Table 6, we present the results of Equation 3
examining the association between sealing judicial records and various types of voluntary
disclosure. In Column 1, the frequency of management forecasts is the dependent variable, and
we find a negative and significant coefficient on SEAL (coefficient = -3.3078; t-stat = 2.02),
which suggests that firms that seal provide fewer management forecasts of annual earnings. This
result is consistent with our expectation and provides evidence that firms with high proprietary
costs are less likely to voluntarily disclose earnings projections. We also find that defendants
with higher stock return volatility, higher bid-ask spreads, and a higher proportion of CEO
compensation from equity-based incentives issue management forecasts more frequently.
We next investigate whether sealing is associated with the choice to hold a conference
call in the year of the appeal. We present the results of Equation 3 with an indicator variable for
26
conference calls as the dependent variable in Column 2 of Table 6. However, we find no
evidence that defendants who seal are less likely to hold a conference call (coefficient = -0.2570;
t-stat = -0.33). In addition, none of the other control variables are significant in this specification,
which may be due to low power caused by our small sample size.
Column 3 of Table 6 reports the results of Equation 3 with the number of 8-Ks as the
dependent variable. The coefficient on SEAL is equal to -2.66 and is significant at the 10% level,
suggesting that defendants with sealed judicial records file fewer 8-Ks. The coefficients on our
control variables suggest that larger firms file more 8-Ks, while those with higher analyst
following file 8-Ks less frequently. The coefficients on the other control variables are not
significant. Finally, in Column 4 we focus only on voluntary 8-Ks and find a negative but
statistically insignificant coefficient on SEAL after controlling for other factors. Overall, the
results in Table 6 support our hypothesis that defendants with high proprietary costs disclose
less. In particular, we observe fewer management forecasts and fewer 8-K filings when
defendants seal judicial records.
Our next test examines the association between sealing and the properties of annual
reports. In Table 7 Column 1, we report the results of Equation 4 with the fog index of the 10-K
as the dependent variable. We find that defendants with sealed judicial records have higher fog
(SEAL coefficient = 0.2029; t-stat = 1.83). None of the other control variables predict annual
report readability. In Column 2, the dependent variable is the length of the 10-K. We find that
defendants who seal have longer 10-Ks (SEAL coefficient = 0.0578; t-stat = 1.73). We also find
that defendants with higher stock return volatility, greater firm age, larger size, and fewer
segments have longer 10-Ks. Overall, the results presented in Table 7 provide additional
evidence in support of our prediction of less disclosure for defendants who seal judicial records.
27
To the best of our knowledge, we are the first to provide a link between proprietary costs and the
textual/linguistic properties of mandatory reports.
In Table 8, we provide the segment reporting results (Equation 5). In Column 1, when the
dependent variable is the cross-segment variability of reported profits, we find a negative
coefficient on SEAL equal to -1.25 (t-stat equal to -1.88). This result suggests that defendants
with sealed judicial records report a lower range of profits across their segments and is consistent
with withholding segment information. The cross-segment variability of reported profits is also
smaller when defendants record higher abnormal profits relative to industry averages, which also
suggests that managers are less forthcoming when proprietary costs are high (Ettredge et al.
2006). We also find that the reported profitability range is higher for more complex firms. In
Column 2, when the number of reported segments is the dependent variable, we find no evidence
that firms with sealed judicial records report fewer segments. However, we do find evidence that
firms in more concentrated industries with a higher Herfindahl index report a larger number of
segments. Finally, in Column 3 the dependent variable is the number of geographic segments.
The coefficient on SEAL is equal to 0.883 and is significant at the 5% level, implying that
defendants who seal generally operate in more geographic areas. The coefficients on our control
variables suggest that defendants with higher ROA and lower size report operations in more
geographic areas. Taken together, our segment reporting results suggest that defendants with
sealed judicial records are more likely to conceal segment profits and report more geographical
segments than defendants who do not seal their judicial records. In general, the various
specifications offer support for our prediction that defendants who seal operate in competitive
environments and disclose less.
28
Sensitivity Tests
We next investigate the sensitivity of our results. In our regression specifications, the
control group includes all defendants without sealed judicial records. However, some of these
firms may request a sealing but be denied by the court. Thus, we examine whether defendants
whose sealing requests are denied also operate in competitive environments. This test speaks to
the ability of the judge to identify firms with proprietary information. Anecdotally, we observe
instances where the judge expresses such sentiments. For example, in a patent lawsuit between
Stryker Corp. (plaintiff) and Zimmer Inc. (defendant), the defendant’s request to seal certain
documents was denied.10 In explaining the decision to deny the request, the judge notes,
The documents at issue are all 15-20 years old (or are undated). The age of the items alone
militates against a finding of any particular competitive sensitivity. Moreover, the content of the
documents is, for the most part, rather generic…. There are no engineering specifications, no
detailed cost accounting or pricing information of a current nature, no specific market strategies
and no concrete and current product development plans. None of the items figures very
prominently in the Zimmer brief, with Exhibit D confined to a single sentence reference, and all
other items at issue included in a string of bullet points for a single point. The parties have not
established good cause sufficient to overcome the presumption of public access for these items.
Accordingly, the Motion to Seal is DENIED.
For such a defendant, we might not expect to observe greater reversion of accounting
rates if the judge correctly determines the competitive concerns to be insignificant. However, an
important caveat is that this prediction might not hold if the defendant operates in a competitive
environment but seeks to redact a filing that does not contain truly proprietary information. Also,
we might not observe this relation if the judge concedes that the information is indeed
proprietary, but it already exists in the public domain, which means the court need not take
10 United States District Court Western District of Michigan Southern Division Case No: 1:10-CV-1223
29
responsibility for controlling its access. For example, in a lawsuit between Warner Chilcott
Company (plaintiff) and Lupin Ltd. (defendant), the court denied Lupin’s request seeking after-
the-fact sealing of a proceeding that was held in open court: “Here, Defendants seek to seal
information which has already entered the public domain, as members of the public were free to,
and did, observe trial proceedings that were not closed to the public. … Defendants’ request to
seal information that was revealed in open court during trial is DENIED.” Thus, we offer no
directional prediction on the nature of the business environment in which they operate as both
explanations may occur in our data.
In our sample, we have 29 cases (~12% of the total sample) where the request is denied.
We present results of the augmented specification that accounts for denied requests in Column 2
of Table 5. We observe diminishing marginal returns similar to the results reported in Column 1.
The reversion is accelerated for defendants with sealed judicial records, but the reversion is not
different for defendants whose requests are denied. We also run an alternative specification
where we exclude the 29 observations whose sealing requests were denied. We present these
results in Column 3 of Table 5. The results are similar to those reported in Column 1. In sum,
both the initial and augmented specifications lend support to our conjecture that sealing judicial
records reflects competitive concerns. These results also suggest that judges deny requests to seal
by defendants with low proprietary costs.
In additional tests, we also examine the market’s reaction to the court’s announcement
that the sealing request is denied. In untabulated results, we observe a slight negative market
reaction. The mean cumulative abnormal returns (CAR) around the three-day window when the
request is denied is about -1.42%. Depending on the length of the window, the results range from
-0.76% (5-day window) to -1.69% (2-day window). This finding suggests that on average, the
30
market perceives that the court’s decision not to grant sealing may reveal some information that
can be useful to the defendant’s rivals.
Finally, we also examine whether our results are robust to the use of industry fixed
effects when there is persistent sealing in certain industries. We continue to find results
qualitatively similar (in sign and significance) to those we report.
CONCLUSION
We develop a novel firm-level proxy of proprietary costs by examining whether courts
permit defendants to seal judicial records involved in patent lawsuits. We find that defendants
with sealed judicial records incur higher R&D expenditures, operate in industries with lower
sales concentration, and make more references to competition in their annual reports, compared
to defendants without sealed judicial records. Interestingly, our measure is only weakly related to
commonly used measures of competition in previous research. Moreover, we observe faster
mean reversion of RNOA from new investments when defendants seal judicial records. This
finding suggests that our measure captures competition, since reversion of accounting rates is
thought to occur faster in a competitive environment. We do not find similar accelerated
reversion for defendants whose sealing requests are denied. Cumulatively, we interpret our
findings as confirming that our measure captures the competitive environment in which
defendants operate and may have useful implications for the ongoing debate around the
transparency of patent litigation. Our evidence supports the notion that judges base their sealing
decision on the potential proprietary costs to the defendant.
We then use our measure to test the relation between proprietary costs and disclosure and
find that defendants with sealed records are less likely to provide earnings guidance, report a
smaller range of segment profitability, and operate in more geographic areas, but they do not
31
report fewer segments. These results corroborate evidence in prior work. We also find that
defendants with sealed records have longer and less readable annual reports and file fewer 8-Ks.
These findings shed light on the effect of proprietary costs on the frequency and linguistic
attributes of mandatory disclosures, which to the best of our knowledge has not been examined
in prior research. Overall, our results are consistent with prior studies that suggest that companies
with high proprietary costs are less forthcoming.
32
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36
Appendix Definition of variables
# Mgmt Forecastst = Number of annual earnings forecasts
ConfCall Dummy = 1 if the defendant holds a conference call during the year, and 0 otherwise #8-Ks = Number of 8-Ks filed during the year VDisc8Ks = Number of voluntary detailed 8K items with the 8Ks filed by a firm within the year Range(ROS) = Range of a firm’s segment return on sales. Calculated as the difference between the
largest and smallest return on sales (ROS). Fog_10K = Li (2008) measure of readability. Calculated as (words_per_sentence +
percent_of_complex_words)*0.4. Complex words are defined as having three syllables or more. Log(Wordcount_10K) = Log(Number of words in the annual report)
Pcompete = Ratio of the number of occurrences of competition words to the total number of words in the 10-K
MDisc8Ks = Number of detailed 8K items with the 8Ks filed by a firm within the year that are not voluntary
SEAL = 1 if the defendant’s response to a plaintiff’s motions to compel is sealed/redacted, or
the defendant requests and is granted a protective order on grounds of protecting sensitive financial information, and 0 otherwise
Δ NOA = NOAt – NOAt-1, where NOA is net accounts receivable + inventories + all other current assets + net PPE + intangibles + all other assets – accounts payable – all other current liabilities – all other liabilities
RNOA = Operating income after depreciation divided by the average NOA
Δ RNOAt+1 = One-year-ahead change in RNOA
BTMt-1 = Ratio of the book-to-market value in the previous year
R&Dt-1 = Ratio of R&D expenditure to total assets in the previous year
Herfindahl = Proportion of firm’s share of sales to total sales by firms in the same industry Sizet-1 = Log(Total assets) in the previous year
37
ROA = Ratio of net income to total assets Loss Dummy = 1 if net income is less than zero, and 0 otherwise Δ Earnings = Net incomet - Net incomet-1
Sign(Δ Earnings) = 1 if Δ Earnings is positive, and 0 otherwise InstOwnt-1 = Percent of shares held by institutional investors
NumAnalystst-1 = Number of analysts following the firm in the previous year. If a firm is not on IBES,
analyst following equals 0. EqCompt-1 = Ratio of CEO annual equity (stock and stock options) compensation to annual total
compensation. The equity portion is valued at grant date.
AvgSprdt-1 = Average of monthly bid-ask spreads in the previous year
Ret Vol = Total stock return volatility in the last 12 months Abn Prof = Firm ROA - Industry ROA Size = Log(Total assets) NumSeg = Number of reported segments disclosed Diverse = Number of different 2-digit SIC industries in which the firm operates Ext_Fin = Net amount of capital a firm raises through debt or equity issuance
Complexity = Square root of the number of geographic areas (numGeo) in which the firm operates Aggregation = Ratio of the number of 4-digit SIC codes assigned in a year to the number of reported
segments
38
TABLE 1: Sample selection Sample selection
Patent infringement cases appealed 2012-2016 (US Court of Appeals, Federal Circuit) 601
Less: Defendant not publicly traded firm
(249) Appealed patent infringement cases with publicly traded defendants
352
Less: Compustat and other data unavailable
(13) Final sample
339
No. of firms (168 parent companies; 50 subsidiaries)
218
39
TABLE 2: Descriptive statistics Descriptive statistics (Patent infringement sample)
N Mean Std. Dev. 25% Median 75% Voluntary disclosure variables
#Mgmt Forecasts 339 0.986 1.381 0.000 0.000 0.000 ConfCall Dummy 339 0.873 0.173 1.000 1.000 1.000 #8-Ks 229 28.030 14.440 18.000 24.000 33.000 VDisc8Ks 229 11.010 7.201 6.000 9.000 13.000 Mandatory disclosure variables
Range(ROS) 339 0.060 0.282 0.036 0.088 0.167 Fog_10K 229 20.640 0.855 20.130 20.570 21.130 Log(Wordcount_10K) 229 10.860 0.323 10.650 10.830 11.030 Pcompete 229 0.00114 0.000543 0.000713 0.00109 0.00146 MDisc8Ks 229 17.020 9.823 10.000 14.000 22.000 Other variables SEAL 339 0.307 0.463 0.000 0.000 1.000 Δ NOA 339 0.025 0.112 -0.032 0.014 0.085 RNOA 339 0.028 0.153 0.017 0.056 0.118 Δ RNOAt+1 339 -0.007 0.137 -0.002 0.005 0.023 BTMt-1 339 0.428 0.300 0.255 0.354 0.515 R&Dt-1 339 0.058 0.061 0.010 0.044 0.086 Herfindahl 339 0.313 0.225 0.171 0.222 0.360 ROA 339 0.051 0.094 0.016 0.065 0.104 Loss Dummy 339 0.187 0.391 0.000 0.000 0.000 Sizet-1 339 9.650 1.962 8.269 9.856 11.340 Δ Earnings 339 -51.510 2,632.000 -334.000 -11.250 200.000 Sign (Δ Earnings) 339 0.475 0.501 0.000 0.000 1.000 Mgmt Forecastt-1 Dummy 339 1.000 0.000 1.000 1.000 1.000 InstOwnt-1 339 0.738 0.192 0.642 0.777 0.872 NumAnalystt-1 339 22.640 12.730 12.000 23.000 30.000 EqCompt-1 339 0.688 0.350 0.675 0.870 0.907 AvgSprdt-1 339 0.006 0.003 0.004 0.005 0.008 Abn Firm ROA 339 0.047 0.113 -0.004 0.047 0.083 Size 339 9.714 1.956 8.377 9.830 11.400 Age 339 43.320 20.060 28.000 38.500 58.000 Ext_Fin 339 0.036 0.167 -0.021 0.004 0.040 Diverse 339 2.036 1.121 1.000 2.000 3.000 NumSeg 339 3.129 1.862 1.000 3.000 4.000 Complexity 339 1.784 0.564 1.414 1.732 2.000 Aggregation 339 0.744 0.260 0.500 0.690 1.000
40
TABLE 3: Correlation Panel A: Spearman and Pearson correlations between SEAL and other proprietary cost proxies
Correlation PCOMPETE R&D BTM HERFINDAHL IND-R&D IND-PPE
Spearman 0.059 0.133 0.062 -0.007 0.089 -0.038 Pearson 0.024 0.085 0.102 -0.007 0.065 -0.047
41
TABLE 4: Determinants of defendant’s approval to seal during case Determinants of the defendants’ approval to seal judicial records during
patent infringement cases
(1)
Dep Var: SEAL
VARIABLES Pcompete
4.6766***
(2.70)
BTMt-1
0.1511
(1.47)
R&Dt-1
0.9236**
(2.38)
Herfindahl
-0.0087*
(1.68)
Size
-0.0114
(-0.20)
Loss_Dummy
-0.1115
(-0.20)
ROA
0.1701
(0.57)
Constant
0.2448
(1.29)
Observations
339
Adj. R-Squared
0.0826 T-stat in parentheses
*** p<0.01, ** p<0.05, * p<0.1
42
TABLE 5: Reversion of accounting rates Relation between sealing filings during litigation and future changes in RNOA
(1) (2) (3)
Dep Var:
Δ RNOAt+1 Δ RNOAt+1 Δ RNOAt+1 VARIABLES
Δ NOAt
-0.1456** -0.1487** -0.1800**
(-1.86) (-1.91) (-2.11)
RNOAt
-0.2189** -0.2163** -0.2316**
(-1.91) (-1.90) (-2.07)
SEAL
0.3409 0.3329 0.3178
(1.43) (1.47) (1.43)
SEAL * Δ NOAt
-0.0386** -0.0386** -0.0387**
(1.87) (1.88) (1.94)
SEAL * RNOAt
-0.1006 -0.1008 -0.0998
(-0.43) (-0.22) (-0.21)
SEAL_Denied
-0.1006
(-0.42)
SEAL_Denied * Δ NOAt
0.0016
(0.21)
SEAL_Denied * RNOAt
0.0001
(0.04
Constant
1.7061* 1.5121 1.3512
(1.65) (1.32) (1.26)
Observations
339 339 310
Adj. R-Squared
0.0826 0.0835 0.0934 T-stat in parentheses
*** p<0.01, ** p<0.05, * p<0.1
43
TABLE 6: The relation between sealing and voluntary disclosure The effect of sealing judicial records during patent litigation on voluntary disclosure
(1) (2) (3) (4)
Dep Var: # Mgmt forecasts ConfCall Dummy #8-Ks VDisc8Ks VARIABLES
SEAL -3.3078** -0.2570 -2.6613* -0.9501
(2.02) (-0.33) (-2.28) (-1.24)
Sizet-1 -0.3833
(-0.31)
ROA -3.7536
-11.1928 0.4466
(-1.63)
(-1.45) (-0.09)
BTMt-1 1.6967
(0.24)
NumAnalystst-1 -0.0039
(-0.02)
Loss Dummy 2.8425*
1.9441 -0.8873
(1.93)
(0.54) (-0.77)
Ret Vol 49.0606**
32.5248 9.1595
(4.30)
(1.05) (0.79)
R&Dt-1 5.8433
(2.47)
Herfindahl 8.1661
(1.05)
Δ Earnings -0.0016
(-0.98)
Sign(Δ Earnings) 6.1615
(0.99)
InstOwnt-1 -18.6420
(-1.53)
EqCompt-1 9.7705***
(2.80)
AvgSprdt-1 1.4226**
(2.32)
Size
0.2761 4.1147*** 0.7929**
(1.25) (4.14) (2.28)
BTM
-0.5720 0.4725 0.7163
(-0.68) (0.28) (0.99)
NumAnalysts
0.0502 -0.4925*** -0.0584
(1.07) (-4.50) (-1.06)
Ext_Fin
0.1576 6.2822 4.2430
(0.06) (1.19) (0.99)
MDisc8Ks
0.2723**
(2.17)
Constant 37.1566* 0.7491 -1.6714 -0.4186
(9.30) (0.44) (-0.19) (-0.13)
Observations 339 339 229 229 Adj. R-squared 0.2180
0.1952 0.2249
McFadden R-squared
0.1539 T-stats in parentheses
*** p<0.01, ** p<0.05, * p<0.1
44
TABLE 7: The relation between sealing and disclosure The effect of the choice to seal during patent litigation on the readability of mandatory
disclosures
(1)
(2)
Dep Var:
Fog_10K
Wordcount_10K
SEAL
0.2029*
0.0578*
(1.83)
(1.73)
Ret Vol
1.0559
1.3242**
(0.16)
(2.03)
Age
0.0007
0.0043***
(0.21)
(2.69)
Size
-0.0064
0.0425***
(-0.18)
(3.27)
NumSeg
0.02
-0.0255*
(0.65)
(-1.73)
Constant
20.5111***
10.2205***
-48.43
(61.39)
Observations
229
229 R-Squared
0.1132
0.2343
Robust pval in parentheses *** p<0.01, ** p<0.05, * p<0.1
45
TABLE 8: The relation between sealing and mandatory disclosure The effect of the choice to seal during patent litigation on geographical areas reported
(1)
(2)
(3)
Dep Var:
Range(ROS)
numSeg
geoSeg
VARIABLES
SEAL
-1.2532*
0.3504
0.8803**
(-1.88)
(1.37)
(1.99)
Abn Prof
-1.2171***
(-5.96)
Herfindahl
2.2056***
0.7504
(4.61)
(0.85)
Size
0.1436
0.2629***
-0.1506**
(0.96)
(5.04)
(-2.35)
ROA
0.3932
2.4451***
(0.46)
(3.67)
numSeg
0.0018
(0.09)
Diverse
0.0153
(0.06)
Ext_Fin
-0.5967
(-0.64)
Complexity
0.1091*
(1.96)
Aggregation
0.0325
(0.87)
Constant
1.3633
-0.4628
3.9418***
(0.82)
(-1.17)
(5.07)
Observations
308 308 308 R-Squared
0.5265
0.1921
0.0544
T-stat in parentheses *** p<0.01, ** p<0.05, * p<0.1
46
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