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THE IMPACT OF MANDATORY DISCLOSURE ON CORPORATE VOLUNTARY DISCLOSURE: PATENT LAW CHANGE AND R&D DISCLOSURE
By
XI WANG
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2017
© 2017 Xi Wang
To all members of my family and many friends who have supported me throughout the process
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ACKNOWLEDGMENTS
I am very grateful to the members of my dissertation committee for their
invaluable guidance and encouragement: Jenny Tucker (chair), Marcus Kirk, Philip
Wang, and Joel Houston. I also thank William Ciconte, Lisa Hinson, Michael Mayberry,
Devin Williams, and workshop participants at the University of Florida, the University at
Buffalo, the City University of Hong Kong, the Chinese University of Hong Kong, the
University of British Columbia, McGill University, the Hong Kong University of Science
and Technology, and the Hong Kong Polytechnic University for helpful comments and
suggestions.
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TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 6
LIST OF FIGURES .......................................................................................................... 7
ABSTRACT ..................................................................................................................... 8
CHAPTER
1 INTRODUCTION ...................................................................................................... 9
2 BACKGROUND AND HYPOTHESES DEVELOPMENT ........................................ 16
2.1 The American Inventors Protection Act of 1999 ................................................ 16
2.2 Voluntary R&D Disclosure ................................................................................ 17 2.3 Hypotheses ....................................................................................................... 19
3 EMPIRICAL ANALYSES ......................................................................................... 24
3.1 Sample Selection .............................................................................................. 24 3.2 Content Analysis ............................................................................................... 26
3.3 Empirical Design ............................................................................................... 28
3.3.1 Measures of Voluntary R&D Disclosures and Patent Applications .......... 28
3.3.2 Regression Models .................................................................................. 28 3.4 Empirical Results .............................................................................................. 33
3.4.1 Descriptive Statistics ............................................................................... 33
3.4.2 Main Results ............................................................................................ 34 3.4.3 Supplementary Analyses ......................................................................... 38
4 CONCLUSIONS ..................................................................................................... 54
APPENDIX
A VOLUNTARY R&D DISCLOSURE AND PUBLICATION OF PATENT INFORMATION TIMELINE ..................................................................................... 56
B VARIABLE DEFINITIONS ....................................................................................... 58
LIST OF REFERENCES ............................................................................................... 59
BIOGRAPHICAL SKETCH ............................................................................................ 63
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LIST OF TABLES
Table page 3-1 Sample selection ................................................................................................ 41
3-2 Content analysis of R&D-related press releases and the preceding patent applications......................................................................................................... 43
3-3 Descriptive statistics ........................................................................................... 44
3-4 Pairwise correlations .......................................................................................... 45
3-5 Interaction effect between the AIPA and patent applications on voluntary R&D disclosure ................................................................................................... 46
3-6 High R&D-intensive firms vs. Low R&D-intensive firms ...................................... 48
3-7 Firms with high product substitutability vs. Firms with low product substitutability ..................................................................................................... 49
3-8 Placebo tests ...................................................................................................... 50
3-9 Changes tests ..................................................................................................... 51
3-10 Content analysis of patent applications .............................................................. 52
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LIST OF FIGURES
Figure page 3-1 Distributions of voluntary R&D disclosure measures. ......................................... 53
A-1 Timeline of voluntary R&D disclosure and publication of patent information ...... 57
B-1 Description of main variables.............................................................................. 58
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
THE IMPACT OF MANDATORY DISCLOSURE ON CORPORATE VOLUNTARY
DISCLOSURE: PATENT LAW CHANGE AND R&D DISCLOSURE
By
Xi Wang
August 2017
Chair: Jennifer Wu Tucker Major: Business Administration – Accounting
Managers must balance the capital-market benefits with the proprietary costs of
voluntary disclosure. A new mandatory disclosure requirement is likely to change this
balance. In this study, I use the American Inventors Protection Act of 1999 (AIPA),
which requires the public disclosure of information contained in firms’ patent
applications, to examine the impact of mandatory disclosure regulation on managers’
voluntary disclosure. I find that after the AIPA, firms with patent applications
substantially increase their voluntary R&D disclosures. I also find that this effect is more
pronounced for R&D-intensive firms, whose costs associated with imitation concerns
decrease significantly after the Act; and less pronounced for firms with high product
substitutability, whose benefits of disclosing early do not increase after the Act. The
evidence suggests a complementary relationship between mandatory and voluntary
disclosures when they convey similar proprietary information.
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CHAPTER 1 INTRODUCTION
Firms communicate their private information with the capital market through both
mandatory and voluntary disclosures. When making voluntary disclosure decisions,
managers must balance the capital-market benefits with the costs of providing
proprietary information. A new mandatory disclosure requirement is likely to change this
balance. Empirically, how a regulation on mandatory disclosure affects firms’ voluntary
disclosure remains largely unknown because prior research tends to focus on either
mandatory or voluntary disclosure in isolation (Healy and Palepu 2001; Beyer, Cohen,
Lys, and Walther 2010).1 Theorists suggest that analyzing either type of disclosure
without considering the other type is flawed unless the two types are uncorrelated (e.g.,
Gonedes 1980; Dye 1985, 1986; Einhorn 2005). In this study, I use the setting of the
American Inventors Protection Act of 1999 (AIPA) to examine the impact of mandatory
disclosure regulation on corporate voluntary disclosure. In particular, I investigate how
the anticipated mandatory disclosure of information contained in patent applications
influences managers’ voluntary disclosure of research and development-related
activities (“R&D disclosure”).
The AIPA is a federal law that requires all patent applications to be published on
the website of the U.S. Patent and Trademark Office (USPTO) 18 months after the
initial application date, which is an exogenous shock to a firm’s mandatory disclosure
1 Two exceptions exist. One study is Bischof and Daske (2013) and investigates the impact of a one-time mandatory disclosure of banks’ sovereign risk exposures on the subsequent voluntary disclosure about sovereign risk in the European Union. The sequence of mandatory and voluntary disclosures in their study is different from that in mine. The other study is Li and Yang (2016) and examines the effect of mandatory IFRS adoption on management earnings forecasts in a cross-country setting. My study differs from theirs by focusing on disclosures associated with high proprietary costs.
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environment.2 This legislation is considered “one of the most significant changes to the
U.S. patent system in the 20th century” (USPTO 2000). Before its enactment on
November 29, 1999, the information contained in a patent application was made public
only when the patent was granted. If a patent was not granted, its application was never
made public. After the AIPA, patent information contained in a patent application,
including details about the invention and its inventor(s), is published regardless of
whether the patent is eventually granted. This new regulation aimed to accelerate the
diffusion of knowledge and help curb wasteful R&D spending by enabling people to
observe whether a technology has already been invented before making R&D
investments. Such patent information is also useful to investors in evaluating firms’
performance (e.g., Deng, Lev, and Narin 1999; Hall, Jaffe, and Trajtenberg 2001;
Hirschey and Richardson 2004).3 Because it takes 36 months on average for a patent to
be approved (Graham and Hegde 2014), the law change expedites patent information
flow to the market—the information is now made public midway through the application
process instead of at the end. The mandatory early disclosure of pre-grant patent
information, however, may harm patent applicants by increasing the risk of imitation.
Some inventors may be discouraged from applying for patents in the new disclosure
regime. The consequences of the AIPA and other mandatory disclosure regulations
have been subjected to a longstanding debate among scholars in various fields (Ferrell
2004).
2 USPTO website: http://www.uspto.gov/patent/laws-and-regulations/american-inventors-protection-act-1999
3 Nowadays, information about patent applications and granted patents can be readily accessed by all market participants on the Internet using USPTO Patent Search or Google Patent Search.
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Voluntary R&D disclosure is important for three reasons. First, innovation is the
backbone of a competitive, knowledge-driven economy (OECD 1996). Corporate R&D
has been the primary measure of a firm’s innovation and technological progress (Lerner
and Wulf 2007). Voluntary R&D disclosure has traditionally been an important channel
that firms use to disclose R&D activities. The mandatory publication of patent
applications also reveals detailed information about the outcomes of firms’ R&D
investments. How this new information channel through patent publications affects
firms’ voluntary disclosure of R&D activities is an open empirical question. Second, R&D
expenses recognized in financial statements alone cannot effectively communicate the
underlying characteristics of firms’ R&D activities because of the idiosyncratic nature
and the high level of uncertainty associated with R&D activities. Many firms rely on
voluntary R&D disclosures to bridge the gap between their financial statement numbers
and the actual level of R&D investments. Third, voluntary R&D disclosure is useful for
market participants. For example, it can help firms explain their changing financial
performance and manage outsiders’ expectations (Merkely 2014) as well as reduce
firms’ costs of capital (Francis, Nanda, and Olsson 2008).
Managers must balance the capital-market benefits of R&D disclosure against
the proprietary costs of making the disclosure. The effects of the AIPA on managers’
voluntary R&D disclosure decisions therefore depend on how this cost-benefit balance
changes in the post-AIPA regime. I predict that for two reasons firms with patent
applications provide more voluntary R&D disclosures after the AIPA than before it. First,
the costs of voluntarily disclosing R&D information should be lower after the AIPA. Once
firms apply for patents, the proprietary costs of disclosing detailed patent information
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become unavoidable, causing the incremental proprietary costs of voluntarily disclosing
R&D to decrease. The proprietary costs associated with the concern over competitors
filing “submarine patents” should also decrease after the AIPA. Before the AIPA,
inventors could intentionally delay the issuance and publication of the so-called
“submarine patents” by constantly making secret amendments to the applications based
on industry-wide and competitors’ disclosure of information until other firms obtained the
granted patents. The goal of these “submarine patents” was to claim priority of the
original ideas in order to squeeze settlements out of the inventors that made the actual
developments. The mandatory early disclosure of patent applications makes “submarine
patents” impossible in the post-AIPA period. When proprietary costs decrease, the
theory predicts a lower disclosure threshold in the equilibrium (Verrechia 1983, 1990).
Therefore, firms should increase the amount of R&D disclosure after the AIPA. Second,
voluntary R&D disclosure should benefit firms more after the AIPA than before it. The
mandatory disclosure of patent information plays a confirmatory role and thus increases
the credibility of voluntary disclosure released before the mandatory disclosure.
Credible disclosure can help analysts and investors more accurately value a firm and
correct mispricing (Healy and Palepu 1993). Firms may also benefit from voluntary R&D
disclosure released before the publication of patent applications by signaling their
technological advantages and gaining early name recognition (Elliott and Jacobson
1994; James 2014). The increased benefits should make firms more likely to provide
R&D disclosures after the AIPA than before it.
I obtain a sample of 26,687 firm-years during 1996-2003 with at least one R&D-
related press release or patent application in the current year, or non-zero R&D
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expenses in the previous three years, to ensure that the firms invest in R&D activities.
To measure the amount of voluntary R&D disclosure, I first expand Merkley’s (2014)
dictionary of common R&D keywords and phrases by using “regular expression”
routines from Perl to examine a pre-collected sample of 500 R&D-related press
releases. I then use this new dictionary to identify all R&D-related press releases and
count both the number of these press releases in each firm-year and the total words
contained in them. I use the NBER patent database for data on the existence and
number of patent applications for each firm-year. I assume that voluntary R&D-related
press releases and the preceding patent applications convey related proprietary
information. I validate this assumption by conducting a content analysis of 136 randomly
selected patent applications after the AIPA and 126 R&D-related press releases issued
by these patent filing firms during the fiscal year immediately after the patent application
year.4 I observe that 64.3% of the R&D-related press releases provide patent-related
information, and 84.0% of these patent-related R&D disclosures provide details on the
specific patents filed in the previous year. The vast majority of these disclosures are
made available in the six months before the information contained in the corresponding
patent applications is made public.
I first use a levels specification to investigate the impact of the mandatory
disclosure of patent applications on firms’ voluntary R&D disclosures. I find that firms
with patent applications increase the amount of voluntary R&D disclosure after the
4 Ideally, R&D-related press releases should be selected from the 18-month period that begins with the patent application date and ends with the publication date of the patent application. Because the NBER patent database records only the application year, not the exact application date, I use the 12-month period after the year in which a patent application was filed to proxy for the 18-month window.
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AIPA. The effect is economically significant. Firms that apply for patents after the AIPA
voluntarily disclose 39.0 percent more words in their R&D-related press releases than
firms that applied for patents before the AIPA. I also find that after the AIPA firms with
more patent applications are more likely to increase their voluntary R&D disclosures
than firms with fewer patent applications. To address the concern that firms responded
to the patent law change by not applying for patents, I construct a sample containing
firms that never applied for patents during my sample period and firms that applied for
at least one patent both before and after the AIPA. The results confirm my primary
findings.
Next, I examine cross-sectional variations in the effect of the AIPA on the amount
of voluntary R&D disclosure by evaluating which types of firms are more (less) likely to
experience the decrease (increase) in costs (benefits) of voluntarily disclosing R&D
information in the post-AIPA period. Before the AIPA, R&D-intensive firms incurred
greater proprietary costs of disclosure than other firms because the former risked losing
their patents to “submarine patent” applicants. Because the AIPA eliminates the practice
of “submarine patents,” I predict that after the AIPA, R&D-intensive firms are more likely
to experience a decrease in proprietary costs and thus are more likely to voluntarily
disclose their R&D activities than firms with low R&D intensity. In addition, firms with
high product substitutability are less likely to make early R&D disclosures before the
publication of patent applications because higher product substitutability means a lower
degree of competitive advantage in the product market and therefore more intense
product competition. These firms do not have technological advantages to deter
competition, so I predict that they are less likely to benefit from and make early
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disclosure of R&D activities than firms with low product substitutability. The empirical
results support these predictions.
I make four contributions to the literature. First, my study enhances the
understanding of the relationship between mandatory and voluntary disclosures. I
answer Beyer et al.’s (2010) call for archival researchers to investigate the extent to
which mandatory disclosure requirements affect the amount of voluntary disclosure.
Second, my study is the first, to my knowledge, to examine such a relationship with a
focus on narrative R&D disclosures. Narrative disclosures have become an increasingly
important channel for communicating mangers’ private information to supplement
financial statements, but only a few studies have considered these disclosures. It is
likely because they are unavailable in large, commercial databases and are difficult to
collect and quantify. I overcome the barrier by using advanced computational
techniques to provide a large-sample study. Third, I contribute to the voluntary
disclosure research by investigating an information item other than management
earnings forecasts, the most commonly used item. In contrast to management earnings
forecasts, the disclosure of R&D activities faces higher proprietary costs as well as
greater uncertainty with regard to the existence of R&D activities among market
participants. Last, this study informs standard setters. The U.S. Financial Accounting
Standards Board (FASB) has long considered a project on the disclosure of intangible
assets (FASB 2004). We have limited understanding about how mandated reporting of
certain intangible assets affects the voluntary disclosure of related information. My
results are relevant to the FASB because patents are an important intangible asset that
is not recognized in firms’ financial statements.
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CHAPTER 2 BACKGROUND AND HYPOTHESES DEVELOPMENT
2.1 The American Inventors Protection Act of 1999
Before the landmark AIPA was signed into law in 1999, the United States was
one of the few industrialized countries that did not publicly disclose information on a
patent application until the patent was granted. For granted patents, the disclosure of
information was delayed by approximately 36 months (Graham and Hegde 2014) from
the initial application date. For patents not granted, the information was never made
public. In the new disclosure regime, the AIPA requires patent applications to be
published electronically after 18 months on the UPSTO website. An application is
required to contain detailed patent information, such as the invention title, abstract,
names of inventor(s) and assignee(s), and claims and descriptions of the invention. I
provide the timeline of voluntary R&D disclosure and publication of patent information in
both the pre- and the post-AIPA periods in Appendix A.
Proponents of this law change offered two arguments. First, mandatory early
disclosure of pre-patent information could speed the diffusion of knowledge and avoid
duplications of R&D investments by multiple firms. Pushing a patent application through
the U.S. system involved a long wait, significantly delaying new technologies and
products available to the market. Companies often feared that they would lose ground
to competitors in Europe and Japan which had more streamlined patent procedures. In
the post-AIPA regime, firms can also observe whether a technology or product has
already been invented before making R&D investments. Second, proponents claimed
that early disclosure would end the practice of “submarine patents.” Before the AIPA,
the applicants of “submarine patents” were a group of people or companies who actively
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monitored the technology trends and their competitors. Based on both industry-wide
and other firms’ disclosure of R&D information, these inventors predicted the future
direction of developments in a technology or the activities of their competitors. The
applications of such patents were constantly amended and secretly kept pending until
some other inventors invested millions of dollars to develop technologies or products
with similar concepts and being granted the patents. The “submarine patent” applicants
would surface and claim to have had the original ideas and squeeze settlements out of
the inventors that made the actual developments. After the AIPA, because other
inventors can observe these patent applications, “submarine patents” become
impossible.
Opponents of the AIPA also provided two reasons against mandatory disclosure.
First, they argued that early disclosure would increase imitation, especially after small
companies filed patent applications. Although a degree of protection is provided to
inventors who file the application, larger competitors are still potentially able to take
advantage of the published information and exploit their richer resources to reap
benefits in the marketplace before the USPTO grants a patent to the original applicant
(Gao 2004). Second, the AIPA might backfire and limit the disclosure of information.
Because the patents most affected in the new regime are those on breakthrough
inventions (Gallini 2002), potential inventors could choose to keep such inventions as
trade secrets and proceed directly to production or to license the ideas without obtaining
patent protection, thus slowing down the diffusion of knowledge.
2.2 Voluntary R&D Disclosure
Financial statement numbers cannot, by themselves, effectively communicate
firms’ underlying economic conditions to outsiders (Lev and Zarowin 1999). Thus, firms
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disclose a large amount of corporate information in a narrative manner. Researchers
are increasingly exploiting advanced computational techniques to gather and analyze
these narrative corporate disclosures (Henry and Leone 2016). Voluntary R&D
disclosure is an important type of narrative disclosure in today's knowledge-based
economy as it facilitates knowledge diffusion (Baruffaldi and Simeth 2015). However,
there are both costs and benefits associated with such disclosure.
On the one hand, voluntary R&D disclosure is costly because it conveys firms’
proprietary information. Entwistle (1999) conducts a series of interviews with executives
and analysts from Canada’s leading technology-based firms. He documents that more
than half of the surveyed executives expressed concerns about competitors using the
R&D disclosure to usurp the firm’s competitive advantage. Guo, Lev, and Zhou (2004)
collect product-related disclosures in the IPO prospectuses of 49 biotech companies
and Jones (2007) obtains R&D-related information disclosed in 10-K filings of 119 firms
from four R&D-intensive industries. 1 They both document that higher proprietary costs
are associated with a lower level of R&D disclosure, consistent with the prediction that
managers limit R&D disclosures based on their concerns about imitation risk. In addition
to imitation risk, there are costs to develop and present the disclosures themselves
(Elliott and Jacobson 1994). For example, because of the importance and the
idiosyncratic nature of R&D activities, managers exert great effort in deciding the timing,
1 Guo et al. (2004) use the availability of a granted patent or patent application on developed products as a proprietary cost proxy and investigate the association between the degree of patent protection and firms’ future disclosure of product-related information. In my study, I only focus on pre-grant patent information.
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type, level of detail, and audience of the disclosure, in order to effectively communicate
the R&D message without harming the firm’s product market competitiveness.
On the other hand, voluntary R&D disclosure benefits firms in various ways.
Elliott and Jacobson (1994) suggest that early disclosures of new products and planned
products can deter competition and give the product a head start in name recognition in
the market. These disclosures also have public relation benefits because they aide
investors and creditors in forming impressions of firms’ openness and forthrightness.
James (2014) examines a sample of 322 publicly traded firms from two industries and
finds that early-stage disclosures are more likely to signal a technological advantage
and deter competitors. Similarly, the survey data in Entwistle (1999) suggests that the
most important benefits of detailed R&D narratives include helping explain the firm’s
financial performance, managing outside parties’ expectations regarding its future
performance, and communicating the importance and current level of R&D investments.
Merkley (2014) provides empirical evidence that managers provide relevant information
in R&D disclosure to explain earnings performance. Francis et al. (2008) find that
voluntary disclosures, including R&D disclosure, lower firms’ cost of capital, conditional
on firms’ earnings quality. The balance of these costs and benefits is likely to change
from the pre- to the post-AIPA period.
2.3 Hypotheses
Although theorists have modeled the relation between mandatory and voluntary
disclosures since the beginning of the 1980s (Gonedes 1980, 1990, 2001), they provide
conflicting conjectures based on different assumptions about the proprietary nature of
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the information.2 It is unclear whether these theoretical models can be used empirically
to derive policy implications (Verrecchia 1982, 2001). To overcome such limitations, I
use the passage of AIPA as an exogenous shock to a firm’s mandatory disclosure
environment and investigate how it changes the firm’s cost-benefit balance in making
voluntary R&D disclosure decisions.
For firms that apply for patents after the AIPA, the costs of voluntary R&D
disclosure should decrease. First, the proprietary costs of disclosing detailed R&D
information, which was avoidable for some firms before the AIPA, becomes unavoidable
for all firms that apply for patents because the AIPA mandates the prompt publication of
proprietary patent information. Consequently, firms’ incremental proprietary costs of
voluntary R&D disclosure should decrease. Second, when a firm made an early
disclosure of its innovation before the AIPA, it was exposed to great risk of having the
concept stolen and filed by a “submarine patent” applicant. After the AIPA, “submarine
patents” are impossible, so the lower risk of being imitated should also reduce firms’
proprietary costs of R&D disclosure. Verrecchia (1983, 1990) suggest that managers
may withhold information in the presence of proprietary costs, and the threshold level of
disclosure decreases as proprietary costs decrease. Thus, managers should be more
likely to make voluntary R&D disclosures after the AIPA.
For firms filing patent applications in the post-AIPA period, the benefits of
voluntary R&D disclosure should increase. Because of the high uncertainty associated
with R&D activities, the credibility of voluntary R&D disclosure was difficult to verify. In
2 For example, Verrecchia (1982) and Dye (1985) predict a substitutive relationship, whereas Dye (1986) and Gigler and Hemmer (1998) predict a complementary relationship.
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equilibrium, unverifiable disclosures are untruthful and hence uninformative (Crawford
and Sobel 1982). The mandatory disclosure of patent applications now offers a
verifiable outcome of firms’ R&D activities.3 The “confirmation hypothesis” in Gigler and
Hemmer (1998) and Ball, Jayaraman, and Shivakumar (2012) suggest that firms benefit
from having a credibility mechanism because it increases the usefulness of managers
truthfully disclosed information that is unverifiable ex ante. This increased benefit of
making voluntary R&D disclosure after the AIPA should make managers more willing to
disclose. Second, knowing that the patent information is to be disclosed in the near
future, firms can deter competition by signaling their technological advantages in early
R&D disclosure and gain early name recognition for their R&D projects (Elliott and
Jacobson 1994; James 2014). Firms can better capture these benefits with low risk of
harming their product market competitiveness because of managers’ great discretion in
deciding the types of information, forms (numerical versus descriptive), and content of
the disclosure.
Given the decreasing costs and increasing benefits of voluntary R&D disclosure,
I predict that after the AIPA, firms with patent applications are more likely to provide
R&D disclosures than before the AIPA.4 Because firms with more patent applications
are more likely to experience such changes in the cost-benefit balance of making
3 One of the most important procedures required by the USPTO in examining a patent application is to “review the claims and the supporting written description to determine if the applicant has asserted for the claimed invention any specific and substantial utility that is credible” (USPTO 2015).
4 Firms that voluntarily disclosed R&D information before the AIPA lose their public relation benefits in the new regime. In order to regain such benefits, managers have to invest more effort to voluntarily provide more private information, increasing the costs of disclosures. When these costs exceed the public relation benefits, managers should decrease their voluntary R&D disclosures after the AIPA. These exceptions only apply to firms that previously made R&D disclosures for public relation benefits but not pervasively to all firms.
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voluntary R&D disclosure, I also predict that firms with more patent applications are
more likely to provide R&D disclosures than firms with fewer patent applications after
the AIPA. I state my first set of hypotheses as follows:
Hypothesis 1a: Firms with patent applications increase the amount of
voluntary R&D disclosure after the AIPA than before the AIPA.
Hypothesis 1b: Firms with more patent applications are more likely to
increase the amount of voluntary R&D disclosure than firms with fewer
patent applications after the AIPA.
Because the balance between costs and benefits in making voluntary R&D
disclosure varies from firm to firm, the AIPA should not equally affect all firms. Thus, I
also consider cross-sectional variations in the consequences of this law change. First, I
examine what type of firms is most likely to experience the decrease in proprietary costs
of making voluntary R&D disclosure in the post-AIPA regime. When firms evaluate the
influence of the mandatory publication requirement of pre-grant patent information, the
costs associated with the risk of being imitated by “submarine patent” applicants are of
special concern. Consistent with this, a large number of comment letters on the AIPA
received by the USPTO suggest that the concern of “submarine patents” was more
profound for firms that invested intensively in R&D activities because they were more
likely to become the targets of “submarine patent” applicants (GAO 2004). After the
AIPA, R&D-intensive firms are exposed to significantly lower risk of losing their patents
to “submarine patent” applicants. As a result, R&D-intensive firms’ propriety costs of
voluntary R&D disclosure should decrease to a greater extent than firms with a lower
level of R&D intensity. I expect that after the AIPA, these firms are more likely to
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increase their R&D disclosures than low R&D-intensive firms. I state my second
hypothesis as follows:
Hypothesis 2: The effect of the AIPA on the amount of voluntary R&D
disclosure is stronger for R&D-intensive firms.
Second, I investigate what type of firms is least likely to experience the increase
in benefits of making voluntary R&D disclosure after the AIPA. Although voluntary R&D
disclosure and the following publication of patent applications likely convey related
information, the timing of disclosure is important. Early disclosure of detailed R&D
information helps deter competition and increase market recognition only for firms with
technological advantages. For firms without such competitive advantages, early R&D
disclosure would only backfire and expose these firms to greater imitation risk. I use a
firm’s product substitutability to proxy for its competitive advantage in the product
market.5 Karuna (2007) defines product substitutability as the extent to which close
substitutes exist for a firm’s products in an industry. Higher product substitutability
means more intense product competition. Because firms with high product
substitutability do not have technological advantages to deter competition, I expect that
after the AIPA, these firms are less likely to increase their R&D disclosures than firms
with low product substitutability. I state my third hypothesis as follows:
Hypothesis 3: The effect of the AIPA on the amount of voluntary R&D
disclosure is weaker for firms with high product substitutability.
5 Prior research suggests that it is difficult to measure a firm’s technological advantages directly and extant studies that use R&D expenditures or granted patent counts as the proxy may overlook the variations in the underlying quality of firms’ R&D activities (James 2014). I therefore focus on firms’ product substitutability.
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CHAPTER 3 EMPIRICAL ANALYSES
3.1 Sample Selection
I identify firms that make R&D investments by requiring each observation to have
at least one R&D disclosure or patent application in the current year, or cumulative R&D
expenses reported in the previous three years. To obtain firms’ R&D disclosures, I
collect R&D-related press releases from Thomson Reuters News Analytics during 1996-
2003.1 Because all press releases need to be manually downloaded, I start the sample
in 1996 and end it in 2003 to have a reasonable sample size and a balanced number of
years before and after the AIPA2. I impose the following requirements during the
downloading process. First, all press releases are released via a newswire such as PR
Newswire, Business Wire, and MarketWire. Second, all press releases are related to
firms’ R&D activities, such as research and development, new product or services, and
product improvement. Third, to ensure that the disclosures are made by firms, as
opposed to analysts or other entities, I require verbs, such as “announce”, “provide”,
and “introduce”, to appear in the headline or the first paragraph of each press release.
Fourth, I expand the dictionary of common R&D keywords and phrases in Merkley
(2014) by using “regular expression” routines from the Perl programming language to
examine a pre-collected sample of 500 R&D disclosures from firms across different
1 Merkley (2014) measures R&D disclosure quantity as the total number of R&D-related sentences in firms’ 10-K filings. Form 10-Ks are easier to process using a computerized content analysis, but may contain more boilerplate language than press releases.
2 The American Inventors Protection Act of 1999 was enacted on November 29, 1999, as part of Public Law 106-113. Kogan, Papanikolaou, Seru, and Stoffman (2017) uses 2000 as the cutoff because the final provisions of AIPA became effective on Nov. 29, 2000. I use 1999 as the cutoff because most of the provisions of the subtitles were effective as of Nov 29, 1999. In addition, the USPTO published the first batches of applications in March 2001, which is much sooner than 18-months after Nov. 29, 2000.
25
industries and years. This newly developed dictionary is then used in the search
process. All press releases are required to contain more than two keywords or phrases
from the dictionary. Lastly, I require each press release to contain more than 150 words
in the main paragraphs after excluding common stop words, titles, disclaimers, and
firms’ contact information.3 I obtain 86,803 R&D-related press releases during 1996-
2003.
For each press release, I use Perl to extract the following information: company
name, disclosure date, TICKER and contact personnel email if available. I then obtain
GVKEYs for these R&D disclosures in four steps. First, I match press releases that
have TICKERs to TICKERs from I/B/E/S and link them to GVKEYs in Compustat,
resulting in 32,182 successfully identified press releases. Second, for the unmatched
press releases, I extract domain names from the contact personnel email addresses
provided at the end of some press releases and match them to domain names from
companies’ websites in Compustat, resulting in 7,081 more identified press releases.
Third, I perform a “fuzzy match” on company names using SAS functions and identify
6,442 press releases with exact matching names. Fourth, I manually match 8,380 more
press releases with relatively high similarity indicators from the “fuzzy match.” In total, I
am able to identify 54,085 R&D-related press releases with GVKEY from 16,615 unique
firm-years.
I obtain the sample of U.S. patent applications during 1996-2003 from the NBER
database.4 I start with 1,199,374 patent-assignee pairs of granted utility patent (the
3 My procedure of colleting R&D disclosures is similar to that in Cao, Ma, Tucker, and Wan (2016).
4 NBER patent data project: https://sites.google.com/site/patentdataproject/Home. This is currently the cleanest patent data freely available to researchers (Hall, Jaffe, and Trajtenberg 2001).
26
most common type of patents) applications filed during 1996-2003 from 219,839 unique
assignee-years.5 After matching each assignee-year with GVKEY from the appropriate
period, I am able to obtain 15,678 firm-years of patent applications during the sample
period.
My initial sample contains 45,830 firm-years that each has at least one R&D
disclosure or patent application, or non-zero R&D expenditures in the previous three
years. I match them with companies in CRSP (PERMNO as the identifier) using the
CRSP/Compustat merged database. I then remove firm-years with missing data to
compute control variables. My final sample contains 26,687 observations. To address
the concern that firms may respond to the AIPA by not applying for patents after the law
change, I also construct a sample of 11,897 observations with firms that either applied
for at least one patent both before and after the AIPA (treatment group) or never applied
for patents during 1996-2003 (control group). Table 3-1 summarizes the sample
selection process.
3.2 Content Analysis
I assume that voluntary R&D disclosures and the preceding patent applications
convey similar information. I conduct a content analysis based on a randomly selected
subsample. First, I identify all patent applications in my sample that were filed in the
post-AIPA period 2000-2003 and had R&D-related press releases in the subsequent
fiscal year. In each of the four sample years, I randomly select 25 firms. Second, I use
the company name to locate a firm’s patent applications on USPTO Patent Search in a
5 A utility patent is issued for the invention of a new and useful process, machine, manufacture, or composition of matter, or a new and useful improvement thereof. It generally permits its owner to exclude others from making, using, or selling the invention for a period of up to twenty years from the date of patent application filing. About 93% of the patents recorded in NBER are utility patents.
27
given year. I am able to find all selected firms and 12 firm-years have more than one
patent application. I obtain 136 patent applications and 126 R&D-related press releases.
Third, I read each patent application and the R&D-related press releases released in the
subsequent year by the same firm together and code the content. I identify whether any
of the press releases contain patent-related information; and if so, whether this
information is directly linked to its preceding patent application. Lastly, I also note the
filing dates of the patent applications and their corresponding R&D-related press
releases.
Table 3-2 provides the findings of my content analysis. Panel A presents the
content relation between R&D-related press releases and the preceding patent
applications. In the sample of 126 selected R&D-related press releases, 81 (64.3%)
contains R&D-related information and 68 (84.0%) of these 81 press releases provide
information about the new technology or product in the corresponding patent
applications. As 54.0% of the examined press releases provide closely related
information with the preceding patent applications, the result provides some comfort in
making the assumption that managers use patent applications and R&D-related press
releases to convey similar information. Panel B presents the timing relation between
R&D-related press releases and the preceding patent applications. Among the 68 press
releases that are directly related to a preceding patent application, 23 (33.8%) are
released during the 13- to 15- month period after the initial filing of the patent
application; and 29 (42.6%) are released during the 16- to 18-month period after the
initial filing of the patent application. These results suggest that the vast majority of R&D
28
disclosures are released during the six months before the publication of patent
applications.
3.3 Empirical Design
3.3.1 Measures of Voluntary R&D Disclosures and Patent Applications
It is challenging to quantify qualitative disclosures, so I follow prior literature and
assume that the number of disclosures or the number of words in these disclosures is
proportional to the amount of information disclosed. I quantify a firm’s R&D disclosures
using the following two measures. The first variable, RD_num, is the total number of
R&D-related press releases from the firm during fiscal year t. The second variable,
RD_word, is the total number of words in all of the firm’s R&D-related press releases
during fiscal year t. I take the logarithmic transformation of RD_word to account for the
skewness of R&D disclosure quantity. Figure 3-1 presents the distributions of these two
voluntary R&D disclosure measures.
I construct two measures for each firm’s patent applications. The first measure,
Patent_dum, is an indicator variable which is 1 if the firm files at least one patent
application during fiscal year t and 0 otherwise. It is the primary measure used in testing
H1a, 2 and 3. The second measure, Patent_total, is the total number of patents the firm
applies for during fiscal year t. It is used in testing H1b.6
3.3.2 Regression Models
My H1a predicts that firms with patent applications increase the amount of
voluntary R&D disclosure after the AIPA than before the AIPA; and my H1b predicts that
6 For the purpose of robustness testing, I also use Patent_total to in testing H2 and 3. The inferences are
largely consistent with the primary analyses, so the results are untabulated.
29
firms with more patent applications are more likely to increase the amount of voluntary
R&D disclosure than firms with fewer patent applications after the AIPA. I estimate
Equation (1) to test this first set of hypotheses. Patent_dum is the independent variable
of interest in testing H1a; and Patent_total is the independent variable of interest in
testing H1b. The dependent variable is either RD_num or the logarithmic transformation
of RD_word.78
RD_numt or Ln(1+RD_word)t = a0 + a1POST + a2Patent_dumt-1 (or Patent_total t-1)
+ a3POST x Patent_dumt-1 (or POST x Patent_total t-1)
+ a4Prod_subt-1 + a5RD_investt-1 + a6Sizet-1+ a7BTMt-1
+ a8Capital_intt-1 + a9Leveraget-1 + a10Losst-1
+ a11Earn_volt-1 + a12Ret_volt-1 + a13Ext_fint-1
+ a14Analystt-1 + a15IOt-1 + a16Voluntary_dist-1
+ industry fixed effects + year fixed effects +ε) (3-1)
where POST is an indicator variable that is 1 for years after the enactment of AIPA in
1999 and 0 otherwise. Both H1a and H1b predict a3 to be positive. I do not have a
prediction for the relationship between the amount of R&D disclosure and the AIPA
because the law change only affects firms that apply for patents. The main effect
between R&D disclosure and Patent_dum or Patent_total is also ex-ante unclear
because many firms that invest in R&D do not seek patents, whereas Koh and Reeb
7 The inference does not change when I use the logarithmic transformation of RD_num as the dependent
variable.
8 I increase RD_word by one word in order to retain the observations with no R&D disclosures during the
current fiscal year.
30
(2015) find that more than ten percent of firms that do not report any information about
R&D file and receive patents.
I include factors that relate to firms’ decisions of making R&D disclosures and
filing for patents. I control for a firm’s competitive advantage in the product market by
using the product substitutability dimension, because it directly relates to a firm’s R&D
effort and patents are high quality outcomes of such effort (Vives 2008).9 Following
Karuna (2007) and Merkely (2014), Prod_sub is calculated as the sales-weighted
average of a firm’s industry gross margin at the beginning of the current fiscal year
multiplied by -1, so a higher value indicates a higher level of product substitutability.
Because prior research suggests that investment mix influences the importance of
capital and product market considerations to a firm (Kothari, Laguerre, and Leone
2002), I control for investment mix by including RD_invest, BTM, and Capital_int.
RD_invest is the intensity of a firm’s R&D investments during the previous fiscal year
because Entwistle (1999) suggests that firms with higher R&D expenditures provide
more disclosures related to their R&D activities. It is calculated as the ratio of R&D
expenses to total operating expenses. The book-to-market ratio at the beginning of the
current fiscal year, BTM, is used to control for firms’ intangible investments. It also
relates to aspects of proprietary costs because on the one hand, frims with high growth
opportunities may have a lower incentives to disclose (Bamber and Cheon 1998), but
on the other hand firms may desire to deter enter try by signaling that a particular
industry has lower opportunities (Merkley 2014). Kothari et al. (2002) find that firms with
9 Three important dimensions of competition include product substitutability, market size, and entry costs (Raith 2003). Market size and entry costs do not directly reflect firms’ R&D effort and quality in an industry.
31
more tangible investments have higher collateral in the form of physical assets whose
value is less uncertain, so firms’ capital intensity may be negatively related to the
amount of R&D disclosure. Capital_int is the total tangible assets divided by total assets
at the beginning of the current fiscal year.
Prior research finds that firm size is positively associated with firms’
transparency, so larger firms are more likely to make voluntary R&D disclosure (e.g.,
Merkley 2014; Jones 2014). Size is the logarithm of total assets at the beginning of the
current fiscal year. I also control for a firm’s leverage because Li (2010) suggests that
leverage is associated with the intensity of competition and Jones (2014) finds that
leverage is negatively associated with firms’ voluntary product disclosures. Leverage is
the ratio of long-term debt to total assets at the beginning of the current fiscal year.
Firms may use their voluntary R&D disclosures to explain the bad performance in
profitability. I control for a firm’s recent performance, Loss, which is 1 if the net income
before extraordinary items in the previous fiscal year is negative and 0 otherwise.
Earnings volatility and return volatility are used to capture information uncertainty
because extant literature provides mixed evidence regarding the effect of information
uncertainty on firms’ disclosure decisions (Lang and Lundholm 1993). Earn_vol is the
standard deviation of earnings (scaled by the average of the beginning and ending total
assets of the year) in the current and previous two years. Ret_vol is the standard
deviation of monthly stock returns during the previous fiscal year. I also control for firms’
financing incentives because firms that are in need of external financing are more likely
to make voluntary disclosures (Francis et al. 2008). Ext_fin is an indicator variable that
is 1 if the firm has a positive net equity or debt issuance at the beginning of the current
32
fiscal year and 0 otherwise. Analyst coverage and institutional ownership are used to
proxy for the influence of outside monitoring. Analysts is the number of analysts that
follow the firm in the last month of the previous fiscal year, according to the IBES
Summary data file. IO is the percentage of common shares held by institutional
investors at the beginning of the current fiscal year. I control for the general level of
voluntary disclosures because Regulation Fair Disclosure (Reg FD) in 2000 and the
Sarbanes–Oxley Act (SOX) of 2002 both fall in my sample period, which may change all
firms’ overall level of voluntary disclosures. Voluntary_dis is the number of management
earnings forecasts issued during the previous fiscal year. Finally, I include industry fixed
effects and year fixed effects to control for changes in firms’ decisions of making
voluntary R&D disclosures or applying for patents in the industry and over time. I cluster
the standard errors by firm because the standard errors within a firm might be positively
correlated even after I control for firm characteristics. Appendix B provides formal
variable definitions.
My H2 predicts that the effect of the AIPA on the amount of voluntary R&D
disclosure is stronger for R&D-intensive firms. For ease of exposition, I partition
observations in the full sample into two groups based on the level of R&D intensity.
Firms in the R&D_High group have a R&D expenditure ratio (RD_invest) greater than or
equal to the sample median, whereas firms in the R&D_Low group have a R&D
expenditure ratio less than the sample median. I remove RD_invest from Equation (1)
and estimate the modified regression separately for each group. H2 predicts that a3 is
more positive for the R&D_High group than for the R&D_Low group.
33
My H3 predicts that the effect of the AIPA on the amount of voluntary R&D
disclosure is weaker for firms with high product substitutability. Again for ease of
exposition, I partition observations in the full sample into two groups based on the level
of product substitutability. Firms in the Prod_sub_High group are with product
substitutability (Prod_sub) greater than or equal to the sample median, whereas firms in
the Prod_sub_Low group are with product substitutability less than the sample median. I
exclude Prod_sub from Equation (1) and estimate the modified regression separately
for each group. H3 predicts that a3 is less positive for the Prod_sub_High group than for
the Prod_sub_Low group.
3.4 Empirical Results
3.4.1 Descriptive Statistics
Table 3-3 provides descriptive statistics of the dependent, explanatory, and
control variables. All continuous variables are winsorized at the 1% and 99% levels. The
average number of R&D-related press releases during a firm-year is 1.17 and the
average number of words in these R&D-related press releases is 979. About 52% of the
sample observations are after the AIPA, indicating that the observations are fairly
evenly distributed between the pre- and the post-AIPA period. 40% of the firm-years
have at least one patent application and the average number of patent applications
during a firm-year is 8.13. Table 3-4 provides the Pearson and Spearman correlations
among my main variables. Both RD_num and RD_word are positively and significantly
associated with POST (Pearson correlation of 0.074 and 0.089; Spearman correlation of
0.108 and 0.116), indicating that the number of R&D-related press releases and the
total words in these press releases have increased after the AIPA.
34
3.4.2 Main Results
Table 3-5 reports the results for my analyses of H1a and H1b. Panel A presents
the results of estimating Equation (1) for testing H1a, so Patent_dum is the independent
variable. The first and second columns use RD_num and Ln(1+RD_word) as the
dependent variable, respectively, and present the results using the full sample. The third
and fourth columns also use RD_num and Ln(1+RD_word) as the dependent variable,
respectively, but present the results using the treatment-control sample. Consistent with
H1a, I find positive coefficients on POST x Patent_dum in all columns (a3=0.289, 0.329,
0.366, and 0.537; t=3.71, 3.48, 3.52, and 4.27), suggesting that firms with patent
applications increase the amount of voluntary R&D disclosure after the AIPA than
before the AIPA. The positive coefficients on POST in the third and fourth columns
(a1=0.121 and 0.110; t=2.40 and 1.68) suggest that the amount of R&D disclosure for
firms with no patent applications increases significantly after the AIPA. This could be
due to the passage of AIPA or other concurrent events, such as Reg FD and SOX. The
positive coefficient on Patent_dum in the first column (a2=0.223; t=3.51) suggests that
firms that apply for patents are more likely to provide voluntary R&D disclosures before
the AIPA.
The estimation results for the control variables are as follows. The significantly
negative associations of Prob_sub with RD_num (a4=-0.398 and -3.300; t=-8.01 and -
5.49) and Ln(1+RD_word) (a4=-1.945 and -2.632; t=-6.12 and -4.83) using both the full
and the treatment-control samples suggest that firms with high product substitutability
are less likely to voluntarily disclose their R&D activities. RD_invest is significantly
positively associated with both RD_num (a5=0.129 and 0.099; t=5.16 and 2.41) and
Ln(1+RD_word) (a5=0.332 and 0.331; t=11.51 and 7.48), consistent with Entwisle
35
(1999) that firms with higher R&D expenditures provide more voluntary R&D
disclosures. The significantly positive associations of Size with both RD_num (a6=1.788
and 3.022; t=8.57 and 5.76) and Ln(1+RD_word) (a6=2.293 and 3.345; t=8.32 and 5.91)
indicate that larger firms are more likely to signal their transparency by voluntarily
disclosing R&D activities. The correlations of BTM with RD_num (a7=-0.072; t=-2.65)
using the full sample and with Ln(1+RD_word) (a7=-0.253 and -0.236; t=-5.88 and -
3.33) using both samples are significantly negative, consistent with Merkley (2014) that
growth firms are more likely to make R&D disclosure in order to deter entry. Similarly,
the correlations of Capital_int with RD_num (a8=-0.617; t=-4.66) using the full sample
and with Ln(1+RD_word) (a7=-1.432 and -0.795; t=-7.81 and -2.68) using both samples
are significantly negative, indicating that firms with more intangible assets provide fewer
voluntary R&D disclosures. The coefficients on Leverage in both the RD_num (a9=-
0.756 and -0.853; t=-4.95 and -3.39) and Ln(1+RD_word) (a9=-1.263 and -1.232; t=-
6.31 and -3.84) models are significantly negative, consistent with Jones (2014). The
significantly positive correlation between Loss and Ln(1+RD_word) (a10=0.279 and
0.324; t=4.58 and 3.70) suggest that firms with negative profitability are more likely to
use their voluntary R&D disclosures to explain their poor financial performance. The
association of Earn_vol with RD_num (a11=0.002 and 0.002; t=4.23 and 3.43) using
both samples and with Ln(1+RD_word) (a11=0.001; t=2.52) using the treatment-control
sample are significantly positive. Ret_vol is also significantly positively associated with
Ln(1+RD_word) (a12=0.158; t=2.72) using the full sample. Both indicate that firms with
higher information uncertainty are more likely to make voluntary R&D disclosure. The
significantly positive correlation of Ext_fin with RD_num (a13=0.078 and 0.083; t=6.96
36
and 4.97) and Ln(1+RD_word) (a13=0.055 and 0.055; t=6.45 and 4.44) are consistent
with Francies et al. (2008) which also find that firm that are in need of external financing
are more likely to make voluntary disclosures. The significantly positive association
between Voluntary_dis and Ln(1+RD_word) (a16=0.035; t=3.41) using the full sample
suggests that the amount of voluntary R&D disclosure increases as the general level of
corporate voluntary disclosures increase during my sample period. I find mixed results
for the effects of outside monitoring. Analyst is significantly positively associated with
both RD_num (a14=0.823; t=4.26) and Ln(1+RD_word) (a14=1.787; t=6.81) using the full
sample, but significantly negatively associated with RD_num (a14=-0.526; t=-2.28) using
the treatment-control sample. IO is significantly negatively associated with both
RD_num (a15=-0.589; t=-3.76) and Ln(1+RD_word) (a15=-0.475; t=-2.31) using the full
sample, but significantly positively associated with Ln(1+RD_word) (a15=1.542; t=3.49)
using the treatment-control sample.
Panel B presents the results of estimating Equation (1) for testing H1b, so
Patent_total is the independent variable. Again, the first and second columns use
RD_num and Ln(1+RD_word) as the dependent variable, respectively, and present the
results using the full sample. The third and fourth columns also use RD_num and
Ln(1+RD_word) as the dependent variable, respectively, but present the results using
the treatment-control sample. Consistent with H1b, I find positive coefficients on POST
x Patent_total in all columns (a3=0.005, 0.008, 0.004, and 0.002; t=2.23, 3.85, 2.05 and
2.14), suggesting that firms with more patent applications are more likely to increase the
amount of voluntary R&D disclosure than firms with fewer patent applications after the
37
AIPA. The estimated coefficients on POST, Patent_dum, and control variables are
largely consistent with those in Panel A.
Table 3-6 reports the results for my analyses of H2. For brevity, I only present the
results of using Ln(1+RD_word) as the dependent variable and Patent_dum as the
independent variable. The inferences are consistent when using RD_num as the
dependent variable and Patent_total as the independent variable. The first column
presents the estimation results for the R&D_High group, whereas the second column
presents the estimation results for the R&D_Low group. The coefficient on POST x
Patent_dum for the R&D_High group (a3=0.412, t=3.15) is significantly positive,
whereas the coefficient on POST x Patent_dum for the R&D_Low group is insignificant.
I use the seemingly unrelated regression techniques to compare these two coefficients.
The significant difference between the two groups (p=0.063) is consistent with my
prediction in H2 that the effect of the AIPA on the amount of voluntary R&D disclosure is
stronger for R&D-intensive firms. The estimated coefficients of controls variables in
Table 3-6 are mostly consistent with those in Table 3-5.
Table 3-7 reports the results for my analyses of H3. I again only present the
results using Ln(1+RD_word) as the dependent variable and Patent_dum as the
independent variable. The first column presents the results for the Prod_sub_High
group, whereas the second column presents the results for the Prod_sub_Low group.
The coefficient on POST x Patent_dum for the Prod_sub_High group is insignificant,
whereas the coefficient on POST x Patent_dum for the Prod_sub_Low group is
significant and positive (a3=0.530, t=4.18). The seemingly unrelated regression test
suggests that there is a significant difference between the two groups (p=0.002),
38
consistent with my prediction in H3 that the effect of the AIPA on the amount of
voluntary R&D disclosure is weaker for firms with high product substitutability. The
estimated coefficients of controls variables in Table 3-7 are also mostly consistent with
those in Table 3-5.
3.4.3 Supplementary Analyses
To mitigate the concern that the increase in voluntary R&D disclosures for firms
with patent applications after the AIPA is driven by an increasing, secular trend of firms’
R&D disclosures over time, I perform placebo tests by using 1998 and 2002 as
“pseudo-event” years. Table 3-8 reports the estimation results. Column 1 presents my
primary findings with Ln(1+RD_num) as the dependent variable. I investigate whether
the positive coefficient on the interaction term POSTxPatent_dum (a3=0.329; t=3.48) is
significant when using “pseudo-event” years. Column 2 presents the results when 1996-
1997 is the pre-event period and 1998-1999 is the post-event period. The coefficient on
POSTxPatent_dum is insignificant. Column 3 presents the results when 2000-2001 is
the pre-event period and 2002-2003 is the post-event period. The coefficient on
POSTxPatent_dum is very weakly significant. These results suggest that my primary
finding is unlikely to be due to an upward trend in firms’ R&D disclosures over time.
To address the potential concern of unobservable correlated variables with firms’
decisions of making voluntary R&D disclosures and applying for patents, I also estimate
Equation (2) to test H1, a changes specification in which time-invariant unobservable
variables cancel out:
ChRD_word%t = b0 + b1POST + b2Patent_dumt-1 (or ChPatent_total t-1)
+ b3POST x Patent_dumt-1 (or POST x ChPatent_total t-1)
+ b4+ChProd_subt-1 + b5ChRD_investt-1 + b6ChSizet-1 + b7ChBTMt-1
39
+ b8ChCapital_intt-1 + b9ChLeveraget-1 + b10ChLosst-1
+ b11ChEarn_volt-1 + b12ChRet_volt-1 + b13ChExt_fint-1
+ b14ChAnalystt-1 + b15ChIOt-1 + b16ChVoluntary_dist-1
+ industry fixed effects + year fixed effects +ε) (3-2)
where all variables are calculated as the change from the prior year to the current,
except the dependent variable ChRD_word% is the percentage change in RD_word
from the prior year. If RD_word during the previous fiscal year is 0, it is set to be 1 in
order to retain the observation. Table 3-9 presents the results of estimating Equation
(2). After the impact of time-invariant unobservable variables is eliminated, the
coefficients on POST x Patent_dum (b3=0.329 and 23.425, t=2.82 and 2.94) are still
positive and significant. The results strongly support my primary finding that firms with
patent applications increase the amount of voluntary R&D disclosure after the AIPA
than before the AIPA.
To investigate whether the AIPA also affects firms’ behavior in making patent
applications, I conduct a content analysis of 40 pairs of patent applications before and
after the AIPA. First, I randomly select 10 pairs of patent applications from each of the
following four groups: Group 1 includes firms that make voluntary R&D disclosure both
before and after the AIPA; Group 2 includes firms that make voluntary R&D disclosure
only before the AIPA; Group 3 includes firms that make voluntary R&D disclosure only
after the AIPA; and Group 4 includes firms that never make voluntary R&D disclosure.
Second, I evaluate each pair of patent applications based on the following aspects:
number of sections, number of figures/tables, and level of detail. Because patent
applications are usually long with multiple sections. I evaluate the level of detail for four
40
major sections: background of the invention, brief summary of the invention, brief
description of the drawings/figures, and detailed description of the invention. If the
evaluated aspect is similar between the two patent applications, the pair gains one
point. The maximum possible score is 6.
Table 3-10, Panel A describes the details of the comparison aspects, and Panel
B presents comparison score of each pair in the four groups. Third, I conduct both a
one-way ANOVA test to compare all groups together and two-tailed t-tests between
every two groups. Panel C presents the results. Only firms that make voluntary R&D
disclosure after the AIPA but not before the AIPA change their behavior in making
patent applications, comparing to firms that never make any voluntary R&D disclosure.
However, the difference is weakly significant. This result provides some comfort in
making the assumption that firms do not change their patent application behavior
dramatically after the AIPA.
41
Table 3-1. Sample selection
Panel A: Voluntary R&D disclosures
Number of R&D disclosures in PR Newswire, Business Wire, and MarketWire during 1996-2003
86,803
Matching procedure
Number of R&D disclosures (TICKER disclosed in the press release as the identifier) with matching TICKER from I/B/E/S and with GVKEY in Compustat
32,182
More R&D disclosures through hand match to obtain GVKEY:
Number of R&D disclosures (domain name from the contact personnel email address as the identifier) with exact matching domain name (web) from the COMPANY table and with GVKEY in Compustat
+7,081
Number of R&D disclosures (applicant name as the identifier) with exact matching company name (conm) from the COMPANY table and with GVKEY in Compustat
+6,442
Number of R&D disclosures manually matched by company name and with GVKEY in Compustat
+8,380
Total number of R&D disclosures with GVKEY in Compustat during 1996-2003
54,085
Number of firm-years with R&D disclosures and GVKEY during 1996-2003
16,615
Panel B: Patent data
Number of patent/assignee pairs for all granted utility patents* applied during 1996-2003
1,199,374
Number of assignee(PDPASS) -YEARs during 1996-2003 (In a given year, one assignee can have multiple patent applications and one patent can have multiple assignees.)
219,839
Number of PDPASS-YEARs with matching PDPASS from the DYNASS file (The DYNASS file contains 13,458 unique assignees, but each assignee matches with up to five GVKEYs during different periods)
27,264
Number of PDPASS-YEARs with matching GVKEY from the appropriate period (One GVKEY may correspond with multiple PDPASSs)
21,937
Number of unique GVKEY-YEARs during 1996-2003 15,678
42
Table 3-1. Continued
Panel C: Final sample
Firm-years with signs of R&D activities:
Number of firm-years with R&D disclosures and with no missing GVKEY during 1996-2003 (See Panel A)
16,615
Number of GVKEY-YEARs with patent applications during 1996-2003 (See Panel B)
15,678
Number of firm-years with non-zero cumulative R&D expenses reported in the previous three years and with GVKEY in Compustat during 1993-2000
39,719
Total number of firm-years with R&D activities (the union of the three numbers above) during 1996-2003
45,830
Less observations with no matching PERMNO in the CRSP/COMPUSTAT merged database
(5,135)
Less observations with missing data to compute control variables
(14,008)
Final sample 26,687
Less observations that applied for patents in either the pre- or the post-AIPA period, but not both
(14,790)
Treatment-control sample (The treatment group includes firms that applied for at least one patent both before and after the AIPA; and the control group includes firms that never applied for patents during 1996-2003.)
11,897
Note: *A utility patent is issued for the invention of a new and useful process, machine, manufacture, or composition of matter, or a new and useful improvement thereof. It generally permits its owner to exclude others from making, using, or selling the invention for a period of up to twenty years from the date of patent application filing. About 93% of the patents recorded in NBER are utility patents. Information about other patents can be found on the USPTO’s website.
43
Table 3-2. Content analysis of R&D-related press releases and the preceding patent applications Panel A: Content relation between R&D-related press releases and the preceding patent applications
Panel B: Timing relation between R&D-related press releases and the preceding patent applications
0-3 months 4-6 months 7-9 months 10-12 months 13-15 months 16-18 months Total
1 4 4 7 23 29 68
Note: This table presents a post-AIPA content analysis of 136 randomly selected patent applications after the AIPA and 126 R&D-related press releases issued by these patent filing firms during the fiscal year immediately after the patent application year. Panel A and Panel B describe the content relation and timing relation, respectively, between the R&D-related press releases and their corresponding patent application
Total number of R&D-related press releases
examined
126
Patent-related information
81
Directly related to a preceding patent
applicaiton
68
Not Directly related to a preceding patent
applicaiton
13
Non-patent-related information
45
44
Table 3-3. Descriptive statistics
Variable N Mean S.D. Min 0.25 Median 0.75 Max
RD_num 26,687 1.17 2.73 0.00 0.00 0.00 1.00 21.00
RD_word 26,687 979 2476.43 0.00 0.00 0.00 783 19952
POST 26,687 0.52 0.50 0.00 0.00 1.00 1.00 1.00
Patent_dum 26,687 0.40 0.49 0.00 0.00 0.00 1.00 1.00
Patent_total 26,687 8.13 32.05 0.00 0.00 0.00 2.00 290.00
Prod_sub 26,687 -0.42 0.17 -0.74 -0.52 -0.37 -0.28 -0.07
RD_invst 26,687 0.11 0.19 0.00 0.00 0.03 0.14 0.92
Size 26,687 5.12 2.12 0.93 3.55 4.88 6.51 11.30
BTM 26,687 0.63 0.62 0.02 0.24 0.45 0.79 6.16
Capital_int 26,687 0.39 0.24 0.01 0.17 0.37 0.55 0.92
Leverage 26,687 0.14 0.16 0.00 0.00 0.07 0.24 0.71
Loss 26,687 0.39 0.49 0.00 0.00 0.00 1.00 1.00
Earn_vol 26,687 0.13 1.16 0.00 0.01 0.06 0.09 1.41
Ret_vol 26,687 0.18 0.12 0.03 0.10 0.16 0.23 0.86
Ext_fin 26,687 0.82 0.36 0.00 1.00 1.00 1.00 1.00
Analyst 26,687 3.17 7.28 0.00 0.00 2.00 7.00 33.00
IO 26,687 0.04 0.15 0.00 0.00 0.00 0.00 0.96
Voluntary_dis 26,687 1.32 2.98 0.00 0.00 0.00 1.00 24.00 Note: All variables are defined in Appendix B.
45
Table 3-4. Pairwise correlations
RD_
num
RD_
word POST
Patent
_dum
Patent
_total
Prod
_sub
RD_
invest Size BTM
Capital
_inv
Lev-
erage Loss
Earn
_vol
Ret
_vol
Ext
_fin
Ana-
lyst IO
Voluntary
_dis
RD_num 0.996 0.108 0.096 0.132 -0.213 0.229 0.161 -0.190 -0.312
-
0.129 0.076 0.256 0.143 0.051 0.255 0.026 0.155
RD_word 0.975 0.116 0.096 0.131 -0.218 0.235 0.160 -0.189 -0.317
-
0.133 0.081 0.258 0.151 0.052 0.254 0.030 0.158
POST 0.074 0.089 -0.009 -0.004 -0.059 0.049 0.122 0.106 -0.114
-
0.021 0.094 0.194 0.235 -0.033 0.033 0.323 0.319
Patent_du
m 0.145 0.145 -0.009 0.958 -0.114 0.269 0.259 -0.166 -0.051 0.020 -0.069 0.260 -0.104 0.013 0.273 0.004 0.127
Patent_tot
al 0.304 0.294 0.014 0.309 -0.110 0.282 0.334 -0.190 -0.048 0.037 -0.093 0.334 -0.126 0.005 0.341 0.006 0.165
Prod_sub -0.208 -0.214 -0.068 -0.102 -0.022 -0.544 0.183 0.246 0.403 0.287 -0.179 0.015 -0.246 -0.056 0.004 -0.052 0.007
RD_invst 0.174 0.187 0.045 0.192 0.030 -0.437 -0.287 -0.285 -0.459
-
0.392 0.334 -0.030 0.359 0.095 -0.048 0.063 -0.037
Size 0.203 0.191 0.121 0.255 0.382 0.168 -0.237 -0.007 0.137 0.388 -0.369 0.802 -0.424 -0.055 0.752 0.078 0.415
BTM -0.136 -0.131 0.154 -0.165 -0.107 0.185 -0.182 -0.056 0.227 0.091 0.013 -0.127 -0.078 -0.140 -0.221 0.093 -0.072
Capital_int -0.236 -0.237 -0.108 -0.072 -0.021 0.409 -0.417 0.115 0.173 0.386 -0.219 -0.046 -0.289 -0.056 -0.008 -0.081 -0.013
Leverage -0.100 -0.103 -0.003 -0.011 0.014 0.232 -0.245 0.325 0.061 0.320 -0.124 0.220 -0.260 0.046 0.144 -0.015 0.072
Loss 0.032 0.038 0.094 -0.069 -0.097 -0.185 0.390 -0.358 0.116 -0.210
-
0.066 -0.070 0.449 0.071 -0.270 0.058 -0.178
Earn_vol 0.259 0.254 0.118 0.146 0.476 0.001 -0.044 0.525 -0.053 -0.027 0.082 -0.045 -0.150 -0.025 0.664 0.133 0.372
Ret_vol 0.092 0.104 0.248 -0.090 -0.104 -0.212 0.285 -0.369 0.001 -0.254
-
0.176 0.408 -0.089 0.129 -0.252 0.006 -0.039
Ext_fin 0.038 0.041 -0.033 0.013 -0.023 -0.054 0.108 -0.055 -0.131 -0.052 0.046 0.071 -0.032 0.113 0.018 -0.017 -0.004
Analyst 0.303 0.293 0.021 0.231 0.449 0.003 -0.069 0.745 -0.208 0.020 0.097 -0.248 0.541 -0.224 -0.006 0.042 0.433
IO 0.019 0.023 0.272 0.041 0.020 -0.034 0.025 0.159 0.022 -0.064 0.022 0.003 0.115 -0.043 -0.018 0.093 0.193
Voluntary
_dis 0.125 0.131 0.325 0.079 0.158 -0.004 -0.069 0.317 -0.074 -0.019 0.034 -0.127 0.199 -0.034 -0.009 0.281 0.315
Note: Spearman (Pearson) correlations are presented in the top (bottom) triangle. Correlations that are statistically significant at the 5% level are in bold. All variables are defined in Appendix B.
46
Table 3-5. Interaction effect between the AIPA and patent applications on voluntary R&D disclosure Panel A: Hypothesis 1a
Full sample Treatment-control sample
RD_num Ln(1+RD_word) RD_num Ln(1+RD_word)
Constant -1.075*** -0.276 -1.172* -0.451
(-3.34) (-0.35) (-1.87) (-0.27)
POST 0.023 -0.135 0.121** 0.110*
(0.37) (-1.16) (2.40) (1.68)
Patent_dum 0.223*** -0.091 0.173 -0.090
(3.51) (-1.11) (1.52) (-0.57)
POSTxPatent_dum 0.289*** 0.329*** 0.366*** 0.537***
(3.71) (3.48) (3.52) (4.27)
Prod_sub -2.398*** -1.945*** -3.300*** -2.632***
(-8.01) (-6.12) (-5.49) (-4.83)
RD_invest 0.129*** 0.332*** 0.099** 0.331***
(5.16) (11.51) (2.41) (7.48)
Size 1.788*** 2.293*** 3.022*** 3.345***
(8.57) (8.32) (5.76) (5.91)
BTM -0.072*** -0.253*** 0.022 -0.236***
(-2.65) (-5.88) (0.42) (-3.33)
Capital_int -0.617*** -1.432*** -0.169 -0.795***
(-4.66) (-7.81) (-0.67) (-2.68)
Leverage -0.756*** -1.263*** -0.853*** -1.232***
(-4.95) (-6.31) (-3.39) (-3.84)
Loss 0.001 0.279*** -0.060 0.324***
(0.02) (4.58) (-0.80) (3.70)
Earn_vol 0.002*** 0.000 0.002*** 0.001**
(4.23) (1.63) (3.43) (2.52)
Ret_vol 0.078* 0.158*** 0.005 0.131*
(1.67) (2.72) (0.08) (1.66)
Ext_fin 0.078*** 0.055*** 0.083*** 0.055***
(6.96) (6.45) (4.97) (4.44)
Analyst 0.823*** 1.787*** -0.526** -0.304
(4.26) (6.81) (-2.28) (-1.25)
IO -0.589*** -0.475** 0.458 1.542***
(-3.76) (-2.31) (1.41) (3.49)
Voluntary_Dis 0.019* 0.035*** 0.031* 0.024*
(1.85) (3.41) (1.87) (1.81)
Industry fixed effects YES YES YES YES
Year fixed effects YES YES YES YES
Observations 26,687 26,687 11,897 11,897
Adjusted R2 26.1% 27.3% 31.8% 30.1%
Note: Panel A reports the regression estimations of the interaction effect between POST and Patent_dum on the amount of R&D disclosure (RD_num or Ln(1+RD_word)), using both the full sample and the treatment-control sample. All variables are defined in Appendix B. t–statistics are in parentheses. The standard errors are clustered by firm. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
47
Table 3-5. Continued Panel B: Hypothesis 1b
Full sample Treatment-control sample
RD_num Ln(1+RD_word) RD_num Ln(1+RD_word)
Constant -1.056*** -0.233 -1.144** -0.494
(-3.20) (-0.37) (-1.97) (-0.29)
POST 0.124 -0.023 0.243** 0.241
(1.21) (-0.21) (2.21) (1.02)
Patent_total 0.015*** 0.018*** 0.015*** 0.012***
(4.97) (4.72) (4.11) (3.79)
POSTxPatent_total 0.005** 0.008*** 0.004** 0.002**
(2.23) (3.85) (2.05) (2.14)
Prod_sub -2.298*** -1.889*** -3.215*** -2.580***
(-7.65) (-5.94) (-5.27) (-4.70)
RD_invest 0.135*** 0.321*** 0.102** 0.324***
(5.25) (11.27) (2.36) (7.54)
Size 1.872*** 2.276*** 3.070*** 3.344***
(8.73) (8.25) (5.52) (5.78)
BTM -0.088*** -0.261*** 0.000 -0.256***
(-3.35) (-6.04) (0.00) (-3.63)
Capital_int -0.614*** -1.437*** -0.153 -0.809***
(-4.71) (-7.88) (-0.63) (-2.74)
Leverage -0.713*** -1.205*** -0.809*** -1.157***
(-4.64) (-6.00) (-3.23) (-3.61)
Loss 0.007 0.284*** -0.048 0.332***
(0.15) (4.67) (-0.64) (3.79)
Earn_vol 0.001* -0.000 0.001 0.000
(1.70) (-0.67) (1.12) (0.63)
Ret_vol 0.095** 0.170*** 0.018 0.143*
(2.10) (2.96) (0.27) (1.83)
Ext_fin 0.062*** 0.044*** 0.059*** 0.038***
(6.07) (5.30) (3.97) (3.15)
Analyst 0.892*** 1.827*** -0.141 0.024
(4.66) (6.94) (-0.66) (0.10)
IO -0.359** -0.301 0.469 1.540***
(-2.42) (-1.47) (1.47) (3.48)
Voluntary_Dis 0.013 0.033*** 0.028 0.024*
(1.24) (3.19) (1.63) (1.83)
Industry fixed effects YES YES YES YES
Year fixed effects YES YES YES YES
Observations 26,687 26,687 11,897 11,897
Adjusted R2 27.8% 27.7% 33.9% 30.6%
Note: Panel B reports the regression estimations of the interaction effect between POST and Patent_total on the amount of R&D disclosure (RD_num or Ln(1+RD_word)), using both the full sample and the treatment-control sample. All variables are defined in Appendix B. t–statistics are in parentheses. The standard errors are clustered by firm. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
48
Table 3-6. High R&D-intensive firms vs. Low R&D-intensive firms
R&D_High group R&D_Low group
Constant -0.211 0.630***
(-0.93) (3.72)
POST 1.196* 0.296***
(1.98) (4.33)
Patent_dum 0.192** 1.232***
(1.99) (15.79)
POSTxPatent_dum 0.412*** 0.088
(3.15) (0.77)
Prod_sub -2.003*** -0.707***
(-8.98) (-3.51)
Size 0.420*** 0.409***
(13.25) (18.24)
BTM -0.383*** -0.320***
(-5.69) (-7.71)
Capital_int -2.816*** -2.977***
(-14.36) (-25.95)
Leverage -2.457*** -0.916***
(-9.07) (-5.54)
Loss 0.541*** 0.403***
(7.11) (6.07)
Earn_vol 0.000 0.001***
(0.89) (4.77)
Ret_vol 3.599*** 1.854***
(11.83) (5.81)
Ext_fin 0.277*** 0.159**
(2.78) (2.36)
Analyst 0.096*** 0.016***
(10.61) (2.61)
IO -1.097*** -0.868***
(-4.83) (-4.73)
Voluntary_Dis 0.054*** 0.005
(4.06) (0.51)
Industry fixed effects YES YES
Year fixed effects YES YES
Observations 13,349 13,338
Adjusted R2 19.1% 18.8%
SUR test for difference (p-value)
POSTxPatent_dum(RD_High)=
POSTxPatent_dum(RD_Low) 0.063**
Note: This table reports the regression estimations of the interaction effect between POST and Patent_dum on Ln(1+RD_word) for the R&D_High group (RD_invest ≥ median) and the R&D_Low group (RD_invest < median). All variables are defined in Appendix B. t–statistics are in parentheses. The coefficients are compared using the seemingly unrelated regression test. The standard errors are clustered by firm. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
49
Table 3-7. Firms with high product substitutability vs. Firms with low product substitutability
Prod_sub_High group Prod_sub _Low group
Constant 0.777*** 0.826***
(4.97) (4.53)
POST 0.161** -0.018
(2.11) (-0.19)
Patent_dum 0.297*** 0.521***
(3.66) (5.60)
POSTxPatent_dum -0.021 0.530***
(-0.18) (4.18)
RD_invest 2.611*** 1.262***
(8.79) (7.08)
Size 0.345*** 0.484***
(14.07) (17.08)
BTM -0.280*** -0.362***
(-6.25) (-5.75)
Capital_int -2.861*** -3.488***
(-22.01) (-20.57)
Leverage -1.804*** -1.832***
(-9.47) (-8.01)
Loss 0.429*** 0.400***
(6.17) (5.05)
Earn_vol 0.001*** 0.000*
(5.10) (1.84)
Ret_vol 2.772*** 3.430***
(8.93) (11.16)
Ext_fin 0.240*** 0.260***
(3.26) (2.79)
Analyst 0.033*** 0.057***
(4.58) (7.08)
IO -0.899*** -0.992***
(-4.54) (-4.55)
Voluntary_Dis 0.041*** 0.024**
(3.87) (1.99)
Industry fixed effects YES YES
Year fixed effects YES YES
Observations 13,350 13,337
Adjusted R2 16.6% 19.3%
SUR test for difference (p-value)
POSTxPatent_dum(Prod_sub_High)=
POSTxPatent_dum(Prod_sub_Low) 0.002***
Note: This table reports the regression estimations of the interaction effect between POST and Patent_dum on Ln(1+RD_word) for the Prod_sub_High group (Prod_sub ≥ median) and the Prod_sub _Low group (Prod_sub ≥ median). The coefficients are compared using the seemingly unrelated regression test. All variables are defined in Appendix B. t–statistics are in parentheses. The standard errors are clustered by firm. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
50
Table 3-8. Placebo tests
POST2000 POST1998 POST2002
Constant -0.276 -0.215* 0.018
(-0.35) (-1.84) (0.16)
POST -0.135 -0.023 -0.009
(-1.16) (-1.10) (-1.09)
Patent_dum -0.091 -0.003* -0.050
(-1.11) (1.98) (-0.82)
POSTxPatent_dum 0.329*** 0.000 0.101*
(3.48) (0.82) (1.79)
Prod_sub -1.945*** -0.534*** -0.190***
(-6.12) (-7.63) (-4.63)
RD_invest 0.332*** 0.054*** 0.042***
(11.51) (8.64) (11.33)
Size 2.293*** 0.514*** 0.258***
(8.32) (9.08) (7.23)
BTM -0.253*** -0.042*** -0.036***
(-5.88) (-5.40) (-5.97)
Capital_int -1.432*** -0.238*** -0.190***
(-7.81) (-6.90) (-7.71)
Leverage -1.263*** -0.231*** -0.152***
(-6.31) (-5.81) (-5.62)
Loss 0.279*** 0.033*** 0.042***
(4.58) (2.68) (5.13)
Earn_vol 0.000 0.000 -0.000*
(1.63) (1.14) (-1.80)
Ret_vol 0.158*** 0.033*** 0.021***
(2.72) (2.98) (2.72)
Ext_fin 0.055*** 0.013*** 0.004***
(6.45) (6.48) (4.16)
Analyst 1.787*** 0.081** -0.031
(6.81) (2.06) (-1.14)
IO -0.475** -0.335*** -0.221***
(-2.31) (-6.60) (-6.29)
Voluntary_Dis 0.035*** 0.005** 0.005***
(3.41) (2.04) (3.41)
Industry fixed effects YES YES YES
Year fixed effects YES YES YES
Observations 26,687 12,810 13,877
Adjusted R2 27.3% 26.9% 27.2%
Note: This table reports the placebo test results on the associations of the interaction effect between POST and Patent_dum on Ln(1+RD_word). Column 1 presents the original results when POST is defined using the real 2000 event year. Column 2 presents results using a sample from 1996 to 1999 and 1998 is the “pseudo-event” year. Column 3 presents results using a sample from 2000 to 2003 and 2002 is the “pseudo-event” year. All variables are defined in Appendix B. t–statistics are in parentheses. The standard errors are clustered by firm. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
51
Table 3-9. Changes tests
ChRD_num ChRD_word%
Constant -0.230*** -45.865
(-3.64) (-1.06)
POST 1.236* 81.293***
(1.93) (2.94)
Patent_dum 0.106 31.245**
(1.54) (2.15)
POSTxPatent_dum 0.329*** 23.425***
(2.82) (2.94)
ChProd_sub 0.014 5.040
(1.20) (0.87)
ChRD_invest 0.017 36.705
(0.17) (1.03)
ChSize 0.113* 47.124**
(1.68) (2.20)
ChBTM -0.035** -10.289
(-2.03) (-0.99)
ChCapital_int 0.174 -62.490
(0.68) (-0.78)
ChLeverage 0.006 30.267
(0.03) (0.48)
ChLoss 0.015 -13.011
(0.31) (-1.24)
ChEarn_vol 0.000 -0.003
(1.03) (-0.36)
ChRet_vol 0.139 21.756
(0.96) (0.47)
ChExt_fin 0.025 -15.975
(0.59) (-1.31)
ChAnalyst 0.034*** 0.773
(2.93) (0.27)
ChIO -0.848** -210.020
(-2.10) (-0.90)
ChVoluntary_Dis 0.029 3.458*
(1.40) (1.67)
Industry fixed effects YES YES
Year fixed effects YES YES
Observations 20,014 20,014
Adjusted R2 1.7% 2.7% Note: This table reports the regression estimations of the interaction effect between POST and Patent_dum on ChRD_word% in changes tests. All control variables are calculated as the change from the prior year to the curren tyear, except the dependent variable ChRD_word% is the percentage change in RD_word from the prior year. If RD_word during fiscal year t-1 is 0, it is set to be 1 in order to retain the observation. The standard errors are clustered by firm. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.
52
Table 3-10. Content analysis of patent applications
Panel A: Aspects of comparison Possible points (Max=6) 1 0 1. Number of sections Diff ≤ 3 Diff > 3 2. Number of figures/tables Diff ≤ 3 Diff > 3 3. Level of detail - Background of the invention
Similar
Not similar - Brief summary of the invention
- Brief description of the drawings/figures
- Detailed description of the invention Panel B: Comparison scores of patent applications before and after the APIA
With voluntary R&D disclosure before (Y or N) and after (Y or N) the AIPA Pair No. Group 1(Y/Y) Group 2(Y/N) Group 3(N/Y) Group 4(N/N) 1. 4 5 4 6 2. 4 3 4 6 3. 4 3 4 6 4. 3 5 4 6 5. 6 6 4 4 6. 5 6 5 5 7. 4 5 5 4 8. 4 4 3 2 9. 3 4 4 6 10. 6 4 2 4 Mean 4.30 4.50 3.90 4.90
Panel C: One-way ANONA and Two-tailed t-test results
One-way ANOVA test
F=1.405; p=0.257
Two-tailed t-tests
Group 1 Group 2 Group 3
Group 2 0.515 Group 3 0.802 1.765 Group 4 0.919 0.667 2.372*
Note: This table presents a content analysis of 10 randomly selected pairs of patent applications before and after the AIPA from each of the following four groups: Group 1 includes firms that make voluntary R&D disclosure both before and after the AIPA; Group 2 includes firms that make voluntary R&D disclosure only before the AIPA; Group 3 includes firms that make voluntary R&D disclosure only after the AIPA; and Group 4 includes firms that never make voluntary R&D disclosure. Panel A describes the comparison aspects for the two patent applications in each pair. If the evaluated aspect is similar between the two patent applications, the pair gains one point. The maximum possible score is 6. Panel B presents comparison score of each pair in the four groups. Panel C presents the one-way ANOVA and two-tailed t-test results between the four groups.
53
A
B
Figure 3-1. Distributions of voluntary R&D disclosure measures. A plots the distribution
of RD_num, the number of R&D-related press releases in a firm-year. B plots the distribution of RD_word, the total number of words in R&D-related press releases in a firm-year. The width of a bar is 2,000 words.
54
CHAPTER 4 CONCLUSIONS
In this study, I examine the impact of mandatory disclosure regulation on
voluntary disclosure. Specifically, I use the AIPA, a mandatory disclosure requirement of
pre-grant patent information, to investigate how this exogenous patent law change
affects firms’ voluntary R&D disclosure decisions. I first conduct a content analysis to
validate the assumption that voluntary R&D-related press releases and their preceding
patent applications convey similar proprietary information. I then use a cost-benefit
framework to examine after the AIPA, how firms change the amount of voluntary R&D
disclosure during the period between the initial patent application date and its
publication date.
I find robust evidence that after the AIPA, firms with patent applications
substantially increase their voluntary R&D disclosures and firms with more patent
applications are more likely to increase their voluntary R&D disclosures after the AIPA
than before the AIPA. I further document cross-sectional variations in the effect of AIPA
on voluntary R&D disclosure. Specifically, I find that this effect is more pronounced for
R&D-intensive firms, whose costs associated with imitation concerns decrease
significantly after the AIPA; and less pronounced for firms with high product
substitutability, whose benefits of disclosing early do not increase after the AIPA. Taken
together, these results suggest that there is a complementary relationship between
mandatory and voluntary disclosures when they convey similar proprietary information.
Although my study focuses on one particular patent law change and disclosure of
only R&D activities, I expect my results to be informative to accounting standard setters
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who are evaluating the costs and benefits of mandating reporting of certain intangible
assets and to generalize to disclosures of other proprietary information.
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APPENDIX A VOLUNTARY R&D DISCLOSURE AND PUBLICATION OF PATENT INFORMATION
TIMELINE
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Figure A-1. Timeline of voluntary R&D disclosure and publication of patent information. In the pre-AIPA period, the
information contained in a patent application was made public only when the patent was granted. In the post-AIPA period, a patent application is published on the website of USPTO 18 months after the initial application date.
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APPENDIX B VARIABLE DEFINITIONS
Dependent variables
RD_num = number of R&D-related press releases during fiscal year t. RD_word = number of words in all R&D-related press releases during fiscal
year t.
Independent variables
POST = 1 for fiscal years ending after December 1999. Patent_dum = 1 if there is at least one patent application during fiscal year t-1 and
0 otherwise. Patent_total = the number of patent applications during fiscal year t-1. Prod_sub = sales-weighted average of the gross margin multiplied by -1 in
fiscal year t-1. RD_invest = ratio of R&D expenses to total operating expenses in fiscal year t-
1. Size = logarithm of total assets at the beginning of fiscal year t. BTM = ratio of book-to-market value of equity at the beginning of fiscal
year t. Capital_int = ratio of tangible assets (PP&E and inventories) to total asset at the
beginning of fiscal year t. Leverage = ratio of total liabilities to total assets at the beginning of fiscal year
t. Loss = 1 if net income before extraordinary items is negative in fiscal year t-
1 and 0 otherwise. Earn_vol = standard deviation of earnings before extraordinary items (deflated
by average total assets of the year) for fiscal year t and the previous 2 years.
Ret_vol = standard deviations of monthly stock returns during fiscal year t-1. Ext_fin = 1 if there is a positive net equity or debt issuance in fiscal year t-1
and 0 otherwise. Analyst = number of analysts whose earnings forecasts for fiscal year t-1 are
included in the last IBES consensus (summary data file) before the earnings announcement for year t-1.
IO = percentage of common shares held by institutional investors at the beginning of fiscal year t.
Voluntary_dis = number of management earnings forecasts issued during fiscal year t-1.
Figure B-1. Description of main variables
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BIOGRAPHICAL SKETCH
Xi Wang’s major was Business Administration – Accounting. She worked as a
research and teaching assistant in the Department of Accounting, Warrington College of
Business. She graduated with the degree of Doctor of Philosophy in the summer of
2017. She received a Bachelor of Business Administration degree with double
concentrations in accounting (CPA track) and finance from Goizueta Business School of
Emory University in 2012. She also completed a major in applied mathematics and a
minor in Japanese language at Emory University. Her research interests include
financial reporting and disclosure, corporate governance, and diversity.