1. hobbies and interests
2. inventors and patents

Standards, IPR, and Inventive Activity:
Evidence from the IETF*
Wen Wen
Chris Forman
Sirkka Jarvenpaa
McCombs School of Business
Scheller College of Business
McCombs School of Business
The University of Texas at Austin
Georgia Institute of Technology
The University of Texas at Austin
2110 Speedway Stop B6500
800 West Peachtree Street NW
2110 Speedway Stop B6500
Austin, TX 78712
Atlanta, GA 30308
Austin, TX 78712
[email protected]
[email protected]
[email protected]
PRELIMINARY AND INCOMPLETE. PLEASE DO NOT QUOTE OR CITE WITHOUT
PERMISSION.
June 2015
Abstract
Standardization of information and communication technologies can influence the rate and direction of
invention activity both by reducing the costs of technical coordination but also by changing the costs of
licensing intellectual property rights (IPR) that are important for standards implementation. Studying the
effects of new standards developed by the Internet Engineering Task Force (IETF) on patenting from firms
who do not contribute to the standards, we find that increases in the number of standards in a technological
area is associated with a decline in patenting in that area. These results are driven by standards that are
created by commercial firms; in contrast, we find that increases in the number of standards developed solely
by non-commercial organizations are positively associated with patenting. We provide evidence that the
effects of standardization vary with the potential costs of infringing and licensing intellectual property rights
(IPR) that are related to the standard, finding that the effects of standardization are more negative in
technological areas in which the standards-contributing firms hold a large fraction of IPR related to the
standards in the area.
Key words: standardization, inventive activity, innovation, intellectual property rights
JEL Classification: L15, L86, O34
We gratefully acknowledge the financial support of this research by the NET Institute, http://www.NETinst.org.
We thank Divyanshu Bansal, Andrew Hayes, Yuan Meng, Geetsikha Pathak, Mahesh Srinivasan, Tracey Tran, Zirui
Wang, Fan Yeung, and Shun Zhang for outstanding research assistance. We also thank participants at the NBER
Productivity Lunch for helpful comments and suggestions. All errors are our own.
*
1. Introduction
Standardization and standards-setting organizations (SSOs) have gained growing attention as
interoperability among products in the information and communication technology (ICT) industries has
grown strategically more important. An increasing body of theoretical and empirical work has investigated
issues such as the implications of rules and policy choices of SSOs and strategic behavior by firms to
influence the standards-setting process. 1
An important topic of research has been to understand the impact of formal standards developed
by SSOs on the rate and direction of inventive activity (e.g., Rysman and Simcoe 2008). Such standards
can reduce the (Coasian) transaction costs of technological coordination and potentially reduce some holdup risks, increasing invention that builds upon the standardized technology (e.g., Besen and Johnson 1988,
Besen and Farrell 1991, Rysman and Simcoe 2008). However, the standardization process may also
increase strategic behavior by standards-contributing firms who may take actions during the standardization
process with the intention to create or maintain market power (Simcoe et al. 2009). Such behavior may
mute incentives to build new inventions on formal standards. Thus, the effects of formal standards on
invention are likely to be nuanced.
Recent work has explored the effects of institutional mechanisms designed to facilitate coordination
and reduce transaction costs among heterogeneous intellectual property rights (IPR) holders. In particular,
Lampe and Moser (2010, 2013) present empirical evidence showing that the creation of patent pools not
only discourages innovation from firms outside of the pool but may also divert the direction of innovation
toward a technologically inferior substitute for the pool’s technology.2 However, while there is a wealth of
qualitative evidence that the formal standards-setting process may lead to strategic behavior by standardscontributing firms that can increase the risks of costly infringement by other firms (e.g., Bekkers et al. 2002,
MacKie-Mason and Netz 2007, Dorth 2003, Farrell et al. 2004), at present there is little systematic empirical
evidence about how such risks might influence the rate and direction of invention.
Motivated by these observations, we seek to investigate how the growth in formal standards and
standards-related IPR holdings by standards-contributing firms in a broad technological area influence the
rate of inventive activity by non-contributing firms in the same area. A primary goal of our paper is to
identify conditions under which the costs of obtaining access to IPR related to formal standards are greater,
and where the implications for inventive activity are likely to be stronger. We argue that the costs of
negotiating access to necessary patents will vary with the incentives of standards-contributing firms to
1
See, for example, Delcamp and Leiponen (2013), Farrell and Saloner 1988, Farrell and Simcoe 2012, Leiponen
(2008), Lerner and Tirole (2006, 2013), and Chiao, Lerner, and Tirole (2007).
2
Other studies have also looked at this question, showing that patent pools and other such institutional mechanisms
can have mixed effects on inventive activity. For further details, see for example Flamm (2013), Hall and Helmers
(2013), and Wen, Ceccagnoli, and Forman (Forthcoming). 1
directly monetize their patents through markets for technology, as well as ex ante uncertainty over which
patents are essential to the standard.
Our primary empirical approach is to investigate how increases in the number of formal standards3
within a technological area over time are associated with changes in inventive activity, as proxied by
patenting activity. Omitted variables, including but not limited to changes in technological opportunities in
an area, have the potential to influence both standardization and inventive activity. This feature of our data
makes identification of a direct causal relationship between standardization and inventive activity difficult.
It is not our goal to recover this relationship. Instead, as noted above, our approach focuses on the
differential effects of standardization when IPR-related risks are high relative to when they are low. More
specifically, we first examine whether standards developed by commercial entities have a less positive
effect on inventive activity than those who are developed solely by not-for-profit entities. We next study
changes in the effects of standardization in technological areas where the strength of patent rights have
increased. We further probe how the effects of standardization vary based on the characteristics of
contributing firms, including their size and holdings of standards-related IPR. Collectively, we employ
these approaches to provide evidence for how IPR infringement risks influence the relationship between
standardization and inventive activity among non-contributing firms.
The use of patenting behavior to proxy for inventive activity has important limitations in our setting.
The patenting activity by a firm reflects not only the output of its R&D investments but also indicates its
propensity to engage in strategic patenting behavior such as patenting for the purpose of engaging in future
ex post licensing transactions (e.g., Hall and Ziedonis 2001, Ziedonis 2004). This is particularly true in ICT
industries where dense patent thickets are common. In fact, as we will detail later, through pooling
important IPR holders together and clarifying the associated licensing terms, standards-setting efforts may
potentially alleviate the patent thickets problem and therefore reduce the need for strategic patenting.
Therefore, a critical identification challenge is to separate the effects of standardization on invention from
strategic patenting. This is currently an area of ongoing research in the paper.
We study standards developed by the Internet Engineering Task Force (IETF), one of the largest
SSOs and one that is fundamental to the development of standards related to the Internet and networking
technologies. The IETF has explicit rules that promote broad participation 4 and that are designed to
minimize licensing costs for standards-essential patents5, suggesting an environment that is likely to be
3
Throughout the remainder of this paper, we will simply refer to such formal standards as “standards,” for brevity.
That is, we will not specifically examine the effects of standards that may be arrived at through other means, such as
“standards wars.”
4
More specifically, any interested participant has free access to the standardized technologies. Participant can also
vote and participate in discussions on any standards.
5
As specified by RFC 2026, the IETF encourages written assurances from the claimants of standards-essential
patents that “any party will be able to obtain the right to implement, use and distribute the technology or works when
2
relatively supportive for non-contributing firms seeking to produce to standards. In our empirical context,
we first identify the set of firms at risk of producing products that built upon IETF standards but who did
not themselves contribute to the standard. To do this, we retrieve the set of firms that attended the tri-annual
IETF meetings but did not develop any documents published by the IETF between 1994 and 2004, an
important period where the development and use of IETF standards accelerated rapidly (Simcoe 2012,
Simcoe 2013). We measure a non-contributing firm’s inventive activity using the number of patents applied
for by the firm. To build our data, we develop a concordance between patent classes and the six
technological areas classified by the IETF based on the expertise and patenting behavior of prior standards
authors.
As a baseline to compare against our other results, we first show the relationship between
standardization (proxied by the number of IETF standards) and inventive activity among non-contributing
firms. We demonstrate that an increase of 100 standards in a technological area is associated with a 13%
decline in patenting in that area. Because our primary interest is to understand how variance in the risks of
potential IPR infringement influence the effects of standardization on inventive activity, throughout the
remainder of the paper we focus our analyses on the effects of standards developed solely by commercial
firms. Using this measure, we find that an increase of 100 standards developed by commercial firms is
associated with an 18% decline in patenting from non-contributing firms.
Our baseline results may be influenced by omitted variables that vary over time and influence both
standardization and patenting. While one prominent source of potential omitted variable bias—the effects
of changes in unobserved market opportunities—would likely bias our coefficient estimates positively, we
acknowledge that we are unable to identify the direct relationship between standards and inventive activity.
The rest of our paper therefore explores how variance in the potential costs of infringing and licensing
standards-related IPR influences the effects of standardization on non-contributor behavior. These results
comprise the core contribution of our paper.
Our first set of analyses probes differences in contributor incentives to enforce and monetize patents
related to standards. In contrast to the negative relationship between the number of standards developed by
commercial firms and inventive activity, we find that the number of standards developed by noncommercial organizations is positively associated with non-contributing firm’s patenting in an area. Next,
we explore the effects of a change in the legal regime covering software patents in 1996 and 1998: namely
the implications of court decisions that strengthened the perceived value of software patents.6 We find that
the effects of standards contributed by commercial firms became more negative after the regime change in
implementing, using or distributing technology based upon the specific specification(s) under openly specified,
reasonable, non-discriminatory terms.”
6
For further details, see Cockburn and MacGarvie (2011), Hall and MacGarvie (2010), and Lerner (2002).
3
technological areas for which the strength of patents was affected. This result is consistent with our
hypothesis that non-contributing firms will perceive risks to invention in technological areas with strong
patent rights and where standards-contributing firms are motivated to monetize their IPR holdings.
Our next set of analyses examine how variance in commercial contributor characteristics influence
the effects of standardization on non-contributor behavior. We find that standardization has a larger (more
negative) impact on patenting when IPR related to the standards is increasingly held by contributing firms.
This relationship is particularly prominent when standards contributions come from small firms. While the
number of standards contributed by small firms is insignificantly and positively correlated with noncontributing firms’ patenting when the fraction of standards-related IPR held by these small firms is at 10th
percentile, an increase of 100 standards contributed by small firms is associated with a significant 28%
decrease of patenting when the fraction of related IPR that are held by small contributing firms is at 90th
percentile, and the difference between 10th percentile and 90th percentile of related IPR is statistically
significant. On the other hand, when the fraction of standards-related IPR held by large contributing firms
is at 10th (90th) percentile, an increase of 100 standards contributed by them is associated with a 10%
(significant 25%) decrease of patenting, and the difference between the two is insignificant.
2. Theoretical Motivation
2.1 The effect of standardization on firm’s inventive activity
In this section we detail the effects of standardization on inventive activity. As noted in the
introduction, standardization may also have implications for strategic patenting behavior. We will discuss
identification issues associated with separating these two mechanisms when describing our estimation
strategy and our results.
Standardization can have positive effects on the inventive activity of a non-contributing firm that
produces to standards. First, a greater level of standardization in a technological area may suggest a greater
level of voluntary consensus has been reached in the area, which reduces uncertainty on the future trajectory
of related technologies. When standards are present, firms who produce complementary products and
technologies may have smaller risks of being locked into technology choices that are eventually stranded
(Shapiro 2000), a possibility that may occur when compatibility is achieved through other means such as a
standards war. Second, in ICT industries characterized with dense patent thickets, one important role played
by the standards-setting process is to pool parties holding essential IPR together, i.e. to solve the
complements problem, and to reduce the risks of the hold-up problem through licensing rules such as
“reasonable and non-discriminatory” or RAND terms on the IPR (Shapiro 2000, 2001). Thus, a
technological area with a greater level of standardization may suggest a lower level of coordination costs
4
and transaction costs of obtaining necessary IPR.7 Third, following the line of argument by Farrell and
Simcoe (2011), while the existence of many standards in an area could reduce the scope for differentiation
on components directly related to the standards, this risk of commoditization might motivate firms to
innovate on extensions and enhancements to the standards. Thus, standardization may reduce the
uncertainty associated with technical compatibility, and may also reduce the uncertainty associated with
obtaining the necessary IP rights.
However, given the potentially intensified competition from the horizontal compatibility, firms
contributing standards may exhibit strategic behavior with the intention to create or maintain power in the
product market or market for IPR (Simcoe et al. 2009). This might increase the uncertainty of making R&D
investments in standardized technologies, and dampen incentives to innovate. For example, some firms
may seek to influence standards to incorporate technologies that give them a substantial time-to-market
lead (Farrell and Simcoe 2012). For example, as has been discussed by Eisenman and Barley (2006), when
the 802.11G standard related to the WLAN technology was under discussion at the IEEE, two market
leaders, Intersil and Texas Instruments, aggressively proposed their own specifications to the IEEE. One
industry participant commented on the matter of development lead times by saying that “If either side got
their version approved, they would get virtually 100% of the marketplace, because they were a year ahead
in terms of development” (Eisenman and Barley 2006, p. 8). These types of activities would increase the
barriers to entry for non-contributing firms, potentially decreasing their incentives to invest in a technology
area.
Moreover, standards-contributing firms may seek to incorporate their IPR into standards to increase
revenues from IPR licensing (e.g., Bekkers et al. 2002, Simcoe et al. 2009). In some cases, contributing
firms can gain by withholding information about IPR that read on the standards. One representative case is
Rambus, who did not disclose that one of its patents read on the DDR-SDRAM standard until it was widely
adopted (Dorth 2003, Farrell et al. 2004). Several issues pertaining to the institutional background of SSOs
in the ICT industries make such behavior feasible. First, while many SSOs require contributing firms to
disclose whether they own IPR essential8 to developing standards, these disclosure statements are often
found to be generic and do not list specific pieces of IPR such as a patent or application numbers (Simcoe
and Catalini 2011). Even if IPR that reads on a standard has been specified and licensed with “reasonable
and non-discriminatory” terms, the definition of “reasonable” royalty rates has attracted much debate (e.g.,
Lemley 2002). Such actions may increase IPR infringement risks for non-contributing firms who wish to
produce for the standard, increasing their costs in two ways: (1) the costs of IPR infringement, including
7
These latter arguments are similar to those often put forth for establishing patent pools. For further details, see
Lampe and Moser (2010, 2013).
8
According to Bekkers et al. (2012), a patent is considered essential if it is not possible to produce products which
comply with a standard without infringing that IPR.
5
the costs of licensing IPR and the costs of litigation such as acquiring a defensive IPR portfolio as well as
injunction and damages, and (2) the transaction costs of acquiring these IPR.
It is also worth noting that even in the absence ex ante strategic behavior, in certain circumstances
the formation of a standard will make some IPR essential to that standard (Lerner and Tirole 2013) or
important to implementing the standard, resulting in a dramatic increase in the value of those IPR. This
would incentivize the contributing firms to assert those IPR aggressively (Galasso and Schankerman 2010),
which are also likely to increase both the infringement costs and transaction costs for firm that produce to
the standards.
In sum, standardization reduces the uncertainty about interoperability among shared components
of product design among heterogeneous producers. This will increase investments in areas that have formal
standards and increase inventive activity, other things equal. On the other hand, attempts by standardscontributing firms to use SSO participation to gain advance in the product market or the market for IPR can
increase the costs of producing complements to the standards. These types of efforts may reduce noncontributing firm’s inventive activity. In our empirical analyses we will examine which of these two effects
dominate and shed light on the overall influence of standardization on a non-contributing firm’s inventive
activity.
2.2 How the effects of standardization vary with the risks of infringement on standards-related IPR
As noted in the introduction, a primary goal of this paper is to investigate how the tradeoffs
identified above will change in environments where IPR that directly or indirectly read on the standards
will be more likely to exist or be enforced. We expect such environments would be associated with higher
IPR infringement risks, so the benefits of standardization to firm’s invention may be lower. In this section
we detail our empirical approaches to studying this issue.
One direct way to examine this issue would be to exploit heterogeneity in the effects of formal
standardization on invention between standards developed by commercial entities and standards written by
individuals with non-commercial affiliations such as universities or non-profit organizations (e.g., IEEE or
the IETF). Throughout most of our analyses we will focus on the effects of standards developed by
commercial entities, and examine variance in the likelihood that these commercial contributors will hold or
enforce IPR important to standard implementation. On the other hand, non-commercial organizations may
be less likely to seek to monetize their patents through markets for technology and engage in some of the
behaviors described above, and so the costs of building on such standards may be lower. Thus, we would
expect the effects from these non-commercial entities on invention to be more positive than the effects from
commercial entities. However, we acknowledge that one risk to this analysis is that standards developed by
non-commercial entities may be different from commercial entities in other ways, making it difficult to
draw causal implications.
6
In a perfect world we would observe an experiment that made some randomly selected technologies
more valuable for firms to enforce their IPRs on. We have no such experiment available to us. However,
we do observe changes in the strength of certain patents over time in our sample. Namely, during our sample
period, the decisions of several court cases clarified and strengthened the IPR around software patents
(Cockburn and MacGarvie 2009, 2011; Hall and MacGarvie 2010; Huang, Ceccagnoli, Forman, and Wu
2013). These changes were associated with an increase in the value and growth of patenting in softwarerelated software categories (Hall 2009, Hall and MacGarvie 2010). Further, prior work has shown that these
changes have had a significant impact on firm strategy, such as the decision to enter into a new market or
to join a platform (Cockburn and MacGarvie 2011, Huang et al. 2013). Some of the technological areas
that we study are software-intensive, so we may expect that in these areas, patents that directly or indirectly
read on the standards will be more likely to be enforced after the legal regime changes.
A range of studies have shown that firms may patent strategically in advance of standardization to
increase the potential benefits from licensing (e.g., Bekker and West 2009). Thus, another empirical strategy
could be to look at standards-contributing firms’ patent holdings related to their contributed standards.
Firms who have a large number of standards-related patents would be more likely to seek licensing revenues
or to otherwise enforce their patents.
While a direct approach is to look at a firm’s holdings of standards-essential patents, as discussed
earlier, firms may strategically choose not to declare some patents as standards-essential9 even though they
may read directly on standardized technologies or on closely related to the technologies (Lemley 2002,
Lerner and Tirole 2013). Meanwhile, the so-called “essential” patents may only represent a very small
fraction of patents important to standard implementation (Bekkers et al. 2012). Therefore, in our study,
instead of investigating the implications of essential IPR specified by standards-contributing firms, we
focus on a broader set of IPR that may include both unspecified standards-essential patents and those not
essential but important to the standard implementation. We label this collection of patents standards-related
IPR. In particular, we study two characteristics of standards-related IPR—the extent to which contributing
firms hold standards-related IPR in an area and the extent to which these standards-related IPR are held by
small firms.
As is well known IPR, particularly patents, serve as both a control right over inventions as well as
an indicator of a firm’s technological capabilities. The extent to which contributing firms hold standardsrelated IPR in an area may not only signal their influence on the trajectory of the standardized technologies
but also indicate the exclusive control they hold over complementary innovation. Therefore, when
contributing firms hold a large fraction of standards-related IPR in an area, it may increase the chance that
9
This is largely because essential IPR are frequently subject to licensing commitments established by the SSO
(Lemley 2002).
7
the standards are developed in a way that implementing them could easily read on these contributing firms’
related IPR. This would imply higher infringement risks of producing to the standards. Moreover, if most
of the standards-related IPR in an area are owned by the contributing firms, there is less opportunity for a
firm to substitute those with other IPR in the area. This may leave the firm in a weaker negotiation position.
In sum, we would expect when contributing firms hold a large fraction of standards-related IPR, the risks
of IPR infringement will be higher, further dampening inventive activity by non-contributing firms.
Moreover, we expect such risks may particularly exist for standards contributed by small firms. As
suggested by Simcoe et al. (2009), large firms may be more interested in competing in the product market
through manufacturing and implementation than seeking rent in the market for IPR. Therefore, given their
lack of incentives in IPR litigation, even when large contributing firms have large holdings of IPR related
to their contributed standards in an area, the associated IPR infringement risks may be of little different
from when they have smaller holdings.
On the other hand, small firms may specialize by competing in the market for technology and thus
have a greater propensity to seek rent through IPR litigation. Nevertheless, this negative rent-seeking effect
of their contributed standards would be highly contingent on their ability to engage such strategic behavior,
namely, the size of their IPR related to their contributed standards in an area. Thus, we would expect
differential effects of standardization led by small firms between environments where they hold large versus
small IPR holdings.
3. Empirical Framework
Several challenges exist in identifying how standardization affects a non-contributing firm’s
inventive activity. First, we do not observe a counterfactual: what would have happened to a noncontributing firm’s innovation if there were fewer standards it could produce to. Second, because our
empirical context covers a range of different technological areas, we must control for unobserved
heterogeneity across technological areas that may be correlated with both firm inventive activity and the
level of standardization. Therefore, our empirical analysis is based on a panel data model with the unit of
analysis as a firm-technological area-year; we employ firm-technological area fixed effects and use the
variance over time within firm in a particular area. Third, our dependent variable, patents, reflects not only
inventive activity but may also reflect strategic patenting by the focal firm.
We measure a firm’s inventive activity in a technological area through the number of patents it
applied for in technological area k in year t10. Thus, we employ a count data model with conditional fixed
10
As mentioned earlier, we acknowledge that some inventive output may not be patented, and on the other hand,
firms may strategically apply for additional patents on the same invention. Ideally, firm’s inventive activity should
be measured based on its R&D investment. If firms change their propensity of strategic patenting in response to the
formation of formal standards, this will result in bias in our estimates. Therefore, our next step is to separate the
effect of standardization on inventive activity from its effect on strategic patenting.
8
effects. Suppose the number of patents applied by firm i in technological area k in year t (denoted as Yikt)
follows a Poisson process with parameter λikt taking the form λikt = exp(Xikt’β). The baseline specification
can be written as E (Yikt | Xikt , αkt) =λikt = exp(Xikt’β), where
Xikt’β = β1 Standardskt + γ1FirmTechFeaturesikt + γ2AreaTechFeatureskt
(1)
+ γ3FirmFeaturesit + αik + τt.
We use the cumulative number of standards in technological area k by year t to capture the level of
standardization in area k in year t, denoted as Standardskt in the equation above. Three types of control
variables are used in the specification. The vector FirmTechFeaturesikt is intended to control for firm’s
innovation capabilities related to an area in a year, consisting of two variables: the stock of the inventive
output (i.e., patents) already held by firm i in area k by year t, denoted as PatentStockikt, and the quality of
the inventive output by firm i in area k by year t, denoted as PatentQualityikt. The vector AreaTechFeatureskt
controls for the overall technological landscape in area k in year t, including variables such as the total
patent stock in an area (denoted as AreaPatentStockkt) as well as the average technological capabilities of
firms in an area (denoted as AreaPatentQualitykt). The vector FirmFeaturesit includes a set of firm-year
controls, including sales volume, number of employees, and total patent stock held by firm i by year t. αik
is time-constant variable that incorporates unobserved differences across firm and area heterogeneity (such
as market activity and technological opportunities). τt includes a full set of yearly dummies. We use robust
standard errors in all regressions, clustered by firm, as a firm’s innovation across different technological
areas may be correlated. We also test the robustness of our results through clustering standard errors by
technological area, and the results are qualitatively similar.
Our interest focuses on the coefficient β1. As discussed earlier, β1 reflects the net effects from
standardization: if it is positive (negative), it may suggest that the benefits of standardization to a noncontributing firm may be greater (less) than the potential costs resulting from contributing firms’ attempts
to gain advance in the product or technology market. However, estimates in this model must be interpreted
carefully, as the number of standards can be influenced by unobserved time-varying factors. In particular,
unobserved market activity and/or rapid technological change that could affect both the number of standards
and a firm’s inventive activity. Further, β1 may reflect an equilibrium response by the standardscontributing firms reacting to the inventive activity by non-contributing firms.
Therefore, as noted in section 2.2., our next specifications focus on examining how changes in the
IPR infringement risks shape how standards influence inventive activity. We first distinguish between the
standards contributed by commercial firms (denoted as ComStandardskt) and the standards contributed by
non-commercial organizations (denoted as NonComStandardskt). Most of our analyses will examine the
effects of standards developed by commercial entities, so the baseline specification of Xikt’β is written as
follows.
9
Xikt’β = β1 ComStandardskt + γ1FirmTechFeaturesikt + γ2AreaTechFeatureskt
(2)
+ γ3FirmFeaturesit + αik + τt.
To test the difference between commercial entities and non-commercial entities in their incentives
to enforce any standards-related patents, we introduce the following specification of Xikt’β where we would
expect β1 to be significantly smaller than β2.
Xikt’β = β1 ComStandardskt + β2 NonComStandardskt + γ1FirmTechFeaturesikt +
(3)
γ2AreaTechFeatureskt + γ3FirmFeaturesit + αik + τt.
We explore the effects of the changes in the legal regime covering software patents in 1996 and
1998 as mentioned earlier. A detailed description of these changes can be found in Hall and MacGarvie
(2010) and Cockburn and MacGarvie (2011). In short, prior to 1996, software was only patentable when it
is tied to physical or mechanical processes. However, a series of court decisions in 1994 and 1995 had
affected the scope of software patents and finally resulted in new USPTO guidelines published in early
1996 which allowed inventors to patent software embedded in physical media. The State Street decision in
1998 then lead to the second important expansion of software patentability—software related business
methods and disembodied algorithms. It is worth noting that these two regime changes not only resulted in
an increase in applications for those types of software patents but also strengthened the validity and
enforceability of the software patents that were granted before the regime changes.
As we discuss more in Appendix A.1, these two regime changes would influence different
technological areas in our sample at different times: we expect that areas with many patents on software
embodied in hardware media such as computers and routers would be particularly affected by the regime
change in 1996. Similarly, areas with many patents regarding business methods and disembodied
algorithms would be particularly influenced by the regime change in 1998. Thus, our identification strategy
is largely similar to the one used in Cockburn and MacGarvie (2011), and the specification of Xikt’β can be
rewritten as follows.
Xikt’β = β1 ComStandardskt + β2 ComStandardskt * D(regime change)kt + γ1FirmTechFeaturesikt
(4)
+ γ2AreaTechFeatureskt + γ3FirmFeaturesit + γ4 D(regime change)kt + αik + τt.
D(regime change)kt is a dummy that turns on in areas that are affected following the regime change
(see Appendix A.1 for details on how this variable is constructed). If our hypotheses hold, standardscontributing firms would be more likely to enforce their IPR in the affected areas after the regime change
and therefore would result in higher IPR infringement risks to a non-contributing firm afterward. As a result,
we would expect β2 to be negative. We note that these legal regime changes may also incentivize the
patenting behavior by non-contributing firms, which is used to measure the dependent variable. We capture
these changes in incentives to patent through the inclusion of the variable D(regime change)kt.
10
Last, we employ the following specification of Xikt’β to investigate how the effect of standards
varies with the extent to which contributing firms hold standards-related IPR in an area (denoted as
RelatedIPRfractionkt). Based on our discussions above, we would expect β2 to be negative.
Xikt’β = β1 ComStandardskt + β2 ComStandardskt * RelatedIPRfractionkt + γ1FirmTechFeaturesikt
(5)
+ γ2AreaTechFeatureskt + γ3FirmFeaturesit + γ4 RelatedIPRfractionkt + αik + τt.
4. Research Setting
We examine the implications of standards developed by the Internet Engineering Task Force (IETF)
for several reasons. First, since it was established in 1986, the IETF has emerged as the de facto forum to
define standards related to the Internet and computer networking, in particular the Internet protocol suite
(TCP/IP) (Simcoe 2012). Therefore, our study is less subject to the issue that firms engage in forumshopping for SSOs (Lerner and Tirole 2006), as there are a few alternatives firms can choose from. Second,
as discussed by Krechmer (2006), when compared with other SSOs such as the ITU and the IEEE, the IETF
exhibits relatively high level of openness in its IPR policy 11 , so it provides a setting that is relatively
supportive for non-contributing firms to produce to standards. Third, the rules for SSOs vary widely
(Lemley 2002) as do the technological areas that they cover, implying that the behavior of contributors and
implications for noncontributors are likely to vary widely across SSOs. We therefore focus on one SSO to
mitigate the extent to which unobserved heterogeneity across SSOs may bias our estimates.
As described on its website12, the IETF divides its work into the following major technological
areas: “Application” which focuses on application protocols such as email and HTTP; “Transport” which
develops protocols such as TCP to support Internet-based applications to exchange data; “Internet” which
covers topics such as IP and the implications of IPv4 and IPv6; “Routing” which is responsible for
developing routing-related protocols and ensuring continuous operation of the Internet routing system;
“Operations and Management” which discusses issues such as network management and DNS operations;
and “Security” which designs security-related protocols on the Internet.
In each of these technological areas, working groups are formed to develop standards and address
specific technical problems. These working groups accept technical proposals, called Internet Drafts in the
IETF’s terminology. Newly submitted Internet Drafts are debated in tri-annual IETF meetings and email
discussions, both of which are open to anyone. After several rounds of revisions and debates, once
consensus is reached on an Internet Draft, it is then published as a Request for Comments (RFC). RFCs not
only include new protocols, which are called standards-track RFCs, but also include documents for
11
More specifically, the IETF encourages royalty-free licensing (Krechmer 2006) and has strong requirement for
disclosure. Although the other SSO, W3C, employs a royalty-free licensing model, contributors to the W3C do not
need to disclose their knowledge of relevant patents.
12
See http://www.ietf.org/iesg/area.html for a detailed description of these different technological areas and related
working groups.
11
informational or experimental purposes, which are called nonstandards-track RFCs. A standards-track RFC
will progress from a Proposed Standard into a Draft Standard and then into an Internet Standard.13
Because of the growth and commercialization of the Internet, an increasing number of firms (as
opposed to non-commercial organizations) have participated in the IETF, from only 50% in 1993 to more
than 80% by 200114 (Simcoe 2012). Participation in the IETF is open to anyone and can take various forms.
Besides directly contributing to the IETF by publishing RFCs, firms could attend tri-annual IETF meetings
as well as getting involved with email discussions hosted by working groups.15While this consensus-based
approach to developing standards could lower the costs of compatibility, Simcoe (2012) shows that the
potential rent-seeking behavior by market participants led to a slowdown in standards production at the
IETF. The IPR policy at the IETF has also gradually changed over time. Although encumbered technologies
were rejected outright prior to 1996, based on a revised policy published as RFC 2026 in 1996, the IETF
has allowed to incorporate those technologies into IETF standards when it is necessary to do so.
Nevertheless, RFC 2026 also makes it clear that any interested parties should be able to obtain the right to
implement, use and distribute these encumbered technologies under RAND terms.
5. Data
5.1 Sample
Because the standards published by the IETF are available to anyone, it is very difficult to identify
the universe of non-contributing firms that potentially produce to the IETF standards. We define our sample
firms as the ones that ever sent employees to attend tri-annual IETF meetings16. Our sample years are from
1994 to 2004, an important period where the number of IETF’s standards had surged from 183 to 1243,
including the widely used ones such as Session Initiation Protocol and Dynamic Host Configuration
Protocol.
More specifically, we implement the following steps to define our sample firms. First, we retrieve
all attendees’ affiliation information from IETF meeting proceedings from 1994 to 2004, either through the
attendee’s email address or through the reported institution name. Second, in order to obtain data on the
size-related variables for the sample firms, we match the firms collected from the first step with the 1994
to 2004 editions of CorpTech directory of technology companies (denoted as CorpTech hereafter).
CorpTech covers over 35k firms in high tech industries and reports detailed annual information such as
13
Further discussion of these institutional details can be found in Simcoe (2012) and Waguespack and Fleming
(2009).
14
These numbers are compiled by Simcoe (2012) and are based on the top-level domain of emails sent to IETF’s
listserv.
15
For further details on the benefits firms may achieve by participating in SSOs, see Waguespack and Fleming
(2009), Leiponen (2008), and Delcamp and Leiponen (2013).
16
We acknowledge that by defining the sample in this way, our research may provide limited implications for very
small firms that never participated the IETF meetings.
12
sales, employment, website, email, and product portfolio; this data set has been used by a body of literature
studying inventive activity and strategic behavior among firms in ICT industries (e.g., Cockburn and
MacGarvie 2009, Huang et al. 2013). The matching procedure is based on both email and organization
name. Because the CorpTech directory primarily focuses on US firms, we next exclude all non-US firms
from the matched results, leading to a sample of 834 firms. Third, because our focus is on firms which do
not contribute standards, we then exclude the set of firms that contributed IEFT standards from 1994 to
2004 from this sample,17 resulting to 566 non-contributing firms. Each of the 566 firms enters our sample
starting from the first year we observed its employee(s) attended the IETF meetings and remains in our
sample unless it exited either through acquisition or bankruptcy.18
5.2 Defining technological areas and the matched patent classes
We utilize the technological areas classified by the IETF to define the technological areas used in
our empirical analysis. As mentioned above, these areas include Application, Transport, Internet, Routing,
Operations and Management, and Security. One important part of our data construction is to identify the
most important patent classes to each of our focal technological areas, for the reasons as follows. First, one
of our key interests in this study is to examine how the characteristics of contributing firms’ IPR related to
their contributed standards in a technological area influence non-contributing firm’s inventive activity in
the same area. Particularly for contributing firms that hold IPR across a wide range of fields, identifying
their related IPR in certain area proves to be difficult, and to the best of our knowledge, there have been
few studies pursuing this endeavor. Relatedly, creating the concordance between patent classes and
technological area allows us to measure several key variables using patents while employing a firmtechnological area-year as the unit of analysis.
At the IETF, each technological area consists of many working groups. The RFCs—the standards
at the IETF—are usually published by working groups, so the technologies described in RFCs published
by the working groups in an area could be used to proxy for the core technologies of that area. Our main
idea is to search for patent classes closely related to each RFC author’s expertise, under the assumption that
each RFC author would only work on certain specific and relatively narrow area(s), so the patent classes
where the author actively patented would be closely related to the technological areas where the author
published RFC(s). Then, based on the RFC’s technological area, we could further map patent classes to
technological areas.
17
As we discuss more below, we use the affiliation information of the authors that published RFCs to obtain the set
of firms that contribute IETF standards.
18
We identify whether a firm was acquired based on whether there is any change on its status from an independent
company to a subsidiary or division of another company, the data of which is directly obtained from CorpTech. If
we do not observe any information about a firm for three consecutive years in CorpTech, we consider it was
bankrupt.
13
More specifically, we create the concordance between technological areas and patent classes with
the following steps. First, for all RFCs published from 1994 to 2004,19 we obtain the full names of their
authors. Second, we match these author names with inventor names from the US Patent Inventor Database
provided by the Harvard Dataverse Network (Lai et al. 2013). 20 These two steps are done through a
combination of automatic and manual search. Third, because the US Patent Inventor Database includes the
mapping between patent inventors and patents, we are then able to identify for each RFC, the set of patents
held by the RFC authors as well as these patents’ main 3-digit USPTO classes. Last, we accumulate all
these RFCs’ patent classes to technological area level, and for each area, we use the top 10% most frequent
patent classes as the representative patent classes for that area.21 See Appendix A.2 for an illustration of the
data construction process.
5.3 Identifying standards-contributing firms and their standards-related IPR holdings
One of our main objectives in this study is to understand how standards-contributing firms’
standards-related IPR shape the effect of standards contributed by them, as well as how such relationship
varies across small versus large contributing firms. Therefore, we identify the set of contributing firms and
their size and standards-related IPR holdings through the following steps. We first retrieve affiliation
information of the authors that published RFCs from 1994 to 2004, including email addresses and reported
institution names. Then, we match them with the 1994 to 2004 editions of CorpTech directory of technology
companies to obtain their size-related data such as founding year, sales, and number of employees. 22
Because our sample starts from 1994, small contributing firms are those that were founded after 1984 and
had sales less than $500 million and employees fewer than 1000 in the first year they contributed to the
IETF. In total, we identify 201 firms that contributed standards-track RFCs from 1994 to 2004, and 104 of
them were small contributing firms. We next use a combination of both automatic and manual search for
these contributing firms’ names in the assignee database provided by the NBER Patent Data Project.
Because the NBER Patent Data Project provides detailed mapping between assignees and patents granted
from 1976 to 2006, we are able to obtain the contributing firms’ patents granted in this period and these
patents’ main 3-digit USPTO classes. Last, a contributing-firm’s standards-related IPR in a technological
19
In all empirical analyses of this study, we focus on RFCs published during our sample, i.e. from 1994 to 2004.
If an author is only matched to one inventor based on his/her full name, we consider it is a correct match; if an
author is matched to multiple inventors, because we also obtain the affiliation and location information for both the
authors at the IETF and the inventors in the U.S. Patent Inventor Database, within the set of inventors that filed
computer and communications-related patents, we choose the inventor(s) that also match on either location or
affiliation.
21
Note that our method creates a many-to-many mapping, where a patent class could be matched to multiple
technological areas and a technological area could be matched with multiple patent classes.
22
More specifically, we first conduct automatic matching using email addresses and then use manual verification
based on company names. Among 2350 combinations of standards-track RFCs and author email addresses, 75% are
either matched to CorpTech or belong to university/non-profit organization email addresses; for the rest 25%, we
manually check their email domains and find most of them are foreign firms.
20
14
area are measured by the stock of patents that are matched to that area based on the concordance between
technological areas and patent classes discussed above.
5.4 Key variables and measures
Standardskt: As noted earlier, this variable captures the level of standardization in area k in year t.
It is equal to the cumulative number of standards-track RFCs (in hundreds) published by all working groups
at the IETF in area k from year 1994 to year t. These standards-track RFCs include RFCs classified as
Proposed Standard, Draft Standard, and Internet Standard. Following Simcoe (2012), we use the authors’
affiliations to identify the set of standards developed by commercial firms (denoted as ComStandardskt)
and the ones developed by non-commercial organizations (denoted as NonComStandardskt): If all authors
writing a standard came from the academic and non-profit entities (i.e. with email’s top-level domain as .edu
or .org or .gov), we define it as one developed by non-commercial organizations; otherwise it is a standard
developed by commercial firms. 23 Relying on authors’ email domains to classify standards into
ComStandards and NonComStandards might be subject to the issue that authors with commercial email
accounts (e.g. hotmail.com) would be classified into ComStandards, even if they were individual
contributors or from non-commercial organizations. To mitigate this concern, we compute the cumulative
number of standards-track RFCs (in hundreds) that are matched to CorpTech firms and use this as an
alternative measure for ComStandards. Moreover, among the set of standards-track RFCs (in hundreds)
that are matched to CorpTech firms, we further decompose those into the ones developed by small
contributing firms (ComStandards_SmallFirms) and the ones from large contributing firms
(ComStandards_LargeFirms).
FirmTechFeaturesikt: To measure the two variables in this vector (i.e., PatentStockikt and
PatentQualityikt), we search for each of our sample firms in the assignee database provided by the NBER
Patent Data Project. After we obtain each firm’s patents, we match them to the six technological areas
according to the concordance between technological areas and patent classes. PatentStockikt is then
measured by the cumulative number of patents related to area k and granted to firm i by January 1st of year
t.24 PatentQualityikt is measured by the cumulative stock of citations received by the patents related to area
k and granted to firm i by January 1st of year t divided by firm i’s total number patents in area k by January
1st of year t, i.e. the average number of citations received by each patent granted to firm i by January 1st of
year t. As shown by the summary statistics below, because these two variables are highly skewed, in the
regression analyses we take the log to reduce the skewness.
23
We are grateful to Tim Simcoe for allowing us to use their data set to measure this variable. These data are
available for download at http://people.bu.edu/tsimcoe/code/SSOCommittees-DataFiles.zip.
24
We essentially lag all patent-based variables by one year. 15
AreaTechFeatureskt: Similarly, we use the NBER Patent Data Project to measure the two variables
(AreaPatentStockkt and AreaPatentQualitykt) in this vector. We identify all patents in area k and granted by
January 1st of year t based on their main 3-digit USPTO classes and grant years, and further exclude the
sample firms’ patents. Then, AreaPatentStockkt is measured by the cumulative number of patents (in
thousands) whereas AreaPatentQualitykt is measured by the average number of citations received by a
patent in an area.
FirmFeaturesit: We obtain data on firm’s sales (denoted as Salesit) and number of employees
(denoted as Employeesit) directly from the 1994 to 2004 editions of CorpTech directory. Besides controlling
for sample firm i’s patent stock related to area k by year t, we include an additional control for the overall
patent stock by firm i by year t (denoted as TotalPatentStockit), measured by the cumulative number of
patents granted to firm i by January 1st of year t. Because of the high skewness of these variables as shown
in table 1 below, we take the log in the regression analyses.
RelatedIPRfractionkt: This variable indicates the fraction of related IPR in area k granted by year t
that were held by firms contributing standards in area k by year t. According to the matching between
standards-track RFCs and CorpTech, our first step is to identify the set of firms that contributed the
standards in area k by year t. Second, based on the concordance between patent classes and technological
areas, we obtain the set of patents that were granted by year t and matched to area k and held by these
contributing firms (i.e. the ones contributing standards in area k by year t). Then, RelatedIPRfractionkt is
measured by the claims-weighted count of patents obtained from the second step, divided by the claimsweighted count of all patents granted by year t and matched to area k. We employ claims-weighted count
as our baseline measure, as claims could better capture the legal scope of an invention and reflects the extent
to which a patent is resistant to invalidation challenges. However, we also measure this variable based on
raw count as robustness check.
To test the effects of small and large contributing firms’ related IPR separately, we then compute
the fraction of related IPR in area k granted by year t that were held by small firms contributing to area k
by year t (denoted as RelatedIPRfraction_SmallFirmskt) as well as the fraction of related IPR in area k
granted by year t that were held by large firms contributing to area k by year t (denoted as
RelatedIPRfraction_LargeFirmskt).
NoOfNonComMsgPerStandardkt: The variable NonComStandardskt measures the number of
standards developed by non-commercial organizations, and we expect that it may have different effect on
firm’s inventive activity than standards developed by commercial firms. However, this variable could also
reflect heterogeneity in interest among academics across different areas. Therefore, we add a control
denoted as NoOfNonComMsgPerStandardkt in all specifications that investigate this variable. We compute
NoOfNonComMsgPerStandardkt using the following steps. First, for each standard, we obtain the number
16
of
email
messages
sent
by
participants
from
non-commercial
organizations.
Then,
NoOfNonComMsgPerStandardkt is computed by the total messages sent from non-commercial
organizations related to all standards in area k by year t, divided by the number of standards in area k by
year t, i.e., the average number of messages sent by academics per standard in area k by year t.
Table 1 below presents the summary statistics for the main variables used in our regression analyses.
On average, 57 standards were developed by commercial firms whereas 11 standards were developed from
non-commercial organizations in an area by a year. Because the standards-related variables are all
cumulative measures, from the statistics below, we see in a few areas no standard was established in 1994
(the first year of our sample) whereas by year 2004, some area already saw 194 standards developed by
commercial firms and 38 standards developed by non-commercial organizations. Interestingly, small and
large contributing firms developed similar number of standards to the IETF. As shown by the summary
statistics of the firm-related variables, our sample firms vary considerably in size and patenting behavior;
however, based on median value of the size-related variables (e.g., sales, number of employees), more than
half of them are small. On average, the sample firms only applied two to three patents in a year in one of
the six technological areas focused by the IETF.
To develop a better understanding on the heterogeneity among these six technological areas, we
present the means of the key variables by technological area in table 2. The average number of patents
applied for per sample firm is similar across these areas. Internet area and Operations and Management area
see greater numbers of standards developed by commercial firms than other areas; non-commercial
participants seem to focus on developing standards in Application area and Internet area, with very few
standards developed in Routing and Operations and Management. Regarding the patent landscape across
these areas, Internet, Transport, and Application areas have more patents than other areas, whereas Operations and Management area and Transport area see greater fraction of standards-related IPR held by
contributing firms. The patent quality is largely similar across all these areas.
Table 1: Summary statistics
Variable names
Technological area-year level
ComStandards
NonComStandards
ComStandards_SmallFirms
ComStandards_LargeFirms
RelatedIPRfraction
RelatedIPRfraction_SmallFirms
RelatedIPRfraction_LargeFirms
AreaPatentQuality
AreaPatentStock
NoOfNonComMsgPerStandard
Firm-year level
Sales
Employees
Obs.
Mean
Std. Dev.
Median
Min
Max
66
66
66
66
66
66
66
66
66
66
.565
.113
.305
.338
15.020
.104
14.917
26.664
46.196
11.505
.477
.106
.254
.315
9.639
.083
9.588
2.029
26.357
3.269
.460
.070
.290
.230
14.312
.097
14.251
26.724
40.715
11.528
0
0
0
0
0
0
0
22.244
11.425
0
1.940
.380
.970
1.210
32.928
.265
32.785
30.541
123.173
17.988
2613
2613
2616.146
12802.43
12214.7
53421.04
29.093
200
0
1
235461.8
819610
17
TotalPatentStock
2613
251.033
1517.302
0
0
23755
Firm-technological area-year level
No. of patents applied
15678
2.695
10.993
0
0
197
PatentStock
15678
10.148
46.242
0
0
760
PatentQuality
15678
14.806
33.459
0
0
427
Notes: The variables ComStandards, NonComStandards, ComStandards_SmallFirms and ComStandards_LargeFirms are all
measured in hundreds; AreaPatentStock is measured in thousands. RelatedIPRfraction, RelatedIPRfraction_SmallFirms, and
RelatedIPRfraction_LargeFirms are all measured in percentages.
Table 2: Summary statistics by technological area
Area
Application
Internet
Operations
and Mgt
Routing
Security
Transport
No. of pats
applied per
sample firm
2.853
3.779
0.242
0.177
Com
Standards_
SmallFirms
0.262
0.416
Com
Standards_
LargeFirms
0.321
0.619
Area
Patent
Quality
27.169
24.876
Area
Patent
Stock
51.520
70.736
Related
IPR
fraction
15.209
16.913
0.760
0.037
0.482
0.459
27.681
32.274
19.555
0.335
0.345
0.499
0.003
0.105
0.115
0.225
0.216
0.231
0.108
0.162
0.358
26.298
28.417
25.482
38.483
28.034
56.097
10.549
8.758
19.149
Com
Standards
NonCom
Standards
0.563
0.885
2.742
2.837
1.932
3.553
Notes:
1)
The
variables
ComStandards,
NonComStandards, ComNonStandards,
NonComNonStandards,
ComStandards_SmallFirms and ComStandards_LargeFirms are all measured in hundreds; AreaPatentStock is measured in
thousands. RelatedIPRfraction is measured in percentage. 2) The number in the cells is the mean value from 1994 to 2004.
6. Empirical Results
6.1 The effect of standardization on firm’s inventive activity
Column (1) in table 3 below reports the estimation results for specification (1), where we examine
the effect of all standards in an area. The coefficient of Standards is significantly negative and indicates
that when 100 more standards are released in a technological area, a non-contributing firm would have 13%
less patenting activity (as measured by the number of patents applied for) in the area. This seems to suggest
that the costs arising from the strategic behavior by the contributing firms may overshadow the benefits of
cooperative efforts among standards-setting participants to a non-contributing firm. Based on specification
(2), we next examine the standards developed by firms in particular, and the results are shown in column
(2) in table 3. The estimated coefficient implies that as firms contribute 100 standards in a technological
area, a non-contributing firm would have 18% less patenting activity. Since our primary interest of this
study is the effect of standardization led by firms, we will focus on ComStandards (i.e. the set of standards
developed by firms) in reminder of our analyses.
As noted earlier, for our baseline measure of ComStandards, we decide whether a standard was
contributed by firms based on author email address’s top-level domain. One concern is that standards
developed by authors with commercial email account would be always categorized into this type of
standards. Therefore, as a robustness check, we measure ComStandards, using the number of standards that
were contributed by our sample of contributing firms (i.e. the ones whose authors’ email addresses are
matched to CorpTech). The results based on this alternative measure are reported in column (3), and they
are consistent with the one in column (2). We next add to the baseline specification a linear and quadratic
time trend interacted with area fixed effects to control for those unobserved time-varying area-specific trend.
18
The estimate on the effect of ComStandards, shown in column (4) below, is similar to our baseline result.
However, we note that the magnitudes of some controls differ from the comparable estimates in baseline
specification, i.e. column (2). This may be due to the multicollinearity among some of our variables, so
cautions need to be taken when interpreting these results. Column (5) then uses 5-year-citations-weighted
count25 of patents applied for to measure a non-contributing firm’s inventive activity whereas column (6)
uses claims-weighted count. The results are consistent to those alternative measures.
Table 3: The effect of standardization on firm’s inventive activity
Dependent variable: Inventive activity
Standards
(1)
-0.141*
(0.086)
ComStandards
log(PatentStock)
log(PatentQuality)
AreaPatentQuality
AreaPatentStock
log(Sales)
log(Employees)
log(TotalPatentStock)
Year dummies
firm-technological area fixed effects
Area-specific time trend
No. of firm-technological area pairs
No. of observations
-0.180
(0.147)
0.100
(0.064)
0.124**
(0.060)
0.004
(0.004)
-0.118
(0.095)
0.286*
(0.163)
-0.091
(0.143)
Yes
Yes
No
1293
7583
(2)
(3)
(4)
(5)
(6)
-0.193*
(0.105)
-0.181
(0.147)
0.099
(0.064)
0.119**
(0.058)
0.005
(0.004)
-0.119
(0.095)
0.286*
(0.163)
-0.090
(0.143)
Yes
Yes
No
1293
7583
-0.187*
(0.110)
-0.181
(0.147)
0.099
(0.064)
0.113**
(0.055)
0.005
(0.004)
-0.119
(0.095)
0.287*
(0.163)
-0.090
(0.143)
Yes
Yes
No
1293
7583
-0.233*
(0.137)
-0.185
(0.143)
0.091
(0.062)
-0.518**
(0.211)
0.355***
(0.107)
-0.121
(0.095)
0.279*
(0.162)
-0.080
(0.138)
Yes
Yes
Yes
1293
7583
-0.235**
(0.092)
-0.140
(0.202)
-0.014
(0.061)
0.030
(0.043)
0.013***
(0.005)
-0.019
(0.105)
0.128
(0.134)
-0.063
(0.227)
Yes
Yes
No
1184
7084
-0.242**
(0.106)
-0.213
(0.148)
0.054
(0.057)
0.125**
(0.060)
0.005
(0.005)
-0.079
(0.085)
0.194
(0.128)
-0.143
(0.133)
Yes
Yes
No
1293
7583
Notes: 1) This number of observations is lower than 15678 because of the use of conditional fixed effects Poisson models, which
drops the panels without any patent over the entire sample period or with only one observation. 2) Robust standard errors, clustered
by firm, are in parentheses. 3) * significant at 10%, ** significant at 5%, *** significant at 1%.
We then exploit how the changes in legal regime affect the effect of standardization and report the
results in table 4. As mentioned above, D(regime change) in specification (4) is measured by a dummy that
turns on in each mostly affected area following the regime change. Consistent with our expectations, the
negative and significant coefficient on the interaction term suggests that as patent rights were strengthened,
the negative effect of standards contributed by firms became stronger, probably due to higher IPR
infringement risks perceived by a non-contributing firm following the regime change. As described in
Appendix A.1, we also construct an alternative measure to capture how much each area is affected by the
two legal regime changes (denoted as I(regime change)), and interact it with ComStandards. The results,
shown in column (1) in table B1 in the appendix, give us qualitatively similar results. In column (2) in table
B1, we consider the effects from the two regime changes are not additive and therefore employ two
dummies—D(regime change 1) turns on for Operations and Management area and Routing area after 1996,
25
The results are qualitatively similar if we use 3-year-citations-weighted count or all-citations-weighted count.
19
and remains zero for all other areas; D(regime change 2) turns on for Application and Internet area after
1998, and remains zero for all other areas. The estimates of the interaction terms seem to indicate that the
change in 1996, i.e. the expansion of patentability on software embedded in physical media, had a
particularly strong effect on the effect of standardization, as shown by the coefficient of ComStandards X
D(regime change 1). Following Cockburn and MacGarvie (2011), we also use two-year lag to allow the
regime changes to take effect, i.e. consider the period following the first regime change to start from 1998
and the period following the second regime change to start from 2000. The results, based on one dummy
and two separate dummies, are reported in column (3) and column (4) in table B1 respectively, and they
are largely similar to the baseline results.
In columns (2) and (3) of table 4 we examine whether the effects of commercial standards appear
before they should, and conduct timing falsification checks. Namely, in columns (2) and (3) we create
dummies that are turned on one year and two years in advance of the regime change, respectively. We show
that these variables, and their interactions with commercial standards, have no effect on patenting activity.
Table 4: Exploiting the effects of the legal regime changes
Dependent variable: Inventive activity
ComStandards
ComStandards X D(regime change)
D(regime change)
(1)
-0.107
(0.095)
-0.130**
(0.064)
0.139***
(0.053)
(2)
-0.111
(0.096)
-0.132**
(0.065)
0.149**
(0.074)
-0.239
(0.265)
0.072
(0.104)
-0.181
(0.148)
0.097
(0.064)
0.126**
(0.061)
0.004
(0.004)
-0.119
(0.095)
0.288*
(0.163)
-0.089
(0.144)
Yes
Yes
1293
7583
-0.181
(0.147)
0.098
(0.064)
0.120**
(0.058)
0.004
(0.004)
-0.119
(0.095)
0.288*
(0.163)
-0.090
(0.143)
Yes
Yes
1293
7583
ComStandards X D(false regime
change_1yrBefore)
D(false regime change _1yrBefore)
ComStandards X D(false regime
change_2yrsBefore)
D(false regime change _2yrsBefore)
log(PatentStock)
log(PatentQuality)
AreaPatentQuality
AreaPatentStock
log(Sales)
log(Employees)
log(TotalPatentStock)
Year dummies
firm-technological area fixed effects
No. of firm-technological area pairs
No. of observations
(3)
-.119
(.098)
-.139**
(.067)
.208**
(.105)
-.295
(.305)
.128
(.133)
-.308
(.395)
.125
(.135)
-.180
(.147)
.098
(.064)
.111**
(.054)
.004
(.004)
-.119
(.095)
.288*
(.163)
-.091
(.143)
Yes
Yes
1293
7583
Notes: 1) This number of observations is lower than 15678 because of the use of conditional fixed effects Poisson models, which
drops the panels without any patent over the entire sample period or with only one observation. 2) Robust standard errors, clustered
by firm, are in parentheses. 3) * significant at 10%, ** significant at 5%, *** significant at 1%.
20
6.2 Examining heterogeneity in the effects of standards on inventive activity
In this section, we conduct a series of tests to examine under what conditions standardization
influences patenting activity. As noted earlier, for all documents at the IETF, we distinguish among the
standards contributed by firms and the standards developed by non-commercial organizations. The results
from our previous section suggest that the standards contributed by firms are negatively associated with a
non-contributing firm’s inventive activity. However, given the substantial differences in the motives to
contribute between firms and non-commercial organizations discussed in section 3, we should not observe
a similar relationship from the standards by non-commercial organizations.
Table 5: Examining heterogeneity in the effects of standards on inventive activity
Dependent variable: Inventive activity
(1)
0.822
(0.504)
(2)
0.828*
(0.494)
-0.224**
(0.107)
(3)
1.050*
(0.545)
(4)
.305
NonComStandards
(.482)
-.135
ComStandards
(.111)
-0.403
.100
NonComStandards X D(regime change)
(0.376)
(.449)
-.121*
ComStandards X D(regime change)
(.068)
0.103
.095
D(regime change)
(0.073)
(.074)
-0.176
-0.180
-0.176
-.180
log(PatentStock)
(0.149)
(0.148)
(0.149)
(.148)
0.100
0.099
0.099
.097
log(PatentQuality)
(0.064)
(0.063)
(0.064)
(.064)
0.072
0.071
0.089*
.097*
AreaPatentQuality
(0.044)
(0.045)
(0.051)
(.054)
0.001
0.005
0.001
.005
AreaPatentStock
(0.004)
(0.004)
(0.004)
(.004)
-0.004
-0.008**
-0.002
-.007
NoOfNonComMsgPerStandard
(0.003)
(0.004)
(0.003)
(.003)
-0.118
-0.119
-0.118
-.119
log(Sales)
(0.095)
(0.095)
(0.095)
(.095)
0.286*
0.288*
0.286*
.289*
log(Employees)
(0.163)
(0.163)
(0.163)
(.163)
-0.096
-0.092
-0.095
-.090
log(TotalPatentStock)
(0.143)
(0.143)
(0.143)
(.144)
Year dummies
Yes
Yes
Yes
Yes
firm-technological area fixed effects
Yes
Yes
Yes
Yes
No. of firm-technological area pairs
1293
1293
1293
1293
No. of observations
7583
7583
7583
7583
Notes: 1) This number of observations is lower than 15678 because of the use of conditional fixed effects Poisson models, which
drops the panels without any patent over the entire sample period or with only one observation. 2) Robust standard errors, clustered
by firm, are in parentheses. 3) * significant at 10%, ** significant at 5%, *** significant at 1%.
The results based on a specification that only includes standards developed by non-commercial
organizations (denoted as NonComStandards) are reported in columns (1) and the results based on
specification (3) are presented in column (2) of table 5. Although the estimate of NonComStandards is
insignificant in column (1) with a p-value of .103, in both specifications it is positively associated with a
non-contributing firm’s patenting activity. A test of the difference between the estimate of
NonComStandards and the estimate of ComStandards in column (2) is statistically significant. This
21
provides us some evidence that the effect identified from ComStandards is not driven by unobservables
such as technological changes that may affect NonComStandards and ComStandards similarly. Moreover,
we speculate that, in contrast to firm-contributed standards, when producing technologies to standards
developed by non-commercial organizations, a non-contributing firm has little concern on being
disadvantaged in product market as well as being exposed to IPR infringement risks. In the absence of those
strategic behaviors by commercial firms, standardization may be beneficial to a non-contributing firm.
Columns (3) and (4) of table 5 then show the results from regressions in which we interact
ComStandards and NonComStandards with the regime change described above. As expected, columns (3)
and (4) show that a change in the strength of software patents has no effect on the relationship between
non-commercial standards and patenting activity. In contrast, as noted in table 4, column (4) shows that
when patent rights are stronger the relationship between commercial standards and patenting becomes more
negative.
6.3 How the effects of standardization vary with the risks of infringement on standards-related IPR
We next investigate how the effect of standardization on inventive activity varies with the risks of
infringement on standards-related IPR held by contributing firms.
Column (1) in table 6 presents the baseline results based on a specification that includes the
interaction between the number of standards contributed by firms and the extent to which the contributing
firms hold standards-related IPR. As reflected by the sign of the interaction term (i.e. ComStandards X
RelatedIPRfraction), the effect of standards is significantly different when the contributing firms hold a
large versus small fraction of related IPR. Given the concern on how ComStandards is measured as
mentioned above, column (2) then reports the results from measuring ComStandards using the set of
standards that are matched to CorpTech, and the results are very similar to column (1).
Columns (3) and (4) examine the effect of standards contributed by small firms (cf. column 3) and
how such effect is affected by the fraction of related IPR in an area held by these small firms (cf. column
4). The marginal effect of ComStandards_SmallFirms is insignificantly negative when other variables are
evaluated
at
means
for
both
columns
(3)
and
(4),
and
insignificantly
positive
when
th
RelatedIPRfraction_SmallFirms is evaluated at 10 percentile for column (4). However, an increase of 100
standards contributed by small firms is associated with a 28% decrease of inventive activity when
RelatedIPRfraction_SmallFirms
is
th
at
90th
percentile,
and
the
difference
between
th
RelatedIPRfraction_SmallFirms at 10 percentile and at 90 percentile is statistically significant. Columns
(5) and (6) then report the effect of standards contributed by large firms (cf. column 5) and how the fraction
of related IPR in an area held by these large firms shapes such effect (cf. column 6). In contrast to the result
observed in column (3), column (5) reveals a significantly negative effect of standards from those large
firms on firm’s patenting activity when all variables are evaluated at means. However, as indicated by
22
column (6), we observe little difference between environments where those large firms holding large and
small fraction of related IPR.26
Table 6: How the effects of standardization vary with the risks of infringement on standardsrelated IPR, baseline results
Dependent variable: Inventive activity
ComStandards
ComStandards X
RelatedIPRfraction
RelatedIPRfraction
(1)
0.093
(0.198)
-0.010**
(0.004)
0.008*
(0.004)
(2)
0.128
(0.188)
-0.010**
(0.004)
0.008*
(0.004)
ComStandards_SmallFirms
(3)
(4)
-0.220
(0.189)
0.136
(0.176)
-2.003**
(0.793)
1.285***
(0.397)
ComStandards_SmallFirms X
RelatedIPRfraction_SmallFirms
RelatedIPRfraction_SmallFirms
(5)
-0.243**
(0.096)
ComStandards_LargeFirms
ComStandards_LargeFirms X
RelatedIPRfraction_LargeFirms
RelatedIPRfraction_LargeFirms
log(PatentStock)
log(PatentQuality)
AreaPatentQuality
AreaPatentStock
log(Sales)
log(Employees)
log(TotalPatentStock)
Year dummies
firm-technological area fixed effects
No. of firm-technological area pairs
No. of observations
-0.181
(0.147)
0.099
(0.063)
0.103*
(0.060)
0.006
(0.004)
-0.119
(0.095)
0.288*
(0.163)
-0.091
(0.143)
Yes
Yes
1293
7583
-0.181
(0.147)
0.099
(0.063)
0.100*
(0.057)
0.005
(0.004)
-0.119
(0.095)
0.288*
(0.163)
-0.091
(0.143)
Yes
Yes
1293
7583
-0.178
(0.147)
0.100
(0.064)
0.106**
(0.051)
0.004
(0.004)
-0.118
(0.095)
0.286*
(0.163)
-0.092
(0.143)
Yes
Yes
1293
7583
-0.181
(0.147)
0.099
(0.064)
0.102*
(0.056)
0.004
(0.004)
-0.118
(0.095)
0.287*
(0.163)
-0.090
(0.144)
Yes
Yes
1293
7583
-0.183
(0.147)
0.099
(0.064)
0.115**
(0.056)
0.005
(0.004)
-0.119
(0.095)
0.286*
(0.163)
-0.089
(0.143)
Yes
Yes
1293
7583
(6)
-0.062
(0.237)
-0.007
(0.006)
0.007
(0.004)
-0.181
(0.147)
0.099
(0.063)
0.111*
(0.058)
0.005
(0.004)
-0.119
(0.095)
0.287*
(0.163)
-0.090
(0.143)
Yes
Yes
1293
7583
(7)
(8)
-0.052
(0.222)
0.364
(0.238)
-1.854**
(0.728)
0.737
(0.452)
0.019
(0.246)
-0.009
(0.007)
0.004
(0.004)
-0.183
(0.148)
0.098
(0.063)
0.110**
(0.054)
0.004
(0.004)
-0.119
(0.095)
0.288*
(0.163)
-0.089
(0.143)
Yes
Yes
1293
7583
-0.216**
(0.086)
-0.182
(0.147)
0.099
(0.064)
0.113**
(0.051)
0.005
(0.004)
-0.119
(0.095)
0.286*
(0.163)
-0.089
(0.143)
Yes
Yes
1293
7583
Notes: 1) This number of observations is lower than 15678 because of the use of conditional fixed effects Poisson models, which
drops the panels without any patent over the entire sample period or with only one observation. 2) Robust standard errors, clustered
by firm, are in parentheses. 3) * significant at 10%, ** significant at 5%, *** significant at 1%.
Columns (7) and (8) then investigate the effects of standards contributed by large firms and small
firms together. In column (7), while we find qualitatively similar results as the ones in the other columns,
the magnitude of ComStandards_SmallFirms changes somewhat; similarly, in column (8), although the
marginal effects of ComStandards_SmallFirms when RelatedIPRfraction_SmallFirms at means and at 90th
percentile are very similar to the ones implied by column (4), the significance level decreases due to larger
standard errors. We note that this is perhaps due to the high correlation between ComStandards_SmallFirms
26
We note that in column (6), although the marginal effect of ComStandards_LargeFirms when other variables are
evaluated at means is insignificant with a p-value of 0.2, its magnitude is -0.2 and thus very similar to the one in
column (5). The insignificance of this result is due to the larger standard error.
23
and ComStandards_LargeFirms (a correlation coefficient of 0.88), so cautions need to be taken when
interpreting the results in columns (7) and (8) where we include both standards by large firms and small
firms in one specification.
We test the robustness of our results where we measure RelatedIPRfraction based on raw count.
The results, presented in columns (1) to (4) in table B2 in the appendix, are largely consistent with the
baseline results in table 6. We also add area-specific time trends and the results are reported in columns (5)
to (8) in table B2, which are qualitatively similar.
Last, we utilize the set of standards contributed by non-commercial organizations to conduct a set
of falsification exercises in this section. We should expect little differential effect of standards by noncommercial organizations between environments where contributing firm holds a large and small fraction
of related IPR. Therefore, we add the interactions of standards by non-commercial organizations with the
fraction of related IPR held by all contributing firms, by small firms, and by large firms. The results are
reported in table B3 in the appendix, which are consistent with our expectation.
7. Conclusion
Although standardization is widespread in ICT products, we still have limited systematic evidence
on the effect of standardization on competition and innovation more broadly. Leveraging data on firms
contributing standards to the IETF, we find that standardization led by commercial firms is associated with
a decrease of inventive activity by non-contributing firms. We provide evidence that these effects may be
due in part to increases in the costs of acquiring rights to standards-essential patents and other related IPR.
Our study is subject to limitations. As has been noted throughout the manuscript, one identification
challenge that we face is that standardization may be correlated with unobserved market-level factors that
may influence both standardization and patenting behavior. While we conduct a wide range of analyses to
attempt to highlight the effects of IPR infringement risks on the economic implications of standardization,
our results must be interpreted with care. Further, our dependent variable may capture both the effects of
standardization on invention as well as its effects on strategic patenting behavior. One of our next steps is
to employ new product entry as an alternative measure for a firm’s inventive activity and exploit the
relationship between standardization and a firm’s product entry decision into an area.
Our study also points to many important questions for future research. For example, how do the
relationships between standards-contributing firms and non-contributing firms shape the effects of
standardization on non-contributors’ inventive activity? Do alliances or partnerships between these two
groups change the effects we find in our paper? We hope our research could illuminate further study in this
important field.
24
References
Bekkers, R., G. Duysters, B. Verspagen. 2002. Intellectual property rights, strategic technology agreements
and market structure: The case of GSM. Research Policy, 31(7): 1141-1161.
Bekkers, R., E. Iversen, K. Blind. 2012. Emerging ways to address the reemerging conflict between
patenting and technological standardization, Industrial and Corporate Change, 21 (4): 901-931.
Besen, S.M., J. Farrell. 1991. The role of the ITU in standardization—Pre-eminence, impotence, or rubber
stamp. Telecommunications Policy 15(4): 311-321.
Besen, S.M., L. Johnson. 1988. Compatibility standards, competition, and innovation in the broadcasting
industry. RAND Report 3453-NSF.
Chiao, B., J. Lerner, J. Tirole. 2007. The rules of standard-setting organizations: an empirical analysis.
RAND Journal of Economics 38(4): 905-930.
Cockburn, I. M., M. MacGarvie. 2009. Patents, Thickets and the Financing of Early-Stage Firms: Evidence
from the Software Industry. Journal of Economics & Management Strategy 18(3): 729-773
Cockburn, I. M., M. MacGarvie. 2011. Entry and Patenting in the Software Industry. Management Science
57(5): 915-933.
David, P. A., S. Greenstein. 1990. The economics of compatibility standards: An introduction to recent
research 1. Economics of innovation and new technology, 1(1-2): 3-41.
Delcamp, H., A. Leiponen. 2013. Innovating standards through informal consortia: The case of wireless
telecommunications. International Journal of Industrial Organization
Dorth, D. A. 2003. The Rambus Shell Game: A Lack of Integrity In the Standards Setting Process. The
John Marshall Review of Intellectual Property Law, 3(1): 138-155.
Eisenman, T., L. Barley. 2006. Atheros Communications. Case no. 806-093, Harvard Business School,
2006.
Farrell, J., J. Choi, A. Edlin, S. Greenstein, B. Hall, and G. Saloner, 2004. Brief Amicus Curiae of
Economics Professors and Scholars in the Matter of Rambus, Inc. U.S. Federal Trade Commission
Docket No. 9302.
Farrell, J., G. Saloner. 1988. Coordination through Committees and Markets. RAND Journal of Economics,
19(2): 235-252.
Farrell, J., T. Simcoe. 2011. Four paths to compatibility. The Oxford Handbook of the Digital Economy,
34.
Farrell, J., T. Simcoe. 2012. Choosing the rules for consensus standardization. RAND Journal of Economics,
43(2): 235-252.
Galasso, A., M. Schankerman. 2010. Patent Thickets, Counts, and the Market for Innovation. RAND
Journal of Economics 41(3): 472-503.
Greenstein, S., V. Stango, 2007. Standards and Public Policy, Cambridge University Press.
Hall, B. H., M. MacGarvie. 2010. The private value of software patents. Research Policy 39: 994-1009.
Hall, B. H., R. Ziedonis 2001. The Patent Paradox Revisited: An Empirical Study of Patenting in the US
Semiconductor Industry, 1979–1995. RAND Journal of Economics 32: 101-128.
Heller, M. A., R. S. Eisenberg. 1998. Can Patents Deter Innovation? The Anticommons in Biomedical
Research. Science 280 (5364): 698-701.
Huang, P., M. Ceccagnoli, C. Forman, D.J. Wu. 2013. Appropriability Mechanisms and the Platform
Partnership Decision: Evidence from Enterprise Software. Management Science 59(1): 102-121
Joshi, A.M., A. Nerkar. 2011. When Do Strategic Alliances Inhibit Innovation by Firms? Evidence from
Patent Pools in the Global Optical Disc Industry. Strategic Management Journal 32(11): 1139-1160
Krechmer, K. 2006. Open Standards Requirements. Journal of IT Standards & Standardization Research
4(1): 43-61.
Lai R., A. D'Amour, A. Yu, Y. Sun, L. Fleming. 2013. Disambiguation and Co-authorship Networks of the
U.S. Patent Inventor Database (1975 - 2010). The Harvard Dataverse Network
Lampe, R., P. Moser. 2010. Do Patent Pools Encourage Innovation? Evidence from the Nineteenth-Century
Sewing Machine Industry. The Journal of Economic History 70(4): 898-920.
25
Lampe, R., P. Moser. 2013. Patent Pools and Innovation in Substitutes - Evidence from the 19th Century
Sewing Machine Industry. RAND Journal of Economics, forthcoming.
Leiponen, A. E. 2008. Competing through cooperation: The organization of standard setting in wireless
telecommunications. Management Science, 54(11): 1904-1919.
Lemley, M. A. 2002. Intellectual property rights and standard-setting organizations. California Law Review,
1889-1980.
Lerner, J. 2002. Where Does State Street Lead? A First Look at Finance Patents, 1971 to 2000. The Journal
of Finance 57(2): 901-930.
Lerner, J., J. Tirole. 2006. A Model of Forum Shopping. American Economic Review, 96:1091–1113.
Lerner, J., J. Tirole. 2013. Standard-essential Patents. NBER Working paper 12563, National Bureau of
Economic Research, Cambridge, MA.
Lerner, J., F. Zhu. 2007. What is the impact of software patent shifts? Evidence from Lotus
Mackie-Mason J., J. Netz. 2007. Manipulating Interface Standards as an Anticompetitive Strategy, in S.
Greenstein and V. Stango, eds., Standards and Public Policy, Cambridge, UK: Cambridge University
Press, 231–259.
Rysman, M., T. Simcoe. 2008. Patents and the Performance of Voluntary Standard Setting Organizations.
Management Science 54(11): 1920-1934.
Shapiro, C. 2000. Setting compatibility standards: cooperation or collusion. University of California at
Berkeley Working Paper
Shapiro, C. 2001. Navigating the Patent Thicket: Cross Licenses, Patent Commons, and Standard-Setting.
A. Jaffe, J. Lerner, S. Stern, eds. Innovation Policy and the Economy, Vol. 1. MIT Press, Cambridge,
MA.
Simcoe, T. 2012. Standard setting committees: Consensus governance for shared technology platforms.
The American Economic Review, 102(1): 305-336.
Simcoe, T. 2013. Modularity and the Evolution of the Internet. In Economics of Digitization. University of
Chicago Press.
Simcoe, T., C. Catalini. 2011. Intellectual Property Disclosure in Open Standards Development. Working
Paper
Simcoe, T. S., S. J. H. Graham, M. P. Feldman. 2009. Competing on Standards? Entrepreneurship,
Intellectual Property, and Platform Technologies. Journal of Economics & Management Strategy.
18(3):775-816.
Waguespack, D. M., L. Fleming. 2009. Scanning the commons? Evidence on the benefits to startups
participating in open standards development. Management Science, 55(2): 210-223.
West, J. 2007. The economic realities of open standards: Black, white and many shades of gray. S.
Greenstein and V. Stango, eds., Standards and Public Policy, Cambridge, UK: Cambridge University
Press, 87-122.
Ziedonis, R.H. 2004. Don't Fence Me In: Fragmented Markets for Technology and the Patent Acquisition
Strategies of Firms. Management Science 50(6): 804-820.
26
APPENDIX A: Additional Data Details
A.1 Additional Details on Determining the Timing of the Regime Shift for the Focal Technological
Areas
To determine the timing of the regime shift for the focal technological areas, we first identify
whether and when each of our focal patent classes was influenced by which regime change. This is mainly
based on a combination of the data from Cockburn and MacGarvie (2011) and manual classification. More
specifically, Cockburn and MacGarvie (2011) reported a list of software product markets and when these
markets were affected by the two legal regime changes. Based on the concordance between product markets
and patent classes they created, we then know whether certain patent classes were affected. For the patent
classes that appear in both our and their study, we directly use the timing of the regime shift reported by
them. For the patent classes that only appear in our study, we determine the timing mainly based on the
discussions by Hall and MacGarvie (2010) and manual reading of the patent class descriptions at the
USPTO website. Table A.1 below presents a complete view on whether and when each of our focal patent
classes was influenced.
Then, based on the concordance between patent classes and technological areas, we could
determine which areas were most affected by each of the two regime changes. This is based on the percent
of pre-sample patent stock in an area that is related to software embedded in physical media (for the regime
change in 1996) and business methods and disembodied algorithms (for the regime change in 1998). Among
the six areas, because Operations and Management area and Routing area have the highest percent of presample patent stock related to software embedded in physical media, we consider these two areas are most
affected by the regime change in 1996. On the other hand, because Application area and Internet area have
the highest percent of pre-sample patent stock related to business methods and disembodied algorithms, we
consider these two areas were most affected by the regime change in 1998. Therefore, D(regime change)kt
dummy in equation (2) is equal to one for Operations and Management area and Routing area after 1996;
it is equal to one for Application and Internet area after 1998; for the other two areas (Security and
Transport), it remains zero during the entire sample.
Besides constructing the dummy variable D(regime change)kt based on the pre-sample patent stock,
we also construct an intensity measure to capture how much each area is affected by the two legal regime
changes, denoted as I(regime change)kt. More specifically, let’s denote the cumulative stock of patents
related to software embedded in physical media (i.e. those affected by the 1996 change) in area k by year t
as SofPatCntkt. Similarly, let’s denote the cumulative stock of patents related to business methods and
disembodied algorithms (i.e. those affected by the 1998 change) in area k by year t as BmPatCntkt. Then,
I(regime change)kt is equal to (SofPatCntkt*After96+ BmPatCntkt*After98)/TotalPatentStockkt, where
27
TotalPatentStockkt is the cumulative stock of all patents in area k by year t, After96 is a dummy that turns
on after 1996, and After98 is a dummy that turns on after 1998.
Table A.1
Patent
class
380
704
707
709
711
713
714
715
719
726
705
340
358
370
375
379
455
702
710
Description
Cryptography
Data processing: speech signal processing, linguistics, language
translation, and audio compression/decompression
Data processing: database and file management or data
structures
Electrical computers and digital processing systems:
multicomputer data transferring
Electrical computers and digital processing systems: memory
Electrical computers and digital processing systems: support
Error detection/correction and fault detection/recovery
Data processing: presentation processing of document, operator
interface processing, and screen saver display processing
Electrical computers and digital processing systems:
interprogram communication or interprocess communication
(ipc)
Information security
Data processing: financial, business practice, management, or
cost/price determination
Communications: electrical
Facsimile and static presentation processing
Multiplex communications
Pulse or digital communications
Telephonic communications
Telecommunications
Data processing: measuring, calibrating, or testing
Electrical computers and digital data processing systems:
input/output
Affected by the
regime change
in 1996
1
Affected by the
regime change
in 1998
Not
affected
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
28
A.2 Additional Details on Mapping Patent Classes to Technological Areas
In the example below, for RFC x that belongs to technological area k, suppose we find its author
Fred Baker holds a patent # US 6742044 through name search in the US Patent Inventor Database. Then,
we consider this patent’s 3-digit USPTO class—709—is matched to RFC x and therefore as a candidate for
technological area k’ patent class. Through this procedure, one RFC could be matched to one patent class
or multiple patent classes. In the latter case, it could be due to one author with patents in different classes
or multiple authors with patents in different classes. After obtaining all possible RFC-patent class matches,
we aggregated them to technological area level, and use the top 10% most frequent patent classes as the
representative patent classes for that area.
Figure A: An Example of Mapping Technological Areas to Patent Classes
US Patent Inventor Database
Technological
area k
RFC z
Patent #6742044
RFC y
RFC x
Author name: Fred Baker
Affiliation: Cisco
Location: Santa Barbara, CA
Patent class: 709
Inventor name: Fred Baker
Affiliation: Cisco
Location: Santa Barbara, CA
29
APPENDIX B: Supporting Empirical Results
Table B1: Robustness check on exploring the regime changes
Dependent variable: Innovation
ComStandards
ComStandards X I(regime change)
I(regime change)
ComStandards X D(regime change 1)
ComStandards X D(regime change 2)
D(regime change 1)
D(regime change 2)
ComStandards X D(regime change lagged)
D(Regime lagged)
(1)
0.392
(0.297)
-1.273*
(0.699)
0.586*
(0.300)
(2)
-0.074
(0.116)
(3)
-0.061
(0.103)
(4)
0.019
(0.121)
-0.217*
(0.122)
-0.073
(0.085)
0.111
(0.077)
0.127
(0.093)
-0.149**
(0.062)
0.109*
(0.057)
-0.234**
(0.113)
-0.124
ComStandards X D(regime change 2 lagged)
(0.092)
0.040
D(regime change 1 lagged)
(0.083)
0.186*
D(regime change 2 lagged)
(0.102)
-0.180
-0.180
-0.181
-0.179
log(PatentStock)
(0.148)
(0.148)
(0.148)
(0.148)
0.097
0.097
0.099
0.098
log(PatentQuality)
(0.064)
(0.064)
(0.064)
(0.064)
0.072
0.079
0.150**
0.047
AreaPatentQuality
(0.047)
(0.065)
(0.065)
(0.048)
0.002
0.002
0.003
0.002
AreaPatentStock
(0.004)
(0.004)
(0.004)
(0.005)
-0.119
-0.119
-0.118
-0.119
log(Sales)
(0.095)
(0.095)
(0.095)
(0.095)
0.288*
0.288*
0.287*
0.288*
log(Employees)
(0.163)
(0.163)
(0.163)
(0.163)
-0.090
-0.090
-0.091
-0.091
log(TotalPatentStock)
(0.144)
(0.144)
(0.144)
(0.144)
Year dummies
Yes
Yes
Yes
Yes
firm-technological area fixed effects
Yes
Yes
Yes
Yes
No. of firm-technological area pairs
1293
1293
1293
1211
No. of observations
7583
7583
7583
7214
Notes: 1) This number of observations is lower than 15678 because of the use of conditional fixed effects Poisson models, which
drops the panels without any patent over the entire sample period or with only one observation. 2) Robust standard errors, clustered
by firm, are in parentheses. 3) * significant at 10%, ** significant at 5%, *** significant at 1%.
ComStandards X D(regime change 1 lagged)
30
Table B2: How the effects of standardization vary with the risks of infringement on standardsrelated IPR, robustness check
Dependent variable: Inventive activity
ComStandards
ComStandards X
RelatedIPRfraction
RelatedIPRfraction
(1)
0.054
(0.188)
-0.009**
(0.004)
0.008*
(0.004)
(2)
ComStandards_SmallFirms X
RelatedIPRfraction_SmallFirms
RelatedIPRfraction_SmallFirms
ComStandards_LargeFirms
ComStandards_LargeFirms X
RelatedIPRfraction_LargeFirms
RelatedIPRfraction_LargeFirms
log(PatentQuality)
AreaPatentQuality
AreaPatentStock
log(Sales)
log(Employees)
log(TotalPatentStock)
Year dummies
firm-technological area fixed effects
Area-specific time trend
No. of firm-technological area pairs
No. of observations
(4)
-0.089
(0.235)
-0.007
(0.006)
0.007
(0.004)
-0.181
(0.148)
0.099
(0.063)
0.113*
(0.059)
0.005
(0.004)
-0.119
(0.095)
0.288*
(0.163)
-0.090
(0.143)
Yes
Yes
No
1293
7583
0.347
(0.215)
-2.400**
(0.959)
0.780
(0.640)
-0.012
(0.259)
-0.007
(0.008)
0.004
(0.004)
-0.182
(0.148)
0.098
(0.064)
0.113**
(0.047)
0.003
(0.004)
-0.119
(0.095)
0.288*
(0.163)
-0.089
(0.143)
Yes
Yes
No
1293
7583
0.208
(0.173)
-2.954***
(1.062)
1.462***
(0.548)
ComStandards_SmallFirms
log(PatentStock)
(3)
-0.180
(0.147)
0.099
(0.063)
0.105*
(0.061)
0.005
(0.004)
-0.119
(0.095)
0.288*
(0.163)
-0.091
(0.143)
Yes
Yes
No
1293
7583
-0.180
(0.148)
0.098
(0.064)
0.119**
(0.052)
0.002
(0.004)
-0.118
(0.095)
0.287*
(0.163)
-0.091
(0.144)
Yes
Yes
No
1293
7583
(5)
0.266
(0.418)
-0.020
(0.016)
0.008
(0.006)
(6)
(7)
(8)
0.435
(0.482)
-0.034
(0.023)
0.009
(0.007)
-0.185
(0.143)
0.091
(0.062)
-0.471**
(0.201)
0.370***
(0.114)
-0.122
(0.095)
0.279*
(0.162)
-0.080
(0.138)
Yes
Yes
Yes
1293
7583
0.324
(0.337)
-3.840**
(1.543)
0.931*
(0.497)
0.516
(0.472)
-0.029
(0.023)
0.006
(0.006)
-0.185
(0.143)
0.091
(0.062)
-0.555***
(0.214)
0.370***
(0.114)
-0.122
(0.095)
0.278*
(0.162)
-0.079
(0.138)
Yes
Yes
Yes
1293
7583
0.179
(0.204)
-4.380**
(1.705)
1.278**
(0.581)
-0.185
(0.143)
0.092
(0.062)
-0.492**
(0.204)
0.363***
(0.113)
-0.122
(0.095)
0.279*
(0.162)
-0.080
(0.138)
Yes
Yes
Yes
1293
7583
-0.185
(0.143)
0.091
(0.062)
-0.610***
(0.224)
0.357***
(0.107)
-0.121
(0.095)
0.278*
(0.162)
-0.079
(0.138)
Yes
Yes
Yes
1293
7583
Notes: 1) This number of observations is lower than 15678 because of the use of conditional fixed effects Poisson models, which
drops the panels without any patent over the entire sample period or with only one observation. 2) Robust standard errors, clustered
by firm, are in parentheses. 3) * significant at 10%, ** significant at 5%, *** significant at 1%.
31
Table B3: How the effects of standardization vary with the risks of infringement on standardsrelated IPR, falsification exercise
Dependent variable: Inventive activity
NonComStandards
NonComStandards X RelatedIPRfraction
(1)
0.888
(0.781)
0.002
(0.021)
NonComStandards
XRelatedIPRfraction_SmallFirms
NonComStandards
XRelatedIPRfraction_LargeFirms
RelatedIPRfraction
(2)
0.900
(0.599)
AreaPatentQuality
AreaPatentStock
NoOfNonComMsgPerStandard
log(Sales)
log(Employees)
log(TotalPatentStock)
Year dummies
firm-technological area fixed effects
No. of firm-technological area pairs
No. of observations
0.002
(0.021)
0.255
(2.455)
0.014
(0.023)
-0.002
(0.003)
-0.176
(0.149)
0.100
(0.063)
0.064
(0.052)
0.001
(0.003)
-0.004
(0.003)
-0.118
(0.095)
0.286*
(0.163)
-0.095
(0.143)
Yes
Yes
1293
7583
0.482
(0.340)
-0.002
(0.003)
-0.178
(0.149)
0.100
(0.064)
0.052
(0.067)
0.001
(0.003)
-0.004
(0.003)
-0.118
(0.095)
0.286*
(0.163)
-0.094
(0.143)
Yes
Yes
1293
7583
-0.002
(0.003)
0.355
(0.349)
RelatedIPRfraction_LargeFirms
log(PatentQuality)
(4)
0.705
(0.831)
0.654
(2.448)
RelatedIPRfraction_SmallFirms
log(PatentStock)
(3)
0.893
(0.779)
-0.176
(0.149)
0.100
(0.063)
0.064
(0.051)
0.001
(0.003)
-0.004
(0.003)
-0.118
(0.095)
0.286*
(0.163)
-0.095
(0.143)
Yes
Yes
1293
7583
-0.177
(0.149)
0.100
(0.064)
0.054
(0.061)
0.002
(0.004)
-0.004
(0.003)
-0.118
(0.095)
0.286*
(0.163)
-0.095
(0.143)
Yes
Yes
1293
7583
Notes: 1) This number of observations is lower than 15678 because of the use of conditional fixed effects Poisson models, which
drops the panels without any patent over the entire sample period or with only one observation. 2) Robust standard errors, clustered
by firm, are in parentheses. 3) * significant at 10%, ** significant at 5%, *** significant at 1%.
32