Licensing activities of Japanese firms in the era of pro-patent system reform and open
innovation
WORK IN PROGRESS, PLEASE DO NOT CITE
BUT COMMENTS ARE WELCOME
Masayo Kania and Kazuyuki Motohashib
Abstract
This paper investigates the determinants of licensing activity of Japanese firms, by
using the firm level dataset from a JPO survey called SIPA (Survey of Intellectual
Property Activities), the IIP patent database, and the Licensing Activity Survey (LAS)
conducted by University of Tokyo. Gambardella et al. (2007) addresses the issue of
technology market imperfection by showing that many potential licenses are not
actually licensing. In this paper, we use two step models of licensing activities; the first
step is to estimate the determinants of potential licensors (willingness to license), and
the second step is for identifying factors behind actual licensing out (licensing
propensity). We have found that the strength of patent protection is important to the
willingness to license out and licensing propensity. In addition, it is found that the
factors of technology market imperfection such as the difficulty in arranging complex
licensing contract prevents a firm from licensing out even it has a willingness to license.
Lecturer, Faculty of Economics, Tezukayama University, 7-1-1 Tezukayama Nara
631-8501 Japan. E-mail address: [email protected], tel:+81-742-48-7148, fax:
+81-742-48-9308
b Professor, Department of Technology Management for Innovation (TMI), University of
Tokyo, 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan. E-mail address:
[email protected], tel: +81-3-5841-1828, fax: +81-3-5841-1829
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1.
Introduction
In 2003, the Intellectual Property Strategy Headquarter, headed by the prime minister,
was created in the Japanese government, and annual reviewing process of IPR policy of
various related ministries were introduced. This initiative is not only for strengthening
patent right, but also for activating technology market to stimulate knowledge diffusion
in a form of intellectual property right. “Development of Knowledge Based Nation by
Intellectual Property” became a slogan of the Japanese government, and pro-patent
policy reforms have been introduced. Alongside of this public policy initiative firms
become aware of the importance of IP as a strategic tool of their competitive advantage.
IP department at firm, which used to be a function of patent application and dealing
with infringement cases, now is actively engaged in activities of strategic use of
intellectual property rights, such as license or not decision.
In addition, licensing becomes one of important variables in Japanese firm’s innovation
strategy in these years. According to the R&D Collaboration Survey conducted by RIETI
in February 2004, firms are treating external collaborations more positively compared
with five years ago, regardless of industries and firm size (RIETI, 2004). Due to the
globalization of the economy and innovation competition spurred by catching up of East
Asia economies such as Korea and Taiwan, it becomes increasingly difficult for
Japanese large corporations to sustain its in-house innovation model. The increasing
importance of scientific knowledge in the R&D process of enterprises in certain
industries, such as pharmaceuticals, is also a factor fostering external collaborations,
particularly with universities and public research institutions (Motohashi, 2005). In
this process of shift to open innovation era, management of intellectual property
consists of key component of innovation strategy of high tech firms. (Chesbrough, 2006)
This paper analyzes Japanese firms’ licensing activities in this era of pro-patent policy
reform and open innovation, by using a novel dataset of licensing activities of Japanese
firms. Licensing activities of firms are influenced by various kinds of factors. It depends
on technology market conditions as well as product market conditions. For example,
patent can be used more as a means of appropriating rents from technological
innovation in pharmaceutical industry, as compared to in other industries (NISTEP,
1997; Cohen et al., 2002). It is found that the licensing propensity is relatively higher for
this industry (Anand and Khanna, 2000). When a firm perceives patent as strong
property right, it may rely more on technology market transactions, instead of
appropriating rent from its invention as a trade secret. It is also found that the firm size
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is an important determinant of licensing propensity (Gambardella et. al, 2007).
Whether a firm licenses or not licenses depends on complementary assets to IPs, such as
marketing and production resources (Arora and Fosfuri, 2003). Licensing of patents
may induce “rent dissipation” of patent owner due to creating a potential competitor in
product market. Therefore, a small company (likely to be a minor player in product
market with small amount of complementary assets) tends to license out more, since
“revenue effect” by licensing fees becomes larger than “rent dissipation effect”. It is also
important to take into transaction costs in the market for technology. Gambardella et. al
(2007) show that large number of firms do not license even thought they are willing to
do so. This finding suggests that the size of transaction cost also matters an ex-post
licensing propensity.
This paper investigates licensing activities in Japanese firms along the line of
Gambardella et. al (2007). A major difference from this existing literature is that our
datasets are based on firm level observations, while Gambardella et. al (2007) use
patent level observations. In addition, our datasets have qualitative information on the
factors hampering licensing activities, enabling us to further dig into the issues of
technology market imperfection. The next section of this paper is devoted for literature
survey of empirical analysis on licensing and technology market. This section is
followed by data description and summary statistics. Then, quantitative analysis
section follows. The determinants of firm’s IP strategy are analyzed with econometrics
models. Finally, a section for conclusion with managerial and policy implications is
provided.
2.
Hypothesis of determinants of licensing propensity
There are various types of incentive for a firm to own patent. A primary purpose is to
prevent its invention from imitation and to gain monopoly rent from a product using its
invention. For a start-up company which does not its own production facility, licensing
its patent is important. Furthermore, all of patents owned by firm are not always used
by in-house use or licensing. It is possible for them to keep patent right which may be
used in future, and a firm may protect its technology by creating patent fence, in a sense
of patenting substitute technologies as well as the one used for its product.
All of these factors influence the licensing propensity of firm, measured by the number
of licensing patents to the number of all patents. But the number of licensing patents
can be decomposed into two parts, i.e., the number of patents which a firm is willing to
license and the share of patents actually licensed to the number of willing to license
patents. Figure 1 depicts this process, that is, all patents owned by a firm are classified
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into three categories i.e., licensing patent, patent that the firm would be willing to
license but could not actually license and patent that are not willing to license.
There are numerous studies investigating the determinants of licensing propensity, but
the novel feature of this study is to disentangle two steps, willingness to license (Step1)
and coming up with actual licensing deal (Step2). Past literature suggest the following
five types of determinants. Here, we provide our hypothesis on which step is relevant to
each type of determinant.

Complementary assets such as production and marketing facilities: A big firm has
production and marketing functions as well as R&D function inside its company.
In
this case, a firm is less willing to license because they can appropriate economic rent
from their invention by using their own assets. In contrast, a firm without
complementary assets, such as a high tech start-up, has a great incentive to license its
patent. Therefore, licensing willingness propensity (Step1) increases as the size of
complementary assets decreases.

Product and technology market competition : Product market competition is also an
important determinant of licensing propensity. The decision of licensing is determined
by a balance between revenue effect (from licensing fees) and rent dissipation effect
(from increasing competition in product market) (Arora and Fosfuri, 2003). If product
market is close to perfect competition, a firm does not have to worry about rent
dissipation effect very much, because monopoly rent is already small. The firm facing
such market competition environment is assumed to be higher. This is the case for
technology market competition as well, since a fierce competition in technology market
also leads to higher licensing propensity. On the other hand, when a firm dominates
certain technology, it is better not license out and gain monopoly rent out of it. Therefore,
license willingness propensity (Step1) increases as product or technology market is
more competitive.

Scientific nature of the technology: In a science based industry, where scientific
contents are important for innovation such as bio pharmaceuticals, the contents of
technology can be expressed more explicitly. This helps licensing deal making because
potential licensor can understand technological contents more clearly. (Arora and
Cambardella, 1994; Arora and Ceccagnoli, 2005). Therefore, scientific nature of
technology increases the licensing propensity (Step2), given the same level of licensing
potential propensity.

Degree of patent right enforcement: Licensing is market transaction of patents so
that a stronger patent may reduce transaction cost and induce more licensing activities.
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A typical case can be found in pharmaceutical companies, where patent protection is
relative effective and active licensing transactions can be found (Anand and Khanna,
2002). This factor is related to both licensing willingness propensity (Step1) and
licensing propensity (Step2), because stronger patent is beneficial to licensor as well as
licensing deal making (more clear in technology market).

Transaction cost : There are substantial number of patents which a firm is willing
to license out, but does not do. In fact, in a process of searching for possible licensees or
making licensing agreement, the firm may have trouble finding licensing partners or
negotiating contracts. Higher transaction cost associated with licensing deal making
reduces the licensing propensity (Step2), even a firm have a great willingness to license.
3.
Data Sources
In order to test the hypothesis in the section above, we have constructed the database,
by combining three types of datasets, the Survey of Intellectual Property Related
Activities (SIPA), IIP Patent Database (IIP-PD) and the Licensing Activity Survey
(LAS). As a results of data linkage, we get a cross section data for about 1,200 firms in
2006. The followings are description of these datasets.
Survey on Intellectual Property Related Activities (SIPA) : The Survey on Intellectual
Property Related Activities (SIPA) are annual statistical survey by JPO (Japanese
Patent Office). JPO started this survey in 2002 for collecting data on various IP related
activities including application, licensing and litigation of patent, utility, design and
trademark. The survey is conducted for all applicants with over a certain threshold
number applications in the previous year1 and randomly sampled ones for the rest of
group（Motohashi, 2008）. In this paper, we use the 2007 survey (for 2006 activities) data.
After throwing out individual investors and public research organizations, we have
about 4,500 samples to be linked with the other datasets.
SIPA covers a broad range of survey items. The survey consists of four parts, (1)
applications of IPR, (2) usage of IPR, (3) information on IPR section at firm and (4) IP
related infringements. In this paper, we mainly use the data from section 2, delineating
detail information on technology (patent) pool. This section covers data on the number
of IPR by various type of status in terms of its usage, such as using by owner, licensed
out and in by type of licensing contract.
IIP Patent Database (IIP-PD): The IIP Patent Database (IIP-PD) is a database constructed, based
The threshold point varies by the type of IPR, 3 for patent, 2 for utility, 4 for design
and 3 for trademark.
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on (Seiri Hyojunka Data) organized and standardized data by JPO to be prepared for innovation
study researchers (Goto and Motohashi (2007)). It includes, for each patent, the data on the date of
each stage from filing to the expiration of a right, data on applicants, right holders and inventors, and
technology classification. In this paper, we use the index of technology market competition and the
scientific nature of the patent, as is explained later in detail. Individual patent level information are
aggregated into a firm level and linked with the other two firm level datasets.
Licensing Activity Survey (LAS): The Licensing Activity Survey is conducted by the
University of Tokyo in 2007. This survey is based on the international project
coordinated by OECD and the common questionnaire was prepared for European and
Japanese firms (OECD, 2008). It surveys three areas, (1) patent propensity,
appropriability importance, (2) licensing activities, (3) changes in licensing activities
and underlying factors. The survey was conducted on 5000 firms that were sampled
from the 2006 patent applicant list of JPO, and 1640 firms responded.
4. Characteristics of license activities
4.1. Share of actual licensor and potential licensor
As for the licensing activities, Gambardella et al. (2007) addresses the issue that many
potential licenses are not licensed, suggesting that the market for technology could be
larger. Therefore, we investigate the determinants of licensing propensity of Japanese
firms considering the licensing out decision making process, regarding the willingness
of firms to license out and how many patents firms license out.
Firstly, we define the willingness to license out by a binary variable, named WILL. It
takes the value 0 if a firm is not willing to license out, and 1 otherwise. We identify it by
using the number of patents licensed and the following question in the LAS: ‘Share of
your patent portfolio that you would be willing to license out but could not actually
license, share in total patents is 0%, 0-2%, 2-6%, 6-15%, or 15-100%.’ The firms that
answered 0% are not willing to license out, or have licensed out all patents which they
are willing to license. Thus, WILL takes the value 0 if the share is 0% and the number of
patents licensed is 0.
Secondly, we denote the actual licensing activity by two variables of licensing propensity.
One is the number of patents licensed out divided by the number of owned patents,
named LICENSE. Another is the number of patents licensed out for license with
positive royalty fee divided by the number of owned patents, named FEE. The difference
between these two variables comes from licensing patents without royalty, mostly
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associated with cross licensing. The information of these two variables is obtained from
SIPA.
We now consider the actual licensor and the potential licensor. Actual licensor means
firms have patents of licensing out, LICENSE>0, and potential licensor means firms
that have patents which they would be willing to license out, WILL=1. We define the
following groups, corresponding to the Figure 1.
 Group A, where firms are actual licensors, or LICENSE>0. The firm actually license
out, and there are 276 samples.
 Group B, where firms are potential licensors but not actual licensors, WILL=1 and
LICENSE=0. The firm would be willing to license but in fact could not license out,
and there are 124 samples.
 Group C, where firms are not potential and actual licensors, or WILL=0. Such a firm
does not a have willingness to license out, and there are 178 samples.
In Group C, firms do not intend to utilize a license market because they plan to use
patent technologies only for own production exclusively. On the other hand, in Group B,
they would be willing to license, but do not come to the stage of an actual deal, and have
been in process of searching for possible licensees, or making licensing agreement.
Although for firms in both Group B and Group C the number of patents licensed out is
zero, firms in Group B and Group C have different licensing out policies. We find that
the number of firms in Group B is unexpectedly larger, and the potential license market
would be larger than the observed market size as is discussed by Gambardella et al.
(2007).
4.2. Comparison of the determinants of licensing out
We give explanations of data corresponding to the determinants of licensing out in
Section 2. Table 1 shows the descriptive statistics by the samples classified by Group A
to C explained in the previous section.
Degree of patent right enforcement: PROTECTION
The degree of patent right enforcement is assessed by the following question in the
Licensing Activity Survey.
‘To bring the benefit to your company of the 4 methods below, which is the best way?
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Could you tell us the rank of patent protection?’
The methods are patent protection, trade secret, complicated manufacturing or
complicated products, and leading products in the market. Therefore, the lower index
expresses the stronger enforcement of patent.
In table 1, average rank is about 1.5, that is patent protection is the most powerful or
second most powerful. The rank of Group C is a little lower, and this suggests that the
firms which are not willing to license out tend to have a lower assessment of the
importance of patent protection.
Difficulty in making deals of licensing out
The following question in the Licensing Activity Survey is supposed to capture the
difficulty in making deals of licensing out: ‘What hampering factors have you been
confronted with in your licensing activity?’ The survey asks about four factors:

Identifying a partner is difficult: PARTNER,

Drafting and negotiating contracts is too complex/costly: NEGOTIATE,

Price offered too low: PRICE,
Respondents are asked to rate the importance of each factor on a scale of 1 to 4 (‘4’ is
very important). We define each factor as a dummy variable, which equals 1 if the factor
is related to licensing (the rating is 2, 3 or 4) and 0 if not.
PARTNER and NEGOTIATE represent high transaction costs in a license market.
These factor cause the situation where the firm which would be willing to license out
could not actually license out.
The average in table 1 shows the proportion of firms that answered that there is a
relation between the factors and licensing, and PARTNER shows the highest score
among the three groups. Comparing the three groups, the averages in Group C are
smaller, but not zero as expected, since the firms in Group C do not have the willingness
to license out.
Firm size: EMP
As the firm size, we use the number of employee (logarithm) in the SIPA. It also
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represents the complementary assets. In table 1, the firm size of actual licensors in
Group A is three times larger than not actual licensors in Group B and C.
Well-developed IP operation: EMP_IP
The IP department in a large firm would be very well organized. Therefore, in order to
control the ability of an organization we use the number of employee in the IP
department. According to table 1, it is also clear that actual licensors would have a more
developed IP operation. The IP department at a firm is not only dealing with patent
applications and infringements, but also involved in licensing strategy. It is implied that
knowledge accumulated in-house is advantageous for licensing activities.
Firm’s age : AGE
We use the age of a firm in the SIPA. The averages of age among the three groups are
about 50 in table 1.
Technology market competition: TECH COMPE
We measure the technology market competition as the Herfindahl index of technology
using patent technology classification data (based on IPC main group) in IIP-DB. As for the
average of index on technology market competition, three groups have almost same averages.
Scientific nature of the technology: SCIENCE
The proxy of scientific nature of the technology is average paper citation by firm. In
table 1, the average of actual licensors in Group A is larger than others.
Specialized R&D: RD
We employ the dummy variable that the firms makes a special business of R&D if the
propensity of R&D denoted by the share of R&D cost in sales is larger than 30%.
Affiliate dealing: AFFILIATE
To identify the type of firm, we use the share of licensing out with affiliated partners in
the LAS. The dummy variable takes the value 1 if the share of licensing out with
affiliated partner is more than 20% and 0 if not. In table 1, the proportion of firms for
which AFFILIATE equals 1 is 50%, such firms could not utilize a licensing market.
Therefore, we control the affiliate dealing factor in our empirical models.
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Although the length of the R&D process and product market competition are not
included, competition is controlled by industry dummy variables（only manufacturing
industry）. In addition, we employ the logarithms of EMP, IP_EMP and AGE in the
econometric model, named log(EMP), log(IP_EMP) and log(AGE).
5. Econometric analysis and discussion
5.1 Determinants of the willingness to license out by the Probit model
First of all, to investigate whether a firm becomes a potential licensor or not, we
estimate a Probit model for having the willingness of licensing out. The dependent
variable is the binary variable of the willingness to license out (WILL), which is 1 if the
firm is willing to license out, and is 0 otherwise. The factors are everything that does not
include “Difficulty in making deals of licensing out (PARTNER, PRICE and
NEGOTIATE)” and “Affiliate dealing (AFFILIATE)”. As for “Difficulty in making deals
of licensing out”, we consider that they are problems for firms that come to an actual
deal stage and not related to having the willingness to license out. Moreover, “Affiliate
dealing” is not related to willingness to license out because it is information about
license partners. Therefore, such factors affect licensing propensity but not the
willingness to license out.
Table 2 shows the estimation results, and the values are the marginal effects of changes
in the covariates on the probability of willingness evaluated at the mean of the
covariates. The degree of patent right enforcement (PROTECT) is statistically
significant for the willingness of licensing, and the sign is expectedly negative because
PROTECT is the ranking of appropriability and the lower index expresses the stronger
enforcement of patent. The strength of patent protection is important role when a firm
plans to license out. Both of the firm size (log(EMP)) and well-developed IP operation
(log(IP_EMP)) are significantly positive.
5.2 Determinants of licensing by the Tobit model
We then estimate determinants of licensing propensity in order to analyze what factors
determine the licensing activity in the actual dealing stage. We present it by the tobit
model, and compare the results of the estimation using two dependent variables, that is
the number of patents licensed out divided by the number of owned patents (LICENSE),
and the number of patents licensed out for license revenue divided by the number of
owned patents (FEE). The number of patents licensed includes three type of license
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contracts; a license for license revenue, a cross license, and a license pool. Unlike the
license for license revenue, a cross license and license pool are for coordination with
other firms2. Therefore, we consider that these could have a different structure of a
licensing activity.
We report the estimation results in the tale 2. Firstly, the inference (2) and (3) in table 2
show the results using our full sample. In inference (2) we employ LICENSE as a
dependent variable, and inference (3) is FEE. Both (2) and (3) are almost same in spite
of the statistical significances. We find that stronger patent protection promotes
licensing activity because PROTECT has a negative sign, and this effect is the same as
willingness to license out. And also, the effect of log(IP_EMP) is the same as the
empirical result of willingness. However, log(EMP) representing the firm size has a
negative sign which is different to willingness. The important determinants of licensing
in this paper is the difficulty in making deals of licensing out (PARTNER, PRICE and
NEGOTIATE). Since these factors hamper licensing out, they cause the situation where
the firm which would be willing to license out could not actually license out. Our result
shows that the complexity of contracts (NEGOTIATE) decreases licensing propensity,
and could be a distortion of licensing activity. On the other hand, PRICE unexpectedly
has a positive sign, and firms that are dissatisfied with the licensing price increase
licensing propensity. Although technology market competition is not statistically
significant in the inference of willingness to licensing, the inference of licensing
propensity shows that a high competition in technology market leads to higher licensing
propensity. As for the difference with the inference of LICENSE and FEE, in the
inference of FEE, or (3), AGE has a negative effect on licensing propensity.
Secondly, we estimate a tobit model for license activities using the sample of having the
willingness to license out (WILL=1), and the results are shown in inference (4) and (5)
in table 2. Although almost significant variables are the same as inference (2) and (3),
technology market competition is statistically insignificant. However, the estimation by
only selected samples could mislead, and then in the following section we modify the
A typical case of substantial cross licensing can be found in semiconductor industry,
where it becomes difficult to make a product by only in-house technology, so that all
firms need in licensing technology. A working solution in this case is to make cross
licensing agreements between big players (Grindley and Teece, 1997). In order to
prepare for future cross licensing agreements, there is some incentive for a firm to build
up strong patent portfolio. Therefore, the share of unused patent for future cross
licensing deal tends to be large. On the other hand, since cross licensing involves
substantial number of patents, a licensing propensity may become large for cross
licensing firms.
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empirical model which generalizes the Tobit model.
5.3 Determinants of licensing by the double hurdle model
As in the previous section 4.1, we classify firms to three groups according to the number
of patent licensed and the willingness to license out, which consist of the actual
licensors (Group A), the potential but not actual licensors (Group B), and not potential
licensors (Group C). In the econometric model where we analyze the determinants of
licensing propensity, the firms in Group B or Group C are observed zero as the licensing
propensity. That is, an observed zero for the dependent variable represents a corner
solution (i.e., due to the intended choice as Group C) or a negative value for the
underlying latent dependent variable (i.e., due to the unintended choice as Group B).
However, in the Tobit model all zero values taken by the dependent variable would
correspond to a corner solution, and this assumption is too restrictive in our study. And
then, we employ the double hurdle model which is overcome the restrictive assumption
by Cragg (1971). Blundell and Meghir (1987) labels the double hurdle model as a
bivariate model against the standard univariate tobit model because ‘bivariate’ is the
definition of a separate process determining the zero-one discrete behavior from that
determining the continuous observation. When we apply the double hurdle model to our
data, the willingness to license and actual licensing are classified. That is, two hurdles
must be crossed in order to observe the non-zero licensing propensity, first hurdle is
whether a firm is willing to license out, and second hurdle is whether the firm can make
deals of licensing out.
Table 4 shows the results of the double hurdle model with independent, homoskedastic
and normally distributed error term3. Note that, in the selection equation we employ
the same determinants as the estimation of willingness described in section 5.1.
Compared with table 3, although almost significant variable do not change,
UNDEVELP is significantly positive.
To summarize, our findings on determinants of licensing activities are shown. First, we
find that the degree of patent protection is an important determinant against the
willingness to license out and licensing propensity. Second, we show the different effect
of complementary assets and well-developed IP operation on licensing propensity. A
large firm has a production and marketing functions as well as a R&D function inside
The estimation program of the double hurdle model has not incorporated in the
standard statistical software, and the user-written programs are used. We use the
program written for STATA by Julian Fennema, http://www.sml.hw.ac.uk/somjaf/Stata/.
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its company. In-house use of patent is possible only if the firm has such complementary
assets, and then licensing propensity could be lower. However, the firm has superiority
on licensing activity if the firm would have a well-developed IP operation and
experienced know-how. We find that complementary assets have a negative effect on
licensing propensity considering the IP operation. Note that, the number of employees
in the IP department would be an endogenous variable. Third, the observed number of
licensing is smaller than the potential number of licensing, and a potential licensing
market would be larger because the potential licensors do not always make deals of
licensing. Our empirical result shows that the complexity of a negotiation prevents a
firm from licensing out.
6.
Conclusion
We have estimated econometric models for firm’s licensing activities in the era of
pro-patent reform and open innovation. According to the results of empirical analyses,
the strength of patent protection is important when a firm plans to utilize own patents.
In the firms where patent protection is a powerful method, licensing out is promoted.
Therefore, it is suggested that pro-patent reform would encourage licensing out.
However, there would be hampering factors of licensing out in the actual stage of
licensing. Our empirical result shows that the complexity of a negotiation prevents a
firm from licensing out. Even now, in the era of open innovation, the environment of
licensing may not be enough to license out effectively, and potential licensors would
exist. It is considered that developing human resources related to intellectual property
would be an effective policy. Moreover, expansion of licensees is also important. They
would react to the development of licensing markets and declining costs of dealing. So,
the arrangement of markets would eliminate the potential licensor’s problem.
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References:
Anand, B. and T. Khanna (2002), The Structure of Licensing Contracts, Journal of
Industrial Economics, March 2000, vol. 48, pp. 103-135
Arora A. and M. Ceccagnoli (2005), Patent Protection, Complementary Assets and Firm’s
Incentive for Technology Licensing, forthcoming Management Science
Arora A. and A. Fosfuri (2003), Licensing the market for technology, Journal of Economic
Behavior & Organization, vol. 52, pp. 277-295
Arora A. and A. Gambardella (1994), The changing technology of technical change: General and
abstract knowledge and the division of innovative labor, Research Policy, vol. 23, pp.
523-532.
Arora, A., Fosfuri, A and A. Gambardella (2001), Markets for Technology, The Economics of
Innovation and Corporate Strategy, MIT Press
Blundell, R. and Meghir, C. (1987), Bivariate Alternatives to the Tobit Model, Journal of
Econometrics, Vol.34, pp. 179-2000.
Cohen,W., A. Goto, A. Nagata, R. Nelson and J. Walsh (2002), R&D spillovers, patents and the
incentives to innovate in Japan and the United States, Research Policy, vol. 31, pp.
1349-1367
Goto, A., and K. Motohashi (2007), Construction of a Japanese Patent Database and a first look
at Japanese patenting activities, Research Policy, Vol. 36, Issue 9, pp. 1431-1442.
Grindley P. and D. Teece (1997), Managing Intellectual Capital: Licensing and
Cross-Licensing in Semiconductors and Electronics, California Management
Review, vol. 39 No. 2 pp. 8-41
Chesbrough H. (2006), Open Business Models: How to Thrive in the New Innovation
Landscape, Harvard Business School Press
Cragg, J. (1971), Some Statistical Models for Limited Dependent Variables with
Application to the Demand for Durable Goods, Econometrica, Vol.39, No. 5,
pp. 829-844.
Gambardella, A., P. Giuri, and A. Luzzi (2007), The market for patents in Europe,
Research Policy, Vol.36, pp.1163-1183.
Hall, B.
and R. Ziedonis (2001), An Empirical Study of Patenting in the US
Semiconductor Industry, 1979-1995, Rand Journal of Economics, Vol. 32, No.
1 pp. 101-128.
14
Kortum and Lerner (1999), What is behind the recent surge in patenting? , Research
Policy, vol. 28, pp. 1-22.
Motohashi, K (2008), Licensing or Not Licensing? : Empirical Analysis on Strategic
Use of Patent in Japanese Firms, Research Policy 37(2008) pp. 1548-1555
Motohashi, K. (2007), The Changing Autarky Pharmaceutical R&D Process: Causes and
Consequences
of
Growing
R&D
Collaboration
in
Japanese
Firms,
International Journal of Technology Management、Vol. 39, Nos. 1/2, 2007
Motohashi, K. (2004), Japan’s patent system and business innovation: eassessing pro-patent
policies, in Patents, Innovation and Economic Performance OECD Conference
Proceedings, OECD Paris
NISTEP (National Institute of Science and Technology Policy) (1997), Appropriability and
Technological Opportunity in Innovation: A Japan-U.S. Comparative study using survey
data, (in Japanese) NISTEP REPORT No. 48
Reitzig, M. (2004), The Private Values of ‘Thickets’ and ‘Fences’: Towards and Updated Picture
of the Use of Patents Across Industries, Economics of Technology and New Innovation,
13(5), pp. 457-476
RIETI (Research Institute of Economy, Trade and Industry) (2004), Report on RIETI’s Survey
on External Collaboration in R&D for Japanese Firms (in Japanese), Research Institute
of Economy Trade and Industry, March 2004
Shane, S. (2001), Technology Regimes and New Firms Formation, Management Science, vol.
47, pp. 1173-1190
15
Figure 1. Firm’s patent portfolio strategy from the licensing activity viewpoint
Patent Portfolio
Willing to license
License
Not willing to license
Not license
C
A
B
16
Table 1. Descriptive statistics
A(278) B(125) C(178)
WILL
SIPA&LAS
1
1
0
LICENSE
SIPA
0.125
0
0
FEE
SIPA
0.072
0
0
PROTECT
LAS
1.540
1.600
1.742
PARTNER
LAS
0.781
0.784
0.562
NEGOTIATE LAS
0.432
0.488
0.399
PRICE
LAS
0.543
0.416
0.303
EMP
SIPA
2225
630
639
IP_EMP
SIPA
8.2
2.4
1.7
TECH COMPE IIPDB
0.053
0.049
0.055
SCIENCE
IIPDB
0.244
0.137
0.123
RD
SIPA
0.025
0
0.011
AFFILIATE
LAS
0.504
0
0
17
Table 2 Determinants of the willingness: the Probit model
Dependent var.
PROTECT
log(EMP)
log(IP_EMP)
TECH COMPE
SCIENCE
RD
Dummy of industry
Chi2
No. samples
WILL
-0.143 (0.074)
0.115 (0.06)
0.388 (0.063)***
-1.888 (1.796)
0.019 (0.153)
0.415 (0.496)
Yes
106.07
581
Note: *, **, *** denote significant at the 10%, 5%, and 1% levels, respectively. We report the marginal effects, and as for Values in
parentheses robust standard errors.
18
Table 3 Estimation of licensing propensity model: the Tobit model
Dependent var.
PROTECT
PARTNER
NEGOTIATE
PRICE
log(EMP)
log(IP_EMP)
TECH COMPE
SCIENCE
RD
AFFILIATE
Dummy of industry
Chi2
No. samples
(2)
LICENSE
-0.037 (0.014)**
0.013 (0.03)
-0.049 (0.029)
0.090 (0.03)***
-0.031 (0.013)**
0.062 (0.015)***
-0.663 (0.28)**
0.014 (0.03)
0.203 (0.138)
0.197 (0.031)***
Yes
139.67
581
(3)
FEE
-0.030 (0.012)**
0.020 (0.024)
-0.058 (0.023)**
0.081 (0.024)***
-0.022 (0.011)**
0.056 (0.014)***
-0.458 (0.217)**
0.008 (0.021)
0.196 (0.142)
0.094 (0.018)***
Yes
133.71
581
(4)
LICENSE
-0.029 (0.014)**
-0.029 (0.03)
-0.055 (0.029)
0.083 (0.03)***
-0.039 (0.013)***
0.042 (0.014)***
-0.428 (0.257)
0.011 (0.028)
0.208 (0.138)
0.112 (0.026)***
Yes
55.01
403
(5)
FEE
-0.026 (0.012)**
-0.009 (0.023)
-0.066 (0.022)***
0.080 (0.024)***
-0.028 (0.011)**
0.043 (0.013)***
-0.243 (0.184)
0.005 (0.019)
0.214 (0.15)
0.038 (0.016)**
Yes
58.24
403
Note: *, **, *** denote significant at the 10%, 5%, and 1% levels, respectively. Values in parentheses are robust standard errors. A
selection probit (1st stage) is dropped. (2) and (3) are the inferences by using all samples, and (4) and (5) are by samples that have
willingness to license out, that is WILL=1.
19
Table 4 Estimation of licensing propensity model: the Double hurdle model
Dependent var.
PROTECT
PARTNER
NEGOTIATE
PRICE
log(EMP)
log(IP_EMP)
TECH COMPE
SCIENCE
RD
AFFILIATE
Dummy of industry
Log Likelihood
No. samples
(6)
LICENSE
-0.023 (0.01)**
0.024 (0.022)
-0.029 (0.02)
0.062 (0.02)***
-0.030 (0.012)**
0.027 (0.011)**
-0.445 (0.182)**
0.004 (0.023)
0.181 (0.128)
0.099 (0.029)***
Yes
38.512
581
(7)
FEE
-0.013 (0.006)**
0.021 (0.013)
-0.029 (0.012)**
0.049 (0.014)***
-0.020 (0.008)**
0.026 (0.008)***
-0.238 (0.109)**
0.001 (0.016)
0.196 (0.128)
0.035 (0.011)***
Yes
214.567
581
Note: *, **, *** denote significant at the 10%, 5%, and 1% levels, respectively. Values in parentheses are robust standard errors.
20