Patent Thicket and Market Value: An
Empirical Analysis*
Mahdiyeh Entezarkheir†
February 2008
Abstract
The pro-patent shift of the United States has created a patent thicket. This has made the
use of other firms’ innovations more costly, due to higher transaction costs and the
possibility of hold up. Using a panel data on publicly traded US manufacturing firms
from 1979 to 1996, this study finds a negative impact from the patent thicket on the
market value of the firm. I also find that firms with larger patent portfolios experience a
smaller effect, likely because stronger bargaining position lowers the occurrence of the
hold-up problem for these firms. The advantage of larger firms is even more prominent
following the pro-patent shift. My results also capture heterogeneity in the impact of the
patent thicket across industries.
Keywords: Innovation, Patent Thicket, Market Value, Fragmentation
JEL Classification: L43, O31, O32, O34, O38
*
Acknowledgements to be added.
PhD Candidate, Department of Economics, University of Waterloo, 200 University Avenue West,
ON N2L 3G1, Canada, mentezar@artsmail.uwaterloo.ca
†
1. Introduction
In this paper, I study how the fragmentation of the ownership of complementary patents
impacts the market value of the firm. This fragmentation builds a patent thicket, which
contains a set of overlapping patents.3 The patent thicket requires obtaining permission
from several right holders to commercialize a product. Firms that face a fragmented
technology market have to pay higher transaction costs and royalty payments to license
external patents, because they are confronted with larger number of entities in the thicket.
They are more prone to opportunistic behaviour by external entities, since the likelihood
of infringing other firms’ patents is high. They are also more exposed to the risk of being
litigated against by other patent holders. Finally, they incur higher costs in finding out
whether the technologies they use have been patented by other firms.4
These problems that result form the fragmentation in the technology market lead
to lower expected profits and, consequently, lower market valuation.5 In this paper I
quantify this patent thicket effect using data on 1975 publicly traded US manufacturing
firms from 1976 to 1996.6
I apply a non-linear approach to estimating a Tobin’s Q equation. My results
show that the larger degree of fragmentation in technology market decreases the market
3
Shapiro (2001)
According to Lanjouw and Schankerman (2001) patent litigations have become more frequent. Their
approach is a Probit estimation based on a sample of 5,452 patent cases during 1975-1991 from the Patent
History CD-ROM of Derwent.
5
As one example of the stock market reaction, is the case of NTP sued RIM. Their negotiation halted
RIM's shares at $72.00
6
The pro-patent shift of the United States Patent and Trademark Office (USPTO), as a responsible
organization for issuing patents, have extensively helped to the distribution of the external rights to patents
through establishing specialized Court of Appeals for the Federal Circuit (CAFC) in mid 80s. The total
number of patent applications granted by the USPTO grew at an average annual rate of 1.35 percent from
1976 to 1985, and this rate was 5.52 percent in the period 1986 to 2002. This average growth was even
more prominent in some of the sectors such as computers and communications (12.13 percent), Drugs and
medical (9.19 percent), and electrical and electronics (7.38 percent).
4
1
value of the firm, and I show that the impact of the patent thicket on the market value is
larger after the pro-patent shift in the United States Patent and Trademark Office
(USPTO) and the establishment of the Court of Appeal for Federal Circuit (CAFC) in
1980s.7 I also find that firms with larger patent portfolios are penalized less than other
firms because these firms face fewer problems in cross-licensing negotiations. The
advantage of firms with the larger patent portfolios is even more prominent following the
pro-patent shift. Finally, my study shows that the impact of patent thicket varies across
sectors. I relate this finding to differences in the nature of innovations across sectors.
2. Background
Hall and Ziedonis (2001) and Ziedonis (2004) examine the impact of the patent thicket on
the patenting behaviour of firms. Hall and Ziedonis (2001) study the semiconductor
industry and find that the pro-patent shift of the US patent system in the 1980s increased
the patenting of capital intensive firms more than other firms.8 Ziedonis (2004) finds that
in the semiconductor industry firms patent more aggressively the more fragmented is the
technology market, and that this effect is more pronounced for capital intensive firms.9
Another group of studies examines the role of patents as a determinant of the
market value of the firm. Hall et al. (2005) show that a larger number of citations per
patent raises the market value of the firm.10 My analysis, in contrast, also includes a
measure of fragmentation of patent ownership as a possible determinant of the market
7
For more detail discussion on these changes in the US please refer to Jaffe and Lerner (2007).
Their sample includes 95 US semiconductor firms during 1979-1995.
9
Ziedonis (2004) sample consists of 67 US semiconductor firms for the period 1980 to 1994. Her
econometric approach is based on a negative binomial specification.
10
They analyze the driving factors of the market value of the firm in a sample of 1982 patenting
manufacturing US firms from 1979 to 1988 in a nonlinear model.
8
2
value of the firm. Noel and Schankerman (2006) analyze strategic patenting and the
market value of the firm in the software industry.11 Their results show that patenting by
rivals declines the market value of the firm in the software industry. I have expanded
their analysis in several aspects. While Noel and Schankerman (2006) focus on the
software industry, my analysis examines patenting in manufacturing sectors. The wider
sample provides a chance to analyze the impact of the patent thicket in different sectors. I
also analyze the heterogeneity of the impact of the patent thicket on the market value in
terms of the patent portfolio size and time periods. Furthermore, my analysis employs a
different measure of strategic patenting than Noel and Schankerman (2006). The
fragmentation measure used in Noel and Schankerman (2006) considers only the citations
of each firm to patents of the four largest rivals in the technology market. I employ the
fragmentation index used in Ziedonis (2004). This measure of fragmentation is based on
the citations to patents of all firms. It also takes into account that firms are possible to be
held-up by small firms.
The patent thickets hold several costs for the firms with cumulative innovations.
They cause the “complement problem” which was first formally examined by Cournot
(1838).12 Shapiro (2001) extends the Cournot idea into the context of intellectual
property. He indicates right holders in the thicket make the prices of invented products
much larger than their marginal costs by imposing the licensing fees. The result is lower
consumer welfare and joint profit of right holders. Heller and Eisenberg (1998) also show
11
Their study is based on a different estimation approach on a panel data of 121 publicly traded US firms in
the software industry from 1980 to 1999. They do not correct for the patent count truncation in their
analysis.
12
Cournot discussed about this problem in the context of complementary monopolists of Zinc and Copper
with whom the Brass producer confronts. The existence of two monopolists lowers consumer welfare by
raising prices and decreases the joint profit of monopolists. The vertical integration of these two
monopolists helps to overcome these negative impacts.
3
that the licensing fees in the thicket lead to underinvestment in innovation or the “tragedy
of anti-commons.” Furthermore, Shapiro (2001) shows that the dense thickets increase
the transaction costs of firms, because identifying complementary patents is harder and
more costly. Innovators usually find about all of the patents after bearing sunk costs. This
means the innovator is faced with a hold-up problem.
The stock market predicts that firms faced with dense thickets have to pay large
licensing fees. It also expects a danger of hold up for these firms, because identifying all
the complementary patents before the production stage is less likely. This means market
foresees the acceptance of high royalty payments in the cross-licensing negotiations for
these firms as an ex-post solution to the hold up problem, or the litigation costs if such
firms do not accept licensing. These costs lower future expected profits of the firm, which
translate into less funding sources for investments in innovations. Consequently, the
stock market values the firms faced with fragmented technology market lower.
3. Empirical Strategy
The foundation of my model specification is based on the studies of Griliches
(1981) and Hall et al. (2005).13 The general empirical framework used in these analyses is
log MVit = log SVt + σ log TAit + σ log(1 + θ
INAit
) + ε it .
TAit
(1)
The variable MVit is the market value of firm i at time t. Market value is calculated as the
sum of common and preferred stocks, long-term debt adjusted for inflation, and short13
Using a sample of 157 large US firms in Compustat data from 1968 to 1974, Griliches finds a significant
relationship between the market value of the firm and its intangible capital, peroxide by past R&D
expenditures and the number of patents through a linear estimation.
4
term debts of the firm. The variable SVt is the marginal shadow value of the assets. The
variables TAit and INAit are tangible and intangible assets, respectively. The variable TAit
is measured by the book value of the firm which is calculated as the sum of net plant and
equipment, inventories, investments in unconsolidated subsidiaries, and intangibles and
others.14 The shadow price of the knowledge asset to tangible asset ratio is measured by
the coefficient θ. Finally, the parameter σ is a scale factor in the value function. With the
assumption of constant return to scale to assets as it usually does in the cross-section, σ
becomes one (Hall et al., 2005). By moving the tangible asset to the left hand side, the
dependent variable becomes log(
MVit
) or Tobin’s Q. Equation (1) becomes
TAit
log Qit = log SVt + log(1 + θ
INAit
) + ε it .
TAit
(2)
Hall et al. (2005) measure the INAit with R&D intensity, patent intensity, and citation
yield per patent. R&D shows the obligation of the firm to innovate. R&D activity might
lead to success which is documented by patents, and the extent of the success is measured
by citations. Equation (2) becomes
log Qit = log SVt + log(1 + θ 1 ×
R & Dit
PATit
CITE it
) + ε it . (3)
+θ2 ×
+ θ3 ×
TAit
R & Dit
PATit
Variables R&Dit, PATit, and CITESit stand for the stock of R&D, patents, and citations.
All of the stock variables are built based on a declining balance formula with depreciation
rate of 15%.
I have augmented equation (3) with the fragmentation index used by Ziedonis
(2004), as a measure of the patent thicket. My empirical specification is in equation (4).
14
The variables selected for calculating MV and BV are based on Hall et al. (2005).
5
log Qit = log SVt + β log Fit + log(1 + θ1 ×
R & Dit
PATit
CITE it
) + ε it
+θ2 ×
+ θ3 ×
TAit
R & Dit
PATit
(4)
The variable Fit is a measure of the patent thicket. The construction of this measure is
discussed in section 3.1. I estimate equation (4) with nonlinear least squares
estimation.15,16,17
3.1 Measuring the patent thicket
USPTO grants patents for inventions, and provides a public document which lists all the
information about the invention, its inventor(s), and the assignee name(s).18 This
15
The R&D data of firms from Compustat is the private R&D expenditure not the public R&D expenditure.
Usually private R&D expenditures have larger impact on GDP than public R&D. I use contemporaneous
R&D because according to Hausman et al. (1984) the within firm correlation of R&D over time is not large
and many firms have short R&D histories
16
I do not follow the fixed effect estimation approach. An important factor that creates heterogeneity across
firm is the differences in their R&D expenditures, which is highly related to firm’s individual features. By
considering fixed effect estimation, I remove this source of difference. Moreover, the explanatory variables
are predetermined and the panel data is short. This requires me using the differenced estimator, but the
explanatory variables change slowly. As a result, a small measurement error could lead to large downward
bias in differenced estimates (Hall et al., 2005). However, for testing the sensitivity of the results to
different specifications, I estimate my empirical framework with unobserved firm fixed effect. Thus
equation (4) becomes
MV
R & Dit
PATit
CITEit
log Qit = log( it ) =α i+mt − σ log Fit + log(1 + θ1Ψ(
) + θ2Ω(
) + θ3Γ(
)) + uit + εit
TAit
TAit
R & Dit
PATit
According to Noel and Schankerman (2006) in the fixed effect specification, the variable log SV in
equation (4) is the sum of parameters αi , mt , and uit . Parameter αi is the firm heterogeneity, mt is a time
dummy, and uit is an iid error term distributed with mean 0 and variance h2. The parameter αi means some
firms have permanently higher market value than others due to omitted firm specific effects. For example,
this could be the result of the stock of past innovations at the beginning of the sample, or a better ability of
absorbing external technologies for reasons that are not explained by independent variables.
According to Bloom et al. (2005) and Noel and Schankerman (2006), it is more convenient to estimate the
market value equation with the firm fixed effects through series expansions. Parameters Ψ, Ω, and Γ show
the polynomial of the knowledge stock variables. Employing this specification, I have found that the fifth
order polynomial is satisfactory. Moreover, I do not consider the multiplicative terms of the knowledge
stock variables in the model because they are too demanding on the data. The estimation results are in
table 6 of the appendix. They are not largely different from the estimates without firm heterogeneity.
17
I would not approximate log(1 + θ × INAit ) with ( θ × INAit ), because such an approximation is right if
TAit
TAit
the ratio of intangible assets to tangible assets is small. However, this ratio is large for high technology
firms of manufacturing sector in my empirical analysis and I can not estimate equation (4) linearly.
6
document also displays the citations or references of this patent to other patents or nonpatented inventions. In order to decide the degree of dispersion in the rights to
complementary patents, I use the fragmentation index of Ziedonis (2004).
This measure, which is based on a normalized Herfindahl index, gives more
weight to the firms faced with higher fragmentation, because the citation shares are
squared.19 The fragmentation index is calculated in equation (4).
K
Fit = 1 − ∑ (
k =1
citeikt 2
)
citeit
(4)
Where cite shows citations made to previous patents in a patent document, firm i cites
patent(s) of firm k, and t refers to the year that citation is happened. In measuring this
index, I do not consider citations made to firm’s own patents, and innovations with
expired or without patent, because they might not impose any problems related to the
fragmentation in the technology market, such as the risk of being hold-up on the citing
firm.
4. Data
The data set used is the result of employing two different sources of US data, NBER
patent data files and Compustat North American Annual Industrial file, linked through a
company identifier file.
The NBER patent data files are constructed by Bronwyn H. Hall.20 They include
patents granted by USPTO from 1963 to 2002 (almost 3 million patents), and Citations
received and made for patents granted from 1976 to 2002 (almost 22 million citations).21
18
19
Assignee is the entity that owns the patent right.
Herfindahl index is usually used for measuring the level of competition in the market
7
The Compustat data based on Standard and Poor’s datasets consists of 500,000
observations on 26,000 US publicly traded firms from 1979 to 2002.22 It includes
information on firm R&D and components of Tobin’s Q. Tobin’s Q is the ratio of the
market value of the firm to its book value. Market value is calculated from common and
preferred stocks, long-term debt adjusted for inflation, and short-term debts of the firm.
Net plant and equipment, inventories, investments in unconsolidated subsidiaries,
intangibles and others are the ones used in calculating the book value of each firm and
they are all adjusted for inflation.23,24 In my analysis, I use R&D capital stock which is
built based on the contemporaneous R&D expenditure adjusted for inflation, and a
declining balance formula with 15 percent depreciation rate.25
Linking the patent counts and citation files to Compustat data by firm name is
facilitated through Hall’s company identifier file. This file is required, because assignees
apply for patents under their name or their subsidiaries. Unfortunately, USPTO does not
specify a unique code for each patenting identity. However, Compustat has a unique code
for each publicly traded firm. Hall’s matching file contains the assignee number of each
firm mentioned on patents in NBER, and its equivalent identifier in Compustat. 26
20
http://elsa.berkeley.edu/~bhhall/
Citations made are the count of references to previous patents in each patent document. Citation received
is the number of times that a patent receives citation.
22
These are the firms which are on the New York, American, and regional stock exchanges, as well as
over-the-counter on NASDAQ.
23
Inflation adjustments are based on CPI urban US index for 1992 (source: http://www.bls.gov)
24
Selecting the components of book value and market value are based on Hall et al. (2005).
25
This depreciation rate is the rate used for building the stock variables in Hall et al. (2000).
26
This file was originally built for the data from 1963 to 1999 and it is available in
http://www.nber.org/patents. Hall et.al. (2001). Hall et al. (2001) provide a detail explanation of this file
and indicate that even though this identifier file is not complete, they have matched almost 50 to 65 percent
of US patents for 1965 to 1995. Later, Hall updates this file for a longer period from 1963 to 2002. I have
used the updated file which is available in: http://elsa.berkeley.edu/~bhhall.
21
8
I select a sample of publicly traded US manufacturing firms (SIC 2000-3999) 27
for the period of 1979 to 2002 from Compustat. This leaves me with an unbalanced panel
of 19,868 firms with 365,589 observations for 1979 to 2002. After polishing for
withdrawn patents, the cited patents granted before 1963, and considering only the
patents of publicly traded firms my sample from NBER data includes almost 19 million
observations.28 In next step, using the Hall’s identifier file, I link my sample from NBER
data to my sample of manufacturing firms in Compustat. After taking care of the missing
observations of the market and book value of firms, my sample size has become into
68,203 observations on 6,402 unique patenting and non-patenting firms from 1979 to
2002 (almost 2000 firms in each year).29 This sample includes 20,852 missing
observations on R&D. The percentage of firms with reported R&D expenditures is about
50-60% annually.
Patent and citation counts have truncation problems. Patent counts truncation is
the result of the lag between the application and grant date of a patent. From the patents
with the application date close to the end of the observed sample, only a small fraction of
them are granted, and the grant date of the rest of them are out of the reach of the sample.
I have followed the approach of the Hall et al. (2000) for correcting this truncation. Based
on the application-grant distribution of patents in the sample, I calculate weight factors
27
SIC is the Standard Industrial Classification by the United States Government.
I do not consider patents without any citations to previous patents or patents with only self-citations in
my sample from NBER. This is because the owners of these patents do not face any sort of the problems
related to fragmentation in the technology market. As a result, later in my analysis, I do not have a
patenting firm without any citation to previous patents in my sample.
29
To build market value (MV) and book value (BV) of firms, I substitute the missing observations of the
components of MV and BV with zero, and then calculate them. In next step, I have dropped MV and BV if
they are zero. This leaves me with 68,203 observations. Instead, if I did not follow this procedure,
calculated the MV and BV with missing components, and then dropped the MV and BV with missing
observations, this would leave me with 52,736 observations. I choose the first approach, since it helps me
to keep more information.
28
9
and correct for the truncated patent counts.
30,31
Figure 1 displays a comparison of
corrected and not corrected patent counts for truncation.
Patents per R&D
.8
1
1.2
1.4
1.6
average of all patenting firms in each year
1975
1980
1985
1990
1995
2000
year
Patent/R&D
Corrected Patent/R&D
Figure 1: Correction for the Patent Counts
I have also corrected for the truncation in the received citation counts of each
patent. Patents keep receiving citations for a long period of time, since they are granted.
As a result, observed citations, are only the ones which are received in the range of the
analyzed sample. This problem is even more prominent for the most recent patents.
Moreover, there is another truncation in citation counts in the beginning of the sample.
Citation data is only available for the patents granted since 1976 in NBER. As a result, I
do not observe the citations of patents granted between 1963 and 1975, which are made
before 1976.
30
This is the formula used for correcting patent counts in my analysis based on Hall et.al. (2000)
patentt
patentt* = 2000 − t
∑ weightk
k =0
1996 ≤ t ≤ 1999
31
I have only corrected patent counts for no later than 1999, because from 2000 to 2002 (end of the data
period) the results are under the "edge effect" according to Hall et al. (2000). This means the 2002 data will
not be usable and 2001 data will have large variance.
10
To correct for this truncation, I have employed the method of Hall et al. (2000),
which is calculating the distribution of the fraction of citations received to each patent at
the lag between the grant year of the citing and cited patents. Using this distribution, I
predict the number of citations of each patent for the part out of the reach of the sample,
maximum to 40 years after the grant date of the patent. Figure 2 displays a comparison of
corrected and not corrected received citation counts for truncation. Using patent and
citation counts as knowledge stock variables in my analysis, I have built the patent and
citation stock variables based on truncation corrected data and a declining balance
formula with depreciation rate of 15%.32
Citations per R&D
0
5
10
15
average of all patenting firms in each year
1975
1980
1985
1990
1995
2000
year
Citation/R&D
Corrected Citation/R&D
Figure 2: Correction for the Citation Counts
Table 1, in appendix, presents the descriptive statistics of the patenting firms’
sample. Knowledge stock variables, book value, and market value are all largely skewed.
However, R&D/book value, patent/R&D, and citation/patent ratios display more
symmetry.33
32
Knowledge depreciates because of imitations by others, personnel move, or machinery wearing out.
The R&D expenditure of the 11% of the patenting firms is missing. This implies most of the firms with
R&D expenditure have patents, but these two sets do not overlap completely.
33
11
Figure 3 displays the change in the fragmentation index of a hypothetical firm
with the change in the number of external right holders, assuming that the total number of
citations made in the patents of this firm is 20 in a given year. The minimum amount of
this index is zero and this happens when all the citations are made to the patents of one
external right holder. The maximum amount of F is almost 1 and this happens when each
citation made is to the patent of a different firm. According to Table 1, in appendix, the
median of fragmentation index in the sample is 0.50, which is ranged from 0 to 0.98. In
26 percent of the observations, patenting firms only self-cite from their own patents.34
0
.2
F index
.4
.6
.8
1
Fragmentation Index and the External Right Holders
0
5
10
number of firms
15
20
Figure 3: Fragmentation Index and the Number of External Right Holders
Figure 4 helps in getting some general understanding of the pattern that actual
data suggests for fragmentation index. It illustrates the trend of fragmentation index, on
average, from 1979 to 1996. The pro-patent shift of 1980s that has made the technology
market more fragmented is prominent in this graph.
34
In building the f index, I do not consider patents with only references to their own patents since they pose
no hold-up. As a result, in my sample these patenting firms’ F index is missing.
12
Trend of Fragmentation Index
.35
.4
F index
.45
.5
.55
.6
average of all firms in each year
1980
1985
1990
1995
year
Figure 4: Trend of Fragmentation Index
6. Results
Table 2 of appendix contains estimation results of equation (4) in section 4. These
results are based on a nonlinear least squares approach applied to the data from 1979 to
1996.35 Moreover, my sample is limited to the firms that have at least one patent over this
period.36 Column 1 displays the baseline estimation of the impact of knowledge stock
variables on the market value of the firm. All of the stock variables have a positive and
significant impact on the market value that supports previous studies in the literature,
such as Hall et al. (2005). The measure of patent thicket or the fragmentation index is
added to the baseline model in column 2 of the Table 2. This variable has a negative and
significant impact on the market value. Moreover, all the knowledge stock variables keep
their positive and significant premium on the market value.37
35
I have limited my analysis to the 17 years of the middle of the sample, because of the truncation problem
at both ends of the sample. As a result, I only focus on when the data is less problematic.
36
This makes it possible to calculate patent stock and citation stock.
37
Based on the explanations in the footnote of the page 6, I have also estimated the model with firm fixed
effect and the results has been shown in the table 6 of the appendix. As it is displayed, log F keeps its
13
Calculating the semi-elasticities, based on the estimates of column 2 of Table 2,
helps me to evaluate the size of the impact of the explanatory variables, especially the
patent thicket, on the market value of the firm. Table 3 in appendix provides the semielasticities as well as the elasticity of the log F at both mean and median of the
variables.38
The coefficient of the log F shows that the market value declines by 1.1% as
fragmentation goes up by 10% i.e. this is the elasticity of market value to the external
allocation of property rights. Since the market value and fragmentation index are
measured in different units, I have also found the standardized impact which is 3.4%. It
means a one standard deviation increase in the log F lowers market value by 0.1127
standard deviation units or actually 3.4%.39
Column 4 analyzes whether the portfolio size of the firm has impact on the effect
of the fragmentation on the market value of the firm. The coefficient of the [log F× log
portfolio size] is positive and significant, while fragmentation preserves its negative and
significant impact on the market value. The implication is that firms with larger patent
portfolios in the fragmented technology market have higher market value than other
firms, because market predicts these firms are less probable to face hold-up and problems
negative and significant impact on the market value of the firm, but the size of the impact is extremely
small or close to no impact.
38
I have considered both because of the skewness in the distribution of the stock variables
39
In terms of the size of the impact of knowledge stock variables, an increase of 1% point in the R&D
intensity of the firm increases market value by 2.3%, an extra patent per million $ of R&D raises market
value by 3%, and an extra citation per patent boosts market value by 0.3%. Past and future citations have
different impacts on the market value of the firm. Citation is not something that happens at one point in
time. It is not clear how market predicts citations, or how evaluates them as they have occurred. At each
point in time I divide the citations received to all the patents of each firm into “citations received before
that date or past citations” and citations that are received after that date to the end of sample or future
citations.” I have corrected both types of citations for truncation errors. Column 3 of Table 2 shows past
citations have a positive and significant impact on the market value of the firm. This implies that past
citations to the patents of the firm do not become old news. However, future citations have a positive and
insignificant impact, which implies investors are not able to forecast the expected value of patented
innovations.
14
of cross-licensing negotiations. This is because of their higher bargaining power in
negotiations.
I also estimate the model for the periods before and after the pro-patent shift of
the USPTO and the establishment of CAFC in 1980s to analyze whether the impact of
patent thicket on the market value is stronger after this change. As the results in Table 4
of appendix display, the impact of patent thicket is more prominent in the period
following the patent policy changes.40 In columns 1 and 3, the coefficient of the log F
shows that the market value declines by 0.98 % as fragmentation goes up by 10% in the
period before the change, and market value declines by 1.29% following the change.
There is almost a 30% decrease in the market value of the firm following the change.
Moreover, according to the columns 2 and 4 of this Table firms with the larger patent
portfolios experience a larger advantage of almost 26% over other firms, in terms of their
market value, in the increase of fragmentation following the mid 80s.
The impacts of fragmented technology market and knowledge stock variables are
different across industries according to Table 5 in appendix.41 The percentage of each
industry in my sample is: Chemical 3.5%, computers 7%, drugs 22%, electrical 28%, and
mechanical 19%. Column 1 illustrates the base specification for the average industry. The
fragmented technology market has a negative premium on the market value of the firm in
chemical and mechanical sectors, all higher than the average industry effect. This is an
implication of the importance of the negotiation power in ex-post contracts of these
40
The reason that I have divided the sample into 1979-1989 and 1990-1996 rather than considering the year
of the establishment of CAFC (1982) is that the impact of the policy needed some time for showing itself.
41
The industry classifications are based on http://elsa.berkeley.edu/~bhhall/mfgind.pdf. In this table, the
chemical industry includes the following classification of Hall-Vopel or IDS: chemical products.
Computers include the computers and computing equipment. Drugs include optical and medical
instruments and Pharmaceutical. Electrical includes Electrical machinery and electrical instrument &
communication equipment. Mechanical includes Primary metal products, fabricated metal products,
machinery & engines, transportation equipment, motor vehicles, and auto parts.
15
industries. The highest negative premium belongs to the chemical sector. The
insignificant impact on the drug sector could be related to the fact that patents are not that
related in this industry and even some of them are important individually. In this sector
firms use patents to stop the development of replacements by rivals and, therefore,
patents are not used for expropriating rivals. In contrast, in chemical and mechanical
sectors, a larger patent thicket translates into higher transaction costs, higher risk of being
held-up and, consequently, lower market value.42
There are differences across sectors in the knowledge stock variables controlling
for fragmentation. In all of the sectors except for computers, the impact of the R&D/asset
is larger than average. The highest premium from R&D/asset is in chemical sector, which
is almost twice the average industry (marginal effects 0.434 in comparison to 0.231).
Patents/R&D is significantly larger than average in chemical and drugs sectors. The
biggest raise in the market value for an extra patent per million $ of R&D is in drugs
sector (almost 11%). The citation/patent is significantly larger than average in electrical.
The larger impacts of patent and citation intensities in the electrical industry imply the
cumulative nature of innovation in this industry. If I consider citations as a way of
capturing the value of patents, this result indicates that market values higher the more
valuable patents in the electrical industry, because it predicts larger negotiation power
from valuable patents for the firm as a way of solving the patent thicket problem.
7. Conclusion
42
For more comparison across industries see Cohen, Nelson, and Walsh (2000)
16
This study provides empirical evidence on the impact of fragmentation of patent
ownership on the market value of firms. The analysis connects to the literature on the
determinants of market value of firms and the literature of the patent thicket problem.
My results show that firms experience a significant decline in their market value
when the technology market is fragmented. The results also show that the pro-patent shift
in the 1980s has increased the size of this impact. I also find that firms with larger patent
portfolios experience a smaller negative premium in their market value. This is likely
because firms with larger patent portfolios face fewer problems in their cross-licensing
negotiations with external entities as the larger portfolio size increases their bargaining
power in the licensing negotiations and lowers the risk of being held-up by their rivals.
This advantage of firms with larger patent portfolios is even more prominent following
the pro-patent shift in the 1980s.
My results also show that the negative impact of the patent thicket varies across
industries. For example, the impact is insignificant in the pharmaceutical sector, but
significant in the chemical and mechanical sectors. In the pharmaceutical sector firms
use patents to block the development of alternatives by rivals and, therefore, patents are
not used for expropriating rivals. In contrast, in the chemical and mechanical industries a
larger patent thicket translates into higher transaction costs, higher risk of being held-up
and consequently, lower market value.
In industries with cumulative innovations higher transaction costs, the risk of
being held-up, and the danger of litigation have created a concern among policy makers
that the current pro-patent policies lead to underinvestment in innovation and,
consequently, lower productivity and growth. The results of this study have obvious
17
merits from the perspective of intellectual property policy, because the results quantify
the cost of the patent thicket and the hypothetical benefits of removing the patent thicket.
Moreover, the smaller negative impact of fragmentation on the market value of firms
with larger patent portfolios implies that the current system of patenting creates an
incentive for aggressive patenting to counter the negative costs of fragmentation. This
could divert the resources of firms from R&D activities to legal activities aimed at
obtaining patents on marginal innovations.
18
References
Bloom, Nick, Mark Schankerman, and John Van Reenen (2005) “Identifying Technology
Spillovers and Product Market Rivalry,” CEPR Discussion Paper 3916
Cohen, Wesley, Richard Nelson, and John Walsh (2000) “Protecting their Intellectual
Assets: Appropriability Conditions and Why US Manufacturing Firms Patent (or
Not),” NBER Working Paper, No. 7552
Griliches, Zvi (1981) “Market Value, R&D, and Patents,” Economic Letters, 17, 183-187
Heler, Michael and Rebecca Eisenberg (1998) “Can Patents Deter Innovation? The
Anti-commons in Biomedical Research,” Science, 280, 698-701
Hall, Bronwyn, Adam Jaffe, and Manuel Trajtenberg (2000) “Market Value and Patent
Citations: A First Look,” NBER Working Paper, No. 7741
Hall, Bronwyn, Adam Jaffe, and Manuel Trajtenberg (2001) “The NBER Patent Citations
Data File: Lessons, Insights and Methodological Tools,” NBER Working Paper,
No. 8498
Hall, Bronwyn, Adam Jaffe, and Manuel Trajtenberg (2005) “Market Value and Patent
Citations,” RAND Journal of Economics, 36 (1), 16-38
Hall, Bronwyn and Rosemarie Ziedonis (2001) “The Patent Paradox Revisited: An
Empirical Study of Patenting in the Semiconductor Industry, 1979-1995,” RAND
Journal of Economics, 32 (1), 101-128
Hausman, Jerry, Bronwyn Hall and Zvi Griliches (1984) “Econometric Models for
Count Data with an Application to the Patents-R&D Relationship,” Econometrics,
52, 909-938
Jaffe, Adam and Josh Lerner (2007) “Innovation and its Discontents: How our Broken
19
Patent System is Endangering Innovation and Progress, and What to do about it,”
Princeton University Press
Jaffe, Adam and Manuel Trajtenberg (1996) “Flows of Knowledge form Universities and
Federal Labs: Modeling the Flow of Patent Citations over Time and across
Institutional and Geographic Boundaries,” NBER Working Paper, No. 5712
Lanjouw Jenny and Mark Schankerman (2001) “Characteristics of Patent Litigation: A
Window on Competition,” Rand Journal of Economics, 32 (1), 129-151
Merges, Robert (2001) “Institutions for Intellectual Property Transactions: The Case of
Patent Pools,” In Rochelle Dreyfuss, Diane Zimmerman, Harry First (eds.),
Expanding the Boundaries of Intellectual Property: Innovation Policy for the
Knowledge Society, Oxford University Press, Oxford, England, 123-165
Noel, Michael and Mark Schankerman (2006) “Strategic Patenting and Software
Innovation,” Downloadable at: http://sticerd.lse.ac.uk/dps/ei/EI43.pdf
Shapiro, Carl (2001) “Navigating the Patent Thicket: Cross Licenses, Patent Pools, and
Standard-Setting,” In Adam Jaffe, Joshua Lerner, and Scott Stern (eds.),
Innovation Policy and the Economy I, MIT press
Ziedonis, Rosemarie (2003) “Don’t Fence Me In: Fragmented Markets for Technology
and the Patent Acquisition Strategies of Firms,” Management Science, 50 (6),
804-820
20
Appendix
Table 143: Descriptive statistics
Variable
Mean
Median
Min
Max
Std.dev
Market Value($M)
970
103
0.06
62755
3018
Book Value($M)
1410.27
113
0
57532
4122.03
Market value/Book value
(Tobin’s Q)
1.33
0.67
0.05
660
10.55
F index44
0.50
0.66
0
0.98
0.40
R&D stock45
346
34
0
28865
1270
Patent stock
85.54
10.87
1
5426
290.10
89
1.19
79115
3460
Total Citation stock
826
R&D /BV
0.90
0.29
0
184.8
4.30
Patent/R&D46
0.98
0.44
0
100.24
2.40
Total citation/patent
10.66
6.45
1.17
346.11
14.71
Past Citation Stock
44.93
5.5
0
7204
238.33
Past Citations/Total
Citations
0.34
0.51
0
1709.23
14.42
D(missing F)
0.26
0
0
1
0.44
D(missing R&D)
0.11
0
0
1
0.31
43
The sample is an unbalanced panel of 10,273 observations on 1975 patenting firms from 1979 to 1996.
Inflation adjusted variables are based on CPI urban US index for 1992. The reason that I have considered
only this part of the sample is in the view of the truncation problem at both ends of the data period. As a
result, I only focus on when the data is the least problematic.
44
It is based on 10448 observations or the ones that F is not missing for them
45
For the 9178 observations in 1979-1996
46
For the 9175 observations with non-missing R&D. In this group only 13 firms have zero R&D stock.
21
Table 2: Non-linear model of the impact of patent thicket on
market value
Dependent variable:
log q
(1)
log F
(2)
(3)
(4)
-0.113**
(0.045)
-0.106 **
(0.045)
-0.137 ***
(0.046)
R&D/BookValue
0.303 ***
(0.051)
0.298***
(0.050)
0.296***
(0.050)
0.297 ***
(0.050)
Patent/R&D
0.039***
(0.011)
0.038***
(0.011)
0.038***
(0.011)
0.038 ***
(0.011)
Totalcitation/
patent
0.004**
(0.002)
0.004**
(0.002)
0.004**
(0.002)
Pastcitation/
patent
0.005**
(0.002)
Futurecitation/
Patent
0.001
(0.002)
0.140***
(0.046)
logF*logPortfolio size
D(R&D=0)
0.126***
(0.033)
D(logF=0)
R2
0.3523
0.118***
(0.033)
0.114 **
(0.033)
0.114 ***
(0.033)
0.024
(0.021)
0.020
(0.021)
-0.002
(0.023)
0.3531
0.3546
0.3544
The sample includes 1975 patenting firms with 10273 observations from 1979 to 1996. In all of the equations
there is a set of time dummies. The signs ***, **, and * mean significance at 1%, 5%, and 10%, respectively. The
numbers in the parentheses are the cluster-robust standard error (clustered at the firm level).
22
Table 3: The size of the impact of the knowledge stock and
fragmentation index on the market value
Ratios evaluated at the
Ratios
Mean
Median
R&D/Assets
0.90
0.29
Patents/R&D
0.98
0.44
Cites /Patents
10.66
6.45
F index
0.66
0.78
logF
-0.18
-0.08
0.231***
(0.033)
0.268***
(0.042)
ðlogq/ð(Patents/R&D)
0.029***
(0.008)
0.0341***
(0.009)
ðlogq/ð(Cites /Patents)
0.0030**
(0.001)
Semi-elasticities
ðlogq/ð(R&D/Assets)
0.0035**
(0.001)
Elasticity
ðlogq/ðlogf
-0.113**
(0.045)
-0.113**
(0.045)
The sample includes 1975 patenting firms with 10273 observations from 1979 to 1996.
23
Table 4: Non-linear model of the impact of patent thicket on
market value before and after the USPTO change in 80s
1979-1989
Dependent variable:
log q
log F
1990-1996
(1)
(2)
(3)
(4)
-0.098**
(0.049)
-0.126**
(0.052)
-0.129 *
(0.045)
-0.149 **
(0.070)
0.0161*
(0.083)
0.128**
(0.043)
logF×logPortfoliosize
R&D/BookValue
0.247***
(0.081)
0.243***
(0.080)
0.322***
(0.055)
0.323 ***
(0.055)
Patent/R&D
0.029***
(0.010)
0.029***
(0.010)
0.056***
(0.024)
0.057 ***
(0.024)
Totalcitation/
patent
0.003*
(0.002)
0.003
(0.002)
0.004**
(0.002)
0.004**
(0.002)
D(R&D=0)
0.071**
(0.034)
0.067*
(0.034)
0.183 ***
(0.060)
0.178 ***
(0.060)
D(logF=0)
0.027
(0.022)
0.002
(0.023)
0.017
(0.034)
-0.012
(0.039)
The sample includes 1975 patenting firms with 10273 observations from 1979 to 1996. In all of the equations
there is a set of time dummies. The signs ***, **, and * mean significance at 1%, 5%, and 10%, respectively. The
numbers in the parentheses are the cluster-robust standard error (clustered at the firm level).
24
Table 5: Industry Effects and patent thicket
Dependent
variable:
log q
Average
(1)
Chemical
(2)
Computers
(3)
Drugs
(4)
Electrical
(5)
Mechanical
(6)
-0.113**
(0.045)
-0.423*
(0.215)
-0.109
(0.167)
-0.130
(0.132)
-0.096
(0.086)
-0.137*
(0.081)
R&D/BV
0.298***
(0.050)
1.139
(0.011)
0.062
(0.053)
0.347***
(0.080)
0.358**
(0.128)
0.626**
(0.216)
Patent/R&D
0.038***
(0.011)
-0.013
(0.021)
0.079
(0.055)
0.121**
(0.045)
0.048**
(0.021)
0.020
(0.019)
Citation/Patent
0.004**
(0.002)
0.022
(0.035)
0.000
(0.001)
0.001
(0.002)
0.008**
(0.003)
0.002
(0.006)
D(R&D=0)
0.118***
(0.033)
0.448**
(0.158)
0.016
(0.121)
1.033***
(0.180)
0.261**
(0.093)
0.060
(0.072)
D(log F=0)
0.024
(0.021)
0.078**
(0.073)
0.008
(0.066)
0.036
(0.069)
0.040
(0.040)
0.050
(0.034)
Observation
10273
545
694
918
2800
2348
Number of
firms
1975
69
138
437
548
384
R2
0.3531
0.6912
0.2053
0.2430
0.3516
0.4398
log F
The sample includes 1975 patenting firms with 10273 observations from 1979 to 1996. In all of the equations
there is a set of time dummies. The signs ***, **, and * mean significance at 1%, 5%, and 10%, respectively. The
numbers in the parentheses are the cluster-robust standard error (clustered at the firm level).
25
Table 6: Patent thicket and market value with firm unobserved
heterogeneity
Dependent variable:
log q
log F
D(logF=0)
R&D/Assets
NLS
Pooled OLS
Fixed Effect
Random Effect
-0.1127647**
(0.045)
0.0241818
(0.021)
0.2980769***
(0.050)
-0.090**
(0.045)
0.010
(0.021)
0.211***
(0.029)
-0.008***
(0.002)
0.0001**
(0.000)
-7.35**
(2.72)
1.49**
(0.0615)
0.104**
(0.034)
0.073**
(0.024)
-0.005
(0.004)
0.0001
(0.0001)
-1.29
(2.30)
0.0312
(1.11)
-0.010*
(0.005)
0.0004**
(0.0001)
-4.06**
(1.51)
0.156**
(5.99)
-0.201**
(7.99)
-0.061**
(0.027)
0.006
(0.011)
0.0172***
(0.035)
-0.006**
(0.002)
0.83547
(0.571)
-4.93
(4.23)
1.02
(1.00)
0.166***
(0.050)
0.131***
(0.026)
-0.014***
(0.004)
0.0005***
(0.0002)
-8.37***
(2.63)
4.08***
(1.30)
0.010*
(0.005)
-0.0002
(0.0001)
2.08
(1.57)
-7.71
(5.84)
9.56
(7.25)
-0.066**
(0.026)
0.008
(0.011)
0.180***
(0.021)
-0.006***
(0.001)
0.00009***
(0.00002)
-5.21***
(1.76)
1.07***
(4.03)
0.150***
(0.037)
0.131***
(0.022)
-0.013***
(0.003)
0.0005***
(0.0001)
-7.80***
(2.34)
3.75***
(1.16)
0.008*
(0.004)
-0.0001
(0.0001)
1.26
(1.39)
-4.71
(5.19)
5.87
(6.42)
0.214
0.200
0.2032
(R&D/Assets)2
(R&D/Assets)3
(R&D/Assets)4×107
(R&D/Assets)5×109
D(R&D=0)
Patent/R&D
0.1181061***
(0.033)
0.037943***
(0.011)
(Patent/R&D)2
(Patent/R&D)3
(Patent/R&D)4×106
(Patent/R&D)5×108
Cites /Patents
0.0038838**
(0.002)
(Cites /Patents)2
(Cites /Patent)3×106
(Cites /Patent)4×109
(Cite /Patent)5×1012
R2
0.3531
There is a set of time dummies in all of the equations.
47
The numbers are multiplied by (×104)
26