1

Appropriability and Firm Performance

Marco Ceccagnoli

College of Management Georgia Institute of Technology

marcoc@gatech.edu

September 2006

Preliminary draft.

Abstract Firms use multiple strategies to appropriate the returns from innovation and some of these of strategies are more effective in achieving and sustaining competitive advantage. To assess the impact of such strategies on firm performance, I merge the Carnegie Mellon appropriability survey with Compustat financial data and the USPTO patent database at the firm level. I find that stronger appropriability, achieved through patent protection or the exploitation of first mover advantages, leads to superior economic performance, as indicated by a superior market valuation of a firm’s stock of R&D knowledge. I also show that preemptive patenting allows incumbent innovators to strengthen their market power and that such impact is substantially higher for incumbents earning higher ex-ante market power, as proxied by higher market share. Finally, I find that the patent per million $ of R&D – a measure of technological performance – depends positively on patent propensity and negatively on R&D. The latter effect is due to the existence of diminishing returns to R&D investments. Since after controlling for R&D, patent propensity, and spillovers, the patent yield has a positive effect on a firm market value, such effect captures the impact of unobserved firm capabilities or unobserved knowledge stock that enhance the productivity of R&D investments. * I would like to thank Wes Cohen for providing me with access to the 1994 Carnegie Mellon R&D survey and Bronwyn Hall, for making the updated NBER patent and financial data used in this paper available for download on her website (http://emlab.berkeley.edu/users/bhhall).

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1. Introduction

A quintessential objective of strategy research is to understand the drivers of

profitability differences across firms. In particular, the introduction of new or improved

products and processes is widely believed to be a central determinant of a firm’s

competitive advantage. Yet, it is also well known that such competitive advantage is

transitory due to the ease with which new knowledge can spill over to rivals, who can

quickly erode rents by direct imitation or through the introduction of substitute products

and processes. The effectiveness of strategies used to appropriate innovation rents, such

as patenting, secrecy, the exploitation of first mover advantages and the ownership of

specialized complementary marketing and manufacturing assets, is therefore essential to

protect innovation-based competitive advantages and a key driver of profitability

differences across firms.

The extent to which appropriability and, more specifically, patenting strategies

affect firm performance has, however, received relatively little scrutiny to date. Recent

work in the economic literature has focused in assessing the value of patents at the firm

level, by estimating the impact of a firm patent stock on its market value after controlling

for their stock of tangible capital (Bloom and Van Reenen, 2002; Hall et al. 2005). This

literature has consistently estimated a positive and significant marginal value of the

patent stock. Bloom and Van Reenen (2002) found that doubling the citation weighted

patent stock would increase the value of UK public firms per unit of capital by about

35%. A positive, but somewhat lower response was found by Hall et al. (2005) for the

US. They also report that, for the US, an extra citation per patent boosts market value by

3%. These effects reflect the change in market value for a given change in the quality – or

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value – of patented innovations. Most of this literature, suffers from the inability to

disentangle the impact of innovation on performance as opposed to the benefits of patent

protection over and above the profits derived from alternative appropriation strategies.1

An important touchstone for the present analysis is the work of Cockburn and

Griliches (1988), who showed that the returns to innovation, as measured by the market

valuation of a firm intangible asset, was critically conditioned by appropriability

conditions, measured by survey-based scores of the effectiveness of patent protection at

the industry level available from the Yale survey. Among the Yale appropriability

measures, only patent protection to prevent duplication appeared to have a significant

impact on the market valuation of innovation.

For the present analysis, I matched the Carnegie Mellon Survey (CMS) on the

appropriability of R&D (Cohen et al., 2000) with Compustat and the NBER patent

database (Hall et al. 2001) at the firm level to analyze the impact of appropriability and

patenting on two measures of firm performance: Tobin’s Q (the market value of the

financial claims on a firm divided by the replacement value of the firm’s assets) and the

patent yield (the number of patents applied for per million $ of R&D expenditures).

As a forward looking measure of firm performance, Tobin’s q captures two

fundamental components of innovation and appropriability: the contribution of firm

intangible assets to its market value and the firm ability to earn supra-normal rents from

its tangible and intangible assets (Hall, 1993). By merging Compustat and USPTO-

NBER patent data to the CMS survey data, I can separately identify the impact of

1 This is also the case of Lerner (1994), for example, who looks at the impact of patent scope on the market

value of a sample of US biotech companies. He finds that a one standard deviation increase in the average

patent scope is associated with a 21% increase in the firm's value.

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innovation and the strategies used to appropriate the rents due to an innovation on firm

performance at the firm level. The CMS, in particular, allows to asses the impact of

specific patent strategies, such the use of preemptive – or blocking – patenting in

conditioning firm performance.

The Carnegie Mellon survey provides a unique data source not previously

available in that the strategies used to appropriate rents have been measured at the firm

level, as opposed to the industry level scores available from the previous Yale survey.

This is important, because the effectiveness of patent protection, for example, depends in

part on characteristics of the exogenous legal system, the nature of technology to be

protected, or the extent and nature of competition in the industry, in part on the firm’s

ability to enforce patents, which only confer a right to exclude others from using the

patented technology. The CMS also provides key information on specific patent

strategies, such as patent preemption or defensive patenting, which allows dissecting the

impact of patents on market power.

A second outcome variable analyzed in the present study is the patent yield, e.g.

the number of patent applications per million dollar of R&D. Itself a measure of

technological performance, the patent yield declined steadily in the U.S. between the mid

50s and the mid 80’s, when it started to change its course, by almost doubling between

1985 and 1997 (NRC, 2004). Hall et al. (2005) have recently shown that the patent yield

has an independent and significant impact on a firm market value. The patent yield is a

function of multiple factors, including a firm’s propensity to patent, it’s level of R&D

investment and the extent of diminishing returns to R&D, spillovers, technological

opportunities and other firm productive capabilities, all of which can be in part be

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measured using the CMS data. It is therefore important for strategy research to

understand the relative importance of the drivers of such measure of R&D productivity

and the channels through which it is influenced by appropriability, thus allowing a better

understanding of its impact on overall firm economic performance.

2. Theory and hypothesis development

2.1 Appropriability and firm performance

The term appropriability refers to the degree to which a firm captures the value

created through the introduction of its innovations. In the industrial organization

literature, appropriability is typically modeled as the extent to which firms are able to

limit the imitation of its own product or process innovations. Strategies used to increase

appropriability considered in previous empirical studies include the use of secrecy, patent

protection, first mover advantages, and the ownership of specialized complementary

marketing and complementary assets (Levin et al. 1987; Cohen et al. 2000).2

Using standard oligopoly models of product and process R&D with imitation and

asymmetric firms, it is straightforward to verify that greater appropriability is associated

with greater market power for the innovator. For the case of product innovation, one

could use a simple innovator/imitator duopoly model with vertical product differentiation

a la Hotelling, with Bertrand price competition in the product market and imitation. In

such setting, the profit of an innovating high-quality firm positively depend on the quality

2 For an excellent review of more recent management literature on first mover advantages see Lieberman

and Montgomery (1998). On the relationship between appropriability and the concept of specialized

complementary assets see Teece (1986).

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differences between firms’ products and greater appropriability (less imitation) typically

increase the innovator’s profits and market power by increasing quality differences.

Similarly, in a standard duopoly model of process innovation with asymmetric

firms and imitation, appropriability is related to firm profits. Indeed, when firms compete

à la Cournot in a homogeneous product market with linear demand and constant

marginal costs, which depend negatively on own process R&D and positively on the

strength of the rival’s appropriability strategy, the profits of the innovator are a positive

function of the difference between marginal costs of innovating and non-innovating

firms. As for the case of product innovations, greater appropriability limits spillovers and

imitation, thus increasing the marginal costs of the imitator, the relative cost differences

due to the introduction of technological innovations, and therefore positively impacts the

ability of an innovator to obtain higher price-cost margins from its own innovative

investment.

I will therefore test the following hypothesis, with particular focus on the

empirical magnitude of this effect:

HYPOTHESIS 1: Stronger appropriability of the profits due to innovative investments

strengthens a firm’s market power, and therefore, its economic performance

Patenting is one mechanism used to protect the competitive advantage due to an

innovation. A patent may be commonly used to commercialize an innovation (vertical

integration), license a technology to other firms, or for other reasons, such preemptive

patenting (e.g. patent blocking), or for defending an incumbent against potential suits.

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Cohen et al. (2000) have shown that such ‘non-conventional’ patenting strategies are

pervasive. In particular, portfolio-based strategies confer greater market power than the

conventional patenting strategies based on direct commercialization or licensing of the

patented innovation, albeit at a higher cost. The benefits of patenting around a firm’s own

innovation may well be worth the additional R&D and patenting costs, if preemptive

patenting, in particular, deters entry in the technology space of substitute technologies,

thus preserving the market power conferred by a related patented innovation

commercialized by the incumbent firm (Gilbert and Newbery, 1982; Bresnahan, 1985;

Scherer and Ross, 1990). In equilibrium, the benefits of building barriers to imitation

using portfolio strategies must outweigh their substantial costs, and therefore I expect

preemptive patenting strategies to increase overall appropriability and firm economic

performance. Theory also suggests that the value of preemptive patenting should be

higher for firms with higher market power, simply because the patent-based entry barriers

would be defending higher profits. Less intuitively, Fudenberg et al. (1983) and Harris

and Vickers (1985) suggest that in asymmetrical patent races technological leaders may

have greater incentives to preempt the laggards, to induce the latter to drop from the race

once the leader is far enough ahead. In these models of R&D competition, the dynamics

of patent races tend to reinforce dominance.3

I will therefore test the following three related hypothesis.

3 See however Doraszelski (2003) for model in which a firm that is behind in the R&D race has stronger

incentives to close the gap with the technological leader. For a recent review of the patent preemption

literature see Gilbert (2006).

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HYPOTHESIS 2a: Preemptive patent strategies allow incumbent innovators to

strengthen the market power associated with their innovations, and therefore, their

economic performance.

HYPOTHESIS 2b: The impact of preemptive patent strategies on firm performance is

higher for incumbents with higher market power.

HYPOTHESIS 2c: The impact of preemptive patent strategies on firm performance is

higher for incumbents with technological leadership.

2.2 Appropriability and technological performance

Past studies that have analyzed the determinants of the patent yield include

Evenson (1984), Griliches (1989, 1990), Kortum (1993). The literature has focused on

the impact of demand and technological opportunities as main drivers of the decline of

research productivity. The findings indicate that most of the observed decline in research

productivity is due to the growth in market size, but they also suggest that a significant

fraction of variation is unaccounted for. Lanjouw and Schankerman (2004) have included

patent quality as an additional control to explain the decline in research productivity in

the U.S., suggesting that both demand and patent quality are two important drivers on the

observed ratio. The present study, thanks to the availability of the CMS survey data,

focuses on two additional sources of variation in the patent yield, those related to patent

propensity and appropriability, which are typically unobserved over time.

To focus ideas and highlight the main relationships at work, I use a standard

innovation production function framework (Pakes and Griliches, 1984; Kortum, 1993;

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Arora et al, 2003). Let ii srm iβ= , where mi is the number of innovations, ri is the R&D

expenditure, β is the elasticity of the number of innovations with respect to R&D, si

represents other factors affecting the average productivity of R&D, such as spillovers,

technological opportunities, and other firm capabilities. Considering that only a fraction

pi of a firm innovations is applied for a patent – the propensity to patent - and letting ai be

the number of patent applications, the ratio of patent applications per $ of R&D

expenditures- or patent yield - can be written as iii srp

ra

i1−= β . It is obvious that the

patent yield depends positively on patent propensity. Moreover, when R&D is

characterized by diminishing marginal returns (e.g. β<1) -- as widely supported by most

empirical studies -- firms with higher R&D will also be characterized by a lower patent

yield.

The previous discussion should be extended by noting that patent propensity and

R&D are choice variables for the firm, and their optimal value will be determined by the

expected net returns from patenting and R&D, respectively. The key drivers of both of

these choice variables are the value created by the innovation and how much of that value

can be captured through available appropriability strategies. Previous work has focused

on observable drivers of the value of an innovation, such as firm size, market size growth,

or the value of a patented innovation (Kortum, 1993; Lajouw and Schankerman, 2004).

For example, Cohen and Klepper (1996) pointed out that larger firms appear to be

characterized by a lower patent yield because they can spread the R&D fixed costs over a

larger volume of output, hence conduct more R&D and, together with the existence of

diminishing marginal returns to R&D (β<1), are characterized by a lower patent yield.

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To address these issues, I test and confirm the endogeneity of patent propensity

and R&D in the patent yield equation, using additional survey evidence as instruments,

such as firm size, appropriability, and other drivers of the returns to R&D. I will therefore

test the following hypothesis:

HYPOTHESIS 3a: The patent yield is positively related to a firm’s propensity to patent

and negatively related to the level of its R&D investments.

HYPOTHESIS 3b: The larger the level of a firm’s R&D, patent propensity, and patent

yield, the higher its economic performance.

3. Specification and estimation

To estimate hypothesis 1 and 2, I estimate the market’s relative valuation of a

firm’s tangible and intangible capital. Following Griliches (1981), I analyze the market

valuation of a firm’s R&D assets using the following specification:

AKq δα +≅log , (1)

where q is the ratio between the firm’s market value and the book value of its assets, A is

book assets and K represents the firm’s stock of intangible assets. As explained in Hall

(1993), this specification can be derived from a simple model where the deviation of the

market value of the firm from its book value depends on three factors: the present value

of the returns to ordinary capital above and beyond those that cover its costs, the relative

magnitude of its intangible capital, and the present value of the supra- or sub-normal

returns to intangible capital.

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Typical factors included on the right-hand-side of (1) have been industry

concentration, a firm’s market share, the stock of R&D, patents, and patent citations, as

well as industry level appropriability variables (cf. Hall, 1999, for a review of this

literature).

I will include most of the previously considered drivers of Tobin’s q, and focus on

appropriability and patenting. Although appropriability has been analyzed by Cockburn

and Griliches (1988), I will use firm-level appropriability measures available from the

Carnegie Mellon survey (CMS) on the appropriability of R&D, which will capture the

profits due to its innovation strategy. I will also use responses from the CMS related to

the strategic use of patents to evaluate their impact on the appropriation of returns to

R&D capital. To evaluate the impact of appropriability on the market valuation of

intangibles I will use interactions between the R&D stock variable as a percentage of

total assets and the available appropriability measures.

To evaluate the drivers of the patent yield I use the following liner specification:

srpra

i 3210 log αααα +++= (2)

The dependent variable is the patent yield, defined as the number of patent applications

divided by the firm’s R&D investments; pi , is the firm-level patent propensity, and s

represents other factors affecting the productivity of R&D, such as information flows

from rivals or universities.

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4. Data and measures

The data used come from three sources: 1) The Carnegie Mellon Survey (CMS)

on industrial R&D (described more fully by Cohen et al. 2000)4; 2) The NBER

manufacturing master file updated through 1995 (described more fully in Hall et al.

1988); 3) The NBER/USPTO patent and citation database covering the 1963-2002 period

(Hall et al. 2001)5. The resulting cross-sectional dataset related to the 1991-1993 period

consists of a subset of 301 firms, for which I have firm-level financial and patenting

information from Compustat and the USPTO, and R&D/appropriability information at

the primary business segment level of each firm from the CMS. I describe the variables

used below.

4.1 Market value equation

4 The population sampled is that of all R&D labs located in the US conducting R&D in manufacturing

industries as a part of a manufacturing firm4. R&D lab managers were asked to answer questions with

reference to the “focus industry” – defined as the principal industry for which the unit was conducting its

R&D. Valid responses were received from 1,478 R&D units, with a response rate of 54%. For the present

analysis I only used the responses from lab’s belonging to the public companies included in the NBER

manufacturing master panel during the 1991-1993 period, which is the time period to which the CMS

responses are related. Moreover, the lab’s focus industry should at least be consistent with the primary

industry of the parent firm at the 2 digit SIC level, in order to be included in the present study. After

dropping observations with missing data for the variables of interest, I obtain a sample of 301 public firms. 5 The updated NBER Compustat manufacturing master file and the patent data have been downloaded from

B. Hall’s website: http://elsa.berkeley.edu/~bhhall/bhdata.html. Compustat data are contained in the R&D

master file updated through 1995 (file ‘pan95export.zip’), which contains data on Tobin’s q, the stock of

firm R&D, and total book assets. The USPTO patent database contains data on patents granted and patent

citations at the firm level (file ‘pat63_02f.zip’). I have matched the CMS and NBER master file by firm

Cusip; whereas the match between the CMS and the USPTO firms data has been done manually by

company name, using 10k information on firm corporate affiliations to identify patents granted to

subsidiaries.

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The NBER master file contains data on Tobin’s q, i.e. the ratio of a firm’s market

value (defined as the book value of a firm equity and total debt) and the firm book value,

TOTASST (defined as the sum of net plant and equipment, inventories, and investments

in unconsolidated subsidiaries, intangibles –which do not include R&D of the current

firm, but may include R&D resulting from acquisitions - and others). I use the log of the

1991-1993 average of Tobin’s q as the main dependent variable in the market value

equation.

The main intangible assets variable is the ratio of R&D STOCK to TOTASSET.

R&D STOCK is computed a cumulated stock of past R&D expenditures (which uses a

15% depreciation rate), available form the NBER master file. I used the end of 1992

stock measures in the analysis.

I also control for the PATENT YIELD, as in Hall et al (2005), defined as the ratio

of the patent stock to R&D stock, and CITATION STOCK, defined as the ratio of

cumulated forward citations stock (with forward citations going up to 2002) divided by

the patent stock. Patent and citation stock variables are constructed from the USPTO

database. The cumulated patent and citation stocks are constructed using a 15% standard

depreciation rate and refer to the end of the 1992 year.6

The key data addition with respect to prior studies are the CMS appropriability

variables which include patent propensity, the effectiveness of patent protection, secrecy,

and first mover advantages, and a measure of ownership of specialized complementary

6 The stock at year t is equal to the current year flow variable + 85% of the previous year stock. The initial

stock variable is the earliest year for which a variable has a non-null value. For patent and citation data, we

have patents and related citations counts for patented granted since 1963, for which the application year

could go as far back as 1920.

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assets, all related to the 1991-1993 period. In particular, PATENT PROPENSITY

represents the percentage of product and process innovations for which a firm applied for

patents in the period 1991-1993 in the US.7 The CMS also asks respondents to indicate

the percentage of their product and process innovations for which patent protection,

secrecy, or being first to market had been effective in protecting their firm’s competitive

advantage from those innovations during the prior three years, form which I build

variables related to the EFFECTIVENESS OF PATENT PROTECTION, SECRECY,

and FIRST MOVER ADVANTAGES.8 The CMS also provides a measure of specialized

complementary assets using information related to the frequency of face-to-face

interaction between personnel from R&D and production, and R&D and sales/marketing

measured in a 4-point Likert scale.9 I constructed a binary variable, COMPASSETS,

which takes value 1 if R&D and manufacturing or R&D and sales/marketing personnel

interact daily (the median value is weekly interaction).

7 I computed a weighted average of product and process patent propensities using the percentage of R&D

effort devoted to product and process innovations, respectively, as weights, as reported by the CMS

respondents. 8 For all such variables, there were five mutually exclusive response categories for product and process

innovations separately: <10%, 10-40%, 41-60%, 61-90%, >90%. I computed a weighted average of the

product and process scores (using mid-points), with the percentage of R&D effort devoted to product and

process innovations as weights. 9 Respondents were asked: “How frequently do your R&D personnel talk face-to-face with personnel from

the ‘Production,’ ‘Marketing or Sales,’ and ‘Other R&D units’ functions?”. In general, measuring the

degree of specialization is difficult but, as Teece (1992) suggests, complementary assets often arise from

the interaction and learning over time of people from different parts of a firm’s organization. This is

especially relevant for the interaction with R&D, which typically requires organizationally embedded inter-

personal and inter-functional activities (Zhao et al. 2005). A similar measure has been used by Arora and

Ceccagnoli (2005).

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The CMS also asked respondents to indicate whether PREEMPTIVE

PATENTING (or “patent blocking”) motivated their most recent decision to apply for

product and process patents. Given that the question is only answered by patentees, I

computed an industry level average of the percentage of firms who indicated a positive

answer to either the product or process question, to construct the PREEMPTIVE

PATENTING variable.10

Other control variables include CR4, the percentage of total industry sales

accounted for by the 4 leading firms in a firm’s primary business segment defined at the

4-digit SIC level, and the firm’s MARKET SHARE, computed as the fraction of total

industry sales captured by the firm in its primary business segment.11 The latter variable

is also commonly used as an indirect measure of a firm’s market power.12 I will test the

hypothesis that the returns to preemptive patenting are higher for firms with stronger

market power (HP 2b) by estimating the market value equation within the sample of low

and high market share firms. TECHNOLOGICAL LEADERSHIP is measured as a

dummy variable equal to one if the rate of product or process introduction by the focal

firm compared to all other firms in its focus industry is reported to be substantially above

average over the previous three years, and zero otherwise. The latter variable is used to

10 I computed a weighted average of the product and process industry level percentages, with the

percentage of firm R&D effort devoted to product and process innovations as weights. 11 The CMS contains data on primary business segment sales, whereas the value of industry shipments and

the 4-firm concentration ratios have been measured at 4-digit SIC level using the 1992 Census of

Manufacturers, U.S. Department of Commerce, Bureau of the Census. 12 A standard industrial organization result is that in an asymmetric oligopoly characterized by firms

competing on output and linear demand, for example, the Lerner index of market power is a positive

function of a firm’s market share and the degree of collusion in the industry as conjectured by the firm and

a negative function of the elasticity of demand.

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estimate the market value equation within the sample of technological laggards vs.

leaders, to test hypothesis 2c.

Finally, I also include exogenous measures of spillovers available from the CMS,

consistent with past work (Jaffe, 1986). In particular, the TECHNOLOGICAL

OVERLAP between the technical goals of the R&D lab’s projects and those of its rivals

in the primary business segment of the firm — a measure of closeness between rivals in

the technology space, which should augment the firm’s stock of intangible capital

through an increased inflow of technical information from rivals13. Similarly,

UNIVERSITY R&D spending by state and field of science should similarly increase the

contribution of university knowledge to the firm stock of intangible assets.14

4.1 Patent yield equation

Equation (2) represents the patent yield, measured as a flow variable, as a

function of other flow variables such as R&D, as opposed to the market value equation

13 The CMS survey asks for a subjective assessment of the percent of each R&D unit’s projects with the

same technical goals as an R&D project conducted by at least one of its competitors. The responses

categories are: 1=0%;2=1-25%;3=26-50%;4=51-75%;5=76-100%. Responses were then recoded to

category midpoints 14 Total R&D spending of doctoral granting institutions by U.S. state and field of science (Source: 1993

NSF/SRS Survey of Scientific and Engineering Expenditures at Universities and Colleges) was assigned to

each respondent according to its location and the importance of each field to its R&D activity. The CMS

survey provides information on the importance, to the lab’s R&D activities, of the contribution of

university or government research conducted over the previous 10 years by field of science and engineering

(possible fields are Biology, Chemistry, Physics, Computer Science, Materials Science, Medical and Health

Science, Chemical Engineering, Electrical Engineering, Mechanical Engineering, Mathematics). These

fields are aggregated by taking average scores of their importance to match the NSF fields (engineering,

physical sciences, math and computer sciences, life sciences). The importance score assigned to each field

is then used to compute a weighted average of the university R&D spending by state.

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which requires measuring the patent yield as a stock variable. Furthermore, in order to

consider a broader indicator of research productivity, I include the total number of

applications (which includes unsuccessful applications) as numerator of the patent yield,

as advocated by Griliches (1989), which is not available from the USPTO for the time

period under study, but is available from the CMS at the primary business segment level

of each firm for the 1991-1993 period15. Similarly, we used the average business segment

R&D expenditures for the same period as a measure of the denominator of the flow-based

patent yield variable. The R&D expenditures are also used on the right-hand-side of (2),

along with patent propensity, has explained in the previous section. I also need to include

the survey-based measures of spillovers and technological opportunities, UNIVERSITY

R&D and TECHNOLOGICAL OVERLAP (described in the previous sections), to

control for other factors external to the firm affecting the productivity of R&D in

equation (2).

Finally, I include a set of 9 industry dummies in all specifications and both

equations (1) and (2), defined at the 2 and 3 digit SIC level of the primary business

segment of the firm.16

5. Results

15 A further comparison of such self-reported measure of patent applications with actual patent grants data

available from the USPTO database reveals an excellent response reliability. 16 The industry dummies are based on the following groupings: Food and tobacco (SIC 20 and 21),

chemicals and allied products (SIC 28, excl. 283), biotechnology and pharmaceuticals (SIC 283),

machinery (SIC 35, excl. 357), computer (SIC 357), electronics (SIC 36), transportation (SIC 37),

instruments (SIC 38, excl. 384), and medical instruments (SIC 384). Note, however, that the results

presented below are qualitatively robust to the use of more disaggregated industry definitions.

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Table 1 and 2 present descriptive statistic and correlations, table 3 the results for

the market value equation, used to test hypothesis 1, 2a-c, and table 4 the results for the

patent yield equation, used to test hypothesis 3a and 3b.

Table 3, specification (1), shows the standard market value equation with only the

R&D/Assets variable included, which with a coefficient of 0.4 – representing the

marginal shadow value of the stock of knowledge assets relative to the tangible assets of

the firm – suggests an impact within the range found in the literature (cf. Hall, 1999). The

addition of the appropriability measures substantially increase the fit of the equation, in

particular when they are interacted with the R&D stock variable in specification (4), even

with a full set of traditional controls. In particular, both the EFFECTIVENESS OF

PATENT PROETECTION and FIRST MOVER ADVANTAGES tend to substantially

and significantly increase the market valuation of R&D, with comparable coefficients.

The results imply that a one standard deviation increase in the effectiveness of patent

protection or lead times is associated to an increase from 0.17 to 0.34 in the marginal

shadow value of intangibles relative to tangible assets. 17

To compare the present results to Cockburn and Griliches (1988), note that

despite the insignificance of the industry-level first mover appropriability measure

available from the Yale survey administered in the mid 80s, the results presented here

suggest that the within industry variation is substantial and systematically related to firm 17Exploration of the complementarities between some of the appropriability strategies is currently

underway . Preliminary results suggest that the strength of patent protection and first mover advantages

appear to be complementary, in the sense that stronger patents increase the impact of first mover

advantages on firm economic performance. This is consistent with Lieberman (2005), who presents

evidence that early entry provides a first mover premium for pioneers with patented innovations for internet

public companies, suggesting that the strength of patent protection and first mover advantages have a

positive interaction effect on the rate of return on R&D.

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performance. Indeed, the proportion of variance of log q explained by our appropriability

measures is almost twice as large the variance explained by the analogous model and

specifications of Cockburn and Griliches (1988).

I also use PATENT PROPENSITY, not available in the previous Yale survey,

instead of patent effectiveness, as a more easily interpretable measure of the

appropriability premium provided by patent protection. Indeed, patent effectiveness is a

broad summary measure of the various costs (such as information disclosure) and

benefits of patenting (including preventing imitation, facilitating technology negotiations

or building patent fences) that is significantly associated with the decision to patent

(Arora et al. 2003, Arora and Ceccagnoli, 2005). Patent propensity is, however, a choice

variable, and the Hausman test of endogeneity performed using patent effectiveness at

both the firm and industry level as instruments, rejects its exogeneity when included on

the right-hand-side of the Tobin’s q equation. The result of the endogeneity test and the

significance of the instruments in the first-stage regression, lead us to use the

instrumental-variable specification (6), which suggests that the interaction between patent

propensity and the R&D stock variable is significant at conventional level, with an

implied elasticity greater than 1 (a 10% increase in patent propensity leads to a 16%

increase in the market valuation of the R&D stock relative to tangible assets). Overall, the

results provide strong support for Hypothesis 1.

To test Hypothesis 2a-c I add the PREEMPTIVE PATENTING variable to the

specification with patent effectiveness, to obtain model (7). Even controlling for patent

effectiveness, I find that in industries where patent preemption is more diffused as a

strategy of appropriation, the market valuation of R&D is substantially and significantly

20

higher, supporting Hypothesis 2a. In particular, a one standard deviation increase in

PREEMPTIVE PATENTING leads to an increase from 0.17 to 0.34 in the market

valuation of the R&D stock relative tangible assets. Finally, results suggest that model

(7), with an R-square of about 0.6, explains a substantial variation in Tobin’s q and

constitutes my benchmark specification.

By estimating specification (7) within the groups of low and high market share

firms and technological leaders vs. laggards, I obtain the results of columns (8)-(11) of

Table 3. The results suggest that the positive impact of PREEMPTIVE PATENTING on

the valuation of R&D more than doubles for firms with high market share, i.e. for firms

characterized by greater market power, consistent with Hypothesis 2b.18

Hypothesis 2c is only weakly supported: although the impact of patent

preemption for the technological leaders increases substantially relative to the sample of

technological laggards, the interaction between the R&D stock and patent preemption is

not significantly different than zero at conventional levels in neither sub-samples.

Hypothesis 3a is clearly confirmed by all specifications in Table 4, where patent

propensity and R&D are included as the main drivers of the patent yield. Additional

testing, not reported here, shows that both patent propensity and R&D are endogenous, in

the sense that they are correlated with the econometric error term in the patent yield

equation. I use appropriability, firm size, industry sales and industry sales growth,

number of technological rivals and whether the firm has global sales to instrument for

18 A less direct test with results consistent with Gilbert and Newbery (1982) theory was performed by

Blundell et al. (1999), who find that higher market share firms are characterized by a higher market

valuation of its intangible assets.

21

R&D and patent propensity.19 Specification (4), in Table 4, is therefore the correct model

which fully support Hypothesis 3a, suggesting a large, positive and significant effect of

patent propensity on the patent yield, and a large, negative, and significant effect of

R&D.

To test Hypothesis 3b, we now need to look back at the results presented in Table

3, where patent propensity was included as one of the determinants of the firm market

value. In particular in specification (6), where patent propensity is treated as endogenous,

patent propensity, the patent yield, and R&D all have a positive and significant effect on

Tobin’s q, consistent with Hypothesis 3b.

5. Concluding remarks

Scholars in the past 2 decades have increasingly considered the strategies used to

capture the value created by innovative investment a fundamental diver of a firm

competitive advantage. To date, however, the empirical evidence on the impact of such

strategies on performance has been poor. The availability of the Carnegie Mellon survey

data matched to Compustat and the USPTO/NBER database is an important and novel

source for innovative empirical work on this topic.

The results show that appropriability at the firm level has a larger impact on firm

performance then previously thought. In particular, the strength of patent protection and

first mover advantages appear to significantly increase the returns captured from R&D,

and therefore, the market power associated with innovative investments.

19 A test of the overidentifying restrictions suggest that the instruments are valid and the first stage

regressions (not shown) show that they have sufficient power (i.e. joint F-statistic>10).

22

The CMS has shown that non-conventional reasons to patent such as patent

preemption are more common than previously thought (Cohen et al., 2000). With this

paper I show that such strategy tends to remarkably improve the appropriability of the

returns to R&D, especially for incumbents with stronger market power, suggesting that

entry deterrence may indeed be the driver of higher profits obtained through broad-based

patent portfolio strategies. The effect of preemptive patenting on market power, however,

could be due not necessarily to complete entry prevention, but, as argued by Waterson

(1990), to a better distribution of innovative products in the economic space at the

industry level by actual and potential competitors. In other words, patent preemption

would increase industry profitability by increasing the effectiveness of R&D-based

differentiation strategies. Whether preemptive patenting is socially wasteful or limits the

rate of technical change is, however, an important and difficult policy question that goes

beyond the scope of this paper.

Finally, how useful is the patent yield as an indicator of firm technological

performance? Controlling for R&D and patent propensity, the patent yield is driven by

intangibles such as knowledge spillovers from rivals or universities, technological

opportunities, and other productive capabilities, which are critical components of a firm

economic performance, but also difficult to measure. Therefore, observation of both

R&D and patent propensity, along with the patent yield, should help in capturing the true

contribution of such intangibles to economic performance by including all the above as

drivers of a firm’s market value.

23

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Table 1. Descriptive statistics, 1991-1993

Variable Mean Median Std Dev Minimum Maximum

Tobin's Q 3.39 1.66 5.35 0.19 44.79Total assets (mil. $) 2,235 418.61 4,655.5 0.68 37,347R&D stock/Assets 0.38 0.21 0.67 0 5.80Citations stock/Successful patent applications stock 9.75 7.70 8.89 0 83.39Successful patent applications stock/R&D stock 3.40 0.55 38.48 0 666.83Total patent applications/R&D 1.56 0.47 4.39 0 53.33R&D (mil. $) 36.18 3.00 127.21 0.01 1,200Patent propensity 0.32 0.31 0.27 0 1Effectiveness of patent protection 0.39 0.3 0.3 0.05 0.95Effectiveness of secrecy 0.50 0.5 0.3 0.05 0.95Effectiveness of being first to market 0.48 0.5 0.29 0.05 0.95Specialized complementary assets (dummy) 0.49 0 0.50 0 1% firms using preemptive patenting 0.77 0.79 0.12 0.23 1Market share 0.02 0.003 0.06 0.0 0.75Technological leader (dummy) 0.43 0 0.5 0 1Concentration Ratio (CR4) 38.25 34.00 17.53 1.00 98% Technological overlap with rivals' R&D 0.56 0.63 0.24 0.0 0.88State university R&D-science field weighted (mil. $) 123.81 84.91 145.93 0.0 1,001

N=301

Table 2. Correlations1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 Tobin's Q 12 Total assets (mil. $) -0.12 13 R&D stock/Assets 0.53 -0.11 14 Citations stock/Successful patent applications stock 0.23 -0.05 0.14 15 Successful patent applications stock/R&D stock -0.01 -0.03 -0.04 -0.02 16 Total patent applications/R&D -0.02 -0.09 -0.05 -0.03 -0.01 17 R&D (mil. $) -0.04 0.37 -0.02 0.05 -0.02 -0.08 18 Patent propensity 0.25 0.10 0.12 0.19 -0.07 0.13 0.18 19 Effectiveness of patent protection 0.31 0.03 0.21 0.20 -0.03 0.23 0.14 0.58 1

10 Effectiveness of secrecy 0.05 -0.01 -0.03 0.00 0.05 -0.01 0.07 0.03 0.10 111 Effectiveness of being first to market 0.02 -0.03 -0.03 0.19 0.06 0.08 0.06 0.05 0.12 0.28 112 Specialized complementary assets (dummy) -0.02 -0.05 -0.04 0.06 0.06 0.15 0.07 0.05 0.08 0.08 0.14 113 % firms using preemptive patenting 0.25 0.02 0.02 0.01 0.09 0.00 -0.05 0.28 0.20 0.05 0.04 0.05 114 Market share -0.11 0.29 -0.11 -0.07 -0.02 -0.06 0.43 0.08 -0.02 0.04 0.03 -0.03 0.01 115 Concentration Ratio (CR4) -0.10 0.24 -0.04 -0.04 0.08 0.03 0.13 -0.13 -0.07 0.13 0.00 -0.01 -0.18 0.25 116 % Technological overlap with rivals' R&D 0.03 0.10 0.07 0.10 -0.10 -0.03 0.13 0.11 0.05 -0.09 0.04 -0.08 0.02 0.13 0.11 117 State university R&D-science field weighted (mil. $) 0.50 -0.03 0.33 0.08 -0.05 -0.05 -0.03 0.12 0.11 -0.08 0.01 0.00 0.11 -0.05 -0.12 -0.07 1

Table 3. Market Value regressions. Dependent Variable: log Tobin's q(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

R&D stock/Assets 0.379 *** 0.357 *** 0.305 *** -0.306 ** -0.017 -0.150 -1.220 *** -1.023 ** -4.863 *** -0.858 -2.448 a0.116 0.111 0.099 0.137 0.147 0.160 0.355 0.400 1.677 0.959 1.466

Effectiveness of secrecy 0.173 0.239 * 0.208 0.151 0.054 0.275 ** 0.300 0.182 0.322 a 0.1210.145 0.134 0.137 0.151 0.163 0.138 0.263 0.229 0.194 0.252

Effectiveness of patent protection 0.468 *** 0.386 *** 0.168 0.167 0.309 -0.240 0.175 0.1550.138 0.129 0.147 0.146 0.248 0.216 0.246 0.239

Patent propensity 0.240 0.2250.182 0.333

% firms using preemptive patenting 0.447 0.269 -0.442 0.494 0.4190.307 0.642 0.565 0.520 0.640

Effectiveness of first mover advantages 0.321 ** 0.219 0.026 0.133 0.322 * -0.097 -0.203 0.452 -0.083 -0.3860.157 0.147 0.163 0.164 0.177 0.168 0.284 0.281 0.251 0.238

Specialized complementary assets 0.019 0.013 0.024 -0.026 0.085 0.074 0.057 -0.023 0.035 0.1690.079 0.074 0.083 0.092 0.104 0.082 0.141 0.121 0.129 0.141

Preemptive patenting × R&D stock/Assets 1.445 *** 1.224 ** 6.073 ** 1.187 1.9210.501 0.588 2.363 1.575 1.438

Patent effectiveness × R&D stock/Assets 0.701 ** 0.567 ** 0.400 2.291 *** 0.344 0.9880.283 0.288 0.355 0.752 0.538 0.604

Eff. first mover advantages × R&D stock/Assets 0.681 *** 0.292 -0.359 0.989 *** 0.929 *** 0.429 0.836 * 1.785 **0.224 0.319 0.419 0.232 0.281 0.944 0.418 0.477

Secrecy effectiveness × R&D stock/Assets -0.024 0.301 0.531 -0.301 -0.250 -0.927 -0.390 -0.0920.229 0.298 0.425 0.240 0.313 0.914 0.319 0.545

Specialized Compl. Assets × R&D stock/Assets -0.026 0.174 -0.359 -0.202 -0.127 0.062 -0.037 -0.3050.164 0.255 0.349 0.158 0.208 0.509 0.386 0.340

Patent propensity × R&D stock/Assets 0.186 1.470 *0.548 0.755

Market share -0.195 -0.211 -0.260 -0.531 -0.196 -98.660 -0.425 -0.058 -0.1920.288 0.280 0.292 0.339 0.284 61.413 0.323 0.704 0.352

Concentration Ratio (CR4) -0.003 -0.003 -0.003 -0.001 -0.002 -0.002 -0.003 0.000 -0.0040.002 0.002 0.002 0.002 0.002 0.003 0.002 0.003 0.003

Citations stock/Successful patent applications stock 0.023 *** 0.023 *** 0.023 *** 0.023 *** 0.022 *** 0.025 *** 0.012 * 0.023 * 0.018 **0.005 0.005 0.005 0.005 0.005 0.006 0.007 0.010 0.006

Successful patent applications stock/R&D stock 0.001 *** 0.001 *** 0.002 *** 0.001 *** 0.001 *** -0.003 0.001 *** 0.018 0.001 **0.0002 0.0002 0.0002 0.0003 0.0002 0.013 0.0002 0.019 0.0004

% Technological overlap 0.227 0.191 0.178 0.143 0.163 0.186 0.086 0.323 -0.1220.162 0.160 0.166 0.163 0.158 0.257 0.171 0.224 0.265

University R&D 0.001 ** 0.001 ** 0.001 ** 0.001 ** 0.0005 * 0.001 * -0.0003 0.0003 0.001 a

0.0003 0.0003 0.0003 0.0003 0.0003 0.0004 0.0003 0.0004 0.0005

R2 0.43 0.48 0.54 0.58 0.56 0.51 0.59 0.66 0.52 0.56 0.67N 301 301 301 301 301 301 301 158 143 172 129

- All specification except (6) are estimated with linear OLS. Specification (6) is estimated using two stage least squares using patent effectiveness to instrument for patent propensity. Specifications (8) and (9) are estimated within low and high market shares firms, whereas (10) and (11) within the sample of technological laggards and leaders.- Robust standard errors in italics.***,**,*: Significantly different than 0 at the .01, .05, and .10 confidence levels.- All specifications include 9 industry dummies and an intercept.

Table 4. Patent yield regressions. Dependent Variable: Number of patent applications per mil. $ R&D

(1) (2) (3) (4)

Patent propensity 4.952 *** 2.414 *** 5.162 *** 10.692 ***1.509 0.672 1.941 3.200

log of R&D -0.902 *** -0.386 *** -0.539 *** -0.948 ***0.303 0.133 0.167 -2.780

% Technological overlap with rivals' R&D 0.871 0.403 0.373 0.5130.923 0.651 0.848 0.540

University R&D -0.001 0.000 0.000 -0.0010.001 0.001 0.001 -0.960

R2 0.21 0.12 0.15 0.11

- Robust standard errors in italics.***,**,*: Significantly different than 0 at the .01, .05, and .10 confidence levels.- All specifications include 9 industry dummies.- Specification (1) is estimated using linear OLS, whereas (2)-(4) are instrumental variable regressions. Specification (2) treats R&D endogenous, (3) instead considers patent propensity as endogenous, and (4) considers both R&D and patent priopensity endogenous. In all IV estimation cases the instruments used are the appropriability variables, firms size as measured by the log of firm employees, the firm number of technological rivals, industry sales, and industry sales growth in the in the firm primary business segment, and a dummy indicating whether the firm has global sales in Japan or Europe.