On the E¤ects of Market Incumbency on Innovative Behaviour

in the Pharmaceutical Industry

Maria Pluvia ZUNIGA�and Gautier DUFLOSy

University of Panthéon-Sorbonne, TEAM & CNRS z

March 15, 2005

Abstract

This paper evaluates the e¤ects of market dominance on �rms�propensity to innovate

and productivity in a panel of 77 pharmaceutical �rms for the period 1975-1991. While a

higher market-share leads to a weaker propensity to innovate, incumbents �rms are found

to be persistent innovators and notably, persistent sources of quality-patenting. Neverthe-

less, incumbent �rms are subject to decreasing R&D productivity likely related to possible

diseconomies of scale and organizational inertia.

Keywords: Market Incumbency, market share, innovation, Pharmaceutical Industry.JEL: L25, L31, O31, O33.

�University of Paris1, Panthéon-Sorbonne, TEAM-CNRS (ESA 8059).yUniversity of Paris1, Panthéon-Sorbonne, TEAM-CNRS and Paris-HEC.zMaison des Sciences Economiques, 106-112 Bd de l�Hôpital 75647 Paris cedex 13 FRANCE. O¤.

+33(0)1.44.07.82.66 - Fax. +33(0)1.44.07.82.67 Mail: Mariapluvia.Zunigalara@malix.univ-paris1.fr (correspond-ing author).

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1 Introduction

Since the seminal work of Schumpeter (1939; 1942), scholars have been interested in identifying

what kind of market structure promotes rapid technological progress. Schumpeter advanced

(1942) the now familiar hypothesis that large �rms with market power accelerate the rate of

innovation: larger-dominant �rms possess superior economic and technological advantages al-

lowing them to dominate an industry through the process of creative destruction. However, this

statement has proved di¢ cult to verify empirically by the intensive literature.1 Our perceptions

about the role of market power on �rms�incentives to innovate and the degree of competition

necessary to ensure persistent innovation in industries remain still inconclusive. Are dominant

�rms powerful and persistent engines of technological progress? Do market power and incum-

bency in�uence in the same way innovative e¤ort? More precisely, how do incumbent �rms

persistently innovate?

It has been shown that in high-technology industries, entrants are more likely to introduce

radical innovations (Henderson, 1993; Lerner, 1997). This phenomenon seems to predominate in

the Pharmaceutical Industry particularly since the mid 1980s with the emergence of molecular

biology (Henderson and Cockburn, 1996; 1998). As documented previously (Arora and Gam-

bardella, 1990; Orsenigo, 1998; Orsenigo et al., 2001), new biotechnology �rms are frequently

source of technological breakthroughs. Nevertheless, some established dominant pharmaceutical

�rms appear also as persistence creators or developers of important innovations. Incumbent

�rms have sustained high levels of growth and R&D expenditures by means of in-house inno-

vation, technological and therapeutic market specialization (Achilladelis and Antonakis, 2000).

Although their technological competences may erode over time, face to the process of creative-

destruction, incumbent �rms may adapt to technological change by relying on their downstream

competences and incurring in cooperation with entrants or through mergers and acquisitions of

companies within and outside the pharmaceutical industry (Orsenigo, 1991; Stern, 1995; Lerner

and Merges, 1998; Gans and Stern, 2001). The determinants or forces leading to persistent

technological performance by these incumbent �rms remain however not clear.2

The distinction between incremental and radical innovation constitutes the basis for un-

derstanding the response of large-established �rms to innovation. The incentives to innovate

for incumbent and entrant �rms may di¤er depending upon the nature of innovation. On the

one hand, it has been argued that dominant �rms have stronger incentives to conduct radical

innovation (Schumpeter, 1942; Gilbert and Newbery, 1982; Bud et al.� 1993) because of the

e¢ ciency-e¤ect related to �rms�ex-ante market power. Financial considerations also support

Schumpeter�s proposition: �rms possessing some market power are more able to use internal

(from past innovation successes) and external funds to support their innovative activity, and

better access quali�ed-labour market and science-networks (Cockburn and Henderson, 1998).

On the other hand the replacement-e¤ect facing a monopolist, states that market power �rms

1Comprehensive surveys on the topic are Kamien and Schwartz (1982); Baldwin and Scott (1987); Levin andCohen (1989); Scherer (1992) and Symeonidis (1996).

2 .As technological-based competences by incumbents can depreciate over time, incumbent �rms may rely ontheir downstream chain value capabilites to cooperate with entrants and bring new discoveries to the market(Teece, 1986; Raethormel, 2001).

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will be declined to win a stochastic patent race too quickly as her existing stream of rents will

be displaced by an entirely new stream of rents (Arrow, 1962; Reinganum, 1982; 1983). R&D

ine¤ectiveness by incumbent �rms to bring radical innovations is also addressed by agency the-

ories. De�ciencies in R&D management (R&D monitoring, projects�choice, coordination) may

lead to a preference incongruity between principal and agent in innovation production deterring

�rms�innovation performance (Berle and Means, 1932; Holmstrom, 1989).3

In many ways, the empirical research to date con�rms the basic thrust of the theoretical

literature: the incentive to bring radical innovation persistently by incumbents is rather un-

certain. While it may be to some extent correlated to incumbency, market power has also an

ambiguous in�uence on �rms�incentives to persistently innovate.4This paper evaluates empiri-

cally these counter-acting views on incumbent �rms�incentives to innovate and their repercus-

sions on �rms� industrial performance in the Pharmaceutical Industry. Our sample concerns

a panel of 77 pharmaceutical �rms for the period 1975-1991. Our results suggest that market

incumbency and market power in�uence in a di¤erent way �rms�innovative behavior. Above

all, incumbent �rms in the pharmaceutical industry show a persistent propensity to introduce

quality-patenting. While a higher market-share seems to deter innovative e¤orts, incumbents

are persistent innovators and responsible of important (leader) patents. Nevertheless, incumbent

�rms are subject to decreasing R&D productivity likely related to possible diseconomies of scale

and organizational inertia.

The study is organized as follows. Section 2 makes a brief review of the literature. Section

3 presents an overview of innovation trends in the Pharmaceutical Industry and describes our

data and methodology. Section 4 presents our empirical models on innovation and productivity.

Section 5 discusses our results and the last section concludes.

2 Theoretical background

An extensive theoretical literature on the relationship between market structure and innovation

contends that incumbent �rms often face di¤erent incentives for innovation than entrants or

small �rms.

A traditional interpretation of the innovation-marked dominance or incumbency correlation

is that concerning �nancial aspects. Firms possessing some market power are more able to use

more internal and external funds to support their innovative activity as well as to better access

quali�ed-labour market (Bhattacharya and Ritter, 1985). Failures in �nancial markets force

�rms to rely on their own supra-normal pro�t to �nance R&D activities. In addition, given

the imperfectness of markets (e.g. asymmetric information, moral hazard), incumbent �rms

may enjoy some reputation which enable them to better access and negotiate �nancial sourcing

(ibid). In the Schumpeterian-perspective, large incumbent �rms may enjoy R&D advantages

3Lastly, organizational theories suggest that incumbent �rms are more imcompetent to answer to technologicalbreakthroughs than entrants given some R&D diseconomies of scale (Arrow, 1974; Nelson and Winter, 1982) andincumbent�s organizational inertia (Mintzberg, 1979; Thusman et Anderson, 1986)

4Scherer (1967, 1980), and recently by Aghion et al (2001) or Aguion et al (2002), suggests that competitionmay have positive e¤ect on innovation up to an in�exion point after which its e¤ect decreases.

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in the production of technology (Schumpeter, 1942; Galbraith, 1952) that facilitates their tech-

nological leadership : �rms�technological experience allow �rms the building of economies of

scale and scope related to R&D production and R&D diversi�cation (Nelson, 1959; Chandler,

1990; Cockburn et Henderson, 1998), a larger spreading of �xed costs over more R&D output

(Teece, 1980, Cohen and Levin, 1989; Cohen et Klepper, 1996) as well as a better appropriation

of external knowledge spillovers (Henderson and Cockburn, 1996).5

In interpreting the e¤ects of market share, other authors have considered the strategic in-

centives arising from current and expected product market power. The distinction between

incremental and radical innovation (or how easily technologies are imitated) constitutes the

basis for understanding the response of large-established �rms to innovation (Henderson, 1993).

A �rst view on this question is that of Gilbert and Newbery (1986). Accordingly, incumbent

monopolists have greater incentives to conduct innovation than potential entrants because total

industry pro�ts decrease when more �rms share the market. This e¢ ciency e¤ect is internalized

by the incumbent but ignored by the entrant. Thus the monopolists will tend to innovate �rst

and industry evolution will be characterized by persistent dominance (Budd et al., 1993; Kamien

and Swartz, 1972). Further, since incumbency may imply better knowledge of market conditions

and identi�cation of the best submarkets to enter, dominant are better positioned to be �rst

innovators (Adner and Levinthal 2002).

Another view is that of Arrow (1962) and Reinganum (1982) or Scherer and Ross (1990)

who model cases where incumbent �rs recognize a radical innovation but �nd it too costly or

risky to pursue and, consequently, ignore the innovation altogether or attempt to thwart the

innovators through blocking-entry strategies such as pre-emptive patenting (Farrell and Saloner,

1986; Gilbert and Newberry, 1982). Under technological uncertainty; incumbent monopolists

will rationally invest less in radical innovation than entrants will for fear of cannibalizing the

stream of rents from their existing products (Reinganum, 1982; 1983). When the competitive-

ness of new innovators or the actual value of the innovation is misestimated and innovations

are radical, incumbents may be displaced by entrants6 Dominant �rms have stronger (fewer)

incentives to invest in incremental innovation (radical) than entrants do, if all �rms have similar

(heterogeneous) research capabilities. Incumbent �rms�motivation to conduct radical innova-

tion will depend on the the size of the revenues related to the existing innovations and the degree

of heterogeneity across �rms�R&D capabilities (Klette, 1996; Klette and Griliches, 1998).7

Subsequent research has established that whether incumbent monopolists or entrants have

greater incentives to invest in innovation is a function of the degree to which innovation destroys

5Others are: managerial competences (logistic, distribution, marketing, etc.), experience in the production ofinnovation (Chandler, 1991; Freeman, 1986), superior access to capital and skilled labour markets (Schumpeter,1945, Galbraith, 1952), greater abilities to appropriate innovation (legal experience, an larger budget on legalpractice, etc.).

6Gilbert and Newbery�s �ndings are only valid in the case of incremental innovation and when these ones arenot related to incumbents�R&D. Incumbent �rms with market power will retain their dominant position in anindustry whose technology evolves incrementally.

7Some empirical studies have shown that persistent R&D di¤erences (e.g. dynamic increasing returns in R&D)seems to explain the distribution of market shares across companies (Gerosky et al, 1997; Blundell et al, 1999).

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existing market-power and of the uncertainty surrounding the innovative process (Gilbert and

Newbery, 1984; Reinganum, 1984; Bresnahan, 1985). The in�uence of these elements can �nally

in�uence di¤erently the innovation behaviour according to the maturity of industries and the

degree of obsolescence of technologies (Klette, 1997).8

Although the Schumpeter hypothesis has come in for some hard empirical knocks, at least

one strand of the literature suggests that it should not be rejected altogether. Theory shows that

market power can stimulate technological progress by market leaders when these ones posses

a superior technological capacity relative to entrants. Segerstrom and Woniereck (1999) or

Hörner (2001) show that if the technological di¤erence between two competitors is su¢ ciently

signi�cant, the company in dominant technological position has stronger incentives to continue

its e¤orts in order to maintain its technological leadership.9 Further, large dominant �rms may

bene�t from dynamic increasing returns in the production of innovations in the form of learning-

by-doing or learning-to-learn, which may lead to strong innovation persistence (Chandler, 1990;

Freeman, 1987). In emphasizing the importance of complementary assets when commercializing

an innovation, Teece (1986) views the fully integrated incumbent as the �rm best positioned to

bene�t from innovation. Though radical technological change generally leads to the deprecia-

tion of incumbents�technological competences, the non-technological assets (e.g. downstream

competences) of incumbent �rms may become more valuable, notably when they are specialized

with respect to commercializing the new technology (Leonard-Barton, 1992).

However, a growing body of work suggest that in certain industries, radical innovations are

actually pursued by market entrants to the detriment of incumbent �rms that presently hold

market power yet lack competence in this innovation (Foster, 1986; Henderson, 1993; Tushman

and Anderson, 1986; Henderson, 1993; Lerner, 1997). The ability to �free-ride�on �rst-mover

investments as well as technological and market uncertainty make it di¢ cult for the incumbent

to adapt to environmental change and may lead a �rm to delay entry into a new sub�eld

(Lieberman and Montgomery 1988).10

The organizational literature provides a number of theories with di¤erent implications for

incumbent innovation. One explanation argues that incumbents may attempt to produce the

innovation but are less successful than challengers due to inertia (bureaucratic process, higher

costs of processing routine information across departments) associated with organizational age

and/or size (Hannan and Freeman, 1989; Miller and Chen, 1994). Another explanation notes

that new knowledge associated with the innovation may render an incumbent obsolete and thus

incompetent in its production (Henderson, 1993; Henderson and Clark, 1990; Tushman and

8For instance, in emergent industries marked by a great turbulence, technology changes very quickly, uncer-tainty is signi�cant and the barriers at the entry are weak (Abernaty and Utterback, 1978; Klepper, 1997). Marketdominance is transistory and the technological advantages of innovating �rms then can be only temporary sincethe innovator in place soon is caught up by a competitor.

9On the other hand, if the di¤erence between the competitors is not too signi�cant, the technological followercan have interest to accentuate his innovating e¤orts in order to prevent further technological lagging (Hörner,2001).10 If a product can be easily imitated, Katz and Shapiro (1987) argue that a dominant �rm is better o¤ by being

a follower into a new submarket. The delay permits the dominant �rm to imitate the �rst mover�s product andavoids having its own products imitated.

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Anderson, 1986). Lastly, several authors have shown that insulation from competitive pressures

discourages innovation and growth, while refutations of Gibrat�s Law imply that smaller, not

larger �rms tend to innovate more than proportionately to their size (Klette and Griliches, 1998).

2.1 Empirical Evidence

The empirical evidence on the in�uence of market dominance on innovation remains inconclu-

sive. They point out however, that this relationship depends upon industry-speci�c factors.11

First cross-sectional studies of the relationship between �rm size, market power and innovative

activity have in general found no systematic relationship (Baldwin and Scott, 1987; Cohen and

Levin, 1989). Some works such as Geroski (1995), Blundell, Gri¢ th and Van Reenen (1995),

Nickell (1996), Levin et al., (1985), and Cohen et al., (1987) have found disproportionate evi-

dence against the Schumpeterian hypothesis. In the opposite, Acs and Audretsch (1987) suggest

that concentration may stimulate innovation in industries highly innovative. Largest �rms have

a superior advantage in the concentrated industries whereas small and medium enterprises may

be more productive in innovation in less concentrated industries. The works by Scherer (1967,

1980), and Aghion et al., (2001) and Aguion et al., (2002), market product competition may

have a positive e¤ect on innovation up to an in�exion point after which its e¤ect decreases. Ac-

cordingly to Aghion et al., (2001), more market competition may increase the incremental pro�ts

from innovation (as it may reduce �rms�pre-innovation rents by more than it reduces its post-

innovations rents), and thereby encourages R&D investment aimed at �escaping competition�.12

Blundell et al., (1993) �nd for a panel of U.K. �rms that, while higher market share �rms

innovate more, �rms in competitive industries tend to have a greater pro�tability of innovat-

ing. Blundell et al., (1999) found a robust and positive e¤ect of market share on observable

headcounts of innovations and patents in the British Industry, when unobserved heterogeneity

is controlled for through the use of pre- entry innovation stocks.13 In the opposite, Crepon et

al., (1995) report weak evidence of Schumpeterian e¤ects on innovation in the French Industry

: they are found to be far less important than the sectoral ones (i.e. product imitation, techno-

logical opportunities and industry-demand pull).14 In a sample of early-twentieth-century U.S.

industrial �rms, Nicholson (2003) �nds strong evidence of Schumpeterian innovation e¤ects:

�rms experiencing disrupted market shares produced fewer patent grants on average, while they

also encountered an unfavourable stock market response to their product market performance.

Furthermore, �rms with strong market positions tended to have higher patent counts especially

11Scherer (1980) have shown that the e¤ect of market concentration on R&D investment depends on thedegree of technological opportunity in the industry: higher concentration in low (high) technological opportunitiesincreasing (decreasing) R&D e¤orts.12This "escape competition" e¤ect dominates for low initial levels of competition, whereas the Schumpeterian

e¤ect dominates at higher levels of competition. The higher the average degree of �neck-and-neckness� in anindustry and that is, the degree in which oligopolistic �rms face more similar production costs, the steeper theinverted-U relationship between product market competition and innovation in that industry.13Though, they also report that increased market competition tends to stimulate innovative activity: while

industry concentration is negatively associated with the number of innovations, import penetration is positivelyassociated with innovation counts.14Although they con�rm that innovation increases proportionally with �rm size but no e¤ect is detected from

concentration on innovation activity.

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in the citation-weighted and scope-weighted (claims) patent production function.

The empirical work evaluating explicitly the role of incumbency on �rms�propensities to

introduce radical innovation arrives also to mixed conclusions. Henderson (1993) and Lerner

(1997) both provide evidence of weak persistence by incumbents, and therefore a constant re-

newal of technological leadership in the American Photolithographic Alignement Equipement

industry and the Disk-Driver industry, respectively Likewise, Cockburn and Henderson (1994)

found no evidence of technology racing behavior (�winner takes all�) in the case case of research

in the Pharmaceutical Industry.

Henderson�s �ndings (1993) show that established �rms invest more in incremental innova-

tion than entering �rms.15 While incumbency seems not to explain larger market shares (Gilbert

and Newbery�s rejection), incumbent �rms are found to have a smaller share of the market for

new products incorporating innovations that were radical in a technological organizational sense.

Lerner (1997) shows for the disk drive industry, that companies who are close-followers to the

leader innovate more, which implies a constant replacement of innovators.16 Lastly, Cockburn

and Henderson (1994) show for a sample of pharmaceutical �rms that the possibility of multi-

ple prizes or multiple patents to innovation makes R&D-rivalry insigni�cant to explain �rms�

innovative e¤ort. Hence there is no evidece of technology-racing a¤ecting �rms� innovative

performance. The discovery of a treatment seems to make easier, for the competitors, to dis-

cover another. Furthermore, spillover between �rms both from internal and external rival R&D

projects, are found to play a major role in increasing research productivity.

The di¤erent views presented in the literature suggest that �rms� innovative behaivor is

a¤ected by co-existing opposing e¤ects related to market dominance and incumbency. Market

dominance and market-structure may actually in�uence in a di¤erent way innovation incentives.

Furthermore, in some industries incumbent �rms could be persistent innovators in the long

run, whereas entrants may be sources of technological breakthrough but with frequently a short

life. One important shortcoming in most of the empirical works (excepting Blundell et al.,

1995; 1999; Duguet et al., 1995) is their aversion to deal with endogeneity arising from �rms�

market power (or market structure) on innovation. In a Schumpeterien economy, �rms gain

market share by innovating but lose it through creative destruction. A second problem relates

to unobserved heterogeneity, deriving, for example, from the di¤erent technological opportunities

and appropriability conditions facing �rms. As explained in the previous section, permanent

technological heterogeneity across �rms contributes to explain di¤erences in innovative behavior.

Lastly, few empirical works consider the role of knowledge dynamic-e¤ects and deal thereby with

the auto-correlation problems imposed by pre-determined variables (Crepon and Duguet, 1997;

Cincera, 1998; Blundell et al., 1995).

15 Investment in incremental R&D appears as 75% superior for old-dominant �rms and even larger when con-sidering organizational innovations (89%).16 Indeed, greatest technical progress is made by the immediate follower - those having e¤ectiveness of 25 and

74% - of the product leader.

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2.2 The Pharmaceutical Industry

To study incumbency and innovative behavior in this sector is particularly appealing for several

reasons. Firstly, the nature of knowledge is unique in the Pharmaceutical Industry. Prior

research shows that a strong link exists between research and development, namely basic research

and innovation in the pharmaceutical and biotechnology industries. For �rms that develop new

drugs, investing in basic research brings tangible bene�ts. In the US, measured as a percentage

of US sales, R&D expenditures rose from less than 12% in 1970 to over 20% in the late 1990s

(see Fig. 1). In 2001, research-based pharmaceutical companies invested US$ 30.5 billion in

R&D (PhRMA, 2002).

Secondly, appropriability is very high in the pharmaceutical industry. Patent protection

is extremely e¤ective (Mans�eld, 1986; Levin et al., 1987) and the rate of imitation is slower

for ethical drugs than for other products (Mans�eld et al., 1981). Thirdly, the industry has

high entry barriers because buyers have low bargaining power, because drug development is a

complex process requiring specialized knowledge, and because extensive human trials are needed

to satisfy regulatory requirements (Pisano, 1997; Scherer, 1994). The after-tax R&D cost per

new chemical entity that is placed in the market has been estimated at between $194 million

and $241 million (Dimasi et al., 1991). Other studies report (PhrMA, 2001) that it requires

an average of $500 million to introduce a new marketed medicine. These costs per marketed

product have risen considerably in real terms in the last decade. A more severe regulatory

framework has contributed to some extent to raising costs of developing new pharmaceutical

solutions.17 Number of studies has highlighted that there is a decreasing R&D productivity

particularly accentuated in pharmaceuticals relative to other manufacturing industries (DiMasi

et al., 1991; Henderson and Cockburn, 1998).

Third, although patents are essentiel to innovate, there is mixed evidence about an e¤ec-

tive patent race behaviour (Cockburn and Henderson, 1994; Henderson and Cockburn, 1995).

Moreover, innovators are confronted to an increasing therapeutic competition (rival patents)

before patent expiration. As evidenced by Lichtenberg (2002), the increasing between patent

competition imposed by rival patents covering similar therapeutic solutions (and the�me too

drugs�) has almost the same e¤ect on �rms�market erosion as the �within�patent competition

coming from generics. Although patenting activity in this sector is rather intensive relative to

other industries, quality of these patented-innovations is increasingly doubtful. The motiva-

tion to conduct low-quality re�ecting incremental innovations but intensive patenting may be

in some way related to �rms�ine¤ectiveness to introduce radical innovations over time (Hender-

son, 1993), strategic-purposes (block-entry, cross-licencing, etc.) or simply �rms�reluctance to

cannibalize existing innovations (Reinganum, 1982; 1983).18

Lastly, the link between drug discovery and basic science has increased over time (Pisano,

1997; Cockburn et al., 1999). Firms have moved away from randomly screening a large number

17For instance, the 1962 Kefauver-Harris Amendments to the Food, Drug and Cosmetic Act18 It is increasingly acknowledged that in some industries patents are frequently used for strategic purposes. For

instance : semiconductors (Hall and Ziedonis, 2001), software (Bessen and Hunt, 2004). See also Grindley andTeece (1997); Saphiro (2000) or Arora et al (2001).

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of potentially useful compounds, towards a more systematic approach called �rational drug de-

sign�. This involves exploiting knowledge about the biochemical mechanisms causing a disease

to identify or develop chemicals that inhibit such mechanisms (Pisano, 1997). Firms are begin-

ning to use biotechnology as a process technology as well as a research tool to improve their

search for new drugs (Cockburn et al., 1999).19 Furthermore, the advent of molecular biology in

the mid-1980 has lead to a new organization of innovation activities, namely the emergence of

a dense network of collaborative relationships among �rms of di¤erent types and other research

institutions.20

The structural break in technology raised by molecular biology has had important implica-

tions on incumbent innovation strategies and �rms�performance. The emergence of biotech-

nology can be interpreted as a technological discontinuity that broke the barriers to entry into

the pharmaceutical industry (Tushman and Anderson, 1986). Consequently, many new biotech-

nology �rms emerged to commercialize this technological breakthrough.21 While the value of

upstream activities (technology-based competences) of incumbent �rms may depreciate in an

environment of radical technological change (Tushman and Anderson, 1986), their downstream

competences (commercialization capabilities, FDA regulatory management, established reputa-

tion and �nancing) may become more valuable, notably when they are specialized with respect

to commercializing the new technology (Rothaermel, 2001; Arora et al., 2001). They may in-

curring in cooperation with entrants (or through mergers and acquisitions of companies), which

frenquently lack of complementary capabilities to put innovations into the market (Orsenigo,

1991; Stern, 1995; Lerner and Merges, 1998; Gans and Stern, 2001). As explained by Rothaer-

mel (2001), there is a symbiotic coexistence between incumbent �rms and new entrants in the

biopharmaceutical industry following radical technological change. The existence of these com-

plementary linkages in the biopharmaceutical industry has been empirically corroborated by

Arora and Gambardella (1990), Lee and Burrill (1994) and recently by Rothaermel (2001).

There are numerous examples of this phenomenon. The cooperation between Genentech and

Eli Lilly is a case in point, as Genentech has preferred to license its human insulin based on

recombinant DNA (Humulin) to Eli Lilly instead of commercializing it on its own (Lee and

Burrill, 1994).22

There is some evidence con�rming the market and technological leadership of incumbent

�rms in the Pharmaceutical Industry. In a study about the evolution of innovation in the

Pharmaceutical industry, Achilladelis and Antonakis (2000) show that most of innovative activity

19A major advance, was the discovery and application of biotechnology processes recombinant DNA and mon-oclonal antibodies in the production of physiological proteins used in therapy or diagnosis of many diseases,particularly cancer, viral and age debilitating diseases for which relatively little progress was made that far.The�rst biotechnology drug, Humulin (human insulin), received �nal FDA approval to be marketed in 1982.20An extensive literature has documented some of the consequences of such technology revolution on the orga-

nization of innovative activities in the Pharmaceutical Industry (Orsenigo, 1989; Henderson, 1994; Gambardella,1995; Orsenigo et al, 2001).21Between 1970 and 1997 alone, 1049 companies entered the industry to commercialize biotechnology. On an

average, 37 companies entered the industry per year in this time period, with 89 entries in 1992 alone (BioScan,1997).22The cooperation between Biogen and Schering-Plough in commercializing Intron A, the �rst biotech-interferon

product approved for cancer treatment, or the cooperation between Chiron and Merck to commercialize the drugEngerix-B for the prevention of hepatitis B, are other examples of these arrangements (Lee and Burrill, 1994).

9

remains concentrated among few large companies: 30 companies introduced more than 70% of all

the innovations of the sample over the period 1950-1990. These �rms have sustained high levels

of growth and R&D expenditures by means of in-house innovation, technological and therapeutic

market specialization, and mergers and acquisitions of companies (Achilladelis and Antonakis,

2000). Even with the emergence of biotechnology, incumbent �rms success to remain market

leaders. In 1993, 6 of the top 10 selling biotechnology drugs where marketed by incumbent

pharmaceutical �rms and not by the NBFs that had developed the products. Those six drugs

alone accounted for almost 60% of the revenues for the top-10 selling biotechnology drugs (Lee

and Burrill, 1994). By 1996 and 1997, the number of top-10 selling biotechnology drugs marketed

by incumbent pharmaceutical �rms had grown to seven.

2.3 Data and Methodology

Our study has two research goals. First, it evaluates empirically the impact of market incum-

bency and market share as well as the in�uence of past-innovation on �rms�propensity to inno-

vate. Second, it assess the e¤ects of the di¤erent knowledge stocks (R&D stocks, non-adjusted

and quality-adjusted patents stocks) owned by incumbents to explain �rms�market sales. Thus

this study attempts to distinguish more adequately incremental innovations from true pioneer

innovations by using patent-citation data.23 If incumbent �rms�tend to innovate incrementally

over time a larger rate of return relative to important and or quality-adjusted patents should be

expected.

To analyze the pharmaceutical inventive activity we use two data sources. First, we ex-

tracted patent data from the NBER patent data �le, by Hall et al. (2001) concerning USPTO

(United States Patent and Trademark O¢ ce) utility patents granted in the classes 514 and

524 over the period 1973-1994 (Drugs Industry). Second, we gathered �rms�patent data and

built �rms�patent portfolio. Third, we match this patent data to �rms�accounting data from

the Standard & Poor�s COMPUSTAT Annual Industrial Files Database. by using the CUSIP

identi�er (Committee on Uniform Security Identi�cation Procedure) code of the �rm.24 Data

are therefore reduced to a sample of 77 Pharmaceutical �rms in a unbalanced panel patenting

at the USPTO between January 1975 and December 1994 and quoted in the American Stock

Exchange. In order to reduce the bias imposed by citation and patent truncation, even after im-

plemented the correction proposed by Hall et al. (2001) to normalize citations, for our empirical

study we reduce our analysis to the period 1975-1991.

The original database on pharmaceutical patents concerns more than 9600 patents referred

in the 514 and 424 industrial USPTO patent classi�cations. Micro-Level Data from COMPU-

STAT concerns R&D spending, capital expenditures, number of employee, net sales, cash �ow,

operating and net income (constant US$94). As noted in Hall et al. (1986), the organization

23To the extent that quality across patents, within a �rms�patent portfolio and across �rms may largely di¤er,it is important to weigh patents�value relative to other innovations in the technological race (Tranjtenberg, 1990;Scotchmer, 1991).24Matching between two sources has been made through an intermediary �le the containing CUSIP code (Hall et

al, 2001) and the corresponding corporate company name. Since Compustat includes �rms that are traded in theUS stock market (manufacturing �rms) only, foreign pharmaceutical patents are not matched, and consequently,our �nal database concerns mainly American Pharmaceutical Firms.

10

remaining in the sample show a size distribution naively tilted toward the larger �rms in the

original universe. We build �rms�quality-innovation adjusted portfolio of patents as the sum of

total patents weighted by their number of received normalized citations.25 Stocks of R&D, and

non-weighted and weighted patents are built following the perpetual inventory method using a

15% depreciation rate (Griliches, 1989; Hall and Mairesse, 1995).

There is an emerging empirical literature using patent citations to study knowledge transfer

and innovation mechanisms. In particular, the front of a patent details other patents which

contributed to the knowledge underlying the new patent (Hall et al., 2001). Patent citation

information can be used therefore to detect the �technological value�of a patent and the �orig-

inality�of innovations: a patent which is cited many times is more likely to be valuable than a

patent that is rarely cited (Ja¤e et al., 1999, Hall et al., 2001).26 As explained by Hall et al.

(2001) and Ja¤e and Trajtenberg (1996), patent citations are themselves as a means to tackle

the underlying innovation (or economic value) heterogeneity across patents as well as a way to

trace spillovers.27

We address the problem of truncation related to patents and citations by implementing the

methodology proposed by Hall et al. (2001). Hall et al. propose to estimate the shape of the

citation lag distribution, i.e. the fraction of lifetime citations (de�ned as the 30 years after the

grand date) that are received in each year after patent grant. Accordingly, the total citations

for any patent (for which we observe a portion of its citation life) is estimated by dividing the

observed citations by the fraction of the population distribution that lies in the time interval for

which citations are observed. It is assumed that this distribution is stationary and independent of

overall citation intensity. In addition, there is an important lag between patent applications and

patent grants (in average about two years).and consequently, we observe only a small fraction

of the patents applied for that eventually will be granted as we approach the last year of patent

data. Count of patents should then be corrected using weighting factors, according to the

estimated application-grant empirical distribution (ibid).We use patent data from 1975, patent

stocks are built for those �rms patenting before this year. Our sample is restricted to the period

1975-1991 in order to avoid any bais related to truncation and patent application lags on our

data.25A point to note is that all patents are granted in USA but there is di¤erent nationality of �rms even if an

American �rm represent 75% of the sample (in the universe of the database). NBER data concerns all patentsgranted in USA meaning by �rms having an international activity and a critical size (over the period 1975 and1995).26Number of researchers have used patent citations as a �paper �ow� to track the way knowledge spills over

between organisations and areas (Henderson et al, 193; Ja¤e and Trajtenberg, 1998).27Nevertheless, citations have also some shortcomings. Frequently, many of the citations reported in the patent

are not really issued by the �rm, but they are added by the inventor�s patent attorney or the patent examiner,representing thus inventions that were wholly unknown to the citing inventor. In addition, identi�cation of originalpatents or placebo patents, is also not quite clear in the citations made (Hall et al. 2001).

11

3 Patent quality Propensity and Industrial performance

3.1 Propensity to innovate: distinguishing nature of patents

A key focus of interest in this paper is the estimation of an innovations equation (Iit). Following

Hausman et al. (1984) and Blundell et al. (1995), for a latent variable of innovation (Iit)

consider the knowledge production function 28,

Iit = f(xit; �i) (1)

where xit is a vector of �rm i characteristics (e.g. past R&D investments) and unobservables

individual �rm-speci�c factors (to the econometrician) are represented by the term �i: It rep-

resents permanent unobservable di¤erences across �rms a¤ecting the production of innovation

�rms (appropriability conditions, marketing or �nancial characteristics). This relationship is

derived as the outcome a �rms�optimal search rule for innovation (Reinganum, 1989; Blundell

et al. 1995). The search process is assumed to generate innovations in future periods. Iit in-

novations can be de�ned by the observed number of patent applications made by the �rm i:

Because the number of patents is a non-negative integrer, we adopt count data regressions as

described by Hausman et al. (1984) and Blundell et al. (1999). We model the conditional mean

as a multiplicative or log link function of explanatory factors as:

E(Iitjxit; �i) = exp(x0it� + �i)

= �itvi; for i = 1; :::::; N and t = 1::T;

where �it = exp(x0it�); vi = exp(�i); xit is a vector of observed explanatory variables, and � is

the corresponding vector of parameters to be estimated including lagged values of �rms�market

share, R&D expenditures and stock of knowledge, etc. These are assumed to be stationary. The

full empirical model is de�ned as :

E(Iitjxit; xit�1; ::xit�m; Git�1; �i; ")

= exp(�1 log(R&D)it + �2 log(Git�1) + �3MSit�1 + �3INCi + �i) "it (2)

for i = 1; :::::; N and t = 1; :::::; T ; where Iit= fNAit; CAit; Litg

Hence in this speci�cation, �rms� innovation propensity is conditional on previous market

power, measured by the lagged value of market-shareMSit�1, previous R&D e¤ortR&Dit:Considering

the dynamics of innovation, we introduce the lagged knowledge stock variables GNA;CA;Lt�1;

where subscripts NA refer to non-adjusted patent stock (GNA;t�1); CA to citation-adjusted

patent stock (GCA;t�1); and L to leader patents stock into the knowledge production function.29

28See also Hausman et al. (1984); Winkelmann and Wimmermann (1992); Crepon and Duguet, (1997); Blundellet al (2000), amongs others.29Poisson and negative binomial with �xed e¤ects have been extended to this approach (Hausman et al, 1984),

Winkelmann and Zimermann (1992). Semi parametric methods letting the distribution of residual unspeci�edhave been proposed by Gouriéroux et al. (1984).

12

A patent is considered as leader or important patent if it has made less citations than the mean

value of citations made by all patents in the current year (application year) and received more

citations (normalized) than the average of the patents that same year. Thus, in a cross-sectional

basis (yearly), we identify the pionner patents, which are those that determine the technological

path followed by �rms in the pharmaceutical industry. These "important" patents are assumed

to generate the process of creative destruction in a technological sense, since they open a new

technological sentier, remplacing existing knowledge (i.e. innovations).30

To identify incumbent �rms�technological superiority on innovation we include the dummy

INCi: It reefers to those �rms that we considered as technological incumbents : those who were

present in 1975 and reported consecutive patenting at least T>3 periods (19 �rms). Further, we

include an interactive term INCi � log(Git�1); to identify the relative e¤ect of incumbent �rms�stocks of knowledge on �rms�propensity to innovate. If incumbency implies stronger permanent

incentives to innovate, the incumbent-dummy should enters positively �rms�propensity to inno-

vate (Gilbert and Newbery, 1982; Segerstrom and Woniereck, 1999; Hörner, 2001). As exposed

in the previous section, incumbent �rms may enjoy several economic and technological advan-

tages facilitating the production of innovation as well as in the extent of patenting (�nancial

strength or R&D economies of scale and scope, amongst others). Inversely, they might su¤er

of diseconomies of scale related to size, in�exible R&D (specialization) and organizational iner-

tia, hampering their abilities to answer to radical innovations by others (technological change).

However, this e¤ect should di¤er across the di¤erent quality levels of patenting.

The e¤ects of market share on innovation propensity are ambiguous. Strategic considerations

imply that the marginal bene�t of an innovation (an additional increment to the �rm�s innovation

stock, Gi;t) may be more valuable to a leader with high market share than a follower with low

market share (Gilbert and Newbery, 1982). In the opposite, high-market share �rms may be

more reluctant to innovate as they �nd more pro�table to exploit existing innovation.31

Individual knowledge stock variables are obtained by accumulating past patents: Gi;t =

Ii;t + (1 + �Git�1). The stock of knowledge is continually increased by the addition of new

patents but is also continually decreased by a constant depreciation rate.� or obsolescence rate

((15%).32 Knowledge-stocks should enter positively innovation-propensity as there are dynamic

returns in the production of innovation. Cumulated technological experience facilitates in some

way current R&D productivity : R&D economies of scale (Teece, 1980, Cohen and Levin, 1989;

Chandler, 1990), learning-by doing, and learning-by learning e¤ects (Rosenberg, 1987; Cohen et

Klepper, 1996). An elasticity superior to one, suggests that there increasing dynamic returns in

cumulated knowledge to further innovate. Gi;t enters the exponential mean multiplicatively.33

30Although, the process of creative destruction in drugs is to some extent limited, both economically andtechnologically. First, as we mentioned previously (Henderson and Cockburn, 1993; 1994), pharmaceutical �rmsmay enjoy each di¤erent "patent" prize as patents may cover similar technological innovations (e.g. patentingaround, evergreening patenting). Second, economically �rms may keep their market power in speci�c therapeuticdomaines through lead time, brand reputation strategies, and other �rst-mover advantages.31 .They may also rely on alternative strategies to keep market power (�rst mover, lead time etc.).32 Initial values of Gi;t correspond to the pre-sample stock of patents for �rms patenting before 1975, otherwhise

patent�stocks for �rms appearing after 1975 are calculated according to the perpetual inventory.33Our speci�cation recalls the standard model used in the R&D- patents literature focuses mainly on the

13

The di¤erent parameterization between the multiplicative feedback model (MFM) and the liner

feedback model (LFM) (Blundell et al., 1999; Cincera, 1998) implies that, in comparison to the

LFM, the coe¢ cients �E(Iit) in the MFM measures the short run impact of a change in Xit.34

In a similar study, Blundell et al. (1999) implement both speci�cations and both models are

found to yield similar estimates for the main parameters of interest.35

We also include three additional lagged terms referring to �rms�innovation degree of gen-

erality, �rms�citations (received) share and the level of market concentration. The two former

constitute the mean value of �rms�patent portfolio (NBER patent �le, Trajtenberg et al., 1997;

Hall et al., 2001). Firms�citations share represents the part of the �rm in total (received) cita-

tions in the pharmaceutical classes 514 and 324. It measures the par of knowledge generated by

the �rm in the previous period or the degree of technological leadership. If technology-leaders

have stronger incentives to remain in the innovation-race, it should enters positively �rms�in-

novation propensity (Segerstrom and Woniereck, 1999; Hörner, 2001). Nonetheless, its impact

must di¤er à priori according to the quality level of patenting (Henderson, 1993).36

Innovation generality constitutes our measure of �rms�R&D diversi�cation. As shown by

Cockburn and Henderson (1998) and Henderson and Cockburn (1996), pharmaceutical �rms

which are more diversi�ed have been found to enjoy a stronger R&D productivity (R&D economies

of scope). The measure of originality is an Her�ndhal concentration index (Trajtenberg et al.,

1997; Henderson et al., 1998): =1-NiXk=1

�Ncitingi;kNcitingi

�2, avec Nciting=nombre of citing patents,

K=number of class (all citing patents). It denotes the share of citations made by patent i that

belong to patent class n, out of Ni patent classes. Thus, if a patent cites previous patents that

belong to a narrow set of technologies, the generality score will be low, whereas citing patents

in a wide range of �elds would render a high score.

3.2 Weakly exogenous variables and dynamics

The introduction of predetermined variables (E(xit; "it+j) = 0; j �; 0 but E(xit; "it�s) = 0; s �1) in the right hand side of the equation in combination with unobserved heterogeneity requires a

special treatment for estimation. Under weak exogeneity of variables, estimation by Conditional

Maximum Likelihood (CML) developed by Hausman et al. (1984) allowing for individual-speci�c

(�xed) e¤ects give inconsistent estimates.37 Likewise, more �exible models allowing for data

distributed lag of past R&D investments. Assumming that historic R&D investments are combined through aCobb Douglas technology to produce knowledge Qit = R&DitR&Dit�1:::vi:, the conditional mean speci�cation ina multiplicative distributed lag model is the standard model: E(yitjxit; xit�1; ::::; xit�m; vi) = exp(x0it�1+x0it�1�2+:::x0it�m�m)vi:34As explained by Blundell et al (1999), in the MFM model the nonlinear dynamic makes it di¢ ccult to judge

the stability properties of the model and the potentially important feedback parameter is sensititive to largeinnovation counts.35Although a non-nested comparison has suggested a preference for the LFM speci�ciation.36Likewise, a weak citation-share indicates that other �rms or competitors own a larger part of the new tech-

nology. Hence competitive spillovers may have a negative rivalry e¤ect on a �rms�likelihood to apply for a patent: the more competitors invet in R&D, the less a �rm is likely to invent a new technology (Loury, 1979; Lee andWilde, 1982).37PoissonThis estimator is the log-link equivalent of the within group sestimator for the linear panel data

modelSemi parametric methods where the distribution of the residual is unspeci�ed have been proposed byGouriéroux et al (1984):

14

overdispersion while controlling for unobserved heterogenity such as the negative binomial model

(Cameron and Trivedi, 1986; Winkelmann and Zimmermann, 1991) also rely on the strong

assuption that the �rms�unobserved e¤ets are independent of the explanatory variables.

To deal with predetermined regressors while allowing for correlation of unobserved individual

e¤ects with regressors, Chamberlain (1992) and Wooldridge (1997) have proposed a non-linear

GMM estimator. It uses the equation in quasi-di¤erences that eliminate the �xed e¤ect from

the multiplicative model, generating orthogonality conditions that can be used for consistent

estimation in count data models with predetermined regressions.38

Considering �it = exp(X 0�); the quasi-di¤erenced transformation by Wooldridge (1991;

1997) for the model may be written :

sit(�) = Iit � exp((xit � xit+1)0�)Iit+1 (3)

Instruments date t or before will be orthogonal to sit(�) and therefore the moment conditions

E(sitxis) = 0 for t > s (4)

The precision of the GMM quasi-di¤erences estimator requires that functions of the xit; xit�1:::xi0,

and Iit�1; Iit�2::::Ii0; are good instruments for the di¤erence term exp((xit � xit+1)0�)Iit+1:39

3.3 Performance and Quality-Adjusted Patents

To evaluate the impact of non-adjusted and quality adjusted patent stocks, we consider now a

Cobb-Douglas production function as follows:

yit = �yit�1 + (1� �)�LLit + (1� �)(1� �L)�kKit + �gGNA;CA;Lit + !it!it = t + �i + vit j�j < 1

vit = �vit + ait vit v AR(1); ait v AR(0) (5)

where yit is a measure of industrial performance, market sales of �rm i in year t, Lit is log

employment, Kit is log capital stock, Git is the knowledge stock (Git NA: non-weighted patents

stocks, Git CA: citations-weighted patents stocks and Git L and GitF , leader and follower patents

stocks) and t is a year speci�c intercept. Of the error components �i is an unobserved �rm

speci�c e¤ect whereas vit is a possibly autoregressive (i.e. productivity/performance) shock

and ait re�ects serially uncorrelated measurement errors. Constant returns to scale would imply

�n + �k = 1; but is not necessarily imposed.

Besides dealing with simultaneity, dynamic panel data methods (IV-GMM estimation) treat

the inherent problem of serial autocorrelation of residuals and the potential heteroscedasticity

38Application of the quasi-di¤erenced GMM estimator to count data models with predeterminated regresorshas been made by Montalvo (1997), Crépon and duguet (1997) and Cincera (1997). Blundell et al. (2000) providean extension of the GMM estimation by including the sample-mean count of patents as a measure to deal withpersistent unobserved heterogeneity.39Blundell et al (1995) have developed an estimator in which the pre-sample information on innovation replaces

the unobservable �rm �xed e¤ect.

15

within panels. We implement the system GMM outlined in Arellano and Bover (1995), and fully

developed by Blundell and Bond (1999), both improving e¢ ciency of GMM-estimation, relative

to standard IV techniques and the Arellano and Bond GMM estimator. As known, estimation

using the Arellano and Bond (1993) GMM estimator presents some shortcomings. It provides

very small and imprecise estimates of �L and �K (Blundell and Bond, 1999). Overidenti�cation

restrictions are typically rejected as fxi;t�2; yi;t�2g are not valid instruments. Further, it su¤ersof the problem of weak instruments in dynamic moders where regressors in �rst di¤erences are

weakly autocorrelated. Lastly, �rst di¤erence transformation also eliminates the cross-sectional

variation an it is suject to the problem of measurement error in input.

The IV-GMM estimator proposed by Blundell and Bond (1999) deal with these issues. The

identifying assumptions are : 1) !it = t + �i + vit; 2)vit = �vit + ait;(AR(1) process), 3) xit =

x�(xi;t�1;!it) and fYi0; Li0;Ki0g is in steady state. Predetermined and endogenous variablesin levels are instrumented with suitable lags of their own �rst di¤erences fxi;t�j;yit�j ; j � 2g.And the �rst di¤erences equation is respectively instrumented with lagged levels of inputs and

outputs f�xi;t�j;�yit�j ; j � 1g. These instruments in the SYS-GMM addresses the problems

of omitted variable bias, endogeneity and unit root e¤ects in the choice of instruments (Blundell

and Bond, 1998). Given its di¤erent advantages to deal with the speci�cities of our data, is our

preferred econometric method.40

We implement the Arellano and Bond test for �rst and second (m(1), m(2)) order serial

autocorrelation of residuals. If vit is not serially correlated, the di¤erence residuals should be

characterized by negative �rst-order serial correlation and the absence of second-order serial

correlation. The Hansen test of over-identifying restrictions are reported at the bottom of

regressions. These tests are inspired from Sargan (1985) and Hansen (1982) and conduct test

for the null hypothesis that the remaining theoretical orthogonality restrictions are equal to zero.

Failure to reject the null hypothesis indicates that the instruments are valid, and then supports

the validity of the model speci�cation.

4 Innovation Trends

Figures 1-2 give an overview of the innovation trends in the American Pharmaceutical Industry.

The evolution of the �rm-average R&D intensity, unadjusted and citation-adjusted patenting

in the industry is displayed in �gure 1. During the period considered 1975-1995, �rms�have

dramatically and continually increased their R&D investment relative to sales from 8% in 1975

to 11% at the end of 1991. This trend re�ect both an expansion in the scale of research e¤ort

and the change in its nature, as the adoption of rational drug discovery techniques increased

the demand for sophisticated equipment and more highly skilled researchers (Henderson and

Cockburn, 1996; Cockburn and Henderson, 1998). Likewise, total patenting has also continuing

40As we are confronted to potential endogeneity arising from simultaneity of input-factors and unobservedheterogeneity, both are likely sources of bias in the OLS estimation. Further, OLS produces inconsistent estimatesof the lagged dependent variable (Nickell, 1981; Bond, 2002), since this explanatory variable is positively correlatedwith the error term due to the presence of the individual e¤ects. Alternatively, within groups estimator is alsoinconsistent here, as they are subject to bias in absence of complete exogeneity.

16

Figure 1: Evolution of Pharmaceutical R&D Productivity

augmented: the number of total patents (applied) almost doubled during this decade from just

over 1,000 patents in 1980 to around 5,000 in 1995.

When adjusting patents by their normalized received citations, the �gures show a di¤erent

trend. The level of quality seems to follow a more stable path, although decreasing at the

end of the period. However, �rms�productivity (patents per R&D) has signi�cantly decreased

(Figure 2). This contracting trend is still more accentuated when considering the number of cites

per patent (industry means). Lastly, when distinguishing leader patents from follower patents

(citations received and forward citation lag larger and smaller, respectively than the yearly-

industry mean). Indeed, the number of incremental patents seems to increase over time whereas

leader patents show a slower and contracting movement along the period. A similar tendency

is found for incumbents�patenting activity (�gures 3 and 4). Although patenting by incumbent

�rms relative to the industry remains important all over the period, their R&D productivity is

notably decreasing particularly in quality-patents at the end of the period.

Henderson and Cockburn (1993; 1996) suggest that one possibility of this declining is that the

transition to more rational methods of drug research has led to a change in patenting strategies

and an increase in the signi�cance of each patent. Real research output may have remained

constant or even risen. Another possibility is that the industry is approaching technological

exhaustion (Grabowsky and Vernon, 1990; Scherer, 1994) but rates of investment in research

are continuing to accelerate despite the decline in patenting, perhaps as a strategic response

to actions of competitors, or simply because changes in demand conditions were increasing the

marginal revenue product of research.

Table 1 reports the 12 most cited patents in pharmaceuticals (514-424 UPSTO patent classes)

in the NBER patent data �le (Hall et al., 2001). All of these patents were applied during the 1970

17

Figure 2: Evolution of Pionner and Incremental Patenting in Drugs

and 1980s. The oldest most-cited patent dates from 1969 (application date) and belong to Alza

Corporation. The most recent cited patent (application date) belongs to DuPont de Nemours

and was applied for protection in 1988. Table 2 summarizes the number of patents considered as

�leader patents�to 1976 and then (cumulated) to 1995. This simple frequency of technological

leadership at the beginning and at the end of the period suggest that pharmaceutical important

innovations are introduced by an increasing number of �rms: there are new innovators in the

industry (with small and medium number of patents), whereas the technological leaders in 1976

continue to bring quality-patents in a relative similar percentage: 7,14% of all quality-patents

(to 1995) belong to Merck and Co., 6,79% to Bristol Myers Squibb, 6% to Ely Lilly and Co.

whereas P�zer Inc. and Schering Plough both own around 5,4% of total leader patents. Total

patents and citations in 1975 and 1990 are reported for a small of large pharmaceuticals in table

3.

Figure 1 in Appendix displays the evolution of total citations (received) and total citations

normalized to correct for the bias of few citations attributed to patens in the lasts year of the

period. We use the de�ator provided in Hall et al. (2001) for the pharmaceutical industry.

Although the normalization of citations reduces the striking contracting tendency at the end of

the period, it does not eliminate completely the problem. For this reason, our empirical analysis

will focus on the period 1975 to 1991, in order to minimize this bias.

4.1 Descriptive Statistics

Table 2-4 presents summary statistics describing the data. Averaging across all �rms and years,

our �rms spent on average in R&D 258,550 dollars and has average sales of 2952,86 (USD

1990). Important di¤erences appear across �rms when looking at the sub-sample of continuing

innovators (77) for which at least three years are reported consecutively, our sample of study;

18

Figure 3: Incumbent Firms�Patenting in Pharmaceuticals (1)

Figure 4:

and at the one concerning incumbent �rms (19). We consider an incumbent �rm, if it appears

in 1975 patenting, and continue patenting at least three periods consecutively. Compared to

non-innovators which reports annual (average) sales of 2589.10 (less than the industry average),

persistent patenting �rms enjoy an average annual sales of 3240.70, report the largest R&D

expenditures (343,11) and an average market share of 0.54. For this sample of 77 �rms the

average number of patents is 6.35, of leader patents is 0.48 whereas of follower patents, 5.86.

Their average citation share (annual) is 0.0075

For the incumbent �rms�sample (19 �rms), the statistics change substantially. Their average

sales reported is around 10,480.11, with an average market share of 1.74, and R&D expenditures

of 1076,53. The average number of citations received (normalized) 2055.78. Their average

number of patents is 26.77 (non-adjusted patents), 1.82 leader patents and 25 follower patents.

Their average citation share (annual) is 0.1871. These simple descriptive statistics suggest

that incumbent �rms show both an economic and technological superiority: they have a more

intensive patenting activity, a higher quality-patenting relative to the rest of the industry and

the largest market and citation share.

5 Empirical Results

5.1 Innovation equation results

In this paper it is argued that by looking at the relationship between incumbency and innovation

together with the impact of patents stocks on �rms�pro�tability it is possible to shed further

light on the importance of the incentives to innovate. Furthermore, it attempts to disentangle

�rms� organizational and strategic e¤ects on �rms� propensity to innovate by looking at the

impact of incumbency as a proxy for the former and of market share for the latter.

19

Table 1 reports estimates of the innovation equation for the sample of 77 �rms using the

GMM-quasi di¤erentiated regression. The set of instrumental variables used are reported at

the bottom of the table, as well as the corresponding auto correlation tests (�rst and second

order). The Sargan test for over-identi�cation justi�es the validity of instrumental variables.

In each case the log-link model (4.1) is estimated across the di¤erent models of patenting. A

di¤erent instrumentation set is used according to the type of knowledge stock (G): subscriptsNA

refer to non-adjusted patent stock (GNA;t�1); CA to citation-adjusted patent stock (GCA;t�1);

and L to the stock of leader or "important" patents (GL;;t�1): As reported by the AR(1) test,

there is signi�cant serial auto-correlation (Blundell et al., 1999) which re�ects the importance of

dynamic e¤ects on �rms�innovation propensity. Nonetheless, it could also indicate the presence

of permanent heterogeneity across our �rms. The coe¢ cients �E(Iit) of the multiplicative

feedback model (MFM) measures the short run impact of a change in Xit. Regressions include

time-dummies in the instrumental variables set.

Regarding the impact of market incumbency (dummy variable) and market-share, it seems

that they a¤ect di¤erently �rms�propensities to innovate. These di¤erences appear more accen-

tuated when distinguishing patents�average quality and leader-patenting. Two main �ndings

appear from our estimations. Corroborating the hypothesis on �rms�incompetence to persis-

tently innovate, our results indicate that �rms�with higher market share show a weaker propen-

sity to introduce quality innovations. Nevertheless, among this group of �rms, incumbent �rms�

appear as persistent innovators.

Whereas past market share a¤ects positively non-adjusted patenting propensity, it is nega-

tively associated with the number of citation-adjusted patenting (columns 2-4). Evaluated at

their means, the elasticity of patenting with respect to market share evaluated at the mean

market share is 0,4. An 1% increase of market share increases non-adjusted patenting by 0,4%

(0,2821/1-0,6408)*0,0054) but decreases quality-patenting by 0,3%.41

Moreover, this deterring-innovation e¤ect related to market-share is more important for

the expected number of leader patents (columns 2-4). These results suggest that �rms�with

increasing market power are less likely to introduce quality-patents: an 1% increase of market

share decreases by 7,6% the probability of applying for an important patent (leader patents).

Hence it seems that there are strategic motives are at stake. The weaker propensity to have

pioneer patents by dominant �rms may obey to strategic reasons such as the reluctance to

cannibalize existing innovations (Reinganum, 1982; 1983) or to reveal new technological options

for followers (Farrell and Saphiro, 1996). Our results diverge from those reported by Nicholson

(2003), who found that high-market power �rms (those having more than 70% of the market

sales) have 72% higher probability in citation-weighted patenting whereas for medium power

�rms the e¤ect is 75% (those �rms having between 40 and 69% of the market sales).

41This e¤ect is larger than the one reported by Blundell et al. (1999) on major innovations for the U.K. industry:the elasticity at the means value is 0,08 when using the MFM (0,10 when using the LFM) for the whole industrywhereas for the pharmaceutical industry the estimate is dramatically larger: 0,50.

20

Tab. 1 GMM-Wooldridge Estimation on Patenting by Pharmaceutical Firms (1975-1991)Non-Adjusted Patents Citation-Adjusted Patents Leader Patents

(NA) (CA) (L)1 2 3 4 5 6

Log(R&D)t�1 0.428 0.446 -0.145 0.3837 1.202 1.049(0.06)*** 0.067*** 0.003*** 0.0097*** 0.234*** 0.232***

Market Sharet�1 0.282 0.354 -0.459 -0.5803 -1.498 -1.0370.061*** 0.065*** 0.002*** 0.0205*** 0.334*** 0.399**

Incumbent Firm (1975) --0.398 0.174 -1.425 -0.2368 0.665 0.9560.351 0.406 0.412*** 0.4149 0.912 1.889

Citations Sharet�1 -3.918 -3.441 -0.757 -0.7515 -8.151 -0.1270.869*** 0.700*** 0.032*** 0.2407*** 7.798 5.941

Innovation Generality t�1 1.778 1.746 -0.264 1.1698 2.523 2.4660.244*** 0.258*** 0.008*** 0.0305*** 1.167** 0.975**

Log(GNA)t�1 0.640 0.8390.048*** 0.070***

Log(GNA)t�1*Incumbent -0.3870.1045***

Log(GCA)t�1 0.121 0.027(0.002)*** 0.006***

Log(GCA)t�1*Incumbent 0.2640.047***

Log(GL)t�1 0.759 0.3900.151*** 0.110***

Log(GF )t�1 0.297 0.2070.2180 0.1811

Log(GL)t�1*Incumbent 1.33751.4872

Log(GF )t�1*Incumbent -0.61170.9682

AR(1) -3.199*** -2.800*** -1.586 -2.148* -1.353 -1.375AR(2) 0.936 0.858 -1.330 1.277 0.838 0.865Sargan Test 44.396 43.642 43.110 42.746 15.119 16.420DOF 41 41 41 41 13 13Robust Standard Errors in parentheses. All instrumental variables sets include time dummies.

In order to identify �rms�organisational e¤ects (experience, age, etc.) a¤ecting �rms�R&D

productivity we look at the estimates on the incumbency dummy variable. As the previous

section exposed, incumbent �rms should show a di¤erent innovative behaviour depending upon

the quality of patenting they are applying for.

According to estimates in columns 1-2, incumbent �rms show a weaker propensity to ap-

ply for non-adjusted patents and leader patents, although the coe¢ cients are not signi�cant.

The only signi�cant e¤ect of being incumbent is reported for citation-adjusted patenting: the

expected number of citation-adjusted patents decreases with an incumbent �rm by 0,76 per-

cent. The weaker propensity to introduce quality-patenting by incumbent �rms would indicate

an increasing �rms�incompetence to conduct radical innovation because of organizational de-

�ciencies (inertia and diseconomies of scale, etc.), extensive experience limiting �rms�answer

to technological change. These results contradict �ndings reported by Nicholson (2003) on the

American manufacturing industry for the early 1920. An additional year of experience increases

un-weighted and scope weighted patent counts by 2,4% and 2,9% respectively (negative binomial

�xed-e¤ects estimation).

The coe¢ cients on �rms� citations share (total yearly-received citations in the industry)

show a negative impact on non-adjusted and citation-adjusted patenting, although the size of

the coe¢ cient di¤er signi�cantly between the two types of patenting. Evaluated at the mean, a

higher citation-share at the previous period reduces �rms�propensity to introduce non-adjusted

21

patent by 7,6% ((-3,92/1-0,64))*0,007 (column 1) and by 0,6% citation-adjusted patents (column

3). Therefore, if the �rm was an importance source of knowledge spillovers to the industry, it

will eventually show a weaker propensity to innovate. Whereas less precisely estimated in the

leader-patents equation, as suggested by the large standard errors (both for citation-share and

the generality index), the e¤ect of citation-share is insigni�cant.

The average innovation generality of �rms�patent portfolio enters positively in almost all

equations, the expected counts of non-adjusted patenting and quality-patenting. Innovation

generality pushing innovative performance can be interpreted as R&D economies of scope. As

in Henderson and Cockburn�s (1996) study on patent output at the research-program level,

larger and more diversi�ed �rms in the pharmaceutical industry are signi�cantly more R&D

productive, ceteris paribus. Nevertheless, some di¤erences appear regarding the size of this

index across the three indicators of innovation: the innovation generality index shows a larger

impact �rstly for leader-patents and secondly, for non-adjusted patenting (NA).

To disentangle more adequately incumbent�s role on patenting and quality patenting and the

importance of knowledge-stocks, we include the interactive terms of these variables (columns 2,

4 and 6). According to estimates in column 2, incumbent �rms with larger patent stocks are

less likely to patent. In the opposite, incumbent �rms having an important citation-adjusted

patent stock show a positive propensity to introducing quality-adjusted patenting. The impact of

�rms�stock of important or �leader�patents shows a positive sign although the coe¢ cient is not

signi�cant. Hence these estimates suggest that R&D e¤orts by incumbents attempting to develop

products that incorporated major or competence destroying innovation are not signi�cantly

di¤erent in productivity terms than those of other �rms (i.e. entrants).

Although there is no in�uence of �rms�cumulated stock of leader patents on their propen-

sity to introduce continually leader patents, the average quality of �rms�intellectual property

stocks does have a positive impact on the expected counts of citation-adjusted patenting. These

estimates suggest therefore that incumbent �rms remain to some extent persistent innovators,

particularly when considering quality patenting. This result con�rms partially recent models by

Segerstrom and Woniereck (1999) or Hörner (2001) about the technological superiority of in-

cumbent �rms enabling them to innovate persistently. According to Segerstrom and Woniereck

(1999) industry leaders devote often substantial resources to R&D activity in order to take ad-

vantage of their R&D advantages (R&D costs, experience) and the persistent di¤erences in R&D

capabilities vis-à-vis followers.

If technological gap (R&D di¤erential costs) between leaders and followers is large under

decreasing returns to R&D, incumbents will continuously participate in innovation races. Lead-

ers discover then the majority of (radical) innovations, with follower (or entrants) occasionally

innovating and becoming industry leaders. If there are increasing returns of R&D for leaders,

only leaders participate to R&D races, and consequently industry (technological) leadership is

maintained forever (Schumpeter, 1942). The pharmaceutical industry is however increasingly

marked by decreasing returns to R&D. The regulatory reforms concerning market-approval un-

dertaken since the mid 1980 have particularly a¤ected the costs of developing new molecules

22

(Scherer, 1994; Dimasi et al., 1999).

Lastly, according to estimates in column (1) and (4), past R&D expenditures a¤ects positively

pharmaceutical �rms�patenting propensity, although its impact is reported to be negative for

citation-adjusted patenting. Above all, results shows that �rms�past R&D investment a¤ects

positively �rms�propensity to introduce leader patents. Inversely, in common with previous

work, we �nd no evidence of (increasing) returns to scale for citation-adjusted patenting and

patenting.

5.2 R&D stocks and Incumbency

Table 2 reports estimates using the R&D stock as indicator for �rms�knowledge assets. The

results of these estimates complement to some extent previous �ndings reported. Essentially,

what these regressions suggest is that while incumbent �rms�may have a stronger propensity to

introduce quality-patenting (as reported in Table 5), they confront increasingly di¢ culties to take

advantage of their R&D assets. Whereas the interactive term enters positively and signi�cantly

non-adjusted patenting (column 1); it in�uences negatively citation-adjusted patenting (column

2) and �rms�propensity to introduce �important�patents (column 3). The coe¢ cient in the

latter is however non signi�cant.

Thus, contrarily to the previous �ndings on patent stocks, incumbent �rms with larger R&D

stocks may show a lower propensity to introduce innovations. The larger their R&D stock, the

less likely they are to introduce quality-patents (citation-adjusted patents). Incumbent �rms

with larger R&D stocks may be subject to inertia and bureaucracy or an increased complexity

in the management of R&D Firms�cumulated R&D experience (R&D knowledge, R&D man-

agement, etc.) may provoke diseconomies of scale in the generation of new knowledge.. This

result corroborates Henderson�s �ndings on the negative impact of �rms� technological expe-

rience on their propensity to introduce radical innovation in the photolithographic alignment

equipment industry (Henderson, 1993). The estimates on the rest of variables di¤er to some

extent from previous estimations. An explanation for this discrepancy is that the inclusion of

R&D stock may reduce some bias related to heterogeneity (individual speci�c e¤ects), reducing

consequently the persistency of the series as suggested by the autocorrelation test of �rst order,

which becomes non signi�cant. Firms�market share (previous period) increases �rms�propen-

sity to patent, although it decreases citation-adjusted patenting and leader patenting. However,

the coe¢ cient on the latter is reported non-signi�cant.

23

Tab. 2 GMM-Wooldridge Estimation on Drugs PatentingNA CA L1 2 3

Market Sharet�1 0.636 -0.108 -0.0880.050*** 0.003*** 0.418

Incumbent Firm (1975) -0.588 2.282 2.7070.411 0.273*** 1.993

Citations Sharet�1 7.587 -0.023 18.1730.131*** 0.027 2.672***

Innovation Generality t�1 2.057 0.123 1.7180.136*** 0.023*** 0.205***

Log(GR&D)t�1 0.4367 0.452 0.8040.0149*** 0.020*** 0.1478***

Log(GR&D)t�1*Incumbent 0.2118 -0.598 -0.1690.0216*** 0.018*** 0.323

AR(1) -2.445** -1.898* -1.507AR(2) 0.7279 -1.230 0.677Sargan Overident. Test 43.293 40.954 16.591DOF 41 41 13Robust Standard Erros reported. IV sets include time dummies.* signi�cant at 10%; ** signi�cant at 5%; *** signi�cant at 1%IV=Stock Capitalt�1�t�2, MSt�2 Incumbency, Citations-Sharet�2,LGR&D2 ;LGR&D2*INC

Our results for the pharmaceutical industry contrast but also con�rm to some extent the

Schumpeterian hypothesis. First, as �rms increase their market-share, they may show lower in-

centives to innovate because of their increased market power and fear to cannibalise innovation.

Nevertheless, not all market-power �rms may be subject to such disincentives. Regarding market

incumbency, two main conclusions stem from our analysis. On the one hand, our �ndings corrob-

orate the Schumpeterian perspective about incumbents�superiority and long-run persistence in

the production of innovations. On the other hand, our results also con�rm the advanced hypoth-

esis that incumbent �rms�R&D productivity is decreasing notably, in the production of major

innovations (leader patents). Technological obsolescence hinders incumbent �rms�innovative ca-

pacity, which con�rms their increasing incompetence to respond to technological breakthrough

(Tushman and Anderson, 1986).

Though, even under depreciation of their technological capacity, incumbent �rms remain

technological leaders. Radical technological change that undermines incumbent �rms�upstream

value chain activities will cause those �rms to seek out a new source of competitive advantage

within the rede�ned technological framework (Dosi, 1982; Pavitt, 1998). Our results suggest that

technological leadership is in�uenced by other non-technological factors, which are captured to

some extent by the incumbency status. For instance, the non-technological assets of incumbent

�rms may become more valuable for the successful commercialization of new technologies, no-

tably; when they are specialized with respect to commercializing in speci�c therapeutic domains

(Leonard-Barton, 1992; Rothaermel, 2001).

The raising of new technologies (e.g. molecular biology and biogenetics) brought by entrants

does not imply the replacement of incumbent �rms. Actually, face to the threat of creative

destruction as it implies a competences�destroying process, incumbent �rms �nd their way to

adapt to technological breakthroughs by means of cooperating with entrant �rms, which often

lack of production and marketing capabilities to bring discoveries into the market. Arora and

Gambardella (1990), Orsenigo et al (2001) and Rothaermel (2001) have provided evidence on

24

the specialization-based division of labour in commercializing a new technology in the Pharma-

ceutical industry following the biotechnology revolution.

5.3 The Productivity Equation

In this stage we evaluate the returns on �rms�sales from the di¤erent knowledge-stocks. Table 3

displays estimates by IV-GMM method for the whole sample and the 77 patenting �rms. In the

whole sample estimation, the R&D stock does not contribute to �rms�economic performance. In

the opposite, the non-adjusted patent stock and citation-adjusted patent stock contributes both

positively and signi�cantly to enhance industrial sales. The impact of patent stock is almost

the double the e¤ect by citation-adjusted patenting stock or citation-stock. This result suggests

that large patent portfolios contribute to increase �rms�pro�tability. Quality of patent stocks

matters in a lesser extent than non-adjusted patent. Thus pharmaceutical �rms are motivated

to patent intensively regardless of the quality-level of innovations as patents serve to increase

or protect market power (entry-blocking). Regarding the sample of the 77 patenting �rms, the

R&D stock has now a large signi�cant impact on performance but it has smaller e¤ect than the

non-adjusted patent stock : the elasticity of real sales respect to the R&D stock ($10 000) is

0,10. Likewise, the double of the patent stock increases sales by 11% whereas the double of the

citation-adjusted patenting and of citation-stocks lead to increases between 5 and 6% of total

factor productivity.

These �ndings suggest that intellectual-property stocks have a large impact on economic

performance than quality-weighted patenting. As patenting is pro�table, this strategy would

explain the increasing patenting trends previously reported in the Industry. The high importance

of patents in industrial performance has been documented previously. As explained by Hall et

al. (2004), drugs are characterized by discrete product technologies where patents serve their

traditional role of exclusion, and some of them are therefore valuable on an individual basis, as

measured by citations.42 In pharmaceuticals, the impacts of patent yield (Patent stock/R&D

stock) and of citations per patents on �rms�market value are more important than the R&D

stock (to assets). Our results contrast to the �ndings by Hall et al. (2002) and Blundell et al.

(2002) on the impact of knowledge-stocks on �rms�market value. Hall et al. (2002) found that

citation-weighted patent stocks are more highly correlated with market value than patent stocks

themselves. They explain that this result is due mainly to the high valuation placed on �rms

that hold very highly cited patents.

42Regarding pharmaceuticals, Hall et al. (2002) show that the impact of patent yield of R&D (Patentstock/R&D stock) for Drugs on �rms�market value is found to be three times the average e¤ect (0.10 versus.031), still larger than that of Computers. Likewise, the impact of the Citations/Patent stock is over 50% higherthan the average e¤ect in the manufacturing industry.

25

Tab. 3 IV-GMM Estimation on Log(Sales): American Pharmaceutical Firms (1975-1991)Whole Sample Patenting Firms (T>=3)

-1 -2 -3 -4 -5 -6 -7 -8Yt�1 0.221 0.19 0.2 0.19 0.06 0.05 0.08 0.07

(0.03)*** (0.04)*** (0.05)*** (0.05)*** (0.04)* (0.04) (0.04)* -0.04Log(L) 0.7321 0.63 0.64 0.64 0.8 0.74 0.74 0.75

(0.53)*** (0.05)*** (0.05)*** (0.05)*** (0.08)*** (0.07)*** (0.10)*** (0.08)***Log(K) 0.148 0.18 0.201 0.19 0.19 0.26 0.27 0.24

(0.05)** (0.06)*** (0.06)*** (0.06)*** (0.08)** (0.08)*** (0.10)*** (0.09)***Log(GR&D)t�1 -0.21 0.10

(0.03) (0.06)*Log(GNA)t�1 0.11 0.11

(0.05)** (0.05)**Log(GCA)t�1 0.13 0.07

(0.05)** (0.05)***Log(GCIT )t�1 0.07 0.05

(0.03)** (0.02)***Constant -1.52 -0.91 -0.98 -0.96 -1.54 -1.08 -1.3 -1.2

(0.18)*** (0.18)*** (0.21)*** (0.19)*** (0.32)*** (0.27)*** (0.37)*** (0.34)***Hansen test 229.79 177.34 172.4 179.19 66.16 70.46 69.26 70.55Prob >�2 0.324 0.374 0.477 0.338 0.98 0.99 0.99 0.99A-R(1) -3.39*** -3.08** -3.13** -3.10** -0.73 -1.07 -1.42 -1.23A-R(2) -0.25 -0.95 -1 -0.9 0.68 0.53 0.53 0.53F-test 674.64 503.55 125.52 260.02 324.9 757.17 365.87 257.99Obs. 4643 4643 4643 4643 809 809 809 809No. Firmes 1151 1151 1151 1151 77 77 77 77Robust standard errors in parentheses* signi�cant at 10%; ** signi�cant at 5%; *** signi�cant at 1%

5.4 Incumbent Firms and Patent-Stocks

Assuming that incumbent �rms may display a di¤erent industrial performance, we include the

dummy of incumbent �rm as well as its interactive term with the di¤erent knowledge stocks.

Our results con�rm again that incumbent �rms�also extract a di¤erent productivity from non-

adjusted and citation-adjusted patenting.

The table 4 displays estimates containing the interaction terms for the 77 �rms sample. These

estimates con�rm previous �ndings reported in table 3 on the higher return on productivity

respect to non-adjusted patent stocks. To be an incumbent �rms with high R&D stocks has no

contribution to �rms�market sales, whereas having a large non-adjusted patent stocks enters

signi�cantly �rms� productivity with an elasticity of 0,11. In the opposite, estimates in the

column (3) show that incumbent �rms with important average quality of patents (citation-

adjusted patent stock) does not matter to explain productivity. Lastly, estimates in the column

(4) con�rm the economic importance of leader patents, and therefore, incumbent �rms�incentives

to persistently introduce technological leader patents. The double of the leader-patents stock,

leads to an increase of 23% of market sales. The e¤ect of follower patent stocks is also positive

and signi�cant with its impact is weaker than the leader-patent stocks.

When looking at the interactive terms on incumbency and leader/follower patent stocks, some

interesting �ndings emerge. While the coe¢ cients are less signi�cant (10%) and the impact is

rather weak, we can note that the e¤ect of follower-patent stocks is the double of leader-patents,

at least for this 77 sample of Pharmaceutical �rms. This result is related notably to the stronger

impact of non-adjusted patenting.

26

Tab. 4 IV-GMM Estimation on Log(Sales): Pharmaceutical Firms (1975-1991)1 2 3 4

Log(L)t�1 0.74 0.67 0.70 0.69(0.13)*** (0.08)*** (0.08)*** (0.09)***

Log(K)t�1 0.38 0.36 0.34 0.35(0.11)*** (0.09)*** (0.08)*** (0.09)***

Log(GR&D)t�1 0.07(0.08)*

Log(GR&D)t�1*Incumbent -0.05(0.06)

Log(GNA)t�1 0.14(0.06)**

Log(GNA)t�1*Incumbent 0.11(0.07)*

Log(GCA)t�1 0.07(0.03)**

Log(GCA)t�1*Incumbent -0.01(0.10)

Log(GL)t�1 0.23(0.19)**

Log(GL)t�1*Incumbent 0.02(0.11)

Log(GF )t�1 0.13(0.12)**

Log(GF )t�1*Incumbent 0.04(0.22)

Hansen test Chi-2 67.32 70.46 69.26 63.81Prob >c2 0.98 0.99 0.99 0.99A-R(1) -0.73 -1.07 -1.42 -0.83A-R(2) 0.68 0.53 0.53 0.3F-test joint Sig. 324.9 757.17 365.87 442.11Observations 809 809 809 809No. Entreprises 77 77 77 77Robust standard errors in parentheses* signi�cant at 10%; ** signi�cant at 5%; *** signi�cant at 1%

To sum up, quality-patenting matters because it allows the �rms to build patent portfolios

around leader patents. Hence, incumbent �rms�are persistent leader innovators but at the same

time they are also intensive active in patenting, regardless of quality. Intensive patenting is

pro�table because follower patents enhance market power and pro�tability. Having patents is

important in the pharmaceutical industry as they permit innovators to exclude rivals in speci�c

technological domains or optimize production by licensing to other �rms

6 Concluding remarks

Our �ndings suggest that market incumbency and market power may in�uence in di¤erent ways

�rms�innovative behaviour. Although a higher market-share seems to deter innovative e¤orts,

amongst this group of �rms, incumbents are found to be constant innovators. Moreover, they

show a persistent propensity to continuously introduce quality-patenting. Nevertheless, incum-

bent �rms are also confronted to increasing di¢ culties to innovate as market incumbency and

technological experience might make �rms�R&D stocks less productive. This result might be re-

lated to possible diseconomies of scale and organizational inertia characterizing some incumbent

�rms.

Hence our study o¤ers to some extent evidence for both arguments in the literature: Schum-

peterian and innovation-pre-emptive e¤ects coexist in the Pharmaceutical Industry. Incumbent

27

pharmaceutical �rms show stronger incentives to innovate not only because they want to keep

their market position but also because it might be easier for them to conduct the innovation

process and bring new products to the market. Although their technological competences erode

over time, incumbent �rms continue to devote substantial resources to R&D in order to take ad-

vantage of their nont-technological assets, such as production and marketing capabilities vis-à-vis

the followers or entrants. Furthermore, incumbent �rms �nd their way to adapt to technologi-

cal breakthroughs by means of cooperating with entrant �rms, which often lack of downstream

capabilities to bring discoveries into the market.

Several policy implications arise from this �rst exploration. First, the threat of creative-

destruction appears as an innovation catalyser for incumbent pharmaceutical �rms. As per-

sistency is con�rmed for dominant �rms, a more e¤ective way of encouraging innovation may

be to focus on policies that facilitate investment and technological creation by new �rms and

technological cooperation. Lastly, it is evident that �rms take advantage of the patent system to

keep market power. The strategic intensive of patents, to the extent that quality of innovation

may largely di¤er remains a debatable matter for patent policy.

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Annexes

Tab. 1 The 12 Most Cited Patents in Pharmaceuticals

Patent No. Date Application Country Class Claims CM CR OR Name

1 3845770 1974 1972 US 424 339 0,52 ALZA CORPORATION

2 4647447 1987 1984 DE 424 68 4 262 0,45 SCHERING AKTIENGESL

3 3916899 1975 1974 US 424 36 1 231 0,40 ALZA CORPORATION

4 4418068 1983 1981 US 514 62 7 225 0,30 ELI LILLY AND COMPANY

5 4327725 1982 1980 US 424 40 5 201 0,41 ALZA CORPORATION

6 3797494 1974 1971 US 424 193 0,58 ALZA CORPORATION

7 4105776 1978 1976 US 514 42 5 191 0,67 BRISTOL-MYERS SQUIBB

8 3598122 1971 1969 US 424 180 0,40 ALZA CORPORATION

9 3773919 1973 1970 US 424 176 0,74 E. I. DU PONT DE NEMOURS

10 3598123 1971 1969 US 424 168 0,53 ALZA CORPORATION

11 4880804 1989 1988 US 514 13 14 156 0,34 E. I. DU PONT DE NEMOURS

12 4200098 1980 1978 US 424 22 3 152 0,36 ALZA CORPORATION

Source: NBER Patent Data File. CM:Citations Made, CR: Citations Received, OR: originality patent index

33

Figure 7: Patents and Citations per R&D (average industry)

Tab. 2 Frequency of Leader Patenting (Main innovators) 1975-1995Year <1976ABBOTT LABORATORIES 1 8,33 8,33ALZA CORP 1 8,33 16,66AMERICAN CYANAMID CO 1 8,33 24,99BRISTOL MYERS SQUIBB 1 8,33 33,32LILLY (ELI) & CO 1 8,33 41,65MERCK & CO 1 8,33 49,98RHONE-POULENC RORER 1 8,33 58,31SEARLE (G.D.) & CO 1 8,33 66,64SMITHKLINE BECKMAN CORP 1 8,33 74,97STERLING DRUG INC 1 8,33 83,3SYNTEX CORP 1 8,33 91,63WARNER-LAMBERT CO 1 8,33 99,96Year <1995KEY PHARMACEUTICALS INC 1 0,36 2,52MILLIPORE CORP 1 0,36 2,88NEO RX CORP 1 0,36 3,24SMITHKLINE BEECHAM PLC -ADR 1 0,36 3,6STRYKER CORP 1 0,36 3,96NOVO-NORDISK A/S -ADR 2 0,71 4,67REPLIGEN CORP 2 0,71 5,38GENENTECH INC 3 1,07 6,45MARION MERRELL DOW INC 3 1,07 7,52BAXTER INTERNATIONAL INC 4 1,43 8,95COLLAGEN AESTHETIC INC 4 1,43 10,38MOLECULAR BIOSYSTEMS INC 4 1,43 11,81BECTON DICKINSON & CO 5 1,79 13,6LIPOSOME COMPANY INC 5 1,79 15,39MYOGEN INC-REDH 5 1,79 17,18ALLERGAN INC 6 2,14 19,32CETUS CORP 6 2,14 21,46ROBINS (A.H.) CO 6 2,14 23,6MALLINCKRODT INC 9 3,21 26,81JOHNSON & JOHNSON 10 3,57 30,38STERLING DRUG INC 10 3,57 33,95RHONE-POULENC RORER 11 3,93 37,88SEARLE (G.D.) & CO 11 3,93 41,81WARNER-LAMBERT CO 11 3,93 45,74SMITHKLINE BECKMAN CORP 12 4,29 50,03ALZA CORP 13 4,64 54,67AMERICAN CYANAMID CO 13 4,64 59,31ABBOTT LABORATORIES 14 5 64,31SYNTEX CORP 14 5 69,31PFIZER INC 15 5,36 74,67SCHERING-PLOUGH 15 5,36 80,03LILLY (ELI) & CO 17 6,07 86,1BRISTOL MYERS SQUIBB 19 6,79 92,89MERCK & CO 20 7,14 100,03

34

Tab. 3 Summary StatisticsPatenting Firms T>=3 (77 �rms)year 809 1984.001 3.928979 1978 1991Sales 809 3240.704 5891.063 0 43992.59Market Share 809 .547478 .9397784 0 4.717773R&D Expenditures 807 343.1113 608.8364 0 4833.788Employees 809 11885.64 19428.78 3 107517Capital Stock 809 240.8685 424.9315 0 2973.079generality (mean) 809 .1292533 .2080372 0 .9024943originality (mean) 809 .116233 .1926539 0 .8175583citations made 809 29.99447 79.69455 0 727.009citations received (nor.) 809 94.64679 240.3633 0 2098.684patents 809 6.346106 15.8945 0 115leader patents 809 .4833127 1.548724 0 21follower patents 809 5.862794 14.78659 0 108Citations Share 809 .0075677 .0189067 0 .1871287Patent Stock 809 30.40346 76.84837 0 536.527Citations per Patent 809 7.31278 15.02915 0 135Stock de Citations/Patent 809 29.12528 58.88933 0 352.5332R&D Stock 807 1391.827 2548.852 .693669 15166.15Leader Patents Stock 809 .5848744 1.020004 0 4.054295Follower Patents Stock 809 27.48742 69.82027 0 485.4362

Incumbent Firms (19 �rms)year 160 1983.856 3.98716 1978 1991Sales 160 10480.11 8314.57 163.8588 43992.59Market Share 160 1.746244 1.197781 .0389504 4.717773R&D Expenditures 160 1076.523 817.6144 6.434748 4833.788Employees 160 34321.9 24305.94 667 98500Capital Stock 160 732.4517 601.1683 5.349589 2973.079generality (mean) 160 .2260729 .1285871 0 .4444444originality (mean) 160 .2260532 .1328437 0 .5084871citations made 160 113.5155 124.9525 0 727.009citations received (nor.) 160 323.0896 380.1524 0 2055.784patents 160 26.775 25.89455 0 115leader patents 160 1.825 2.904237 0 21follower patents 160 24.95 24.01357 0 108Citations Share 160 .0265308 .0307665 0 .1871287Patent Stock 160 129.8837 124.3246 0 536.527Citations per Patent 160 10.16091 12.41903 0 78.21574Stock de Citations/Patent 160 52.97886 67.98738 0 307.1345R&D Stock 160 4454.923 3573.849 28.33968 15166.15Leader Patents Stock 160 1.962561 1.179905 0 4.054295Follower Patents Stock 160 118.3471 112.6362 0 485.4362

35