Not Your Stepping Stone: Collaboration and the Dynamics of ...

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Not Your Stepping Stone: Collaboration and the Dynamics of Industry
Evolution in Biotechnology






Kenneth Koput





Walter W. Powell


University of Arizona




Stanford University


Rough Draft for


Org
anization Science Winter Conference





February, 2000











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INTRODUCTION



Contemporary research on organizations and industrial performance is replete
with reports of a marked upsurge in various forms of interorganizational collaboration,
including
research consortia, joint ventures, strategic alliances, extensive subcontracting
and/or outsourcing of key functions. Astute observers of these developments, such as
Richard Rosenbloom and William Spencer (1996), suggest that industrial competition
today

resembles less a horse race and more a rugby match in which players frequently
change uniforms.


A recent National Research Council analysis of trends in industrial research and
development (R&D) reports that the innovation process has undergone a signifi
cant
transformation over the past decade (Merrill and Cooper, 1999). This transformation
appears to be both “substantial” in magnitude and consequential to economic
performance, both positively and negatively (p. 104). The four components of this
reorien
ting of R&D are: 1.) A shift in the industries and sectors that dominate R&D
towards new emerging technologies and nonmanufacturing industries; 2.) A change in
the time horizons of R&D, with industry focusing more on shorter
-
term development
and relying mo
re on universities for basic research; 3.) A change in the organizational
structure of R&D, with greater decentralization of research activities and increased
reliance on both outsourcing and collaboration among firms, universities, and
government laborato
ries; and 4.) Changes in the location of R&D, with successful
research increasingly dependent on geographic proximity to clusters of related
organizations.


A companion National Research Council survey of eleven industries,
purposefully diverse in characte
r and technology but all resurgent in the 1990s, notes
that common to each industry is: 1.) Increased reliance on such external sources of
R&D as universities, consortia, and government laboratories; 2.) Greater collaboration
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with domestic and foreign comp
etitors, as well as customers, in the development of
new products and processes (Mowery, 1999: 7). National Science Foundation data
show a marked increase in the number of international alliances between U.S. and
Western European countries between 1980 an
d 1994; but by the mid
-
1990’s, the
formation rates for intranational alliances linking U.S. firms with their domestic
competitors outpace international linkages (National Science Foundation, 1998).
Similarly, there is now ample research showing the growin
g links between U.S. firms
and universities (Cohen et al 1994), a greater involvement by firms and government
labs in research joint ventures (Link, 1996; 1999), and a much greater foreign presence
in U.S. R&D through collaboration with U.S. universities (
NSF, 1998).
In the realm of
science, Hicks and Katz (1996) find that research papers are more and more likely to be
co
-
authored and involve authors with mult
iple institutional affiliations spanning the
public and private sectors.

In short, as Mowery (1999: 9) observes, “the diversity of
institutional actors and relationships in the industrial innovation process has increased
considerably, even as the investments by U.S. firms now appear to focus on shorter
time horizon
s.” Complex networks of firms, universities, and government labs now
play a critical role in many industries, and especially so in a number of newer
industries such as computers, semiconductors, pharmaceuticals and biotechnology
(Powell and Owen
-
Smith, 19
98; Mowery and Nelson, 1999).


Divergent Accounts


Taken together, th
e
s
e

developments in industry

and

scienc
e

suggest a significant
restructuring of organizations, work arrangements and the

organization of innovation
.

There is
, however,

considerable debate about both the causes and consequences of this
r
estructuring, and much scholarly and po
pular discussion about
the purported new
economy. We cannot

resolve these
large
issues here, rather we focus on
one aspect:
the
role of the small science
-
based startup firm. The startup firm, whether as a spinoff out
of existing companies or universities or as
a stand
-
alone entity, has played a greater role
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in the U.S. economy over the past two decades. Moreover, these smaller units appear to
operate according to a different organizing logic, with extensive linkages to other
organizations and partnerships with
outside parties for key business functions. But
beyond these observations, there is disagreement about both the contributions and the
developmental path of smaller firms.


While small firms may be bountiful, some dismiss them as trivial and controlled
by
larger firms. Harrison (1994) argues that we are witnessing the growth of
decentralized power, where there is growing concentration of corporate power but
without centralization. He dubs this phenomena the “lean and mean” strategy, and
provides anecdotal

data drawn selectively from a few firms such as Nike to portray an
intensive spider’s
-
web world of outsourcing, contract labor, demanding work practices,
but growing control at the center where corporate power is lodged. In his view, small
firms are larg
ely dependent on corporate giants. Another perspective on recent
developments is offered by ecologist Glenn Carroll (1985; 1994; Carroll and Hannan,
1995: 215
-
21), who suggests a resource
-
partitioning process that involves the
simultaneous expansion in th
e number of small firms and a contraction in number but a
concentration in size of large firms. Carroll (1994) notes that there has been a
proliferation of smaller organizational units, with the average size of a firm declining by
roughly 30


40% over the

period 1960
-
1989. Some of this transformation is due to
compositional shifts in the U.S. economy, reflected in service sector growth and
manufacturing decline. But Carroll and his colleagues offer a resource
-
based argument
that applies especially to fie
lds in which production or marketing activities exhibit
economies of scale and price competition. As competition for scale economies
increases, only a few generalist organizations survive, and they do so by offering fairly
homogeneous products or services

to a mass market audience. But the interesting twist
is that the concentration of generalists on the mass market opens small pockets of
resources on the periphery of the market, where smaller specialist firms emerge and
thrive. Thus, in the words of Car
roll and Hannan (1995: 217) “increasing market
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concentration enhances the life chances of specialist organizations.” Such a process has
been observed in the newspaper, beer, wine, microprocessor, music, and book
industries. In this account, small firms p
roliferate by catering to specialized tastes


An alternative view is found in recent studies of the innovation process, cited
above. Rather than dependency or specialism, these analysts suggest a refashioning of
the division of labor in which smaller firm
s, and the linkages between them and centers
of innovative activity play a much more prominent role, especially in research
-
intensive
fields. A somewhat discordant chorus of voices are found in this camp, with some
suggesting that networks represent an al
ternative
means for governing economic
exchange
(Powell, 1990),
while
others point

to a profound blurring
of
organizational
boundaries and a remaking of the production process (Sobel, 1991), and still others
contending that new information technologies allow
more disaggrega
ted, and flexible
means for organizing production and delivering services (Morton, 1991; Brynjolfsson et
al, 1994; Shapiro and Varian, 1999).


Older, Bigger, Wiser?



We want to add some empirical flesh to these discussions of the structure of the
firm, an
d to debates over the relevant role of small firms and the growing salience of
alliances and various types of collaborations. We have the advantage of having good
longitudinal data on firm arrangements over the period 1988
-
1999. We have the
disadvantage
that the data are based on only one industry
--
biotechnology.
Nevertheless, the commercial field of the life sciences is purportedly one of the key
components of the new economy, so it is a strategic site for analysis. Moreover, the data
we describe belo
w are based on formal contractual agreements and not informal ties,
handshake deals, or social embedding, hence they afford a strict test of whether
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horizontally based external relationships are a critical feature of the contemporary
science
-
based firm.
1


One way to sift through the divergent arguments concerning the structure of
contemporary organizations is to examine a sample of firms in the same field over a
sufficient time period that changes at the firm
-
level and field
-
level, as well
transformations i
n the larger economy, can be observed. Viewed through the lens of
more than a decade’s changes, one can examine patterns of growth for individual firms,
changes in the nature and number of interorganizational relationships, and industry
evolution. To acc
ount for changes in the repertoire of organizational practices, we
analyze whether there is persistence, decline, or expansion in the use of external
collaborations. The crux of the argument for changes in strategy and structure is that
interorganizationa
l networks are an increasingly fundamental cornerstone that enables
firms to both gain and hold competitive advantage, rather than a transitional stage.
Thus, we attempt to measure how organizational strategy and structure have evolved
in one industry.


W
e assess the consequences of organizational growth, aging, and success for the
types of collaborative arrangements that firms in the commercial field of biotechnology
employ. Each process
--

growth in size, the gaining of experience, and the successful
la
unching of a new product
--

presents a challenge to a firm in terms of how it chooses
to organize. Growth brings problems of communication and coordination as the
number of employees grows. As an organization ages, its stock of knowledge increases



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There is clearly an important difference between formal and informal organizational linkages.
Contractual relati
onships are crafted with considerable care and typically entail milestones or covenants
dictating certain types of expected performance. Informal linkages more typically involve unwritten
understandings, quid pro quos, and tacit agreements. Moreover, inf
ormal relationships are often
entangled in ongoing friendships among employees of organizations. Such interpersonal ties are often
less calculative and voluntaristic than formal ties. Our focus here is on direct organization to
organization relationships

that involve the transfer of resources and/or information. In companion work,
we are collecting “founding stories” for all the firms in our database, and it is clear that at the point of
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and it
s routines for organizing core tasks become more well developed. With a
successful product, an organization faces the challenge of reproducing its initial success
but now has new resources to deploy. Viewed differently, growth, aging and success
are mile
posts for organizations, and we can look at these road markers and see whether
the mix of arrangements that were used to reach a particular milepost are continued,
disbanded, or diversified as movement toward the next marker occurs.


In our earlier work
on biotech firms, which covered a much shorter time period
than the analyses presented here, we showed that centrality in the industry network
heightened a firm’s reputation and generated access to resources. Firms so positioned
attracted new employees, p
articipated in more new ventures, and developed deeper
experience at collaborating with other parties. Put colloquially, a firm grew by
becoming a player; it did not become a player by growing. Growth and financial
success resulted from centrality in ind
ustry networks (Powell, Koput, Smith
-
Doerr,
1996; Powell, Koput, Smith
-
Doerr, Owen
-
Smith, 1999).


We begin these analyses by asking how achieving growth influences subsequent
behavior. As firms add employees and expand their operations internally, they fa
ce a
series of choices. Organizations could opt to pursue more activities internally and
fewer externally as they increase internal capacity. Or they could use additional staff to
assist in expanding the number of outside collaborations. Moreover, growt
h could
result in increased differentiation internally (following Blau’s [1970] classic arguments
about growth leading to an expansion of roles and structures), or promote a
diversification externally into collaborations for different types of activities.

Finally,
added internal capacity could lead to more tangential external relations, or could allow
for deeper and/or more intensive external linkages. Stated formally, we test to see
whether:






organizational founding, the ties linking organizations are more of
ten person
-
to
-
person linkages, and that
formal affiliations come later.

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As firms grow in size, they deepen their portfolio of collabor
ations; expand their
number of external ties; diversify the types of collaborative activities they engage in; and
engage in more complex arrangements.
(Hypothesis 1).


The effects of age have been well studied in the organizations literature.
Building on

Stinchcombe’s (1965) key insights, there has been a great deal of attention
paid to the liability of newness (Hannan and Freeman, 1989: 245
-
70). Younger firms
need to build credibility with consumers, suppliers, creditors and the like and overcome
initial

reservations toward a new entity. Given these obstacles, it is not surprising that
startup companies turn to venture capital firms, law firms, and established companies
for assistance, as well as rely on universities and government laboratories for key
t
echnologies. Moreover, many startup science
-
based firms are spin
-
offs from
universities and, less frequently, government laboratories and established firms.
Consequently, survival often hinges on location in a supportive network in order to
overcome init
ial questions about credibility and reliability. As a small firm ages, it faces
a choice. The company can become less reliant on external parties. Or as the firm
matures and expands its activities downstream from its initial research focus into
product
development, production, and sales, it may opt to become involved with more
outside partners as it takes on new tasks. More pivotally, firms may engage with a
wider span of partners for key activities, selecting and being selected by a
heterogeneous group

of collaborators for critical functions such as R&D, production, or
sales. Put simply, as companies age, they make decisions about both the volume and
the span of activities done internally or externally. Hence we test to see whether:


As firms grow old
er, they become involved in a wider variety of external
activities, with a more heterogeneous group of partners for each activity.
(Hypothesis
2).


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In many emerging industries with a strong science base, companies spend much
of their early years burning u
p investors’ money on costly initial research. External
support is the lifeblood during this phase. As the research program develops and
moves into application and testing, the prospect of a new product looms larger. As the
product cycle unfolds, compan
ies have differential needs, and the nature of the reliance
on others shifts depending on the stage of development. For the successful firm,
products are eventually released and sales generated. Some firms plow revenues back
into R&D, others generate pro
fits to repay investors who have been patient during the
development stages. We have argued that companies in knowledge
-
intensive
businesses are involved in learning races, that is, in a cycle of learning in which initial
success generates the resources t
hat allow advancement to the next level (Powell et al,
1999). The key question is whether that initial success triggers changes in current
organizational arrangements or “restarts” the process again in pursuit of new avenues
of research.


Thus the fruits
of initial success present a branching point for many young
companies. The founders could choose to “take the money and run,” cashing out by
selling the company or agreeing to a merger. The founders could take the money and
attempt to build a vertically
integrated firm, with the full range of organizational
functions, and with key tasks performed only internally. The cycles of learning
argument that we have posited suggests that organizations respond to the initial
success of sales and/or profits by expa
nding their absorptive capacity (Cohen and
Levinthal, 1989; 1990), that is, by enhancing
BOTH

their ability to generate new
products internally as well as their portfolio of external research affiliations. An
organization’s absorptive capacity allows it t
o make sense of news generated elsewhere
and to make news on its own (Nelson, 1994). Seen in this light, we assess whether, as
firms reach an initial “finish” line, they reinvest in the process of research exploration,
choose to pursue research internally
, or choose to exit. More formally stated, we test
the argument that:

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As companies first reach an initial plateau of earnings, they subsequently engage
in more external R&D collaborations.

(Hypothesis 3).



Researchers from several disciplines have stre
ssed the importance of research
prowess as an admission ticket to information networks in science
-
based fields
(Mowery and Rosenberg, 1989; Arora and Gambardella, 1994; Powell et al, 1996). A
persistent finding from a diverse set of empirical studies is t
hat R&D intensity and the
level of technological sophistication are positively correlated with the number and
intensity of strategic alliances (C. Freeman, 1991; Hagedoorn, 1995). More generally,
there is widespread agreement that in technologically advan
ced sectors, the locus of
innovation is often found in networks of relationships (Powell et al, 1996) or sectoral
innovation systems (Mowery and Nelson, 1999). But we do not have a clear sense of
which actors bind these innovation networks together, provi
ding them with coherent
agendas.


Indeed, one would expect substantial variation in network structure based on
differences in technology, the supportive institutional infrastructure, public policy, and
the endowments of large and small participants. We ca
nnot provide a complete picture
of this complex process here. Rather we are interested in the role and evolution of the
small science
-
based firm. Consider the following range of possible roles performed by
startups. Small firms may provide the ideas tha
t jumpstart the innovation process.
Because these firms are both closer to the underlying basic science and have a deeper
understanding of the technology, they continue to exert influence as the ideas are
translated into commercial products. In this scen
ario, the small firm is able to use its
knowledge to orchestrate production through an innovation network. In contrast, small
firms could serve as the sources of new ideas or novel products, but, due to their small
scale they are unable to develop, produc
e, or market the final product. Under this
scenario, the small firm would remain a specialist research boutique that generates
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ideas, while larger and more established firms would pick off the most promising leads
and thus reap the lion’s share of the ben
efits from innovation. Alternatively, an inner
circle of key participants may dominate the innovation network, and these centrally
positioned actors are able to reap greater rewards from their strategic location in the
chain of production. This inner cir
cle might well compromise an elite mix of large and
small firms, research institutes, financiers, and providers of other key resources that
play a disproportionally large role. Finally, industrial structure might be highly fluid,
with different organizati
ons taking on more central roles at different times and in
different places, as well as for different stages of the product life cycle. Under this latter
scenario, there is neither an inner circle of participants, nor a boutique or orchestrator
role for s
mall firms, but a changing division of labor in which industrial leadership
varies according to capability and resource availability. We assess the prevalence of
these divergent accounts of the divisions of labor by testing the following arguments:


Small

firms play an active role in each stage of product development rather than
solely in research and development.
(Hypothesis 4A)
.


There is a heterogeneous group of participants for each stage of product
development, rather than an elite cadre that dominat
es all stages.
(Hypothesis 4B)
.


Small firms play a linking role, hence they are active in all stages of product
development, while other participants play more specialized roles.
(Hypothesis 4C)
.


Background on Industry Origins



The science underlying
the field of biotechnology had its origins in university
laboratories. The scientific discoveries that sparked the field occurred in the early
1970s. These promising discoveries were initially exploited by a handful of science
-
based start
-
up firms founde
d in the mid to late 1970s. The year 1980 marked a sea
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change, with the U.S. Supreme Court ruling in the
Diamond v. Chakrabaty
case that
genetically engineered life forms were patentable. Congress passed the Bayh
-
Dole Act
in the same year, which allowed
universities, nonprofit research institutes, and small
businesses to retain the intellectual property rights to discoveries funded by federal
research grants. And Genentech, which along with Cetus was then the most visible
biotech company, had its initial

public offering, drawing great interest on Wall Street,
with a single day stock price run up exceeding any previous one
-
day jump. Over the
next two decades, hundreds of biotech firms (BFs) have been founded, mostly in the
United States but more recently
in Canada, Australia, Britain, and Europe.


The initial breakthroughs


most notably Herbert Boyer and Stanley Cohen’s
discovery of recombinant DNA methods and George Köhler and Cesar Milstein’s cell
fusion technology that creates monoclonal antibodies


d
rew primarily on molecular
biology and immunology. The early discoveries were so path
-
breaking that they had a
kind of natural excludability, that is, without interaction with the university scientists
who were involved in the research, the knowledge was
difficult to transfer (Zucker,
Darby and Brewer, 1994). But what was considered a radical innovation two decades
ago has changed considerably as the science diffused rapidly. Genetic engineering,
monoclonal antibodies, polymerase chain reaction amplifica
tion, and gene sequencing
are now a standard part of the toolkit of microbiology graduate students. To stay on
top of the field, participants have to be at the forefront of knowledge
-
seeking and
technology development. Moreover, many new areas of scienc
e have become
inextricably involved in the biotech enterprise, ranging from genetics, biochemistry, cell
biology, general medicine, and computer science, to even physics and optical sciences.


The commercial potential of biotechnology appealed to many sci
entists and
entrepreneurs even in its embryonic stage. In the early years, the principal efforts were
directed at making existing proteins in new ways, then new methods were developed to
make new proteins, and today the race is on to design entirely new m
edicines. The
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firms that translated the science into feasible technologies and new medical products
faced a host of challenges. Alongside the usual difficulties facing start
-
up firms, biotech
firms needed huge amounts of capital to fund costly research,
assistance in managing
themselves and in conducting clinical trials, and eventually experience with the
regulatory approval process, manufacturing, marketing, distribution, and sales. In
time, established pharmaceutical firms were also attracted to the fi
eld, initially allying
with BFs in research partnerships and in providing a set of organizational capabilities
that BFs were lacking. Eventually, the considerable promise of biotechnology led nearly
every established pharmaceutical corporation to develop,

to varying degrees of success,
both in
-
house capacity in the new science and a wide portfolio of collaborations with
BFs (Arora and Gambardella 1990; Gambardella 1995).



Thus, the field is not only multi
-
disciplinary, it is multi
-
institutional as well.
In
addition to research universities and both start
-
up and established firms, government
agencies, nonprofit research institutes, and leading hospitals have played key roles in
conducting and funding research, while venture capitalists and law firms have p
layed
essential parts as talent scouts, advisors, consultants, and financiers (Gilson and Black
1996; Lerner and Merges 1998; Powell and Owen
-
Smith 1998). Biotechnology emerged
at a time, in the 1970s and 1980s, when a dense transactional infrastructure f
or relational
contracting was being developed, especially in Silicon Valley (Suchman 1994; Powell
1996). This institutional infrastructure of venture capital firms, law firms, and
technology talent scouts greatly facilitates interorganizational collaborat
ion.



Taking all these elements into account, two factors are highly salient. One, all the
necessary skills and organizational capabilities needed to compete in biotechnology are
not readily found under a single roof (Powell and Brantley 1992). Two, in

such fields
such as biotech, where knowledge is advancing rapidly and the sources of knowledge
are widely dispersed, organizations enter into an array of alliances to gain access to
different competencies and knowledge. Progress in developing the technol
ogy goes
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hand
-
in
-
hand with the evolution of the industry and its supporting institutions.
Following Nelson (1994), we argue that the science, the organizations, and the
associated institutional practices are co
-
evolving. Universities are more attentive t
o the
commercial development of research, BFs are active participants in basic science
inquiry, and pharmaceuticals are much more involved with BFs and universities.


DATA SOURCES



We began assembling a database in 1990, using
BioScan
, an independent
indu
stry directory, founded in 1988 and published six times a year, that covers a wide
range of organizations in the life sciences field. We attend to those companies that are
independently operated, profit
-
seeking entities involved in human therapeutic and
d
iagnostic applications of biotechnology. This focus results in a sample that covers 482
firms, of which as many as 180 are in existence in all years over the 12
-
year period,
1988
-
99. During this period, 229 firms were founded and entered the database, an
d 91
exited, due to failure, departure from the industry, or merger. The database, like the
industry, is heavily centered in the U.S., although in recent years there has been
expansion in Europe. In 1999, eighty percent of the companies were located in t
he U.S.
and ten percent in Europe.



We stress that our focus is on dedicated human biotech firms. We do not include
companies involved in veterinary and agricultural biotech, which draw on different
scientific capabilities and operate in a much different

regulatory climate. We do not
include large pharmaceutical corporations, health care companies, hospitals,
universities, or research institutes in our primary database; these participants enter the
database as partners that collaborate with dedicated bio
tech firms. We also exclude
companies that are wholly owned subsidiaries of pharmaceutical or chemical
corporations. We do, however, include BFs that have minority or majority investments
in them by other firms, as long as the company continues to be ind
ependently traded on
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the stock market. We observe and study the process by which some independent
companies are acquired. Our rationale for excluding both small biotech subsidiaries
and large, diversified chemical, medical, or pharmaceutical corporations

is that the
former do not make decisions autonomously and that biotechnology may represent
only a minority of the activities of the latter. Both circumstances generate serious data
ambiguities.



The reference source
BioScan

reports information on a firm
’s ownership,
financial history, formal contractual linkages to collaborators, products, and current
research. Firm characteristics reported in
BioScan

include founding data, employment
levels, financial history, and for firms that exit, whether they were

acquired or failed.
The data on interorganizational agreements cover the time frame and purpose of the
relationship. Our database draws on
BioScan
’s April issue, in which new information is
added for each calendar year. Hence the firm
-
level and network

data are measured
during the first months of each year. When information was missing from
BioScan
, we
consulted other sources including various editions of
Genetic Engineering and
Biotechnology Related Firms Worldwide
, Dun and Bradstreet’s
Who Owns Whom?
,
and
Standard and Poor’s
. In addition, we utilized annual reports, Securities and
Exchange Commission filings, and, when necessary, made phone calls to companies.



We have constructed a relational database that allows us to examine biotech
firms, their
ties to other participants, and the evolving network structure of the field.
Thus the database contains separate files for 1.) Biotech firms, 2.) The formal agreements
involving biotech, and 3.) The parties to these agreements. We treat each agreement as

a tie, and code each tie for its purposes and duration, using an implicit logic of
production to classify them into categories: R&D, clinical trials, manufacturing,
marketing, sales, and so forth. Table 1 describes each type of tie, and provides
illustra
tions of typical participants. The “partner” datafile for all organizations that
appear as partners on any tie with a biotech firm is large, expanding annually
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(numbering more than 2900 organizations active by 1999), and exceptionally diverse in
both form

and nationality, including multinational corporations, government agencies,
hospitals, universities, and pharmaceutical companies.



Data on financial performance are available only for publicly held firms, and
were obtained from COMPUSTAT, a widely used
electronic data service operated by
Standard and Poor. COMPUSTAT contains information compiled from public records
filed by firms listed on NYSE, AMEX, or NASDAQ. We obtained annual performance
data for 154 biotech firms listed on COMPUSTAT.


MEASURES



We utilize a variety of measures of firm characteristics, interorganizational
relations, and financial performance. Descriptive statistics are presented in Table 2 for
the measures described below.


Dependent variables: Hypothesis 1
-
4 predict the subsequ
ent depth, configuration, and
R&D intensity of a firm’s network. To measure the
depth

of a firm’s collaborations, we
use the total number of ties, the number of types of activity, and the number of complex
ties. The means for these variables are 8.5 coll
aborations, 2.4 types of collaboration, and
1.1 complex collaborative arrangements. To measure the
configuration

of a firm’s
collaborations, we use the number of ties for each type of activity and the number of
kinds of partners for each activity. To cap
ture R&D involvement after initial success,
we use the number of R&D agreements.


Independent variables: Our predictions are based on firm size, measured by the
number of employees, calendar age, and performance, captured by positive earnings.
The mean f
or size is 153 employees, but the distribution is highly skewed as the largest
firm has 7500 employees. The average age is 7.75 years, though the oldest firm is 42.
16


The earnings measure is taken from the firm’s income statements and is the difference
bet
ween operating income and expenses before dividends. We form an indicator
variable that is 1 if a firm’s earnings are positive and 0 if otherwise, thus identifying
those firms that have generated sufficient funds for reinvestment in new activities.
Twent
y one out of 154 firms have showed positive earnings.



RESEARCH METHODS



To test the arguments developed above, we utilize within firm statistics,
employing a panel regression model. We are not comparing large pharmaceutical
corporations to small biotec
h companies, nor are we measuring the relative mix of
activities performed internally or externally. Both assessments would, of course, be
interesting but would entail exceptional data access. Rather we focus on the more
tractable question of whether and

how biotech companies change the amount, variety,
and scope of collaborative arrangements they engage in over the period 1988
-
99.



To eliminate any spurious effects due to differences between firms, we
incorporated a fixed effects, or dummy variable, mod
el. Consequently, the estimated
coefficients will capture only the amount by which a dependent variable shifts within a
firm in response to a preceding change in that firm’s predictor variable. This fixed
-
effects approach is preferable to the alternative

random
-
effects, because the predictor
variables are likely to be correlated with the firm effects and we have the population of
biotech firms over our observed time period and not a random sample. We are
interested in estimating a dynamic model, in which

the independent variables are
lagged one year. Some firms may be “imprinted” or otherwise start on developmental
trajectories before the start of our observation period. The trajectories may have
naturally evolving patterns that change over time in cohe
rent ways, but ones that we
cannot foresee or measure. The omission of an important factor that changes over time
within firms will result in autocorrelated errors and may bias estimates of the
17


parameters in which we are most interested. One way of break
ing the correlation over
time, so as not to overestimate the effects of our hypothesized independent variables, is
to sample on a lagged dependent variable, y. That is, our arguments involve whether
firms that have grown, matured, or proven successful by
using collaboration then
retreat from or expand their network of involvement. We are not, here, concerned with
the antecedents of a firm’s initial collaboration. For example, in testing our hypotheses
predicting number and depth of ties at
t

based on siz
e, age, or success at
t
-
1
, we use
only those firms who had ties at
t
-
1
.



A second theoretical consideration is that the dynamics we are studying involve
the co
-
evolution of firms and networks. This process leads to an additional source of
statistical non
independence across our observations. Hence, we need to control for
effects that vary over time but are constant across firms, such as the overall number of
outside partners, the density of the industry’s network, government budgets for
medical research,
or the economic circumstances of pharmaceutical companies. To do
so, we included fixed year effects: a dummy variable for each year.


FINDINGS


In table 3 we present the effects of firm growth on depth of collaboration, to test
hypothesis 1. Recall tha
t we wish to assess whether firms that use collaboration to
overcome liabilities of smallness subsequently choose to go it alone, or whether they
deepen their collaborations. Consequently, we include in the analysis only firms that
have at least one prio
r tie. We use three measures of depth of collaborations: the overall
number of ties, the number of types of activities pursued with those ties, and the
number of complex ties.


The first row of the table contains GLS regression estimates of the effect of
size at
time t
-
1 on three measures of the depth of collaboration at time t, each in a separate
18


column. The standard errors for the estimates are presented just below the estimates in
parentheses. Also presented for each model are the within
-
firm and ful
l r
-
squares, the
number of records, and the number of firms involved in those records. The within
-
firm
r
-
square indicates, for each measure of depth, the amount of variance over time from a
firm’s average on each measure that is explained by size. The
full r
-
square explains the
amount of overall variation in depth, both between and within firms, that is explained
by size and by the fixed firm and year effects that are included as controls.


The results are clear and consistent across the three measure
s of depth. In each
case, as a firm grows, it subsequently deepens its portfolio of collaborations. For the
overall number of ties and the number of complex ties, the effects are significant beyond
the .0001 level. For each of these measures, prior si
ze explains about 10% of the within
-
firm variation in subsequent collaboration. Prior size explains only about 3% of the
within
-
firm variation in the subsequent scope of production activities; however, the
effect is significant beyond the .001 level. Gi
ven the restricted range for the number of
types of tie activity, ranging from 1 to 6, there is doubtless some attenuation.


In Tables 4 and 5, we turn to the effects of age on the scope and reach of
collaboration. In Table 4, we test whether firms that

use collaboration to overcome
liabilities of newness subsequently choose to restrict or expand the types of activities
they collaborate on. To do so, we separately model the initial use of each type of
collaborative activity, including in each analysis f
irms that only have ties of other types.
Recall that the six types of activity, presented in Table 1, are: R&D, Finance, Licensing,
Evaluation, Commercialization (Manufacturing, Marketing, etc), and Complex.



The GLS estimates (and their standard errors
) of the effect of prior age on
subsequent initial use of each type of collaborative activity are found in the first row of
Table 4. There are six columns, each presenting a separate model for the type of
activity listed. The table also contains the wi
thin
-
firm and full r
-
squares, as described
19


above, along with the number of records and number of firms that meet the inclusion
criteria for each model.


Aging consistently increases the scope of activities for which firms use
collaboration. For all si
x types of activity, among firms that only have other types of
ties, the effect of prior age on subsequent number of ties of that type is positive. The
results are significant beyond the .0001 level for all types, except evaluation, where the
effect is si
gnificant beyond the .05 level.


In Table 5, we ascertain whether firms that utilize alliances to overcome liabilities
of newness subsequently rely on the same partners for each type of activity, or whether
they broaden the reach of their affiliations. To

do so, we separately model the number
of kinds of partners used for each type of collaborative activity, including in each
analysis only firms that previously have at least one kind of partner for that type. The
identity of partners is defined by their i
ndustry or sector, including government,
university, nonprofit, pharmaceutical, venture capital, biotech, etc., as suggested in
column two of Table 1.


The GLS estimates (and their standard errors) of the effect of prior age on
subsequent number of kinds o
f partners for each type of collaborative activity are found
in the first row of Table 5. There are six columns, each presenting a separate model for
the type of activity listed. The table also contains the within
-
firm and full r
-
squares, as
described
above, along with the number of records and number of firms that meet the
inclusion criteria for reach model.


Aging increases the reach of collaboration for all types of ties save complex,
where the effect is positive, but not statistically significant.

For each of the remaining
types of activity, among firms that have prior ties for that type of activity, the effect of
prior age on the subsequent number of kinds of partners used for that particular
20


activity is positive. The results are significant b
eyond the .0001 level for finance and
licensing, significant beyond the .001 level for commercialization, and beyond .01 and
.05 for R&D and evaluation, respectively.


In table 6 we present the effects of firm success on involvement in collaborative
R&D,

in order to test hypothesis 3. Here we test whether firms that initially get
involved in collaborative R&D to fund expensive research subsequently choose to
emphasize exploitation by taking R&D inside, or whether they reinvest in exploration
collaborat
ively. We include in the analysis only firms that have at least one prior R&D
alliance. Success is indicated by a firm having reported positive earnings. Our
measure of involvement in collaborative R&D is simply the number of R&D alliances.
The GLS
estimate (and its standard error) for the effect of positive earnings at time t
-
1
on number of R&D alliances at time t is found in the first row of Table 6. The estimate
demonstrates that when firms that are engaged in collaborative R&D cross the earning
s
threshold, they subsequent add, on average, two more R&D alliances to their portfolio
of collaborations. This positive effect is significant beyond the .0001 level. Table 2
showed the weighted
-
average number of R&D ties for a firm to be just under 2.

This
reinvestment, therefore, is not only statistically significant, but substantial from a
practical standpoint as well.


Turning to hypothesis four, concerning the division of labor within the industry,
our aim is to discern whether BFs play a speciali
zed, boutique role or are active in
multiple stages of production. We also assess whether there is an elite cadre of either
BFs or partners that dominate all types of activity. Finally, we look to see if there is
evidence as to whether BFs play an orches
trating role in knitting together the industry.
With respect to hypothesis 4A, whether BFs are involved extensively rather than
narrowly, 27 biotech firms have collaborations for all six types of activities, while
another 128 are involved in five types of

collaborations. Thus 32% of the biotech firms
have interorganizational ties for at least five different business activities. The extensive
21


involvement of BFs in a wide array of ties suggest that BFs are not just specialized
participants.



On the other
hand, among the partners there are 25 organizations that have ties
to BFs for all six activities. Recognize that this is not a symmetrical comparison because
while our data cover biotech firms’ ties to all partners, we have only the partners’ ties to
biot
ech firms and not to one another. Still, the composition of the 25 most active
organizations is interesting. First, four of the larger biotech firms (Chiron, Genentech,
Genzyme, and Immunex) are among this group. Key branches of government


the
Nationa
l Institutes of Health, the U.S. Army, and the National Cancer Institute (a
division of NIH)


are included too. Chemical and healthcare companies, such as
Mitsubishi, DuPont, BASF, Hoechst, Kodak and Johnson and Johnson, Proctor and
Gamble, and Baxter Tr
avenol are deeply involved. Finally, the large pharmaceuticals


Novartis, Pfizer, Merck, Hoffman La Roche


are included, but by no means are all of
the largest pharmaceuticals present. In short, both the biotech participants and the
partner group show
considerable variety rather than specialization.



Turning to hypothesis 4B, regarding whether there is a heterogeneous group of
players for each stage in the development process, the answers are a bit more complex.
On the biotech side, just thirteen comp
anies have an average of two or more ties for
each activity in all twelve years. This suggests there may be a small group with
extensive involvement in all phases of development. But there are another thirty
companies with at least one type of tie every
year. Recall, also, that there are but 180
firms in existence for the entire period 1988
-
1999. So if there is an inner circle, it is a
fairly large one.



On the partner side, because the competencies are diverse in purpose, we would
not necessarily expe
ct a tight inner core. Investment activities are dominated by
venture capital firms and financial institutions. Universities play a significant role in
22


licensing. Government institutes are clearly dominant in research collaborations.
Several smaller bi
otechs have become specialists in running clinical trials. And large
pharmaceuticals loom large in importance on the manufacturing side. Thus neither
among biotechs nor among partners is there an identifiable tight inner core.



The linking pin role, hyp
othesis 4C, is harder to assess with descriptive statistics.
But consider that when we count which organizations are among the most active in
terms of ties to biotech firms, that is, in the top 10% for 3 or more business functions, 20
organizations emerge
. Among these 20 are the ever
-
present NIH and eight large
pharmaceutical corporations, including Novartis, Lilly, SmithKline, Merck, Bristol
Meyers, Schering Plough, Hoffman La Roche, and Boehringer. (But note the absence of
such giants as Warner Lamber
t, Pfizer and Glaxo Wellcome.) There are also eight
biotechs among this group. The largest biotechs


Genzyme, Genentech, and Chiron are
there, but so is Genetics Institute, which was acquired by American Home Products.
And present as well are several n
ew startup firms, including ArQule, founded in 1993,
and Metra, founded in 1990. Thus with regard to hypothesis 4C, concerning whether
biotechs play a subsidiary role or a linking role, we find biotechs


both large and small


playing active and diverse

roles alongside government institutes and pharmaceutical
companies.


DISCUSSION AND IMPLICATIONS



We find
c
o
n
s
i
s
t
e
n
t

a
n
d

c
l
e
a
r

e
v
i
d
e
n
c
e

for three trends. One, as BFs grow in size,
they increase the number of alliances they engage in, deepen existing one
s, and
diversify their alliances into new business functions. Two, as BFs age, they become
involved with a more heterogeneous set of partners. We have referred to these
developments as increases in the depth, scope, and reach of their collaborative portf
olio.
Three, as firms encounter some degree of success, they reinvest and engage, on average,
in two more R&D collaborations. In short, we see a strong pattern in which
23


“successful” BFs are pursuing multiple collaborations with a more diverse group of
pa
rtners.
2

In our view, there are clear gains from this strategy as well as obvious
difficulties associated with it. But in a fast
-
moving field there are also possible costs to
not forging ahead and expanding the network portfolio. We discuss each of thes
e
consequences in turn.



The advantages of a heterogeneous group of contacts is well established in both
social theory and network analysis. A strong tradition of theory and research, running
from Simmel (1954) to Merton (1957) to Granovetter (1973) to B
urt (1992), makes
abundantly clear that there are informational, status, and resource advantages to having
a broad and diverse social circle. In the area of commercializing scientific advances, we
note additional gains from network diversity. One, hetero
geneity in the portfolio of
partners allows BFs to learn from a wider stock of knowledge.
3

Organizations with
broader network range are exposed to more experiences, different competencies, and
added opportunities for discussion and debate. Such a setting

creates an environment
in which “creative abrasion,” the synthesis that is developed from multiple points of
view, is more likely to occur. In this view, “innovation occurs at the boundaries
between mind sets, not within the provincial territory of one k
nowledge and skill base”
(Leonard
-
Barton, 1995:62). By having access to a more diverse set of activities,
experiences, and collaborators, BFs are broadening the resource and knowledge base
that they draw on.



By developing more multiplex ties with indivi
dual partners, either through
pursuing multiple collaborations or expanding an existing R&D partnership into
clinical development or manufacturing, BFs are increasing the points of contact between



2

Obviously, success is a relative term. None of these firms are hugely successful in comparison to
software giant Microsoft or a computer firm like Dell. But success is used multi
-
d
imensionally here;
these firms are growing in size, surviving, and, in some cases, generating sufficient revenues for
reinvestment.

3

We thank Steve Klepper for first emphasizing this point to us.

24


the two organizations. When relationships are deepened, gr
eater commitment and
more thorough knowledge sharing should follow. Organizations with both multiple
and/or multifaceted ties to others are likely to have developed better protocols for the
exchange of information and the resolution of disputes (Powell, 1
998).



We also find that BFs are pursuing new types of collaborations. If a firm did not
have an R&D or a marketing (or any other business function) collaboration, as it got
older it was very likely to add one. Moreover, if a firm had an R&D tie to a un
iversity,
as it aged it was likely to add an R&D alliance with a government lab, a pharmaceutical
company, or a research institute. The general approach appears to be one of filling out
the portfolio of collaborations. In the rapidly developing field of
the life sciences, the
value of a BF is its scientific expertise and technological leadership in a specific disease
category. Maintaining that expertise enables the BF to be an attractive partner. Linking
with diverse participants for different activitie
s permits a company to leverage its skills
across a range of relationships with parties that have few interdependencies among
themselves, but are connected through the biotech firm. Such a strategy has obvious
payoffs for a small science
-
based firm. If s
uccessfully pursued, the small firm
contributes to the agendas of a diverse set of organizations without rendering itself
redundant by allowing too much of its knowledge to migrate to others. By interacting
with diverse participants, the small firm plays
an orchestrator role rather than a
specialist or dependent one. Diversity thus allows little encroachment on the small
firm’s scientific competence. Yet this is not a situation of a third party broker playing
off others to maximize its own gain. The pro
cess of drug development is lengthy,
costly, and protracted. The orchestrator role requires building across relationships for
the advantage of all the participants. The better the transfer of knowledge and skills
among the participants, and the better co
nnected that partners are to others, the richer
the flow of information to all involved. Rather than exploiting one’s position through
leverage, participants in these learning races must find a way to improve both their and
their parties’ capabilities.

25




Seen in this light, two further findings fit the general pattern. Among the
relatively small set of firms with positive earnings, there was a strong tendency to
reinvest in R&D. Successful firms added, on average, two more R&D collaborations, a
strong s
ign that maintaining scientific leadership is critical for maintaining one’s
position in the industry. We also observed in the descriptive statistics that BFs were
actively linked to a great variety of different types of organizations


commercial,
govern
mental, nonprofit, domestic and foreign. We suspect, but cannot demonstrate at
this point, that these heterogeneous links represent alliance webs in different research
and therapeutic areas. Put differently, competition in this field is not firm vs. firm

or
one firm’s network portfolio vs. another’s, but multilateral competition of different
partners and different alliances on divergent projects. A collaborator on a cancer drug
may well be a competitor in Alzheimer’s research.



But such complex webs of
multiple relationships present organizational
challenges and tax the ability of an organization to sustain numerous external ties.
4

Most organizations have ample trouble managing their internal operations, these
diverse linkages post additional problems o
f control and coordination (see Gomes
-
Casseres, 1996: 157
-
166 and Doz and Hamel, 1998: 195
-
220). Maintaining productive
linkages with multiple parties is difficult, learning how to learn across relationships is
even more so. Add to these operational chal
lenges the strategic challenge of
simultaneously learning from a partner while protecting core skills and knowledge base
and the task is daunting. There is inherent tension in this new form of
interorganizational collaboration. An organization has a set
of skills that makes it
attractive to others and provides it with bargaining power. To be a valuable partner, it
must share some of those skills and the knowledge or resources it acquires to create



4

An important question is just how much diversity can an o
rganization sustain. In a recent paper, we
found declining returns to network diversity and experience after an organization crossed a threshold of
connected ness. Once centrally positioned, with a portfolio of partners, there were scant returns to
perfo
rmance from adding more collaborators (Powell et al, 1999).

26


something it cannot create on its own. But it has to pro
tect itself from letting its skills
leak into the public domain, from learning less rapidly than its partners, and from being
only a provider of a resource that others exploit to greater use. Given these challenges,
heterogeneity is one solution. By havi
ng diverse collaborators, each participant
provides a resource that the other party values and cannot readily find through
alternative means.



Finally, we should consider that in fields where knowledge is developing rapidly
and the sources of scientific e
xpertise are broadly distributed, there is a huge cost to
inaction or inertia. In previous research, we have seen that repeat trading with similar
partners lead to restricted access and cognitive lock
-
in. Powell (1985: 202
-
7) found that
repeated exchange

among a small circle of book editors and authors lead to
parochialism and ossification. Glassmeier (1991) attributes the failure of Swiss
watchmakers to adapt to digital technology to the restricted nature of their contacts.
Similarly, Grabher (1993) su
ggests overembeddedness led to organizational inbreeding
in the Ruhr steelmaking district, leading to the decline of the German steel industry.
Thus, even though the challenges of coordinating heterogeneous networks may be
considerable, the alternative of

parochialism is not viable.


Conclusion



We find
strong confirmation

for the argument that commercial organizations in
the life sciences are actively expanding their range of collaborations, and diversifying
the array of business functions they collaborat
e on, as they grow larger, older, and
become successful. We attribute these results to a general process of organizational
learning in which firms linked to others with more diverse ties are exposed to a broader
stock of knowledge. We draw from these res
ults the conclusion that interfirm
collaboration is not a transitional stage, or stepping stone, to success or maturity, but a
significant organizational practice in this technologically advanced field. Extending this
27


argument, we suggest this strategy of

interfirm collaboration represents neither
dependency nor specialization but an alternative way of accessing knowledge and
resources.



Our claim is that this form of organizing is viable needs to be tempered in three
ways. First, we have consistently li
nked these biotech firms to the adjectives emerging
or small. Some of them, however, are not so small; the largest has 7500 employees and
a number have sales in excess of one billion dollars. These are hardly weak or tiny
firms. But all of them are rela
tively young, and even the largest biotechs pale in size
compared to the large pharmaceutical corporations, all with employee counts
numbering in the tens of thousands, or government research institutes, or elite research
universities with whom they collab
orate. To be sure, small is used as a relative term
throughout, but the BFs are typically the smaller and younger party in the exchange
relation.



Second, we argue that the BFs are not specialists because they are diversifying
their activities, both inte
rnally and externally, to all stages in the production process.
Thus, they are not focused on only one stage of production. But resource
-
partitioning
arguments have primarily, though not exclusively, focused on product
-
market
segmentation. Were we to an
alyze our companies in terms of the disease categories
they concentrate on, we suspect we would find a persistent relationship: the smaller the
company the more focused they are on a single medicine or disease; as they grow they
branch into related therape
utic areas. But with success and reinvestment in R&D, the
BFs branch into new therapeutic areas. This observation, however, represents
conjecture at this stage in our research and awaits empirical testing.



Finally, these findings need to be considered
in the context of the life sciences
field, which has some idiosyncratic features and other characteristics shared with a
number of high technology industries. Advances in basic science have continued to
28


play a critical role in this field, and universities

have a key hand in this process (Zucker
et al, 1994; Liebeskind et al, 1996; Powell and Owen
-
Smith, 1998). In few other high tech
industries have universities continued to exert such considerable influence for such a
sustained period of time. Consequent
ly, the industrial structure has evolved, in part,
out of the invisible college structure of the academy. Moreover, scientific leadership is
divided rather than concentrated, with diverse sources of expertise located in many
advanced industrial nations.
Again, the fact that scientific excellence is broadly
distributed promotes interorganizational collaboration. Finally, interorganizational
relations are largely focused on mutual learning, that is, developing new medicines or
treatment regimes, and creati
ng new medical markets. Few alliances are driven by cost
cutting considerations, or outsourcing to replace existing internal functions. This
orientation toward mutual gain extends the shadow of the future and lends itself to the
expectation of future int
eraction. Effective sharing of knowledge is enhanced by
awareness of a shared destiny.



This open
-
ended time frame and focus on joint learning is not unique to the life
sciences, however. Many new technologies, much more nascent than biotech, involve
th
e delivery of new products or services and/or tap markets that did not previously
exist. And fields such as e
-
commerce or wireless communications do not face the long
arduous period of new product development, which presents many occasions for
disappointm
ent and defection, that characterizes biotech. The life sciences have also
benefited from a decade of increases in research funding, both in the public sector at the
National Institutes of Health and in the private sector, where increases in research
spen
ding have even outpaced government spending. But, again, a wide range of new
research fields have benefited from this decade
-
long economic expansion. Indeed, the
availability of venture capital funding has spurred the growth of a number of new
industries
.


29



The coincidence of industry growth and a favorable economic environment raise
the question as to whether these organizational arrangements are the product of a
munificent environment. Many commentators have suggested that an eventual
shakeout, or cons
olidation, will occur in these new fields. Others have contended that
biotech and other new industries will eventually be absorbed by more mature,
established firms. Still other pundits anticipate that with maturity will come a reversion
to more traditio
nal organizational arrangements. Obviously, access to capital in
research
-
intensive fields is critical and the terms of contractual agreements vary with
respect to the availability of capital (Lerner and Merges, 1998). But despite stiff
competition for f
inancing from other emergent industries, and the opening and closing
of windows of opportunity for going public, the strategy of accessing external sources
of expertise and support has not only continued, it has been deepened and refined.



In sum, a singl
e case over a little more than a decade does not provide a
definitive answer as to whether small science
-
based firms and collaborative practices
are supplanting more traditional organizational arrangements based on hierarchy and
economies of scale. Nevert
heless, we follow March (1991) in arguing that all
organizations face the challenge of balancing the demands of exploration, or
experimentation with novel alternatives, and exploitation, the refinement and extension
of existing competencies and technologie
s. We are persuaded that a good deal more
attention needs to be paid to the processes of exploration and to those industries in
which success is based on winning learning races. In such fields, more horizontally
-
based interorganizational collaborations a
ppear to be a cornerstone of organizational
practice, and
these

new routines are being actively developed.




30


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36


Table 1:

Description of Biotechnology Agreements


Type of Tie


Typical Kinds of Partners


R&D
: Biotech firm develops re
search
program with another organization for
a specific target.


Finance:

Partner invests funds in a BF,

or BF invests funds (and usually

scientific expertise) in a partner.


Other biotechs, pharmaceutical corps.,
universities, research institut
es.



Venture capital firms, larger biotech
companies, pharmaceutical corps.



Licensing
: BF either licenses its
intellectual property (IP) to another
party, or acquires license to others’ IP.

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Clinica
l Trials/Evaluation
: BF has partner
conduct trials on human subjects for
FDA approval.

Research hospitals, organizations
specializing in clinical trials.



Commercialization
: BF contracts with
partner to manufacture and market its
product, or BF agrees t
o supply
product to a distributor for sales.

Large pharmaceutical or chemical
corps., larger BFs.






Complex
: a tie between a BF and
another party that involves multiple
activities (i.e., R&D and marketing)


Any partner (except venture capital).









37


Table 2: Descriptive Statistics for Biotechnology Firms



Mean

Standard
Deviation

Maximum

N firm
-
years*

Size
(employees)

153.52

435.42

7500

2946

Age (years)

7.7575

5.7670

42

4144






Number of
ties:





R&D

1.8727

2.9779

25

4178

Finance

2.7252

4
.1790

52

4178

Licensing

1.5785

3.1784

43

4178

Evaluation

0.0764

0.3983

6

4178

Commercial

1.1309

2.5444

23

4178

Complex

1.1168

2.4496

32

4178

Total

8.5005

10.5118

91

4178






Number of
types

2.4050

1.5371

6

4178






Number of
ties per
partner

1.1
501

.2821

5

3864






Earnings ($M)

-
48.0

32.33

879.4

1061












* The differences in the number of firm years reflect missing data for some companies
with respect to size and/or age. With respect to the number of ties, a small number of
firms h
ave no ties of any kind. The earnings data are based on 154 publicly traded
firms in the United States and cover those years for which financial reports are
available.





38






39


Table 3: Effects of firm growth on depth of collaboration.*




Dependent Varia
ble at time t


Overall
number of ties

Number of
types of tie
activity

Number of
complex ties

Size at time t
-
1

.6160****


(.0392)

.0294***

(.0061)

.1113****

(.0119)





Within
-
firm r
-
square

.10

.03

.09





Full r
-
square

.83

.69

.84





N firm
-
years

2349

2349

2349





N firms

407

407

407


All models include both firm and year fixed effects.

Significance levels: ***=p<.001; ****=p<.0001


* Only firms with at least one tie at time t
-
1 are included in these analyses.













40


Table 4: Effects of a
ging on scope (initial use of each type) of collaborative activity.*



Number of ties of specific type of tie activity at time t


R&D

Finance

Licensing

Evaluatio
n

Commerci
al

Complex

Age at
time t
-
1

.0311****

(.0048)

.0267****

(.0050)

.0288****

(.0037)

.
0180*

(.0012)

.0246****

(.0030)

.0281****

(.0038)








Within
-
firm r
-
square

.03

.02

.04

.01

.03

.03








Full r
-
square

.35

.32

.33

.24

.28

.32








N firm
-
years

1482

1363

1658

3220

2136

1839








N firms

319

291

327

439

374

360


All models

include fixed firm and year effects.

Significance levels: *=p<.05; ****=p<.0001.


* Only firms without specific type at time t
-
1, but with at least one other type at time t
-
1are included in these analyses.












41


Table 5: Effects of aging on reach (n
umber of kinds of partners) for each type of
collaborative activity.*



Number of kinds of partners by specific type of tie activity at time t


R&D

Finance

Licensing

Evaluatio
n

Commercial

Complex

Age at
time t
-
1

.0530**

(.0107)

.0715****

(.0106)

.0471**
**

(.0096)

.0260*

(.0107)

.0502***

(.0139)

.0047

(.0038)








Within
-
firm r
-
square

.03

.02

.02

.01

.01

.00








Full r
-
square

.55

.70

.66

.61

.61

.57








N firm
-
years

1570

2239

1495

1616

1986

1140








N firms

306

381

295

312

358

244


All

models include fixed firm and year effects.

Significance levels: *=p<.05; **=p<.01;***=p<.001;****=p<.0001.


* Only firms with at least kind of partner for specific type at time t
-
1are included in
these analyses.











42


Table 6: Effects of positive ea
rnings on reinvestment in collaborative R&D.*



Number of R&D alliances at time t

Positive earnings at time t
-
1

2.0500****

(.5038)



Within
-
firm r
-
square

.03



Full r
-
square

.73



N firm
-
years

716



N firms

133


All models include fixed firm and
year effects.

Significance levels: ****=p<.0001


* Only firms with at least one R&D alliance at time t
-
1are included in these analyses.