Organizational Legacy and the Internal Dynamics of Clusters:
The U.S. Human Bio-Therapeutics Industry,
1976-2002
Maryann Feldman
Rotman School of Management
University of Toronto
105 St. George Street
Toronto, ON M5S 3E6 CANADA
maryann.feldman@rotman.utoronto.ca
Elaine Romanelli
McDonough School of Business
Georgetown University
Washington D.C. 20057
elaine.romanelli@msb.edu
Abstract: Using data on the human biotherapeutics industry in the United States, over the period
1976 through 2002, this paper examines the growth of the industry and tests hypotheses about
factors underlying the growth of clusters. Our study reveals that early entrepreneurial activity in
the clustered regions were important, however other factors, such as the tendency of the
entrepreneurial firms to spin off new firms, distinguished regions that have exhibited long term
cluster growth. Our empirical results, examining the importance of organizational legacy,
demonstrate that there are observable patterns of internal cluster dynamics that affect their long
term viability.
January 18, 2006
Introduction
There are very few chances to observe the start of a new industry and to trace its spatial
and temporal development. Biotechnology, the commercial application of scientific discoveries
in genetic engineering, is a notable recent exception. Genentech, arguably the first
biotechnology firm, was founded in 1976 by Bob Swanson, a venture capitalist with Kleiner &
Perkins and Herbert Boyer, a biochemist at the University of California, San Francisco. In 1973,
Boyer along with Stanley Cohen, a Stanford molecular biologist, developed a technique that
enabled the purposeful recombination of genetic material. The resulting intellectual property was
awarded a patent in 1980 and set off an explosion of product development activity (Feldman et
al. 2005). The chance to participate in what promised to be a revolution in the diagnosis and
treatment of human disease along with allure of significant monetary reward motivated many
academic scientists, pharmaceutical company executives, business investors and others to start
new firms (Kenney 1986). Thus, biotechnology, which might have simply been a research
technique, became the platform of an entirely new industry.
The economic potential of biotechnology was not lost on policymakers and economic
development officials. Biotechnology is the type of innovative activity that benefits from
agglomeration economies and the tendency of biotech firms to locate in close proximity to
universities and research institutions has been studied extensively (Prevezer 1995; Audretsch and
Stephan 1996; Zucker, Darby and Brewer 1998; Owen-Smith and Powell 2003; Stuart and
Sorenson 2003). It is no surprise that governments offer a variety of incentives and subsidies in
an attempt to build the industry in their region. After all, every region has some resource
endowment that may be leveraged to create or attract firms. Despite these efforts, most efforts
do not succeed.
The mechanisms underlying geographic clustering and the reasons why an industry
thrives in one location while languishes in another is poorly understood. Case histories of cluster
development enrich our understanding of the development of clusters within particular regions;
however, they provide little systematic explanation for the location and vitality of industry
clusters. Anecdotal evidence notwithstanding, virtually no research has attempted to
characterize the conditions that determine the location of the biotech industry or the processes of
regional competition for industry clusters. Unless researchers systematically compare
differences in the development processes of regions that have similar resource endowments, we
2
will be unable to explain with any confidence why industries develop in some regions but not
others. Until researchers track the geographic patterns of entrepreneurial and organizational
migrations across regions over time and begin to systematically investigate the characteristics of
regions that attract and repel firm investment, we will be left to assume simply, if
unsatisfactorily, that the location of industry clusters was preordained – the obvious result of
resource endowments or historical accidents. Regional leaders will continue to invest and to
compete, but social science will have little to say about how they might compete in more
effective ways and influence the ultimate outcomes.
This paper begins to fill the empirical gap in our understanding of the development of
industrial clusters. Using data on the human biotherapeutics industry in the United States, over
the period 1976 through 2002, we investigate the growth of the industry by examining the
number of firms in a region over time and testing hypotheses about factors underlying the growth
of clusters. Human biotherapeutics -- the discovery and production of drugs for the prevention
and treatment of human disease using biotechnological techniques -- has emerged as a distinct
industry. Our study reveals that the tendency of the entrepreneurial firms to spin off new firms,
distinguished regions that have exhibited long term cluster growth. Our work is the first that we
know of to consider the internal industrial demography of cluster development, including both
the organizational and geographic origins of entrepreneurs and firms who came to populate
biotech clusters. It is also one of the few studies—Saxenian (1994) and Sorenson and Audia
(2000) being important exceptions—to consider cluster development in the context of multiple,
competing regions. Though our findings are limited to patterns that we can observe in just one
U.S. industry, they establish a basis for theorizing about the dynamics of clustering both within
and across geographic regions that may be formally tested in this and other emerging industries.
Our empirical results, examining the importance of organizational legacy, demonstrate that there
are observable patterns of internal cluster dynamics that affect their viability.
The next section of this paper examines spatial clustering in the human biotherapeutics
industry starting in 1976. Basically, there are three competing hypothesis that explain cluster
growth: resource endowments, the persistence of initial events and organizational legacy.
Simple geographic patterns support neither resource endowments nor the long term impact of
initial events. Our focus is on organizational legacy which is developed in Section III. Section
IV discusses our data and presents methodological issues related to studying cluster dynamics.
3
Section V provides empirical results. The final section concludes with a discussion of the
development of industrial clusters.
The Development of Human Therapeutics Clusters
Industry clusters are a pervasive and persistent feature in the organization of economic
activity. For almost a century, ever since Marshall (1920) perhaps first identified the
phenomenon, economists, geographers, and historians have explored and debated explanations
for the tendency of firms to locate in close geographic proximity to one another. Prevailing
theory has emphasized the importance of resources as an explanation for the location of industry
clusters, arguing that innovative activity tends to cluster in regions where resources relevant to
the performance and survival of firms are most abundant (Feldman 1994). Many resources,
including the presence of skilled labor and access to transportation (Krugman 1991), proximity
to markets and input suppliers (Storper and Christopherson 1987; Baum and Haveman 1997), the
presence of universities and research organizations (Zucker, Darby and Brewer 1998), and
cultural and institutional supports for entrepreneurial activity (Saxenian 1994; Sorenson and
Audia 2000; Stuart and Sorenson 2003), have been considered. Yet when we consider spatial
patterns there is always the concern of endogeniety: firms and resources develop in tandem and
causality is difficult to attribute. The presence of specific resources may affect productivity but
not account for the location of firms.
Prior to 1980, only a few companies worked in the emerging field of biotechnology.
Genentech’s first commercial success was the clone of a human insulin gene and when Eli Lilly,
the world’s largest producer of insulin licensed this technology from Genentech, the commercial
viability of the biotech industry was established. Genentech’s initial public offering on October
14, 1980, started at $35 and closed at $80 a share, initiating what Science writer Nicholas Wade
described as a Gold Rush (Wade, 1980). In addition, existing firms started working with rDNA.
For example, Cetus Corporation, located in San Francisco, was founded in 1971 to work on
cancer therapies but publicly announced in 1978 that it was beginning to work with rDNA
techniques. In March 1981, Cetus raised $107 million, making it the largest IPO by a new
corporation in US history, even though the company prospectus mentioned no potential for
profitability until 1985 at the earliest. As a result of this attractive investment climate, many
4
companies, both new start-ups and existing firms, entered the industry and heralded a steeper
growth trajectory for the industry in the mid-1980s.
[Figure 1 here]
Figure 1 displays the growth of the U.S. human bio-therapeutics industry nationally, from
1976 to 2002. After 1984, the rate of growth increased as new firms entered the industry. The
number of U.S. companies reached 452 in 2000, at the peak of the technology bull market and
then declined. The recent decline is perhaps due to the particular sensitivity of the industry to
fluctuations in the stock market (Lerner 1994). In 2002, there were 435 firms nationally in
human biotherapeutics.
The geographic reach of the industry increased as new firms entered. By 1985, human
biotherapeutics firms could be found in 30 regions in the U.S. By 2002, firms could be found in
60 regions. In sum, over the 26 year time period from 1976 to 2002, 75 regions had at least one
human biotherapeutics firm at some time.
[Table 1 here]
Table 1 examines the geographic distribution of the industry in 1985 and 2002, along
with an industry location quotient. The industry location quotient is calculated as the proportion
of human biotherapeutics firms in the region relative to the national number as the numerator
normalized by the population in the region relative to national population. A location quotient of
one indicates that the distribution of firms mirrors what we expect given the region’s population.
For example, there were 35 firms in New York in 1985 and 47 in 2002. Compared to New
York’s size, the industry is over-represented in New York and over 10% of the industry is
located there. However given location quotients of over 30 for Durham or 12 for San Diego, it
appears that these locations may have some underlying advantage. Notably missing are places
such as Chicago with its prominent research universities and existing pharmaceutical firms that
might appear to be logical locations for human biotherapeutics firms. There were six firms in
Chicago in 1985 and eight in 2002. These descriptive results suggest that resources do not fully
explain the location of the industry.
5
Arthur (1990) and Rauch (1993) challenge the primacy of resources as an explanation for
the location of industry clusters, arguing instead that expectations and positive feedback may
generate clusters in regions with few or demonstrably inferior resources. Arthur (1990)
demonstrates theoretically, how a small early lead in a region’s concentration of similar new
firms can promote the formation of an industry cluster independent of the quality of regional
resources. This result is based on positive feedback and suggests that early entry may be a
simple heuristic for predicting the future locations of industry clusters.
Table 2: Companies established prior to 1980, by region
Region
Albuquerque
Boston-Cambridge-Quincy
Dallas-Fort Worth-Arlington
Durham
Philadelphia-Camden-Wilmington
San Diego-Carlsbad-San Marcos
San Francisco-Oakland-Fremont
Washington-Arlington-Alexandria
Company (Date Established or Entered the
Industry)
Summa Medical (1979)
Biogen (1978);
Wadley Biosciences (1978);
Medco Research (1978)
Centocor (1979);
Hybritech (1978)
Genentech (1976); Cetus Corp (1978);
Hana Biologics (1979)
Genex (1977);
Number of Companies,
2002
0
53
2
20
19
54
57
14
The earliest, pioneering firms were rather geographically dispersed and perhaps not in
predictable locations (see Table 2). The conventional wisdom is that biotech companies would
locate near universities with strong academic departments, and in locations with significant
amounts of venture capital financing. Certainly that might account for the early firms located in
San Francisco and Boston. But it is interesting to consider other locations. Genex, the first firm
located in Washington DC was started by venture capitalist, Bob Johnson, in 1977. Although
Johnson was based in New Jersey, he strategically located the start-up near the U.S. National
Institutes of Health (NIH). Genex struggled in this location even given relatively abundant
resources and eventually partnered with Searle to scale up aspartame sweetener in 1987,
ostensibly exiting the human therapeutics industry. Other places, such as Albuquerque, may be
somewhat surprising as a location for a human biotherapeutics firm. Summa Medical began in
Albuquerque in 1979 but moved to Washington DC in 1991. While Albuquerque has been the
site of other start-ups in human biotherapeutics it has never had more than two companies at any
point in time.
[Figure 2 here]
6
Figure 2 provides the growth in the number of human biotherapeutics firms for the eight
metropolitan regions where firms were located prior to 1980. The national growth trajectories
are mirrored by the fastest growing clusters of Boston and San Francisco. San Diego started
slower but caught-up to the fastest growing group – an interesting feat given its relatively smaller
size and resource base. Although theoretical work hypothesizes that early entry is associated
with agglomeration development, these data reveal that, despite an early lead, the industry failed
to develop in Albuquerque and Dallas. Neither location has had more than three firms at any one
point in time. In addition to the fast growing regions and the regions where the industry did not
develop, there is a middle group of regions. Durham, North Carolina, Philadelphia and
Washington, D.C. have experienced slower growth but their experiences are not uniform. For
example, Durham experienced a substantial increase in the number of biotherapeutics firms since
1991. Many of these were new start-ups whose entrepreneurs were previously employed at the
biotherapeutics firms that slowly became established in the region during the preceding 15 years.
These observed differences suggest that internal dynamics may account for different underlying
patterns of growth.
The concept of industrial clusters draws attention to the internal organization of firms
within a geographically defined area. There is general agreement that clusters tend to persist
because of positive externalities that result from close geographic proximity: greater geographic
density of firms facilitates social interaction among individuals at related and competing firms
and promotes innovation and experimentation. The exchange of valuable information about
R&D, production, and markets, may improve the performance and survival chances of the
individual firms. The fortune of firms and regions may be linked in ways that have not been
previously explored.
For a given technology and place, the propensity of firms to share information may be a
differentiating characteristics that drives cluster growth (Rosenthal and Strange 2003; Saxenian
1994). Rosenthal and Strange (2003) find that a concentration of similar firms, a proxy for local
cohesion and culture, yields subsequent greater firm formation. This suggests the salience of
considering internal cluster dynamics on the ability of a cluster to grow and realize critical mass.
Few studies, however, have explored either the organizational or geographic origins of
entrepreneurs and firms who populate an industry cluster. Although some (e.g., Stinchcombe
1965; Romanelli 1989; Gompers, Lerner and Scharfstein 2003) have considered the
7
characteristics of organizations that may be more or less prolific generators of new organizations,
little empirical evidence has been brought to bear on the question of how organizational legacy
affects the growth of industrial clusters.
Entrepreneurship, Organizational Legacy and Cluster Growth
Jane Jacobs (1969: 97-99) suggests that cluster growth is driven by what she describes as
breakaway firms: firms that are started by entrepreneurs with experience in the same industry.
This is a British term that was formalized by the practices of medieval guilds: “An apprentice
learned the work in an existing organization, then became a journeyman employed in the same
organization or others similar, and then, if all went well, he set up a shop on his own as a master
and took on apprentices “(Jacobs 1969: 66). The process is tied to innovation as breakaways
experiment with variations learned from the prior work and find new ways of creating value.
Jacob’s conceptualization has a decided local orientation that grounds the entrepreneur in a
community of practice and social relationships. In more formal terms, employees from existing
organizations have the intellectual capital both in terms of their knowledge of the technology and
their professional networks to start companies in the location in the same or related industry.
Established firms may serve as a training ground – an advanced apprenticeship for entrepreneurs.
Successful firms provide a blueprint of successful organizational practices, business models and
strategies (Klepper and Sleeper, 2005). Even employment at a firm that is not particularly
successful provides an opportunity to learn about an industry and to identify potentially
profitable new products and opportunities.
This observation is certainly part of the lore of Silicon Valley as semiconductor firms
form a family tree in a cascading series of spin-offs from Fairchild Semiconductor, the original
firm of its type in Silicon Valley (Kenney and Von Berg 1999). The story is quite similar to the
automobile industry. Klepper (2002) found that firms started by entrepreneurs with experience
either in related industries such as carriages and wagons, bicycles, or engines, or in incumbent
firms in the auto industry, performed better than firms founded by inexperienced entrepreneurs.
A particularly prominent incumbent was the Olds Motor Works whose spin-offs included Dodge,
Cadillac, and Ford. Thus, Olds played much the same role as Fairchild in Silicon Valley: it was a
training ground for subsequent entrepreneurs and formed the core of the new industry in the
region.
In addition to providing relevant experience to increase the success of a firm, certain
8
types of organizational legacies may encourage networking and information sharing that are
associated with clusters. A collection of firms started by individuals with certain types of
organizational legacies may be more likely to have the types of network and share information.
Saxenian (1994) argues that semiconductor producers in Silicon Valley, most of whom had been
the founders of their firms, were far more supportive of employees (managers and engineers)
who sought to strike out on their own than producers elsewhere. We may speculate that certain
types of founder backgrounds may provide experience useful to the success of their new firms
and also set expectations about how to work with other firms.
Organizational Legacy in Human Biotherapeutics
Table 3 provides the number of human biotherapeutics organizations established in the
U.S. over the period 1976 through 2002, broken down by organizational legacy. The majority of
firms (455, or 66%) were established as startups. Approximately half of all firms were created
by scientists out of universities or private research institutes. This accounts for the origins of
almost one-third of all biotherapeutics firms. This activity may represent the large number of
state and local initiatives in the U.S. which attempt to leverage academic expertise in the biotech
industry (BIO 2004). While academic scientists may start firms close to their universities we
may question if they have the appropriate organizational legacy to create a sustainable cluster
and to position a region of a higher growth trajectory. It may be that start-ups founded by
academics account for the geographic dispersion of the industry, mirroring the geographic
distribution of universities. The question is whether clusters with a high proportion of start-ups
generated by academics are associated with larger cluster size.
[Table 3 here]
The importance of academic scientists in biotech is at odds with our understanding of
other technology-based industries like computers, semiconductors, and the disk drive industry.
The most successful clusters may be those in which Jacob’s description of breakaway firms is
operative. The most fruitful organizational legacy may stem from entrepreneurs who were
previously employed in human therapeutics firms. Table 3 demonstrates that 10% of start-ups
were formed by entrepreneurs who were previously employed in the human therapeutics
9
industry. This suggests that once a firm exists in the industry in a location, regardless of how it
entered, it may become an incubator for other breakaways. Cluster growth may be driven by
individuals who gain experience with the industry and then start their own firms. We expect that
firms with this type of legacy will have be associated with greater cluster growth. And that the
most vibrant clusters will have a higher proportion of firms with this legacy.
In a new industry at first there are no existing firms and entrepreneurs may be drawn
from other experiences such as related outside industry or commercial activity. Chesborough and
Rosenbloom (2002) document the development of new technologies through spinoffs from
Xerox Palo Alto Research Center. Existing firms are often unable to move aggressively into new
technologies and spin-offs provide a means to realize the potential of discoveries. The
pharmaceutical industry probably provides the most relevant industrial predecessors for human
biotherapeutics, our specific case. A difference is that the traditional pharmaceutical industry
focuses on chemistry while the new industry focuses on genetics (Galambos and Sturchio 1998).
Part of the folklore in the industry is the importance of start-up firms created by venture
capitalists that identify an opportunity and then create a company by hiring the appropriate
human capital and licensing technology. The venture capital model is associated with great
industry contacts and ability to network. Gompers et al (2003) showed that venture-capital
funded organizations were more likely to generate other new entrepreneurial firms than
organizations created or funded in other ways.
Another common story is the hybrid model of scientists with business people who form a
diverse team and start a company. Certainly this was the case at Genentech as an early example.
The hybrid form is expected to increase access to different types of expertise and a higher
proportion of these firms may create a high velocity of information flows.
The relevant question is what factors allow clusters to become vibrant in some regions
while an industry fails to develop in other regions. Certainly as the data demonstrates having
resource endowments or an early start are no guarantee. The next section will set up an empirical
model to test the effects of organizational legacy on the size of clusters.
Data and Methodology
When we think about clusters the concern is the size of the cluster or the number of firms
that exist and are active at any given time. Our dependent variable is the number of firms in the
10
Metropolitan Statistical Areas (MSA) annually from 1976 to 2002. This is a count variable and
pooled cross-section data was used in the estimation. There were 75 regions that had at least one
human therapeutics firm at some point over the time period. The distribution of the number of
firms exhibits over-dispersion and an excess number of zeros. The negative binomial regression
model is used with spatial fixed effects to account for unobserved heterogeneity due to location
and yearly fixed effects to account for period effects and heterogeneity over time.
We identified 748 firms—including both U.S. and foreign-owned organizations—that
were engaged in human biotherapeutics research and product development in the U.S. over the
study period. Firm data were collected primarily from Bioscan (1987 through 2004), a
comprehensive industry directory that provides information about the characteristics of
biotechnology firms as well as product research, strategic alliances, and management teams.
Firms in the human bio-therapeutics industry were identified based on a review of actual
products in research or production as indicated in BioScan and supplemented by an extensive
review of business and industry press publications.1 These data were used for all the tables and
maps included in this paper.
We tracked firms over time with specific attention to firm name changes and changes in
organization form. Human biotherapeutics is a turbulent industry with a large number of firm
failures, acquisitions, mergers, or cessation of activities in human biotherapeutics. In some
cases, acquired firms were left intact as separate operating subsidiaries of the acquiring
organizations, while in other cases their assets were absorbed into the activities of the acquiring
organization. In the first scenario, even if the name of the organization did not change, we coded
the exit of the original organization and the entry of a new organization as a subsidiary.
Similarly, in the case of mergers, even when the merged organizations continued under the name
of one of the merging organizations, we coded the exits of the merging organizations and the
entry of a new organization. To avoid double counting, we coded the last year of existence for
the acquired or merging organizations as the one in which the acquisition or merger occurred and
the first year of existence for the new firm as the year following the acquisition or merger event.
We cannot claim that our sample is exhaustive of all human biotherapeutics firms
operating in the U.S. over the study period; in particular, we suspect that very small firms,
1
We are indebted to Martin Doyle, an MBA graduate of the McDonough School of Business at Georgetown
University whose earlier Master’s degree in microbiology and extensive industry experience aided in the
classification of firms engaged in human biotherapeutics research and product development.
11
especially those that existed during the earliest and latest periods of the study, may not have been
identified. Nonetheless, our extensive inquiries into the histories of biotherapeutics
entrepreneurs and firms, as well as the evolution of the biotechnology industry as a whole,
revealed only a few that were never listed in BioScan. These were included in our database
when we found them. Thus, we believe that our coverage of the industry is comprehensive. Of
particular interest to questions explored in this paper, we have no reason to believe our coverage
is biased toward biotherapeutics activity in particular regions.
Additional data were collected from published sources such as Security and Exchange
Commission (SEC) 10K reports, databases of newspapers and other published sources, extensive
web-based searches, and personal interviews. Data were verified and triangulated from these
multiple sources.
Geographic Coding
Geographic origins and destinations of entrepreneurs and organizations were coded using
the 2003 U.S. Office of Management and Budget Bulletin No. 03-04 which lists metropolitan
statistical areas (MSAs) and combined MSAs, among other groupings, based on information
obtained in the 2000 census. As described in the bulletin, MSAs were designated based on their
having “at least one urbanized area of 50,000 or more population, plus adjacent territory that has
a high degree of social and economic integration with the core as measured by commuting ties.”
A total of 362 MSAs (not including Puerto Rico) were listed, encompassing 1090 counties,
approximately 35 percent, and about 83 percent of the U.S. population. Combined MSAs, e.g.,
the Washington D.C. and Baltimore MSAs, were defined as two or more geographically adjacent
MSAs with employment interchanges (i.e., commuting ties) of at least 25 percent (or, in a few
cases with employment interchanges of only 15 percent, but considered highly integrated based
on the opinions of Congressional delegations). The bulletin lists 113 Combined MSAs in the
U.S., encompassing 310 MSAs. We classified the MSA locations of firms in the study, as well
as the organizational origins of their entrepreneurs and antecedent firms using zip code data that
allowed us to identify counties and thus the relevant MSAs.
The use of MSAs and combined MSAs, rather than city or state geographic boundaries, is
attractive for identifying regions of activity in that MSAs are designated on the basis of regional
economic integration independent of political boundaries. We use the 2003 Office of
12
Management and Budget geographic classification scheme which is based on the 2000 Census of
Population. The study period is long, and the distribution of population across regions as well as
the economic integration within regions has not been static. The decision to classify location
raises the possibility that a firm may be incorrectly attributed to a location at the time of an entry
or exit event. For example, under the 2003 classifications, a firm located in Worcester County,
Massachusetts would be part of the Worcester MSA. Previously, Worcester was considered of
the Boston MSA. Thus, without a consistent classification, a firm could be differentially
classified as located in Boston and then Worcester, without ever changing its physical address.
Our procedure classifies the organization as located in the Worcester MSA over the entire
period.
To explore the extent of this difficulty, we tracked the classifications of counties (which
are the core units of MSAs) from 1976 through 2002. The classification schemes changed most
dramatically every ten years following the decennial census; new MSAs were designated and old
MSAs were reorganized in terms of their county components, sometimes combining counties
that were previously classified as separate MSAs and sometimes separating counties that were
previously classified under another MSA. With the exception of the addition of wholly new
MSAs such as Corvallis, Oregon in the 2003 classification, the MSA classifications over the
study period are remarkably consistent. Approximately 85 percent of the MSAs listed in the
2003 bulletin were also listed in the 1976 bulletin; changes in their county compositions reflected
mainly the addition of one or more adjacent counties.2 Thus, assuming that the 2003
classifications represent trends in population growth and regional economic integration that were
developing long before the official classifications, we feel comfortable using the single
classification scheme to designate regions over the entire study period.
Organizational Legacy
To track the organizational origins of entrepreneurs and firms in the industry, we
collected data on the types of organizations where founders were previously employed. Six
2
Though the OMB bulletins reporting MSA classifications describe numerical criteria, based on commuting ties, for
designating the inclusion or exclusion of counties in particular MSAs, the system is not scientific and we found
substantial evidence of human judgment in the decennial classification systems. For example, even in years in
between the decennial census reports, certain counties, usually on the geographic edges of metropolitan regions
might be included or excluded from one year to the next. Moreover, by report, the classifications were also
influenced by the opinions of Congressional delegations.
13
categories of organizational origins were used: (1) universities or research institutes, (2) existing
human biotherapeutics firms, (3) traditional pharmaceutical firms, (4) venture capital firms, (5)
hybrids of the above categories in cases where two or more entrepreneurs emerged from different
sources, and (6) other types of organizations. Classification is based on the founder’s
immediately prior place of employment and is restricted to entities in the same MSA. Sixtyeight percent of firms were founded by entrepreneurs in the regions in which they already
previously employed.
The legacy variables are calculated as the number of firms whose founders had a specific
background divided by the total number of firms in the cluster, or local density. Thus, the
science legacy variable is the proportion of academics who started firms in the cluster that were
still in existence in the prior year and so on. The omitted category is the proportion of firms
founded by entrepreneurs with a variety of experiences in other sectors of biotechnology or
completely different industries.
Control Variables
All variables in the analysis use the same geo-coding. We control for the total dollar
amount of research awards from the National Institutes of Health (NIH). The average grant lasts
approximately three years and we use a simple arithmetic average of the total dollar amount of
NIH awards received in a region, lagged by one year. We also control for the dollar amount of
venture capital awards made to firms in the region, also lagged one year. The number of human
biotherapeutics firms that existed nationally, National Density, and National Density Squared, is
also included, following the organizational ecology literature. Population is included as a control
for region size. Table 4 provides descriptive statistics.
[Table 4 here]
We estimate the model separately for two time periods: 1976 – 1984 and 1985 – 2002.
Theoretically we believe that the industry started on a higher growth trajectory after 1984 due to
the convergence of factors such as several successful IPOs, a general increase in patenting of
14
university intellectual property and greater acceptance of the involvement of scientists in
commercial activity.
[Table 5 here]
Empirical Results
Table 5 presents the results for the early time period. Model 1 provides a baseline with
our control variables. Population size was negatively and statistically significantly associated
with cluster location. This was expected as the industry was located in smaller cities. The log of
three year average of total NIH expenditures was not statistically significant. The majority of
NIH funding is allocated to basic research and the association with commercial activity is not
direct. There are many locations that receive large amounts of NIH funding but have not
generated clusters in the life sciences. The coefficient on the log of venture capital financing in
the prior year is positive and statistically significant. The causal link is not specified as the
results may be either directly due to venture capital funding or it may be that more firms are
started in regions where there may be a possible expectation of VC funding. The coefficient on
National Density is positive and statistically significant as expected while the coefficient on the
squared term is only significant in the final model. Models 2 – 6 add in the organizational legacy
variables separately. Model 7 includes them simultaneously. The results are robust across the 7
specifications. The relative ranking of the different influences of proportions of organizational
founders on cluster growth remain constant.
In the early time period, the largest impact on cluster growth is due to having a greater
percentage of founders with experience in human biotherapeutics. Larger cluster size was
associated with founders who had prior experience working in the industry. Certainly, the
importance of experience in the human therapeutics industry is part of the story of the San Diego
cluster as many employees left Hybritech after it was sold to Eli Lilly in 1986 to found new
firms. The result before 1985 indicates that this was a general pattern earlier. As expected, a
greater percentage of founders with specific prior experience working in the industry are
associated with the development of clusters.
Founders with experience in the pharmaceutical industry were next in terms of their
impact on the development of clusters. The percentage of human therapeutics firms founded by
15
venture capitalist, also had a positive effect on cluster development. A lower impact is
associated with academic scientists. Early studies of the biotech industry emphasize the role of
academic scientists as entrepreneurs and demonstrate an unambiguous local orientation in
starting companies. Kenny (1986), documenting the changes that accompanied the early origins
of the industry, describes biotech as a university-industrial complex with a prominent role for
academic scientists who started companies to commercialize their discoveries. Many academic
scientists start companies without resigning their academic jobs and locating their companies
near their universities was a natural outcome. Zucker, Darby and Brewer (1998), extend this
finding and suggest that the location of biotech firms is primarily due to the presence of star
scientists, who had published a large number of genetic sequence discoveries. These discoveries
created the intellectual capital for new companies and the stars’ prominent academic status
created scientific credibility that attracted investors.
The proportion of founding teams with diverse experiences – the hybrid had a negative
effect on cluster development in the first time period. This type of start-up team, while
generally regarded as beneficial, was not associated with cluster growth in the early time period.
This suggests that it might be fruitful to probe further on different types of experiences for the
hybrid founding teams.
[Table 6 here]
Table 6 presents results for the later time period from 1985 to 2002. Model 1 provides
the baseline model with the control variables. Following the previous table, the legacy variables
are entered separately in models 2 -6 and all together in model 7. The results are robust across
the specifications.
The organizational legacy of founders had a differential effect on the growth of clusters
in the second time period when compared to the earlier time period. A larger proportion of firms
founded by individuals from the pharmaceutical industry did not translate into a larger cluster.
This supports the assertion that experience in the pharmaceutical industry does not translate into
creating an environment conducive to cluster growth. The ways of conducting business may be
too different. It seems likely, based on Saxenian’s (1994) reasoning, that the established
pharmaceutical firms, having little experience in memory of entrepreneurial activity—most of
16
the large pharmaceutical firms are close to 100 years old—were not encouraging of any
inclinations on the part of their talented employees to leave the firms to pursue an entrepreneurial
enterprise. The results suggest that these large pharmaceutical firms also exerted a stultifying
influence on the pace of regional growth. Certainly, this may explain the lack of development of
the industry in New York City. We may speculate that the interconnectedness of firms in the
regions with a large number of local pharmaceutical founders may be lower.
The largest impact was associated with entrepreneurs who had experience in human biotherapeutics. This type of organizational legacy is expected to be associated with new activity in
the industry and conducive to sharing ideas and networking. A higher proportion of firms that
were founded by academic scientists had a negative and statistically significant effect on cluster
development. There may be several explanations for this result. The first is that many academic
start-ups remain small, employing students and post-docs. The lab family mentality may prevent
these employees from breaking away. Second, while the traditional norms of open science favor
the open dissemination of knowledge, academic scientists may not carry these norms over when
they engage in commercial activity. Most notable, there are several places that are dominated by
academic startups but which never progressed to second stage breakaways.
Venture capital was not statistically significant in the second time period. This may be
due to the entry of venture capitalists entering an industry where others had realized high returns.
The results suggest that VC funding is associated with cluster growth, however a larger
percentage of firms started by VCs are not. The relatively large and statistically significant
coefficient on the proportion of hybrid firms suggests that this model, which brings together
academic scientists with venture capitalists or others with business experience, had become a
superior method by the second time period. A larger proportion of firms with this type of
organizational legacy suggest that both commercial and academic interests were integrated in the
local networks.
The largest coefficient is again associated with a large proportion of organizational
founders with prior work in the human biotherapeutics industry. A large proportion of these
breakaway firms in a region suggest greater cluster growth.
Reflections on the Development of Industrial Clusters
17
Industry clusters develop within complex contexts of national and even international
influence. Although explanations for the location and growth of industry clusters have
emphasized local factors, including the presence of important resources that promote the
formation of new firms, few studies have examined the growth of industry clusters in the context
of multiple regions. Although it often seem obvious, in retrospect, that a cluster would have
developed in a particular region, and historical analysis may point to seemingly unique resources
that were available in the region, policy recommendations cannot assume this inevitability.
We find that while the human biotherapeutics firms developed in 75 metropolitan regions
in the U.S. the greatest spur to growth appears to be a tendency of entrepreneurs to leave local,
established biotherapeutics firms to found additional biotherapeutics firms – the breakaway
phenomenon that Jacob’s (1969) describes. Regions with a high proportion of these individuals
are in a beneficial position with regard to an organizational legacy that facilitates the sharing of
information and perhaps a common vision for the cluster. Many regions continued to generate
new biotherapeutics by entrepreneurs out of local universities and research institutes at a
relatively steady pace. Only those regions, however, that exhibited this secondary, or secondgeneration growth, from the biotherapeutics firms themselves grew to substantial sizes relative to
other clusters.
We can only speculate about the conditions and processes that led the entrepreneurs and
firms in some regions to produce the second-generation growth, but the findings are consistent
with many case histories of cluster development that emphasize regional cultures and patterns of
social interaction (Saxenian 1994; Murtha et al 2001; Storper and Venables 2002) as critical to
the rise of an industry cluster. Second-generation growth, which involves entrepreneurs leaving
established firms in a cluster to found competing new organizations, requires that the
entrepreneurs believe in their abilities to attract capital and especially human resources to
support their new organizations. It is difficult to conclude that such beliefs could develop unless
the leadership of the earlier organizations was supportive of new entrepreneurial efforts.
Industry clusters are not only a universal feature in the spatial arrangements of industries,
but also an essential component in the economic evolution of industries. Clusters provide not
only a near term source of economic wealth, but also a long term foundation for future economic
growth. Underlying the clustering phenomenon are mechanisms that facilitate the interchange
and flow of information between firms. The organizational legacy of founder in an industry may
18
provide training and experience that is useful in subsequent entrepreneurial efforts and also
influence the density of local networking possibilities.
19
REFERENCES
Arthur, W. Brian. 1990. ‘Silicon Valley’ Locational Clusters: When Do Increasing Returns
Imply Monopoly? Mathematical Social Sciences, 19: 235-51.
Audretsch, David B. and Stephan Paula E. 1996. Company-Scientist Locational Links: The Case
of biotechnology. American Economic Review, 86: 641-652.
Baum, Joel A.C., and Heather A. Haveman. 1997. Love Thy Neighbor? Differentiation and
Agglomeration in the Manhattan Hotel Industry, 1898-1990. Administrative Science
Quarterly, 42: 304-338.
BioScan, 1987-2004. Atlanta, GA: Thomson Group.
Biotechnology Industry Organization (BIO). 2004. Laboratories of Innovation: State Bioscience
Initiatives 2004, Washington, D.C. .
Chesborough, H. and Rosenbloom, R.S. 2002. The role of the business model in
capturing value from innovations: evidence from Xerox corporation’s technology
spin-off companies. Harvard business school, # 01-002.
Feldman, M.P., 1994. The Geography of Innovation. Kluwer Academic Publishers,
Boston.
Feldman, Maryann P., Alessandra Colaianni and Kang Liu. 2005. Commercializing Cohen
Boyer: 1980 – 1997. Working paper.
Galambos, Louis and Jeffrey L Sturchio. 1998. Pharmaceutical firms and the transition to
biotechnology: A study in strategic innovation. Business History Review 72: 250-278.
Gompers, Paul, Josh Lerner and David Scharfstein. 2003. Entrepreneurial Spawning: Public
Corporations and the Genesis of New Ventures, 1986-1999. NBER working paper.
Jacobs, Jane. 1969. The Economy of Cities. New York: Random House.
Kenney, Martin. 1986. Biotechnology: The University-Industrial Complex. New Haven: Yale
University Press.
Kenney, Martin and Urs Von Berg. 1999. Technology, Entrepreneurship, and Path
Dependence: Industrial Clustering in Silicon Valley and Route 128. Industrial and
Corporate Change, 8: 67-103
Klepper, Steven. 2002. The Capabilities of New Firms and the Evolution of the US Automobile
Industry. Industrial and Corporate Change, 11: 645-666.
20
Klepper, Steven and Sally Sleeper. 2005. Entry by Spinoffs. Management Science 51: 12911306.
Krugman, Paul. 1991. History versus Expectations. Quarterly Journal of Economic: 651-667.
Lerner, Joshua. 1994. The Importance of Patent Scope: An Empirical Analysis. RAND Journal
of Economics, 25: 319-333.
Marshall, Alfred. 1920. Principles of Economics. New York: MacMillan.
Murtha, Thomas P., Stephanie Ann Lenway and Jeffrey A. Hart. 2001. Managing New Industry
Creation. Stanford: Stanford University Press.
Office of Management and Budget, Bulletin No. 03-04. 2003.
Owen-Smith, Jason and Walter W. Powell. 2003. The Expanding Role of University Patenting in
the Life Sciences: Assessing the Importance of Experience and Connectivity. Research
Policy, 32: 1695-1711.
Prevezer, Martha. 1995. The Dynamics of Industrial Clustering in Biotechnology. Small
Business Economics. 9: 255-271.
Rauch, James E. 1993. Does History Matter When It Matters Little? The Case of City-Industry
Location. The Quarterly Journal of Economics, 108: 843-867.
Romanelli, Elaine. 1989. Organization Birth and Population Variety: A Community Perspective
on Origins. In B. Staw and L.L. Cummings (eds.), Research in Organizational Behavior,
11: 211-246. Greenwich, CT: JAI Press.
Rosenthal, Stuart S. and William C. Strange. 2003. Geography, Industrial Organization, and
Agglomeration. The Review of Economics and Statistics, 85: 377-393.
Saxenian, AnnaLee. 1994. Regional Advantage: Culture and Competition in Silicon Valley and
Route 128. Cambridge MA: Harvard University Press.
Sorenson, Olav, and Pino G. Audia. 2000. The Social Structure of Entrepreneurial Activity:
Footwear Production in the United States, 1940-1989. American Journal of Sociology,
106: 424-462.
Stinchcombe, Arthur L. 1965. Organization-Creating Organizations. Trans-Actions,
2:34-35.
Storper, Michael, and Susan Christopherson. 1987. Flexible Specialization and Regional
Agglomerations: The Case of the U.S. Motion Picture Industry. Annals of the
Association of American Geographers, 77: 104-117.
21
Storper, Michael, and Anthony J. Venables. 2002. Buzz: The Economic Force of the City.
Paper presented at the DRUID Summer Conference on “Industrial Dynamics of the New
and Old Economy—Who is Embracing Whom?” Copenhagen.
Stuart, Toby E., and Olav Sorenson. 2003. Liquidity Events and the Geographic Distribution of
Entrepreneurial Activity. Administrative Science Quarterly, 48: 175-201.
Wade, Michael J. 1980. An Experimental Study of Kin Selection. Evolution, 34: 844-855.
Zucker, Lynne G., Michael R. Darby and Marilynn B. Brewer. 1998. Intellectual Human
Capital and the Birth of U.S. Biotechnology Enterprises. American Economic Review,
88: 290-306.
22
Figure 1: Number of U.S. Human Biotherapeutics Firms
500Authors’ computation from BioScan.
Source:
400
300
natdensi
200
100
0
1970
1980
1990
year
2000
2010
23
Figure 2. Number of Human Biotherapeutics Firms by Region,
Regions with at least one firm prior to 1980
70
San Francisco
Boston
60
Local Density
50
San Diego
40
Durham
30
Philadelphia
20
Washington DC
10
Albuquerque
Dallas
0
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
Year
24
25
Table 1: Regional Distribution of Human Bio-therapeutics Industry, 1985 and 2002
Count
Albuquerque, Nm (MSA)
Atlanta-Sandy Springs-Marietta, Ga (MSA)
Austin-Round Rock, Tx (MSA)
Baltimore-Towson, Md (MSA)
Birmingham-Hoover, Al (MSA)
Boston-Cambridge-Quincy, Ma-Nh (MSA)
Boulder, Co (MSA)
Chicago-Naperville-Joliet, Il-In-Wi (MSA)
Cleveland-Elyria-Mentor, Oh (MSA)
Dallas-Fort Worth-Arlington, Tx (MSA)
Durham, Nc (MSA)
Houston-Baytown-Sugar Land, Tx (MSA)
Los Angeles-Long Beach-Santa Ana, Ca (MSA)
Madison, Wi (MSA)
Miami-Fort Lauderdale-Miami Beach, Fl (MSA)
Minneapolis-St. Paul-Bloomington, Mn-Wi (MSA)
New Haven-Milford, Ct (MSA)
New York-Northern New Jersey-Long Island, Ny-Nj-Pa (MSA)
Philadelphia-Camden-Wilmington, Pa-Nj-De-Md (MSA)
Salt Lake City, Ut (MSA)
San Diego-Carlsbad-San Marcos, Ca (MSA)
San Francisco-Oakland-Fremont, Ca (MSA)
San Jose-Sunnyvale-Santa Clara, Ca (MSA)
Seattle-Tacoma-Bellevue, Wa (MSA)
Trenton-Ewing, Nj (MSA)
Washington-Arlington-Alexandria, Dc-Va-Md-Wv (MSA)
2
1
0
0
1
18
2
6
1
1
2
5
9
0
0
1
1
35
5
2
9
20
8
5
4
9
167
1985
% of Industry
1.20
0.60
0.00
0.00
0.60
10.78
1.20
3.59
0.60
0.60
1.20
2.99
5.39
0.00
0.00
0.60
0.60
20.96
2.99
1.20
5.39
11.98
4.79
2.99
2.40
5.39
100.00
LQ
3.034
0.617
0.000
0.000
0.000
6.492
15.446
1.228
0.775
0.459
10.563
0.907
1.436
0.000
0.000
0.000
2.110
2.963
1.248
2.252
5.440
8.073
9.116
3.659
21.039
3.107
Count
0
3
3
2
2
53
5
8
3
2
20
12
15
4
5
3
8
47
19
6
54
57
21
17
5
14
435
2002
% of Industry
0.00
0.69
0.69
0.46
0.46
12.18
1.15
1.84
0.69
0.46
4.60
2.76
3.45
0.92
1.15
0.69
1.84
10.80
4.37
1.38
12.41
13.10
4.83
3.91
1.15
3.22
100.00
LQ
0.000
0.583
1.481
0.732
1.185
7.659
10.654
0.479
0.872
0.240
30.774
1.580
0.654
4.956
0.621
0.628
5.297
1.632
2.303
4.495
12.154
9.206
7.174
3.886
10.654
2.071
27
Table 3.
U.S. Biotherapeutics Firms by Types of Entry and Organizational Origins
All Firms
Number
Startups
from university or research institute
from an existing biotherapeutics organization
from pharmaceutical organization
hybrid, from multiple source types
from venture capital firm
from other type of firm
no information
Percent
455
205
70
24
50
64
21
21
66
30
10
03
07
09
03
03
Spin-off from Exiting Organization
55
08
Direct Entries by Established Firms
62
09
Subsidiaries
75
11
Mergers
30
04
Joint Ventures
11
02
TOTAL
688
Relocated Firms
60
Table 4: Descriptive Statistics, Regions before 1985
1
2
3
4
5
6
7
8
9
10
Pharmaceutical Legacy
Human Biotherapeutics Legacy
Academic Science Legacy
Venture Capital Legacy
Hybrid Legacy
Log of Population
Log of Total NIH Expenditure
Log of Venture Capital Investment
Local Density
National Density
Mean
Std.Dev.
Min
Max
1
2
3
4
5
6
7
8
9
10
0.04
0.00
0.06
0.01
0.01
13.54
14.34
0.44
0.89
51.22
0.16
0.00
0.22
0.08
0.10
1.22
5.19
1.24
2.74
45.81
0.00
0.00
0.00
0.00
0.00
10.53
0.00
0.00
0.00
1.00
1.00
0.07
1.00
1.00
1.00
16.64
19.73
5.52
30.00
129.00
1.00
0.01
0.10
0.03
0.00
0.12
0.07
0.21
0.41
0.21
1.00
0.06
0.06
-0.01
0.05
0.04
0.16
0.21
0.07
1.00
0.12
0.00
0.12
0.14
0.39
0.36
0.25
1.00
0.01
0.10
0.08
0.17
0.20
0.17
1.00
0.02
-0.21
0.20
0.07
0.05
1.00
0.54
0.31
0.39
0.03
1.00
0.16
0.20
0.02
1.00
0.56
0.32
1.00
0.24
1.00
Descriptive Statistics for Regions after 1984
1
2
3
4
5
6
7
8
9
10
Pharmaceutical Legacy
Human Biotherapeutics Legacy
Academic Science Legacy
Venture Capital Legacy
Hybrid Legacy
Log of Population
Log of Total NIH Expenditure
Log of Venture Capital Investment
Local Density
National Density
Mean
Std.Dev.
Min
Max
1
2
3
4
5
6
7
8
9
10
0.12
0.05
0.26
0.06
0.03
13.70
15.43
1.53
4.71
334.75
0.28
0.16
0.38
0.19
0.14
1.21
5.12
2.09
10.74
104.73
0.00
0.00
0.00
0.00
0.00
10.56
0.00
0.00
0.00
144.00
1.00
1.00
1.00
1.00
1.00
16.72
20.85
6.26
61.00
452.00
1.00
-0.04
-0.18
-0.09
-0.02
0.09
-0.04
0.10
0.09
0.04
1.00
-0.09
-0.03
0.01
0.26
0.14
0.12
0.27
0.18
1.00
-0.03
-0.09
-0.01
0.25
0.12
0.08
0.13
1.00
-0.04
0.21
0.13
0.11
0.09
0.03
1.00
0.02
-0.25
0.14
0.08
0.04
1.00
0.55
0.47
0.45
0.05
1.00
0.32
0.29
0.07
1.00
0.55
0.09
1.00
0.12
1.00
29
Table 5: Negative Binomial Model on Count of Firms per Region before 1985
Model 1
Pharmaceutical Legacy
Model 2
2.946**
(0.493)
Human Bio-Therapeutics Legacy
Model 3
Model 4
Model 5
90.801**
(20.850)
Academic Science Legacy
1.238**
(0.337)
Venture Capital Legacy
2.094**
(0.906)
Hybrid Legacy
-7.525**
(1.876)
0.101
Log of Total NIH Expenditure
(0.067)
0.674**
Log of Venture Capital Investment
(0.070)
0.019**
National Density
(0.004)
-0.073e-03
National Density Square
(0.042e-03)
100.372
Constant
(25.329)
675
N
* Significant at 5%; ** significant at 1%
Log of Population
Model 6
-5.431**
(1.651)
0.076
(0.092)
0.616**
(0.061)
0.0118**
(0.003)
-0.031e-03
(0.036e-03)
72.249
(22.217)
675
-6.720**
(1.651)
0.0926
(0.093)
0.638**
(0.061)
0.0153**
(0.003)
-0.045e-03
(0.036e-03)
89.419
(22.234)
675
-7.737**
(1.666)
0.094
(0.094)
0.609**
(0.063)
0.0148**
(0.003)
-0.040e-03
(0.037e-03)
103.1431
(22.433)
675
-7.370**
(1.880)
0.120
(0.106)
0.652**
(0.070)
0.0194**
(0.004)
-0.082e-03*
(0.042e-03)
97.921
(25.303)
675
-2.680**
(0.864)
-6.952**
(1.883)
0.123
(0.106)
0.674**
(0.069)
0.020**
(0.004)
-0.081e-03
(0.042e-03)
92.254
(25.340)
675
Model 7
3.158**
(0.537)
85.676**
(22.402)
1.699**
(0.361)
1.907*
(0.856)
-2.395**
(0.820)
-5.496**
(1.817)
0.074
(0.064)
0.515**
(0.069)
0.015**
(0.004)
-0.085e-03*
(0.040e-03)
73.345
(24.519)
675
Table 6: Negative Binomial Model on Count of Firms per Region after 1984
Model1
Pharmaceutical Legacy
Model 2
-0.778
(0.629)
Human Bio-Therapeutics Legacy
Model 3
Model 4
Model 5
6.974**
(0.966)
Academic Science Legacy
-2.014**
(0.446)
Venture Capital Legacy
-0.922
(0.909)
Hybrid Legacy
Log of Population
Log of Total NIH Expenditure
Log of Venture Capital Investment
National Density
National Density Square
Constant
N
* Significant at 5%; ** significant at 1%
Model 6
2.261
(2.168)
-0.049
(0.060)
0.154*
(0.075)
0.016**
(0.004)
-0.091e-04
(0.066e-04)
-30.258
(29.456)
1200
1.175
(2.154)
0.002
(0.058)
0.194*
(0.078)
0.014**
(0.004)
-0.027e-04
(0.065e-04)
-16.269
(29.273)
1275
-0.839
(2.126)
-0.005
(0.057)
0.221*
(0.076)
0.014**
(0.004)
-0.047e-04
(0.064e-04)
11.036
(28.928)
1275
1.444
(2.135)
0.004
(0.057)
0.228*
(0.078)
0.016**
(0.004)
-0.051e-04
(0.065e-04)
-20.053
(29.045)
1275
2.211
(2.170)
-0.008
(0.055)
0.157*
(0.076)
0.016**
(0.004)
-0.095e-04
(0.066e-04)
-30.144
(29.491)
1275
4.756**
(1.454)
2.068
(2.159)
0.017
(0.055)
0.139
(0.075)
0.015**
(0.004)
-0.075e-04
(0.066e-04)
-28.422
(29.349)
1275
Model 7
-0.713
(0.618)
6.501**
(0.987)
-1.641**
(0.435)
-0.028
(0.886)
3.827**
(1.414)
0.397
(2.120)
-0.029
(0.059)
0.197**
(0.074)
0.017**
(0.004)
-0.099e-04
(0.064e-04)
-5.035
(28.799)
1275
31