1. Jason Clark
San Francisco State University
Page 1 of 11
What Causes Biotech Firms To Cluster?
The biological technology industry in the US is a high growth industry due to new entrants
entering the market and existing firms expanding their operations. This expansion is due to many factors
including agricultural usages and pharmaceutical development. When we see growth in any industry,
firms are presented with the question of where to locate. A firm’s decision can not only affect their level
of output but may also have an effect on local labor markets, universities, governments and so on.
In recent years we have observed an increase in jobs in the US move from low to high skill labor
inputs; a trend that seems to be focused in specific industries. Low skill manufacturing jobs and similar
companies are moving overseas in order to realize the low cost of labor inputs, while the US economy
has moved to a higher concentration of high skill industries in order to stay competitive in the global
economy. As a result local governments are looking to replace the outflow of low skill jobs with new
high skill jobs. This is a tricky task, especially when the market for locating high skill industries is so
competitive across regions and local budgets are not endless.
The interesting thing with a biotech firm’s decision on location is that their supply chain is
somewhat abstract. Most of their input factors are things like human capital, intellectual property, and
financial capital (Stuart and Sorenson, 2003). These input factors are very different from the input
factors that drove urban development during the industrial revolution. Firm locations during that period
depended on proximity to raw natural resources, transportation, and high amounts of cheap raw labor.
(Glaeser, Shapiro, 2003).
So why is it that we see biotech firms clustering into specific regions and not others? Well, some
recent literature attempts to answer this question. In most of these papers they focus on agglomeration

2. Jason Clark
San Francisco State University
Page 2 of 11
economies where they look for positive returns to scale at the regional level (Marshall, 1920; Arthur,
1990; Krugman, 1991). One effect would be the spill-over effect, where technical knowledge in one
industry may have a positive effect on another industry. For example, biotech firms may want to locate
near venture capital firms to take advantage of their specialized skills in business development
(Kolympiris, Kalaitzandonakes, Miller, 2011). A second effect might be the available quantity of a highly
talented workforce that is also highly productive (Beckstead, Brown, Gellatly, 2008). Finally, bio tech
firms need access to specialized lab equipment and space in which to conduct their trials. Universities
often lease space in labs to biotech start-ups, which in turn reduces the cost of production for biotech
firms. One example of this type of collaboration can be seen in incubators such as QB3 (Mission Bay, San
Francisco, CA (UCSF)).
My theoretical approach in the explanation of biotech’s location choice will be based off input
factors from the production function and local externalities. The biotech industry is a high risk / high
reward industry which commercial banks tend not to lend to because of the lack of collateral. Without
traditional sources of investment, biotech firms tend to seek venture capital. I will use the VCs location
relative to the firm to determine the capital input factor in the production function for each given area.
Biotech firms rely heavily on the amount of human capital available in their location to produce
their products. This human capital comes from a local labor force that possesses skills in biology,
chemistry, mathematics and other similar fields of study. Without access to a highly skilled labor force it
would be extremely difficult for these types of firms to produce goods.

3. Jason Clark
San Francisco State University
Page 3 of 11
My population regression model takes the form of:
)()()()()( 654321 NSNVCNEAreaPLaborCNBF  
My predictions for this model are that the total amount spent on employee wages should
decrease the number of biotech firms in the area because an increase in wages leads to an increase in
production costs. The patents issued within a given MSA should increase the number of biotech firms in
the area. With an increase in medical patents issued I would expect to see a positive knowledge spillover
effect for similar biotech firms. The number of employees should have a positive effect on number of
biotech firms in an area because if an area has a large number of workers that possess specialized skills
in the biotech field I would expect to see more start-ups enter the market due to the decrease costs
associated in poaching skilled workers from similar firms. The number of venture capital firms in a given
area should have a positive effect on the number of biotech firms in the area. When we increase
competitive investment for venture capital firms they are willing to take on more risky investments in
order to realize greater profits over their competitors thus making it easier for risky industries like
biotech’s to secure the investment they need. The number of medical schools within a given area should
have a positive effect on the number of biotech firms. With an increase in the number of universities I
would expect to see an increase in available lab space for biotech research. With state budgets
decreasing the funding for universities is also decreasing therefore universities must look for alternate
sources of revenue in order to keep tuition costs from increasing. Please note that I haven’t calculated
the elasticity of demand for medical universities and that I’m assuming that as costs to students raise
enrolment will decrease. Universities have large fixed operating costs so it’s important for universities to
offset these costs.

4. Jason Clark
San Francisco State University
Page 4 of 11
Data Description:
Parameters used in this model are as follows:
Dependent Variable
Number Of Biotech Firms
(NBF)
Total number of Bio tech
firms within each MSA
Explanatory Variables
Labor costs ($1K)
(LaborC)
Salary paid to employees in
US$ in 2007
Patents Issued
(AreaP)
Total number of medical
patents issued within each
MSA
Number of Employees
(NE)
Total number of employees
that reported to have
worked full time for bio tech
firms within the given year
Number of Venture
Capital firms
(NVC)
Total number of venture
capital firms within each
MSA
Number of medical
schools
(NS)
Total number of schools that
offer medical degrees within
each MSA

5. Jason Clark
San Francisco State University
Page 5 of 11
Data Sources:
 NBF = direct biotech firms (2007 county business patterns & economic census)
 LaborC = Total amount of US$ spent on employee wages in the bio tech industry by MSA
(2007 county business patterns & economic census)
 AreaP = number of medical patents issued within each MSA (U.S. PATENT AND
TRADEMARK OFFICE Electronic Information Products Division Patent Technology
Monitoring Team (PTMT) Technology Profile Report Patent Examining Tech Center
Groups 1630-1660, Biotechnology All Classified Patents (OR/XR))
 NE = Total number of full time employees within the given year that reported working
for bio tech firms, separated by MSA codes (2007 county business patterns & economic
census)
 NVC = amount of VC firms in the area (2007 county business patterns & economic
census)
 NS = Total number of medical schools as reported by the National Center for
Educational Statistics (NCES)

6. Jason Clark
San Francisco State University
Page 6 of 11
In this paper I define biotech firms from the following list:
NAICS code 2007 NAICS Definition Sector
325411 Medicinal and Botanical Mfg Biopharmaceutical
325412 Pharmaceutical Preparation Mfg Biopharmaceutical
325413 In-Vitro Diagnostic Substance Mfg Biopharmaceutical
325414 Biological Product (exc. Diagnostic) Mfg Biopharmaceutical
334510
Electromedical & Electrotherapeutic
Apparatus Mfg
Medical Devices, Instruments and
Diagnostics
334516 Analytical Laboratory Instrument Mfg
Medical Devices, Instruments and
Diagnostics
334517 Irradiation Apparatus Mfg
Medical Devices, Instruments and
Diagnostics
339112 Surgical and Medical Instrument Mfg
Medical Devices, Instruments and
Diagnostics
339113 Surgical Appliance and Supplies Mfg
Medical Devices, Instruments and
Diagnostics
339114 Dental Equipment and Supplies Mfg
Medical Devices, Instruments and
Diagnostics
339115 Ophthalmic Goods Mfg
Medical Devices, Instruments and
Diagnostics
339116 Dental Laboratories
Medical Devices, Instruments and
Diagnostics
541380* Testing Laboratories R&D and Testing Laboratories
541711 R&D in Biotechnology R&D and Testing Laboratories
541712*
R&D in the Physical, Engineering, and Life
Sciences (exc. Biotechnology) R&D and Testing Laboratories
*only a portion taken into account

7. Jason Clark
San Francisco State University
Page 7 of 11
In this paper I define venture capital firms by the 2007 NAICS 6-digit code: 523910
Index entries that bring you to this industry:
o Individuals investing in financial contracts on own account
o Investment clubs
o Mineral royalties or leases dealing (i.e., acting as a principal in dealing royalties or leases to
investors)
o Oil royalty dealing (i.e., acting as a principal in dealing royalties to investors)
o Tax liens dealing (i.e., acting as a principal in dealing tax liens to investors)
o Venture capital companies
o Viatical settlement companies
Summary statistics:
Quantitative
Variables Mean
Standard
Deviation
Number Of
Biotech Firms 48.655 120.1
Cost of labor for
biotech firms in
each area $41,959,700 $158,078,000
Patents issued
by area 2373.494 4421.984
Size of labor
force within
biotech firms by
area 1814.304 8327.938
Number of
Venture Capital
firms 18.842 51.889
Number of
medical schools
in the area 0.373 0.851

8. Jason Clark
San Francisco State University
Page 8 of 11
 F-test for the full set of regression parameter’s:
0: 54320  H
:1H at least one 0i
F-Stat= 488
310519004
54085063)1(



knRSS
kESS
2.697702)310,5,01(. testF
 At the 1% significance level I reject the null hypothesis when taken together at least one
variable has a significant impact on the number of biotech firms that locate within specific
MSA’s.
To test my hypothesis that biotech firms prefer to locate in areas that have high levels of
production inputs, I used OLS regression estimate the empirical model. I used the number of biotech
firms within each metropolitan statistical area (MSA) as the dependent variable. The sample included 15
six digit NAICS industries within each MSA that had at least one firm and at least one employee. The
resulting sample size was 316 observations. The independent variables are listed below. The adjusted R-
squared for this model was 0.8855 which indicates a fairly high goodness of fit overall. Approximately
89% of the variation from the mean in the number of biotech firms within an MSA can be explained by
the independent variables used in this model.

9. Jason Clark
San Francisco State University
Page 9 of 11
Regression Results:
Debendent Variable: Number of Biotech Firms
Independent
Variable
Coeffcient
Estimate Elasticity t-statistic p-value
Cost of labor
for biotech
firms in each
area ($1K) 0.0001608 138.67 6.15 0.000
Patents
issued by
area 0.0022503 0.110 4.18 0.000
Size of labor
force within
biotech firms
by area 0.0012188 0.045 3.52 0.000
Number of
Venture
Capital firms 1.216607 0.471 16.69 0.000
Number of
medical
schools in
the area 31.98006 0.245 7.50 0.000
316)(
8855.
2


nSample
RA
The positive sign on all the coefficient estimates for production input factors are positive and is
consistent with this papers research hypothesis that regional access to these factors of production
attract biotech firms to these locations. The most significant factor, with a t-statistic of 16.69 and a p-
value of approximately zero, is the proximity to venture capital firms. This makes sense intuitively
because biotech firms tend to be very risky yet they need access to capital in order to operate. Venture

10. Jason Clark
San Francisco State University
Page 10 of 11
capital firms reduce this risk when they fund firms close to their offices in order to observe the biotech
firm’s day-to-day operations.
The overall size of the effect that venture capital firm proximity has to biotech firm clustering
was measured in the elasticity results above. The 0.471 elasticity estimate indicates that a 10% increase
in the number of venture capital firms in the MSA should lead to an approximate 4.7% increase in the
number of biotech firms for that MSA. To put this in other terms, if 1 venture capital firm starts-up,
relocates, or expands operations into an MSA then it would be about a 5% increase above the mean for
that MSA. Increasing the number of venture capital firms by 5% would increase the number of biotech
firms in the MSA by approximately 2.4%. The average number of biotech firms in an MSA is 48.7, so a
2.4% increase would lead an additional 1.2 biotech firms.
The other empirical results suggest the same type of increase in inputs would lead to an
increase in the number biotech firms within that MSA.
Conclusion
In this paper, I document that biotech firms strongly tend to locate near their factors of
production. This finding suggests that increases in access to capital and skilled labor (knowledge
accumulation) inputs have a significant effect on where a biotech firm will locate their operations.
Another factor that might have a significant effect is the population size of the MSA. While
collecting my raw data I noticed that several areas (NY, San Diego, San Francisco…) had large numbers of
venture capital firms and biotech firms, these areas were well above the average. I also noticed the
areas surrounding these large clusters (suburban areas) had an exponentially decreasing number of
venture capital firms but the number of biotech firms only dropped off by a small amount.

11. Jason Clark
San Francisco State University
Page 11 of 11
Also, in this paper I’ve included 15 six-digit NAICS codes to define the biotech industry. Many of
these industries have a wide variety of input factors. For example, if this paper were to just focus on
R&D as opposed to medical goods manufacturing, my model would look very different. In R&D, they
tend to have a greater need for capital inputs relative to sear quantity of workers, while at the same
time their need for highly specialized workers increases.
It is my hope to continue this research to sort out these other factors.
References:
 Arthur, B., 1990. Positive feedback in the economy. Scientific American. February 92–99.
 Beckstead, Desmond, W. Mark Brown, and Guy Gellatly. 2008. "The Left Brain of North
American Cities: Scientists and Engineers and Urban Growth." International Regional Science
Review 31, no. 3: 304-338.
 Glaeser, Edward L., and Jesse M. Shapiro. 2003. "Urban Growth in the 1990s: Is City Living
Back?." Journal Of Regional Science 43, no. 1: 139-165.
 Kolympiris, Christos, Nicholas Kalaitzandonakes, and Douglas Miller. 2011. "Spatial
Collocation and Venture Capital in the US Biotechnology Industry." Research Policy 40, no. 9:
1188-1199.
 Krugman, P., 1991. Increasing returns and economic geography. Journal of Political Economy
99, 483–499.
 Marshall, A., 1920. Principles of Economics, 7th ed. Macmillan, London.
 Stuart, Toby, and Olav Sorenson. 2003. "The Geography of Opportunity: Spatial
Heterogeneity in Founding Rates and the Performance of Biotechnology Firms." Research
Policy 32, no. 2: 229-253.