Biotech Cluster Analysis 2006

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UNIVERSITY OF CINCINNATI

Date: April 18, 2006

I, Ke Chen ,

hereby submit this work as part of the requirements for the degree of:

Doctorate of Philosophy (Ph.D.)in:

Geography

It is entitled:

Biotechnology Cluster Analysis across Metropolitan Areas in the United

States

This work and its defense approved by:

Chair: Dr.Roger Selya

Dr.Howard Stafford

Dr.Robert South

Dr.Chris Kelton


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Biotechnology Cluster Analysis across

Metropolitan Areas in the UnitedStates

A dissertation submitted to the Division of Research and

Advanced Studies of the University of Cincinnati

In partial fulfillment of the requirements for the degree ofDoctorate of Philosophy (Ph.D.)

In the Department of GeographyOf the College of Arts and Sciences

2006

By

Ke ChenB.S., Nanjing University, 1998

M.S., Beijing Normal University, 2001

Committer members:

Dr. Roger Selya (chair)

Dr. Howard A Stafford

Dr. Robert South

Dr. Chris Kelton

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Abstract

This dissertation explores the spatial distribution of biotechnology industry activities

across fifty major metropolitan areas in the United States in 2002. The biotechnology

industry is composed of the pharmaceutical and medicine manufacturing industry and the

physical, engineering, and life science research and development industry. When

measured by the number and density of establishments, employment amount and density,

and specialization, metropolitan areas where the majority of biotechnology industrial

activities are clustered in the United States in 2002 include New York City, Boston, San

Diego, San Francisco, Washington DC, Chicago, Los Angeles, Philadelphia, and Raleigh.

Using Porters (1990, 1998) cluster theory as analytical framework, this study finds out

that local input factor conditions, represented by local life science research base, local

biotechnology innovation capability, local supply of life science PhDs, and local

entrepreneurship are very important for biotechnology industry development. A local

atmosphere of creation and networking among biotechnology professionals is important

as well. Also, biotechnology industry is generally located close to its buying, related, and

supportive industries at metropolitan level. However, there is no clear agglomeration

effect from biotechnology anchor establishments, and anchor impacts may depend on

individual anchors business culture.

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Acknowledgement

I am greatly indebted to Dr. Howard A Stafford for guiding me through the development

of this dissertation. He has consistently given valuable advice on this research and a

related research I did for the Hamilton County Regional Planning Commission. Professor

Stafford has a gift for posing questions and helping to structure research in a way that is

both rigorous and straightforward. I am also very grateful to professors Roger Selya,

Chris Kelton and Robert South for their instruction, comments, and kind help during my

dissertation writing. I thank all other geography faculty for their tutelage and moral

support. I am also grateful to my fellow students with whom I have established

friendships. Special thanks are given to Susan Jakubowski for her careful proofreading of

my dissertation. Finally, I thank my husband Lei Chen and my parents for their love and

support without which I would not have completed my four years of PhD study in the

United States.

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Table of contents

Abstract ................................................................................................................................ iAcknowledgement ............................................................................................................... iTable of contents.................................................................................................................. iList of tables....................................................................................................................... iiiList of figures...................................................................................................................... vChapter 1 Introduction ........................................................................................................ 1

Research problem statement ........................................................................................... 1Development of biotechnology industry......................................................................... 1Research objective .......................................................................................................... 4Significance of research.................................................................................................. 6Structure of the dissertation ............................................................................................ 9

Chapter 2 Spatial variation of biotechnology industry across fifty metropolitan areas inthe United States ............................................................................................................... 10

Biotechnology firms industry codes ............................................................................ 11

Survey by the United States Department of Commerce ........................................... 11Biotechnology industry definition in three national studies ..................................... 13Biotechnology firms in two business directories...................................................... 14

Biotechnology industry distribution across fifty metropolitan areas............................ 18Spatial distribution of biotechnology establishments ............................................... 20Spatial variation in density of biotechnology establishment .................................... 24Spatial distribution of biotechnology industry employment..................................... 26Spatial variation in density of biotechnology industry employment ........................ 31Spatial variation of biotechnology industry specialization....................................... 34

Overall comparison of biotechnology industrys spatial distribution and biotechnologyclusters .......................................................................................................................... 38

Chapter 3 Literature review .............................................................................................. 46Definition of industrial clusters .................................................................................... 46Cluster theory and cluster diamond .............................................................................. 49

Importance of local factor input conditions .............................................................. 50Local context............................................................................................................. 56Demand conditions ................................................................................................... 58Related and supporting industries............................................................................. 60Firm strategy, structure and rivalry........................................................................... 61

Summary....................................................................................................................... 64Chapter 4 Hypothesis, methodology, and data ................................................................. 65

Hypotheses.................................................................................................................... 65

Methodology ................................................................................................................. 66Data ............................................................................................................................... 71

Chapter 5 Results .............................................................................................................. 73Importance of local factor input conditions .................................................................. 73

Life science research base, biotechnology innovation capabilities, life science PhDs,and venture capital overall utilization....................................................................... 73Local usage of biotechnology venture capital .......................................................... 96Entrepreneurship....................................................................................................... 99

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Importance of local context - creativity and networking............................................ 102Spatial proximity between biotechnology industry and its value chain linked industries..................................................................................................................................... 106

Biotechnology industrys demand conditions, related and supportive industries... 107Spatial proximity between biotechnology industry with its value chain linked

industries................................................................................................................. 110Agglomeration effects from biotechnology anchors................................................... 120Pharmaceutical anchors .......................................................................................... 121Physical, engineering, and life science research and development anchors ........... 128

Chapter 6 Conclusion...................................................................................................... 134Major findings............................................................................................................. 134Implications for economic development..................................................................... 138Future research............................................................................................................ 140

References....................................................................................................................... 142Appendix......................................................................................................................... 148

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List of tables

Table 2-1 Biotechnology establishments NAICS codes by the US Department ofCommerce (2003) ............................................................................................................. 12Table 2-2 Biotechnology industry definition used in the Ernst & Young (2000) ............ 14Table 2-3 Biotechnology firms in D & B million dollar database.................................... 15Table 2-4 Comparison of biotechnology industry definition............................................ 17Table 2-5 Top ten biotechnology clusters by different measurements ............................. 40Table 2-6 Comparison of biotechnology cluster rankings................................................ 44Table 5-1 Correlation matrix among various variables .................................................... 75Table 5-2 Multiple regression (biotechnology employment as dependent variable, venturecapital excluded) ............................................................................................................... 88Table 5-3 Multiple regression results (pharmaceutical employment as dependent variable,venture capital excluded) .................................................................................................. 90Table 5-4 Multiple regression results (R & D employment as dependent variable, venturecapital excluded) ............................................................................................................... 91

Table 5-5 Multiple regression results (biotechnology employment as the dependentvariable) ............................................................................................................................ 93Table 5-6 Multiple regression results (pharmaceutical employment as the dependentvariable) ............................................................................................................................ 94Table 5-7 Multiple regression results (R&D employment as Y)...................................... 95Table 5-8 Comparing factor loadings from factor analysis using different methods ..... 108Table 5-9 Frequency distribution of the pharmaceutical establishments........................ 121Table 5-10 F test on the number of pharmaceutical establishments between metropolitanareas that are not anchored and those with one pharmaceutical anchor ......................... 122Table 5-11 T test on the number of pharmaceutical establishments between metropolitanareas that are not anchored and those with one pharmaceutical anchor ......................... 122

Table 5-12 F test on the number of pharmaceutical establishments between metropolitanareas that are not anchored and those with two pharmaceutical anchors ...................... 123Table 5-13 T test on the number of pharmaceutical establishments between metropolitanareas that are not anchored and those with two pharmaceutical anchors ....................... 123Table 5-14 Frequency distribution of R & D establishments ......................................... 124Table 5-15 F test on the number of R & D establishments between metropolitan areas thatare not anchored and those with one pharmaceutical anchor ......................................... 125Table 5-16 T test on the number of R & D establishments between metropolitan areasthat are not anchored and those with one pharmaceutical anchor .................................. 125Table 5-17 F test on the number of R & D establishments between metropolitan areas thatare not anchored and those with two pharmaceutical anchors........................................ 126

Table 5-18 T test on the number of R & D establishments between metropolitan areasthat are not anchored and those with two pharmaceutical anchors................................. 126Table 5-19 Description of the biotechnology R & D establishments ............................. 128Table 5-20 F test on the number of R & D establishments between metropolitan areas thatare not anchored and those with one R & D anchor ....................................................... 129Table 5-21 T test on the number of R & D establishments between metropolitan areasthat are not anchored and those with one R & D anchor ................................................ 129Table 5-22 Description of the pharmaceutical establishment groups............................. 130

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Table 5-23 F test on the number of pharmaceutical establishments between metropolitanareas that are not anchored and those with one R & D anchor ....................................... 131Table 5-24 T test on the number of pharmaceutical establishments between metropolitanareas that are not anchored and those with one R & D anchor ....................................... 131Table 5-25 F test on the number of pharmaceutical establishments between metropolitan

areas that are not anchored and those with two R & D anchors ..................................... 132Table 5-26 T test on the number of pharmaceutical establishments between metropolitanareas that are not anchored and those with two R & D anchors ..................................... 132

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List of figures

Figure 2-1 Histogram of biotechnology establishments ................................................... 21Figure 2-2 Histogram of pharmaceutical establishments.................................................. 21Figure 2-3 Histogram of research and development establishments ................................ 22Figure 2-4 Spatial Distribution of the Biotechnology Establishments in 2002 ................ 23Figure 2-5 Spatial Variation in Density of the Biotechnology Establishment.................. 25Figure 2-6 Spatial Variation in Density of Pharmaceutical Establishment ...................... 25Figure 2-7 Spatial Variation in Density of Physical, Engineering, and Life Science R & D........................................................................................................................................... 26Figure 2-8 Spatial Distribution of Biotechnology Industry Employment ........................ 27Figure 2-9 Histogram of biotechnology industry employment......................................... 28Figure 2-10 Histogram of biotechnology industry employment (log transformed) ......... 28Figure 2-11 Histogram of pharmaceutical industry employment ..................................... 29Figure 2-12 Histogram of pharmaceutical industry employments (log transformed) ...... 29Figure 2-13 Histogram of employments in the R & D industry ....................................... 30

Figure 2-14 Histogram of employments in the R & D industry (log transformed) .......... 30Figure 2-15 Spatial Variation in Density of the Biotechnology Industry Employment ... 32Figure 2-16 Spatial Variation in Density of Pharmaceutical Industry employment......... 33Figure 2-17 Spatial Variation in Density of R & D Industry Employment ...................... 33Figure 2-18 Spatial variation of biotechnology industrys specialization ........................ 35Figure 2-19 Spatial variation of the pharmaceutical industrys location quotients .......... 35Figure 2-20 Spatial variation of physical, engineering, and life science research anddevelopment industrys location quotients ....................................................................... 36Figure 2-21 Histogram of biotechnology industrys LQ .................................................. 36Figure 2-22 Histogram of pharmaceutical industrys LQ................................................. 37Figure 2-23 Histogram of R & D industrys LQ............................................................... 37

Figure 2-24 Biotechnology establishment, employment, and location quotient .............. 38Figure 2-25 US biotechnology clusters............................................................................. 43Figure 3-1 Porters cluster diamond illustration ............................................................... 50Figure 3-2 Marshalls triad of external economies of industrial localization................... 52Figure 5-1 Spatial variation of input factors in biotechnology industry........................... 74Figure 5-2 Histogram of NIH funding .............................................................................. 76Figure 5-3 Histogram of NIH funding (after log transform) ............................................ 77Figure 5-4 Scatter plot between NIH funding and biotechnology R & D industryemployment....................................................................................................................... 77Figure 5-5 Histogram of amount of patents filed in biotechnology.................................. 80Figure 5-6 Histogram of patents after log transformation ................................................ 80

Figure 5-7 Scatter plot between patents and pharmaceutical industry ............................. 81Figure 5-8 Scatter plot between patents and R & D industry .......................................... 81Figure 5-9 Histogram of life science PhDs....................................................................... 83Figure 5-10 Histogram of log transformed PhDs ............................................................. 84Figure 5-11 Scatter plot between PhDs and biotechnology R & D industry employment84Figure 5-12 Histogram of venture capital investment ...................................................... 86Figure 5-13 Histogram of venture capital investment after log transformation ............... 87Figure 5-14 Scatter plot between biotechnology employment and venture capital.......... 87

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Figure 5-15 Histogram of the local source of venture capital .......................................... 97Figure 5-16 Percentage of local venture capital investors................................................ 97Figure 5-17 Hybritech family tree .................................................................................. 101Figure 5-18 Histogram of creativity index...................................................................... 103Figure 5-19 Scatter plot between creativity index and biotechnology employment ...... 103

Figure 5-20 Input output relationships among value chain linked industries to thebiotechnology industry.................................................................................................... 109Figure 5-21 Histogram of the hospital employment ....................................................... 110Figure 5-22 Histogram of the hospital employment (log transformed).......................... 111Figure 5-23 Histogram of the ambulatory health care employment............................... 111Figure 5-24 Histogram of the ambulatory health care employment (log transformed).. 112Figure 5-25 Histogram of the veterinary services employment...................................... 112Figure 5-26 Histogram of the veterinary services employment (log transformed) ........ 113Figure 5-27 Scatter plot between hospital employment and biotechnology employment......................................................................................................................................... 113Figure 5-28 Scatter plot between ambulatory health care services employment and

biotechnology employment............................................................................................. 114Figure 5-29 Scatter plot between veterinary employment and biotechnology employment......................................................................................................................................... 114Figure 5-30 Histogram of medical equipment industrys employment .......................... 117Figure 5-31 Histogram of medical equipment industrys employment (after logtransform)........................................................................................................................ 118Figure 5-32 Scatter plot between medical equipment industry and biotechnology industryemployment..................................................................................................................... 118Figure 5-33 Histogram of educational service industrys employment.......................... 119Figure 5-34 Histogram of educational service industrys employment (log transformed)......................................................................................................................................... 120Figure 5-35 Scatter plot between educational service and biotechnology industry........ 120

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Chapter 1 Introduction

Research problem statement

The purpose of this dissertation is to explain the spatial variation of biotechnology

industry across major metropolitan areas in the United States.

Development of biotechnology industry

In the Merriam-Webster Dictionary, biotechnology is defined as biological

science when applied especially in genetic engineering and recombinant DNA

technology. Similarly, the National Library of Medicine, the National Agricultural

Library, and the Library of Congress state that biotechnology is that body of knowledge

which relates to the use of organisms, cells, or cell-derived constituents for the purpose of

developing products which are technically, scientifically and/or clinically useful. In

essence, biotechnology is the use of biological science to make products, such as drugs,

to improve human and animal health (Audretsch and Stephan, 1996; Biotechnology

Industry Organization, 2001; Brookings Institute; 2002; Paugh and LaFrance, 1997).

The branch of basic life science research that gives birth to biotechnology is

genetic research. It includes biochemical genetics, cytogenetics, ecological genetics,

extrachomosomal inheritance, gene mapping, gene sequencing, and gene

immunogenetics. Genetic research is then applied to genetic engineering, which includes

cell culture, conjugation, transformation, gene manipulation, gene transfer,

microinjection, and molecular cloning. Genetic engineering then leads to protein studies,

including protein crystallography, protein sequencing, and protein structure. Based upon

these studies, biotechnology products such as amino acids, antibiotics and anti-tumor

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agents, antibodies, carbohydrates, enzymes and cofactors, food additives, and vaccines

become possible. These biotechnology products are applied in the fields of medicine,

therapy, drug design and therapy, gene therapy, diagnostic services, agriculture, plant

breeding, aquaculture, animal breeding, aquaculture, pest control, soil organisms, waste

treatment, environmental engineering, energy, chemical, feedstocks, and fermentation

and process engineering1

Biotechnology as an industry, and especially the commercial activities that utilize

biotechnology to make profits, started with the establishment of the first biotechnology

company, Genetech, in 1977 in San Francisco. The biotechnology industry then

developed quickly, especially since the 1990s. According to the Biotechnology Industry

Organization, from 1992 to 2001, total number of biotechnology establishments increased

by 18 percent, total employment by 140 percent, revenues increased by 250 percent, and

capital by 300 percent2. This fast growth of the biotechnology industry is due to its

success in the drug market for human and animal health care, which is the major source

of profits for biotechnology companies (United State Department of Commerce, 2003).

However, the process of making drugs using biotechnology is risky and time and

capital consuming. There are three major steps to produce drugs through the use of

biotechnology: research, development, and delivery. The first stage includes gene

identification, target identification, and lead identification. This stage takes two to ten

years. In the second stage, development, there comes the preclinical testing on animals

and then clinical trials on patients. Clinical trials are very time consuming and costly.

They include three phases. Phase I studies are primarily concerned with assessing the

1 Source: http://www.nal.usda.gov/acq/cdbiotec.htm2 Data source : www.bio.org

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drug's safety and are done on a small number of healthy volunteers. This initial phase of

testing typically takes several months. About seventy percent of experimental drugs pass

this initial phase of testing. Once a drug has been shown to be safe, it must be tested for

efficacy. This second phase of testing may last from several months to two years, and

involves up to several hundred patients. Only about one third of experimental drugs

successfully complete both Phase I and Phase II studies. In a Phase III study, a drug is

tested in several hundred to several thousand patients. This large-scale testing provides

pharmaceutical companies and the Federal Drug Administration (FDA) with a more

thorough understanding of the drug's effectiveness, benefits, and the range of possible

adverse reactions. Phase III studies typically last several years. Seventy percent to ninety

percent of drugs that enter Phase III studies successfully complete this phase of testing 3.

Once a Phase III study is successfully completed, a pharmaceutical company can request

FDA approval for marketing the drug. Then there comes the last stage, the delivery of

biotechnology products. During this average 14-year trek to get a potential biotechnology

treatment to market, biotechnology companies can spend some $800 million to

commercialize a drug. [According to the Tufts Center for the Study of Drug

Development] (Thomas, 2005).

Despite this expensive, risky, and time-consuming process, biotechnology

industry is becoming a strong economic engine (Ernst & Young, 2004). The

biotechnology industry itself is a high paying industry. According to the Bureau of Labor

Statistics, in 2004, life science occupations have a mean hourly wage of $29, much

higher than average of $17.8 for all occupations. In addition, the biotechnology industry

can boost other economic sectors. In the Ernst & Youngs (2000) report, in 1999, one job

3 Source : http://www.answers.com/topic/clinical-trial-1

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in a biotechnology firm could generate 1.9 jobs in its backward linked firms, and $1

revenue from the biotechnology industry could generate $1.3 revenues in its backward

linked firms. Although biotechnology firms 2003 employment multiplier of 1.9 is lower

than the overall manufacturing sectors employment multiplier of 2.9 nationwide, it is

higher than health service sectors employment multiplier of 1.2 and business services

sectors multiplier of 1.54.

With the potential benefits that biotechnology firms can bring to the economy, it

is not surprising that many governments at various levels are eager to develop

biotechnology industries (Carlson, 2005; Bazley, 1999; Kemme, 2003). According to a

survey by the Biotechnology Industry Organization (2001), out of 77 local and 36 state

economic development agencies surveyed, 83 percent list biotechnology as one of their

top target industries.

Research objective

The objective of this dissertation is to understand the spatial variation of

biotechnology firms across major metropolitan areas in the United States. In other words,

why do some places have more biotechnology industry activities than other places?

Studies have shown that biotechnology firms are concentrated in certain places in

the United States. For example, according to the United State Department of Commerce

(2003), in 2001, California alone accounts for 25.6 percent of all biotechnology firms,

the other nine leading states are Massachusetts, Maryland, New Jersey, North Carolina,

Pennsylvania, Texas, Washington, New York and Wisconsin.

4 Data source: http://www.epinet.org/workingpapers/epi_wp_268.pdf

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To understand this spatial variation, scholars have recognized that it is useful to

consider biotechnology industry development in a cluster frame, where markets,

competitors, suppliers and related institutions are tightly linked in a geographic area

(Feldman, 2002; Munroe el at, 2002; Porter, 1990, 1998; Sainsbury, 1999; San Diego

INFO, 2001). However, while most current studies are aimed at identifying

biotechnology centers or clusters (Brookings Institute, 2002; Milken Institute, 2004), few

have answered the question of why some metropolitan areas have developed more

biotechnology industry activities than other places. Without knowing location factors that

are important for biotechnology industries, it is hard for economic geographers or

economic planners to give solid policy suggestions for governments to initiate

development plans to boost biotechnology industry. This dissertation explores the

importance of various location factors for successful biotechnology industry

development, and thus offers some insights for economic development policies.

Questions to be answered in this dissertation include the following.

First, where are biotechnology industry clusters in metropolitan areas in the

United States in 2002? Second, for biotechnology industry developments, how important

are local factor input conditions, such as a research base, labor, capital, and

entrepreneurship? Third, how important is a local high technology creativity atmosphere

and the volunteer connection among biotechnology professionals for biotechnology

industry development? Fourth, how important is the local demand market size in

influencing biotechnology industry size? Fifth, how important is it for the biotechnology

industry to be located close to its related industries? Lastly, do biotechnology anchor

firms exert agglomeration effects and increase competition among biotechnology firms

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of smaller sizes? These problems are analyzed by applying Porters (1998) cluster theory

(discussion below, chapter three).

Significance of research

There are three major contributions of this study. First, results from this study can

potentially help economic practitioners initiate their policies in regard to biotechnology

industry development. Second, it provides a possible way to test Porters (1990, 1998)

cluster theory. Third, by integrating appropriate quantitative techniques in econometric

analysis with traditional economic development measurements in the field of economic

geography, this dissertation improves methodology in industrial location analysis.

By measuring biotechnology industry across major metropolitan areas and

treating it as the dependent variable, this study is very helpful for governments to initiate

their economic development policies. The ability of giving policy suggestions from this

study is a big improvement upon many cluster studies where little explanation of

biotechnology industry clustering has been offered. Take the biopharmaceutical cluster in

Harvard Business Schools cluster mapping project for example. A biopharmaceutical

cluster is composed of three industries: biotechnology firms, health care product

manufacturers, and the container industry5. Which industries do the economic

development planning practitioners care: the biotechnology firms, health care product

manufacturers, or the container industry? Assuming that the biotechnology industry is

the focus for economic development planning practitioners, do we really need to have the

health care product manufacturers or container industry to develop biotechnology

industry? Or does the existence of the health care product manufacturers or container

5 Source: http://www.isc.hbs.edu

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industry help to improve the competitiveness of biotechnology firms? If places do have a

great amount of biotechnology firms without having other industries, are they at a

disadvantage? What kind of policy suggestions can economic practitioners get without

analyzing the interdependence of biotechnology industry upon other industries or upon

other locational factors at the local level? This dissertation then aims to understand the

importance of various factors that explain why some places have more biotechnology

industry than others. If local infrastructure, such as research base, labor, capital, and

entrepreneurship, is tested to be important for successful biotechnology industry

development, governments may consider nurturing such factors in the local area. If there

is no causal link between biotechnology product market size and biotechnology industry

size, then a smaller market may not be a disadvantage for biotechnology industry

development. If the results indicate that it is important for biotechnology firms to be

close to their suppliers or other related firms, local governments may consider

developing suppliers in order to attract biotechnology firms. Another concern is about

biotechnology anchor firms, since many local governments try to boost biotechnology

industry development by offering tax incentives to big firms, such as did Hamilton

County in Ohio (Hamilton County Regional Planning Commission, 2005). But does the

existence of biotechnology anchor firms increase the overall competition among

biotechnology firms in the locality? By answering the above questions, the results could

be used directly for local government economic development agencies and business

communities.

Second, this dissertation provides a feasible way to test arguments put forward by

Porter (1990, 1998), which state that when various business and institutions are clustered,

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the competitiveness of such a place is enhanced. Porter (1998), however, does not offer a

feasible way to test the validity of his clustering theory. Many scholars merely accept the

cluster concept but do not test it as a model (Bergman & Feser, 1999; Feser, 2000, 2002;

Feser & Bergman, 2000; Feser & Luger, 2002; Feser & Sweeney, 2000; Feser &

Sweeney; 2002). In this study, by testing whether there are strong spatial linkages

between biotechnology industry and other locational variables, I examine whether

Porters (1998) cluster model holds true for biotechnology industry. The methodology

used in this dissertation is also applicable to other industrial cluster analysis. In contrast

to Feser and Lugers (2002) argument that cluster analysis should be adapted to the local

policy needs, I argue that the cluster model is subject to scientific testing.

Third, this study integrates appropriate quantitative techniques with traditional

economic development measurements and thus improves methodology in industrial

location analysis. Using statistical cluster analysis, metropolitan areas are grouped into

different classes according to their biotechnology industrys size, structure, and

specialization. This is an improvement upon the traditional way of calculating a

composite index to compare regions with multiple measurements of industrial activities,

which blurs the difference among measurements. Another method used in this

dissertation is to do a principal component analysis on national input output table. While

input output analysis is used by regional economists, geographers are less familiar with it

as well as its related analyzing methods. This study then shows that principal component

analysis on the industry transaction matrix is a useful tool for geographers to understand

industrial linkages.

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Structure of the dissertation

The remainder of dissertation is divided into five chapters. Chapter two describes

the spatial variation of the biotechnology industry in major metropolitan areas in the

United States in 2002. Chapter three then explores cluster theory and frames explanations

for the biotechnology industry in the cluster theory. Chapter four states the hypotheses,

methodology, and data source of the research. Chapter five tests hypotheses and

describes results. The sixth and last chapter summarizes the major findings of this

research.

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Chapter 2 Spatial variation of biotechnology industry

across fifty metropolitan areas in the United States

This chapter explores the spatial variation of biotechnology industry activities

across fifty metropolitan areas in the United States, and identifies biotechnology clusters

where these industrial activities are concentrated. Since biotechnology is a technology,

not a product, biotechnology industry has no corresponding industry codes in the North

American Industry Classification System (NAICS) system or the Standard Industry

Classification (SIC) system. Neither is there one universal NAICS code nor SIC code of

biotechnology industry in the literature. For example, the Biotechnology Industry

Organizations (2001) bioscience survey of 29 US states shows that while drugs and

pharmaceuticals, research and development, testing and medical instruments are included

by a majority of states, other industries like animal and veterinary specialists, agriculture

and other organic chemicals, lab and analytical instrumentation, hospitals and medical

laboratories are not universally included. As a result, definitions of biotechnology

industry were investigated in various sources. This is explored in the first section in this

chapter. The second section then compares the spatial variation of biotechnology industry

across fifty major metropolitan areas in the United States. Though there is no agreed

upon geographic scale of an appropriate industrial cluster (Acs et al, 1994; Audretsch,

2001; Botham el at, 2001; Brookings Institute, 2002; Feldman, 2000; Lee et al, 2003;

Milken Institute, 2004; Munroe et al, 2002; Nettles, 2003; Porter, 1998; Prevezer, 1997;

Rex, 1999; Rey & Mattheis, 2000; Rosenfeld, 1997; Stanford, 2005; Swann and

Prevezer, 1996), in the United States Census definition, each metropolitan unit functions

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as a distinct social, economic, and cultural area, and thus is an appropriate scale for

biotechnology industry cluster analysis. Fifty major metropolitan areas in this dissertation

include 18 Consolidated Metropolitan Statistical Areas (CMSA) and 32 Metropolitan

Statistical Areas (MSA). They are the same metropolitan areas as those in the Brookings

Institutes study (2002) except San Juan, for which the industry data are not available in

the County Business Patterns database. Appendix 1 shows the list of fifty CMSAs and

MSAs as well as their component MSAs.

Biotechnology firms industry codes

Three groups of resources are used to identify biotechnology industrys NAICS

Codes. The first resource group comes from the national biotechnology industry survey

by the United States Department of Commerce in 2003. The second resource group

comes from three national biotechnology studies, including the Ernst & Youngs The

Economic Contributions of the Biotechnology Industry to the United States Economy in

2004, the Brooking Institutes Signs of Life: The Growth of Biotechnology Centers in

the United States in 2002, and the Milken Institutes Americas Biotech and Life

Science Clusters in 2004. The last resource comes from two major business directories,

Dun & Bradstreet Million Dollar Database and Hoovers. Industries that appear in all of

the above resources are then defined as biotechnology industry. Thus this dissertation

adopts a narrow biotechnology industry definition.

Survey by the United States Department of Commerce

A survey by the United States Department of Commerce in 2003 reveals that

firms that label themselves biotechnology fall into over 60 NAICS codes, but scientific

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research and development industry and pharmaceutical firms are the major players in

biotechnology commercial activity (Table 2-1).

Table 2-1 Biotechnology establishments NAICS codes by the US Department of Commerce (2003)Biotechnology NAICS codes by Department of Commerce

Basic industries & materials NAICS codes Percent share of companiesAgriculture, forestry, fishing & hunting 11 1.1

Food, beverage, tobacco manufacturing 311, 312 1.6

Furniture & laboratory apparatus manufacture 337, 33911 .7

Other basic industries activity 22, 23, 323, 327, 3390 .5

Plastics & rubber products manufacture 326 .3

Paper & wood manufacture 322 .1

Chemical manufacture

Basic chemical manufacture 3251 1.8

All other chemical product manufacture 3250, 3259 1.5

Agricultural chemical manufacture 3253 .8

Resin, synthetic rubber & fibers manufacture 3252 .2

Paint, coatings, adhesives, cleaning, surface agent 3255, 3256 .1

Information & electronics

Instrument manufacture 334511-19 3.3

Software publishers 5112 .5Computer systems design & related services 5415 .2

Motion picture & sound recording industries 512 .1

Semiconductor & related device manufacturing 334413 .1

Computer peripheral equipment & terminal manufacture 334113, 334119 .1

Machinery manufacture

Commercial & service industry machinery manufacture 3333, 33321, 33322, 333291-4 .1

Other industrial machinery manufacturing 333298, 3334, 3335, 3339 .5

Medical substances & devices

Non-diagnostic biological product manufacture 325414 12.3

Pharmaceutical & medicine manufacture 3254, 325412 11.3

In-vitro diagnostic substance manufacture 325413 4.8

Medicinal & botanical manufacture 325411 .7

Medical instruments, equip. & supplies manufacture 334510 3.5

Various services

Scientific R & D services 5417 32.3

Professional, scientific and technological services except test lab.Computer & sci. R & D service

54 except 54138, 5415 & 5417 2.2

Testing laboratories 54138 1.9

Medical & diagonostic laboratories 6215 1.9

Wholesale & retail, transport & warehousing 42, 44, 45 excpet 45411, 48, 49 1.5

Management of companies & enterprises 55 .6

Other services 61, 62, 71, 72, 81 except 6215 .4

Admin., support, waste management & remediation 56 .1

Source: Department of Commerce (2003)

The biggest biotechnology group in the survey is scientific research and

development establishments (NAICS 5417), which make up 32.3 percent of all the

companies surveyed. In the NAICS definition from the census website6, scientific

research and development industry (NAICS 5417) includes two sub groups, NAICS

54171 and NAICS 54172. NAICS 54171 are those mainly engaged in research and

6www.census.gov

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development in physical, engineering, and life science, such as agriculture, electronics,

environment, biology, botany, biotechnology, computers, chemistry, food, fisheries,

forests, geology, health, mathematics, medicine, oceanography, pharmacy, physics,

veterinary, and other allied subjects. NAICS 54172 establishments are those mainly

engaged in research and development in social sciences and humanities. The second

group is made up of pharmaceutical and medicine manufacturers (NAICS 3254), which

make up 29.1 percent of the total firms surveyed. This industry comprises establishments

primarily engaged in one or more of the following: (1) manufacturing biological and

medical products; (2) processing (i.e., grading, grinding, and milling) botanical drugs and

herbs; (3) isolating active medicinal principals from botanical drugs and herbs; and (4)

manufacturing pharmaceutical products intended for internal and external consumption in

such forms as ampoules, tablets, capsules, vials, ointments, powders, solutions, and

suspensions.

These two categories make up 61.4 percent of all the firms surveyed. The rest of

the companies that label themselves biotechnology firms include navigational,

measuring, electromedical, and control instruments manufacturing (NAICS 3345), testing

labs (NAICS 54138), computer service (NAICS 5415), health care services (NAICS

6215), chemical manufactures (NAICS 3251), and wholesale trade (NAICS 42).

Biotechnology industry definition in three national studies

In the Ernst & Youngs (2000) study, biotechnology companies have SIC codes

of 2833, 2834, 2835, 2836 or 8731, whose corresponding industries in the NAICS system

are pharmaceutical and medicine manufacturing (NAICS 3254) and research and

development in physical, engineering, and life sciences (NAICS 54171) (table 2-2).

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In the Brooking Institutes study, it is stated that most biotechnology firms are

assigned to one of two broader industry categories encompassing research and

development and drug manufacturing - namely, NAICS five-digit industry 54171

(research and development in the physical, engineering, and life sciences) or NAICS

industry group 3254 (pharmaceutical and medicine manufacturing) (Brookings, 2002).

Table 2-2 Biotechnology industry definition used in the Ernst & Young (2000)

SICcodes

Name in SIC system CorrespondingNAICS codes

Name in NAICS system

2833 Medicinal Chemicals andBotanical Products

325411 Medicinal and BotanicalManufacturing

2834 Pharmaceutical Preparations 325412 Pharmaceutical PreparationManufacturing

2835 In Vitro and In Vivo DiagnosticSubstances

325412 and325413

Pharmaceutical PreparationManufacturingIn-Vitro Diagnostic SubstanceManufacturing

2836 Biological Products, ExceptDiagnostic Substances

325414 Biological Product (exceptDiagnostic) Manufacturing

8731 Commercial Physical andBiological Research

541710 Research and Development in thePhysical, Engineering, and LifeSciences

Source: Ernst & Young (2000), edited by author

So both the Ernst & Young (2000) and the Brookings (2002) agree that

biotechnology industry may be defined as the combination of two industries: research and

development in physical, engineering, and life sciences (NAICS 54171) and

pharmaceutical and medicine manufacturing (NAICS 3254).

In the Milken Institutes (2004) study, biotechnology firms also fall intothesetwo

categories, although two subgroups within these industries, NAICS 325412 and NAICS

5417101 are excluded.

Biotechnology firms in two business directories

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In the Dun & Bradstreet database, when the industry keyword biotechnology is

typed in the search engine, 126 records show up. Firms listed range from agricultural and

industrial chemical manufacturing to research service industries. Among them, 67.4

percent are commercial physical and biological research companies (SIC 873,

corresponding NAICS code is 54171); 8.7 percent belong to drug manufacturing

companies; 4.0 percent are medical and professional equipment companies; and 3.2

percent are medical labs (table 2-3).

Table 2-3 Biotechnology firms in D & B million dollar database

4-digit SIC Industry category Number of

companies2048 Prepared Feeds & Feed Ingredients for Animals & Fowl, Except Dogs &

Cats1

2741 Miscellaneous Publishing 1

2834 Pharmaceutical Preparations 5

2835 In Vitro and In Vivo Diagnostic Substances 2

2836 Biological Products, Except Diagnostic Substances 4

2844 Perfumes, Cosmetics, and Other Toilet Preparations 1

2869 Industrial Organic Chemicals 1

2899 Chemicals and Chemical Preparations 1

3821 Laboratory Apparatus and Furniture 1

3829 Measuring and Controlling Devices 1

3841 Surgical and Medical Instruments and Apparatus 14222 Refrigerated warehousing and storage 1

5047 Medical, Dental, and hospital equipment and supplies 3

5049 Other professional equipment and supplies 1

5122 Drugs, drug propritaries, and druggists sundries 1

7371 Computer programming service 2

7389 Other business services 1

8071 Medical laboratories 4

8731 Commercial physical and biological research 85

8733 Non-commercial research organizations 8

8734 Testing laboratories 1

Total 126

Source: Dun & Bradstreet, edited by the author

It appears that majority of biotechnology firms fall into NAICS 54171 categories.

However, a detailed examination of the most famous biotechnology firms reveals that

they are classified into the pharmaceutical industry. For example, the top five

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biotechnology firms in terms of sales Amgen, Genetech, Genzyme, Serono and

Centocor have their primary industry codes in pharmaceutical manufacturing (NAICS

3254), but secondary industries codes in physical, engineering, and life science research

and development (NAICS 54171).

In Hoovers, biotechnology industry is a subdivision of pharmaceuticals.

Biotechnology firms include biopharmaceuticals & biotherapeutics firms, biotechnology

research equipment manufacturing firms, and biotechnology research services.

Several facts can be summarized from these two industry databases. One is that

biotechnology companies fall into broad NAICS industry codes, and this is in accordance

with the United States Department of Commerces (2003) survey finding. Second,

biotechnology companies are concentrated in a few fields, such as commercial

biotechnology research and development. Third, most successful biotechnology firms are

actively engaged in drug manufacturing.

To summarize findings from these three groups of sources, it is very clear that the

major players in biotechnology commercial activities are physical, engineering, and life

science research and development establishments (NAICS 54171) and pharmaceutical

manufacturing companies (NAICS 3254). They are the definition of biotechnology

industry in this dissertation. Table 2-4 shows that these two industries appear in all of the

sources examined, therefore they are used as the biotechnology industry definition in this

study. Other industries may also have some biotechnology activities; however, they do

not have big presentation in the arena of biotechnology and their primary activity is not

directly involved in biotechnology, thus they are not considered as major biotechnology

industry components in this dissertation.

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Table 2-4 Comparison of biotechnology industry definition

Biotechnology clusterindustries

USDep. ofComme

rce

Ernst &Young,

Brooking

Milken Dun &Bradstr

eet

Hoover Totalcount

NAICS 3251 Basic ChemicalManufacturing

X 1

NAICS 3313 Alumina andAluminum Production andProcessing

1

NAICS 3254 Pharmaceuticaland Medicine Manufacturing

X X X X(Excluding

325412)

X X 5

NAICS 3259 Other ChemicalProduct and PreparationManufacturing

X 1

NAICS 334510Electromedical and

Electrotherapeutic ApparatusManufacturing

X 1

NAICS 334516 AnalyticalLaboratory InstrumentManufacturing

X X 2

NAICS 334517 IrradiationApparatus Manufacturing

X 1

NAICS 3391 MedicalEquipment and SuppliesManufacturing

X X(339111)

2

NAICS 42345 Medicalequipment merchantwholesalers

X 1

NAICS 42346 Ophthalmicgoods merchant wholesalers

X 1

NAICS 4461 Health &personal care stores

X 1

NAICS 54138 testinglaboratories

X 2

NAICS 5417 scientificresearch and development(including NAICS 54171 and54172)

X X X X(5417102)

X X(541710)

6

NAICS 5419 OtherProfessional, Scientific, andTechnical Services

X 1

NAICS 562211 Hazardouswaste treatment & disposal

X 1

NAICS 621 AmbulatoryHealth Care Services

X X(6215)

X(6215)

3

NAICS 622 hospitals X 1

NAICS 623 Nursing &residential care facilities

X 1

Source: compiled by author

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Biotechnology industry distribution across fifty metropolitan

areas

Five groups of measurements are used to compare the spatial variation of

biotechnology industry activities across fifty metropolitan areas: number of

biotechnology establishments, number of biotechnology establishments normalized by

metropolitan area size (establishment density), biotechnology industry employment size,

biotechnology industry employment size normalized by metropolitan area size

(employment density), and location quotients.

Number of biotechnology establishments is used to measure localization

economies (Atwood, 1928). It indicates the degree of co-location of a group of

biotechnology firms in metropolitan areas (Swann and Prevezer, 1996). A greater number

of biotechnology establishments should represent more of a perfect competitive market

(Porter, 1998).

Some metropolitan areas may be much larger than others in terms of land size,

and can in theory host more biotechnology establishments. For example, New York

CMSA is much larger than San Diego MSA in terms of land size. However, there may be

a higher biotechnology establishment density in a smaller metropolitan area, where the

average distance between establishments is smaller, and there may be more geographic

proximity between establishments. If the distance decay rule holds true, there may be

more interactions between establishments that are closer to each other than

establishments that are farther away. Due to this, the number of biotechnology

establishments in each CMSA or MSA is divided by the area size in square miles to

calculate establishment density. Higher establishment density indicates higher degree of

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biotechnology industry co-locating. Although it is possible that biotechnology

establishments may be distributed unevenly within metropolitan areas, this problem is not

uncommon with other measurements when aggregated geographic units are used. For

example, the location quotient (described below), a well-adopted measurement for

industry specialization, when used at the metropolitan level, does not measure internal

variance either.

A third measurement is biotechnology industry employment size. Due to various

firm sizes, two metropolitan areas with the same number of biotechnology establishments

may have different employment size. Biotechnology industry with a greater employment

size contributes more to local economy and may be more competitive in the national

economy. Bigger size also leads to specialization and localization economies (Atwood,

1928; Hanson, 1996; Walters, 1984).

Employment size is also normalized to take effect of urban land size to compare

employment density.

Another way to measure industry development is location quotient (LQ), which

measures specialization of individual industry in a place and may be used to identify the

driving industry in a place (Hill and Brennan, 2000). The equation to calculate LQ is as

the following.

ni

aiaia

EE

EELQ

/

/=

WhereLQia is the location quotient for industry i in area a,Eia is the employment

in industry i in area a, Ea is the total employment in area a, Ei is the employment in

industry i in the nation, andEn is the total employment in the nation.

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Fifty metropolitan areas are compared using these measurements for

biotechnology industry as well as its two component industries. Industry data come from

County Business Patterns, and metropolitan land size data come from the U.S. Census.

Since County Business Patterns do not provide exact employment data for many

metropolitan areas, a narrowing down process is used to get approximate employment

data. This procedure is listed in appendix 2. Establishments, employment, and LQ data

are listed in appendix 3. Rankings are provided in appendix 4. Finally, a statistical cluster

analysis is used to classify metropolitan areas according to their biotechnology industry

size, density and specialization.

Spatial distribution of biotechnology establishments

In 2002, there are 13,384 biotechnology establishments (including the two

component industries: pharmaceutical and medicine manufacturing, and physical,

engineering, and life science research and development industry) in the United States.

Among them, 74.0 percent are located in the fifty metropolitan areas under study. Among

these biotechnology establishments, 13.0 percent of them are pharmaceuticals, and 87.0

percent are physical, engineering, and life science research and development

establishments. Figures 2-1 to 2-3 show the histograms of establishments in

biotechnology industry as well as those in its two component industries7.

7 Physical, engineering, and life science research and development industry is also labeled R & D industryin the following contents. Metropolitan is labeled metro in graphs.

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Figure 2-1 Histogram of biotechnology establishments

Figure 2-2Histogram of pharmaceutical establishments

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Figure 2-3Histogram of research and development establishments

Biotechnology establishments are highly concentrated in a few metropolitan

areas. The top ten metropolitan areas account for 49.1 percent of the total biotechnology

establishments in the United States in 2002. Among them, the number one location for

biotechnology establishments is New York CMSA with 1160 establishments in 2002. It

is also the number one location for biotechnology component pharmaceutical firms in the

nation. New York CMSA is followed closely by San Francisco CMSA with a number of

ts the most physical,

ngine

1049. Different from New York CMSA, San Francisco CMSA hos

e ering, and life science research and development establishments in the nation. The

third location is Washington CMSA, which hosts 1017 establishments. These three

metropolitan areas together host 24.1 percent of the total biotechnology establishments in

the United States in 2002. The other seven metropolitan areas in the top ten include Los

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Angeles CMSA, Boston CMSA, San Diego MSA, Philadelphia CMSA, Chicago CMSA,

Denver CMSA, and Seattle CMSA. The number of biotechnology establishments in these

seven areas range from 268 to 780. When considering the spatial pattern, three of the top

ten metropolitan areas are located in the southwest of the US, four are in the northeast,

one is in the northwest, one in the Midwest, and one in the central region (figure 2-4).

Figure 2

0th in the

number of pharmaceutical establishments, while 21st

in the number of physical,

engineering, and life science research and development establishments. In contrast,

Seattle is ranked 10th in the number of engineering, and life science research and

-4 Spatial Distribution of the Biotechnology Establishments in 2002

Figure 2-4 also shows that some metropolitan areas have comparatively more

pharmaceutical establishments than others. For example, Saint Louis is ranked 1

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development establishments, but 21st in the number of pharmaceutical establishments.

And Norfolk is ranked 47th

in the number of pharmaceutical establishments, but 27th

in

the number of research and development establishments.

Spatial variation in density of biotechnology establishment

When the density of biotechnology establishments is calculated through dividing

the number of biotechnology establishments by metropolitan areas land size8, the top ten

metropolitan areas with highest establishment density are Boston, San Diego, San

Francisco, Washington DC, New York, Raleigh, Philadelphia, Salt Lake City, Miami,

and Providence (figure 2-5). Among these ten metropolitan areas, New York City, San

Diego, and Boston have the highest establishment density in the biotechnology

component pharmaceutical manufacturing industry (figure 2-6), while Boston, San

Francisco, and San Diego have the highest establishment density in the biotechnology

component physical, engineering, and life science research and development industry

(figure 2-7). In general, the northeast and southwest metropolitan areas have higher

density of the physical, engineering, and life science research and development

establishment than other places.

Spatial variation of establishment density is quite different from spatial

distribution of establishments. For example, places like Salt Lake City, Miami, and

Providence rank higher in the area density than in their establishment numbers.

8 Unit: number of establishment per 10 square miles

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Figure 2-5 Spatial Variation in Density of the Biotechnology Establishment

Figure 2-6 Spatial Variation in Densityof Pharmaceutical Establishment

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Figure 2-7 Spatial Variation in Density of Physical, Engineering, and Life Science R & D

Spatial distribution of biotechnology industry employment

Figure 2-8 shows the spatial distribution of employment in biotechnology industry

as well as its two component industries across fifty metropolitan areas. Figures 2-9 to 2-

14 are histograms of employment distribution in the biotechnology industry as well as in

d distributions (data

is log transformed to minimize distortion by extreme values when correlation and

regression are conducted in the hypothesis testing chapter).

Biotechnology employees are highly concentrated. The top ten metropolitan areas

account for 57.5 percent of all biotechnology industry employment in the United States in

2002. These top tens are Washington DC CMSA, New York CMSA, San Francisco

CMSA, San Diego MSA, Boston CMSA, Los Angeles CMSA, Philadelphia CMSA,

its two component industries, and their corresponding log transforme

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Seattle CMSA, Chicago CMSA, and Raleigh MSA. Among them, New York CMSA, Los

Angeles CMSA, and San Francisco CMSA have the most pharmaceutical employees,

while Washington CMSA, New York CMSA, and San Francisco CMSA have the most

physical, engineering, and life science research and development employees.

Figure 2-8 Spatial Distribution of Biotechnology Industry Employment

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Figure 2-9Histogram of biotechnology industry employment

Figure 2-10 Histogram of biotechnology industry employment (log transformed)

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Figure 2-11 Histogram of pharmaceutical industry employment

Figure 2-12Histogram of pharmaceuticalindustryemployments (log transformed)

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Figure 2-13 Histogram of employments in the R & D industry

Figure 2-14 Histogram of employments in the R & D industry (log transformed)

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This spatial distribution of biotechnology employment is different from the

distribution of biotechnology establishments. In the pharmaceutical component industry,

for example, Indianapolis is ranked 7th in terms of employment size, but 30th in terms of

the number of pharmaceutical establishments, suggesting industry concentration in a few

big pharmaceutical firms. Some similar cases include Grand Rapids and Rochester.

Grand Rapids is ranked 11th in pharmaceutical employment, but 34th in the number of

establishments; and Rochester is ranked 17th and 43rd individually. In contrast, Portland is

ranked 39th in terms of pharmaceutical employment, but 14th in the number of

establishments, suggesting that the majority of firms are of small or medium size.

Minneapolis is similar to Portland, with rankings of 25th and 11th accordingly. In the other

biotechnology component, physical, engineering, and life science research and

development industry, for example, Miami is ranked 32nd in the total am nt of

th ajority of the

firms are small to medium sized. Columbus is the opposite. It is ranked 17th in the total

amount of employments, but 29th in the number of establishments, suggesting that there

are more medium to large sized establishments.

ngton

CMSA

ou

employment, but 17 in the number of establishments, suggesting that m

Spatial variation in density of biotechnology industry employment

The top ten metropolitan areas that have the highest employment density are San

Diego MSA, New York CMSA, Boston CMSA, San Francisco CMSA, Washi

, Philadelphia CMSA, Raleigh CMSA, Buffalo CMSA, Indianapolis MSA, and

Chicago CMSA (figure 2-15). Among these top ten, New York City, Indianapolis, and

Philadelphia have the highest pharmaceutical employment density (figure 2-16), while

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San Diego, Washington DC, and Boston have the highest employment density in

physical, engineering, and life scientific research and development industry (figure 2-17).

Spatial variation of employment density is different from spatial distribution of

employment. For example, San Diego MSA, Indianapolis MSA, and Buffalo MSA rank

higher in employment density than their ranking in the absolute employment size,

suggesting more condensed spatial agglomeration of the industry.

Figure 2-15 Spatial Variation in Density of the Biotechnology Industry Employment

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Figure 2-16 Spatial Variation in Density of Pharmaceutical Industry employment

Figure 2-17 Spatial Variation in Density of R & D Industry Employment

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Spatia

istograms of LQs in the biotechnology industry as

well as in its two component industries in the fifty metropolitan areas under study.

Overall, eighteen metropolitan areas are specialized in biotechnology industry.

The most specialized metropolitan area in the biotechnology industry is San Diego, with

a location quotient of 5.3, followed by Raleigh with a location quotient of 3.6. The other

eight metropolitan areas in the top ten are Washington CMSA, San Francisco CMSA,

Buffalo MSA, Boston CMSA, New York CMSA, Austin MSA, Indianapolis MSA, and

Philadelphia CMSA.

Considering the pharmaceutical component of biotechnology industry, the most

specialized metropolitan area is Indianapolis MSA with a location quotient of 3.6,

followed by Grand Rapids MSA with a value of 3.4. As for the other biotechnology

elopment industry,

MSA with a location quotient of 7.5.

l variation of biotechnology industry specialization

Location quotient (LQ) measures the degree of specialization in biotechnology

industry across fifty metropolitan areas. A metropolitan area with an LQ higher than one

is viewed as specialized in the biotechnology industry. Figures 2-18 to 2-20 show the

spatial variation of LQ in biotechnology industry as well as in its two component

industries. Figures 2-21 to 2-23 are h

component, physical, engineering, and life science research and dev

the most specialized metropolitan area is San Diego MSA, with a location quotient of

14.0. It is followed by Washington CMSA with a location quotient of 8.0 and Raleigh

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Figure 2-18 Spatial variation of biotechnology industrys specialization

Figure 2-19 Spatial variation of the pharmaceutical industrys location quotients

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Figu - a s l, , life science research and development

indu n

re 2

stry

20 Sp

s loca

ati

tio

l var

quo

iatio

tient

n of

sphy ica engineering and

gure 2-21 Histogram of biotechnology industrys LQFi

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37

2-22Histogram of pharmaceutical industrys LQFigure

Figure 2-23 Histogram of R & D industrys LQ


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Ranking of metropolitans by LQ is quite different from ranking by number of

establishments or by employment size. As figure 2-24 shows, the two most highly

specialized metropolitan areas, San Diego and Raleigh, are not the highest ranked in

terms of total number of biotechnology establishment or employment. In contrast, some

places rank high in terms of establishment and employment but are not highly

specialized, such as New York City.

Figure 2-24 Biotechnology establishment, employment, and location quotient

Overall comparison of biotechnology industrys spatial

distribution and biotechnology clusters

biotechnology clusters. For example, New York is the number one biotechnology cluster

Different measurements produce various rankings across the fifty metropolitan

areas. Table 2-5 shows the top ten metropolitan areas when five groups of biotechnology

industry activity measurements are used. Each measurement gives varied result of ten top

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when measured by biotechnology establishments; Boston ranks number one in

biotechnology establishment density; San Francisco ranks number one in the number of

physical, engineering, and life science research and development establishments;

Washington DC ranks number one in terms of biotechnology industry employment; and

San Diego is the number one biotechnology cluster in terms of biotechnology

employment density and location quotient.

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able Top t bi echnol c rs i easurem ts2-5 en ot ogy luste by d fferent m enE hstablis ment Establishm dent ensity E entmploym E mmploy ent density

# of bio-est.

# of phmest.

# f R&Doest.

bio- est.den.

p m est.hden.

R&D est.den.

# of bioemp.

# of phmemp.

# of R&Demp.

b p.io- emden.

phm mp.ed .en

R&Demp. den.

1 New York New York SanFrancisco

Boston New York Boston W hingtason

New York Washington

San Diego New York San Diego

2 SanFrancisco

LosAngeles

Washington

San Diego San Diego SanFrancisco

New York LosAngeles

New York New York Indianapolis

Washington

3 W tashingon

SanFrancisco

N w Yorke SanFrancisco

Boston San Diego SanFrancisco

SanFrancisco

SanFrancisco

Boston Philadelphia

Boston

4 LosAngeles

Boston Boston Washi tngon

Philadelphia

Washington

LosAngeles

Philadelphia

San Diego SanFrancisco

SanFrancisco

S naFrancisco

5 Boston San Diego LosAngeles

New York Salt LakeCity

New York Boston Chicago San Diego W tashingon

Boston New York

6 San Diego Philadelphia

S Diegoan Raleigh SanFrancisco

Raleigh San Diego Boston LosAngeles

Philadelphia

Chicago Raleigh

7 Philadelphia

Washington

P adelphhilia

Philadelphia

Miami P lphilade hia

Philadelphia

Indianapolis

Philadelphia

Raleigh San Diego Philadelphia

8 Chicago Chicago Chicago Salt LakeCity

Raleigh Salt LakeCity

Seattle Washington

Seattle Buffalo Raleigh Buffalo

9 Denver Den rve Denver Providence

W hingtason

P enrovid ce

Chicago San Diego Chicago Indianapolis

GrandRapids

Co slumbu

10 Seattle St. L isou Seattle Miami St. ouisL Seattle Raleigh Raleigh Raleigh Chicago Rich ondm S naAntonio

T

Abbreviation:Bio represents ec ogEst. represen l enPhm. Represe ha eDen. Represe densiR & D repres h l, e ng, and l fe ntEmp. Represe m me

biot hnol yts estab ishm tnts p rmac uticalnts tyents p ysica engin eri i science research and developments e ploy nt


51/170

As different measurements reflect various characteristics of biotechnology

industry distribution, it is not appropriate to average the rankings in each measurement to

calculate a composite index since it blurs the difference among measurements. Instead, a

statistical cluster analysis is used to sort metropolitan areas into groups in a way that the

degree of association between two metropolitan areas is maximal if they belong to the

same group and minimal otherwise. The K-means algorithm method is selected since it

allows experimenting on cluster numbers (or the metropolitan area groups in this study)

and since the best number of clusters leading to the greatest separation (distance) is not

known as a priori, different numbers of clusters are compared using the supplementary

knowledge (NCSS software help).

A detailed discussion of K-means cluster statistics and experiment on K numbers

is listed in appendix 5. Based on various experiments, the 6-cluster method provides a

fairly satisfactory result, as it provides explainable groupings. Statistics for these

metropolitan areas are listed in appendix 6. Figure 2-25 shows the spatial distribution of

six types of metropolitan areas in terms of their biotechnology industry activities.

Cluster type one is labeled super sized biotechnology cluster. New York City is

this type. It has the largest amount of biotechnology establishment, biotechnology

employment, biotechnology employment density, and pharmaceutical establishment

density.

The second type of cluster is labeled highly concentrated and specialized

biotechnology research clusters. It has four metropolitan areas, including Boston, San

Diego, San Francisco, and Washington DC. They are second to New York in terms of the

absolute value of biotechnology establishments and employment. However, they have the

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highest biotechnology establishment density, especially in physical, engineering, and life

ong all the fifty metropolitan

areas. They are also m

ore specialized in the physical, engineering, and

life science research and development than in the pharmaceutical manufacturing.

medium sized biotechnology centers. It

include

shments and density,

employ

science research and development establishment density am

ost specialized in physical, engineering, and life science research

and development.

The third type of cluster is labeled upper level biotechnology clusters. It

includes four metropolitan areas: Chicago, Los Angeles, Philadelphia, and Raleigh. The

average value of establishment, employment, density, and specialization in these

metropolitan areas is smaller than that in type-two clusters. In terms of the specialization,

these four biotechnology clusters are m

The fourth type of cluster is called

s seventeen metropolitan areas: Atlanta, Austin, Buffalo, Columbus, Denver,

Detroit, Houston, Kansa City, Miami, Minneapolis, Norfolk, Pittsburgh, Salt Lake City,

San Antonio, Seattle, Saint Louis, and Tampa. They are the medium biotechnology

centers in terms of the total number of biotechnology establi

ments and density, and specialization. In addition, these metropolitan areas are

relatively more specialized in the physical, engineering, and life science research and

development than in the pharmaceutical manufacturing.

The fifth type of cluster is called highly dominated pharmaceutical centers. It

includes four metropolitan areas: Indianapolis, Rochester, Grand Rapids, and Richmond.

They have the least amount of biotechnology establishments. However, their average

amount of the biotechnology employment is close to type-four clusters, and their average

pharmaceutical employment density is smaller than type-three clusters. These four

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metropolitan areas are relatively more specialized in the pharmaceutical manufacturing

than in the physical, engineering, and life science research and development.

The sixth type of cluster is called low level biotechnology centers. It includes

twenty metropolitan areas: Charlotte, Cincinnati, Cleveland, Dallas, Greensboro,

Hartford, Jacksonville, Las Vegas, Louisville, Memphis, Milwaukee, Nashville, New

Orleans, Oklahoma City, Orlando, Phoenix, Portland, Providence, Sacramento, and West

Palm Beach. They have the lowest value of biotechnology industry employment and

density among fifty US metropolitan areas. They are not specialized in any biotechnology

component industry.

Figure 2-25 US biotechnology clusters

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Type 1 Super sized biotechnology cluster, Type 2 Highly concentrated and specialized biotechnology

centers, Type 5 Highly dominated pharmaceutical centers, Type 6 Low level biotechnology centers

This spatial distribution results are co

research clusters, Type 3 Upper level biotechnology clusters, Type 4 Medium sized biotechnology

mpared with the Brookings Institute (2002),

and the

Metropolitan areas Rank in Label in Brooking Label in this dissertation

Milken Institutes (2004) studies (table 2-6).

Table 2-6 Comparison of biotechnology cluster rankings

Milken

San Diego MSA 1 Biotechnology Centers Highly concentrated and specializedbiotechnology cluster

Boston PMSA 2 Biotechnology Centers Highly concentrated and specializedbiotechnology cluster

Raleigh MSA 3 Biotechnology Centers Upper level biotechnology clusters

San Jose MSA (part of SanFrancisco CMSA)

4 Biotechnology Centers Highly concentrated and specializedbiotechnology cluster

Seattle PMSA 5 Biotechnology Centers Medium sized biotechnology centers (in the top

list among the mediums)Washington PMSA 6 Biotechnology Centers Highly concentrated and specializedbiotechnology cluster

Philadelphia PMSA 7 Biotechnology Centers Upper level biotechnology clusters

San Francisco PMSA 8 Biotechnology Centers Highly concentrated and specializedbiotechnology cluster

Oakland MSA (part of SanFrancisco CMSA )

9 Biotechnology Centers Highly concentrated and specializedbiotechnology cluster

Los Angeles PMSA 10 Biotechnology Centers Upper level biotechnology clusters

Orange County MSA (par of LosAngeles CMSA)

11 Biotechnology Centers Upper level biotechnology clusters

Austin MSA 12 Medium Medium sized biotechnology centers(in the top list among the mediums)

New York CMSA Biotechnology Centers Super sized biotechnology cluster

Chicago CMSA Research centers Upper level biotechnology clusters

Detroit CMSA Research centers Medium sized biotechnology centers

(in the top list among the mediums)Houston CMSA Research centers Medium sized biotechnology centers(in the top list among the mediums)

Saint Louis MSA Research centers Medium sized biotechnology centers(in the top list among the mediums)

In the Milken Institutes (2004) study, among their top twelve biotechnology

clusters, San Diego MSA, Boston PMSA, San Francisco CMSA, Washington PMSA, and

San Francisco PMSA correspond to highly concentrated and specialized biotechnology

cluster in this study, while Raleigh MSA, Philadelphia PMSA, and Los Angeles CMSA

correspond to upper level biotechnology clusters in this study. The only exceptions are

Seattle and Austin, which are labeled medium sized biotechnology centers in this

study.

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The Brookings Institute (2002) identified nine biotechnology centers: Boston

CMSA, San Francisco CMSA, San Diego MSA, Raleigh MSA, Seattle CMSA, New

York CMSA, Philadelphia CMSA, Los Angeles CMSA, and Washington CMSA. In this

dissertation, New York is labeled super sized biotechnology cluster, Boston CMSA,

San Francisco CMSA, Washington CMSA, San Diego, and San Francisco CMSA are

labeled

ry activities

measured by establishments, employment, density, and specialization are in general

accordance with other measurements, such as basic life science research, high quality

labor, a

highly concentrated and specialized biotechnology cluster, Raleigh MSA,

Philadelphia CMSA, and Los Angeles CMSA are labeled upper level biotechnology

cluster, and Seattle is labeled medium sized biotechnology cluster. The Brookings

Institute also identified four research centers: Chicago CMSA, Detroit MSA, Houston

CMSA, and St. Louis MSA. In this study, while Chicago is labeled upper level

biotechnology cluster, Detroit, Houston and St. Louis appear in the medium sized

biotechnology center.

Obviously there is some commonality and some difference among the results of

this study and of the results of the Brookings Institute and the Milken Institute. The three

studies all suggest that some metropolitan areas biotechnology indust

nd commercialization activities (Brookings, 2002; Milken, 2004). The difference

suggests that even though some places may be rich in non-industry measurements, they

do not necessarily have strong industry presence. And the difference further leads us to

the research question of this dissertation: does a place need to have all other elements,

such as life science research, human capital, and commercialization locally, to have a

strong biotechnology industry?

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Chapter 3 Literature review

The objective of this dissertation is to explain the spatial variation of the

biotechnology industry across major U.S. metropolitan areas, using industrial cluster

theory as a framework. Thus it is necessary to understand literatures in these two fields of

biotechnology industry and industrial cluster. This chapter starts with literature on

industrial c

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Biotech Cluster Analysis 2006