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Pittsburgh Technology Strategy
Benjamin YeoRelated papers
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Capitalist modernity not only contains profound contradictions, but is also undergoing a significant transformation. Far from acting as a driving force for the development of social productive forces, it has become a parasitic entity with an essentially rentier and speculative function. Underlying this is an institutional framework that favors the private appropriation and the concentration of the products of general intellect.
Pittsburgh Technology Strategy
November 2006
by Perry Wong,
Benjamin Yeo
and Ross DeVol
Pittsburgh Technology Strategy
Research Report
Presented to
The Greater Oakland Keystone Innovation Zone
Completed by
Milken Institute
By
Perry Wong
Benjamin Yeo
Ross DeVol
1250 Fourth Street, Santa Monica, California 90401
www.milkeninstitute.org
NOVEMBER 2006
Acknowledgements
The Milken Institute would like to acknowledge the support of the Greater Oakland Keystone Innovation Zone in providing
the assistance and support to this project, without which this project would not have become a reality. We are especially grateful
to Don Smith and Timothy White for their comments and advice throughout the course of the project.
In addition, we would like to thank our colleagues for their assistance in various ways. Of note, the contributions of Dinah
McNichols, editor, and Armen Bedroussian, senior research analyst, deserve considerable recognition.
Cover photographs of “New Technology,” “Point at Dusk,” and “Duquesne Incline” are reprinted with permission from the
Pittsburgh Post-Gazette. The cover photograph of the Mellon Institute is reprinted with permission from the Department of
Chemistry, Carnegie Mellon University.
The Milken Institute is an independent economic think tank whose mission is to improve the lives and economic conditions
of diverse populations in the U.S. and around the world by helping business and public policy leaders identify and implement
innovative ideas for creating broad-based prosperity. We put research to work with the goal of revitalizing regions and
inding new ways to generate capital for people with original ideas.
We do this by focusing on human capital – the talent, knowledge and experience of people, and their value to organizations,
economies and society; financial capital – innovations that allocate inancial resources eficiently, especially to those who
ordinarily would not have access to it, but who can best use it to build companies, create jobs and solve long-standing social
and economic problems; and social capital – the bonds of society, including schools, health care, cultural institutions and
government services, that underlie economic advancement.
By creating ways to spread the beneits of human, inancial and social capital to as many people as possible – the democratization
of capital – we hope to contribute to prosperity and freedom in all corners of the globe.
We are nonproit, nonpartisan and publicly supported.
© 2006 Milken Institute
Pittsburgh Technology Strategy
Milken Institute
Table of Contents
Executive Summary ...................................................................................................1
SWOT Analysis .........................................................................................................5
Introduction...........................................................................................................5
Pittsburgh MSA .....................................................................................................5
Project Objectives ..................................................................................................6
Summary of Analysis .................................................................................................6
Knowledge-Base Analysis .........................................................................................12
1. Innovation Pipeline ..........................................................................................12
2. Education Infrastructure..................................................................................23
3. Business Climate ..............................................................................................25
Industry Analysis .....................................................................................................30
1. Industry Development Capacity .......................................................................30
2. Industry Concentration....................................................................................37
3. Industry Productivity .......................................................................................39
Pittsburgh Industry Cuts..........................................................................................44
High-Tech Sector Strengths .................................................................................51
High-Tech Sector Weaknesses ..............................................................................51
High-Tech Sector Opportunities ..........................................................................52
High-Tech Sector Threats ....................................................................................52
Summary of Industry Cuts ..................................................................................53
Regional Economic Overview ..................................................................................56
Potential Areas of Focus for the Above Assessment ..............................................56
Kick-Start the Business Environment ...................................................................56
Attract and Retain Talent ....................................................................................60
Boost Knowledge-Base Incubation .......................................................................62
Assessment Summary ...........................................................................................64
Brief Recommendations .......................................................................................65
Appendix A: Definition of High-Tech Manufacturing .............................................67
Appendix B: Definition of High-Tech Services ........................................................68
Appendix C: Data Sources .......................................................................................69
Appendix D: Top 100 Performing Metros 1999–2001 .............................................70
Appendix D: Top 100 Performing Metros 2002–2003 ............................................72
Appendix D: Top 100 Performing Metros 2004–2005 ............................................74
Appendix E: Miscellaneous Charts ..........................................................................76
About the Authors....................................................................................................96
Pittsburgh Technology Strategy
Milken Institute
List of Tables
Table 1: Tabulated Summary of High-Tech Economy — Human Resources ....................................7
Table 2: Tabulated Summary of High-Tech Economy — Performance .............................................7
Table 3: Milken Institute Tech Pole Index, 2003...............................................................................8
Table 4: Milken Institute Ranks of Best Performing MSAs, 1999–2005 ...........................................9
Table 5: Ranked MSAs by Job Growth Rate (Non-Farm Employment), 1990–2002 ...................... 10
Table 6: Fortune 500 Companies in Pittsburgh .............................................................................. 11
Table 7: Innovation Pipeline — Summary of Strengths and Weaknesses ........................................ 12
Table 8: Education Infrastructure — Summary of Strengths and Weaknesses ................................23
Table 9: Business Climate — Summary of Strengths and Weaknesses ............................................ 25
Table 10: Cost of Living and Cost of Doing Business by MSA........................................................ 25
Table 11: Industry Development Capacity — Summary of Strengths and Weaknesses ...................30
Table 12: Industry Concentration — Summary of Strengths and Weaknesses ................................ 37
Table 13: Industry Productivity — Summary of Strengths and Weaknesses ...................................40
Table 14: National Rankings of Overall GDP, 2004 .......................................................................40
Table 15: National Rankings of High-Tech GDP, 2004 .................................................................. 41
Table 16: National Rankings of Overall Productivity, 2004 ............................................................ 41
Table 17: National Rankings of High-Tech Productivity, 2004 .......................................................42
Table 18: Pittsburgh Industry Cuts SWOT Summary ....................................................................48
Table 19: Key Recommendations for High-Tech Manufacturing Sectors ........................................54
Table 20: Key Recommendations for High-Tech Services Sectors ................................................... 55
Table 21: Combined State and Local Tax Burden by State, 2005 .................................................... 58
Table 22: Qualitative SWOT Assessment — Strengths and Weaknesses.........................................64
Table 23: Qualitative SWOT Assessment — Opportunities and Threats........................................ 65
Pittsburgh Technology Strategy
Milken Institute
List of Figures
Figure 1: MSA Total Publications, 1998–2002 ............................................................................... 13
Figure 2: Total Patents Filed and Issued at Universities, 1996–2003 ............................................... 13
Figure 3: Annual Patents Issuance Rate by Universities, 1996–2003 ............................................... 14
Figure 4: Annual Patents Filed by Universities ................................................................................ 14
Figure 5: Annual Patents Issued to Universities ............................................................................... 15
Figure 6: University R&D Expenditure, 1996–2003 ($Billions) ..................................................... 15
Figure 7: Average Patents Issued per $10 million R&D Expenditure ............................................... 16
Figure 8: Average Licenses Executed per $10 million R&D Expenditure ........................................ 16
Figure 9: University VC Startups, 1996–2003 ................................................................................ 17
Figure 10: Average University VC Startups per $10 million R&D Expenditure .............................. 17
Figure 11: University-Based and Corporate R&D Assets (Standardized Scores) .............................. 18
Figure 12: High-Tech Location Quotients, 2004 ............................................................................ 19
Figure 13: High-Tech LQ Trends, 1990–2004 ................................................................................ 19
Figure 14: Number of VC Investments, 1990–2005........................................................................20
Figure 15: Sum of VC Investments, 1990–2005 ($Billions).............................................................20
Figure 16: Average Investment Dollars per Initial Stage Deal ($Millions) ....................................... 21
Figure 17: Average VC Investment Size per Year, 1990-2005 ($Millions) ........................................ 21
Figure 18: Average Investment Dollars at Early Stages per Year ($Millions) ....................................22
Figure 19: Average VC Investment Dollars per Investor by Year ($Millions) ...................................22
Figure 20: Types of Sci./Eng. Degrees Awarded (Standardized) ......................................................23
Figure 21: Number of Accreditations, 2004 ....................................................................................24
Figure 22: Faculty per 100 Students, 2003–2004 ...........................................................................24
Figure 23: Total Wages Disbursed per Worker ($Thousands) .........................................................26
Figure 24: Total Wages per Worker Indexed Growth, 1990–2004 ..................................................26
Figure 25: High-Tech Wages Disbursed per High-Tech Worker ($Thousands) ...............................27
Figure 26: Adjusted High-Tech Wages Disbursed per High-Tech Worker, 2004 ($Ths) .................27
Figure 27: High-Tech Wages Disbursed per High-Tech Worker, Indexed Growth, 1990–2004 ......28
Figure 28: Corporate and Personal Income Tax Rates, 2005 ........................................................... 29
Figure 29: High-Tech GDP, 2004 ($Billions) .................................................................................. 29
Figure 30: High-Tech Productivity Levels, 2004 .............................................................................30
Figure 31: High-Tech Firm Sizes, 2004 ........................................................................................... 31
Figure 32: Percentage of High-Tech Manufacturing Firms, 2004 ................................................... 32
Figure 33: Percentage of High-Tech Services Firms, 2004 ............................................................... 33
Figure 34: Percentage of Clustered High-Tech Firms ......................................................................34
Figure 35: Sum of Investment Dollars and Number of VC Investments per Year, 1990–2005 ........34
Pittsburgh Technology Strategy
Milken Institute
Figure 36: Current Status of Companies Receiving VC Support ..................................................... 35
Figure 37: Average VC Investment per Investment Round ($Millions), 1990–2005 ........................36
Figure 38: Percentage of Startups Receiving VC Dollars, 1990–2005 ............................................36
Figure 39: MSA Population, 1970–2004 (Millions) ........................................................................ 37
Figure 40: Adjusted Income of Inward and Outward Migrants ($Thousands) ................................38
Figure 41: Percentage of Total VC Investment Dollars
by Industry Sub-Group ($Thousands), 1990–2005 .................................................................... 39
Figure 42: Overall GDP, 2004 ($Billions) .......................................................................................40
Figure 43: High-Tech GDP, 2004 ($Billions) .................................................................................. 41
Figure 44: Overall Productivity, 1990–2004 ($Thousands per Worker)..........................................42
Figure 45: High-Tech GDP, 1990–2004 ($Billions) ........................................................................43
Figure 46: High-Tech Productivity, 1990–2004 ($Thousands per High-Tech Worker) ...................43
Figure 47: Pittsburgh Benchmarked High-Tech Manufacturing (Standardized).............................. 45
Figure 48: Pittsburgh Benchmarked High-Tech Services (Standardized).........................................46
Figure 49: Degree Awards by Discipline, 2002–2003 .....................................................................50
Figure 50: High-Tech Degrees Awarded per High-Tech Worker (Standardized) .............................50
Figure 51: Non-High-Tech Wages per Non-High-Tech Worker, 1990–2004 ($Thousands) ............ 76
Figure 52 Non-High-Tech Wages per Non-High-Tech Worker, Indexed to 1990 ............................ 76
Figure 53: Communications Equipment Manufacturing LQ, 1990–2004 ......................................77
Figure 54: Manufacturing and Reproducing Magnetic and Optical Media LQ, 1990–2004 ..........77
Figure 55: Communications Equipment Productivity, Indexed Growth.......................................... 78
Figure 56: Magnetic and Optical Media Productivity, Indexed Growth ......................................... 78
Figure 57: Semiconductor and Other Electronic Component LQ, 1990–2004 ............................... 79
Figure 58: Nav/Measuring/Medical/Control Instruments LQ, 1990–2004 .................................... 79
Figure 59: Aerospace Products and Parts Manufacturing LQ, 1990–2004 ......................................80
Figure 60: Software Publishers LQ, 1990–2004 .............................................................................80
Figure 61: Satellite Telecomm LQ, 1990–2004 ............................................................................... 81
Figure 62: Data Processing, Hosting and Related Services LQ, 1990–2004.................................... 81
Figure 63: Semiconductor and Electronic Component Manufacturing Productivity,
Indexed Growth .......................................................................................................................... 82
Figure 64: Nav/Measuring/Medical/Control Instruments Productivity, Indexed Growth ............... 82
Figure 65: Aerospace Products and Parts Manufacturing Productivity, Indexed Growth ................ 83
Figure 66: Software Publishers Productivity, Indexed Growth ........................................................ 83
Figure 67: Satellite Telecomm Productivity, Indexed Growth..........................................................84
Figure 68: Data Processing Services, Hosting and Services Productivity, Indexed Growth..............84
Figure 69: Computer and Peripheral Equipment Manufacturing LQ, 1990–2004 .......................... 85
Figure 70: Other Information Services LQ, 1990–2004 ................................................................. 85
Pittsburgh Technology Strategy
Milken Institute
Figure 71: Computer and Peripheral Equipment Productivity, Indexed Growth .............................86
Figure 72: Other Information Services, Productivity Indexed Growth ............................................86
Figure 73: Audio and Video Equipment Manufacturing LQ, 1990–2004 ......................................87
Figure 74: Medical Equipment and Supplies Manufacturing LQ, 1990–2004 ................................87
Figure 75: Cable and Other Programs Distribution LQ, 1990–2004 ..............................................88
Figure 76: Other Telecomm LQ, 1990–2004..................................................................................88
Figure 77: Computer Systems Design and Related Services LQ, 1990–2004 .................................. 89
Figure 78: Audio and Video Equipment Manufacturing Productivity, Indexed Growth ................. 89
Figure 79: Medical Equipment and Supplies Productivity, Indexed Growth ...................................90
Figure 80: Cable and Other Programs Distribution Productivity, Indexed Growth ........................90
Figure 81: Other Telecomm Productivity, Indexed Growth ............................................................ 91
Figure 82: Computer Systems Design and Services Productivity, Indexed Growth ......................... 91
Pittsburgh Technology Strategy
Milken Institute
Executive Summary
Look at the economic base of every advanced industrial nation, and you’ll ind lower manufacturing employment igures, but substantially higher production output. Over the past three decades, every region that has
enjoyed economic growth anticipated the need to redirect its efforts toward the development of a knowledgebased economy comprising high-tech industries (with both manufacturing and service sectors) that recruit and
retain the best human capital they can afford.
The foundation for this new economy, based in knowledge (and information) and enriched with knowledge
assets (colleges, universities and research centers), requires viable information systems, innovation facilitators, a
supportive institutional regime and human capital.1 The demand for its high-tech “knowledge goods,” as well
as monetary value of those goods and the salaries of the knowledge workers who produce them, relects the
importance of high technology in today’s world.2 (In fact, income disparities are increasingly shaped by gaps
in knowledge and information capacity.3) Therefore, leveraging human capital — talent, education, skills and
experience — is crucial for planning and generating economic development.
It was, of course, heavy manufacturing that made Pittsburgh prosperous for much of its modern history. And
when the steel industry collapsed in the 1970s and the region’s manufacturing base deteriorated, Pittsburgh
continued to be well served by Carnegie Mellon University and the University of Pittsburgh — the former
strong in computing and engineering, the latter acclaimed for life-sciences research and its organ transplant
institute. Yet even with such an established knowledge base, the Pittsburgh region has proved unable to achieve
its aspirations and foster a high-tech economy that would rival the area’s previous economic achievements. In
fact, the metropolitan-area economy has been in decline.
In 2003, according to that year’s Milken Institute Tech Pole Index — the measure of a region’s high-tech industry concentration and growth in relation to other U.S. Metropolitan Statistical Areas (MSAs) — Pittsburgh
ranked 39th in a ield of 100. The index also measures each MSA’s contribution to U.S. high-tech output, or
real GDP. San Jose, California, in the heart of Silicon Valley and No. 1 on the index, scored a perfect score of
100. In contrast, Pittsburgh scored 4.28.
Nor did Pittsburgh fare much better in the Milken Institute’s Best Performing Cities Index, an annual compilation based on such factors as job-, wage- and salary growth, and technology growth, over a ive-year period.
From 1999 to 2005, Pittsburgh ranked low in overall economic performance; 2003 was the only year the region
appeared in the top 100 MSAs for this index, and even that was at the 96th position. The region scored lowest in 2000, at 188. These rankings suggest that the region lacks the triggers to simulate growth, despite its
boomtown legacy.
For this study, we selected ive MSAs — Baltimore, Indianapolis, Phoenix, Seattle and St. Louis — with which
to compare and contrast Pittsburgh’s high-tech employment and industry growth rate, for example, and local
trends in venture capital investment and university spending in research and development. Each region has
some similarity to Pittsburgh in economic makeup. They are all, for example, midsized metros in transition
[1]
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Milken Institute
that are competing in the global arena. The result of the study, which uses both quantitative and qualitative assessments, is a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis of Pittsburgh’s high-tech base.
The quantitative analysis was broken down into seven components: the innovation pipeline, education infrastructure, business climate, industry development capacity, industry concentration, industry productivity and
industry cuts (or sectors). We analyzed the strengths and weaknesses of Pittsburgh’s high-tech industry and
compared the tech base with those of the ive other MSA cities.
Our indings show that while Pittsburgh has great development potential, it lacks the energy to take the region
to the next level of economic performance. This may be due to a number of factors:
•
•
•
•
aversion to risk
lack of productivity growth
high corporate tax rates
low high-tech wage-income base
•
•
•
•
lack of anchor irms
poor entrepreneurial environment
low immigration
low workforce development
Finally, we break down Pittsburgh’s high-tech industry into 25 industry “cuts”, or sectors. Examining the cuts’
strengths, weaknesses, opportunities and threats, we propose recommendations for ive of them. In brief, the
recommendations are to:
• develop policies that leverage the university talent by encouraging university startups and primary research
and development;
• develop policies that encourage venture capital investments at the corporate level;
• maintain the status quo and develop policies to increase output;
• aggressively support local high-tech development and make Pittsburgh a favorable destination for expansion
or relocation;
• increase enrollment for relevant programs at colleges and universities, and develop scholarship programs and
local industry networks for relevant university programs.
The qualitative analysis examines Pittsburgh’s leadership and business environment, and includes examples of
best practices from other MSAs. A summary of the qualitative SWOT assessment follows:
Strengths:
Strong, university-based knowledge assets; active grassroots political leadership; active incubators; the presence
of established high-tech irms; low personal state and local tax burdens.
Weaknesses:
High risk aversion, negative migration dynamics; no real policy for high-tech development from previous
political leadership; lack of political collaboration; unfavorable corporate tax structure.
Opportunities:
Newly elected political leadership; scholarship programs in Pennsylvania; proximity to high-tech talent;
university collaborations.
[2]
Pittsburgh Technology Strategy
Milken Institute
Threats:
Pittsburgh’s inancial deicit, competition from more economically favorable regions; drain of the knowledge
base; aging population.
In addition, we offer proposals as starting points for the implementation of strategies:
• Initiate technology scholarship programs at leading Pittsburgh universities.
• Develop and offer business services, targeted to opportunity areas, to help high-tech businesses connect
with national venture capital irms and other companies.
• Increase lobbying efforts so that the new regional leadership places industry development high on its agenda.
A more aggressive shift toward the knowledge-based economy offers the potential for Pittsburgh to regain its
growth of the Fifties. This SWOT analysis lays the groundwork for planning initiatives that have the potential
to reinvigorate the area’s dampened economy.
[]
Pittsburgh Technology Strategy
Milken Institute
SWOT Analysis
Introduction
The world’s advanced industrial nations experienced a fundamental economic change three decades ago. In
the 1970s, they began to shift their emphasis from manufacturing to service industries and increased their
industrial dependency on technical information and knowledge.4 To remain viable, local governments and
businesses, for example, found it necessary to use information and communications technologies, thus facilitating computerization and the so-called digital revolution.5 The current demand for technical knowledge, the
monetary value placed on it and the salaries paid to knowledge workers all relect the immense societal value of
the new knowledge industries.6 Income disparities, and regional economic growth, are increasingly shaped by
gaps in knowledge and innovation capacities.7
Four factors, each of which must be sustainable, form the basis of the knowledge economy: information systems, innovation facilitators, a supportive institutional regime and human capital.8 Of these, human capital
(ideas and innovation) lies at the center of every link and interdependence among irms and industries.9
However, just as important as the availability of human capital (high-tech workers) is the ability to attract and
retain that capital and to generate an ongoing low of intellectual capital.10 Countries like Korea, Singapore
and Taiwan, for example, are poor in natural resources but have fared better than their resource-rich neighbors
Thailand, China, Malaysia and Indonesia. Their success is due to their ability to leverage human capital assets. They were quick to focus their economic policies on creating vibrant R&D environments for scientists
and entrepreneurs.11 India, too, learned to leverage human capital to build a robust software industry: Seven
India Institutes of Technology (administered overall by an ITT Council at whose head is the national Minister
of Human Resource Development) provide a steady stream of engineers and entrepreneurs into the country’s
work force.12
Across the United States, regional economies are feeling the effects of technological innovation and assimilation,
making the leveraging of human capital assets a requisite among economic development initiatives.13 The process,
however, requires analysis and collaboration among agencies, and between the private and public sectors.
Pittsburgh MSA
The Pittsburgh region’s economy has been dampened by a number of factors over the past 25 years. Global
shifts in heavy industry and manufacturing affected both population and economic growth. Even with two
world-class universities, Carnegie Mellon University and the University of Pittsburgh, that provide a well-established knowledge base, the population remains lat and the region’s economic performance lags behind the
national economy in some sectors.
Carnegie Mellon is ranked No. 14 worldwide in science and engineering by the U.K.’s Times Higher Education
Supplement (THES) in its 2005 “World University Rankings.” In overall university rankings, THES rated
Carnegie Mellon 21st in the United States. According to the university’s web site, U.S. News & World Report
[5]
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ranked Carnegie Mellon second nationwide in management information systems and ifth in computer engineering. Also last year, The Wall Street Journal rated the graduate school of business second nationally for its
information technology programs and ifth for its entrepreneurship academics. “More than 70 startup companies in the Pittsburgh region have emerged from research conducted by Carnegie Mellon faculty and students,”
the university web site notes, “in ields such as computer science, software engineering and robotics.”14
The University of Pittsburgh, one of the country’s oldest universities, is renowned for its life-sciences research.
This was where a young Jonas Salk, then director of the medical school’s Virus Research Lab, developed a vaccine for polio.15 This past December, the National Medal of Science, the nation’s highest scientiic honor, was
awarded to Thomas Starzl, “the father of transplantation” and a Distinguished Service Professor of Surgery at
the university’s medical school. It was Starzl who made Pittsburgh the organ transplant capital of the world.16
The two schools have a history of innovative collaboration. In 2000, the National Sciences Foundation awarded the Pittsburgh Supercomputing Center, a joint operation of the University of Pittsburgh, Carnegie Mellon
University and Westinghouse Electric Company, a three-year, $45 million grant to create, with help from
Compaq Computer Corp., an open-source Terascale Computing System (TCS), the world’s most powerful
nonmilitary computer system.
Despite these initiatives and assets, Pittsburgh scored 141st in the Milken Institute’s 2004-2005 Best Performing Metros Index, slightly ahead of St. Louis, which ranked 144th. Baltimore, which also has highly respected
universities and enjoyed a strong economy in the 1950s, came in at 60th. Clearly, Pittsburgh’s economic performance is not in sync with its exceptional knowledge base. The region has failed to leverage its assets and
potential.
Project Objectives
Our objectives are to provide an analysis of Pittsburgh’s high-tech industry and to recommend strategies to
develop that industry for regional economic growth.
The best strategy, of course, involves a clearly deined focus, and this research justiies key areas in which to
formulate development initiatives.
Summary of Analysis
In Appendixes A and B, we provide a breakdown of the high-tech industry into numerous manufacturing and
service sectors. Our analysis is based on these deinitions.
Appendix C provides the data sources for the Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis.
[6]
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Milken Institute
The following two tables offer key indicators we used for all six MSAs, including population, high-tech employment, the percentage of high-tech employment, wages for high-tech workers, university R&D activity,
high-tech GDP and productivity, the cost of living and the cost of doing business.
Table 1: Tabulated Summary of High-Tech Economy by MSA - Human Resources
Population 2004
est. (mil)
High-Tech
Employment (ths)
Percentage High-Tech
Employment 2004 (%)
High-Tech Wages per
Worker (2004) ($ths)
Baltimore
2.64
82.9
8.1
$65.40
Indianapolis
Phoenix
1.62
3.72
58.1
117.7
7.8
8.4
$55.20
$71.80
Pittsburgh
2.4
62.7
6.4
$65.10
Carnegie Mellon U.
U. of Pittsburgh
185.3
80.1
16.7
7.1
$79.50
$63.00
U. of Washington
Washington U.
MSA
Seattle
3.17
St. Louis
2.79
Source: Economy.com, U.S. Census Bureau
Key Univ.
John Hopkins U.
U. of Maryland
Indiana U.
Arizona State U.
Table 2: Tabulated Summary of High-Tech Economy by MSA - Performance
HT Productivity
($Ths)
Baltimore
$9.30
$112.39
Indianapolis
$8.40
$144.16
Phoenix
$19.20
$163.00
Pittsburgh
$7.50
$118.84
Seattle
$27.60
$148.75
St. Louis
$8.90
$111.48
Source: Economy.com, U.S. Census Bureau, AUTM
HT GDP ($Bil)
HT Growth
(I=1990)
80.75%
140.79%
210.47%
112.12%
101.11%
55.00%
Overall Growth
(I=1990)
44.50%
84.93%
146.09%
43.72%
69.04%
41.60%
The Milken Institute Tech Pole Index illustrates the high-tech industry concentration and growth for MSAs,
and their U.S. rankings. It is a composite measure, based on the percentage of national high-tech output, or
GDP, multiplied by the high-tech location quotient (LQ). Location quotients are calculated as a measure of a
region’s economic base in a speciic industry, with respect to a larger context. In this case, the larger region is
the United States. Scoring in the tech pole index can range from 0.0 to 100.0. A high score means the region
is exerting a strong technology gravitational pull.
In 2003, the latest rankings compiled by the Milken Institute, all six MSAs ranked among the top 50 metros.
In terms of high-tech industry growth and concentration, San Jose, in irst place here, scored a perfect 100.0.
Note the difference between San Jose and Washington, D.C., which ranked second yet scored just 54.0; and
the even greater disparity between San Jose and the remaining MSAs. Among those, Seattle and Phoenix led, at
third and eighth, with scores of 42.1 and 18.8, respectively. Pittsburgh scored 39th, with a score of 4.3, slightly
ahead of last-place St. Louis, which scored 4.1.
Pittsburgh’s high-tech location quotient (LQ) and high-tech real GDP were lower than four of the benchmarked MSAs. The scores also show large differences in LQ and real GDP between Seattle (and San Jose) at
the high end and Indianapolis, Baltimore, Pittsburgh and St. Louis, at the bottom.
[]
Pittsburgh Technology Strategy
Rank
Score
1
100
Milken Institute
Table 3 Milken Institute Tech Pole Index, 2003
Rank
Score
MSA
26
San Jose, CA
MSA
9.469 Minneapolis-St. Paul, MN-WI
2
54.027
Washington, DC-MD-VA-WV
27
3
42.061
Seattle-Bellevue-Everett, WA
28
7.681 Wichita, KS
8.36 Kansas City, MO-KS
7.367 Boise City, ID
4
40.049
Los Angeles-Long Beach, CA
29
5
32.87
Boston, MA
30
6.708 Huntsville, AL
6
32.445
Dallas, TX
31
7
26.906
Portland-Vancouver, OR-WA
32
6.481 Indianapolis, IN
New Haven-Bridgeport6.437 Stamford, CT
8
18.82
Phoenix-Mesa, AZ
33
6.408 Baltimore, MD
9
16.05
Atlanta, GA
34
5.964 Fort Worth-Arlington, TX
10
15.93
Chicago, IL
35
11
15.148
Philadelphia, PA-NJ
36
5.57 Nassau-Suffolk, NY
5.278 Colorado Springs, CO
Tampa-St. Petersburg5.192 Clearwater, FL
12
15.116
Orange County, CA
37
13
14.688
San Diego, CA
38
4.366 Rochester, NY
14
13.682
San Francisco, CA
39
4.283 Pittsburgh, PA
15
13.302
New York-Newark, NY-NJ-PA
40
4.174 Ventura, CA
16
13.302
Albuquerque, NM
41
4.102 St. Louis, MO-IL
17
12.686
Denver, CO
42
4.085 Tucson, AZ
18
12.146
Detroit, MI
43
3.919 Salt Lake City-Ogden, UT
19
12.105
Austin-San Marcos, TX
44
3.651 Sacramento, CA
Melbourne-Titusville-Palm
3.22 Bay, FL
20
11.894
Raleigh-Durham-Chapel Hill, NC
45
21
11.541
Oakland, CA
46
3.131 Bergen-Passaic, NJ
22
10.931
Houston, TX
47
3.051 Cincinnati, OH-KY-IN
23
10.661
Newark, NJ
48
3.005 San Antonio, TX
24
10.651
Middlesex-Somerset-Hunterdon, NJ
49
2.834 Orlando, FL
25
9.538
Boulder-Longmont, CO
50
2.801 Hartford, CT
Source: Milken Institute
To assess the tech pole in terms of a region’s overall economic performance, we turned to the Milken Institute’s
Best Performing Cities Index. It measures growth in jobs, wages and salaries, and growth over a ive-year period. Such numbers are increasingly relevant in a knowledge-based economy; the best performing cities have
top economic performance and create the most jobs.17
In the same year (2003), Phoenix performed well economically. It enjoyed a good rate of job growth, 2.80 percent (11th) and high-tech GDP growth (15th), and ranked 43rd among the best performing cities.
However, even though Seattle rated third in the tech pole index, it ranked outside the top 100 in the index
of best performing cities. Its high-tech GDP was dropping (80th), yet it enjoyed a very strong high-tech location quotient of 250 (sixth). It suffered a very low rate of wage growth (179th) and job growth (151st) yet still
managed to inish at 138th. Seattle’s good location quotient and a solid percentage (17.32) of its work force in
high-tech industry contributed to its economic performance. The workforce percentage dropped from 18.68
percent in the previous year, but Seattle’s other strengths remained a driving force.
[]
Pittsburgh Technology Strategy
Milken Institute
Looking at its overall economic performance, Pittsburgh ranked consistently below Seattle, Phoenix and Baltimore. In fact, in indexes compiled by the Institute from 1999 to 2005, Pittsburgh was listed just once in the
top 100 MSAs, in 2003, and then at 96th. That year, Pittsburgh experienced a strong GDP growth (indexed to
1997 and 2001). In all other years, the region performed below the other benchmarked MSAs, with the exception of St. Louis. A summary of ratings from 1999 to 2005 follows.
Table 4: Milken Institute Ranks of Best Performing MSAs, 1999-2005
MSA/Ranks
1999
2000
2001
2002
2003
2004
Pittsburgh
111
188
193
132
96
132
Baltimore
137
187
107
81
91
56
Indianapolis
41
40
52
74
101
60
Phoenix
20
24
28
17
43
3
Seattle
1
13
74
92
138
94
St Louis
77
202
217
170
186
151
Source: Milken Institute
2005
141
60
104
15
127
144
Pittsburgh’s low ranking, consistently within the bottom two MSAs, is largely attributable to the slow rate
of job growth. As shown in the following table, the region ranked 41st nationally, with a rate of job growth
reaching only 10 percent from 1990 to 2002. This was far below the 55 percent for Phoenix and 20 percent
for Indianapolis.
Pittsburgh’s rankings were lowest in 2000 and 2001. Ironically, during this period when the state was focusing efforts on regional economic development, the Pittsburgh ranking dropped from 40th to 44th.18 Based
on reports from the U.S. Department of Housing and Urban Development, the median family income for
Pennsylvania’s metropolitan areas increased by just 40 percent between 1989 and 2001, or 20 percent below
the national average.19 Thus, while the state was actively implementing efforts to boost the economy, it is clear
that these efforts were not immediately effective. We explain this further in qualitative assessment. In contrast,
Seattle’s ranking was very good, at No. 13, with its wage growth indexed to 1993. Of course, Seattle also has a
large high-tech industry base.
Pittsburgh’s position rose sharply in 2002, when Comcast Corp., the largest cable company in the United
States, acquired AT&T’s broadband cable systems and entered the Pittsburgh market, where it acquired regional ofices through the acquisition.20 The following year, Pittsburgh again enjoyed marked GDP growth in
its high-tech industry. Its growth, indexed to 1997, ranked 48th nationally; indexed to 2001, it ranked 32nd. In
addition, the region enjoyed a high wage growth, indexed to 2000, at 72nd nationwide. Baltimore fared slightly
better because of its high indexed growth in salaries, but its industry base remained moderately low, at 113.
Then in 2004 and 2005, Pittsburgh’s position dropped to 167th, its rate of job growth having shrunk to -0.57
percent from 2003 to 2004. In 2004, the region suffered large-scale, high-proile job losses that lowered conidence in the area. This may have led to increased out-migration and a slight decrease in population.21 Phoenix,
on the other hand, showed strong job growth and a robust tech industry base.
[9]
Pittsburgh Technology Strategy
Milken Institute
Table 5: Ranked MSAs by Job Growth Rate (Non-Farm employment)
1990 to 2002
Rank
MSA
State
Job Growth Rate
1
Las Vegas
Nevada
90%
2
Austin
Texas
70%
3
Mesa-Phoenix
Arizona
55%
4
Colorado Springs
Colorado
43%
5
San Antonio
Texas
39%
6
Atlanta
Georgia
38%
7
Dallas
Texas
38%
8
Nashville-Davidson
Tennessee
37%
9
Tucson
Arizona
37%
10
Denver
Colorado
36%
11
Charlotte
North Carolina
35%
12
Jacksonville
Florida
35%
13
Albuquerque
New Mexico
34%
14
Fort Worth
Texas
32%
15
Houston
Texas
30%
16
Sacramento
California
30%
17
San Diego
California
28%
18
Oklahoma City
Oklahoma
26%
19
Portland
Oregon
26%
20
Tulsa
Oklahoma
26%
21
Minneapolis
Minnesota
25%
22
Columbus
Ohio
25%
23
Omaha
Nebraska
25%
24
Kansas City
Missouri
24%
25
Fresno
California
23%
26
El Paso
Texas
21%
27
Indianapolis
Indiana
20%
28
Memphis
Tennessee
20%
29
Miami
Florida
19%
30
Oakland
California
19%
31
Seattle
Washington
19%
32
San Jose
California
18%
33
Washington,
DC
18%
34
Virginia Beach
Virginia
17%
35
Wichita
Kansas
14%
36
Milwaukee
Wisconsin
13%
37
New Orleans
Louisiana
12%
38
Boston
Massachusetts
11%
39
Chicago
Illinois
11%
40
Detroit
Michigan
10%
41
Pittsburgh
Pennsylvania
10%
42
Baltimore
Maryland
9%
43
St. Louis
Missouri
8%
44
San Francisco
California
8%
45
Philadelphia
Pennsylvania
7%
46
Cleveland
Ohio
7%
47
New York
New York
2%
48
Honolulu
Hawaii
0%
49
Long Beach/Los Angeles
California
-0.90%
Sources: US Department of Labor, The Allegheny Institute for Public Policy
[ 10 ]
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh is home to seven Fortune 500 companies, listed in the chart below with their corresponding 2003
revenues. On average, the 2005 rankings are down from 2004. Although Alcoa Inc. has revenue higher than
the national average of $14.93 billion, just four companies earned revenues higher than the national median
of $8.19 billion.
From these, Pittsburgh shows potential for growth (with the seven companies) but fails to sustain it (declining
ranks and low revenue). As our analyses in subsequent sections show, the region in fact has few facilitators for
sustainability. This is clear from the charts: a sudden rise from 2001 to 2003, followed by a sharp decline that
brought the region to a ranking lower than it held in 1999.
Table 6: Fortune 500 Companies in Pittsburgh
Company
Alcoa Inc
United States Steel Corp
H J Heinz Co
PPG Industries Inc
PNC Financial Services Group Inc
Mellon Financial Corp
Wesco International Inc
National Average
National Median
Source: USA Today
Rank 2005 Rank 2004
86
82
209
264
213
194
236
233
309
277
385
350
490
461
Revenue 2003 ($Bil)
21.728
9.458
9.328
8.756
5.969
4.55
3.287
14.931
8.193
Baltimore is similar to Pittsburgh in several ways; both regions have renowned universities and underwent economic industrial booms in the 1950s. However, over the seven-year period, and with the exception of university
R&D assets, Baltimore ranks higher overall.
The tech pole and the best performing cities indexes summarize Pittsburgh’s national position. And while
there are many areas to address for remedy, the high-tech industry deserves special attention for this reason:
Regions with greater concentrations of high-tech industries perform better economically.22 More important,
MSAs experiencing the best growth rates share a common characteristic: They attract and nurture high-tech
clusters that can leverage knowledge assets for commercialization and expansion. This is the key to sustained
economic health.
Industry conditions can be improved for the short term with shrewd planning, but university-based assets cannot be changed overnight, and Pittsburgh’s greatest asset — and chief advantage over potential competitors
— lies in the entrenched strength of its universities.
We analyzed the 25 industry cuts, or sectors, within Pittsburgh’s high-tech industry. Using data that capture
industry trends from 1990 to 2004 and tech-irm presence, we targeted speciic industries within those sectors
and reduced the list further to those demonstrating clear strengths, weaknesses, opportunities and threats. We
recommend key focus areas for each cut in the shortlist.
[ 11 ]
Pittsburgh Technology Strategy
Milken Institute
The qualitative assessment of Pittsburgh also highlights key regional strengths, weaknesses, opportunities and
threats. The local knowledge base again shows up as a strength that is not being suficiently leveraged because
of weaknesses in the industrial and political environment. At the same time, grassroots political leadership
shows tremendous activity, and we see strengths in the availability of early risk capital and business coaching
from the nonproit Innovation Works and other tech-based economic development providers.
Knowledge-Base Analysis
1. Innovation Pipeline
The following table provides a summary of strengths and weaknesses, and their corresponding ranks in Pittsburgh’s innovation pipeline. (The innovation pipeline consists of knowledge-base assets, such as universities
and venture capital.)
1. A high strength among peers (strength) is an attribute where Pittsburgh ranks irst or second.
2. A low weakness among peers (weakness) is an attribute where Pittsburgh ranks ifth or sixth.
3. A moderate strength among peers (moderate strength) is an attribute where Pittsburgh ranks third.
4. A moderate weakness among peers (moderate weakness) is an attribute where Pittsburgh ranks fourth.
As in the previous MSA comparisons, it is clear that similar economic makeups do not result in similar performances. Overall, Pittsburgh does not leverage its knowledge-base assets as well as the other ive MSAs.
Table 7: Innovation Pipeline – Summary of Strengths and Weaknesses
Strength
University startups
University patents filed 96-03
University patents issued 96-03
University R&D assets
Corporate R&D assets
R&D expenditure
University licenses executed 96-03
Students enrolled for credit
High tech economic base
No. of VC Investments 05
Sum of VC Investments 05
Average VC investment/investor 05
Maximum VC Investment 05
[ 12 ]
Moderate Moderate
Weakness
Strength Weakness
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh does show considerable strengths in the academic side of its innovation pipeline, ranking third for
publications (igure 1). This is a moderate strength, yet the number of publications is considerably greater than
in Phoenix, Indianapolis and St. Louis.
Figure 1: MSA Total Publications
1998-2002
Thousands
50
44.4
40
34.3
28.0
30
18.5
20
10
6.8
0
6.7
Baltimore Indianapolis
Phoenix
Pittsburgh
Seattle
St. Louis
Source: CEST
In terms of patents iled and issued (igure 2), Pittsburgh again shows academic strengths. At about 40 percent,
its rate of issued patents (patents issued per patent iled) was considerably higher than Baltimore’s (24 percent)
and Phoenix’s (20 percent). Indianapolis scored 45 percent, St. Louis 66 percent and Seattle 38 percent.
Figure 2: Patents Filed and Issued at Universities
1996-2003
4000
Patents Filed
Patents Issued
3024
3000
2000
1000
917
342
0
874
717
512
154
Baltimore Indianapolis
Source: AUTM
366
532
335
349
101
Phoenix
Pittsburgh
Seattle
St. Louis
The calculations are based on total patents iled and issued between 1996 and 2003; the cumulative total is
more important than rates for speciic years because the number of patents issued in a given year may not include just the patents iled that year. (Although the computation of an annual patent issuing rate is problematic,
it can be used to gauge relative R&D quality.)
[ 1 ]
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh shows considerable R&D activity, and the overall quality is veriied by its high patent issuing rate
(igure 3). In fact, the rate surpassed the national average from 1999 to 2002. More important, the steady rates
of Pittsburgh and Seattle show that these two cities have sustainable R&D.
Figure 3: Annual Patents Issuance Rate by Univ.
1996-2003
1.6
Baltimore
Phoenix
Seattle
U.S. Average
1.4
1.2
Indianapolis
Pittsburgh
St. Louis
1.0
0.8
0.6
0.4
0.2
0.0
1996
1997
1998
1999
2000
2001
2002
2003
Source: AUTM
From 1996 through 2003, all universities increased their patent ilings (igure 4); as of 2001, universities were
iling on average more than 100 patents per year.
As expected, Baltimore iled a high number of patents every year. Pittsburgh iled fewer, between 48 and 172, but
showed a trend of increased patent iling. Overall, its iling rate is also slightly higher than the peer MSAs.
Figure 4: Annual Patents Filed by Universities
1996-2003
200
150
Indianapolis (L)
Pittsburgh (L)
St. Louis (L)
Baltimore (R)
700
Phoenix (L)
Seattle (L)
U.S. Average (L)
600
500
100
400
300
50
200
0
1996
1997
1998
1999
2000
Source: AUTM
[ 14 ]
2001
2002
2003
100
Pittsburgh Technology Strategy
Milken Institute
In terms of patents issued (igure 5), Pittsburgh’s R&D strengths show up: Issued patents jumped in 1998 and
remained higher than for the other benchmarked MSAs, except Baltimore, until 2003.
Figure 5: Annual Patents Issued to Universities
1996-2003
160
Baltimore
Phoenix
Seattle
U.S. Average
140
120
Indianapolis
Pittsburgh
St. Louis
100
80
60
40
20
0
1996
1997
1998
1999
2000
2001
2002
2003
Source: AUTM
Pittsburgh’s university R&D expenditure (igure 6) was a moderate strength for the years 1996 to 2003. At
more than $4 billion, the metropolitan area trailed Seattle closely and scored substantially better than Phoenix,
Indianapolis and St. Louis. However, Baltimore’s R&D expenditure was considerably greater.
Figure 6: University R&D Expenditure
1996-2003
US $Billions
12
11.2
10
8
6
4.5
4.9
4
3.0
2.0
2
0.5
0
Baltimore Indianapolis Phoenix Pittsburgh
Source: AUTM
[ 15 ]
Seattle
St. Louis
Pittsburgh Technology Strategy
Milken Institute
To determine the effectiveness of R&D expenditure, we look at the average number of patents issued per $10
million in R&D expenditure (igure 7). Pittsburgh exceeded the national average in 1999 but fell below in
2003. Phoenix and St. Louis performed better for the same period.
Figure 7: Avg. Patents Issued per $10 mil. R&D Exp.
1996-2003
3.5
Baltimore
Phoenix
Seattle
U.S.Average
3.0
Indianapolis
Pittsburgh
St. Louis
2.5
2.0
1.5
1.0
0.5
0.0
1996
1997
1998
1999
2000
2001
2002
2003
Source: AUTM
The average licensing of patent rights executed per $10 million in R&D expenditure (igure 8) shows the extent
to which regions leverage the products of their R&D. Pittsburgh ranked below the national average.
This inding is important. It may indicate that university patents do not see commercialization. On the other
hand, between 1998 and 2001 (the peak and initial downturn of the dot-com boom), Seattle — which ranked
consistently below the national average in terms of patents issued —executed more licenses than the national
average. Seattle is quick to catch on to economic trends, and its research is eficient and cost-effective because
of commercialization.
Figure 8: Avg. Lic. Executed per $10 mil. R&D Exp.
1996 to 2003
4.0
3.0
Baltimore
Phoenix
Seattle
U.S.Average
Indianapolis
Pittsburgh
St.Louis
2.0
1.0
0.0
1996
1997
1998
1999
Source: AUTM
[ 16 ]
2000
2001
2002
2003
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh led in university venture capital startups (AUTM data, igure 9). Despite spending $6.74 billion less
than Baltimore in university R&D, Pittsburgh produced 25 percent more VC startups. Seattle and Pittsburgh
spent roughly the same amount on university R&D, but again Pittsburgh produced 25 percent more VC startups. Local university-based growth potential could surpass Baltimore’s and even be better than that of Seattle,
which led the six MSAs in economic growth.
Figure 9: University VC Startups
1996-2003
70
64.0
60
51.0
50.0
50
40
30
20
17.0
14.0
13.0
10
0
Baltimore Indianapolis Phoenix Pittsburgh
Seattle
St. Louis
Source: AUTM
Pittsburgh’s university VC startups (igure 10) were close to the national average of between 0.1 and 0.2 startup per $10 million expended in R&D. In fact, the region performed consistently well in this regard, compared
to Baltimore, Indianapolis and St. Louis. Overall, the trend reinforces the argument that Pittsburgh’s R&D
strengths are stable and not easily removed from the region.
Figure 10: Avg. U. VC Starts per $10 mil. R&D Exp.
1996-2003
0.70
Baltimore
Phoenix
Seattle
U.S. Average
0.60
Indianapolis
Pittsburgh
St. Louis
0.50
0.40
0.30
0.20
0.10
0.00
1996
1997
1998
1999
Source: AUTM
[ 1 ]
2000
2001
2002
2003
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s university R&D assets ranked second among the MSAs, higher even than Baltimore’s. The measure of university R&D assets is computed by multiplying the rate of patents issued by the licensing revenue
per patent and total R&D expenditure.
The measurement of corporate R&D assets is not so straightforward. It is determined by a composite measure
of sales and the number of R&D service workers. This method is limited, but research has shown that sales and
employment are signiicant positive determinants of corporate R&D investment for the information technology (IT) industry.23 Because R&D investment of irms relects their respective knowledge capital, we used this
measure as a proxy because of data limitations that do not allow as rigorous a computation as for university
R&D assets. We used employment in the R&D service industry to compute corporate R&D, instead of employment in general.
Figure 11 shows standardized scores of university-based and corporate R&D assets for the six MSAs. The
university-based assets are based on cumulative data from 1996 and 2003; corporate assets are derived from
2004 data.
Pittsburgh showed strengths in university R&D but lagged behind Baltimore, Seattle and St. Louis in corporate R&D. Again, despite the strong number of university VC startups, Pittsburgh’s licenses executed (with
respect to research dollars expended) remained low. The sales and employment igures were also not on par.
Again, Pittsburgh is not suficiently leveraging its university R&D assets.
Figure 11: Univ.-Based and Corporate R&D Assets
Standardized Scores
7
University-based
Corporate
6.00 6.00
6
5
4
3.01
3
2.09
2
2.73
2.33
1.46 1.38
1.63
1.38
1.00 1.00
1
0
Baltimore Indianapolis Phoenix Pittsburgh
Source: AUTM, Selectoryonline.com, Economy.com
[ 1 ]
Seattle
St. Louis
Pittsburgh Technology Strategy
Milken Institute
We use location quotients (LQs) to determine the economic base of a region’s high-tech industries relative to
that of the United States in general. From igure 12, we see that Pittsburgh’s base fell below the national level
and, in last place, constitutes a weakness. This is a major concern since the high-tech base is the key to sustained economic growth. To reiterate, MSAs experiencing the best growth rates attract and nurture high-tech
clusters that can leverage knowledge assets for commercialization and expansion. This is the key to sustained
economic health.
Figure 12: High-Tech Location Quotients
2004
2.5
2.14
2.0
1.5
1.04
1.00
1.0
1.07
0.91
0.82
0.5
0.0
Baltimore Indianapolis Phoenix Pittsburgh
Seattle
St. Louis
Source: Milken Institute, Economy.com
Figure 13 illustrates the high-tech location quotient trends from 1990 to 2004. Pittsburgh’s location quotients
for the 14-year period remained consistently below the national average, set at 1.0. Baltimore, meanwhile,
showed signs of growth as it intersected the national average in 2003 and hit 1.04 in 2004. Note that Pittsburgh’s high-tech LQ remained very weak among its peers throughout the period.
Figure 13: High-Tech LQ Trends
1990-2004
3.0
2.5
Baltimore
Phoenix
Seattle
Indianapolis
Pittsburgh
St. Louis
2.0
1.5
1.0
0.5
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ 19 ]
Pittsburgh Technology Strategy
Milken Institute
For all MSAs, the numbers of VC investments (igure 14) follow a similar pattern. The numbers increased in
the late 1990s and then dropped. This comes as no surprise since the IT boom led to increased investments
and the dot-com crash resulted in a decline. The only exception is Indianapolis, which saw no sharp relative
increase in VC investments.
Figure 14: Number of VC Investments
1990-2005
80
Baltimore (L)
Phoenix (L)
St. Louis (L)
Indianapolis (L)
Pittsburgh (L)
Seattle (R)
60
350
300
250
200
40
150
100
20
50
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
0
Source: Venture Economics
Looking at the sum of VC investment dollars per year (igure 15), Pittsburgh was one of the slowest to pick up
the growth momentum of the IT boom. In 1999, its total VC investments ranked No. 6. At its peak, in 2000,
Pittsburgh ranked second, after Baltimore, which had a consistently higher amount of VC investment dollars
during the period.
In 2004, the sum of total VC investment dollars in Pittsburgh ranked No. 3. Although Pittsburgh’s VC investment dollars were slower to gain and to lose momentum, it ranked ifth in 2005, with a sharp decline from
its No. 3 position in 2004. This could be the result of risk adversity in its investment climate and/or a low
concentration of high technology.
Figure 15: Sum of VC Investments
1990-2005
US$ Billions
4.0
Baltimore (L)
Phoenix (L)
Seattle (R)
Indianapolis (L)
Pittsburgh (L)
St. Louis (L)
3.0
2.0
1.0
0.0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
Source: Venture Economics
[ 20 ]
Pittsburgh Technology Strategy
Milken Institute
Taking a closer look at initial-stage deals (i.e., early stage, seed and startup venture capital), Pittsburgh’s VC
investment dollars were greatest in 2000 and 2003 (igure 16). In almost all years before 2000, Pittsburgh did
not perform as well as the peer MSAs. After 2000, signs of growth emerged, but the decline afterward sent
Pittsburgh far below Seattle. Still, in 2005 its performance in initial-stage deals was comparable to that of the
other MSAs.
Figure 16: Avg. Invest. Dollars per Initial Stage Deal
1990-2005
US$ Millions
400
Baltimore
Phoenix
Seattle
Indianapolis
Pittsburgh
St. Louis
300
200
100
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
Source: Venture Economics
Pittsburgh’s average VC investment dollars showed a similar pattern as the sum of VC investment dollars for
the same period (igure 17), with the exceptions of 1994 and 2004. This suggests outliers (anomalies) in those
two years.
Figure 17: Average VC Investment Size per Year
1990-2005
US$ Millions
35
30
Baltimore
Phoenix
Seattle
Indianapolis
Pittsburgh
St. Louis
25
20
15
10
5
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
Source: Venture Economics
[ 21 ]
Pittsburgh Technology Strategy
Milken Institute
Looking at early-stage VC investments, which include seed, startup and early stage investments (igure 18),
Pittsburgh showed signs of growth toward 2000, but decline afterward, a trend found across the benchmarked
MSAs. In 2003 and 2004, however, Pittsburgh’s performance fell below that of most of the other MSAs. It
picked up in 2005 but remained below Phoenix and Seattle. This suggests insuficient investment conidence
in Pittsburgh.
Figure 18: Avg. Invest. Dollars at Early Stages per Yr.
1990-2005
US$ Millions
16
Baltimore
Phoenix
Seattle
14
Indianapolis
Pittsburgh
St. Louis
12
10
8
6
4
2
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
Source: Venture Economics
On average, VC investment per investor in Pittsburgh (igure 19) was lower than that for the other MSAs.
This measure is a proxy to suggest the quality of VC investments in a region. A higher investment amount per
investor suggests a higher quality of the deal. Trends in this measure for Pittsburgh were consistently low, again
with the exception of 1994 and 2004 and their anomalies. Despite the IT boom in the late 1990s, increases in
VC investment per investor were moderate, compared with increases in Indianapolis, Phoenix and St Louis.
Baltimore’s trends were similar to Pittsburgh’s, but consistently higher. Again, exceptions occurred in 1994 and
2004. (Analysis of anchor irms and VC startups appears in later sections.)
Figure 19: Average VC Investment per Investor by Yr.
1990-2005
US$ Millions
25
20
Baltimore
Phoenix
Seattle
Indianapolis
Pittsburgh
St. Louis
15
10
5
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
Source: Venture Economics
[ 22 ]
Pittsburgh Technology Strategy
Milken Institute
In 2004, NEP Broadcasting received a venture capital investment of $101 million ($30 million more than the
next largest local VC investment that year). This single outlier pushed up the sum of VC investment dollars and
average VC investment per investor. The investment qualiies as an outlier because NEP Broadcasting is in the
communications industry, which does not show up as an industry strength in our examination of sectors.
2. Education Infrastructure
Education infrastructure refers to university-related attributes, which are the foundation for the development of
the knowledge base built on human capital. As we found for the innovation pipeline, Pittsburgh also exhibits
strengths in its education infrastructure. Table 8 summarizes strengths and weaknesses.
Table 8: Education Infrastructure – Strengths and Weaknesses Summary
Strength
Moderate
Strength
Moderate Weakness
Weakness
Sci/Engin BA degrees awarded
Sci/Engin MA degrees awarded
Sci/Engin Ph.D. degrees awarded
Sci/Engin assoc degrees awarded
National accreditation
Full time instructional faculty
Regional accreditation
Faculty/Student ratio
Pittsburgh awarded high numbers of science and engineering degrees, and the most doctorates among the six MSAs
for the 2002-2003 academic year (igure 20). The scores were standardized due to data limitations. We scored the
MSAs on a scale of 1.0 to 6.0, whereby a higher score indicates higher awards of the respective degrees.
Figure 20: Sci./Eng. Associate Degrees Awarded
Standardized, 2002-2003
7.5
Bachelor
Doctorate
Master
Associate
5.6
3.8
1.9
0.0
Baltimore Indianapolis Phoenix Pittsburgh
Source: NCES
[ 2 ]
Seattle
St. Louis
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s universities enjoyed high recognition from national and special accreditation agencies (igure 21)
in 2004. Yet while Pittsburgh has strength in national/special accreditations, in the same year its accreditations
from regional agencies were moderately weak.
Figure 21: Number of Accreditations
2004
80
From National/Special Agencies
From Regional Agencies
67
59
60
39
40
31
28
25
26
22
18
20
14
8
3
0
Baltimore Indianapolis Phoenix Pittsburgh
Seattle
St. Louis
Source: NCES
Pittsburgh did have the highest number of faculty per 100 students in the group, yet the number of students
enrolled for credit was comparatively low. One interpretation could be that Pittsburgh attracts solid numbers of
full-time faculty members, but too few students. The situation was similar in Baltimore, which had 15 faculty
members per 100 students but which also attracted more students. However, Pittsburgh’s leading universities are
research universities. Hence having a larger faculty-student ratio is advantageous to transferring the region’s assets
to a small group of high-quality students. Particularly, the University of Pittsburgh and Carnegie Mellon University are not inclined to expand their top departments so as to preserve the quality of their pedagogical practice.
Figure 22: Faculty per 100 Students
2003-2004
18
16
15
14
12
9
7
5
5
5
0
Baltimore Indianapolis Phoenix Pittsburgh
Source: NCES
[ 24 ]
Seattle
St. Louis
Pittsburgh Technology Strategy
Milken Institute
. Business Climate
In order to examine Pittsburgh’s attractiveness as a destination for high-tech business, it is important to understand the region’s basic business conditions. The following assessments apply to the general business climate
but are useful when looking at the high-tech industry, as well.
Table 9: Business Climate – Strengths and Weaknesses Summary
Strength
Cost of living
Cost of doing business
Personal income tax rate
Corporate income tax rate
High-tech wage/High-tech worker 2004
High-tech GDP
High-tech productivity
Moderate Moderate
Weakness
Strength Weakness
In 2004, Pittsburgh had a lower cost of living than four of the other MSAs. Table 10 summarizes that year’s
cost of living and doing business. (The costs of living and doing business are measured relative to the national
average; a score of 100 percent means the regional cost of living equals the national average.)
Table 10: Cost of Living and Cost of Doing Business by MSA
Cost of Living
Cost of Doing Business
Expressed as Percentage Standardized Expressed as Percentage Standardized
MSA
of U.S.
Score
of U.S.
Score
Pittsburgh
86%
1.38
102%
4.44
Baltimore
105%
5.04
106%
5.69
Phoenix
102%
4.46
94%
1.94
Indianapolis
84%
1.00
93%
1.63
St. Louis
87%
1.58
91%
1.00
Seattle
110%
6.00
107%
6.00
Source: Economy.com
Pittsburgh’s cost of living was 14 percent below the national average in 2004. Given the area’s cultural and
natural assets, this was a bargain. Phoenix and Baltimore cost more than average, and Seattle was much more
expensive. In the irst quarter of 2005, the median price of a single-family home in Pittsburgh was about
$106,000, far lower than the national average of $188,000, according to the National Association of Realtors.
This constitutes a strength.
However, Pittsburgh showed a moderate weakness in the cost of doing business, which was 2 percent higher
than the 2004 national average. And among the six MSAs, Pittsburgh showed the greatest difference between
the cost of living and the cost of doing business. The low cost of living may not be a suficiently motivating
factor for industry development.
[ 25 ]
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Milken Institute
For all MSAs, the wages disbursed per worker from 1990 to 2004 (igure 23) were consistently higher than the
national average.
Figure 23: Total Wages Disbursed per Worker
1990-2004
US$ Thousands
70
Baltimore
Phoenix
Seattle
U.S. Average
60
Indianapolis
Pittsburgh
St. Louis
50
40
30
20
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Sources: Milken Institute, Economy.com
However, with the exception of St. Louis, the increments in total wages per worker, indexed to 1990, varied.
Indexed-growth charts demonstrate the annual growth relative to a single year of reference. Given that most
data went back to 1990, we use that as the reference year. The 14-year period from 1990 to 2004 captures the
upswings and downturns of the high-tech industry nationwide.
In 1994, the wage increment for Pittsburgh dropped below the average national increment, where it has remained. Baltimore and Phoenix picked up in 2002 to be on par with the national increment. However, Pittsburgh, Seattle and Indianapolis did not.
Figure 24: Total Wages per Worker Indexed Growth
1990-2004
Percent Change, Index 1990=100
120
100
Baltimore
Phoenix
Seattle
U.S. Average
Indianapolis
Pittsburgh
St. Louis
80
60
40
20
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Sources: Milken Institute, Economy.com
[ 26 ]
Pittsburgh Technology Strategy
Milken Institute
From 1990 to 2004, high-tech wages stood considerably and consistently better than the national average for
all six MSAs (with the exception of St. Louis, which came close in 1992). Although Pittsburgh’s disbursement
of high-tech wages per high-tech worker is higher than the national average, and slightly higher than the average of the top 50 metros, it is lower than that in Baltimore, Indianapolis, Phoenix and Seattle. Between 1999
and 2000, when the IT boom peaked and declined, high-tech wages dropped slightly for Pittsburgh and Indianapolis. Given the relatively low high-tech wages in Pittsburgh, it will be a challenge to attract and retain
talent, regardless of its exceptional knowledge base. Wages may also relect the output quality of the industry.
The area’s low high-tech wages suggest the low-quality output, which leads to lower performance in industry.
Figure 25: HT Wages Disbursed per HT Worker
1990-2004
US$ Thousands
90
80
Baltimore
Phoenix
Seattle
U.S. Average
Indianapolis
Pittsburgh
St. Louis
Top 50 Metros in 2005
70
60
50
40
30
20
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
Adjusting for the cost of living, Pittsburgh’s high-tech wages remained low (igure 26). Still, they appear comparable to nearby Baltimore and St. Louis, which are competitors despite their weaker R&D assets.
Figure 26: Adj. HT Wages Disbursed per HT Worker
2004
US$ Thousands
79.45
80
75
71.77
70
65.36
65.10
65
63.04
60
55.20
55
50
Baltimore Indianapolis Phoenix Pittsburgh
Sources: Milken Institute, Economy.com
[ 2 ]
Seattle
St. Louis
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh does show signs of growth (igure 27), albeit slow growth. Indexing the increase in high-tech wages
to 1990, we see an overall positive and consistent trend (with the exception of 1999 to 2000, when wages
dropped slightly below the national average). In 2002, the annual local increments rose above the national
average again. Still, the igures suggest that while high-tech workers are becoming increasingly valued, local
wages aren’t suficient to attract or keep them, given the rate of inlation. Note that although Pittsburgh’s hightech wages disbursed per high-tech worker are slightly higher than the average of the top 50 metros, its growth
falls behind. This explains its relatively poorer performance as captured by the institute’s best performing cities
indexes.
Figure 27: HT Wages per HT Worker Indexed Growth
1990-2004
Percent Change, Index 1990=100
140
120
100
Baltimore
Phoenix
Seattle
U.S. Average
Indianapolis
Pittsburgh
St. Louis
Top 50 Metros in 2005
80
60
40
20
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
In other industry jobs, Pittsburgh wages scored higher than the national average, but consistently below the
other MSAs (see Appendix E). Indexed to 1990, wages are dropping for non-high-tech workers. Since 1993, the
annual increments have shrunk and show no obvious signs of recovering. In 2004, Pittsburgh was among four
MSAs whose non-tech industry annual wage disbursements increments stood below the national average.
Pittsburgh, Phoenix and St. Louis are all increasing their value placed on the high-tech industries. Baltimore,
on the other hand, appears to be valuing its non-high-tech industries more than its high-tech industries (see
Appendix E). It bodes well that Pittsburgh’s indexed growth in high-tech wages is higher than both the national average and the wages disbursed to non-high-tech workers.
[ 2 ]
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Milken Institute
Although Pittsburgh had the lowest average personal state income tax rate of the MSAs last year, it also had the
highest corporate tax rate, a discouraging sign for startups and spin-offs (igure 28). This may be detrimental
to the development of anchor irms too.
Figure 28: Corporate & Personal Income Tax Rates
2005
Percent
14
State Corporate Tax Rate
State Personal Income Tax Rate
12
10
9.99
8.50
8
7.00
6.97
6.25
6
4
3.07
3.38
3.96
3.40
3.75
2
0.00 0.00
0
PA
MD
AZ
IN
MI
WA
Source: Federation tax Administration
The rankings of the real GDP in high-tech sectors (Figure 29) show that Pittsburgh rated last among the six
MSAs in 2004. We used nominal numbers for NAICS codes 3341, 3342 and 3344 in computing GDP, to account for the decline in prices for computer-related products. Given that the region ranked No. 4 in terms of
high-tech productivity, there would seem to be a fairly low number of people working in the high-tech sector.
Figure 29: High-Tech GDP
2004
US$ Billions
30
27.56
25
19.19
20
15
10
5
9.32
8.92
8.37
7.45
Baltimore Indianapolis Phoenix Pittsburgh
Sources: Milken Institute, Economy.com
[ 29 ]
Seattle
St. Louis
Pittsburgh Technology Strategy
Milken Institute
Productivity was computed by dividing the total real high-tech GDP (with corresponding adjustments made to
the three NAICS codes) by total high-tech employment. In 2004, Pittsburgh ranked second in tech sector productivity and ifth in terms of overall productivity. As with the other MSAs, Pittsburgh’s high-tech sector was
considerably more productive than its overall productivity, suggesting that the sector is a potential strength (igure
30). We provide a closer national comparison of tech productivity trends under Pittsburgh Industry Cuts.
Figure 30: High-Tech Productivity Levels
2004
US$ Thousands per Worker
180
High Tech Productivity
162.98
Overall Productivity
160
148.74
144.13
140
120
119.02
118.83
112.37
111.49
101.38
100
79.50
80
60
78.47
82.45
77.83
Baltimore Indianapolis Phoenix Pittsburgh
Sources: Milken Institute, Eocnomy.com
74.62
Seattle
72.89
St. Louis U.S. Average
Pittsburgh’s low high-tech GDP and productivity fuels the low value of work in its high-tech industry. This
is relected in their low wage-income base. Without a competitive wage-income base, it is not surprising that
Pittsburgh has a declining population since 1970 (see the section on Industry Concentration). It becomes critical therefore, to develop measures to attract and retain its human capital, which constitutes its most valuable
asset for its high-tech industry.
Industry Analysis
1. Industry Development Capacity
Table 11 illustrates how regional conditions affect the growth of high-tech industries.
Table 11: Industry Development Capacity – Strengths and Weaknesses Summary
Strength
University startups
University R&D assets
Corporate R&D assets
High-tech anchors
High-tech establishments
Percentage of high-tech anchors
Entrepreneurial environment
[ 0 ]
Moderate Moderate
Weakness
Strength Weakness
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh leads its peer MSAs in university startups and university R&D assets. However, to have a robust
regional economy, private-sector growth needs to be more developed to absorb university startups and spawn
corporate spin-offs.
Anchor irms, companies with 500 employees or more, play important roles in a region, sparking latent entrepreneurship and providing credibility and inspiration.24 But in 2004, Pittsburgh showed a weakness in hightech anchor irms. With only 17 anchor irms, it stood below all peer MSAs except Indianapolis.
Figure 31: High-Tech Firm Sizes
2004
300
HT Firms (>100 Employees)
HT Firms (>300 Employees)
HT Firms (>500 Employees)
250
HT Firms (>200 Employees)
HT Firms (>400 Employees)
235
199
200
167
139
150
119
111
100
83
56
50
0
32
2317
46
3226
Pittsburgh Baltimore
94
90
63
49
36
68
32
161412
44
3329
Phoenix Indianapolis St. Louis
71
45
37
Seattle
Source: Selectoryonline.com
Pittsburgh again showed a weakness in the number of smaller high-tech businesses in 2004. Looking at numbers of both small and anchor irms, we see that Seattle and Baltimore both scored very well, though most of
their high-tech irms were not anchors. Phoenix, meanwhile, was home to both anchors and smaller irms. The
results suggest that Pittsburgh currently lacks suficient anchor and small irms to support a high-tech industry
base, the driver of sustained economic growth.
Figures 32 and 33 compare the percentages of high-tech manufacturing and service irms in 2004. The bar
for each NAICS code is broken up into colored segments, each representing one MSA. Each MSA segment
includes its percentage of high-tech industries for that NAICS code and shows how it compares with the other
cities. St. Louis, for example, had the greatest share of Medical Equipment and Supplies Manufacturing (NAICS code 3391), with 4.43 percent of its high-tech industries in that sector. The percentages show the portfolio
composition for each MSA for each NAICS code.
In igure 32, Pittsburgh beats the other MSAs in its percentage of Computer and Peripheral Equipment
Manufacturing (NAICS code 3341) and Nav/Measuring/Medical/Control Instruments Manufacturing irms
(NAICS code 3345). Yet the number of these irms among all local high-tech irms stood at just 0.53 percent
and 2.66 percent. Although Pittsburgh led other cities in these speciic manufacturing areas, the local hightech industry didn’t relect this.
[ 1 ]
Pittsburgh Technology Strategy
Milken Institute
Figure 32: Percentage of High-Tech Manufacturing Firms, 2004
3.46%
3391
3364
2.70%
1.87%
0.13%
0.23%
3346
3.35%
0.74%
0.34%
0.07%
3343
0.50%
3342
0.30%
3254
0%
0.64%
0.43%
10%
0.44%
20%
Pittsburgh MSA
1.27%
0.05%
0.30%
0.37%
0.73%
3333
0.39%
0.91%
0.22%
0.53%
3341
2.65%
1.52%
0.36%
0.48%
Phoenix MSA
40%
0.69%
1.93%
0.71%
0.32%
0.20%
0.20%
0.39%
0.85%
Indianapolis MSA
60%
Baltimore MSA
0.40%
0.44%
1.41%
50%
0.94%
0.27%
0.64%
0.13%
0.23%
1.04%
0.08%
0.61%
30%
3.53%
0.65%
2.17%
0.16%
4.43%
0.42%
1.48%
1.40%
3344
0.32%
0.17%
2.66%
3345
1.98%
0.42%
0.64%
70%
80%
St Louis MSA
90%
100%
Seattle MSA
Source: Bureau of Labor Statistics
Pittsburgh also led (igure 33) in Other Information Services (NAICS code 5191) and Medical and Diagnostic Laboratories (NAICS code 6215). Other Information Services include news syndicates, libraries and
archives, as well as various information services that cannot be classiied under existing information services
categories. Again, however, the percentages of local high-tech irms focused in this area were just 2.96 percent
and 4.22 percent, respectively. On a positive note, Pittsburgh had a fairly high percentage of tech irms working in Architectural, Engineering and Related Services (NAICS code 5413) and Computer Systems Design
and Related Services (NAICS code 5415). Nonetheless, Pittsburgh can do better, considering its strong engineering and computer science knowledge base.
[ 2 ]
Pittsburgh Technology Strategy
Milken Institute
Figure 33: Percentage of High-Tech Services Firms, 2004
4.49%
5417
5415
25.13%
5413
5191
0.93%
2.96%
5182
3.16%
5181
1.83%
5179
5175
0.83%
5174
0.13%
5173
1.23%
5172
5171
5121
5112
1.99%
0%
10%
1.76%
20%
Pittsburgh MSA
1.52%
Phoenix MSA
40%
50%
Indianapolis MSA
0.32%
0.10%
1.89%
1.74%
2.06%
2.86%
6.43%
3.48%
3.38%
6.96%
3.53%
1.18%
30%
0.08%
1.47%
3.33%
3.96%
2.53%
3.46%
0.07%
0.99%
4.29%
6.31%
3.70%
1.51%
1.86%
1.71%
1.78%
2.08%
2.18%
4.15%
0.06%
1.67%
2.18%
0.55%
0.59%
0.17% 0.66%
0.05%
0.00%
0.23%
6.18%
28.33%
0.44%
0.78%
0.37%
1.83%
27.69%
0.37%
0.00%
0.84%
29.65%
1.89%
1.91%
2.65%
31.22%
2.50%
2.13%
5.28%
0.12%
0.07%
28.20%
29.24%
34.78%
32.48%
38.29%
35.55%
29.67%
7.25%
3.92%
6.42%
3.57%
2.28%
1.67%
2.62%
2.33%
2.61%
2.26%
4.22%
6215
60%
Baltimore MSA
70%
80%
St Louis MSA
90%
100%
Seattle MSA
Source: Bureau of Labor Statistics
In igure 34, Pittsburgh shows its strength in bioscience irms, but a comparatively low percentage of advanced
manufacturing and IT irms. With a heavier emphasis on biosciences, Pittsburgh may be able to leverage its
concentration of bioscience irms. On the same note, the low concentration of advanced manufacturing and
IT irms shows that Pittsburgh is not suficiently leveraging its assets in science and technology.
[ ]
Pittsburgh Technology Strategy
Milken Institute
Figure 34: Percentage of Clustered High-Tech Firms
2004
Percent
60
Advanced communication and IT
Bioscience
Mass media and telecomm
50
Advanced manufacturing and materials
Internet specific
48
44
42 41
40
43
41
41
39
39
36
35
30
30
20
15
10
0
4
1
1
10
9 10
5
14
14
12
8
7
3
3
4
1
Baltimore Indianapolis Phoenix Pittsburgh
Seattle
1
St. Louis
Source: Bureau of Labor Statistics
The capacity for development depends also on the environment for venture capital. As igure 35 shows, between 1990 and 2005 Pittsburgh’s VC investment climate was comparable to that of the other MSAs, with
the exception of Seattle. Indeed, Seattle scored far better on average than the remaining MSAs. (Seattle had
six investment years, and Phoenix one investment year, in which some VC investments fell outside the range
of the graph. These have been excluded to show more clearly the distribution of investment dollars and number of investments.)
This indicates a fairly optimistic situation for Pittsburgh. The sum of its VC investment dollars and number
of its companies receiving VC investments are quite similar to igures for Baltimore, Indianapolis, Phoenix
and St. Louis. We must note, however, that Pittsburgh experienced a single year (2004), in which investments
were substantially larger.
Figure 35: Sum of Investment Dollars and No. of VC Investments
Per Year 1990-2005
120
100
80
60
40
20
0
$0
$1
Billions
Baltimore
Indianapolis
Phoenix
Source: Venture Economics
[ 4 ]
Pittsburgh
Seattle
St Louis
Pittsburgh Technology Strategy
Milken Institute
Figure 36 shows the breakdown of the current status of companies receiving VC support. The length of the
colored bars relects the breakdown of each status, such as going public, pending acquisition, etc., among
the six MSAs. The percentages in the bars show the breakdown of companies in each status category within
Pittsburgh. In Pittsburgh, 62 percent of companies that received VC funding are in “active investment,”
as shown in igure 36. Of the region’s companies receiving VC investment, 23 percent have been acquired.
These numbers, while positive, remain quite low compared to the totals in Seattle, which again scores better
than the other ive MSAs.
On the negative side, a high percentage of VC startups in Pittsburgh have iled for bankruptcy (both Chapters
7 and 11), in comparison to its peer MSAs. These percentages are far lower than Seattle’s, but higher than
Baltimore’s. Companies receiving VC investments in Pittsburgh tend to have substantially short life cycles.
While the absolute numbers are small, the numbers for the other MSAs are smaller.
Figure 36: Current Status of Companies Receiving VC Support
3.49%
Went Public
Pending Acquisition
0.58%
0.00%
Other
2.33%
Merger
0.00%
LBO
In Registration
0.00%
5.23%
Defunct
1.74%
Bankruptcy - Chapter 7
1.16%
Bankruptcy - Chapter 11
62.21%
Active Investment
23.26%
Acquisition
0%
20%
Baltimore
40%
Indianapolis
Phoenix
60%
Pittsburgh
Seattle
80%
100%
St Louis
Source: Venture Economics
Venture capital can be an indicator for entrepreneurial environment.25 By taking ratios of the sum of investment dollars to the sum of VC investment rounds per company, we can compute the potential for entrepreneurial activity. Figure 37 shows the trends of average VC investment per round from 1990 to 2005.
Pittsburgh’s growth in VC investments per investment round luctuates considerably. Similar to earlier indings, Pittsburgh’s sum of VC investment dollars in 2004 was exceptional, resulting in a positive upturn for the
[ 5 ]
Pittsburgh Technology Strategy
Milken Institute
year (and hence an anomaly). Overall, it seems that entrepreneurial environment based on VC investment per
round in Pittsburgh is not stable. From igure 37, the momentum dropped in 2005. Baltimore, too, shows a
slowdown in growth. Seattle and Phoenix were on the decline but now show signs of recovery. St. Louis has
fallen since 1998 without recovery.
From igure 37, Pittsburgh’s peak in 2000 was lower than that of the other MSAs, and its performance was
optimistic only in 2004. Its average VC investment per round each year was lower than most of its peer MSAs
despite its knowledge resources. Its companies are surviving, but an entrepreneurial climate does not exist
that would enable them to grow. The mechanism to transform its resources to commercial value appears to
be lacking.
Figure 37: Avg. VC Investment per Investment Rnd
1990 to 2005
US$ Millions
25
Baltimore
Phoenix
Seattle
20
Indianapolis
Pittsburgh
St. Louis
15
10
5
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
Source: Venture Economics
From 2000, all the MSAs scored low for the percentage of startups receiving VC support (igure 38). Only
Baltimore and Seattle have enjoyed some recovery in subsequent years.
Figure 38: Percent. of Startups Receiving VC Dollars
1990-2005
Percent
35
Baltimore
Phoenix
Seattle
30
Indianapolis
Pittsburgh
St. Louis
25
20
15
10
5
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05
Source: Venture Economics
[ 6 ]
Pittsburgh Technology Strategy
Milken Institute
2. Industry Concentration
Industry concentration shows the makeup of the economy (table 12). In a knowledge-based economy, hightech industries rely on human resources, which then become the fundamental basis for development.
Pittsburgh shows its weakness in its high-tech economic base, as shown earlier in its innovation pipeline.
From 1990 to 2004, its location quotient (relative to the U.S. location quotient) ranked last among the six
MSAs, and it was the only region whose population continued to decline (igure 39). This weak demographic
trend limited job growth and demand for housing and services, which in turn contributed to general economic slowdown and sluggishness in high-tech industry growth.26
Figure 39: MSA Population
1970-2004
Millions
4.0
Baltimore
Phoenix
Seattle
3.5
Indianapolis
Pittsburgh
St. Louis
3.0
2.5
2.0
1.5
1.0
0.5
70
75
80
85
90
95
00
Source: Economy.com
If we compare adjusted income levels between those moving away from Pittsburgh and those moving to the
region (igure 40), we see that between 1993 and 2004, the median income of those leaving Pittsburgh was
higher than that of those arriving. Pittsburgh and Seattle are the only two MSAs with a lower median income
of inward than outward migrants. Note that the median income of inward migrants of Seattle is similar to
the median income of outward migrants from Pittsburgh. Coupled with low high-tech GDP and productivity, this migration fuels a downward spiral which inhibits the region in leveraging its human capital assets for
long-term growth.
[ ]
Pittsburgh Technology Strategy
Milken Institute
To adjust for the different costs of living in different MSAs, median income values were divided by the cost
of living percentage. Pittsburgh and Seattle are the only two MSAs with a lower average median income of
inward migrants compared to outward migrants. The low income relects the lower value of production by
the inward migrants to Pittsburgh.
Figure 40: Adj. Income of Inward / Outward Migrants
2004
US$ Thousands
55
Median Income of Inward Migrants
Median Income of All Workers
50
Median Income of Outward Migrants
50.1
47.7
44.7
45
44.2
41.6
39.9
40
35
32.2 32.0
30.2 30.4
30
25
20
28.6
24.8
25.5
26.5
26.4
26.4 26.5
24.0
Baltimore Indianapolis Phoenix
Pittsburgh
Seattle
St. Louis
Source:Economy.com
The following chart (igure 41) shows Pittsburgh’s VC investment portfolio broken down by industry subgroups. The length of the bars relects percentage share of VC investment dollars in comparison with other
benchmarked MSAs in each subgroup — utilities, transportation, etc. The number in the bars shows the VC
investment dollars in Pittsburgh from 1990 to 2005. From the chart, Pittsburgh leads its peer MSAs in investments in the “computer/other” subgroup. Yet this amounts to just $16 million. Meanwhile, the $264 million
Pittsburgh received in the “computer and software” subgroup leaves it no stronger than fourth among MSAs.
Given its strong knowledge base in computer science, Pittsburgh should theoretically draw a higher amount
of VC investments in this area. Pittsburgh’s VC investment portfolio shows that money is invested in areas
where the benchmarked MSAs are not focusing their efforts on. Consider Internet-speciic investments,
whereby Pittsburgh’s VCs showed an investment of more than $778 million. However, this was less than one
third of Seattle’s investment in this area. The same applies to medical and health investments.
[ ]
Pittsburgh Technology Strategy
Milken Institute
Figure 41: Percentage of Total VC Investment Dollars by Industry Sub-Group 1990-2005
US$Thousands, 1990-2005
Utilities
Transportation
$4,960
Semiconductor/Electr
$39,080
Other
Medical/Health
$230,566
Manufact.
$8,500
Internet Specific
$778,940
Industrial/Energy
$35,694
Financial Services
$122,214
Consumer Related
$62,061
Construction
$20,340
Computer Software
$264,007
Computer Other
$16,200
Computer Hardware
$75,250
Communications
$336,788
Business Serv.
$63,830
Biotechnology
$171,585
Agr/Forestr/Fish
$3,737
0%
Baltimore
10%
20%
Indianapolis
30%
40%
Phoenix
50%
60%
Pittsburgh
70%
80%
Seattle
90%
100%
St Louis
Source: Venture Economics
. Industry Productivity
The output of a high-tech economy shows how much it produces. And the value of this production is relected
in the wages disbursed. In addition, the ratio of output to input illustrates how productive the economy is.
We use industry GDP as the output measure, and employment as the input measure. We use real GDP for
all industry cuts, and adjusted GDP for NAICS codes 3341, 3342 and 3344. Given the industry reliance on
human capital, we use high-tech employment as the input measure.
[ 9 ]
Pittsburgh Technology Strategy
Milken Institute
Table 13: Industry Productivity – Strengths and Weaknesses Summary
Strength
Moderate
Strength
Moderate Weakness
Weakness
Overall GDP
High-tech GDP
GDP contribution from high-tech
Overall productivity
High-tech productivity
The overall GDPs of the MSAs in 2004 are given in igure 42. Pittsburgh had a weakness in this measure, despite the sharp increase in VC investments for the year. The region rated far below Phoenix and Seattle, whose
GDPs totaled more than $110 billion. Baltimore again draws very close to Pittsburgh in overall GDP.
Figure 42: Overall GDP
2004
US$ Billions
130
116.22
112.40
111
93
84.20
81.04
76.58
74
58.58
56
37
21.35
19
0
Baltimore Indianapolis
Phoenix
Pittsburgh
Seattle
St. Louis U.S. Average
Sources: Milken Institute, Economy.com
Looking at national rankings (table 14), Pittsburgh rated a strong 27th among 379 MSAs. Still, it held the
next-to-last position among the benchmarked MSAs.
Table 14: National Rankings of Overall GDP, 2004
Overall GDP ($Millions)
Phoenix
116,221.18
Seattle
112,395.33
St Louis
84,196.53
Baltimore
81,043.42
Pittsburgh
76,579.88
Indianapolis
58,584.04
Source: Economy.com
[ 40 ]
Ranks 2004
11
12
23
25
27
39
Pittsburgh Technology Strategy
Milken Institute
Despite its VC investment dollars, knowledge assets and high overall GDP, Pittsburgh’s high-tech GDP also
scored low, last among the six MSAs (igure 43). This again suggests that the region is not fully leveraging its
knowledge assets and that the local economy is not suficiently driven by its high-tech industry. The effects of
VC investments in 2004, however, remain to be seen.
Figure 43: High-Tech GDP
2004
US$ Billions
30
27.56
25
19.19
20
15
10
9.32
8.92
8.37
7.45
5
0
2.58
Baltimore Indianapolis
Phoenix
Pittsburgh
Seattle
St. Louis U.S. Average
Sources: Milken Institute, Economy.com
Though it rates above the national average of $3 billion, Pittsburgh’s $7 billion high-tech GDP is not a promising number, despite its position at 33rd.
Table 15: National Rankings of High Tech GDP, 2004
MSA
High-Tech GDP 2004 ($Millions)
Rank
Seattle
$27,558.90
5
Phoenix
$19,187.70
13
Baltimore
$9,321.03
25
St. Louis
$8,923.49
27
Indianapolis
$8,368.18
30
Pittsburgh
$7,450.85
33
Source: Economy.com
Productivity values were computed by dividing GDP by the number of workers for the same period. Table
16 and igure 44 illustrate overall productivity levels. Pittsburgh’s overall productivity is only $5,000 above
the U.S. average. Looking at national rankings, Pittsburgh occupies 124th place. Still, among the peer MSAs,
only Seattle made the top 100.
Table 16: National Rankings of Overall Productivity, 2004
Overall Productivity
($Thousands per worker)
Ranks 2004
Seattle
93.88
26
Phoenix
79.31
109
Pittsburgh
77.54
124
Baltimore
77.37
128
Indianapolis
75.89
146
St. Louis
69.78
209
Source: Economy.com
[ 41 ]
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh did perform considerably and consistently better than the national average from 1990 to 2004, in
terms of its overall productivity.
Figure 44: Overall Productivity
1990 to 2004
US$ Thousands per Worker
100
90
Baltimore
Phoenix
Seattle
U.S. Average
Indianapolis
Pittsburgh
St. Louis
80
70
60
50
40
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
In overall economy, Pittsburgh still faces a big threat from a population downturn. Given its relatively high
overall GDP and low productivity, the metropolitan area appears to have a comparatively high number of
employees who add little value to the high-tech industry. Thus, as employee numbers decline, the quantity or
value of output may be reduced even more.
At this point, we turn to the high-tech industries (see again, igure 30). In Phoenix, the high-tech industry
is substantially driving its economy because of its greater productivity. In Pittsburgh, as well, high-tech productivity surpasses overall productivity. Just the same, its high-tech productivity is comparable to the national
average of $119 per worker.
Nor does Pittsburgh’s high-tech productivity rank within the top 100 MSAs (table 17). At just $119,000, it
ranks lower than national overall productivity but still surpasses Baltimore on both measures. Phoenix tops the
high-tech productivity rankings. Given that Pittsburgh’s high-tech GDP is substantially higher than its overall
GDP, its high-tech industry is clearly driving its economy. However, the comparatively low high-tech GDP is a
cause for concern for the region. This goes beyond its high-tech industry and applies to its overall economy.
Table 17: National Rankings of High-Tech Productivity, 2004
High-Tech Productivity ($Thousands per
high-tech worker)
Ranks 2004
Phoenix
$163.00
41
Seattle
$148.75
74
Indianapolis
$144.16
81
Pittsburgh
$118.84
174
Baltimore
$112.39
199
St Louis
$111.48
202
Source: Economy.com
[ 42 ]
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s high-tech GDP remained consistently low from 1990 to 2004 (Figure 45). In fact, it ranked last
among the MSAs throughout the period, exhibiting few signs of growth during the IT boom. On the other
hand, it did not show signs of sharp decline after the IT crash in 2000.
Figure 45: High-Tech GDP
1990-2004
US$ Billions
30
25
Baltimore
Phoenix
Seattle
U.S. Average
Indianapolis
Pittsburgh
St. Louis
20
15
10
5
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
Pittsburgh’s high-tech productivity also ranked consistently last until 2003, when it moved up to fourth (igure 46). The region still has substantial room for high-tech growth if it can attract more high-tech workers.
Worker retention and the reversal of outward migration are of paramount importance.
Figure 46: High-Tech Productivity
1990-2004
US$ Thousands per High Tech Worker
180
160
Baltimore
Phoenix
Seattle
U.S. Average
Indianapolis
Pittsburgh
St. Louis
140
120
100
80
60
40
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
From 1999 to 2001, growth in local high-tech productivity stagnated but remained relatively unharmed by
the IT crash. However, that productivity remains far below productivity in Phoenix, Indianapolis and Seattle.
And while Pittsburgh’s productivity picked up sharply in 2002, it still could not match the top three MSAs.
[ 4 ]
Pittsburgh Technology Strategy
Milken Institute
In sum, Pittsburgh’s high-tech industry is a key driver of its economy because of the high GDP from its hightech industry compared to its overall GDP. However, its low high-tech productivity suggests that the industry
does not produce high-value, high-tech products and services comparatively. This is relected in its comparatively low high-tech wages. Without a vibrant industry base that produces high-value products and services,
wages will be low. Hence, it becomes dificult to attract and retain quality talent, even those produced by
Pittsburgh’s renowned institutions.
Pittsburgh Industry Cuts
Looking at individual industry sectors, Pittsburgh is strong in two manufacturing sectors, “manufacturing and reproducing optical media” and “communications equipment manufacturing.” Among high-tech
services sectors, Pittsburgh showed no signs of a clear leadership among its peer MSAs.
The next two charts summarize manufacturing and service industry cuts in 2004. The two axes depict standardized scores of the number of establishments and the employment numbers for each of the 25 industry
categories. The scores were computed by standardizing and comparing the number of establishments and
employment numbers for the six MSAs. This method accounts for ordinal differences, as well as the magnitude of the differences.
The scores for employment and establishments were obtained by ranking the absolute numbers of each
attribute against the ive peer MSAs. The magnitude of differences in ranks were taken into consideration and
mapped into a scale of 1 to 6. The resulting scores were then used to plot the following two scatter grams to
illustrate Pittsburgh’s strengths, weaknesses, opportunities and threats in its high-tech industry sectors.
Deinitions
A strength is deined as a sector that scores > = 4.5 in both number of establishments and employment.
A weakness is deined as a sector that scores < = 2.5 in both number of establishments and employment.
An opportunity is deined as a sector that has a score of > = 4.5 in one of the two axes and a moderate score
(2.5 < = x < = 4.5) in the other. Boosting the moderate scores, Pittsburgh could potentially gain another
strength.
A threat is deined as a sector with a score of > = 2.5 in one of the two axes and < = 5 in the other. The lowranking axis may result in the loss of the strength in the corresponding axis without intervention.
[ 44 ]
7
6
Audio & Vide o
Equipm e nt
M anufacturing
5
[ 45 ]
Employment
Employment
M anufacturing &
Re producing M agne tic
& Optical M e dia
M e dical Equipm e nt &
Supplie s
M anufacturing
Com m unications
Equipm e nt
M anufacturing
Pittsburgh Technology Strategy
Figure 47: Pittsburgh Benchmarked High-Tech Manufacturing (Standardized)
Com pute r & Pe riphe ral
Equipm e nt
M anufacturing
Com m e rcial & Se rvice
Indus try M achine ry
M anufacturing
4
3
Se m iconductor & Othe r
Ele ctronic Com pone nt
M anufacturing
2
Nav/M e as uring/M e dical/
Control Ins trum e nts
M anufacturing
Pharm ace utical &
M e dicine
M anufacturing
1
Ae ros pace Products &
Parts M anufacturing
0
1
Source: Economy.com, Bureau of Labor Statistics
2
3
4
Establishments
Establishments
5
6
7
Milken Institute
0
Pittsburgh Technology Strategy
Figure 48: Pittsburgh Benchmarked High-Tech Services (Standardized)
7
Othe r Infor m ation
Se r vice s
Cable and Othe r
Progr am s Dis tr ibution
6
[ 46 ]
Employment
Employment
5
Archite ctur al,
Engine e r ing & Re late d
Se rvice s
Inte r ne t Se r vice
Pr ovide r s and We b
Se ar ch Por tals
4
Te le com m Re s e lle r s
Scie ntific R&D Se r vice s
3
Sate llite Te le com m
M otion Pictur e & Vide o
Indus trie s
2
Data Proce s s ing
Se r vice s , Hos ting, and
Re late d Se r vice s
M e dical & Diagnos tic
Labor ator ie s
Othe r Te le com m
Wir e le s s Te le com m
Car rie rs (e xce pt
Sate llite )
1
Softw ar e Publis he rs
Wir e d Te le com m
Car rie r s
Com pute r Sys te m s
De s ign & Re late d
Se r vice s
0
1
2
3
4
Establishments
Establishments
Establishments
5
6
7
Milken Institute
0
Pittsburgh Technology Strategy
Milken Institute
A summary of the strengths, weaknesses, opportunities and threats is given in table 18. These sectors deserve
special attention. Four sectors were added to the advanced manufacturing and materials cluster for more indepth analysis. These four additional sectors—“basic chemical manufacturing,” “resin, synthetic rubber and
artiicial synthetic ibers and ilaments manufacturing,” “paint, coating, and adhesive manufacturing,” and
“other chemical product and preparation manufacturing”—are not classiied under the Milken Institute’s
high-tech deinition.
Pittsburgh’s strong clusters are its bioscience and internet-speciic clusters, with location quotients around
1.0. However, only 7.95 percent of its high-tech irms are in the internet-speciic cluster. Its irm location
quotients for the bioscience and internet-speciic clusters are 0.57 and 0.78 respectively, a marked difference
from its employment location quotients.
Although the region’s advanced communication and IT cluster showed low location quotients by employment and irms, “communications equipment manufacturing” showed up as a strength in the region, with an
employment location of 1.25. However, its location quotient by irms was only 0.12. This means the sector
is dominated by a small number of companies. Looking at other location quotients by irms for the region,
Pittsburgh’s high-tech clusters do not seem to have a large irm base to match the high-tech employment in
the region. A closer look at the individual industry sectors is given in the subsequent section.
[ 4 ]
Pittsburgh Technology Strategy
Milken Institute
Table 18: Pittsburgh Industry Cuts SWOT Summary
NAICS Cluster
3342
3343
3344
3346
5112
Advanced Communication and IT
3341
3333
3364
3351*
3352*
3355*
Advanced Manufacturing and
Materials
5415
3359*
3254
3391
5413
Bioscience
3345
Industry 2004
SWOT
Emp
LQ
Cluster
Cluster
Firm LQ
Firm LQ
Emp LQ
O
0.46
0.13
S
1.25
0.12
T
4.27
0.15
W
0.46
S
1.5
0.26
W
0.49
0.33
T
0.66
0.80
1.17
0.28
0.04
0.01
Computer and peripheral equipment
manufacturing
Communications equipment
manufacturing
Audio and video equipment
manufacturing
Semi-conductor and other electronic
component
Manufacturing and reproducing
magnetic and optical media
Software publishers
Computer systems design and related
services
Commercial and service industry
machinery manufacturing
Aerospace products and parts
manufacturing
W
0.37
0.83
Basic chemical manufacturing
Resin, synthetic rubber & artificial
synthetic fibers & filaments mfg
Paint, coating, & adhesive
manufacturing
Other chemical product &
preparation manufacturing
Pharmaceutical and medicine
manufacturing
Nav/measuring/medical/control
instruments manufacturing
Medical equipment and supplies
manufacturing
Architectural, engineering and
related services
0.17
0.35
0.70
0.48
2.09
0.23
1.99
0.41
1.10
0.59
0.45
0.04
W
0.54
0.22
T
1.53
0.50
1.01
0.57
1.2
0.87
Scientific r&d services
0.89
0.29
6215
Medical and diagnostic laboratories
1.23
0.81
0.93
0.78
5191
5121
5171
5172
5173
5174
5175
5179
Mass Media and Telecommunications
5182
Internet-Specific
5417
5181
Internet service providers and web
search portals
Data processing services, hosting,
and related services
0.99
W
0.76
O
2.63
2.54
Motion picture and video industries
0.45
0.66
Wired telecomm carriers
0.95
0.39
Wireless telecomm carriers (except
satellite)
0.93
0.36
Telecomm resellers
0.51
Other information services
0.79
0.47
0.41
Satellite telecom
W
0.23
0.47
Cable and other programs
distribution
T
1.76
0.35
Other telecomm
T
0.37
0.86
Source: Milken Institute, Economy.com, Bureau of Labor Statistics
*Additional industries not classified under the Milken Institute's high-tech definition
[ 4 ]
0.54
0.78
0.43
Pittsburgh Technology Strategy
Milken Institute
On the manufacturing side, Pittsburgh has strengths in both “communications and equipment manufacturing,” and “manufacturing and reproducing magnetic and optical media.” “Aerospace products and parts
manufacturing,” “nav/measuring/medical/control instruments manufacturing” and “semiconductor and
other electronic component manufacturing” show up as weaknesses.
“Audio and video equipment manufacturing” constitutes a sector threat. While the sector is very strong in
terms of employment, its number of establishments is very low. Sony and Robicon are the only two irms in
this sector with a presence. The same can be said of “medical equipment and supplies manufacturing.” Cook
Vascular Inc. alone accounts for 1,790 employees. The dominating presence of these large establishments is
advantageous, in terms of sales and employment, and as potential anchor irms that could bring in subsidiary
companies. But the corresponding low number of total establishments in this industry shows a vulnerability
considering the strong dependency on these few establishments.
“Computer and peripheral equipment manufacturing” shows up as an opportunity, but not a clear opportunity because of its moderately high employment numbers.
On the services side, Pittsburgh shows no strengths in any sector. The “software publishers,” “satellite telecom”
and “data processing services, hosting and related services” sectors show up as weaknesses. This inding appears
to reinforce the argument that Pittsburgh is lacking the infrastructure to commercialize university R&D.
Three sectors amount to threats: “cable and other programs distribution,” “computer systems design and
related services,” and “other telecomm.” In “computer systems design and related services,” iGate Corp. accounts for 500 employees. Multiple companies in the other two sectors employ very few workers.
On a positive note, “other information services” is a sector on which Pittsburgh can capitalize. Sector examples include news syndication (NAICS code 51911) and library archives (NAICS code 51912). Currently,
the sector has high employment, but only a moderate number of establishments.
Pittsburgh can exploit its sector “opportunities” by encouraging greater expansion and development to utilize
the high numbers of people trained in these ields. Figure 49 illustrates the distribution of degrees awarded
by discipline. The primary degree disciplines were aggregated to show their direct support for the ive clusters
used in our analysis.
Degrees awarded in biosciences support the bioscience cluster, those awarded in information technology and
communications support the advanced communication and IT, telecommunications, and internet-speciic
clusters, and inally, degrees awarded in engineering and manufacturing disciplines support the advanced
manufacturing and materials cluster. It must be noted that a direct match is dificult to obtain, because degree
awards are not organized by NAICS codes. Also, some disciplines allow the degree holder to cross over to
other industries. However, aggregating the NAICS codes and degree-award disciplines, it is possible to gauge
the degree of support given to the ive major clusters.
[ 49 ]
Pittsburgh Technology Strategy
Milken Institute
Figure 49: Degree Awards by Discipline
2002-2003
10000
Supporting BioSciences
Supporting Engineering and Manufacturing
Supporting IT & Communications
7676
8000
6000
4000
3181
3087
2757
2159
2000
1405
1165
944
311
0
Baltimore Indianapolis
1081
904
642 560
Phoenix
Pittsburgh
1318
1115
Seattle
Source: NCES
Here, Pittsburgh shows obvious strengths in its university assets. Although the number of information technology degrees lagged far behind Phoenix’s in academic year 2002–2003, Pittsburgh’s rankings were comparable to the other four MSAs in life science, natural science and engineering/technician degrees. Of note,
Pittsburgh ranks second in the number of information technology degrees.
The Pittsburgh metropolitan area is large, but its population ranked ifth in 2004. Given the decline in population
since the 1970s, we must interpret the number of degrees relative to the number of local high-tech workers.
Pittsburgh ranks second for all three categories of high-tech degrees awarded. Phoenix awards the most information technology and engineering/technician degrees, most likely attributable to its fast-growing optics
industry. Although Pittsburgh’s high-tech industry does not exhibit the same strengths, its universities produce many technical degrees. Given that these are consistent across the three categories, the key to develop
the high-tech sectors in Pittsburgh is to provide employment opportunities that leverage the knowledge assets
and retain technical talent.
Figure 50: HT Degrees Awarded per HT Worker
Standardized
8
Supporting Biosciences
Supporting Engineering and Manufacturing
Supporting IT and Communications
6.006.00
6
6
5
4.82
4.15
4
3.38
2.95
3
2.59
2
2
1.85
1.62
1 1.57
1 1.001.00
0
Baltimore Indianapolis Phoenix Pittsburgh
Sources: Milken Institute, NCES, Economy.com
[ 50 ]
Seattle
St. Louis
Pittsburgh Technology Strategy
Milken Institute
High-Tech Sector Strengths
Pittsburgh shows strengths in two sectors: “communications and equipment manufacturing,” and “manufacturing and reproducing magnetic and optical media.” The igures in Appendix E show the LQ trends for these
two sectors from 1990 to 2004. We use LQs to show the employment levels relative to the national employment average. In this and the following three sections, we attach the relevant igures in Appendix E.
This “communications and equipment manufacturing” sector is a strength in terms of LQ (see Appendix E,
igure 53) but nevertheless shows poor productivity (Figure 55). Indexed to productivity levels in 1990, the
sector was lower in 2004 than at the start of the measure. Still, the other ive MSAs show growth in productivity for this sector, even though the indexed growth of the U.S. average (1.0) is not promising.
Pittsburgh ranked below both the national average and the other MSAs in 2004. Thus, while the region has
a large economic base for this sector, it is not leveraging human resources as well, which makes this sector a
conditional strength.
Pittsburgh’s “manufacturing and reproducing magnetic and optical media” sector picked up in 1996 and
continued to beat the benchmarked MSAs through 1998 (see Appendix E, igure 54). That year, it crossed
the national average of 1.00. Since then, it has remained a sector strength.
Local productivity dropped below the national average in 1997, however, as did the rate of productivity. In
2004, productivity remained below the national average (which had itself declined sharply in 2003). Compared
to Baltimore, Indianapolis and Phoenix, Pittsburgh did not fare well in productivity growth but managed,
in 2004, to rank third among the benchmarked MSAs. As such, this strength again, becomes a conditional
strength, dependent on whether or not Pittsburgh can leverage human resources to maintain its position.
Neither manufacturing sector had a strong base in 1990, but both showed rapidly increasing employment
with the advent of the dot-com boom and remained strong in 2004, this despite Pittsburgh’s comparatively
lower performance in sector productivity. Their strengths lie in the availability of irms to employ large numbers of tech graduates. It is therefore vitally important to retain these industries and provide a steady low of
graduates for them.
High-Tech Sector Weaknesses
In high-tech manufacturing, Pittsburgh exhibits three weak sectors: “semi-conductor and other electronic
component manufacturing,” “nav/measuring/medical/control instruments manufacturing” and “aerospace
products and parts manufacturing” (see Appendix E). Pittsburgh has not shown strengths in these sectors
over the 14-year period, and they remain undeveloped with low employment. While these sectors may be addressed in the long run, we do not recommend a focus on them for Pittsburgh’s high-tech strategy, especially
since the weaknesses are entrenched.
[ 51 ]
Pittsburgh Technology Strategy
Milken Institute
As expected, productivity growth was lower than the national average and most of the benchmarked MSAs. As
shown in Appendix E, these trends resemble those in the weak high-tech manufacturing sectors. Surprisingly, the
“software publishers” sector does not leverage longstanding local university R&D assets. In contrast, Seattle, which
does not produce the same numbers of high-tech graduates per high-tech worker, clearly leverages its R&D assets,
not just in “software publishers” but also in “data processing services, hosting and related services.”
Pittsburgh has failed to boost employment in these sectors, despite the proximity of renowned universities.
The reason may be attributed to the lower wages paid to workers in these sectors. While the metropolitan area
produces qualiied graduates, it seems unable to keep them from moving elsewhere.
A strategy to develop and attract irms in these three sectors is important. It is interesting to note that existing
software publishing companies themselves fail to exploit their proximity to Pittsburgh’s graduates. The data
processing services sector, in fact, seems not to have exploited it throughout the 14-year period. It is plausible
that the existing companies in these sectors are not suficiently focused on high value-added activities, resulting in a mismatch between their operations and the region’s leading university talents.
For the medium- to long term, Pittsburgh might promote its leading information technology and engineering tertiary programs. In the short term, the area may experience dificulty in addressing these weaknesses,
given the longstanding low employment trends.
High-Tech Sector Opportunities
Appendix E shows the LQ opportunity trends in Pittsburgh’s high-tech sectors from 1990 to 2004. The economic base for “computer and peripheral equipment manufacturing” and “other information services” has
been strong and fairly consistent.
Again in Appendix E, the indexed productivity growth for the two sectors shows that Pittsburgh has not
performed as well as the other MSAs, but does show room for growth.
Intervention to develop or attract new businesses could bolster these sectors. As the data show, there are high
numbers of workers, but a relatively moderate number of businesses. In the short term, this leaves a strong
possibility for development through venture capital support to leverage knowledge assets. Equally important
is the effective leveraging of human resources.
High-Tech Sector Threats
“Audio and video equipment manufacturing,” “medical equipment and supplies manufacturing,” and “cable
and other programs distribution” are three sectors where Pittsburgh shows strengths in employment numbers,
but weaknesses in the number of irms. As explained with the case of “audio and video equipment manufacturing” (see Appendix E, igure 73), it is risky to rely on a few anchor irms to support the entire sector.
[ 52 ]
Pittsburgh Technology Strategy
Milken Institute
The “other telecom” and “computer systems design and related services” sectors feature a low number of
high-tech workers and a high number of businesses. “Other telecom” sectors refer to sectors that do not fall
into the existing telecom categories. With insuficient workers, irms may pull out and trigger a decline in
the sector base.
The indexed productivity growth for these ive sectors are shown in Appendix E. Pittsburgh performed better
than the national average, and potential exists for development. Still, sectors with high numbers of tech irms
lack trained employees; and sectors with high numbers of trained employees lack the irms to hire them.
Leveraging Pittsburgh’s knowledge assets becomes the key to averting threat. In the short term, of course,
it is dificult to boost either employment or the number of irms, but it is possible to avert the threats with
planning. Pittsburgh stands to lose workers and businesses, both of which are mobile, if they are suficiently
motivated by economic opportunity elsewhere. Knowledge assets, however, are the result of a cumulative
process over long periods of R&D using excellent human capital.
Summary of Industry Cuts
Pittsburgh’s high-tech industry shows overall signs of weakness that can chiely be attributed to the fact that
it fails to leverage the area’s strong knowledge base. With the exception of two sectors with manufacturing
strength — “computers and peripheral equipment” and “other information services” — that weakness has
been in evidence since 1990. We also found opportunities, but the entire industry shows threats, including
low productivity and insuficient human capital, that must be addressed in order to avoid decline.
We propose key strategies that target speciic sectors:
For sector strengths, maintaining the status quo will allow Pittsburgh to retain its assets and potentially
strengthen these sectors. At the same time, policies may be introduced that encourage practices to maximize
the human resource potential.
For sector weaknesses, we recommend focusing on primary university R&D as a way to encourage university-based startups and venture capital support. Pittsburgh remains strong in R&D assets and awards a large
number of relevant degrees, and further leveraging could address weaknesses, even in the short term.
For sector opportunities, business expansion will increase employment and the potential for startups and
spin-offs. Increasing both employees and businesses will boost these sectors and offer short-term beneits.
For sector threats, an effective strategy must increase either the number of irms or the number of workers.
This depends on whether the targeted sector has a comparatively low number of irms or workers.
[ 5 ]
Pittsburgh Technology Strategy
Milken Institute
The following table provides a summary of recommendations.
Table 19: Key Recommendations for High-Tech Manufacturing Industry
High-Tech Manufacturing Industry
3254 Pharmaceutical and medicine
manufacturing
3333 Commercial and service industry
machinery manufacturing
3341 Computer and peripheral equipment
manufacturing
3342 Communications and equipment
manufacturing
3343 Audio and video equipment
manufacturing
3344 Semi-conductor and other electronic
component manufacturing
3345 Nav/Measuring/Medical/Control
instruments manufacturing
3346 Manufacturing and reproducing
magnetic and optical media
3364 Aerospace products and parts
manufacturing
3391 Medical equipment and supplies
manufacturing
Key Strategy for Short-Term Development
Develop policies that will encourage VC
investments at corporate level
Maintain status quo and develop policies to
increase output
Encourage the development or inward migration
of companies in these industries
Develop policies to benefit relevant university
startups and encourage primary R&D
Develop policies to benefit relevant university
startups and encourage primary R&D
Maintain status quo and develop policies to
increase output
Develop policies to benefit university startups
and encourage primary R&D
Encourage the development or inward migration
of companies in these industries
[ 54 ]
Pittsburgh Technology Strategy
Milken Institute
Table 20: Key Recommendations for High-Tech Services Sectors
High-Tech Services Industry
5112 Software publishers
Key Strategy for Short-Term Development
Develop policies to benefit relevant university
startups and encourage primary R&D
5121 Motion picture and video industries
5171 Wired telecomm carriers
5172 Wireless telecomm carriers (except
satellite)
5173 Telecomm resellers
5174 Satellite telecom
5175 Cable and other programs distribution
5179 Other telecomm
Develop policies to benefit relevant university
startups and encourage primary R&D
Encourage the development or inward migration
of companies in these industries
Increase intake for relevant programs at
universities.
Develop scholarship programs and local industry
linkages in relevant university programs
5181 Internet service providers and web
search portals
5182 Data processing services, hosting, and Develop policies to benefit relevant university
related services
startups and encourage primary R&D
5191 Other information services
Develop policies that will encourage VC
investments at corporate level
5413 Architectural, engineering and related
services
5415 Computer systems design & related
Increase the intake for relevant programs at
services
universities. Develop scholarship programs and
local industry linkages in relevant university
programs
5417 Scientific R&D services
6215 Medical and diagnostic laboratories
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Regional Economic Overview
Pittsburgh has a strong knowledge base but lacks the necessary industry conditions to exploit it. Development
potential certainly exists, but the region appears to lack the conditions to take the region to the next level of
economic performance. This can be attributed to several factors:
• Aversion to risk: Pittsburgh’s investment climate is slow to gain momentum, relative to other cities in this
analysis. A comparable number of companies may receive venture capital, but the investment dollars are
lower. This is surprising, considering the strong knowledge assets in its innovation pipeline.
• High corporate tax rates: The standard of living in Pittsburgh makes it a favorable region for business and
professional development. However, the high state corporate tax rates may deter business development and
expansion. Baltimore and Indianapolis offer lower corporate taxes.
• Low high-tech wage-income base: The low high-tech industry base does not involve a suficiently high
level of high-tech production and is coupled with low high-tech productivity levels. Without a suficient
industry base to engage in high-value production, it becomes dificult to offer high wages to high-level
high-tech workers. Taken together, the comparatively lower wage-income in the high-tech industry presents
challenges. Without competitive wages, Pittsburgh may be unable to attract and retain the talent that is
concentrated in its renowned academic institutions and research base.
• Lack of anchor irms: A dearth of anchor irms means less potential for spin-offs and clusters. Some established high-tech clusters do exist, but with just a few anchor irms, the short- to medium-term business
climate is vulnerable. This also makes Pittsburgh less desirable for high-tech talent.
• Insuficiently developed entrepreneurial environment: Pittsburgh is not strategically positioned to attract large VC investments. The absence of entrepreneurial capacity, coupled with risk aversion, places the
area in a highly disadvantaged position for tech development.
• Lack of immigration: As explained before, the low wage-income base, the smaller high-tech industry base
and lower productivity are weakening the high-tech industry base. As such, the region cannot attract and
retain the labor force that can leverage its strong R&D base. Although Pittsburgh’s leading institutions produce a quality work force (i.e., the student body), retaining these talents and attracting others become the
most critical elements to ensure commercial success in building a high-tech industry.
• Low workforce development: Again, while Pittsburgh remains strong in the quality of its work force (i.e.,
the student body), their numbers in the industry remain low. The good news is that Pittsburgh’s high-tech
industry productivity remained consistent during the IT crash.
Potential Areas of Focus for the Above Assessment
Kick-Start the Business Environment
Taxation impact is an economic factor that affects decisions of large corporations to locate their headquarters and startups beyond the seeding stage. An unfavorable tax environment therefore, hinders the business
development for both large and small companies, and hence entrepreneurial activities. Workers’ decision to
select safer wage jobs versus risky self-employment is dependent on the differential taxation between these
two choices.27
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Pennsylvania has the third highest corporate net income tax rate in the United States, at 9.99 percent, and
the highest lat tax, as well.28 The high corporate tax rate is disadvantageous to business development on large
and small scales. With the understanding that this tax structure is obsolete, members of the Greater Oakland
Greater Innovation Zone (GO KIZ) have been pushing for change. But in December 2005, Gov. Edward
Rendell vetoed House Bill 515, aimed at corporate tax reform.29
Still, Pennsylvania’s tax burden, as a percentage of personal income, is relatively low. The state ranks 35th
(Table 21) nationwide, and 5th among the six benchmarked MSAs, making it an inexpensive state in which
to live. The tax burden includes business taxes because businesses pass on their costs to consumers.30
The average U.S. tax burden in 2005 is 10.1 percent; the average U.S. tax burden per capita is more than
$3,000.31 Among the six MSAs, Maryland and Indiana rank highest, with 10.3 percent each. Arizona and
Washington come close, at 10.2 percent and 10.0 percent. Pennsylvania follows, with 9.7 percent, and Missouri comes in last, with a state tax burden of 9.4 percent. Though the percentages do not differ substantially,
Pennsylvania’s tax burden per capita in 2005 was $3,700 compared to Maryland’s at $4,500. Pennsylvania’s
tax burden is comparable to the U.S. average. Considering the cost of living in Baltimore and Pittsburgh,
these numbers are not surprising. Baltimore’s cost of living in 2004 was 105 percent of the national average,
compared to the national average, while Pittsburgh’s was only 86 percent.
However, in December 2005, the state House of Representatives passed an increase in the state personal
income tax and an expansion of state sales tax to new items and services. These were initiated to make up
for property tax reductions.32 Currently there are proposals submitted that are pushing for ways to shift the
property tax.
Missouri: Tax Incentives for the Life Science Industry
The state of Missouri has a comprehensive biotech initiative that is poised to boost its high-tech industry.
The Equipment Tax Credit brings beneits to both the industry and distressed communities by means of
a three-year, 40 percent tax credit (capped at $75,000). Meanwhile, the Research and Development Tax
Credit allows companies to claim up to 6.5 percent of incremental increases over a starting base period
of three years.
To develop its life sciences work force, Missouri provides reimbursements to students who work in biotechnology for the irst two years of their higher education. In addition, trade organizations—such as the
Missouri Biotechnology Industry Organization, Technology Gateway Organization and the American
Soybean Association—provide a supply of labor, as well as various services to support projected growth.
The tax incentives are coupled with VC support from Prolog Ventures, RiverVest Venture Partners, Oakwood Medical Investors and Ascension Health Ventures, which raised more than $284 million from
2002 to 2005. Other St. Louis venture capital funds are expected to raise another $250 million.
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Table 21: Combined State and Local Tax Burden by State, 2005
Maine
New York
Hawaii
Rhode Island
Wisconsin
Vermont
Ohio
Nebraska
Utah
Minnesota
Arkansas
Connecticut
West Virginia
New Jersey
Kansas
Louisiana
Maryland
Indiana
Kentucky
California
Arizona
Michigan
Wyoming
Washington
Iowa
Mississippi
Idaho
North Carolina
New Mexico
Illinois
Georgia
Massachusetts
South Carolina
Virginia
Pennsylvania
Oregon
Colorado
Nevada
Montana
Oklahoma
Missouri
North Dakota
Texas
Florida
South Dakota
Alabama
Tennessee
Delaware
New Hampshire
Alaska
Tax Burden
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Tax Burden as a
Percentage of Income
13.00%
12.00%
11.50%
11.40%
11.40%
11.10%
11.00%
10.90%
10.90%
10.70%
10.50%
10.50%
10.50%
10.40%
10.40%
10.40%
10.30%
10.30%
10.30%
10.30%
10.20%
10.10%
10.10%
10.00%
10.00%
10.00%
10.00%
10.00%
9.90%
9.80%
9.80%
9.80%
9.70%
9.70%
9.70%
9.60%
9.50%
9.50%
9.50%
9.40%
9.40%
9.40%
9.30%
9.20%
8.80%
8.70%
8.30%
8.00%
7.40%
6.40%
Sources: Tax Foundation and Bureau of Economic Analysis.
[ 5 ]
Tax Burden
Per Capita
$4,390
$5,170
$4,161
$4,327
$4,141
$4,005
$3,906
$3,984
$3,122
$4,409
$2,993
$5,400
$2,996
$4,971
$3,629
$3,200
$4,501
$3,503
$3,199
$4,078
$3,184
$3,686
$3,802
$3,990
$3,479
$2,739
$3,014
$3,268
$2,882
$3,920
$3,377
$4,608
$2,976
$3,820
$3,747
$3,271
$3,758
$3,423
$2,878
$2,876
$3,282
$3,170
$3,167
$3,271
$2,976
$2,656
$2,757
$3,128
$3,040
$2,452
Pittsburgh Technology Strategy
Milken Institute
Tax reform efforts in the state House indicate the realization by some politicians that Pennsylvania’s business
taxes are hurting the state’s competitiveness.
Currently, Allegheny County Chief Executive Dan Onorato is focusing on three economic development
goals: Brownields Economic Redevelopment, the airport corridor, and university-based expansion to fuel the
high-tech sector. Of note, the airport corridor is deemed as central to the economic growth of the region. 34
At the municipal agenda-setting level, however, Pittsburgh’s leadership has not been stellar. Former Mayor
Tom Murphy targeted real estate focused towards amenities and tourism rather than high-tech, a focus out
of sync with the best strategy for economic development: leveraging the region’s knowledge assets to build
up the high-tech industry. The 2005 real estate market, understandably, did not perform well under conditions of low job growth and stagnant population igures.35 And the region’s plans to promote Pittsburgh as
a tourist and entertainment attraction may also prove ineffectual for economic growth, including growth in
the high-tech industry.36
Worse, despite the high corporate tax rates (and outdated tax structure) and sluggish job growth, Mayor
Murphy proposed tax hikes, modeling his plan after San Francisco and Philadelphia, both of which had even
lower job growth than Pittsburgh from 1990 to 2002.37
In November 2005, newly elected Mayor Bob O’Connor proposed a new plan for restoring the area’s economic health, among them more cooperation among regional leadership and across different levels. His
approach appears to be far less confrontational.38 The collaboration of Dan Onorato and Bob O’Connor in
setting the regional agenda was a good irst step for developing the region’s high-tech industry.
Seattle’s Leadership and Development of High-Tech Industries 9
We cannot overstate the importance of coordinated leadership in the area of high-tech development.
Local governments and agencies often have resources to nurture organizations and industry associations
that develop entrepreneurship. Some cities have established special funds to invest in startups, “smart”
buildings and incubators.
In Seattle, the Washington Software Alliance was created with support from a state economic development agency, and the local chamber of commerce brought together entrepreneurs from several tech sectors to create the Technology Alliance. Public agencies have hosted meetings and offered support services,
including a personal computer user group, activities of the MIT Enterprise Forum, an artiicial intelligence Forum and a science-and-technology roundtable within the Technology Alliance.
Legal guidelines address the city’s for-proit investments. Using those guidelines, the city’s Ofice of Economic Development initiated a revolving loan fund to assist in the creation of a biotech incubator, using
lab space made available when the Fred Hutchinson Cancer Research Center moved to a new site. Because
the city could not legally establish venture capital funds, the local chamber organized a network of “angel”
investors among area residents with training in high-tech disciplines and vested interests in these areas.
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The region’s business climate remains quite conservative. Many companies remain small, with just 10 to 20
employees over years, and exhibit little propensity for expansion. Business expansion, of course, implies sharing
control with external parties, an anathema to those accustomed to conservative operational methods. But reticence only hinders progress in an economy where innovation and industry linkages are key drivers of growth.
To capture a favorable economic position and technology leadership, rapid expansion of irms and production
becomes critical to the transfer of innovation to markets that yield high economic wealth and jobs. Without
this transfer or commercialization on a large scale, the region cannot beneit from the high-tech innovation
in R&D institutions, regardless of their quality.
Regional leaders may consider initiatives that would boost access to venture capital. Local companies have not received
competitive VC funding, and the region could help them gain access to national VCs to sustain initial growth.40
Changes in the state corporate tax environment, as well as entrepreneurial culture issues, will serve the region
well. These issues inhibit Pittsburgh’s ability to leverage its R&D assets. It becomes crucial therefore to initiate
these changes at the state level.
Attract and Retain Talent
High-tech companies depend on talented developers and entrepreneurial thinkers, and they reward them
with high salaries and sometimes with stock options. High-tech leaders also recognize that rival companies
may try to lure away their workers, and that it is crucial to keep them from leaving. An appealing urban environment is one carrot companies use to attract and retain employees.41
Migration patterns are largely determined by unfavorable employment and career prospects.42 These are push
factors because people tend to move to places that have more employment and career advantages. Pittsburgh
is not immune to this: Since 1990, economic opportunities across all local industries have shrunk, and so has
the region’s population. People leaving Pittsburgh tend to have higher incomes than do those moving into the
area. While the income gap is smaller than it was in the 1980s,43 it does suggest that the quality of outward
migrants remains higher than that of inward migrants.
The area’s high-tech industry has experienced longstanding weakness, but successive administrations have failed to
address the problems of low employment and sluggish growth. All the while, comparatively attractive regions are
not so far away (such as Baltimore and Indianapolis) and pose potential threats to local high-tech development.
Pittsburgh’s strength lies in its human capital and its R&D base. The region cannot risk under-utilizing these
assets and eventually losing them. But when companies decide to expand or relocate elsewhere, the human
capital will move. Spinnaker and Seagate are based in Pittsburgh precisely to exploit the local talent in data
storage. Seagate, originally based in California, relocated some of its operations for the express purpose of
being close to Carnegie Mellon University.44 Other high proile companies attempting to exploit Pittsburgh’s
university assets are Intel and Apple. More recently, Google expanded its operations to Pittsburgh to leverage
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the local expertise at Carnegie Mellon University with the aim to create more than 100 high-paying jobs.45
With low employment numbers, however, and fewer skilled workers, companies such as these may see less
economic motivation to stay in Pittsburgh.
Indeed, the data show few signs of potential for employment, so Pittsburgh would do well to take full advantage of existing opportunities, such as the presence of Spinnaker and Seagate, and work quickly to enhance
their sustainability. Spinnaker was acquired by Network Appliance for $300 million.46 It is actively enhancing
their presence in Pittsburgh.
One way to do this is to attract foreign talent while retaining local graduates. Again, there is the competitive
threat from regions that offer more employment opportunities. A 1999 survey of students majoring in business, computer science and engineering at Carnegie Mellon University showed that half wanted to start their
own companies but preferred other regions, such as Boston or California.47
Also in 1999, the Pennsylvania High Education Assistance Agency introduced two scholarship programs
designed to keep students in the area.48 To qualify for aid, students must agree to work for a full year in
Pennsylvania after they graduate. The scholarships could serve as an attractive lure for companies seeking a
steady stream of local talent.49 That same year, the state launched it Sci-Tech scholarships, targeting residents
enrolled full time in science or technology disciplines.50 Sci-Tech recipients must complete internships with
Pennsylvanian companies as part of their degree requirements.
The 1999 GI Bill for the New Economy offers inancial aid to Pennsylvania high school graduates who don’t
plan to attend a college or university, but do want technical training.51 While Pittsburgh may stand to beneit
from these scholarship programs, the region faces in-state competition for graduates from Philadelphia. More
incentives are crucial to attract and retain high-tech workers. Among which, better wage income packages
coupled with the low cost of living in Pittsburgh may work very well to the region’s advantages.
Arizona: Commercialization of the Optics Industry 52
The optics industry in Phoenix (and Arizona) provides a good example of industry linkages; it cuts across
the biotech, aerospace, automotive, semiconductor and telecommunications industries, and represents
a milestone in local high-tech industry development. The early 1990s saw scientists and entrepreneurs
leaving universities to form their own startups, and the state’s economic base began to shift from defense
and aerospace to optics. Today the optics industry is thriving in Phoenix and elsewhere in the state.
Key to this was the fact that R&D originating from area universities remained local. And the high-tech
industry grew through linkages with the optics ield. Arizona can, of course, beneit from further collaboration and increased leverage of its knowledge base, but the existing synergy of high-tech talent in
optics R&D has created strong commercialization links. This research infrastructure remains one of the
internal drivers of the high-tech industry.
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The most successful technology companies build near their talent pools. Hence, Pittsburgh might ind that
tax-free zones work as strong incentives to leverage the region’s knowledge assets in attracting new business.53
In addition, tax-free zones can feed into smart zones, such as the Michigan SmartZones. SmartZones constitute partnerships among universities, industry, research organizations, government, and various community
institutions to facilitate the development of clusters to fuel the growth of high-tech businesses and jobs. Primarily, these are oriented around idea commercialization, patents, and other R&D efforts.54
Just as the new economy requires companies to be lexible, so too must local governments, universities and
businesses think creatively to commercialize innovations. Regions prosper if they develop a human infrastructure of knowledge workers in economic production.55 For Pittsburgh, the challenge is to broaden the focus
of university R&D spending — the R&D pipeline — from primary innovation toward commercialization.56
Nurturing an eficient center for R&D partnerships with universities might serve as a useful strategy to attract
anchors to boost the high-tech industry.
Boost Knowledge-Base Incubation
Incubators boost business growth.57 They provide support to skilled entrepreneurs who need inancial backing. Successful incubation increases the potential to produce high-tech industry leaders. And the success of
the U.S. high-tech industry is at least partly attributable to linkages made easier because universities, government agencies and businesses all share proximity.58
Successful incubation requires support services, including VC investments. In Pittsburgh, nonproits like Innovation Works provide such inancial and strategic assistance to budding technology companies, and work
with universities to boost business opportunities. These kinds of organizations are crucial to the region’s
growth and, in particular, to growth in high-tech. According to a study by Innovation Works, links to large,
national venture capital is necessary for sustained growth.59
In its number of VC investments, Pittsburgh is similar to the other MSAs in our analysis but demonstrates
little momentum for increasing the investments or the dollar amounts invested. As a result, its entrepreneurial
environment remains bleak. With few anchor irms, it may be dificult to establish ties with national VCs and
corporations whose investments sustain local development.
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Indiana: Incubation, Development Initiatives and Tax Incentives 60
Indiana University’s Advanced Research and Technology Institute (ARTI) has more than a hundred technologies available for licensing and recently established its own life-science incubator in Indianapolis. Statewide, Lilly BioVentures (part of the venture capital division of Eli Lilly and Company) is a $75 million VC
fund targeting biotechnology research. The Training Skills Enhancement Fund reimburses up to 50 percent
of employee training costs (capped at $200,000) with grants from the Indiana Department of Commerce.
The state also offers two 10-year inancial incentives to boost the life sciences industry. In 2003, the governor signed into law Energize Indiana, a $1.25 billion plan to create 200,000 high-wage, highly-skilled
jobs in the life sciences and other high-tech ields. Energize Indiana’s Indiana Venture Fund is a $144
million investment that allows companies to leverage state money for startups.
The Pittsburgh Life Sciences Greenhouse can serve as an impetus for additional collaborative efforts. In addition, two non-proit organizations, the Pittsburgh Digital Greenhouse—comprising members from the
Commonwealth of Pennsylvania, member irms and three leading universities in Pennsylvania, and The Robotics Foundry—an organization aimed at accelerating the robotics industry, merged to become The Technology Collaborative (TTC), whose aim is to develop collaborative industry clusters in advanced electronics,
cyber security, and robotics.61
Overall, Pittsburgh will need VC support to help leverage its knowledge base so efforts to create incubators
can succeed. Local universities have high national, but low regional accreditation, suggesting that the market
demand is largely foreign. Innovation Works and similar organizations can work even more closely with universities and companies. A successful strategy will result in long-term collaborations between the universities
and businesses in receipt of venture capital support. Tax incentives may also be advantageous to Pittsburgh’s
high-tech incubation.
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Assessment Summary
A summary of strengths, weaknesses, opportunities and threats from the qualitative assessment is given in the
following tables.
Table 22: Qualitative SWOT Assessment—Strengths and Weaknesses
Strengths
SWOT
Assessment
Strong university-based knowledge assets:
Pittsburgh’s strengths in its universities come as no surprise. However, the
transference of these assets to the high-tech industry is another issue altogether.
Active incubators:
As with any industry anywhere, the presence of incubators is important for creating
active mechanisms to transfer the knowledge base to commercialization. Pittsburgh’s
active incubators strengthen the region’s high-tech development.
Presence of established high-tech firms:
Pittsburgh’s regional talent have attracted world-class technology companies. Among
which, Seagate has established operations in Pittsburgh to leverage the local talent.
Weaknesses
Low state and local individual tax burden:
Pittsburgh’s relatively low cost of living and tax burdens constitute an attraction for
individuals.
High risk aversion:
This is reflected in the VC lag. This cultural characteristic has major effects on the
entrepreneurial capacity in the region.
Negative migration dynamics:
Migration patterns for Pittsburgh show that inward migrants have lower income than
outward migrants. These patterns suggest that the quality of the population is
decreasing. Furthermore, there is a challenge of talent retention, despite CMU being
a net importer of students.
Lack of clear political focus for high-tech development at the state level
In the existing political leadership, there is a lack of a focused direction on high-tech
development. Given the region’s insufficiently leveraged knowledge base, leadership
is important in establishing the institutional infrastructure, such as effective policies,
to enable businesses to capitalize on the region’s strengths.
Unfavorable corporate tax structure:
The high corporate net income and CSFI tax structures make Pittsburgh a less
attractive region for entrepreneurial activities, as does recent legislation to increase
individual income tax.
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Table 23: Qualitative SWOT Assessment—Opportunities and Threats
Opportunities
SWOT
Qualitative Assessment
Scholarship programs in Pennsylvania:
The state’s scholarship programs provide opportunities to attract and retain talent in
Pennsylvania, though not necessarily Pittsburgh. Given the region’s knowledge base,
however, similar programs can be developed locally.
Proximity to high-tech talent:
Major firms prefer to be close to major talent. The University of Pittsburgh and Carnegie
Mellon University can provide a steady stream of skilled graduates.
University collaborations:
Current R&D collaborations often inspire further collaborations. As we have stated, the
knowledge assets in Pittsburgh are not well leveraged. Collaborative efforts are
opportunities to increase that leverage and boost the high-tech industry.
Threats
Competition from economically favorable neighboring regions:
The limitations of the tech industry in Pittsburgh make neighboring regions a draw for
Pittsburgh’s local talent and for companies in search of a relocation or expansion.
Knowledge base drain:
There is tremendous local talent, but not enough for the high-tech industry. Pittsburgh
risks losing its graduates to regions offering greater economic opportunity.
Aging population:
Migration patterns place Pittsburgh at the risk of losing its young talent, without attracting
them from various regions. A high-tech industry needs a continuous supply of young and
entrepreneurial people. The quality of life in Pittsburgh appears to be more attractive to
an older population. Since migration patterns are largely determined by economic
opportunities, Pittsburgh’s relative lack of these deters inward migration and retention of
younger talent.
Brief Recommendations
We propose three recommendations to spur Pittsburgh’s high-tech development, each aimed at creating the
economic environment Pittsburgh must have if it is to leverage its knowledge base and boost industry performance. Ultimately, this will feed medium- and long-term strategies.
• Initiate scholarship programs at Pittsburgh universities.
Given that Pittsburgh lacks the necessary value in the high-tech workforce, the region will beneit from
aggressive promotion of scholarship programs for its young people. Pittsburgh’s universities can develop
more assistance for students entering the technology ields. Considering the competition in Philadelphia for
outstanding students, Pittsburgh’s high-tech industry might fund scholarships, and anchors such as Seagate
Corp. could serve as industry links within the scholarship programs. The maintenance of such industry linkages through scholarship programs facilitates value adding to its high-tech workforce to boost its high-tech
industry base.
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• Develop and offer business services to help new and smaller high-tech businesses connect with
national VCs and irms. These must be targeted at industry opportunity areas.
Pittsburgh boasts a number of active companies in receipt of VC investment. These businesses will need to connect with national VCs and corporations if they wish to expand. Likewise, incubators and business development
organizations, such as Innovation Works, while helpful, can do more to create sustainable development. Perhaps
they can offer more services and develop ways to connect small and new businesses to national players.
The presence of seven key incubators in and around Baltimore indicates the emphasis it places on R&D and
commercialization. In 2001, irms in six of Maryland’s incubators generated between $184 million and $530
million in gross state product; they also paid an average of more than $36,000 per worker.62 This was higher
than the 2002 state average wage of $35,000.
Pittsburgh covers a larger geographic region and has excellent incubator facilities, but has not had comparable
success with the commercialization of its R&D. The area has both for- and nonproit business incubators,63
but their effectiveness remains to be seen. Its university assets — its knowledge base — show strengths and
opportunities in key industry areas.
• Increase lobbying efforts to push the high-tech industry on the agenda of the new government.
Last year’s mayoral election brings opportunities to Pittsburgh, although the deicit crisis may divert attention
from the equally pressing problem of sluggish economic growth. A high-tech agenda is a regional and state
issue and should be taken up to the state government. However, effective representation from the local level is
an initial step the region can take to elevate the agenda to the regional and state level. It is not unreasonable to
suggest that Pittsburgh may ind a short-term solution to the budget crisis by looking at its high-tech industry
and the extensive knowledge base found in its universities.
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Appendix A: Deinition of High-Tech Manufacturing
3254
3333
3341
3342
3343
3344
3345
Pharmaceutical and Medicine Manufacturing
325411 Medicinal and Botanical Manufacturing
325412 Pharmaceutical Preparation Manufacturing
325413 In-Vitro Diagnostic Substance Manufacturing
325414 Biological Product (except diagnostic) Manufacturing
Commercial and Service Industry Machinery Manufacturing
333311 Automatic Vending Machine Manufacturing
333313 Ofice Machinery Manufacturing
333314 Optical Instrument and Lens Manufacturing
Computer and Peripheral Equipment Manufacturing
334111 Electronic Computer Manufacturing
334112 Computer Storage Device Manufacturing
334113 Computer Terminal Manufacturing
334119 Other Computer Peripheral Equip Manufacturing
Communications Equipment Manufacturing
33421 Telephone Apparatus Manufacturing
33422 Radio and TV Broadcasting and Wireless Comm Equip Mfg
33429 Other Comm Equip Manufacturing
Audio and Video Equipment Manufacturing
33431 Audio and Video Equipment Manufacturing
Semiconductor and Other Electronic Component Manufacturing
334411 Electron Tube Manufacturing
334412 Bare Printed Circuit Board Manufacturing
334413 Semiconductor and Related Device Manufacturing
334414 Electronic Capacitor Manufacturing
334415 Electronic Resistor Manufacturing
334416 Electronic Coil, Transformer, and Other Inductor Mfg
334417 Electronic Connector Manufacturing
334418 Printed Circuitry Assembly
334419 Other Electronic Component Manufacturing
Nav/Measuring/Medical/Control Instruments Manufacturing
334510 Electromedical and Electrotherapeutic Apparatus Manufacturing
334511 Search, Detection, Navigation, Guidance, Aeronautical Instruments
334512 Automatic Environmental Control Mfg for Residential, Comm,
and Appliance
334513 Instruments and Related Products Mfg for Measuring Displaying
334514 Totalizing Fluid Meter and Counting Device Manufacturing
334515 Instrument Mfg for Measuring and Testing Electricity and Electric Signals
334516 Analytical Laboratory Instrument Manufacturing
334517 Irradiation Apparatus Manufacturing
334518 Watch, Clock and Part Manufacturing
334519 Other Measuring and Controlling Device Manufacturing
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Pittsburgh Technology Strategy
3346
3364
3391
Milken Institute
Manufacturing and Reproducing Magnetic and Optical Media
334611 Software Reproduction
334612 Prerecorded Compact Disc (Except Software), Tape, and Record Mfg
334613 Magnetic and Optical Recording Media Manufacturing
Aerospace Products and Parts Manufacturing
336411 Aircraft Manufacturing
336412 Aircraft Engine and Engine Parts Manufacturing
336413 Other Aircraft Parts and Auxiliary Equipment Manufacturing
336414 Guided Missiles and Space Vehicle Manufacturing
336415 Guided Missile and Space Vehicle Propulsion Unit and Parts Mfg
336419 Other Guided Missile and Space Vehicle Parts and Auxiliary Equip Mfg
Medical Equipment and Supplies Manufacturing
339111 Laboratory Apparatus and Furniture Manufacturing
339112 Surgical and Medical Instrument Manufacturing
339113 Surgical Appliance and Supplies Manufacturing
339114 Dental Equipment and Supplies Manufacturing
Appendix B: Deinition of High-Tech Services
5112
5121
517
518
5191
5413
Software Publishers
51121 Software Publishers
Motion Picture and Video Industries
51211 Motion Picture and Video Production
512191 Teleproduction and Other Postproduction Services
512199 Other Motion Picture and Video Industries
Telecommunications
5171 Wired Telecomm Carriers
5172 Wireless Telecomm Carriers (except Satellite)
5173 Telecomm Resellers
5174 Satellite Telecomm
5175 Cable and Other Programs Distribution
5179 Other Telecomm
Internet Service Providers, Web Search Portals, and Data Processing Services
5181 Internet Service Providers and Web Search Portals
5182 Data Processing Services, Hosting, and Related Services
Other Information Services
Architectural, Engineering and Related Services
54131 Architectural Services
54133 Engineering Services
54136 Geophysical Surveying and Mapping Services
54137 Surveying and Mapping (except geophysical) Services
54138 Testing Laboratories
[ 6 ]
Pittsburgh Technology Strategy
5415
5417
6215
9271
Milken Institute
Computer Systems Design and Related Services
541511 Custom Computer Programming Services
541512 Computer Systems Design Services
541513 Computer Facilities Management Services
541519 Other Computer Related Services
Scientiic R&D Services
54171 R&D in the Physical, Engineering, and Life Sciences
54172 R&D in the Social Sciences and Humanities
Medical and Diagnostic Laboratories
Space Research and Technology
Appendix C: Data Sources
The following databases were used extensively for analysis:
Association of University Technology Managers (AUTM)
http://www.autm.net
Selectoryonline.com
http://www.selectoryonline.com
National Center for Education Statistics (NCES)
http://nces.ed.gov/
PriceWaterHouse Coopers (PWC) Money Tree, Venture Economics
http://www.pwcmoneytree.com/moneytree/
http://ventureeconomics.com/
U.S. Census Bureau
http://www.census.gov/
Economy.com
http://www.economy.com
Internal Revenue Service
http://www.irs.gov
Bureau of Labor Statistics
http://www.bls.gov
Bureau of Economic Analysis
http://www.bea.gov
Federation of Tax Administrators
http://www.taxadmin.org
Tax Foundation
http://www.taxfoundation.org/
[ 69 ]
Pittsburgh Technology Strategy
Milken Institute
Appendix D: Top 100 Performing Metros 1999-2001
1999
1 Seattle WA
2000
1 Austin TX
2001
1 San Diego CA
2 Austin TX
2 Atlanta GA
2 Dallas TX
3 Dallas TX
3 Santa Rosa CA
3 Las Vegas NV-AZ
4 Ventura CA
4 Boulder CO
4 Santa Rosa CA
5 Oakland CA
5 Boise City ID
5 Boise City ID
6 Middlesex NJ
6 San Diego CA
6 McAllen TX
7 Denver CO
7 Orange County CA
7 Oakland CA
8 San Jose CA
8 San Antonio TX
8 Ventura CA
9 Houston TX
9 West Palm Beach FL
9 San Luis Obispo CA
10 Atlanta GA
10 Colorado Springs CO
10 Brownsville TX
11 Orange County CA
11 Fort Collins CO
11 Austin TX
12 San Diego CA
12 Oakland CA
12 Laredo TX
13 Omaha NE
13 Seattle WA
13 Orange County CA
14 Santa Rosa CA
14 Charlotte NC-SC
14 West Palm Beach FL
15 Tampa FL
15 Fort Worth TX
15 Riverside CA
16 San Francisco CA
16 Decatur IL
16 Boulder CO
17 Boulder CO
17 Cedar Rapids IA
17 Fort Worth TX
18 Raleigh NC
18 Portland OR-WA
18 Vallejo CA
19 Ann Arbor MI
19 Provo UT
19 Raleigh NC
20 Phoenix AZ
20 Sacramento CA
20 Orlando FL
21 Washington DC
21 Salt Lake City UT
21 Reno NV
22 Sioux Falls ND
22 Raleigh NC
22 Tampa FL
23 Portland OR
23 Sioux Falls SD
23 Sacramento CA
24 Wilmington DE
24 Phoenix AZ
24 Houston TX
25 Minneapolis MN
25 Dallas TX
25 Washington DC-MD-VA-WV
26 Columbus GA
26 Wilmington DE-MD
26 Fayetteville AR
27 Madison WA
27 Omaha NE-IA
27 Stockton CA
28 Sacramento CA
28 San Luis Obispo CA
28 Phoenix AZ
29 Boston MA
29 Tucson AZ
29 San Francisco CA
30 Albuquerque NM
30 Iowa City IA
30 Naples FL
31 Chicago IL
31 San Jose CA
31 Las Cruces NM
32 Tulsa OK
32 Las Vegas NV-AZ
32 Kansas City MO-KS
33 Wichita KS
33 Tampa FL
33 Houma LA
34 Cincinnati OH
34 Ventura CA
34 Santa Barbara CA
35 Ft Worth TX
35 Brownsville TX
35 San Antonio TX
36 San Antonio TX
36 Riverside CA
36 Jacksonville FL
37 Nashville TN
37 Wilmington NC
37 Albuquerque NM
38 Salt Lake City UT
38 McAllen TX
38 Fort Lauderdale FL
39 Columbus OH
39 Tallahassee FL
39 Lubbock TX
40 Baton Rouge CA
40 Indianapolis IN
40 Denver CO
41 Indianapolis IN
41 Houston TX
41 Colorado Springs CO
42 Kansas City MO
42 Kansas City MO-KS
42 Chico CA
43 Jackson MS
43 Columbia SC
43 Santa Cruz CA
44 Lafayette IN
44 San Francisco CA
44 Sarasota FL
45 Lubbock TX
45 Washington DC-MD-VA-WV
45 Iowa City IA
46 Tucson AZ
46 Yuma AZ
46 Tyler TX
47 New Haven CT
47 Minneapolis MN-WI
47 Melbourne FL
48 New York City NY
48 Naples FL
48 Fort Myers FL
[ 0 ]
Pittsburgh Technology Strategy
Milken Institute
1999
49 Vallejo CA
2000
49 Eau Claire WI
2001
49 Punta Gorda FL
50 Riverside CA
50 Chico CA
50 Sioux Falls SD
51 Boise City ID
51 Columbus GA-AL
51 Modesto CA
52 Milwaukee WA
52 Sarasota FL
52 Indianapolis IN
53 Eugene OR
53 Greenville NC
53 Fort Walton Beach FL
54 Grand Rapids MI
54 Orlando FL
54 Boston MA
55 Fresno CA
55 Salinas CA
55 Salt Lake City UT
56 Tallahassee FL
56 St. Cloud MN
56 Greenville NC
57 Trenton NJ
57 Boston MA
57 Charleston SC
58 Santa Barbara CA
58 Vallejo CA
58 Asheville NC
59 Orlando FL
59 Santa Cruz CA
59 Green Bay WI
60 Stockton CA
60 Nashville TN
60 Brazoria TX
61 Beaumont TX
61 Greeley CO
61 Fort Collins CO
62 Los Angeles CA
62 Myrtle Beach SC
62 Dutchess County NY
63 Pensacola FL
63 Dutchess County NY
63 Provo UT
64 Tacoma WA
64 Waco TX
64 San Jose CA
65 Anchorage AK
65 Baton Rouge LA
65 Anchorage AK
66 Columbia MO
66 Pueblo CO
66 Tallahassee FL
67 Newark NJ
67 Fort Walton Beach FL
67 Tucson AZ
68 Louisville KY
68 Salem OR
68 Myrtle Beach SC
69 Burlington VT
69 Eugene OR
69 Bakersfield CA
70 Memphis TN
70 Lawrence KS
70 Charlotte NC-SC
71 Lexington NE
71 Fargo ND-MN
71 Columbus OH
72 Provo UT
72 Fort Myers FL
72 Pocatello ID
73 Charlotte NC
73 Lincoln NE
73 Wilmington DE-MD
74 Detroit MI
74 Rochester MN
74 Seattle WA
75 Des Moines IA
75 Ann Arbor MI
75 Springfield MO
76 El Paso TX
76 Nassau NY
76 Nassau NY
77 St Louis MO
77 Fort Lauderdale FL
77 Rochester MN
78 Charleston SC
78 Greenville SC
78 Tulsa OK
79 Nassau NY
79 Bloomington IL
79 Portland OR-WA
80 Chattanooga TN
80 Monroe LA
80 Monmouth NJ
81 Fort Lauderdale FL
81 Philadelphia PA-NJ
81 Greeley CO
82 Monmouth NJ
82 Reno NV
82 Atlanta GA
83 Jersey City NJ
83 Denver CO
83 Yuba City CA
84 Philadelphia PA
84 Gainesville FL
84 Billings MT
85 Melbourne FL
85 Tyler TX
85 Trenton NJ
86 Canton OH
86 Lexington KY
86 Bryan TX
87 Birmingham AL
87 Bryan TX
87 Middlesex NJ
88 Cedar Rapids IA
88 Hamilton OH
88 Fresno CA
89 Daytona Beach FL
89 Richland WA
89 Wilmington NC
90 Charleston WV
90 Green Bay WI
90 Nashville TN
91 Elkhart IN
91 Wichita KS
91 Monroe LA
92 Little Rock AR
92 Grand Rapids MI
92 Lancaster PA
93 Gainesville FL
93 Laredo TX
93 Lafayette LA
94 West Palm Beach FL
94 Olympia WA
94 Charlottesville VA
95 Jacksonville FL
95 Tulsa OK
95 Richland WA
96 South Bend IN
96 Springfield MO
96 Medford OR
97 Richmond VA
97 Biloxi MS
97 Visalia CA
98 Appleton WI
98 Lancaster PA
98 Madison WI
[ 1 ]
Pittsburgh Technology Strategy
Milken Institute
Top 100 Performing Metros 1999-2001, cont.
1999
2000
99 Modesto CA
2001
99 La Crosse WI-MN
100 Davenport WA
99 Louisville KY-IN
100 Tacoma WA
100 Huntsville AL
Source: Milken Institute
Appendix D: Top 100 Performing Metros 2002-200
2002
1 San Diego CA
2003
1 Fayetteville AR
2 Santa Rosa CA
2 Las Vegas NV-AZ
3 Las Vegas NV-AZ
3 Fort Myers FL
4 Ventura CA
4 West Palm Beach FL
5 McAllen TX
5 San Diego CA
6 Boise City ID
6 San Luis Obispo CA
7 San Luis Obispo CA
7 Laredo TX
8 Oakland CA
8 Brownsville TX
9 Brownsville TX
9 McAllen TX
10 Orange County CA
10 Monmouth NJ
11 Riverside CA
11 Anchorage AK
12 West Palm Beach FL
12 Raleigh NC
13 Boulder CO
13 Chico CA
14 Dallas TX
14 Ventura CA
15 Vallejo CA
15 Sacramento CA
16 Laredo TX
16 Houma LA
17 Phoenix AZ
17 Vallejo CA
18 Sacramento CA
18 San Antonio TX
19 Austin TX
19 Washington DC-MD-VA-WV
20 Raleigh NC
20 Riverside CA
21 Houston TX
21 Madison WI
22 Reno NV
22 Reno NV
23 Fayetteville AR
23 Naples FL
24 Stockton CA
24 Modesto CA
25 Tampa FL
25 Houston TX
26 Fort Worth TX
26 Stockton CA
27 Lubbock TX
27 Tampa FL
28 Washington DC-MD-VA-WV
28 Tallahassee FL
29 Colorado Springs CO
29 Fort Lauderdale FL
30 Fort Lauderdale FL
30 Oakland CA
31 Santa Barbara CA
31 Orange County CA
32 Naples FL
32 Fresno CA
33 Houma LA
33 Fort Worth TX
34 Albuquerque NM
34 Dutchess County NY
35 Orlando FL
35 Charleston SC
36 San Antonio TX
36 Colorado Springs CO
37 Fort Myers FL
37 Albany NY
38 Santa Cruz CA
38 Lancaster PA
39 Sarasota FL
39 Albuquerque NM
40 Modesto CA
40 Tucson AZ
41 Chico CA
41 Sarasota FL
42 Kansas City MO-KS
42 Bakersfield CA
43 Dutchess County NY
43 Phoenix AZ
44 Jacksonville FL
44 Middlesex NJ
[ 2 ]
Pittsburgh Technology Strategy
Milken Institute
2002
2003
45 Boston MA
45 Orlando FL
46 Middlesex NJ
46 Charlotte NC-SC
47 Melbourne FL
47 Jacksonville FL
48 Anchorage AK
48 Knoxville TN
49 Fort Collins CO
49 Wilmington NC
50 Monmouth NJ
50 Lincoln NE
51 Denver CO
51 Green Bay WI
52 Myrtle Beach SC
52 Santa Rosa CA
53 Bakersfield CA
53 Nashville TN
54 San Francisco CA
54 Myrtle Beach SC
55 Portland OR-WA
55 Miami FL
56 Provo UT
56 Brazoria TX
57 Tucson AZ
57 Lubbock TX
58 Wilmington DE-MD
58 Tacoma WA
59 Tulsa OK
59 Austin TX
60 Salt Lake City UT
60 Visalia CA
61 San Jose CA
61 Kansas City MO-KS
62 Brazoria TX
62 Lafayette LA
63 Atlanta GA
63 Olympia WA
64 Allentown PA
64 Newburgh NY-PA
65 Trenton NJ
65 Wilmington DE-MD
66 Columbus OH
66 Nassau NY
67 Fresno CA
67 Norfolk VA-NC
68 Lancaster PA
68 Springfield MO
69 Tallahassee FL
69 Richland WA
70 Lafayette LA
70 Melbourne FL
71 Huntsville AL
71 Merced CA
72 Nassau NY
72 Boise City ID
73 Wilmington NC
73 Tulsa OK
74 Indianapolis IN
74 Corpus Christi TX
75 Richland WA
75 Savannah GA
76 Nashville TN
76 Lexington KY
77 Green Bay WI
77 Atlanta GA
78 Olympia WA
78 Dallas TX
79 Minneapolis MN-WI
79 Odessa TX
80 Madison WI
80 Fort Collins CO
81 Baltimore MD
81 Waco TX
82 Visalia CA
82 Ann Arbor MI
83 Odessa TX
83 Longview TX
84 Oklahoma City OK
84 Atlantic NJ
85 Miami FL
85 Eugene OR
86 Charlotte NC-SC
86 Baton Rouge LA
87 Charleston SC
87 Des Moines IA
88 Asheville NC
88 Asheville NC
89 Omaha NE-IA
89 Denver CO
90 Newark NJ
90 Killeen TX
91 Ann Arbor MI
91 Baltimore MD
92 Seattle WA
92 Boulder CO
93 Gainesville FL
93 Salinas CA
94 Hamilton OH
94 Lakeland FL
[ ]
Pittsburgh Technology Strategy
Milken Institute
Top 100 Performing Metros 2002-200, cont.
2002
95 Salinas CA
2003
95 Birmingham AL
96 Albany NY
96 Pittsburgh PA
97 Springfield MO
97 Jackson MS
98 Spokane WA
98 Appleton WI
99 Longview TX
99 Minneapolis MN-WI
100 Los Angeles CA
100 Santa Barbara CA
Source: Milken Institute
Appendix D: Top 100 Performing Metros 2004-2005
2005
2004
1 Fort Myers Coral FL
1 Palm Bay FL
2 Las Vegas NV
2 Cape Coral Myers FL
3 Phoenix AZ
3 Naples Island FL
4 West Palm Beach Raton FL
4 McAllen TX
5 Daytona Beach FL
5 Deltona Beach FL
6 Sarasota FL
6 Orlando FL
7 Fayetteville AR
7 Washington DC Metropolitan Division
8 Riverside Bernardino CA
8 Fayetteville AR-MO
9 Fort Lauderdale FL
9 Fort Lauderdale Beach FL
10 Monmouth NJ
10 Riverside Bernardino CA
11 Washington DC
11 Las Vegas NV
12 Tampa Petersburg FL
12 Port St. Lucie Pierce FL
13 Boise City ID
13 Ocala FL
14 Portland ME
14 Tucson AZ
15 Naples FL
15 Phoenix AZ
16 San Diego CA
16 Santa Barbara Maria CA
17 Tucson AZ
17 Santa Ana CA
18 McAllen TX
18 Bremerton WA
19 Trenton NJ
19 Camden NJ
20 Albuquerque NM
20 Clarksville TN-KY
21 Anchorage AK
21 Reno NV
22 Sacramento CA
22 Charleston SC
23 Vallejo CA
23 Provo UT
24 Brownsville Benito TX
24 Sarasota FL
25 Merced CA
25 Tampa Petersburg FL
26 Reno NV
26 Gainesville FL
27 Huntsville AL
27 West Palm Beach Raton Beach FL
28 Laredo TX
28 Huntsville AL
29 Orlando FL
29 San Diego Marcos CA
30 Ventura CA
30 Bakersfield CA
31 Melbourne Bay FL
31 Stockton CA
32 Tacoma WA
32 Boise City ID
33 Green Bay WI
33 Lakeland FL
34 Raleigh Hill NC
34 Sacramento CA
35 Orange County CA
35 Madison WI
36 Knoxville TN
36 Trenton NJ
37 Provo UT
37 Tacoma WA
38 New London CT
38 Oxnard Oaks CA
39 Newark NJ
39 Ogden UT
[ 4 ]
Pittsburgh Technology Strategy
Milken Institute
2004
40 Stockton CA
2005
40 Fresno CA
41 Gainesville FL
41 Vallejo CA
42 Pensacola FL
42 Portland ME
43 Madison WI
43 Albuquerque NM
44 Des Moines IA
44 Killeen Hood TX
45 Norfolk Beach News VA
45 Raleigh NC
46 Providence RI
46 Fort Collins CO
47 Fresno CA
47 Modesto CA
48 Jacksonville FL
48 Poughkeepsie NY
49 Visalia CA
49 Anchorage AK
50 Charlotte Hill NC
50 Manchester NH
51 Barnstable MA
51 Jacksonville FL
52 Savannah GA
52 Nashville TN
53 Santa Barbara Maria CA
53 Bethesda MD
54 Newburgh NY
54 Knoxville TN
55 Spokane WA
55 Pensacola Pass FL
56 Baltimore MD
56 Honolulu HI
57 Charleston SC
57 San Antonio TX
58 Olympia WA
58 Austin Rock TX
59 Tallahassee FL
59 Wilmington NC
60 Indianapolis IN
60 Baltimore MD
61 Dutchess County NY
61 San Luis Obispo Robles CA
62 Springfield MO
62 Des Moines IA
63 Honolulu HI
63 Richmond VA
64 Austin Marcos TX
64 Virginia Beach News VA-NC
65 Lancaster PA
65 Oklahoma City OK
66 Jackson MS
66 Albany NY
67 Lakeland Haven FL
67 Visalia CA
68 Nashville TN
68 Amarillo TX
69 Hamilton OH
69 Charlotte NC-SC
70 Salem OR
70 Allentown PA-NJ
71 Amarillo TX
71 Colorado Springs CO
72 Atlanta GA
72 Lincoln NE
73 Modesto CA
73 Miami Beach FL
74 San Luis Obispo Robles CA
74 Norwich London CT
75 Wilmington DE
75 Providence Bedford River RI-MA
76 Waco TX
76 Nassau NY
77 Miami FL
77 Springfield MO
78 San Antonio TX
78 Savannah GA
79 Fort Pierce St. Lucie FL
79 Hagerstown MD-WV
80 Lincoln NE
80 Fort Worth TX
81 Eugene OR
81 Fayetteville NC
82 Minneapolis/St. Paul MN
82 Tallahassee FL
83 Chattanooga TN
83 Minneapolis Paul MN-WI
84 Philadelphia PA
84 Jackson MS
85 Odessa TX
85 Lubbock TX
86 Montgomery AL
86 Lake County County IL
87 Colorado Springs CO
87 Salt Lake City UT
88 Atlantic May NJ
88 Edison NJ
89 Harrisburg PA
89 Oakland CA
[ 5 ]
Pittsburgh Technology Strategy
Milken Institute
Top 100 Performing Metros 2004-2005, cont.
2004
2005
90 Bakersfield CA
90 Merced CA
91 Baton Rouge LA
91 Wilmington DE
92 Asheville NC
92 Boulder CO
93 Allentown PA
93 Spokane WA
94 Seattle WA
94 Little Rock Little Rock AR
95 Fort Worth TX
95 Portland OR-WA
96 Nassau NY
96 Eugene OR
97 South Bend IN
97 Lexington KY
98 Ocala FL
98 Brownsville TX
99 Boulder CO
99 Green Bay WI
100 Longview TX
100 Salem OR
Source: Milken Institute
Appendix E: Miscellaneous Charts
Non-High-Tech Wages
Figure 51: Non-HT Wages per Non-HT Worker
1990-2004
US$ Thousands
80
Baltimore
Phoenix
Seattle
U.S. Average
70
Indianapolis
Pittsburgh
St. Louis
60
50
40
30
20
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
Figure 52: Non-HT Wages per Non-HT Worker
Indexed Growth, 1990-2004
US$ Index 100=1990
100
80
Baltimore
Phoenix
Seattle
U.S. Average
Indianapolis
Pittsburgh
St. Louis
60
40
20
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ 6 ]
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s Sector Strengths
Figure 53: Comms. Equipment Manufacturing LQs
1990-2004
1.4
1.2
Baltimore
Phoenix
Seattle
Indianapolis
Pittsburgh
St. Louis
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
Figure 54: Mfg./Reproducing Mag./Optic. Media LQs
1990-2004
3.0
Baltimore
Phoenix
Seattle
2.5
Indianapolis
Pittsburgh
St. Louis
2.0
1.5
1.0
0.5
0.0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ ]
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s Sector Strengths
Figure 55: Communications Equipment Productivity
Indexed Growth
Percent, Index 100=1990
500
Percent
USA (R)
St. Louis (R)
Indianapolis (L)
Phoenix (L)
400
Pittsburgh (R)
Seattle (L)
Baltimore (L)
300
200
300
100
200
0
100
-100
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
-200
Source: Economy.com
Figure 56: Magnetic & Optical Media Productivity
Indexed Growth
Percent, Index 100=1990
600
500
400
USA (L)
St. Louis (L)
Indianapolis (R)
Phoenix (R)
Percent
Pittsburgh (L)
Seattle (L)
Baltimore (R)
5000
4000
300
3000
200
2000
100
1000
0
-100
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ ]
0
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s Sector Weaknesses
Figure 57: Semiconductor & Other Elec. Comp. LQs
1990-2004
0.80
0.70
Baltimore (L)
Pittsburgh (L)
St. Louis (L)
6.0
Indianapolis (L)
Seattle (L)
Phoenix (R)
5.5
0.60
5.0
0.50
0.40
4.5
0.30
4.0
0.20
0.10
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
3.5
Source: Economy.com
Figure 58: Nav/Measuring/Med./Ctrl. Instr. LQs
1990-2004
2.5
Baltimore
Phoenix
Seattle
2.0
Indianapolis
Pittsburgh
St. Louis
1.5
1.0
0.5
0.0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ 9 ]
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s Sector Weaknesses
Figure 59: Aerospace Products & Parts Mfg. LQs
1990-2004
4.0
Baltimore (L)
Phoenix (L)
St. Louis (L)
16
Indianapolis (L)
Pittsburgh (L)
Seattle (R)
15
3.0
14
2.0
13
12
1.0
11
0.0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
10
Source: Economy.com
Figure 60: Software Publishers LQs
1990-2004
1.00
Baltimore (L)
Phoenix (L)
St. Louis (L)
0.80
Indianapolis (L)
Pittsburgh (L)
Seattle (R)
14
12
0.60
10
0.40
8
0.20
6
0.00
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ 0 ]
4
Pittsburgh Technology Strategy
Milken Institute
Figure 61: Satellite Telecomm LQs
1990-2004
1.4
1.2
Baltimore (L)
Pittsburgh (L)
St. Louis (L)
Indianapolis (L)
Seattle (L)
Phoenix (R)
10
8
1.0
6
0.8
0.6
4
0.4
2
0.2
0
0.0
-0.2
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
-2
Source: Economy.com
Figure 62: Data Pro., Hosting & Related Serv. LQs
1990-2004
1.8
1.6
1.4
Baltimore
Phoenix
Seattle
Indianapolis
Pittsburgh
St. Louis
1.2
1.0
0.8
0.6
0.4
0.2
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ 1 ]
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s Sector Weaknesses
Figure 63: Semiconductor & Elec. Comp. Productivity
Indexed Growth
Percent, Index 100=1990
Percent
200
150
300
USA (L)
St. Louis (L)
Indianapolis (R)
Phoenix (R)
Pittsburgh (L)
Seattle (R)
Baltimore (L)
250
200
100
150
50
100
0
50
-50
-100
0
-50
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
Figure 64: Nav/Meas./Med./Ctrl. Instr. Productivity
Indexed Growth
Percent, Index 100=1990
800
600
USA (L)
Indianapolis (L)
Pittsburgh (R)
St. Louis (R)
Percent
Seattle (L)
Baltimore (L)
Phoenix (R)
2000
1500
400
1000
200
500
0
-200
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ 2 ]
0
Pittsburgh Technology Strategy
Milken Institute
Figure 65: Aero. Products & Parts Mfg. Productivity
Indexed Growth
Percent, Index 100=1990
350
300
250
USA
St. Louis
Indianapolis
Phoenix
Pittsburgh
Seattle
Baltimore
200
150
100
50
0
-50
-100
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
Figure 66: Software Publishers Productivity
Indexed Growth
Percent, Index 100=1990
Percent
100
50
0
USA (L)
St. Louis (L)
Indianapolis (L)
Phoenix (L)
Pittsburgh (L)
Seattle (L)
Baltimore (R)
-20
-40
0
-60
-50
-100
-80
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ ]
-100
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s Sector Weaknesses
Figure 67: Satellite Telecomm Productivity
Indexed Growth
Percent, Index 100=1990
Percent
8500
2000
1500
USA (L)
St. Louis (L)
Indianapolis (L)
Phoenix (R)
Pittsburgh (L)
Seattle (L)
Baltimore (L)
7067
5633
1000
4200
500
2767
0
-500
1333
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
-100
Source: Economy.com
Figure 68: Data Pro., Hosting & Serv. Productivity
Indexed Growth
Percent, Index 100=1990
300
200
USA (R)
St. Louis (L)
Indianapolis (L)
Phoenix (L)
Pittsburgh (L)
Seattle (R)
Baltimore (R)
100
0
-100
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ 4 ]
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s Sector Opportunities
Figure 69: Comp. & Peripheral Equip. Mfg. LQs
1990-2004
1.00
Baltimore
Phoenix
Seattle
0.80
Indianapolis
Pittsburgh
St. Louis
0.60
0.40
0.20
0.00
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
Figure 70: Other Information Services LQs
1990-2004
1.6
Baltimore (L)
Phoenix (L)
St. Louis (L)
Indianapolis (L)
Seattle (L)
Pittsburgh (R)
3.8
1.2
3.3
0.8
2.9
0.4
2.4
0.0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ 5 ]
2.0
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s Sector Opportunities
Figure 71: Comp. & Peripheral Equip. Productivity
Indexed Growth
Percent, Index 100=1990
Percent
300
USA (R)
Indianapolis (L)
Pittsburgh (L)
St. Louis (L)
200
Baltimore (L)
Phoenix (L)
Seattle (L)
800
600
400
100
200
0
-100
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
-200
Source: Economy.com
Figure 72: Other Info. Serv. Productivity
Indexed Growth
Percent Change, Index 100=1990
250
200
Baltimore
Phoenix
Seattle
U.S. Average
Indianapolis
Pittsburgh
St. Louis
150
100
50
0
-50
-100
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ 6 ]
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s Sector Threats
Figure 73: Audio & Video Equipment Mfg. LQs
1990-2004
6
Baltimore
Phoenix
Seattle
5
Indianapolis
Pittsburgh
St. Louis
4
3
2
1
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
Figure 74: Med. Equip. & Supplies Mfg. LQs
1990-2004
2.0
Baltimore
Phoenix
Seattle
Indianapolis
Pittsburgh
St. Louis
1.5
1.0
0.5
0.0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ ]
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s Sector Threats
Figure 75: Cable & Other Programs Distribution LQs
1990-2004
2.0
Baltimore
Phoenix
Seattle
Indianapolis
Pittsburgh
St. Louis
1.5
1.0
0.5
0.0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
Figure 76: Other Telecomm LQs
1990-2004
12
Baltimore
Phoenix
Seattle
10
Indianapolis
Pittsburgh
St. Louis
8
6
4
2
0
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ ]
Pittsburgh Technology Strategy
Milken Institute
Figure 77: Comp. Sys. Design & Related Serv. LQs
1990-2004
1.8
Baltimore
Phoenix
Seattle
1.6
Indianapolis
Pittsburgh
St. Louis
1.4
1.2
1.1
0.9
0.7
0.5
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
Figure 78: Audio & Video Equip. Mfg. Productivity
Indexed Growth
Percent, I=1990
600
Percent
USA (L)
St. Louis (L)
Indianapolis (R)
Phoenix (R)
400
Pittsburgh (L)
Seattle (R)
Baltimore (L)
1200
1000
800
600
200
400
200
0
0
-200
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ 9 ]
-200
Pittsburgh Technology Strategy
Milken Institute
Pittsburgh’s Sector Threats
Figure 79: Med. Equipment & Supplies Productivity
Indexed Growth
Percent, Index 100=1990
150
100
USA
St. Louis
Indianapolis
Phoenix
Pittsburgh
Seattle
Baltimore
50
0
-50
-100
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
Figure 80: Cable & Other Prog. Distri. Productivity
Indexed Growth
Percent, Index 100=1990
Percent
400
4000
USA (L)
St. Louis (L)
Indianapolis (L)
Phoenix (R)
308
Pittsburgh (L)
Seattle (L)
Baltimore (L)
3000
217
2000
125
1000
33
0
-58
-150
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ 90 ]
-1000
Pittsburgh Technology Strategy
Milken Institute
Figure 81: Other Telecomm Productivity
Indexed Growth
Percent, Index 100=1990
Percent
20000
1200
USA (R)
St. Louis (L)
Indianapolis (L)
Phoenix (L)
15000
Pittsburgh (L)
Seattle (R)
Baltimore (R)
1000
800
10000
600
400
5000
200
0
0
-5000
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
-200
Source: Economy.com
Figure 82: Comp. Sys. Design & Serv. Productivity
Indexed Growth
Percent, I=1990
80
60
USA
St. Louis
Indianapolis
Phoenix
Pittsburgh
Seattle
Baltimore
40
20
0
-20
-40
90 91 92 93 94 95 96 97 98 99 00 01 02 03 04
Source: Economy.com
[ 91 ]
Pittsburgh Technology Strategy
Milken Institute
Endnotes
1
Porter, Michael, E. 2002. Pittsburgh. Clusters of Innovation. Harvard University.
Clusters on Competitiveness.
2
Drucker, P. 1993. Post-Capitalist Society. New York: Harper Business.
3
Rincon, M. & R. Kadi. 2004. Strategies Towards the Knowledge Society: Case Study from a Developing
Country. Working Paper.
4
Bell, D. 1973. The Coming of the Post-Industrial Society. A Venture in Social Forecasting.
New York: Basic Books.
5
Schiller, D. 1999. Digital capitalism: Networking the Global Market System.
Cambridge, MA: MIT Press.
6
Drucker, P. 1993. Post-Capitalist Society. New York: Harper Business.
7
Rincon, M. & R. Kadi. 2004. Strategies Towards the Knowledge Society: Case Study from
a Developing Country. Working Paper.
8
United Nations Economic Commission for Europe. 2002. Concept, Outline, Benchmarking and
Indicators. New York. Geneva: United Nations Publications.
9
DeVol, R., Perry Wong, Junghoon Ki, Armen Bedroussian & Rob Koepp. 2004. America’s Biotech
and Life Science Clusters. San Diego’s Position and Economic Contributions. Santa Monica:
Milken Institute.
10
DeVol, R., Rob Koepp, John Ki, and Frank Fogelback. 2004. California’s Position in Technology
and Science. Santa Monica: Milken Institute.
11
Saxenian, A. 1999. Reversed Brain Drain.
12
DeVol, R., Perry Wong, Junghoon Ki, Armen Bedroussian & Rob Koepp. 2004. America’s Biotech and
Life Science Clusters. San Diego’s Position and Economic Contributions. Santa Monica: Milken Institute.
13
DeVol. Ross. 1999. America’s High-Tech Economy. Growth, Development, and Risks for Metropolitan Areas.
Santa Monica: Milken Institute.
14
http://www.cmu.edu/home/about/about_history.html.
15
http://www.salk.edu/jonassalk/.
16
http://members.tripod.com/~MitchellBrown/samples/CNET_java.html.
17
Devol, R. and Frank Fogelbach. 2003. Milken Institute Best Performing Cities. Santa Monica: Milken Institute.
18
Haulk, Jake, Paul F. Stiflemire 2002. Pittsburgh’s Job Growth Rate has been Dismal.
The Allegheny Institute for Public Policy.
[ 92 ]
Pittsburgh Technology Strategy
Milken Institute
19
Haulk, Jake, Paul F. Stiflemire 2002. Pittsburgh’s Job Growth Rate has been Dismal.
The Allegheny Institute for Public Policy.
20
http://www.bizjournals.com/pittsburgh/stories/2003/07/28/daily42.html.
21
Pittsburgh. Economics Division, PNC Financial Services Group.
22
DeVol, Ross. America’s High-Tech Economy: growth, Development and Risks for Metropolitan Areas.
Santa Monica: Milken Institute.
23
Lee, Myeong-Ho & In Jeong Hwang. 2003. Determinants of Corporate R&D Investment:
An Empirical Study Comparing Korea’s IT Industry with its Non-IT Industry.
ETRI Journal, 25: 4. Aug 2003.
24
Gertler, Meric S., Uyen Quach & Tara Vinodrai. 2005. Life Science Innovation Systems: Lessons
from ISRN. http://www.utoronto.ca/onris/pdf/ONRIS%20Fall%20Workshop%202005%20%20Life%20Sciences%20v3.ppt.
25
Entrepreneurial Vitality Scorecard, Carnegie Mellon University. 1998.
26
Pittsburgh. Economics Division, PNC Financial Services Group.
27
Schuetze, Herbert, J. and Donald Bruce. 2004. The Relationship Between tax Policy and Entrepreneurship:
What We Know and What We Should Know. Conference on Self-Employment, march 22nd, 2004.
Stockholm, Sweden. Organized by the Economic Council of Sweden.
28
http://www.issuespa.net/articles/13001/.
29
http://www.state.pa.us/papower/cwp/view.asp?Q=448542andA=11.
30
http://www.retirementliving.com/RLtaxes.html.
31
http://www.retirementliving.com/RLtaxes.html.
32
http://www.pabar.org/legisspecialsession05.shtml.
33
http://www.bio-itworld.com/archive/061503/insights_midwest.html.
34
http://www.post-gazette.com/pg/06054/659564.stm. Accessed: Mar 1, 2006.
35
http://online.wsj.com/public/article/SB112119640720383572KpZLmVTMBhPKDA3gpvQ8cTjh87w_20060712.html?mod=tff_main_tff_top.
36
Haulk, Jake, Paul F. Stiflemire 2002. Pittsburgh’s Job Growth Rate has been Dismal.
The Allegheny Institute for Public Policy.
37
Haulk, Jake, Paul F. Stiflemire 2002. Pittsburgh’s Job Growth Rate has been Dismal.
The Allegheny Institute for Public Policy.
[ 9 ]
Pittsburgh Technology Strategy
Milken Institute
38
http://www.post-gazette.com/pg/05313/603160.stm.
39
Sommers, Paul, Daniel Carlson, Michael Stanger, Saijun Xue, Mika Miyasato. 2000. Ten Steps to a High
Tech Future: The New Economy in Metropolitan Seattle. Center on Urban and Metropolitan Policy.
The Brookings Institution.
40
Innovation Works. 2005.
41
Sommers, Paul, Daniel Carlson, Michael Stanger, Saijun Xue, Mika Miyasato. 2000. Ten Steps to a HighTech Future: The New Economy in Metropolitan Seattle. Center on Urban and Metropolitan Policy.
The Brookings Institution.
42
Gradeck, R. and Jerry Paytas. 2000. Migration and Regional Growth. Center for Economic Development,
Carnegie Mellon University.
43
Center for Economic Development, Carnegie Mellon University. 2003. The Root of Pittsburgh’s
Population Drain.
44
H. John Heinz III School of Public Policy and Management. 2000. Entrepreneurial Pittsburgh:
Uncovering the New Economy. Key Findings and Policy Recommendations.
45
http://pittsburghlive.com/x/tribune-review/business/s_404601.html.
46
http://www.netapp.com/news/press/news_rel_20040218.
47
Survey of 104 students pursuing degrees in Business, Engineering, and Computer Science at
Carnegie Mellon. 1999.
48
http://www.pheaa.org/specialprograms/nets/New_Economy_Technology_Scholarship.shtml.
49
http://www.pahouse.com/youngblood/pr/198102199.
50
http://www.pheaa.org.
51
H. John Heinz III School of Public Policy and Management. 2000. Entrepreneurial Pittsburgh:
Uncovering the New Economy. Key Findings and Policy Recommendations.
52
The Emerging Cluster of Arizona. Collaborative Economics.
53
H. John Heinz III School of Public Policy and Management. 2000. Entrepreneurial Pittsburgh:
Uncovering the New Economy. Key Findings and Policy Recommendations.
54
http://medc.michigan.org/smartzones/program/.
55
Florida, Richard, “Toward the Learning Region,” Futures 27, 5 (June 1995): 527-536.
56
H. John Heinz III School of Public Policy & Management. 2000. Entrepreneurial Pittsburgh: Uncovering
the New Economy. Key Findings and Policy Recommendations.
[ 94 ]
Pittsburgh Technology Strategy
Milken Institute
57
Gonzalez, Marisela, & Rafael Lucea. 2001. The Evolution of Business Incubation. Center for Economic
Development, Carnegie Mellon University.
58
RESI Research nd Consulting. 2001. Maryland Incubator Impact Analysis.
59
Innovation Works. 2005.
60
http://www.bio-itworld.com/archive/061503/insights_midwest.html.
61
http://www.techcollaborative.org/default.aspx?id=about_ttc.
62
Feldman, Maryann and Francis Johana. “Fortune Favors the Prepared Region.” Forthcoming in European
Planning Studies. http://www.rotman.utoronto.ca/feldman/papers/MDBiotechCluster2.0.pdf.
Accessed on Feb 28, 2006.
63
Gonzalez, Marisela, and Rafael Lucea. 2001. Pittsburgh’s Business Incubators.
http://www.smartpolicy.org/pdf/incproiles.pdf.
[ 95 ]
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Milken Institute
About the Authors
Ross DeVol is Director of Regional Economics at the Milken Institute. He oversees the Institute’s research on
the dynamics of comparative regional growth and performance, and technology and its impact on regional
and national economies. DeVol is an expert on the intangible economy and how regions can prepare themselves to compete in it. He authored the study America’s High-Tech Economy: Growth, Development, and Risks
for Metropolitan Areas, an examination of how clusters of high-technology industries across the country affect
economic growth in those regions, and America’s Biotech and Life Science Clusters, among many others. He
also created the Best Performing Cities Index, and annual ranking of U.S. metropolitan areas that shows where
jobs are being created and economies are growing. Prior to joining the Institute, DeVol was senior vice president of global Insight, Inc. (formerly Wharton Econometric Forecasting), where he supervised the Regional
Economic Services group. DeVol earned an M.A. in economics at Ohio University.
Perry Wong is a Senior Managing Research Economist in Regional Economics at the Milken Institute. He is
an expert on regional economics, development and econometric forecasting and specializes in analyzing the
structure, industry mix, development, and public policies of a regional economy. He designs, manages, and
performs research on labor and workforce issues, the relationship between technology and economic development, trade and industry, with a focus on economic policy development and implementation in both leading and disadvantaged regions. Prior to joining the Institute, Wong was a senior economist and director of
regional forecasting at Global Insight, Inc. (formerly Wharton Econometric Forecasting), where he managed
regional, state, and metropolitan area forecasts and provided consultation. Wong earned a master’s degree in
economics at Temple University in 1990 and completed all course requirements for his Ph.D.
Benjamin Yeo is a Research Analyst in Regional Economics at the Milken Institute and a Ph.D. candidate at
Pennsylvania State University. Yeo’s doctoral dissertation is aimed at developing a theory of sustainability for
the knowledge economy. His expertise comprises information technology, planning and knowledge management for e-business and economic development, information systems/process management, and national
information policy studies. Yeo obtained his undergraduate and graduate degrees at the School of Communication and Information at Nanyang Technological University in Singapore.
[ 96 ]
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