Transcript Indicators for the Washington Innovation Economy

Indicators for the Washington
Innovation Economy
Paul Sommers, Seattle University
Andrew Wenzl, University of Washington
William Beyers, University of Washington
Report for the Washington Economic Development Commission, June 2010
Presentation Version for LMEA Conference, October 2010
What this presentation is about
Consulting team hired to create a “dashboard” – a system of indicators that could be used to assess
Washington’s progress in creating a healthy innovation-based economy
 RFP called for indicators in five areas organized in a model of innovation, and for comparisons of
Washington to other states and nations
 Major conclusions:

 More than one path to success is apparent
 We import talent but have a strong workforce; we could decide to grow more of our own in
selected areas
 There are weaknesses in each of the input dimensions that could be improved
 It is not necessary (or even possible) to be best at everything
 We have strong business performance but are not turning that into as much broad based public impact
as some other states – possible implications for public policy here

Suggested extensions of the work:
 Investigate alternative weighting systems
and their implications
 Use indicators for POG goals & cluster strategies
 Focus policy on improvements on selected indicators
 Explore innovation models (policies, industrial
base, and outcomes) in peer states
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5
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3
2
1
0
California
Colorado
Connecticut
Massachusetts
Virginia
Virgina
Washington
Peer States Chosen Based on
Similarities in Industry Structure
(5 peers chosen from initial list of 15 states)
Location Quotients
Industry
Crop production
Washington California Colorado Connecticut Massachusetts Virginia
4.98
2.83
0.63
0.53
0.22
0.37
11.10
3.56
1.07
4.59
0.42
0.89
0.06
n.d.
0.73
0.18
0.01
0.37
n.d.
0.06
0.62
0.16
1.2
0.78
8.11
0.28
n.d.
0.15
2.82
1.65
Breweries
Wineries
3.71
1.05
2.25
0.91
1.02
6
1.73
7.58
0.23
1.25
ND
0.23
0.36
0.45
n.d.
2.09
1.97
0.7
Wood product manufacturing
2.03
0.70
0.62
0.32
0.35
1.63
7.68
1.26
0.84
5.07
0.97
0.1
2.27
0.56
n.d.
n.d.
0.14
5.52
1.67
0.76
0.93
1.27
1.50
0.41
3.68
9.03
n.d.
1.43
0.6
2.73
1.71
0.65
1.66
3.44
0.69
0.75
1.44
1.63
1.31
0.72
1.05
0.81
1.17
0.94
2.37
1.21
2.84
1.54
Non-citrus fruit and tree nut farming
Forestry and logging
Food manufacturing
Seafood product preparation and
packaging
Coffee and tea manufacturing
Aerospace product and parts
manufacturing
Ship and boat building
Electronic shopping and mail-order
houses
Sea, coastal, and Great Lakes
transportation
Software publishers
Scientific research and development
services
Grant-making foundations
EDC Model of Innovation’s Economic Impact
Talent
Innovation
Business Performance
Public Impact
Infrastructure
Three kinds of
inputs influence
business performance
Business
performance
determines public
impacts
22 Indicators Organized in 5 Broad Categories
Talent
Investment
Infrastructure
Business
Performance
Public Impact
Education
Pipeline
R&D Intensity
Business Climate
Productivity
State GDP
Transportation
Exports
Employment
Growth
Workforce Quality Business Startups
R&D Potential
Inward Investment Broadband
Market
Capitalization
Income
Distribution
STEM Production
Capital Access
Energy
New products and
services
-no data found
State Revenues
Sustainability
Profitability
-no data found
Standard of living
Lifelong Learning
Fact Sheet for each indicator shows the trend in Washington, a comparison with peer
states, and a comparison of the U.S. to selected other nations
Example Fact Sheet (1 of 22 in the report)
Science and Engineering Degrees Granted
1997
2002
2007
1997
2002
2007
California
66,347
77,904
89,947
43.8
44.1
43.9
Colorado
11,427
13,308
13,729
40.4
42.4
38.2
7,153
7,294
9,052
33.6
31.8
32.6
Massachusetts
22,537
24,538
26,363
33.8
34.4
33.6
Virginia
16,270
17,956
20,679
35.3
38.4
37.4
Washington
10,761
12,292
14,026
24.9
25.6
23.6
538,702
597,517
685,914
32.9
33
32.1
Connecticut
United States
Science and Engineering Degrees Granted, 1997 to 2002
Science and Engineering Degrees Granted by Washington’s
Higher Education Institutions, 1997 to 2007
 Technological progress depends on presence of scientists
and engineers in the workforce
 Improvement from 1997 to 2002, but a sharp drop from
2002 to 2007 in Washington and peer states
 U.S. produces as many science and engineering
graduates as a proportion of all university degrees as five
other nations, but a much lower level than So. Korea and
Sweden, and somewhat less than several European
countries
 Data source: National Science Foundation
Science and Engineering Graduates by Country
(as percentage of all first university and doctoral degrees)
The Other 21 Fact Sheets
 Detailed fact sheets are provided in the report at the WEDC
website: http://www.wedc.wa.gov/Publications.htm
 The focus in this presentation is on findings at the broad
category level
Composite Scores
All 5 Broad Indicators – The Dashboard
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Composite scores created by
indexing each indicator and
combining indicators into a
composite for each of the five
broad indicators; the five
broad indicator composites
are then combined into an
overall composite
“dashboard” indicator shown
on this slide
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5
4
3
2
1
0
California
Colorado
Connecticut Massachusetts
Virgina
Virginia
Washington
The overall composite measure shows Washington performing
well below California, Connecticut, and Massachusetts, in a
similar range to Colorado, and ahead of Virginia
Composite Scores by 5 Broad Components
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5
4
3
2
Talent
Investment
Infrastructure
Business_Performance
Public_Impact
1
0
 Washington has low scores on talent, investment, and public impact; we do
well on business performance and infrastructure
 Colorado reaches a similar overall score in a very different manner, almost the
reverse of the pattern in Washington
 The high performing states (CA, CT, and MA) also have different patterns on
the five broad components
 There are different strategies available for success in the innovation economy
Talent Composite
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6
5
4
3
2
1
0
California
Colorado
Connecticut
Massachusetts
Virginia
Virgina
Washington
Washington is in last place on the talent index
Talent Components
6
5
4
3
2
Education Pipeline
Workforce Quality
R&D Personnel
Stem Proficiency
1
0
Washington has relatively low scores for the education pipeline,
R&D personnel, and STEM proficiency, but does better on
workforce quality
Investment/Entrepreneurship
Composite
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6
5
4
3
2
1
0
California
Colorado
Connecticut
Massachusetts
Virginia
Virgina
Washington
Washington is fifth among the six peer states on this composite indicator
Investment/Entrepreneurship
Components
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5
4
3
2
R&D Intensity
Business Startups
Inward Investment
Capital Access
1
0
Washington scores relatively poorly on inward investment and
capital access, relatively well on R&D intensity, and in the middle
on business startups
Infrastructure
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6
5
4
3
2
1
0
California
Colorado
Connecticut
Massachusetts
Virginia
Virgina
Washington
Washington is in second place on this composite indicator
Infrastructure Components
6.00
5.00
Business Climate
4.00
Transportation
3.00
Broadband
2.00
Energy
Sustainability
1.00
0.00
Washington has high scores on three of the five indicators:
business climate, transportation, and broadband
Business Performance
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6
5
4
3
2
1
0
California
Colorado
Connecticut Massachusetts
Virginia
Virgina
Washington
Washington has the highest score on this indicator
Business Performance Components
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5
4
Productivity
3
Exports
2
Market Capitalization
1
0
Washington has a high score on exports, and very good
position on market capitalization and productivity
Public Impact
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6
5
4
3
2
1
0
California
Colorado
Connecticut
Massachusetts
Virginia
Virgina
Washington
Washington’s overall public impact score is low, below all peer states except California.
Public Impact Components
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5
4
3
2
State GDP
Employment Growth
State Revenues
Standard of Living
Income Distribution
1
0
Washington has high scores on state GDP and employment growth, but
lower scores on state revenues, income distribution, and standard of living
Weighting the Variables
 Other index reports such as
Atkinson’s State New Economy
Index do not give equal weight to
each variable; equal weights are used
in all charts in this presentation
except the two on this page
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5
4
Average Score
3
2
Weighted Score
1
0
 We used example arbitrary weights;
the ranking of state scores is
unchanged for the dashboard, but for
the talent index, the ranking of states
shifts considerably
 The EDC could explore alternative
weights, for example weighting
inputs more than outputs, or the
other way around, or giving greater
weight to individual variables
considered especially important
The Dashboard Weighted and Not Weighted
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5
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3
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Average Score
Weighted Score
The Talent Composite Index Weighted and Not Weighted
Major Conclusions
Major conclusions:
More than one path to success is apparent
We import talent but have a strong workforce; we could decide to
grow more of our own in selected areas
There are weaknesses in each of the input dimensions that could be
improved
It is not necessary (or even possible) to be best at everything
We have strong business performance but are not turning that into as
much broad based public impact as some other states – possible
implications for public policy here
Suggested extensions of the work:
Investigate alternative weighting systems
and their implications
Use indicators for POG goals & cluster strategies
Focus policy on improvements on selected indicators
Explore innovation models (policies, industrial base, and outcomes)
in peer states