creative regional strategies january 31, 2011. gridland 100 400 200 5,000 400 3,000 700 6,000 2,000...
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Creative Regional Strategies
January 31, 2011
Gridland
100
400
200
5,000
400
3,000
700
6,000
2,000
10,000
2,000
7,500
200
2,000
500
8,000
1,250
4,000
Total Population:
45,900
Total Number of X:
7,350
Want to compare how distribution of X compares to distribution of population.
Gridland
100
400
200
5,000
400
3,000
700
6,000
2,000
10,000
2,000
7,500
200
2,000
500
8,000
1,250
4,000
Average across all of Gridland =
16.01%
= 7,350 / 45,900
How does each location compare to the average?
Gridland
25%
= 100
/ 400
4%
= 200
/ 5,000
13.3%
= 400
/ 3,000
11.7%
= 700
/ 6,000
20%
= 2,000
/ 10,000
26.7%
= 2,000
/ 7,500
10%
= 200
/ 2,000
6.25%
= 500
/ 8,000
31.25%
= 1,250
/ 4,000
Average across all of Gridland =
16.01%
= 7,350 / 45,900
How does each location compare to the average?
•Concentration within a region•Compared to•Average Concentration across all regions
•LQ =(X in region / total for region)÷ (total X all regions / total all regions)
Location Quotient (1)
Gridland – Location Quotients
1.56= 25%
÷ 16.01%
0.25= 4%
÷ 16.01%
0.83= 13.3%
÷ 16.01%
0.73= 11.7%
÷ 16.01%
1.25= 20%
÷ 16.01%
1.67= 26.7%
÷ 16.01%
0.62= 10%
÷ 16.01%
0.39= 6.25%
÷ 16.01%
1.95= 31.25%
÷ 16.01%
Average across all of Gridland =
16.01%
= 7,350 / 45,900
How does each location compare to the average?
Gridland – Location Quotients
1.56 0.25 0.83
0.73 1.25 1.67
0.62 0.39 1.95
LQ shows high & low concentrations within individual regions – compared to entire geography
100
400
200
5,000
400
3,000
700
6,000
2,000
10,000
2,000
7,500
200
2,000
500
8,000
1,250
4,000
• Share of “item of interest” in a region• Compared to• Share of total population in the same region
• LQ =(X in region / total X all regions)
÷ (total for region / total all regions)
• Exactly the same – depends on data available
Location Quotient (2)
•Porter – Clusters– Industry-level (SIC or NAICS)–Total employment, sales–Predefined “clusters”
–Suppliers, buyers, related industries
•Milken – Tech-Pole– “High tech” industries
• (Stolarick) Occupational Clusters
Using Location Quotients
• Includes software, electronics, biomedical products, and engineering services (appendix)•Combination of two measures–Region’s High Tech LQ
–Small, concentrated regions–Region’s total share of High Tech Output
–Larger, producing regions
Milken “Tech-Pole” Index
•Total “High Tech” employment•Base is US & Canada•Each region compared to base•As with Milken, NA Tech Pole =
High Tech LQ
x
Share of NA High Tech Employment
North American “Tech-Pole”
High-Tech Metros by LQ
High-Tech Metros by Output Share
Tech-Poles
• Patents–Current per capita–Average patent growth over time–The good, the bad and the ugly with patents
• Industry Clusters–Specific industries–“Evolutionary” vs. “created” clusters
• Occupational Clusters• Industry & Occupation Simultaneously
Other Measures
Other Technology Measures?
•Managerial, professional, tech jobs•Education (talent)•Exporting•Gazelles• Job churning•New publicly traded companies•Online population•Broadband telecom
Other Measures
•Computers in schools•Commercial internet domains• Internet backbone•High-tech jobs•Sci & Eng degrees•Patents•Academic R&D (also AUTM)•Venture Capital
Other Measures
Samples
Prince Edward County
Upstate New York Super-Region
Growth Benchmarks
Overall Growth
Technology Benchmarks
Upstate “High-Tech”
Syracuse Benchmarks
Toronto
Toronto: Overall
Toronto: Technology
•www.census.gov–American Fact Finder–Data Set Access
•http://censtats.census.gov/–County Business Patterns–USA County Data
Data Sources
•www.statcan.gc.ca–Community Profiles–Data Set Access
•http://dc1.chass.utoronto.ca/–Canada, OECD, International Data
•http://www.chass.utoronto.ca/datalib–Canada, US, International Data
Data Sources