Creative Regional Strategies

1. Creative Regional Strategies January 31, 2011

2. 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.

3. 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?

4. 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?

5. Location Quotient (1) • 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)

6. 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?

7. Gridland – Location Quotients 0.39 1.56 0.25 0.83 0.73 1.25 1.67 0.62 1,250 4,000 500 8,000 100 400 200 5,000 400 3,000 700 6,000 2,000 10,000 2,000 7,500 200 2,000 1.95 LQ shows high & low concentrations within individual regions – compared to entire geography

8. Location Quotient (2) • 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

9. Using Location Quotients • 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

10. Milken “Tech-Pole” Index • 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

11. North American “Tech-Pole” • 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

12. High-Tech Metros by LQ

13. High-Tech Metros by Output Share

14. Tech-Poles

15. Other Measures • 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

16. Other Technology Measures?

17. Other Measures Managerial, professional, tech jobs Education (talent) Exporting Gazelles Job churning New publicly traded companies Online population Broadband telecom

18. Other Measures Computers in schools Commercial internet domains Internet backbone High-tech jobs Sci & Eng degrees Patents Academic R&D (also AUTM) Venture Capital

19. Samples

20. Prince Edward County

25. Upstate New York Super-Region

27. Growth Benchmarks

28. Overall Growth

31. Technology Benchmarks

42. Upstate “High-Tech”

45. Syracuse Benchmarks