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. 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)

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 1.560.250.83 0.731.251.67 0.620.39 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

8. 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)

9. 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

10. 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

11. 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”

12. High-Tech Metros by LQ

13. High-Tech Metros by Output Share

14. Tech-Poles

15. 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

16. Other Technology Measures?

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

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

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

54. Toronto

55. Toronto: Overall

63. Toronto: Technology

69. www.census.gov – American Fact Finder – Data Set Access http://censtats.census.gov/ – County Business Patterns – USA County Data Data Sources

70. 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