Creative Regional Strategies January 31, 2011

Gridland , , ,000 2,000 10,000 2,000 7, , ,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 , , ,000 2,000 10,000 2,000 7, , ,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, % = 400 / 3, % = 700 / 6,000 20% = 2,000 / 10, % = 2,000 / 7,500 10% = 200 / 2, % = 500 / 8, % = 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 LQ shows high & low concentrations within individual regions – compared to entire geography , , ,000 2,000 10,000 2,000 7, , ,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

– American Fact Finder – Data Set Access – County Business Patterns – USA County Data Data Sources

– Community Profiles – Data Set Access – Canada, OECD, International Data – Canada, US, International Data Data Sources