1. Understanding Location Quotients (LQ) Dr. Kevin Stolarick

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