Creative Regional Strategies

January 30, 2012

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 xShare 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