Evaluating Tallahassees and Other Medium Sized MSAs with the New Economy Index: Lessons Learned: A S

1
Evaluating Tallahassees and Other Medium Sized
MSAs with the New Economy IndexLessons
Learned A Seminar on Understanding and
Analyzing the Knowledge Economy

• Tim Lynch, Ph.D., Director
• Julie Harrington, Ph.D., Asst. Dir.
• Center for Economic Forecasting and Analysis
• Florida State University
• www.cefa.fsu.edu
• Ken Stackpoole, Doctoral Candidate
• University of Central Florida, College of Public
  Affairs
• A presentation for ACCRAs 42nd Annual Conference
  
• Creating Competitive Communities By Supporting
  Quality of Life and Economic Diversity
• Charleston, South Carolina
• June 11-15, 2002

2
US Manufacturing Worker Hourly Production Value
vs the IT Investment Rate (Constant 2002)
Info Tech of All US Private Investments
US Worker Hourly Manuf Production
IT Investment Rate
Old economy
New economy
3
Tallahassees Final Ranking Among The US MSAs
Evaluated
TALLAHASSEE RANKS 11th OUT OF 66 US MSAs
EVALUATED
Source The State New Economy Index Benchmarking
Economic Transformation in the States,
Progressive Policy Institute,Technology New
Economic Project, July, 1999, Atkinson, et al.
www.neweconomyindex.org
4
Data, Sources, and Calculations
Issues and Lessons Learned

• We attempt to exactly duplicate data sources and
  calculations in order to provide proper
  integration and comparisons
• 16 Indicators 28 total variables to collect
• 20 MSAs in Florida 5 were reported in the PPI
  New Economy Report (http//www.neweconomyindex.org
  /)
• Variables with same data source located
• Variables with similar data source located
• Variables with no data source, proxies had to be
  calculated
• Smooth the data to be compatible with PPI New
  Economy Data using the 5 known Indicators
• Calculation of Final Index

5
Data, Sources, and Calculations Issues and
Lessons Learned
Variables with same data source

• Export Sales ITA Web Site
• IPOs Edgar Online
• Broadband Providers per zip code FCC Web site
• Commercial Internet Domain Names Matt Zook
• Internet Backbone Capacity Ed Malecki
• SE Degrees NSF CASPAR Database
• Patents USPTO
• Academic RD NSF CASPAR Database

6
Data, Sources, and Calculations Issues and
Lessons Learned
Variables with similar source and data

• Man/Prof/Tech Jobs BLS CPS (BLS OES, Web site,
  soc codes)
• Workforce Education BLS CPS (1980-90 Census
  data linear forecast)
• Gazelles Cognetics (Brandow data, high growth
  jobs rate)
• Job Churning Cognetics (FL Dept of Labor Open,
  Expand, Contract, Close)
• Computer Use in Schools BLS CPS (FL Dept of Ed
  Survey)
• High Tech Jobs Census Bureau CBP (BLS ES-202
  data, sic codes)
• Venture Capital Money Tree Report (Florida
  Venture Forum)

7
Data, Sources, and Calculations Issues and
Lessons Learned
Variable with dissimilar source, proxy was
calculated

• Online Population Scarborough Research (1999
  Census Bureau Computer Use in U.S. - estimated
  relationship between education level and computer
  use)

Variables used as divisors to provide control for
size of metro

• Total Employment DOC/BEA/REIS (BLS ES-202)
• Gross Metro Product Standard Poors
  (BEA/REIS/Implan)
• Total Firms/Businesses County Business Patterns
• Zip Codes (Dynamap ZIP Code File)
• children US Census, County Population Est.
  (FL Dept of Education)

8
Data, Sources, and Calculations Issues and
Lessons Learned
Smoothing the data to fit with known five FL
Metros

• We knew we had some differences in the data
  collected, even with the 5 known metros
• Used these five knowns to determine average
  difference between our data and the PPI data
• Adjusted all metros by this known difference to
  bring the data in line

Calculation of Final Index

• Convert all PPI data to raw scores (some were
  reported as z-scores)
• Convert all calculations to z-scores, apply
  appropriate weight
• Sum all weighted z-scores, then add 20 to make
  all positive
• Divide this sum by sum of highest for each
  indicator
• Therefore, final score is a function of the total
  score a metro would have achieved if it had
  finished first in every category

9
Comparison of CEFA New Economy Index Rankings
with Policom Rankings
10
CEFA New Economy Index Rank of MSA and Output
from a 1 Million Investment in High Tech
Industries within the MSA
11
CEFA New Economy Index Ranking of MSA and of
State of Florida Output Compared to Shannon
Weaver Diversity Index
12
CEFA New Economy Index and Policom Ranking of
MSAs and Output from 1 Million Stimulus
Compared to Shannon Weaver Diversity Index