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Tim Lynch, Ph.D., Director Julie Harrington, Ph.D., Asst. Dir.

Evaluating Tallahassee’s and Other Medium Sized MSAs with the New Economy Index: Lessons 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

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Tim Lynch, Ph.D., Director Julie Harrington, Ph.D., Asst. Dir.

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  1. Evaluating Tallahassee’s and Other Medium Sized MSAs with the New Economy Index:Lessons 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$) $300 60% 54% 42% $200 40% 29% 22% $100 20% 13% 7% $0 0% 1950 1960 1970 1980 1990 2000 Source: Chase Econometrics, 4-15-2002 Info Tech % of All US Private Investments US Worker Hourly Manuf Production % IT Investment Rate Old economy New economy

  3. Tallahassee’s 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 • S&E Degrees – NSF CASPAR Database • Patents – USPTO • Academic R&D – 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 & Poor’s (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

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