USF Libraries
USF Digital Collections

Tampa Bay economy

MISSING IMAGE

Material Information

Title:
Tampa Bay economy
Physical Description:
Book
Language:
English
Creator:
University of South Florida -- Center for Economic Development Research
Publisher:
University of South Florida, College of Business Administration, Center for Economic Development Research.
Place of Publication:
Tampa, Fla

Subjects

Subjects / Keywords:
Economic conditions -- Periodicals -- Tampa Bay Region (Fla.)   ( lcsh )
Economic conditions -- Statistics -- Periodicals -- Tampa Bay Region (Fla.)   ( lcsh )
Commerce -- Periodicals -- Tampa Bay Region (Fla.)   ( lcsh )
Genre:
non-fiction   ( marcgt )

Record Information

Source Institution:
University of South Florida Library
Holding Location:
University of South Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
usfldc doi - C63-00072
usfldc handle - c63.72
System ID:
SFS0000346:00001


This item is only available as the following downloads:


Full Text
xml version 1.0 encoding UTF-8 standalone no
record xmlns http:www.loc.govMARC21slim xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.loc.govstandardsmarcxmlschemaMARC21slim.xsd
leader nam a22 u 4500
controlfield tag 008 d20002006flu 000 0 eng d
datafield ind1 8 ind2 024
subfield code a C63-00072
0 245
Tampa Bay economy.
n Vol. 4, no. 2 (winter 2004).
260
Tampa, Fla. :
b University of South Florida, College of Business Administration, Center for Economic Development Research.
505
High-Tech Jobs in Florida -- From the Editor -- Household Income Distribution in Tampa Bay, 1989-1999 -- Economic Contributions of the Finance and Insurance Sector in Florida's High Tech Corridor and the Rest of Florida -- USF's Basic Economic Development Course -- Market Analysis of Hillsborough County's Community Development Block Grant Areas -- Update on CEDR's Data Center.
651
Tampa Bay Region (Fla.)
x Economic conditions
v Periodicals.
Tampa Bay Region (Fla.)
Economic conditions
Statistics
Periodicals.
Tampa Bay Region (Fla.)
Commerce
Periodicals.
2 710
University of South Florida.
Center for Economic Development Research.
4 856
u http://digital.lib.usf.edu/?c63.72



PAGE 1

Volume 4, No. 2 Winter 2004 High-Tech Jobs in Florida By Michael Bernabe, Graduate Research Assistant, Center for Economic Development Research E ditors note: The following article provides an analysis of High-Tech Jobs in Florida from 1998 to 2003. Because of a change in industry classification systems from the Standard Industrial Classification s ystem to the North American Industry Classification System in the year 2000, the analysis is separated into two periods, 1998 – 2000 and 2001 – 2003. Due to the change in industry classification systems a different methodology in determining High-Tech Jobs is used for the latter period, and therefore no direc t comparisons can be made between the two periods. High-Tech employment is calculated using a list of science and engineering intensive industr y grou p s compiled by the Bureau of Labor Statistics (BLS). High-Tech industries typically use state-ofthe-art techniques, devote a high proportion o f expenditures to research and development, and employ scientific, technical, and engineering personnel. The BLS list of High-Tech Industr y Groups is generated using data on the amount o f employment in an industry accounted for by scientific, technical and engineering personnel engaged in research and development activities. Industries are considered High-Tech if employment in both research and development and in all technology-oriente d occupations accounts for an amount of employment that is at least twice the average amount of employees for all industries in the 1998 Occupational Employment Statistics (OES) survey. This list is the b asis of the USF Center for Economic Development Research (CEDR) analysis of High-Tech jobs in the state of Florida from 1998 through 2000. But this list was based on the Standard Industrial Classification (SIC) system, which was replaced by the North American Industry Classification System (NAICS). In 2002 the BLS updated the OES with conversions to the NAICS system. In “Gauging Metropolitan ‘High-Tech’ and ‘ITech’ Activity: Some Thoughts and Commentary” authors Chapple et al use the 1998 OES to identify all three-digit SIC manufacturing and servicep roducing industries with 9% (three times the average of the economy as a whole) of their national workforce in science and engineering jobs to develop a list of HighTech Industry Groups. Then, Carnegie Mellon University Center for Economic Developmenttakes this list of High-Tech Industry Groups (SIC based) and converts it to a list of High-Tech Industries (NAICS based). Using employment data from the updated 2002 OES and following the same methodology as Chapple, et al, the Carnegie Mellon University Center for Economic Development makes a new list, which is the basis of the USF-CEDR’s analysis of High-Tech jobs in the state of Florida from 2001 through 2003. Table 1 shows the number of High-Tech jobs in Florida in 1998, 1999 and 2000. High-Tech jobsin Florida increased by 2.84% from 1998 to 1999, and increased by 5.32% from 1999 to 2000. Ou r Summary Indicator for “High-Tech Jobs” is the percentage of High-Tech jobs to total jobs in Florida. This indicator assesses not only the rate of growth o f High-Tech jobs, but also whether High-Tech jobs are increasing relative to total employment. In 1998, 5.37% of jobs in Florida were in High-Tech industries. (Continued on Page 3)

PAGE 2

2 Table 1 The Tampa Bay Economy Volume 4, No. 2 Winter 2004 Table of Contests High-Tech Jobs in Florida…………………………...1 From the Editor……………………………..…….....2 Household Income Distribution in Tampa Bay, 19891999………….……………...…14 Economic Contributions of the Finance and Insurance Sector in Florida’s High Tech Corridor and the Rest of Florida……………..……..19 USF’s Basic Economic Development Course……………………………..…21 Market Analysis of Hillsborough County’s Community Development Block Grant Areas….…..22 Update on CEDR’s Data Center……………………25 CEDR Staff Dr. Dennis Colie………………………..……Director Dodson Tong…………………………..Data Manager N olan Kimball………………………...Coordinator o f Information/Publications Alex McPherson…………….………..……Economist Dave Sobush……………………………….Economist Anand Shah……………………….……Web Designer Michael Bernabe……......Graduate Research Assistant Jason Rodriguez…….…..Graduate Research Assistant University of South Florida From the Editor… This is the second issue of The Tampa Bay E conom y (TBE) for 2004, published solely in electronic form. Toconserve resources, we will no longer be mailing printed copies of the TBE. “High-Tech Jobs in Florida” is the lead report in this issue. The articles analyzes, for the perio d 1998-2003, trends in Florida high-tech employment, and compares Florida’s experience to those of other selected states. Also in this issue is “Household Income Distribution in Tampa Bay, 1989-1999,” which utilizes a Gini coefficient to describe income distribution trends in our seven-county region. “Economic Contributions of the Finance an d Insurance Sector in Florida’s High Tech Corridor and the Rest of Florida” summarizes a CEDR research report completed in December 2003. CEDR conducted the 28th annual USF Basic Economic Development Course in October 2004. This issue of the TBE includes a brief report about the course. “Market Analysis of Hillsborough County’s Community Development Block Grant Areas” summarizes a CEDR report commissioned by the Hillsborough County Economic Development Department. We conclude this issue of the TBE with a n “Update on CEDR’s Data Center.” To help us make the journal add even more value to Tampa Bay’s economic development community, we ask the journal’s readers to send us their comments at: cedr@coba.usf.edu with subject line “Journal Comments.”

PAGE 3

3 Table 1 Private Sector High-Tech Jobs in Florida Employment SIC Code Industry Group 1998 1999 2000 281 Industrial Inorganic Chemicals 522526567 282 Plastics Materials and Synthetics 3,2143,2303,425 283 Drugs 3,0523,9414,321 284 Soap, Cleaners, and Toilet Goods 2,8922,9113,059 285 Paints 1,4921,5661,632 286 Industrial Organic Chemicals 1,8401,5971,006 287 Agricultural Chemicals 6,4976,5636,307 289 Miscellaneous Chemical Products 1,7781,8021,650 291 Petroleum Refining nd*nd38 348 Ordinance and Accessories, N.E.C. 631735789 351 Engines and Turbines 2,3742,4393,315 353 Construction and Related Machinery 3,2153,6093,270 355 Special Industry Machinery 2,6482,4162,562 356 General Industrial Machinery 5,0734,9645,281 357 Computer and Office Equipment 6,0195,9385,558 361 Electric Distribution Equipment 2,2062,2282,110 362 Electrical Industrial Apparatus 2,3152,6012,251 365 Household Audio and Video Equipment 2,6152,5772,598 366 Communications Equipment 21,16019,50820,293 367 Electronic Components and Accessories 22,59721,45222,593 371 Motor Vehicles and Equipment 8,1027,8807,442 372 Aircraft and Parts 17,37117,06116,239 376 Guided Missiles, Space Vehicles 10,0148,7298,326 381 Search and Navigation Equipment 9,5439,4448,464 382 Measuring and Controlling Devices 6,4845,8445,981 384 Medical Instruments and Supplies 15,35315,09115,101 386 Photographic Equipments and Supplies 385310270 737 Computer and Data Processing Services 66,25871,10182,271 871 Engineering and Architectural Services 47,01951,58056,403 874 Management and Public Relations 82,06887,15191,088 Total Florida High-Tech Jobs 354,737364,794384,210 Total Florida Jobs (Public and Private Sector) 6,605,9876,844,6497,060,986 Summary Indicator 5.37%5.33%5.44% Source: Compiled by CEDR from US Department of Labor, Bureau of Labor Statistics, State and County Employment Wages from Covered Employment and Wages, available at http://data.bls.gov/cgi-bin/dsrv?ew *n/d: Not Disclosable data do not meet BLS or State Agency disclosure standards, usually because a minimum employment amount has not been met. (Continued from p. 1) In 1999, the Summary Indicator dropped slightly to 5.33%. In 2000, the percentage of jobs in High-Tech industries to total jobs increased to 5.44%. Table 1 also shows the following industry groups have the most Hi g h-Tech Jobs in Florida: Mana g ement an d Public Relations (SIC 874), Computer and Data Processing Services (SIC 737), Engineering an d Architectural Services (SIC 871), Electronic Components and Accessories (SIC 367), an d Communications E q ui p ment ( SIC 366 ) .

PAGE 4

4 Table 2 Summary Indicators for Private Sector High-Tech Jobs Year Measure State: Florida Arizona N. Carolina Texas 1998 High-Tech jobs 354,737 157,452249,246 715,267 Total jobs 6,605,987 2,072,7263,721,309 8,818,172 Summary Indicator 5.37% 7.60%6.70% 8.11% 1999 High-Tech jobs 364,794 157,913261,075 720,236 Total jobs 6,844,649 2,150,5383,804,369 9,016,641 Summary Indicator 5.33% 7.34%6.86% 7.99% 2000 High-Tech jobs 384,210 170,444266,220 754,439 Total jobs 7,060,986 2,220,7123,862,782 9,289,286 Summary Indicator 5.44% 7.68%6.89% 8.12% Source: Compiled by CEDR from U.S. Department of Labor, Bureau of Labor Statistics, State and County Employment and Wages from Covered Employment and Wages, available at http://data.bls.gov/cgi-bin/dsrv?ew Chart 2 Source: US Department of Labor, Bureau of Labor Statistics, State and County Employment and Wages from the Quarterly Census of Employment and Wages (2001 forward), http://www.bls.gov/data/home.htm Table 2 provides a comparison of the Summary Indicators for High-Tech Jobs in Florid a with other selected states. Chart 2 depicts the comparisons. All states experienced a slight downturn in the percent of jobs in High-Tech industries from 1998 to 1999, but in 2000 they increased their percents of High-Tech jobs over 1998 levels. In 2000, Texas had the highest percentage of High-Tech jobs among the benchmarking states, exceeding Florida’s Summary Indicator by 2.67%. However, that is down from a 2.74% difference in 1998, indicating Florida’s relative success in attracting High-Tech jobs.

PAGE 5

5 Table 3 Percent Change in Private Sector High-Tech Jobs in Florida (by Industry Group) 1998 to 1999 1999 to 2000 SIC Code Industry Group % Change % Change 281 Industrial Inorganic Chemicals 0.77%7.79% 282 Plastics Materials and Synthetics 0.50%6.04% 283 Drugs 29.13%9.64% 284 Soap, Cleaners, and Toilet Goods 0.66%5.08% 285 Paints 4.96%4.21% 286 Industrial Organic Chemicals -13.21%-37.01% 287 Agricultural Chemicals 1.02%-3.90% 289 Miscellaneous Chemical Products 1.35%-8.44% 291 Petroleum Refining nd*nd 348 Ordinance and Accessories, N.E.C. 16.48%7.35% 351 Engines and Turbines 2.74%35.92% 353 Construction and Related Machinery 12.26%-9.39% 355 Special Industry Machinery -8.76%6.04% 356 General Industrial Machinery -2.15%6.39% 357 Computer and Office Equipment -1.35%-6.40% 361 Electric Distribution Equipment 1.00%-5.30% 362 Electrical Industrial Apparatus 12.35%-13.46% 365 Household Audio and Video Equipment -1.45%0.81% 366 Communications Equipment -7.81%4.02% 367 Electronic Components and Accessories -5.07%5.32% 371 Motor Vehicles and Equipment -2.74%-5.56% 372 Aircraft and Parts -1.78%-4.82% 376 Guided Missiles, Space Vehicles -12.83%-4.62% 381 Search and Navigation Equipment -1.04%-10.38% 382 Measuring and Controlling Devices -9.87%2.34% 384 Medical Instruments and Supplies -1.71%0.07% 386 Photographic Equipments and Supplies -19.48%-12.90% 737 Computer and Data Processing Services 7.31%15.71% 871 Engineering and Architectural Services 9.70%9.35% 874 Management and Public Relations 6.19%4.52% Source: Compiled by CEDR from US Department of Labor, Bureau of Labor Statistics, State and County Employment Wages from Covered Employment and Wages, available at http://data.bls.gov/cgi-bin/dsrv?ew *nd: Not Disclosable data do not meet BLS or State Agency disclosure standards, usually because a minimum employment amount has not been met. Table 3 indicates the year-over-year percent change in High-Tech jobs in Florida by industr y group. From 1998 to 1999 the Drugs (SIC 283) industry group experienced the highest percent increase just over 29% while the Industrial Organic Chemicals (SIC 286) industry group absorbed the highest percent decrease of about 13%. In the 1999 to 2000 period, the Industrial Organic Chemicals industry group continued to lose jobs with another 37% decline. The big gainer in 1999 to 2000 was the Engines and Turbines (SIC 351) industry group with an almost 36% increase in jobs. Table 3 also shows that three of the industry groups holding the most High-Tech jobs (SIC 874, SIC 737, SIC 871) experience positive growth in number of High-Tech j obs in both 1999 and 2000. The other two (SIC 367, SIC 366) have negative growth in the number of HighTech jobs in 1999, but then rebound in 2000 with p ositive g rowth.

PAGE 6

6 Table 4 Private Sector High-Tech Jobs in Florida EMPLOYMENT NAICS Industry 2001 2002 2003(p) 211111 Crude Petroleum and Natural Gas Extraction 224 nd172 325110 Petrochemical Manufacturing nd ndnd 325120 Industrial Gas Manufacturing 323 296293 325131 Inorganic Dye and Pigment Manufacturing 84 8382 325188 All Other Basic Inorganic Chemical Manufacturing nd ndnd 325192 Cyclic Crude and Intermediate Manufacturing nd N/And 325199 All Other Basic Organic Chemical Manufacturing 426 nd416 325411 Medicinal and Botanical Manufacturing 114 163132 325412 Pharmaceutical Preparation Manufacturing 3,924 3,9164,176 325413 In-Vitro Diagnostic Substance Manufacturing nd 95100 325414 Biological Product (except Diagnostic) Manufacturing nd 37 333210 Sawmill and Woodworking Machinery Manufacturing 48 41nd 333292 Plastics and Rubber Industry Machinery Manufacturing nd ndnd 333293 Textile Machinery Manufacturing 270 215200 333294 Printing Machinery and Equipment Manufacturing 335 425403 333295 Semiconductor Machinery Manufacturing nd ndnd 333298 All Other Industrial Machinery Manufacturing 897 813848 333313 Office Machinery Manufacturing 1,249 1,0001,003 333314 Optical Instrument and Lens Manufacturing 1,297 1,2521,413 333315 Photographic and Photocopying Equipment Manufacturing 99 8598 333319 Other Commercial and Service Industry Machinery Manufacturing 1,037 972920 334111 Electronic Computer Manufacturing 2,094 1,8441,857 334113 Computer Terminal Manufacturing 122 103159 334119 Other Computer Peripheral Equipment Manufacturing 1037 972920 334210 Telephone Apparatus Manufacturing 2,407 1,9861,471 334220 Radio and Television Broadcasting and Wireless Communications Equipment Manufacturing 10,178 8,2386,899 334290 Other Communications Equipment Manufacturing 4,211 3,6123,173 334310 Audio and Video Equipment Manufacturing 2,096 1,9111,049 334412 Bare Printed Circuit Board Manufacturing 5,835 4,6203,546 334413 Semiconductor and Related Device Manufacturing 8,742 8,6117,856 334414 Electronic Capacitor Manufacturing 584 507378 Table 4 shows the number of jobs in Florida in 2001, 2002 and 2003 (preliminary data) within each High-Tech industry, classified by NAICS. High-Tech j obs in Florida decreased by 5.57% from 2001 to 2002, and decreased by 1.22% from 2002 to 2003. The Summary Indicator shows that in 2001, 2.88% o f j obs in Florida were in High-Tech industries. In 2002, the number of jobs in Florida’s High-Tech industries dropped to 2.72% and fell to 2.65% in 2003. Table 4 also shows the following industries to hold the mos t High-Tech jobs: Engineering Services (NAICS 541330), Custom Computer Programming Systems (NAICS 541511), Computer Systems Design Devices (NAICS 541512), Research and Development in the Physical, Engineering, and Life Sciences (NAICS 541710), and Radio and Television Broadcasting and Wireless Communications Equipment Manufacturing (NAICS 334220).

PAGE 7

7 Table 4 (Continued) Private Sector High-Tech Jobs in Florida EMPLOYMENT NAICS Industry 2001 2002 2003(p) 334415 Electronic Resistor Manufacturing 438 317290 334417 Electronic Connector Manufacturing 640 516525 334418 Printed Circuit Assembly (Electronic Assembly) Manufacturing 5,006 3,9292,881 334419 Other Electronic Component Manufacturing 2,099 2,0971,961 334510 Electromedical and Electrotherapeutic Apparatus Manufacturing 2,703 2,9433,555 334511 Search, Detection, Navigation, Guidance, Aeronautical, and Nautical System and Instrument Manufacturing 8,353 8,0568,081 334512 Automatic Environmental Control Manufacturing for Residential, Commercial, and Appliance Use 394 391357 334513 Instruments and Related Products Manufacturing for Measuring, Displaying, and Controlling Industrial Process Variables 1,065 924983 334514 Totalizing Fluid Meter and Counting Device Manufacturing 672 554533 334515 Instrument Manufacturing for Measuring and Testing Electricity and Electrical Signals 1,225 948905 334516 Analytical Laboratory Instrument Manufacturing 910 746575 334517 Irradiation Apparatus Manufacturing 40 2823 334519 Other Measuring and Controlling Device Manufacturing 421 481519 336411 Aircraft Manufacturing 4,726 3,8253,958 336412 Aircraft Engine and Engine Parts Manufacturing 5,330 4,3773,651 336413 Other Aircraft Part and Auxiliary Equipment Manufacturing 2,851 2,5522,349 336419 Other Guided Missile and Space Vehicle Parts and Auxiliary Equipment Manufacturing nd ndnd 511210 Software Publishers 5,910 6,2766,376 541310 Architectural, Engineering, and Related Services 9,793 9,6389,772 541330 Engineering Services 42,639 43,59345,175 541370 Surveying and Mapping (except Geophysical) Services 6,016 6,3266,828 541380 Testing Laboratories 3,773 3,8134,004 541511 Custom Computer Programming Services 22,130 21,35023,002 541512 Computer Systems Design Devices 19,338 17,12516,867 541710 Research and Development in the Physical, Engineering, and Life Sciences 10,254 10,3109,842 541720 Research and Development in the Social Sciences and Humanities 1,690 1,7041,618 Total High-Tech Jobs in Florida 206,049 194,582192,201 Total Jobs in Florida (Public and Private Sector) 7,153,589 7,164,5237,248,097 Summary Indicator 2.88% 2.72%2.65% Sources: Compiled by CEDR from 1) Carnegie Mellon University Center for Economic Development (CED), Table 1: Technology Employers, http://www.ssti.org/Publications/online.htm 2) US Department of Labor, Bureau of Labor Statistics, State and County Employment and Wages from the Quarterly Census of Employment and Wages (2001 forward), http://www.bls.gov/data/home.htm. *n/d: Not Disclosable data do not meet BLS or State Agency disclosure standards, usually because a minimum employment amount has not been met. p: preliminary data from 2003

PAGE 8

8 Table 5 Summary Indicators for Private Sector High-Tech Jobs Year Measure State: Florida Arizona Texas N. Carolina 2001 High-Tech jobs 206,049 117,438528,128128,577 Total Jobs 7,153,589 2,243,6529,350,7703,805,498 Summary Indicator 2.88% 5.23%5.65%3.38% 2002 High-Tech jobs 194,582 105,591512,777114,003 Total Jobs 7,164,523 2,240,2349,261,0893,751,648 Summary Indicator 2.72% 4.71%5.54%3.04% 2003 High-Tech jobs 192,201 98,907461,834111,278 Total Jobs 7,248,097 2,272,3939,208,4733,719,444 Summary Indicator 2.65% 4.35%5.02%2.99% Source: US Department of Labor, Bureau of Labor Statistics, State and County Employment and Wages from the Quarterly Census of Employment and Wages (2001 forward), http://www.bls.gov/data/home.htm Chart 5 Source: US Department of Labor, Bureau of Labor Statistics, State and County Employment and Wages from the Quarterly Census of Employment and Wages (2001 forward), http://www.bls.gov/data/home.htm Table 5 provides a comparison of the Summary Indicators for Private Sector High-Tech jobs in Florida with a group of selected states as benchmarks. Chart 5 is a visual comparison. All states consistently experienced a decrease in the percent and absolute number of jobs in High-Tech industries from 2001 through 2003. Texas led with the highest percent of High-Tech jobs to total jobs, while Florida had the least percent of High-Tech jobs to total j obs out of all four states in each of the three years. But Florida was the state that experienced the least p ercent decrease from 2001 through 2003 indicating some stability in High-Tech employment, relative to the benchmark states.

PAGE 9

9 Table 6 Percent Change in Private Sector High-Tech Jobs in Florida (by Industry) % Change NAICS Code Industry Group 2001 20022002 2003 211111 Crude Petroleum and Natural Gas Extraction N/A*N/A 325110 Petrochemical Manufacturing N/AN/A 325120 Industrial Gas Manufacturing -8.36%-1.01% 325131 Inorganic Dye and Pigment Manufacturing -1.19%-1.20% 325188 All Other Basic Inorganic Chemical Manufacturing N/AN/A 325192 Cyclic Crude and Intermediate Manufacturing N/AN/A 325199 All Other Basic Organic Chemical Manufacturing N/AN/A 325411 Medicinal and Botanical Manufacturing 42.98% -19.02% 325412 Pharmaceutical Preparation Manufacturing -0.20%6.64% 325413 In-Vitro Diagnostic Substance Manufacturing N/A5.26% 325414 Biological Product (except Diagnostic) Manufacturing N/A 133.33% 333210 Sawmill and Woodworking Machinery Manufacturing -14.58%N/A 333292 Plastics and Rubber Industry Machinery Manufacturing N/AN/A 333293 Textile Machinery Manufacturing -20.37%-6.98% 333294 Printing Machinery and Equipment Manufacturing 26.87%-5.18% 333295 Semiconductor Machinery Manufacturing N/AN/A 333298 All Other Industrial Machinery Manufacturing -9.36%4.31% 333313 Office Machinery Manufacturing -19.94%0.30% 333314 Optical Instrument and Lens Manufacturing -3.47%12.86% 333315 Photographic and Photocopying Equipment Manufacturing -14.14%15.29% 333319 Other Commercial and Service Industry Machinery Manufacturing -6.27%-5.35% 334111 Electronic Computer Manufacturing -11.94%0.70% 334113 Computer Terminal Manufacturing -15.57%54.37% 334119 Other Computer Peripheral Equipment Manufacturing -6.27%-5.35% 334210 Telephone Apparatus Manufacturing -17.49%-25.93% Table 6 indicates the year-over-year percent change in High-Tech jobs in Florida by industry. From 2001 to 2002 the Medicinal and Botanical Manufacturing (NAICS 325411) industry experienced the largest percent increase of about 43%, while the Irradiation Apparatus Manufacturing (NAICS 334517) industry experienced the largest percent decrease o f about 30%. From 2002 to 2003 (preliminary data) it was the Biological Product (except Diagnostic) Manufacturing (NAICS 325414) industr y experiencing the largest percent increase of 133%, and the Audio and Video Equipment Manufacturing (NAICS 334310) industry experiencing the largest percent decrease of about 45%. Notably, of the 61 High-Tech industries profiled, only 4 ex p erienced p ositive growth in consecutive years (2002 and 2003) with the latter year’s growth being larger than the first. Those industries are: Electromedical and Electrotherapeutic Apparatus Manufacturing (NAICS 334510), Engineering Services (NAICS 541330), Surveying and Mapping (except Geophysical) Services (NAICS 541370), and Testing Laboratories (NAICS 541380). Table 6 also shows that only one o f the industries holding the most High-Tech jobs, Engineering Services (NAICS 541330), is experiencing positive growth in number of High-Tech j obs in both 1999 and 2000.

PAGE 10

10 Table 6 (Continued) Percent Change in Private Sector High-Tech Jobs in Florida (by Industry) % Change NAICS Code Industry Group 2001 2002 2002 2003 334220 Radio and Television Broadcasting and Wireless Communications Equipment Manufacturing -19.06%-16.25% 334290 Other Communications Equipment Manufacturing -14.22%-12.15% 334310 Audio and Video Equipment Manufacturing -8.83% -45.11% 334400 Semiconductor and Other Electronic Component Manufacturing N/AN/A 334412 Bare Printed Circuit Board Manufacturing -20.82%-23.25% 334413 Semiconductor and Related Device Manufacturing -1.50%-8.77% 334414 Electronic Capacitor Manufacturing -13.18%-25.44% 334415 Electronic Resistor Manufacturing -27.63%-8.52% 334417 Electronic Connector Manufacturing -19.38%1.74% 334418 Printed Circuit Assembly (Electronic Assembly) Manufacturing -21.51%-26.67% 334419 Other Electronic Component Manufacturing -0.10%-6.49% 334510 Electromedical and Electrotherapeutic Apparatus Manufacturing 8.88% 20.80% 334511 Search, Detection, Navigation, Guidance, Aeronautical, and Nautical System and Instrument Manufacturing -3.56%0.31% 334512 Automatic Environmental Control Manufacturing for Residential, Commercial, and Appliance Use -0.76%-8.70% 334513 Instruments and Related Products Manufacturing for Measuring, Displaying, and Controlling Industrial Process Variables -13.24%6.39% 334514 Totalizing Fluid Meter and Counting Device Manufacturing -17.56%-3.79% 334515 Instrument Manufacturing for Measuring and Testing Electricity and Electrical Signals -22.61%-4.54% 334516 Analytical Laboratory Instrument Manufacturing -18.02%-22.92% 334517 Irradiation Apparatus Manufacturing -30.00% -17.86% 334519 Other Measuring and Controlling Device Manufacturing 14.25%7.90% 336400 Aerospace Product and Parts Manufacturing N/AN/A 336411 Aircraft Manufacturing -19.06%3.48% 336412 Aircraft Engine and Engine Parts Manufacturing -17.88%-16.59% 336413 Other Aircraft Part and Auxiliary Equipment Manufacturing -10.49%-7.95% 336419 Other Guided Missile and Space Vehicle Parts and Auxiliary Equipment Manufacturing N/AN/A 511210 Software Publishers 6.19%1.59% 541310 Architectural, Engineering, and Related Services -1.58%1.39% 541330 Engineering Services 2.24% 3.63% 541370 Surveying and Mapping (except Geophysical) Services 5.15% 7.94% 541380 Testing Laboratories 1.06% 5.01% 541511 Custom Computer Programming Services -3.52%7.74% 541512 Computer Systems Design Devices -11.44%-1.51% 541600 Management, Scientific, and Technical Consulting Services N/AN/A 541700 Scientific Research and Development Services N/AN/A

PAGE 11

11 Table 6 (Continued) Percent Change in Private Sector High-Tech Jobs in Florida (by Industry) % Change NAICS Code Industry Group 2001 20022002 2003 541710 Research and Development in the Physical, Engineering, and Life Sciences 0.55%-4.54% 541720 Research and Development in the Social Sciences and Humanities 0.83%-5.05% Sources: Compiled by CEDR from – 1) Carnegie Mellon University Center for Economic Development (CED), Table 1: Technology Employers, http://www.ssti.org/Publications/online.htm 2) US Department of Labor, Bureau of Labor Statistics, State and County Employment and Wages from the Quarterly Census of Employment and Wages (2001 forward), http://www.bls.gov/data/home.htm. *N/A: Not Available – a percent change was not available due to no data disclosed to make a calculation We are also able to examine the number o f High-Tech establishments in Florida. See Table 7 The table indicates the year-over-year percent change in number of establishments within each High-Tech industry. From 2001 to 2002 the Other Measuring and Controlling Device Manufacturing (NAICS 334519) industry experienced the largest percent increase o f about 24%, while the Sawmill and Woodworking Machinery Manufacturing (NAICS 333210) industr y experienced the largest percent decrease of 25%. From 2002 to 2003 (preliminary data) it was the Computer Terminal Manufacturing (NAICS 334113) industry experiencing the largest percent increase o f 40%, and the Semiconductor and Related Device Manufacturing (NAICS 334413) industr y experiencing the largest percent decrease of about 15%. Only 6 High-Tech industries experienced p ositive growth in both 2002 and 2003, with 2003’s growth being larger than 2002’s. Those industries are: Other Electronic Component Manufacturing (NAICS 334419), Aircraft Manufacturing (NAICS 336411), Architectural, Engineering, and Related Services (NAICS 541310), Surveying and Mapping (except Geophysical) Services (NAICS 541370), Custom Computer Programming Services (NAICS 541511), and Computer Systems Design Devices (NAICS 541512). In comparing Table 6 to Table 7 both industries, Irradiation Apparatus Manufacturing (NAICS 334517) in 2002 and Audio and Video Equipment Manufacturing (NAICS 334310) in 2003, that experienced the largest percent decrease in HighTech jobs also experienced a percent decrease in High-Tech establishments. There is a strong correlation between the number of jobs and the number of establishments within the High-Tech industries. There is also a moderate correlation between percent change in High-Tech jobs an d percent change in High-Tech establishments. Table 7 also shows that three of the industries with the most High-Tech Jobs: Engineering Services (NAICS 541330), Custom Computer Programming Services (NAICS 541511), and Computer System Design Devices (NAICS 541512) also have the most establishments. Those same three are also three of the 6 High-Tech Industries that experienced positive growth in both 2002 and 2003 with 2003’s growth being larger than 2002’s.

PAGE 12

12 Table 7 Private Sector Establishments in the High-Tech Industries in Florida ESTABLISHMENTS% Change NAICS Industry 20012002 2003(p) 2001-20022002-2003 211111 Crude Petroleum and Natural Gas Extraction 19nd 19N/A*N/A 325110 Petrochemical Manufacturing ndnd ndN/AN/A 325120 Industrial Gas Manufacturing 1720 1817.65%-10.00% 325131 Inorganic Dye and Pigment Manufacturing 55 60.00%20.00% 325188 All Other Basic Inorganic Chemical Manufacturing ndnd ndN/AN/A 325192 Cyclic Crude and Intermediate Manufacturing nd0 ndN/AN/A 325199 All Other Basic Organic Chemical Manufacturing 10nd 11N/AN/A 325411 Medicinal and Botanical Manufacturing 1514 13-6.67%-7.14% 325412 Pharmaceutical Preparation Manufacturing 5755 61-3.51%10.91% 325413 In-Vitro Diagnostic Substance Manufacturing nd8 80.00%0.00% 325414 Biological Product (except Diagnostic) Manufacturing nd4 40.00%0.00% 333210 Sawmill and Woodworking Machinery Manufacturing 86 nd -25.00% 0.00% 333292 Plastics and Rubber Industry Machinery Manufacturing ndnd ndN/AN/A 333293 Textile Machinery Manufacturing 3128 25-9.68%-10.71% 333294 Printing Machinery and Equipment Manufacturing 2319 19-17.39%0.00% 333295 Semiconductor Machinery Manufacturing ndnd ndN/AN/A 333298 All Other Industrial Machinery Manufacturing 3736 38-2.70%5.56% 333313 Office Machinery Manufacturing 1513 12-13.33%-7.69% 333314 Optical Instrument and Lens Manufacturing 3529 25-17.14%-13.79% 333315 Photographic and Photocopying Equipment Manufacturing 1312 14-7.69%16.67% 333319 Other Commercial and Service Industry Machinery Manufacturing 104100 96-3.85%-4.00% 334111 Electronic Computer Manufacturing 4541 42-8.89%2.44% 334113 Computer Terminal Manufacturing 65 7-16.67% 40.00% 334119 Other Computer Peripheral Equipment Manufacturing 3842 4410.53%4.76% 334210 Telephone Apparatus Manufacturing 4441 38-6.82%-7.32% 334220 Radio and Television Broadcasting and Wireless Communications Equipment Manufacturing 10395 95-7.77%0.00% 334290 Other Communications Equipment Manufacturing 4847 46-2.08%-2.13% 334310 Audio and Video Equipment Manufacturing 4640 38-13.04%-5.00% 334412 Bare Printed Circuit Board Manufacturing 7165 63-8.45%-3.08% 334413 Semiconductor and Related Device Manufacturing 5053 456.00% -15.09% 334414 Electronic Capacitor Manufacturing 87 6-12.50%-14.29% 334415 Electronic Resistor Manufacturing 87 6-12.50%-14.29% 334417 Electronic Connector Manufacturing 1211 12-8.33%9.09% 334418 Printed Circuit Assembly (Electronic Assembly) Manufacturing 4649 426.52%-14.29% 334419 Other Electronic Component Manufacturing 3940 42 2.56% 5.00% 334510 Electromedical and Electrotherapeutic Apparatus Manufacturing 4340 45-6.98%12.50% 334511 Search, Detection, Navigation, Guidance, Aeronautical, and Nautical System and Instrument Manufacturing 5351 52-3.77%1.96% 334512 Automatic Environmental Control Manufacturing for Residential, Commercial, and Appliance Use 2725 22-7.41%-12.00% 334513 Instruments and Related Products Manufacturing for Measuring, Displaying, and Controlling Industrial Process Variables 5751 53-10.53%3.92% 334514 Totalizing Fluid Meter and Counting Device Manufacturing 2421 20-12.50%-4.76%

PAGE 13

13 Table 7 (Continued) Private Sector Establishments in the High-Tech Industries in Florida ESTABLISHMENTS% Change NAICS Industry 20012002 2003(p) 2001-20022002-2003 334515 Instrument Manufacturing for Measuring and Testing Electricity and Electrical Signals 5145 45-11.76%0.00% 334516 Analytical Laboratory Instrument Manufacturing 2320 21-13.04%5.00% 334517 Irradiation Apparatus Manufacturing 109 12-10.00%33.33% 334519 Other Measuring and Controlling Device Manufacturing 2936 4024.14%11.11% 336411 Aircraft Manufacturing 4449 5611.36%14.29% 336412 Aircraft Engine and Engine Parts Manufacturing 5754 52-5.26%-3.70% 336413 Other Aircraft Part and Auxiliary Equipment Manufacturing 5152 491.96%-5.77% 336419 Other Guided Missile and Space Vehicle Parts and Auxiliary Equipment Manufacturing ndnd ndN/AN/A 511210 Software Publishers 210230 2499.52%8.26% 541310 Architectural, Engineering, and Related Services 1,5761,629 1,7013.36%4.42% 541330 Engineering Services 3,3543,584 3,7756.86%5.33% 541370 Surveying and Mapping (except Geophysical) Services 682698 735 2.35% 5.30% 541380 Testing Laboratories 379379 3880.00%2.37% 541511 Custom Computer Programming Services 3,3373,511 3,8585.21%9.88% 541512 Computer Systems Design Devices 2,9532,991 3,1061.29%3.84% 541710 Research and Development in the Physical, Engineering, and Life Sciences 601580 594-3.49%2.41% 541720 Research and Development in the Social Sciences and Humanities 184171 167-7.07%-2.34% As shown by our summary indicators, over the p eriod of 1998 to 2000, Florida experienced growth in the number of jobs in High-Tech industries relative to j obs in the economy as a whole. While positive growth was consistent among the benchmark states, Florida’s .07% growth over the three years falls below N orth Carolina’s .19% growth and Arizona’s .08% growth for that same period. For the period of 2001 to 2003, under the new industry classification system and using a different definition of High-Tech industries, our summary indicators show that over the period Florida experiences a decline in the number of jobs in the High-Tech industries relative to jobs in the econom y as a whole. Again this trend is consistent with the benchmark states for the period, but Florida’s .23% decline was the least, ranking ahead of Arizona’s .88% decline, Texas’s .63% decline, and North Carolina’s .39% decline. Thus the state of Florida was able to keep relatively more jobs in the High-Tech industries than the benchmark states.

PAGE 14

14Household Income Distribution in Tampa Bay, 1989-1999 By Dave Sobush, Economist with the Center for Economic Development Research CEDR’s annual publication, Tampa Bay M arket Report reveals economic information including per capita income and population for the Tampa Bay region. Multiplying per capita income b y the population calculates the aggregate value o f income dollars in a given region, but neither the aggregate value nor the per capita value provide much insight into the distribution of income within an area. A Gini coefficient provides this insight. This article discusses the calculation of the Gini coefficient, reports household income distribution measured by a Gini coefficient for Florida, the seven-county Tampa Bay area, and Tampa Bay’s three cohort Metropolitan Statistical Areas (MSAs) fo r years 1989, 1999, and estimates the current (2004) distribution of household income. Finally, this article discusses possible uses of Gini coefficients for grantseeking economic developers. Calculating the Gini Coefficient For any set of numbers, the Gini coefficient, develo p ed by Italian statistician Corrado Gini (d. 1965), is a number between zero and one, where p erfect equality between the numbers is denoted by a zero value, and perfect inequality is denoted by a value of one. For example, a society in which ever y household had the same income would have a Gini coefficient of 0.0. A society in which one househol d earned all income and all other households earned no income would have a Gini coefficient of 1.0. The Gini coefficient is calculated as the ratio of areas under the line of perfect equality (the 45 line) and the Lorenz curve. The Lorenz curve graphs the p ercentage of cases (for the purposes of this article, households) on the x-axis and the cumulative percentage of the variable of interest (for the purposes of this article, total household income) on the y-axis. In our example Lorenz curve illustrated above, we conclude that the bottom 80% of households earn roughly 45% of household income. Or in other terms, theupper 20% of households earn roughly 55% of all income. The Gini coefficient would be calculated as the shaded area divided by the total area under the line of equality. M ethodolog y The Lorenz curve is created by first arranging the household income data in ascending order. After the cases have been ordered, the cumulative p ercentage of household income is assigned to each case. For a sample of 10 households, a spreadsheet set up to graph the Lorenz curve may look like this: Cumulative % of HouseholdsCumulative % of Household Income

PAGE 15

15 A B C D 1 Case Number Household Income (HHI) Percentage of Total HHI Cumulative % of HHI 2 1 $ 1,000 0.15% 0.15% 3 2 $ 2,500 0.36% 0.51% 4 3 $ 6,000 0.87% 1.38% 5 4 $ 32,000 4.66% 6.05% 6 5 $ 42,500 6.19% 12.24% 7 6 $ 55,000 8.01% 20.26% 8 7 $ 56,250 8.20% 28.45% 9 8 $ 75,235 10.96% 39.42% 10 9 $ 90,750 13.22% 52.64% 11 10 $ 325,000 47.36% 100.00% To graph the Lorenz curve, plot the data in column D in line-chart format, as shown below: Sample Lorenz Curve0% 20% 40% 60% 80% 100% 120% 0%20%40%60%80%100% % of HouseholdsCumulative % of Household Income Precise measurement of the area under the Lorenz curve requires that (a) the curve be mathematically defined as a function and (b) that the integral of that function be taken. Recognizing that this level of mathematic finesse may not be readil y available to an individual or organization, we propose a method of estimating the area under the Lorenz curve. Rather than using calculus to create the curve, and then to determine the area underneath it, a p proximate Lorenz curves can be constructed and measured by a process of division, multiplication, and addition. Instead of integral calculus, the alternative method involves the creation of n rectangles, where n is the number of cases.

PAGE 16

16 A B C D E F 1 Case Number Household Income (HHI) Percentage of Total HHI Cumulative % of HHI 1/n D x E 2 1 $ 1,000 0.15% 0.15% 0.1 0.0001 3 2 $ 2,500 0.36% 0.51% 0.1 0.0005 4 3 $ 6,000 0.87% 1.38% 0.1 0.0014 5 4 $ 32,000 4.66% 6.05% 0.1 0.0060 6 5 $ 42,500 6.19% 12.24% 0.1 0.0122 7 6 $ 55,000 8.01% 20.26% 0.1 0.0203 8 7 $ 56,250 8.20% 28.45% 0.1 0.0285 9 8 $ 75,235 10.96% 39.42% 0.1 0.0394 10 9 $ 90,750 13.22% 52.64% 0.1 0.0526 11 10 $ 325,000 47.36% 100.00% 0.1 0.1000 12 SUM(F2:F11) 0.2611 To graph the approximate area under Lorenz curve, plot the data in column D in column-chart format, as shown below: Approximation of Area Under Lorenz Curve0% 20% 40% 60% 80% 100% 120% 10%20%30%40%50%60%70%80%90%100% % of HouseholdsCumulative % of Household Income Each rectangle has a width of 1/n, and a height equal to the cumulative percentage of HHI assigned to each case. By taking the sum of the areas of the rectangles, we approximate the area under the Lorenz curve. To do so, modify our earlier spreadsheet b y adding two columns: 1/n, where n is the number o f cases, and D x E, where the value displayed for each row is the product of column D and column E. In our example, n equals 10 therefore 1/n equals 0.1.

PAGE 17

17 GINI Coefficient Values for Household Income Area 1989 1999 10-Year Rate of Change 2004* Florida 0.5580 0.56481.21% 0.5682 Tampa Bay 0.5321 0.55063.49% 0.5602 Lakeland-Winter Haven, FL MSA 0.5604 0.5450-2.75% 0.5374 Tampa-St. Petersburg-Clearwater, FL MSA0.5288 0.54322.73% 0.5505 Sarasota-Bradenton, FL MSA 0.5381 0.57637.09% 0.5964 CEDR estimate based on annual compound growth rate, 1989-1999 The line of equality forms a triangle with base and height both equal to 1, thus the area under the line of equality is 0.5*1*1=0.5. To calculate the ap p roximate area under the Lorenz curve, sum the values of cells F2 through F11, as shown above in cell F12. Subtract this value from the total area under the line of equality to yield the area between the Lorenz curve and the line of equality: 0.5 0.2611 = 0.2389 Divide this result by 0.5 to yield the Gini coefficient: 0.2389/0.5 = 0.4778 For large sample sizes, the width of the rectangles will decrease, producing a smoother curve and thus a more accurate approximation of the area under the Lorenz curve. D ata Source and Gini Coefficients The data used to create the Lorenz curve and thus the Gini coefficient is the U.S. Census Bureau’s Public Use Microdata Sample (PUMS). The PUMS data, gathered concurrent with the decennial census, contains a wide array of demographic and economic data for both people and households. We use the 1990 and 2000 5%-sample PUMS databases, hence ou r household income data corresponds to years 1989 and 1999, respectively. PUMS data is available for a variety of geographic areas, although finer detail (i.e. the municipal level) will yield smaller and sometimes insufficient sample sizes than a larger area. We calculate our Gini coefficients using the methodology described above, with one exception. For the state of Florida, taking a random sample o f 65,000 cases reduces the sample size of 350,000+. The following table reports Gini coefficients for Florida, Tampa Bay, and Tampa Bay’s three cohort MSAs:

PAGE 18

18 As measured by the Gini coefficient, reported 1989 and 1999 household income was distribute d more evenly in the Lakeland-Winter Haven an d Tampa-St. Petersburg-Clearwater MSAs than in the state as a whole. The Tampa Bay region followed this trend. However, Gini coefficients for househol d income distribution in Tampa Bay and the Tampa-St. Petersburg-Clearwater MSA grew at a faster rate during the ten-year period of interest. While our 2004 estimate still shows a higher Gini coefficient for the State, should the current trends continue Tampa Ba y and the Tampa-St. Petersburg-Clearwater MSA will eclipse that of the state in the future, signifying a growing divide between the upper-income and lowerincome households. For all years reported, household income inthe Sarasota-Bradenton MSA was distributed less equall y than the other examined areas. This pattern is p redicted to continue and the difference between this area and others to increase. As shown above, the 10year rate of change in the Gini coefficient for the Sarasota-Bradenton area was twice that of the nex t highest rate of change. Uses of Household Income Distribution Data Many economic development and/or neighborhood enhancement grants, such as those given by the U.S. Economic Development Administration (EDA) or private-sector foundations, require demonstration of need. Many times, per capit a incomes below national or state averages suffice as that demonstration. However, in geographicall y smaller areas, such as MSAs, counties, an d municipalities, outliers at the top of the income scale may push per capita income levels above that which demonstrates need. Thirdp arty data sources produce income and dataat very detailed levels, such as block groups, but often these sources are priced beyond the financial means of ad hoc researchers. By creating Lorenz curves, researchers and grant applicants can produce per capita and househol d income statistics for segments of the population. Fo r instance, if a Lorenz curve shows that the bottom 20% of households earn 5% of the income, divide 5% o f the total income by 20% of the total households to calculate the average household income for the lowest-earning 20% of households. Even if an area as a whole cannot demonstrate need, if large segments o f the population can demonstrate to have need, grant applicants may find greater success.

PAGE 19

19Economic Contributions of the Finance and Insurance Sector in Florida’s High Tech Corridor and the Rest of Florida By Dennis G. Colie, Ph.D., Director, Center for Economic Development Research E ditor’s note: The following article is a s ummary of a CEDR research report of the same titl e and dated December 2003. The original CEDR research report can be found on CEDR’s website a t http://cedr.coba.usf.edu The purpose of this research is to estimate the economic contributions of the Finance and Insurance (F & I) sector of the economy within the Florida High Tech Corridor and the Rest of Florida. We employ the R EMITM Policy Insight model to perform the estimates. The by-county geographic coverage of the model allows us to examine the principal component counties of the Florida High Tech Corridor : Brevard, Hernando, Hillsborough, Lake, Manatee, Orange, Osceola, Pasco, Pinellas, Polk, Sarasota, Seminole and Volusia counties. Florida’s counties other than the principal component counties are aggregated in the model as the Rest of Florida. The conceptual foundation of this analysis is the understanding that job creation in one industr y begets additional jobs in related industries. In addition, further jobs are created to support an increased level of aggregate household income and spending resulting from the inter-industry job creation. This phenomenon of job creation, with concomitant increased levels of income and production, is called the multiplier or ripple effect. In 2003, there are about 175,800 jobs in the F & I sector. These jobs represent 4.81% of total employment in the Corridor. In the Rest of Florida there are about 276,600 F & I jobs, or 4.97% of total employment. Within the F & I sectors of both the Corridor and the Rest of Florida, we expect the number of Banking jobs to slightly decrease between 2003 and 2007, while we expect jobs in Credit & Finance and Insurance to increase during that same time period. In 2003, output of the F & I sector within the Corridor approximates $27.5 billion, or 7.65% of the Corridor’s total economic activity. The Rest o f Florida produces F & I output equal to about $44.6 b illion, or 8.61% of total output in that area. Although we expect Banking jobs to decline, we anticipate that Bankingoutput will grow at an over 2% average annual rate throughout Florida. Declining employment and growing output is consistent with p roductivity gain (and consolidation) in the Banking industries. Overall, we expect F & I output throughout Florida to grow by more than 3% per annum. Also in 2003, wages of the F & I sector within the Corridor are nearly $6.4 billion, or 6.25% of the Corridor’s total wage bill. In the Rest of Florida, F & I wages equal about $11.5 billion, or 7.45% o f total wages paid in the Rest of Florida. Between 2003 and 2007, we anticipate that total wages and salaries p aid to workers in the F & I sector will increase b y more than an average 4% per annum. We assess the economic contributions of the F & I sector of the economy using the traditional counter-factual approach. With this approach, we use the REMITM Policy Insight model to virtually remove the baseline output produced by the primary industries of the F & I sector. The model tabulates the direct effects of the removal of the baseline economic activities as well as the ripple, or secondary, effects throughout the economy. First, we virtually remove the output of the F & I sector within the High Tech Corridor, but allow finance and insurance activities in the Restof Florida. This first counter-factual analysis yields the economic contribution of the F & I sector to the High Tech Corridor. Second, we virtually remove the output o f the F & I sector from both the Corridor and the Rest o f Florida. Hypothetically, finance and insurance activities now only take place outside the state o f Florida. This second counter-factual analysis yields the economic contribution of the F & I sector to the stateofFlorida.

PAGE 20

20 From the first analysis, we find that in 2003 the F & I sector contributes about 457,000 jobs, o r 12.53% of total employment, to the High Tech Corridor’s economy. The largest contributions are in Hillsborough County and Pinellas County at 129,500 j obs and 106,600 jobs, respectively. Measured b y output, the F & I sector contributes over $57 billion, or about 15.85% of total output, to the Corridor’s economy. The largest contributions are in Hillsborough County at over $15.7 billion, or 21.06% of Hillsborough County’s total economic activity an d in Pinellas County at over $13.9 billion, or 22.55% o f Pinellas County’s total economic activity. And, as measured by wages, the F & I sector contributes over $14.9 billion, or about 14.70% of total wages an d salaries, to the Corridor’s economy. The largest contribution is in Hillsborough County at over $4.5 billion, which is approximately 19.61% of the County’s total wage bill. The contribution in Pinellas County is over $3.4 billion, which is the highest p ercentage, 20.77%, of any Corridor county’s wage bills. From the second analysis, we find that in 2003 the F & I sector’s contri b ution to the state of Florida’s economy is approximately 1,228,000 jobs, over $158 b illion of output, and wage and salary disbursements for workers totaling over $42.5 billion. From our analyses, we conclude that the F & I sector is a large and growing segment of Florida’s economy. The center of this economic activity is i n the Tampa Bay region in the western portion o f Florida’s High Tech Corridor, particularly clustered within Hillsborough County and Pinellas County. This research was done in support of the Florida Financial Service Cluster Initiative (FFSCI) under the coordination of Guy Hagen, President, Innovation Insight, Inc. In the next column, Mr. Hagen describes the FFSCI: The Florida Financial Service Cluster Initiative (FFSCI) is a public-private partnership with objectives including the expansion, attraction, and creation of high value financial services companies in Florida. In particular, the FFSCI has targeted non-retail finance operations including securities and commodities, insurance, transaction processing, and technology / operations facilities. The FFSCI is a statewide partnership led by top executives from the private sector and with representatives from across Florida. FFSCI’s efforts to date have helped to attract key companies like Depositors Trust & Clearing Corporation (DTCC), and sponsoring a comprehensive strategic research project to guide marketing and positioning, economic development, and other collaborative activities. The FFSCI’s first objective is to obtain Florida ‘high impact’ designation for selected financial services sectors. The FFSCI has been working closely with state officials toward this goal, which would be an important and high profile step toward establishing Florida as one of the top international clusters in financial services. The FFSCI is in the process of formal incorporation, and expects to unveil some highprofile industry events and announcements in early 2005. The official website of the FFSCI is http://financialflorida.com Guy Hagen, President Innovation Insight, Inc.

PAGE 21

21USF’s Basic Economic Development Course By Nolan Kimball, Coordinator of Information/Publications with the Center for Economic Development Research The Center for Economic Development Research (CEDR) conducted the 28th annual USF Basic Economic Development Course during the week of October 24 29, 2004. The course was hel d at the Hilton Tampa Airport Westshore in Tampa, Florida. Thirty-three students from six states p articipated in the 2004 course. CEDR is a unit o f the College of Business Administration. Dennis G. Colie, Director of CEDR was the Course Directo r and the Course Coordinator was Nolan Kimball, Coordinator of Information/Publications for CEDR. The International Economic Development Council (IEDC) accredits the course. The Course Director received valuable input from the Advisory Committee, whose members are economic development practitioners. The 2004 Course Advisory Committee members were: Beatriz Bare, Director of Corporate Recruitment an d Expansion, Greater Tampa Chamber of Commerce, Committee of One Hundred Richard “Buzz” David, CEcD, Director, Pinellas County Economic Development Marilyn Hett, Business Development Administrator, Hillsborough County Economic Development Department Michael McHugh, Director, Hernando Count y Office of Business Development Regina Smith, Director, Lee County Office o f Economic Development Mary Jane Stanley, CEcD, President/CEO, Pasco Economic Development Council and Chairperson o f the Florida Economic Development Council. CEDR structured the 2004 USF Basic Economic Development Course around the core topics established by IEDC. Those topics are Marketing/Attraction, Business Retention and Expansion, Entrepreneurship/Small Business Development, Economic Development Finance, Real Estate Development Reuse, Workforce Development, Strategic Planning and Community/Neighborhoo d Development. Field trips also highlighted urban redevelopment and environmental issues in economic development. Nine of the 19 presenters at this course are IEDC members. The presenters were drawn from diverse working environments: Fifteen from not-forp rofit economic development organizations, One from manufacturing, Two from academia, and One from an economic development consulting firm. Tuition for this year’s course was $755. The tuition included expenses for instruction, course materials, refreshments, field trips, a group photo an d two luncheons. The Florida Economic Development Council (FEDC) sponsored the Opening Night N etworking Dinner as well as providing one scholarship for a qualified participant. The Mosaic Company – formerly Cargill Crop Nutrition, Inc. served box lunches during the environmental fiel d trip. CEDR will hold the 29th Annual USF Basic Economic Development Course during the week o f October 23 -28, 2005 at a location (to be determined) in the Tampa Bay area.

PAGE 22

22Market Analysis of Hillsborough County’s Community Development Block Grant Areas Creating the Benchmark Area To create the CDBG benchmark area, we obtained geographic information systems (GIS) shapefiles of the nine Hillsborough County CDBG target areas from the County’s website. Then, using the ArcView Business Analyst extension, and its associated databases, we determined the cohort census tracts small, relatively permanent statistical subdivisions of a county populated by b etween 2,500 and 8,000 persons of the nine CDBG areas. We defined a cohort census tract as one contained by -in part or in whole or tangent to a CDBG area. We selected, at random, nine of these census tracts to create the benchmark area. The figure below shows the geographic location of the benchmark, USF, Ruskin, and Palm River CDBG areas relative to Hillsborough County. By Dave Sobush, Economist with the Center for Economic Development Research In order to assess economic conditions within the county’s Community Development Block Grant (CDBG) program target areas, the Hillsborough County (FL) Economic Development Department commissioned CEDR to create an inventory o f b usiness establishments within a benchmark CDBG area and to compare this inventory to the demographic characteristics of the benchmark area. We then compare the USF, Ruskin, and Palm River CDBG areas to the benchmark for analysis of the relative abundance or scarcity of businesses within those CDBG areas.

PAGE 23

23 Demographic Summary of Benchmark, USF, Ruskin, and Palm River CDBG Areas CDBG Benchmark USF Ruskin Palm River Population (2005) Amount % Amount % Amount % Amount % White 62,96478.28%24,65764.87%5,83689.92% 4,81857.87% Black 12,02814.95%10,42927.44%811.25% 2,84134.12% American Indian, Eskimo, or Aleut 3120.39%1680.44%240.38% 640.77% Asian or Pacific Islander 1,1671.45%1,2833.38%370.57% 1591.91% Other 3,9674.93%1,4713.87%5127.88% 4445.33% Hispanic Origin 22,47427.94%7,22719.01%2,25734.78% 2,40928.93% Total (Excluding Hispanic Origin) 80,437100.00%38,008100.00%6,490100.00% 8,326100.00% Age & Gender (2005) Population Age <18 21,35326.55%8,05921.20%1,40621.66% 2,65631.90% Population Age 65+ 9,28811.55%4,36311.48%1,49122.97% 7348.82% Population Male 40,03449.77%18,87649.66%3,23349.81% 4,01748.25% Population Female 40,40450.23%19,13250.34%3,25850.19% 4,30951.75% Income (2005) Per Capita $ 19,515 n/a $ 17,078 n/a $ 17,192 n/a $ 15,052 n/a Average Household Income $ 55,986 n/a $ 36,549 n/a $ 44,045 n/a $ 47,135 n/a D emographic Characteristics The ArcView Business Analyst software package contains demographic data collected at the b lock group level. The table below summarizes the demography of the benchmark, USF, Ruskin, an d Palm River CDBG areas. Calculation and Analysis of Descriptive Statistics To calculate the descriptive statistics, we take the number of establishments for each industr y category, divide by a demographic characteristic, an d multiply by 1,000. Therefore, each descriptive statistic represents the number of establishments for every 1,000 of the specified demographic characteristic. For instance, in the 3rd quarter of 2003, the CDBG benchmark area had 17 Child Day Care Services establishments (NAICS industry 624410), and a projected 2005 population of 80,437. Thus, the descriptive statistic is 0.2113. Put in other words, in the CDBG benchmark area, for every 1,000 p eople, there are 0.2113 Child Day Care Services establishments. Conclusions In order to focus our analysis, we report on establishments when normalized by total population and by per capita income, due to their straightforward effects on economic behavior. The number of establishment types found to be relatively scarce in a CDBG area varies greatly if the establishment types are normalized by population o r b y per capita income. Normalizing by population consistently yields fewer scarce establishment types, suggesting that this characteristic contributes more greatly to the decision of an entrepreneur to establish a business in an area. For example, our findings suggest that the Palm River CDBG area, based on the per capita income of its residents, has 28.08 fewer

PAGE 24

24 Offices of Physicians (except Mental Health Specialists) – NAICS code 621111 – establishments than it should. However, based solely on the total p opulation of the area, a deficit of 2.04 NAICS code 621111 establishments is revealed. We feel that 28 establishments is too much for the market to “miss;” entrepreneurs would have filled the gap i f establishments normalized by per capita income was the descriptive statistic of interest in the decision to open a business. Therefore we conclude that an establishment type scarce in terms of population is a more accurate assessment of under-representation in the marketplace. USF Not surprisingly, given the area’s proximity to the University of South Florida (the University is itsel f exogenous of the USF CDBG), offices of physicians and dentists are relatively abundant in the USF CDBG area. On the other hand, engineering, accounting, and certain computer services are under-represented. These industries are prime targets for expansion, and should take advantage of the University’s presence in the area, as both a source of employees and clientele. Given the USF CDBG area’s relatively high p opulation density, it is surprising to see the relative scarcity of grocery and convenience stores, making these also logical priorities for business expansion. Ruskin In addition to those establishment types present in the benchmark CDBG area, but absent i n the Ruskin CDBG area, business expansion shoul d focus on attracting professional services, more specifically the offices of physicians and dentists, to the Ruskin CDBG area. The data also suggests that insurance and real estate operations are underrepresented within the Ruskin CDBG area. Temporary Help Services (NAICS code 561320) establishments are relatively scarce in the Ruskin area, but due to the nature of this industry –providing temporary labor to other businesses –we expect that this industry will thrive only when its customers set up operations within or close to the Ruskin CDBG area. Palm River -Business expansion should focus on attracting professional services, such as offices o f p hysicians, lawyers, and dentists, to the Palm Rive r CDBG area. The data also suggests that accounting and real estate operations are under-represented withi n the Palm River CDBG area. Restaurants –both limited-service and full-service –are a good opportunity for expansion in the Palm River CDBG area. Not only are restaurants generally underrepresented in the area, but would offer flexible employment opportunities for local residents as well as provide a sense of place and community. This article is a summary of the CEDR research project “Community Economic an d Demographic Research, September 2004” available from CEDR’s website at http://cedr.coba.usf.edu. Thesetypes of analyses are good beginnings for neighborhood development efforts, as they provide focus and measurable indicators of program effectiveness.

PAGE 25

25Update on CEDR’s Data Center By Dodson Tong, Data Manager for the Center for Economic Development Research CEDR’s online Data Center has updated its databases with the most currently available data an d will continue to update these datasets as they are released throughout 2005. Note that there will be a p lanned major data change included in the Local Area Unemployment Statistics and in the Metropolitan Area Designations beginning with the release of data for January 2005 in March. The Bureau of Labor Statistics (BLS) will implement a redesigned method for producing labor force estimates for census regions, divisions, states, and selected substate areas. The redesigned method encompasses a number o f changes:(1) the introduction of improved time-series regression models for all states, the District o f Columbia, New York City, the Los Angeles-Long Beach-Glendale metropolitan division (currently the Los Angeles-Long Beach metropolitan area), and the respective balances of New York and California, (2) the introduction of real-time benchmarking to national Current Population Survey (CPS) estimates o f employment and unemployment, and (3) the introduction of time-series regression models for six additional substate areas and their respective state balances. The estimates will also reflect the routine annual updates to population estimates from the U.S. Census Bureau. More information on these changes is available from the Bureau of Labor Statistics, Divisio n of Local Area Unemployment Statistics website at http://www.bls.gov/lau/lauschanges2005.htm Other changes affecting the estimation methodology for the substate areas include implementation of Census 2000b ased geographic area definitions. In the Tampa Bay area, this change is reflected in the renaming of the Sarasota-Bradenton, FL Metropolitan Statistical Area (MSA) to the Sarasota-Bradenton-Venice, FL MSA as a result of the latter city’s recent growth. All data from Januar y 2000 forward will eventually be revised to incorporate all of these changes. Also, in order to present a more consistent substate series, the substate data for 199099 will be revised to reflect new area definitions and statewide controls. More information about the changes to metropolitan areas is available from the Bureau of Labor Statistics, Division of Local Area Unemployment Statistics Web site at http://www.bls.gov/lau/lausmsa.htm Due to the stormy weather over the past few months, questions on how many people lost their jobs or were directly affected by the hurricanes came up. One way to help address this question is to look at the monthly filings of initial unemployment claims b y workers. Therefore, CEDR’s Data Center has recentl y created this additional online database to its website. For example, the largest number of initial unemployment claims filed in August 2004 (right after Hurricane Charley) occurred in Charlotte County wit h 3,791, representing a monthly change of 1,285%. The largest filing of initial unemployment claims for September 2004 (right after Hurricanes Frances, Ivan, and Jeanne) was Palm Beach County with 4,829 initial claims for a monthly change of 133%. However, the largest percentage increase in initial claimsduring this same period came from Indian River County with an increase of 809%, representing 2,815 initial claims. This database can be accessed at http://cedr.coba.usf.edu and “Query CED R Databases.” The Regional and State database section under Initial Unemployment Claims, has “BY COUNTY” link to a database which enables the use r to access this type of data. Please note that historical “data-inserts” accompanying previous journals are available for downloading under the “Tampa Bay Econom y Journal” link off CEDR’s website. In addition to the Initial Unemployment Claims data, the Regional and State database section continues to make available the following:

PAGE 26

26 Cost of Living This data set provides relative costs of living for Florida's 67 counties and is released annually by the Florida Department o f Education. Starting with period 1993-1994, Florida’s cost of living in a given year is set at 100% and then each Florida county’s cost of living is expressed relative to 100%. Education Indicators. The indicators in the dat a set are graduation rates, drop out rates, SAT scores, average class size, and per pupil expenditures for Florida’s public high schools. The Florida Department of Education distributes the data. CEDR presents the data organizedb y county and covering four academic years beginning with 1996-1997. ES202 This data set is a Bureau of Labor Statistics (BLS) sponsored collection of job and wage data from all employers participating in Florida's unemployment insurance program. It is organized by 1-digit level Standard Industrial Classification (SIC) codes (and totals for all SIC codes), and describes the number of units (i.e. an establishment designated as a single reporting uni t for the unemployment insurance system), the number of covered employees, total wages o f those employees, and average wages. The data set is partitioned for each Florida county and provides monthly data (by quarter) from first quarter 1988 to first quarter 2004. A version with annual data from 1988 to 2003 is also available. Beginning with year 2001, these datasets will be available b y 2-digit North American Industrial Classification System (NAICS) 2002 handbook manual codes Note that there is not an exact bridge from the previous SIC system to the new NAICS system due to the newer industries that NAICS now tracks. Gross Sales This data series provided by the Florida Department of Revenue is intended as a measure of economic activity. Gross sales are the sum of taxable and non-taxable sales as reported by businesses to the Florida Department o f Revenue. The Florida Department of Revenue reports gross sales and taxable sales to CEDR by ninety-nine "kind" codes. In order to protect the confidentiality of businesses reporting to the Florida Department of Revenue, CEDR has aggregated certain kind codes and converted the aggregations into 8 categories. The data set is p artitioned by Florida county and provides monthly data beginning in 1994. Housing Permits This data set of construction authorized by building permits is distributed by the Manufacturing and Construction Division, Bureau of the Census. The data set is primarily based on reports submitted to the Bureau by local building p ermit officials in response to a mail survey, although some data may be generated by Census Bureau interviewers or imputed from past data. The data on CEDR's web site is organized by state, b y county, and by Metropolitan Statistical Area (MSA) for each month of a year beginning with January 1996 to November 2004. The data describes the number of units and aggregate value for which building permits have been issued by: single-family, 2-family, 3&4-family, and 5-famil y units. Note that beginning with January 2004 data, the Residential Construction Branch began using the new OMB Metropolitan and Micropolitan Statistical Area definitions that were released in June 2003. Local Area Unemployment Statistics (LAUS). This labor force data set is prepared monthly b y the Bureau of Labor Statistics (BLS) and describes labor force partici p ation, employment, unemployment, and unemployment rate by count y of residence (data is also included by Florid a MSA). The self-employed are counted as employed persons in the LAUS data. The LAUS estimates are based on a combination of data from the Current Population Survey (CPS), unemployment insurance claim data, the Current Employment Statistics (CES) survey o f establishments, and ES-202 data. Statewide an d Florida counties' data are available. The data can be displayed beginning with the month of Januar y 1990 to December 2004. Annual averages are also available.

PAGE 27

27 Personal Income Per Capita Personal Income, and Population These three data sets are organized by county, or by MSA, per year and are released annually through the Regional Economic Information System (REIS) of the Bureau o f Economic Analysis (BEA). The data is based on place of employment and reflect annual averages. In producing REIS, BEA makes use of data that are byproducts of the administration of various federal and state programs, including unemployment insurance, Social Security, federal income taxes, veterans benefits, and militar y payroll. Hence, the REIS data series, which includes farming and non-farming, military an d civilian, proprietorships (i.e. self-employment) an d wage and salary employment, is more comprehensive than ES202. ES202 data covers non-farming and salary employment only. BEA defines Personal Income as the current income received by persons from all sources (including investment income and transfer payments) minus their personal contributions for social insurance. Personal income includes both monetary income (including non-paycheck income such as employer contributions to pensions) and non-monetar y income (such as food stamps and net rental value to owner-occupants of their homes). The REIS county and MSA data are issued about 16 months after the year in which the observations were made. Currently CEDR’s data center has this information from 1969 to 2002. The 2003 dat a should be available in mid 2005. Zip Code Business Patterns This dataset contains the number of business establishments located within a postal ZIP code area throughout Florida. The database also reports the number o f employees by industry. CEDR has ZIP code b usiness pattern data from 1997 to year 2000. For each year, drop-down menus allow the researcher to specify a ZIP code area by name (ordere d alphabetically) or by ZIP code (ordere d numerically). Additionally, the researcher can specify a ZIP code and a Standard Industrial Classification (SIC) code for the 1997 data. Beginning in 1998, the data is organized by North American Industry Classification System (NAICS) codes, so the researcher may specify a ZIP code and a NAICS code. Most economic activity is covered by this data set. However, data are excluded for self-employed persons, domestic service workers, railroad employees, farm workers, most government employees, maritime workers on ocean-going vessels, and persons working outside the U.S. ZIP Code Business Patterns data items are extracted from the Standard Statistical Establishments List, a file of all known single and multi-establishment firms. The list is maintained and updated by the U.S. Bureau of the Census. CEDR has developed and provided ZIP code maps for each of Florida's counties that will help researchers identify and define a local area of interest. ZIP Code Business Pattern maps are available from 1997, which are a graphical representation of the data. In conjunction to this ZIP Code Business Patterns data, maps from 1999 ZIP code boundaries are also made available. CEDR has also recently received from the Bureau of Economic Analysis (U.S. Dept. of Commerce) State P ersonal Income, 1929 2003. There are tables with annual measures for each of the states of the U.S.: Income and employment summary, Personal income by major source and earning by industry, Compensation of employees by industry, Wage and salary disbursements by industry, Total full-time and part-time employment by industry, State economic profiles, Personal current transfer receipts, Farm income and expenses, and Personal current tax receipts. Although the State Personal Income, 1969 2003 tables are not available online, you can go to CEDR’s home page and click on “Request Data from CEDR” to e-mail your individualized data request.

PAGE 28

28 Other items that can be found at CEDR’s web site are reports of research reports and other p ublications as well as links to other sites containing data of interest for economic development. CEDR’s online data center continues to garner wide interest. In 2004, annual web hits reached over 239,806 and averaged a monthly count of 19,984. Since CEDR began putting its research reports available online, the numbers of downloads has also gone up. In 2004 there were 38,172 CED R research report downloads. During the most recent month, users remained at the site for an average o f 14.55 minutes per visit. Check CEDR's web site at http://cedr.coba.usf.edu for new projects an d continuously updated data sources.