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Tampa Bay economy.
n Vol. 3, no. 1 (spring 2002).
Tampa, Fla. :
b University of South Florida, College of Business Administration, Center for Economic Development Research.
Tax Refund Program for Qualified Target Industry Business -- From the Editor -- Economic Patters in Hillsborough County in 1997: Hillsborough County Zip Code Business, Employment and Farm Patterns Analysis -- Demographic Changes in Southeast Pasco County -- The 2001 USF Economic Development Course -- The Relocation of Brooksville Regional Hospital -- Update on CEDR's Data Center -- Census 2000 Date Release Schedule -- Population Growth in Florida Counties During the 1990s; Regional Amenities and Business Climate.
Tampa Bay Region (Fla.)
x Economic conditions
Tampa Bay Region (Fla.)
Tampa Bay Region (Fla.)
University of South Florida.
Center for Economic Development Research.
Volume 3, No. 1Spring 2002THE Tampa Bay EconomyQuarterly Journal of the Center for Economic Development ResearchTax Refund Program for Qualified Target Industry Businesses Continued on page 2 OUTLINE OF THE STATUTE 288.106 Tax refund program for qualified target industry businesses. (1) DEFINITIONS (2) TAX REFUND; ELIGIBLE AMOUNTS (3) APPLICATION AND APPROVAL PROCESS (4) TAX REFUND AGREEMENT (5) ANNUAL CLAIM FOR REFUND (6) ADMINISTRATION By Dennis G. Colie,Associate Director of the Center for Economic Development Research with Alex McPherson,Economist with the Center for Economic Development Research The purpose of this article is to estimate the economic impact of the State of Florida Qualified Target Industry (QTI) Tax Refund program for a hypothetical increase of jobs in the Tampa Bay region. QTI is an economic development incentive that may be offered to businesses by all levels of government within the state of Florida. A Summary of Florida's Qualified Target Industry Program The Qualified Target Industry Tax Refund Program is available to Florida communities to encourage the expansion of existing businesses or the location of new-to-Florida businesses. The program currently provides tax refunds to pre-approved applicants of $3,000 per new job created ($6,000 in an Enterprise Zone or rural county). A company that pays an average of at least 150 percent of area wages receives an additional $1,000 per job. And a company that pays an average of at least 200 percent of area wages receives an additional $2,000 per job. A maximum of 25% of the total tax refund may be paid in a single fiscal year. According to the Office of Trade,Tourism and Economic Development (OTTED),the objective of the QTI incentive is "to create high-value jobs and encourage the growth of corporate headquarters and other targeted high-value industries."1The QTI financial incentive is one of eleven incentives listed at the State of Florida's website, MyFlorida. OTTED certifies,disburses and monitors these incentive programs,which are administered by Enterprise Florida,Inc. When the State's Legislature did away with the Florida Department of Commerce in 1996,the state's economic development functions were placed with a public / private partnership named Enterprise Florida,Inc. (EFI). However, certain related activities,such as oversight of financial incentives,have remained in the public sector. OTTED fulfills the oversight of financial-incentives function. The QTI program is authorized by section 288.106 F.S. A sense of purpose of the program is implied by definitions provided in the statute. Under the definition of a "target industry business"the statute authorizes OTTED,in consultation with Enterprise Florida,Inc.,to develop the list of targeted industries using the following criteria: 1. Future growth "... strong expectation for future growth in both employment and output ..." 2. Stability "... not ... subject to periodic layoffs ... also relatively resistant to recession ..." 3. High wage "... pay relatively high wages compared to statewide or area averages." 4. Market and resource independent "The location of industry businesses should not be dependent on Florida markets or resources as indicated by industry analysis." 5. Industrial base diversification and strengthening "... contribute toward expanding or diversifying the state's or area's economic base ..." 6. Economic benefits "strong positive impacts on or benefits to the state and regional economies."
2 CEDRStaffDr. Kenneth Wieand........................................Director Dr. Dennis Colie..............................Associate Director Dodson Tong..........................................Data Manager Alex McPherson..........................................Economist Gina Space..................................................Economist Nolan Kimball......................................Coordinator of Information/Publications Anand Shah............................................Web Designer David Sobush..................................Graduate Assistant 2THETampa Bay EconomyVolume 3, No. 1 Spring 2002From The Editor. . This issue of The Tampa Bay Economy contains an analysis of Population Growth for Florida's 67 counties over the timeframe 19902000. There are also three articles regarding specific areas within the Tampa Bay region"Demographic Changes in Southeast Pasco County,""Hillsborough County's Zip Code Business, Employment and Farm Patterns Analysis"and the proposed "Relocation of the Brooksville Regional Hospital." An economic impact analysis of the "Tax Refund Program for Qualified Target Industry Businesses"in the Tampa Bay region, an update on CEDR's data center,the Schedule for the Census 2000 Data Release and a report on the 2001 USF Economic Development Course round out the articles for this journal. This is the first issue of the journal for 2002,and it contains the regional economic development data inserts for both,3rd and 4th quarters of 2001. CEDR's distribution list has reached over 1,400 recipients since the first edition of The Tampa Bay Economy was published in spring 2000. Now we ask you,the journal's reader,to help us make the journal add even more value to Tampa Bay's economic development community. Please send us your comments and suggested improvements to:email@example.com with subject line "Journal Comments."Tax Refund Program for Qualified Target Industry Businesses Continued from page 1 The statutory language of criterion 4 and criterion 5,suggests an "industry analysis."Criterion 5 further requires "analysis of employment and output shares compared to national and regional trends."Notwithstanding analysis for determining qualified industries,certain businesses do not have a chance to become qualified. They are excluded from the QTI program by law. The law excludes "any industry engaged in retail activities; any electrical utility company; any phosphate or other solid minerals severance,mining,or processing operation; any oil or gas exploration or production operation; or any firm subject to regulation by the Division of Hotels and Restaurants of the Department of Business and Professional Regulation." See the box,on page 3,for a list of qualified target industries. The numbers in parentheses are 2-digit major industry group codes (except 88,which designates corporate headquarters) in accordance with the Standard Industrial Classification (SIC) system.2Tax Refund Program for Qualified Target Industry Businesses..........................1 From The Editor..........................................................2 Economic Patterns in Hillsborough County in 1997: Hillsborough County Zip Code Business,Employment and Farm Patterns Analysis................................................9 Demographic Changes in Southeast Pasco County........................................12 The 2001 USF Economic Development Course......19 The Relocation of Brooksville Regional Hospital....20 Update on CEDR's Data Center................................21 Census 2000 Date Release Schedule........................22 Population Growth in Florida Counties During the 1990s; Regional Amenities and Business Climate................24
To receive a tax refund a business in a qualified target industry must submit an application to OTTED. To qualify for review of its application by OTTED the business must establish that it will: 1. pay an estimated annual average wage of at least 115% of the average private-sector wage in the area where the business will locate or statewide,i.e. high-wage jobs, 2. create at least 10 of these high-wage jobs,or if an expansion,increase high-wage employment by not less than 10% of existing jobs,and 3. be in targeted "high-value added industries that contribute to the area and to the economic growth in the state and that produce a higher standard of living for citizens of this state in the new global economy ..." QUALIFIED TARGET INDUSTRY (QTI) TAX REFUND TARGET INDUSTRIES Effective September 1,1996 APPAREL AND OTHER TEXTILES (22,23) BUSINESS SERVICES (73) CHEMICALS AND ALLIED PRODUCTS (28) COMMUNICATIONS (48) CORPORATE HEADQUARTERS (88) ELECTRONIC AND OTHER ELECTRIC EQUIPMENT (36) FABRICATED METAL PRODUCTS (34) FOOD AND KINDERED PRODUCTS (20) FURNITURE AND FIXTURES (25) HOLDING AND OTHER INVESTMENT OFFICES (67) INDUSTRIAL MACHINERY AND EQUIPMENT (35) INSTRUMENTS AND RELATED PRODUCTS (38) INSURANCE CARRIERS (63) LUMBER AND WOOD PRODUCTS (24) MISCELLANEOUS MANUFACTURING (39) MOTION PICTURES* (78) NON-DEPOSITORY CREDIT INST. (61) PAPER AND ALLIED PRODUCTS (26) PRIMARY METAL INDUSTRIES (33) PRINTING AND PUBLISHING (27) RESEARCH AND DEVELOPMENT (87) RUBBER AND MISC. PLASTICS (30) SECURITY AND COMMODITY BROKERS (62) STONE,CLAY AND GLASS (32) TRANSPORTATION EQUIPMENT (37) WHOLESALE DISTRIBUTION (50,51) Once OTTED accepts an application for review,the statute further prescribes,at a minimum,that the evaluation of that application is based on: 1. the expected contribution of the business to the state's economic development plan,which is adopted by Enterprise Florida,Inc., 2. the economic benefit of the jobs to be created "taking into account the cost and average wage of each job," 3. the amount of capital investment to be made, 4. local commitment and support for the project, 5. the effect on the local community "taking into account the unemployment rate for the county where the project will be located," 6. the effect of any tax refunds granted and the probability the project will be undertaken in Florida if such tax refunds are granted "taking into account the expected long-term commitment of the applicant to economic growth and employment"in Florida, 7. the expected long-term commitment to the state by the business,and 8. a review of the business's past activities including criminal or civil fines and penalties. Finally,if tax refunds are offered by the State to the applicant,the business must enter into a tax refund agreement. Refunds can then be made for the corporate income and insurance premium taxes due and paid beginning with the first taxable year of the business after entering into the agreement. In addition,refunds for the following types of taxes due and paid can be made after entering into the agreement: 1. sales and use taxes, 2. intangible taxes, 3. excise taxes,and 4. ad valorem (property) taxes. There are also some important limitations on the tax refunds that can be received by a qualified business. Refunds may not exceed 25 percent of the total tax refunds specified in the agreement or $1.5 million ($2.5 million in an Enterprise Zone) in a single fiscal year. Furthermore, the total refund over all fiscal years cannot exceed $5 million ($7.5 million in an Enterprise Zone). The use of economic development incentives is not a new policy tool,rather incentives have been around for years. Notwithstanding the longevity of incentive-policy,incentives are becoming increasingly controversial. In a study prepared for the Economic Development Administration, an agency of the US Department of Commerce,Poole (1999) et al. list the following reasons of concern: 1. the cumulative costs of incentive programs over time, 2. competition for public revenues among various government agencies and levels of government, 3. the offering of incentives to large corporations,which are often perceived as not needing the benefit,creates a negative public view of incentive-policy,and 4. studies intended to evaluate incentive programs have inadequately defined the comprehensive fiscal and economic impacts of these programs. Continued on page 43 Only motion picture sound recording and reproducing studios Source:ENTERPRISE FLORIDA,Inc., The Atrium Building,Suite 201,325 John Knox Road, Tallahassee,Florida 32303
There is intense competition among communities for new and expanding industry. The lack of a profitability constraint on public enterprise leads to the danger that states and municipalities may end up over-bidding for new businesses. For example,Clark (1999) relates that in 1993 Alabama reported an incentive package for a new Mercedes plant that equaled $168,000 per new job. Presumably,the competition for Mercedes centered about maintaining and expanding local employment opportunities. It is hard to imagine an economic scenario in which there would be a rational payback period for the taxpayers of Alabama for such a high cost. Another issue complicating the role of tax incentives is the changing structure of modern economies. Stucki and Andrews (1999),for example,argue that a transition from an industrial economy to an Information-Age Economy is a worldwide phenomenon. The Information-Age Economy is deemed to be a knowledge-based economy requiring the creation of an intellectual infrastructure based on active links among academia,industry,and economic development organizations. In Stucki and Andrew's InformationAge Economy,traditional economic incentives,such as tax abatement,reduce the cost to a business of a factor input (labor,capital,land or some combination thereof) but they do not have the catalytic effect of knowledge. The on-going transition to information based economies feeds into the perceived role of economic development,an important goal of which has long been to bring (or to retain) jobs into a region. This goal makes sense when a region has pools of unemployed capital and workers. However,beginning in the mid-1990s the unemployment rate consistently declined in most regions,reaching 30-year lows in 2000. In response to the changing economic environment,the goal of economic development has shifted from attracting jobs to attracting jobs paying good wages And,good wages are assumed to be associated with "competitive,profitable firms employing educated workers in high-skill,high-wage industries."(Clark,pp. 3-4) Accordingly,much economic development activity in the Tampa Bay region focuses on attracting high-tech industries. The emphasis on attracting high-tech industry poses a conceptual challenge for programs,such as the QTI Tax Refund Program,that are based upon reducing a firms' labor costs. According to economic principles,the consequence of relatively less costly labor will be some substitution of labor for capital. The substitution of labor for capital may not be conducive to developing an Information-Age,high-tech economy. Financial incentives that effectively reduce the cost of labor relative to the cost of capital favor manufacturing i.e. labor intensive,businesses over knowledge-intensive businesses. Mackay (1994) reports on a survey of economic development professionals and corporate real estate executives. Interestingly,in the survey,49% of economic developers indicated they would prefer a situation in which no incentives were offered for projects,while only 17% of corporate real estate executives "thought positively of a situation in which incentives would not play a role in corporate relocation-expansion."3Although the corporate real estate executives stated that their companies were continuously seeking increasing amounts of incentives,they stressed that incentives are secondary to other location factors. Only 25% of the NACORE respondents felt that a community not offering incentives would be at a competitive disadvantage. However,63% of economic developers indicated their belief that not offering incentives would place their communities at a competitive disadvantage. Among location screening factors,incentives were ranked 14th of 17 factors by the NACORE respondents. Mackay concludes that,although economic development incentives from communities are far down the list of criteria for selecting a site location for a new plant or other facility,they remain significant to both economic development organizations and corporations,particularly when it comes down to the final relocation / retention decision. Incentives are a big part of Florida's economic development strategy. The Florida Economic Development Council (FEDC) "support[s] continued funding of the Qualified Targeted Industry Tax Refund Program (QTI)" as one of "Florida's three most important key investment tools or 3 Keys."4The importance of the QTI Program for high-tech industry development is less certain in one such initiative,the Florida High Tech Corridor. The Florida High Tech Corridor Council's list used to "report and populate the florida.high.tech 2001 corporate guide"does not include most QTI industries.5 However,commonality exists within Chemicals and Allied Products (SIC 28),Industrial Machinery and Equipment (SIC 35),Electronic and Other Electric Equipment (SIC 36),Instruments and Related Products (SIC 38),and Communications (SIC 48). Estimating the Impact of QTI on Tampa Bay In order to estimate the economic impact of adding jobs to Tampa Bay's economy in targeted industries of the QTI Program,we use the IMPLAN ProfessionalTMSocial Accounting and Impact Analysis Software with 1998 (the most recent available) data by county.6The IMPLAN software is an economic input-output model. The historical data are used to develop a model that quantifies economic interactions in terms of the flow of dollars from purchasers to producers within the Tampa Bay region. From the descriptive model we calculate a set of multipliers and use them to predict changes in employment,income,output,and government's tax revenues for each industry in the model. For each target industry we direct the IMPLAN computer model to inject 100 hypothetical jobs into a specified industry and observe the model's prediction of economicTax Refund Program for Qualified Target Industry Businesses Continued from page 34
5 Continued on page 6 At the top of Table 1 are the 1998 Tampa Bay baseline population and aggregate personal income,from which we calculate personal income per capita. Before adding the 100 jobs in Carpets and Rugs,personal income per capita in Tampa Bay was $26,569.87. We use the change in regional per capita personal income as the principal gauge of the impact of the 100 added jobs. Other measures of the impact that are also shown in Table 1 are increases in employment,output,and government revenue. Section 288.106 F.S. states,"Average private sector wage in the area means the statewide private sector average wage or the average of all private sector wages and impact. The impact is comprised of three effects:direct, indirect and induced. The direct effect is the 100 new jobs and their concomitant increases in income,output and tax revenues. The indirect effect is the increases in employment,income,output,and tax revenues attributable to other businesses in the supplier chain of the industry. The induced effect is the increases in employment,income,output,and tax revenues due to more income,and consequently greater spending by the households that have directly or indirectly benefited from the originally added 100 jobs. Table 1 presents a detailed economic impact analysis for one industry. The purpose of Table 1 is to demonstrate the method of analysis we use to determine economic impacts. Subsequent tables,with less detail,will be used to explain the findings of our analysis. IMPLAN employs an industry sector scheme that can be bridged to the Standard Industrial Classification (SIC) system. Table 1 shows the impact of an injection of 100 jobs into the Carpets and Rugs manufacturing industry. The results presented in Table 1 are based on IMPLAN sector 117,which bridges to SIC 2270 Carpets and Rugs. Tampa Bay Baseline 1998 Population3,393,498 Aggregate Personal Income (000s)$90,164,790 Personal Income per Capita$26,569.87 IMPLAN Sector Name = 117: Carpets and Ru g s SIC 2270 Avg. 1998 Wages SIC 2270$27,521(Note 1) 100 New Jobs -Impacts on SIC 2270 DirectIndirectInducedTotalDirectIndirectInducedTotalEmployment10048.559.0207.5Emp. Comp. (Note 2)$3,357,922$1,425,817$1,480,948$6,264,687$33,579$29,398$25,101$30,191Proprietors Inc.$136,070$146,220$160,394$442,684Labor Inc.$3,493,992$1,572,037$1,641,342$6,707,371$34,940$32,413$27,819$32,325Property Inc.$2,048,734$692,917$845,881$3,587,532Personal Income$5,542,726$2,264,954$2,487,223$10,294,903$55,427$46,700$42,156$49,614Indirect Bus. Tax$190,381$246,930$286,404$723,715Value added$5,733,107$2,511,884$2,773,627$11,018,618Output$19,415,554$4,468,821$4,171,768$28,056,143$194,156$92,141$70,708$135,210Federal Taxes$1,943,231$9,365State/Local Tax$718,564$3,463Total Taxes (Note 3)$2,661,795$12,828 Tampa Bay after Adding 100 Jobs 1998 Population3,393,498(no change in population) Aggregate Personal Income (000s)$90,175,085 Personal Income (PI) per Capita$26,572.90 Increase in PI per Capita $3.03 Population3,393,706(increase in population equal to new jobs) Aggregate Personal Income (000s)$90,175,085 Personal Income (PI) per Capita$26,571.28 Increase in PI per Capita $1.41 Note 1:Source is ES 202 data for unemployment compensation. Avg. wages do not include the cost of benefits paid by an employer. Proprietors are not included. Note 2:Source is IMPLAN input-output model estimation. Employment compensation includes the cost of benefits paid by an employer. Note 3:Does not include an incremental ad valorem (property) tax paid by corporations.Table 1Impact of Adding 100 Jobs in Carpets and Rugs Aggregated ImpactAverage Impact per Added Job salaries in the county or in the standard metropolitan area in which the business is located."However,the statute does not make clear how the average wage is calculated. One way to calculate an average wage is to use ES202 data to find the employment-weighted average of wages reported by firms in the area. The ES202 data set is a collection of job and wage data from all employers participating in Florida's unemployment insurance program. Because self-employed proprietors do not contribute to the unemployment insurance system,they are not included in the ES202 data. ES202 wages include only monetary payments to employeesbenefits received in-kind,such as medical insurance,are not included in ES202 wages. In 1998,on average,the annual wage in the Carpets and Rugs industry in Tampa Bay was $27,521. The average industry wage calculated using ES202 data is a different measure from personal income per capita used in this analysis. As reflected in Table 1,personal income is the sum of employee compensation,proprietors'income and property income. Personal income per capita is a more comprehensive measure of the economic impact of jobscreation than an average ES202 industry wage. In the illustration of Table 1,the 100 jobs,which are directly added to the regional economy,motivate the creation of 107.5 more jobs. Thus,the total increase in employment is 207.5 jobs. Employee compensation is a component of personal income. The IMPLAN model estimates a direct increase in employee compensation of $3,357,922 or an average of $33,579 per added job. Because the model's employee compensation measurement includes non-cash benefits,but ES202 wages do not,we estimate that on average a "benefits package"in the Carpets and Rugs industry costs the employer $6,058 per job. ($6,058 is the difference between employee compensation and the ES202 wage in the Carpet and Rug manufacturing industry.) That is,the employer spends about 18 percent of employee compensation for benefits such as health insurance and retirement plans. The IMPLAN estimate of the total increase in employment compensation (which includes direct,indirect and induced employment) is $6,264,687,or $30,191 per added job. Note that the indirect and induced jobs offer lower average levels of employee compensation than do the direct jobs. Thus,it may be seen that,while an incentive program may lure relatively high-wage jobs,the program may also be responsible for the creation of additional jobs paying lower employee compensation. These lower-wage jobs tend to reduce the total impact of the 100 jobs directly added to the economy. Continuing the illustration of Table 1,total labor income due to the hypothetical 100 added jobs is $6,707,371,or $32,325 per job.7Self-employed persons receive somewhat higher compensation than employees do when performing work in the Carpets and Rug manufacturing industry. Thus,average labor income exceeds average employee compensation.
6 Another component of personal income is property income. Property income consists of payments from rents, royalties,dividends,interest,as well as firms'retained earnings. Property income is an estimate of the money flows to the owners of capital due to the hypothetical 100 added jobs. Total property income is $3,587,532. Personal income is the sum of labor income and property income. Total personal income generated in Tampa Bay by the hypothetical 100 added jobs is $10,294,903,of which $5,542,107 is directly attributable to the added 100 jobs. Direct personal income per capita is $55,427 per added job,but the total effect (including direct,indirect, and induced jobs) is $49,614 per capita. The average per capita personal income from new jobs is much higher than Tampa Bay's baseline personal income per capita. But, how much would overall personal income per capita increase? That is,what is the average per person benefit for each resident of Tampa Bay? The average per person increase in personal income is a gauge of the impact of an incentive program that adds jobs to a region's economy. In the case illustrated by Table 1,and assuming no increase in the region's population,the model's projected increase in personal income per capita is $3.03. However,the assumption of "no population"increase is realistic only when there is some slack in the region's labor market. If there is no slack in the labor market (the region is at full employment) all new workers must migrate into the region. In that case,the total of 207.5 newly created jobs increases Tampa Bay's per capita personal income by $1.41. From this example,it is seen that the utility of an incentive to add jobs to a region's economy is sensitive to the region's labor market slack. When there is slack in the labor market,i.e. qualified and unemployed residents already available in the region,an incentive program to create jobs has a higher utility than when the regional economy is already at full employment. Output is the value of goods and services produced in the region. Output is a measure of the total value of intermediate purchases of an industry plus the value added by the industry to the intermediate purchases. In Table 1,the direct output expected from the hypothetical 100 new jobs is $19,415,554 or about $194,156 per job. The total effect (including direct,indirect,and induced output) is $28,056,143 of output,or $135,210 per job. The IMPLAN model also provides an estimate of new tax revenues that will be generated as a result of the hypothetical addition of 100 jobs to the regional economy. The model's estimate,shown in Table 1,is over $2.66 million in new tax revenue:$1.94 million in federal revenue and $0.72 million in State and local revenue. That is,federal tax revenue is expected to increase $9,365 per new job and combined state / local tax revenues are expected to go up by $3,463 per new job. Table 1 illustrated the method we use to analyze the economic impact of creating 100 jobs in the Carpets and Rugs industry. An industry is identified by a four-digit SIC code which is 2270 in the case of carpets and rugs manufacturing. However,qualified target industries are designated at the two-digit SIC code level. According to the nomenclature of the SIC system,a two-digit code identifies a "major group" of industries. The Carpets and Rugs industry is a part of major group 22,which is called Textile Mill Products. Table 2 summarizes the findings of the analysis of the Textile Mill Products major group. The IMPLAN sector scheme bridges to 15 industries or grouping of industries within the Textile Mill Products major group. Out of the 15,six were manufacturing products in Tampa Bay in 1998. One of the six is a grouping of industries designated in Table 2 as SIC 22XX,Broadwoven Fabrics. The remaining five are individual industries. The findings of Table 2 are based on an input-output analysis of majorgroup-22 businesses that already exist in Tampa Bay.Tax Refund Program for Qualified Target Industry Businesses Continued from page 5 Tampa Bay Baseline 1998 Population3,393,498 Aggregate Personal Income (000s)$90,164,790 Personal Income per Capita$26,569.87 100 New Jobs -Impacts on Major Industry Group 22 SICDescriptionDirectIndirectInducedTotal 22XX (Note 1)Broadwoven Fabrics$2,461,926$1,815,973$1,565,942$5,843,841 2240Narrow fabrics$5,109,645$746,506$2,371,286$8,227,437 2253Knit Outerwear$1,634,049$683,841$878,375$3,196,265 2270Carpets and Rugs$5,542,726$2,264,954$2,487,223$10,294,903 2295Coated Fabrics$2,043,492$4,296,415$2,376,717$8,716,624 2298Corda g e$3,095,908$1,766,418$1,873,720$6,736,046 Average$3,314,624$1,929,018$1,925,544$7,169,186 SICDescriptionDirectIndirectInducedTotal 22XX (Note 1)Broadwoven Fabrics10041.037.1178.1 2240Narrow fabrics10016.656.3172.9 2253Knit Outerwear10016.320.8137.1 2270Carpets and Rugs10048.559.0207.5 2295Coated Fabrics10085.356.4241.7 2298Corda g e10034.344.4178.7 Average10040.345.7186.0 SICDescriptionDirectIndirectInducedTotal 22XX (Note 1)Broadwoven Fabrics$11,076,791$3,290,939$2,626,523$16,994,253 2240Narrow fabrics$8,558,966$1,354,781$3,977,308$13,891,055 2253Knit Outerwear$7,292,071$1,246,214$1,473,281$10,011,566 2270Carpets and Rugs$19,415,554$4,468,821$4,171,768$28,056,143 2295Coated Fabrics$18,339,128$8,478,033$3,986,419$30,803,580 2298Corda g e$9,645,316$3,428,743$3,142,741$16,216,800 Average$12,387,971$3,711,255$3,229,673$19,328,900 SICDescriptionFederalState/LocalTotal 22XX (note 1)Broadwoven Fabrics$1,172,032$446,606$1,618,638 2240Narrow fabrics$1,714,553$453,273$2,167,826 2253Knit Outerwear$649,687$213,278$862,965 2270Carpets and Rugs$1,943,231$718,564$2,661,795 2295Coated Fabrics$1,782,071$779,177$2,561,248 2298Corda g e$1,390,402$519,963$1,910,365 Average$1,441,996$521,810$1,963,806 Tampa Bay after Adding 100 Jobs -1998 Population(no change)3,393,498 Aggregate Personal Income (000s)$90,171,959 Personal Income (PI) per Capita$26,571.98 Increase in PI per Capita $2.11 Population(increase = new jobs)3,393,684 Aggregate Personal Income (000s)$90,171,959 Personal Income (PI) per Capita$26,570.52 Increase in PI per Capita $0.66 Note 1:22xx includes SICs 2210, 2220, 2230, 2261, and 2262 Increase in Output Increase in Taxes Table 2 Impact of Adding 100 Jobs in Textile Mill Products Increase in Personal Income Increase in Employment
7 Continued on page 8 On average,100 new jobs in the Textile Mill Products major group in Tampa Bay would be expected to motivate 86 additional jobs for a total of 186 jobs. The average increase in personal income is $7,169,186. If all 186 jobs were filled by existing residents,i.e. no population change, the increase in personal income per capita for Tampa Bay's residents is $2.11. On the other hand,if all 186 jobs were filled by in-migrants to Tampa Bay,the increase in personal income per capita is only $0.66. The creation of 100 new jobs in Textile Mill Products major group would also be expected to increase annual regional output by $19,328,900 and combined federal, state and local tax revenues by $1,963,806 per year. Furthermore,Table 2 illustrates that there is a wide range of potential outcomes for job-creation targeted at a major group as opposed to an industry. In the illustration, 100 jobs in Carpets and Rugs (SIC 2270) increase total personal income by $10,294,903. This is the high end of the range for personal income. At the low end of the range, 100 jobs in Knit Outerwear (SIC 2253) increase total personal income by $3,196,265. Table 3 reports increases in personal income per capita for QTI-qualified major industry groups. One hundred jobs are created in each industry that had at least one firm operating in Tampa Bay during 1998. The reported increases are the average of the increases for each industry within the QTI-qualified major industry group. The anticipated increases in personal income per capita assuming there is no in-migration to fill the newly created jobs range from a high of $6.86 for the Chemical and Allied Products major industry group to a low of $1.85 for the Miscellaneous Manufacturing major industry group. No Pop Incr.w/ Pop Incr. SICMajor Group NameDirectIndirectInducedTotalTotal 20Food and Kindred Products$1.99$1.86$1.18$5.03$2.45 22Textile Mill Products$0.98$0.57$0.57$2.11$0.66 23Apparel and Other Textile Products$0.96$0.44$0.50$1.90$0.54 24Lumber and Wood Products$1.37$0.74$0.73$2.84$1.20 25Furniture and Fixtures$1.22$0.65$0.68$2.55$0.98 26Paper and Allied Products$1.79$1.00$0.94$3.73$1.82 27Printing and Publishing$1.33$0.68$0.72$2.73$1.11 28Chemicals and Allied Products$2.93$2.36$1.57$6.86$3.97 30Rubber and Miscellaneous Plastics$1.23$0.88$0.78$2.88$1.16 32Stone, Clay, and Glass$1.75$0.87$0.86$3.48$1.69 33Primary Metal Industries$1.79$1.58$1.13$4.51$2.19 34Fabricated Metal Products$1.94$0.75$0.83$3.52$1.81 35Industrial Machinery and Equipment$1.78$0.97$1.07$3.82$1.84 36Electronic and Other Electric Equipment$2.08$0.85$1.01$3.88$2.03 37Transportation Equipment$1.54$0.96$0.95$3.46$1.54 38Instruments and Related Products$1.10$1.11$0.90$3.12$1.14 39Miscellaneous Manufacturing$1.03$0.38$0.44$1.85$0.58 48Communications$3.18$1.13$1.27$5.58$3.38 50, 51Wholesale Trade$1.80$0.53$0.82$3.14$1.54 61, 67Credit Agencies$1.32$0.11$0.52$1.96$0.80 62Security and Commodity Brokers$2.39$0.40$1.33$4.12$2.35 63Insurance Carriers$1.45$0.91$0.85$3.21$1.28 73Business Services$1.12$0.35$0.55$2.02$0.69 78Motion Pictures$0.76$0.66$0.62$2.04$0.37 87Research and Development$1.13$0.44$0.69$2.26$0.77 88Corporate HeadquartersN/A Table 3 QTI Major Industry Groups Personal Income per Capita Average Impact of Adding 100 Direct Jobs Increase in Per Capita Personal Income Totals Increase in Per Capita Personal Income SICMajor Group NameDirectIndirectInducedTotal 20Food and Kindred Products100134.895.1329.9 22Textile Mill Products10040.345.7186.0 23Apparel and Other Textile Products10033.240.2173.4 24Lumber and Wood Products10050.958.9209.8 25Furniture and Fixtures10046.054.8200.8 26Paper and Allied Products10067.675.6243.2 27Printing and Publishing10048.558.3206.8 28Chemicals and Allied Products100143.0126.3369.3 30Rubber and Miscellaneous Plastics10058.262.6220.8 32Stone, Clay, and Glass10059.169.5228.6 33Primary Metal Industries100105.391.0296.3 34Fabricated Metal Products10051.367.0218.3 35Industrial Machinery and Equipment10066.186.3252.4 36Electronic and Other Electric Equipment10059.476.7236.1 37Transportation Equipment10068.276.4244.5 38Instruments and Related Products10079.872.8252.6 39Miscellaneous Manufacturing10026.935.4162.3 48Communications10079.2102.0281.2 50, 51Wholesale Trade10038.865.7204.5 61, 67Credit Agencies1006.742.1148.8 62Security and Commodity Brokers10019.1107226.1 63Insurance Carriers10078.368.7247 73Business Services10025.444.1169.5 78Motion Pictures10063.450.1213.5 87Research and Development10035.255.4190.6 88Corporate HeadquartersN/A Increase in Employment Table 4 QTI Major Industry Groups Employment Average Impact of Adding 100 Direct Jobs SICMajor Group NameDirectIndirectInducedTotal 20Food and Kindred Products28,647,446 $ 12,421,364 $ 6,725,114 $ 47,793,924 $ 22Textile Mill Products12,387,971 $ 3,711,255 $ 3,229,675 $ 19,328,901 $ 23Apparel and Other Textile Products9,671,018 $ 2,872,217 $ 2,841,075 $ 15,384,309 $ 24Lumber and Wood Products13,569,391 $ 5,079,451 $ 4,166,914 $ 22,815,756 $ 25Furniture and Fixtures12,358,199 $ 4,249,312 $ 3,873,972 $ 20,481,482 $ 26Paper and Allied Products19,477,865 $ 6,761,507 $ 5,345,396 $ 31,584,767 $ 27Printing and Publishing11,830,695 $ 4,192,704 $ 4,122,600 $ 20,145,999 $ 28Chemicals and Allied Products34,338,825 $ 16,210,120 $ 8,929,791 $ 59,478,737 $ 30Rubber and Miscellaneous Plastics13,917,304 $ 6,156,973 $ 4,429,382 $ 24,503,659 $ 32Stone, Clay, and Glass15,043,703 $ 5,729,821 $ 4,917,502 $ 25,691,026 $ 33Primary Metal Industries26,517,928 $ 10,116,123 $ 6,432,353 $ 43,066,404 $ 34Fabricated Metal Products17,610,155 $ 4,876,749 $ 4,734,691 $ 27,221,595 $ 35Industrial Machinery and Equipment18,673,516 $ 6,248,741 $ 6,101,971 $ 31,024,227 $ 36Electronic and Other Electric Equipment17,061,472 $ 5,555,915 $ 5,425,336 $ 28,042,722 $ 37Transportation Equipment20,391,288 $ 6,660,484 $ 5,398,965 $ 32,450,737 $ 38Instruments and Related Products15,203,929 $ 7,311,940 $ 5,147,391 $ 27,663,260 $ 39Miscellaneous Manufacturing8,353,216 $ 2,460,574 $ 2,504,362 $ 13,318,151 $ 48Communications19,830,554 $ 7,312,169 $ 7,208,687 $ 34,351,409 $ 50, 51Wholesale Trade11,276,723 $ 3,054,399 $ 4,648,312 $ 18,979,434 $ 61, 67Credit Agencies5,396,045 $ 572,105 $ 2,980,147 $ 8,948,297 $ 62Security and Commodity Brokers11,219,484 $ 1,960,050 $ 7,567,016 $ 20,746,550 $ 63Insurance Carriers10,814,124 $ 4,282,159 $ 4,858,729 $ 19,955,012 $ 73Business Services6,267,029 $ 1,933,580 $ 3,120,209 $ 11,320,817 $ 78Motion Pictures7,636,158 $ 5,136,308 $ 3,542,158 $ 16,314,624 $ 87Research and Development8,984,199 $ 3,328,024 $ 4,772,028 $ 17,084,251 $ 88Corporate Headquarters N/A Increase in Output Table 5 QTI Major Industry Groups Output Average Impact of Adding 100 Direct Jobs If all of the newly created jobs were filled by inmigrants to the Tampa Bay region,then the increases in personal income per capita range from a high of $3.97 for the Chemical and Allied Products major group to a low of $0.37 in the Motion Pictures major group. Table 4 reflects the average increase in employment in the selected major industry groups,for which the increases in personal income per capital were reported in Table 3. Tables 5 and 6 show expected increase in output and tax revenue,respectively,for each of the selected major industry groups. Increase in Taxes SICMajor Group NameFederalState & LocalTotal 20Food and Kindred Products2,967,074 $ 1,742,804 $ 4,709,878 $ 22Textile Mill Products1,441,996 $ 521,810 $ 1,963,806 $ 23Apparel and Other Textile Products1,276,946 $ 425,441 $ 1,702,387 $ 24Lumber and Wood Products1,864,510 $ 684,094 $ 2,548,604 $ 25Furniture and Fixtures1,712,891 $ 578,120 $ 2,291,011 $ 26Paper and Allied Products2,458,453 $ 937,655 $ 3,396,107 $ 27Printing and Publishing1,841,785 $ 623,768 $ 2,465,553 $ 28Chemicals and Allied Products4,243,422 $ 1,637,514 $ 5,880,936 $ 30Rubber and Miscellaneous Plastics1,978,121 $ 736,084 $ 2,714,204 $ 32Stone, Clay, and Glass2,264,352 $ 819,111 $ 3,083,463 $ 33Primary Metal Industries2,882,684 $ 1,347,740 $ 4,326,393 $ 34Fabricated Metal Products2,224,291 $ 796,747 $ 3,021,038 $ 35Industrial Machinery and Equipment2,680,115 $ 936,382 $ 3,616,498 $ 36Electronic and Other Electric Equipment2,500,294 $ 834,015 $ 3,334,308 $ 37Transportation Equipment2,382,034 $ 803,055 $ 3,185,089 $ 38Instruments and Related Products2,237,147 $ 795,111 $ 3,032,258 $ 39Miscellaneous Manufacturing1,166,202 $ 402,811 $ 1,569,013 $ 48Communications3,451,693 $ 1,575,181 $ 5,026,874 $ 50, 51Wholesale Trade2,250,443 $ 1,915,912 $ 4,166,355 $ 61, 67Credit Agencies1,324,153 $ 448,387 $ 1,772,540 $ 62Security and Commodity Brokers3,116,541 $ 1,295,820 $ 4,412,361 $ 63Insurance Carriers2,202,877 $ 1,048,558 $ 3,251,435 $ 73Business Services1,365,804 $ 417,442 $ 1,783,246 $ 78Motion Pictures1,495,559 $ 494,898 $ 1,990,457 $ 87Research and Development1,599,899 $ 403,991 $ 2,003,889 $ 88Corporate Headquarters N/A Table 6 QTI Major Industry Groups Tax Revenue Average Impact of Adding 100 Direct Jobs
8A Summary of Findings In summary,we analyze the economic impact of the QTI Tax Refund program for a hypothetical increase of 100 jobs in the Tampa Bay region. The analysis includes the indirect and induced jobs also generated as the multiplier effect of the 100 direct jobs ripples through Tampa Bay's economy. Although the enabling legislation implies that an increase in the average wage be used as a measure of impact,our primary yardstick for the analysis is the increase in personal income per capita when the 100 jobs are created within Tampa Bay's economy. We use personal income per capita because it is a more comprehensive measure of economic impact of job-creation than average wages. Personal income per capita measures the impact with regard to the economic well being of all residents of Tampa Bay. Average wages only measure the impact on those persons (and indirectly their households) who are employed. Furthermore,a wage measurement usually excludes employee benefits and always leaves out the property income of the owners of capital. Our analysis reveals two important observations with respect to Florida's QTI Tax Refund program. First,the QTI program actually targets major industry groups,not specific industries. As the example,in Table 2,of the Textile Mill Products major industry group shows,there can be a large variation in the personal income impacts among the industries within a major group. In Textile Mill Products the largest total increase in personal income (Carpets and Rugs,SIC 2270) is more than three times the smallest increase in personal income (Knit Outerwear,SIC 2253). Hence,targeting at the industry level increases the efficiency of a job-creation scheme. Our second observation is that the condition of the region's labor market is a determinant of the impact of jobcreation on personal income per capita. When the Tampa Bay's unemployment rate is low and the workforce participation rate is high,many of the newly created jobs will be filled by in-migrants. Increased population dilutes the impact of job-creation as measured by personal income per capita. The example in Table 2 shows that the increase in personal income per capita will be reduced by as much as two-thirds,if the new jobs were filled by in-migrants. Thus,it seems prudent to suspend job-creation programs when unemployment is low and workforce participation is high. Alternatively,the job-creation program should mandate the number of newly created direct jobs that must be filled by current residents of Tampa Bay. References Clark,Cal, 135 Great Ideas on Economic Development privately published by UtiliCorp United,1999. Mackay,John W.,"The Evolving Importance of Incentives," Economic Development Review ,Fall 1994,Vol. 12, Issue 4,pp. 4-8. Poole,Kenneth E. et al.,"Evaluating Business Development Incentives,"National Association of State Development Agencies,Washington,D.C.,August 1999. Stucki,Heinz,Ph.D.,MBA and Gregg L. Andrews, "Knowledge-Based Economic Development:A Simplified Model for Practitioners," Economic Development Review: Strategic Planning Issue ,1999,Vol. 16,Issue 2,pp. 97100. Endnotes1See http://www.myflorida.com/myflorida/government/ learn/otted/financial_incentives.html2The Standard Industrial Classification (SIC) system was developed by the federal government during the 1930s to consistently group together establishments that use the same or similar processes to produce goods or services. 3The economic developers polled were members of the American Economic Development Council (AEDC) and the real estate executives polled were members of the International Association of Corporate Real Estate Executives (NACORE).4Reference "FEDC Sets Legislative Agenda,"by Fred A. Martin,FEDC Legislative Director,in On Track,the newsletter of the Florida Economic Development Council,Inc.,Fall 2001,page 3. See also "The 3 Keys to Florida's Business Success,"undated,published by FEDC,P.O. Box 3186,Tallahassee,Florida 32315 3186.5See "Report on Central Florida's Technology Clusters," The Florida High Tech Corridor Council Inc.,Spring 2001,pages 30 and 31. Florida's High Tech Corridor Council was created in 1996. The Corridor extends from Tampa Bay,through the Orlando metropolitan area,to Florida's Atlantic coast. The goal of the Council is the attraction,retention and growth of high-tech industry and sustaining workforce. The Council's website is at http://www.floridahightech.com.6We define the Tampa Bay region as a seven-county area composed of Hernando,Hillsborough,Manatee, Pasco,Pinellas,Polk and Sarasota counties.7Labor income is the sum of employee compensation and proprietors'income. When estimating the impact of adding jobs to a regional economy,the IMPLAN model maintains the ratio of employees to self-employed workers in an industry.Tax Refund Program for Qualified Target Industry Businesses Continued from page 7
Continued on page 109 Economic Patterns in Hillsborough County in 1997: Hillsborough County Zip Code Business,Employment and Farm Patterns AnalysisBy Gina B. Space,Economist with the Center for Economic Development Research When conducting local economic development,it is important to know both the composition of the local economy and those factors that set your community apart from its neighbors. Fortunately,there are a number of sources of public data concerning regional economies. Most data is available for standard political jurisdictions such as counties and metropolitan statistical areas (MSAs). MSAs are groups of economically integrated counties. Much less public information is available below the county level. The dearth of information can make planning and execution of local development difficult. Local economic development officials can frequently help to fill that gap,based upon their intimate knowledge of their community. But,as useful as individual knowledge of an area can be,accurate data on the distribution of economic activities on a sub-county basis will generally be viewed as more reliable. In addition,a single data source is comparable across communities. Sub-county economic data is available in the Zip Code Business Patterns,but here too,many find the identification of zip codes in a county a difficult and cumbersome process. Since zip codes cross political boundaries,government officials may hesitate to use zip code data to conduct policy analysis and planning activities. Two methods of disaggregating zip code level data can improve its usefulness in regional analysis. First,if the data is mapped geographically,zip code identification becomes a much smaller problem. When viewed in map format,neighborhoods and commercial activity centers can be viewed by reference to streets and other natural boundaries. Second,if the zip code data is allocated to sub-county political jurisdictions such as incorporated cities,totals can be provided for each jurisdiction,facilitating government planning and analysis. This analysis summarizes a report conducted in collaboration with the Hillsborough County Economic Development Department. The report mapped economic activity,measured by number of business and farm establishments and by employment,and allocated the data by sub-county political jurisdictions in Hillsborough County. The Economic Development Department administers programs that sustain and encourage the economic growth of the local economy,including job creation programs. Knowing the distribution of businesses,farms and employment helps the Department balance the needs of the entire community,including businesses,farmers and employees,when implementing programs supporting the county business community. Data The analysis is based on three data sources: the 1997 Zip Code Business Patterns,published in May 2000 by the Economics and Statistics Administration of the U.S. Department of Commerce; the 1997 U.S. Census of Agriculture Zip Code tabulation; and ESRI's ArcView Geographic Information System,a desktop mapping software program which maps zip code boundaries. The Zip Code Business Patterns data cover all private business establishments with one or more paid employees. Data are not included for self-employed persons,domestic service workers,railroad employees,agriculture production workers and most government employees. For example,the data for zip code 33620,which includes the University of South Florida's Tampa Campus,does not reflect the large number of employees at USF,because these employees are government employees. Business establishments are further defined as:"a single physical location at which business is conducted or services or industrial operations are performed."Thus,a single business establishment could be only one of several branches of a corporation and is not a measure of the number of companies in a geographic area. Total employment covers all fulland part-time employees,and salaried officers and executives of corporations. Employees on paid leave are also included in the count. However,proprietors and partners of unincorporated businesses are not included. The Census of Agriculture is conducted every five years by the U.S. Department of Agriculture. The 1997 Census of Agriculture Zip Code Tabulation report covers all farms in all 50 states. The Census of Agriculture was conducted in 1998 and refers to farms and farming activity conducted in 1997. The Census defines a farm as "any place from which $1,000 or more of agricultural products were produced or sold,or normally would have been sold during the census year."(See the endnotes for limitations of the Zip Code Tabulation report.1) The zip code locations and boundaries were identified using ESRI's ArcView Geographic Information System, a desktop mapping software program. The zip codes used in this analysis were released on the 1999 "Data and Maps"compact disc under license to ESRI from Geographic Data Technology,Inc (GDT). GDT zip code boundaries are based primarily on 1998 U.S. Postal Service data. As zip codes and boundaries change from time to time,it is important to keep in mind that these are 1998 zip codes. However,because the business and farm
10Economic Patterns in Hillsborough County in 1997: Hillsborough County Zip Code Business,Employment and Farm Patterns Analysis Continued from page 9 data are from 1997,a high amount of geographic consistency is expected throughout the data. Methodology Several zip codes within Hillsborough County cross political jurisdictions. Other zip codes contain addresses in as many as three different jurisdictions. Still other zip codes contain land and associated businesses and employment located in counties adjacent to Hillsborough County. In order to obtain summary data by sub-county political jurisdiction,it was necessary to allocate the number of business establishments and employees in each multi-jurisdictional zip code that fall within and outside of each political jurisdiction. The allocations are made for zip codes containing two or more political jurisdictions within the county and for zip codes that include areas outside of the county boundary. Establishments and employment in multi-jurisdictional zip codes are allocated by calculating the percentage of land area in each political jurisdiction within the zip code. The percentages are then applied to the totals for that zip code. The result is an estimate of the number of businesses,farms and employment contained in each political jurisdiction's portion of each multi-jurisdictional zip code. This technique necessarily assumes that businesses and employment are uniformly distributed throughout each multi-jurisdictional zip code. While this assumption may be inexact,there is no systematic bias in using this technique,which is expected to randomly result in underestimation in some places and overestimation in others. Additionally,given the large number of establishments and employment in the county and number of estimates calculated,it is likely that the net effect is negligible. Analysis It is estimated that in 1997 there were 25,800 private businesses in Hillsborough County,including all incorporated political subdivisions,employing 470,573 persons. Estimates show Hillsborough County contained 2,695 farms with 1,250 persons with farming as their principal occupation. Of these totals,an estimated 13,696 establishments,221,113 employees,2,340 farms and 1,104 principal occupation farmers are in the unincorporated portion of Hillsborough County. (See Table 1.) By these estimates, the unincorporated portion of Hillsborough County contains 53.1% of all business establishments,47.0% of all employment,86.8% of all farms and 88.4% of all principal occupation farmers. The City of Tampa contained an estimated 11,395 business establishments,237,029 employees,210 farms and 77 principal occupation farmers. The City of Tampa contains a smaller proportion of the county's business establishments than the unincorporated county (44.2% to 53.1%),but a larger proportion of the employment (50.4% to 47.0%). This reflects in part the higher structural density for commercial office space in certain locations within the City of Tampa. Higher structural density is found in places such as the Central Business District (downtown) and much of Westshore. The farm distribution also reflects land use values and patterns. The majority (88.4%) of farms and principal occupation farmers (85.5%) are located in unincorporated Hillsborough County. The remainder are split mostly between Plant City (4.8%) and the City of Tampa (7.8%). However,the relatively high number of farms in the data for Tampa is most likely a result of farm owners'mailing
11 addresses in city zip codes,rather than substantial farming activity in the city. Business Establishments Eight zip codes in Hillsborough County contain over 1,000 business establishments. Of these,two zip codes are exclusively in unincorporated Hillsborough County (33511 and 33618) and two are in the City of Tampa (33609 and 33602). The largest number of businesses in unincorporated Hillsborough County is the Brandon area with 1,326,followed by Carrollwood with 1,002. The City of Tampa's businesses are concentrated in the Westshore area,which contains 1,649 establishments,and downtown Tampa,which is home to 1,152 establishments. The other four zip codes with more than 1,000 business establishments are not exclusive to a single political jurisdiction; these zip codes have areas in both the unincorporated county and Tampa. In these zip codes,it is estimated that the City of Tampa has 3,202 business establishments and the unincorporated county contains 2,497. However,unincorporated Hillsborough County contains several additional large zip codes with a substantial number of businesses. As a result, it is estimated that the unincorporated county contains 2,301 more business establishments than the City of Tampa. Employment Two zip codes in Hillsborough County contain over 50,000 employees:33607 with 58,112; and 33619 with 54,093. Zip code 33607 is almost entirely in the City of Tampa (96.5%) and the majority of 33619 lies in unincorporated Hillsborough County (86.6%). Several other zip codes that encompass parts of both unincorporated Hillsborough County and the City of Tampa contain large numbers of employees. Although there are a large number of establishments distributed throughout much of the county,employment in the unincorporated portion of Hillsborough County is concentrated around the City of Tampa's legal boundaries. Total employment in the City of Tampa is estimated to be slightly greater than employment in the unincorporated county. The employment distribution reflects high structural density buildings in the downtown area (zip code 33602) and the Westshore business district (33609 and 33607),which contain larger business establishments with more employees per firm. Farms and Principal Occupation Farmers Unincorporated Hillsborough County contains by far the largest number of farms and principal occupation farmers in the county. The unincorporated portion of the county contains 2,340 farms,or nearly 87% of all farms in the county,and 1,104 principal occupation farmers. Tampa registers 210 farms and 77 principal occupation farmers; however,it is difficult to discern if that is a result of the mailing list method of data collection or if there are active farms within the City of Tampa. Plant City contains 128 farms and 64 principal occupation farmers. As expected,farms and farmers are concentrated in the eastern and southern parts of Hillsborough County. A relatively large number of farms appear also in the northwest portion of Hillsborough County. While there are a substantial number of principal occupation farmers in Hillsborough County,it is important to note that more farm owners have occupations other than farming. This could be an indication of lifestyle farmers,tenant farming and/or an inability to generate sufficient income from farming alone. Further study would be required to draw conclusions. ZIPNotes Number of Business Establishments Total Employment #Farms Principal Occupation Farmer Other Principal Occupation % FT FarmersPolitical Jurisdiction Subtotal7,683 82,521 1,449 683 766 47.1%Subtotal Hillsborough County Exclusively HC769 10,642 736 356 379 48.4%Hillsborough County Portion Subtotal of Plant City/HC Shared Zip Codes HC4,775 119,845 134 59 74 44.2%Hillsborough County Portion Subtotal of Tampa/HC Shared Zip Codes HC470 8,105 22 6 16 28.6%Hillsborough County Portion Subtotal of Tampa/Temple Terrace/HC Shared Zip Codes 13,696 221,113 2,340 1,104 1,236 47.2% Subtotal7,299 122,322 162 61 101 37.7%Subtotal City of Tampa Exclusively T3,870 110,809 37 13 25 34.5%City of Tampa Portion Subtotal of Tampa/HC Shared Zip Codes T226 3,898 11 3 8 28.6%City of Tampa Portion Subtotal of Tampa/Temple Terrace/HC Shared Zip Codes 11,395 237,029 210 77 133 36.6% TT355 6,128 17 5 12 28.6%Temple Terrace Portion Subtotal of Tampa/Temple Terrace/HC Shared Zip Codes 355 6,128 17 5 12 28.6% PC353 6,303 128 64 65 49.7%Plant City Portion Subtotal of Plant City/HC Shared Zip Codes 353 6,303 128 64 65 49.7% 25,800 470,573 2,695 1,250 1,445 Hillsborough County Zip Code Business and Farm PatternsSources: ESRI ArcView 1999 Data and Maps (for zip codes and boundaries), 1997 Zip Code Business Patterns, US Bureau of the Cen sus (for business establishments and amployment), and 1997 Census of Agriculture, US Department of Agriculture, National Agriculture Statistics Service (for farms, and farmer occupa tions).City of Tampa ESTIMATED TOTAL City of Temple Terrace City of Temple Terrace ESTIMATED TOTAL City of Plant City SUMMARY Hillsborough County Hillsborough County ESTIMATED TOTAL City of Tampa City of Plant City ESTIMATED TOTAL Total for All of Hillsborough County Continued on page 12
12Economic Patterns in Hillsborough County in 1997: Hillsborough County Zip Code Business,Employment and Farm Patterns Analysis Continued from page 11 Demographic Changes in Southeast Pasco CountyBy Gina B. Space,Economist with the Center for Economic Development Research It is well publicized that Florida's population is growingrapidly. Population grew from 9,746,324 in 1980 to 12,937,926 in 1990 and to 15,982,378 in 20001. This represents a twenty-year growth rate of 64.0%,compared to the national rate of 21.8%. It is also widely appreciated that some parts of the state have grown,and may continue to grow,faster than others. Counties may grow at different rates for several reasons. Some areas may be built-out to capacity,leaving little room for growth. Other areas start with large population bases and therefore show smaller growth rates since the rate of growth declines with the size of the base. Moreover,economic activity and opportunity in an area influences individuals' choices of residency and may either foster or dampen growth. Especially in metropolitan areas,as the population grows outward from the central city,land use and zoning regulations tend to steer growth and development in specific directions. The Tampa Bay area is a big contributor to Florida's high population growth rates. Other studies have documented rapid population growth in Tampa Bay and the Tampa-St. Petersburg-Clearwater Metropolitan Statistical Area. This article examines one rapidly urbanizing area in Tampa Bay,the southeast section of Pasco County. We examine recent growth patterns and their effect on the age distribution of the population. For the purpose of this article,southeast Pasco County is defined by census tracts. The area includes the following tracts:321.01,321.02,328,329,330.01,330.02,330.03, 330.04,and 331. Geographically,this area is bounded by Interstate 75 on the west and the county line on the south and east. The northern boundary follows approximately State Road 52a and State Highway 579a. The area will be referred to as southeast Pasco and is shown in Map 1. From 1980 to 2000,the population in southeast Pasco more than doubled. The area's population grew at a twenty-year rate of 119.8%almost twice as fast as Florida's rate of 64.0%. Pasco County's population grew 78.0% over the twenty years,also greater than the state rate,but substantially less than southeast portion of the county. The overwhelmConclusions We find that the unincorporated portion of Hillsborough County contains the majority of:business establishments (53.1%) in the county; farms (86.8%); and farmers,both principal occupation farmers (88.4%) and farmers with other occupations (85.5%). The City of Tampa contains just slightly more than half of all employment in the county (50.4%). The unincorporated county's employment comes in a close second to Tampa containing 47.0% of total county employment. Plant City and Temple Terrace combined contain less than 3% of business establishments and employment. The geographic distribution of businesses shows that commerce is spread across the county. However,the distribution of employment does not follow precisely the same pattern. While employment is concentrated in a few areas,business establishments flourish throughout populated areas. This is a result of the natural business model. As establishments locate where customers can be found, establishments found in the outlying areas of the county presumably serve retail demand from the suburbs. Further analysis of the data by industry sector should confirm these conclusions. 1The Zip Code Tabulation report of farm data has some limitations. One limitation is the method used to gather the data. The data is collected using mailing addresses and there are some instances in which the mailing address zip code for a farm is not the same as,or included in,the zip code where the farm is located (i.e. post office boxes,other business addresses,etc.). Thus,a farm may be reported in a central business district since the farm owner receives business mail there,but the farm can be physically located miles away. Additionally,zip codes with fewer than five farms are aggregated in a separate category for each state and are not reported for the zip code of record. Likewise,zip codes with no farms do not appear in the zip code tabulation. A lack of data should not be taken as an indication of no farms or farming activity,because such zip code could contain fewer than four farms. Alternatively,one cannot assume that a small number farms exist in zip codes with no data.
13 ing majority of the growth in southeast Pasco is found in the area just north of Hillsborough County,running from I-75 to Zephyrhills,with State Road 54 cutting almost through the middle from west to east. This area contributed 82.7% of the total population growth experienced in that section of the county. The proximity of I-75 and the use of SR 54 as a connector to I-75 have served as an inducement for new residential growth as they lower the cost of transportation to major employment and commercial centers in the Tampa Bay area. Growth is changing the overall demographics of southeast Pasco. In 1980,almost half of the area's population was over age 55 (49.3%) and over one-third of the population was over 65 (34.5%). Today,those figures are smaller:41.9% of the population is older than 55 and 30.3% is over 65. These figures are still substantially higher than the average population distribution nationally,where 21.1% of the population is over 55 and only 12.4% is over 65. As southeast Pasco continues to urbanize,will the population become more representative of the U.S.? Or will the area continue to be a retirement destination? An examination of the changing demographics will help answer those questions. There are two ways to analyze the changing age of the southeast Pasco population. A straightforward method is to analyze the growth in population by age range in stasis. The second method is more complicated. It consists of examining the changes in population figures over time,as we follow age group cohorts over twenty years. The results of the two methods can be dramatically different. The population in southeast Pasco is shown in Table 1 by age range for census years 1980,1990 and 2000. Table 2 shows the absolute changes and Table 3 shows the percent change in each age group. Both tables cover three time spans: 1980 to 1990; 1990 to 2000; and 1980 to 2000. Separating the growth by decade highlights the shift in the age of the typical newcomer to southeast Pasco. From 1980 to 1990,it was much more likely for an older,established person to move into southeast Pasco than for someone under 25. Between 1980 and 1990,southeast Pasco added 5,828 residents over 65 to only 2,334 under 25,a ratio of two and one-half to one. In the same time period,southeast Pasco showed the largest absolute growth (6,427) in persons of labor-force age (25 to 64). For the 1990's decade,the population growth shifted. Continuing to dominate the growth are persons between the ages 25 and 64,adding 10,149 people and accounting for 56.8% of total growth. However,from 1990 to 2000, growth was much more pronounced in the younger generation than in the elder one. Southeast Pasco added 4,813 residents under 25,compared to 2,911 residents over 65, almost the reverse of the previous ten years. For the sum total twenty years,the population growth distribution was slightly skewed to the older age ranges,but the overall growth was still dominated by working-age persons. The two age ranges with the highest twenty-year growth rates are those aged 34 to 45 and 45 to 54,growing 263.2% and 199.7% respectively. The turnaround in the population growth of the younger ages in the 1990's helped propel these age ranges into the group of fastest growing,with residents aged zero to 4 registering the third largest gain (158.1%). As a result,the young and the old achieved a near-balance in twenty-year absolute population change:southeast Pasco added 7,147 residents under 25 and 8,739 residents over 65. The effect of the population growth on the relative proportion of each age range in the area is shown by the percentage of the population in each range. Chart 1 (See page 14) shows the population distribution by age for southeast Pasco for 1980,1990 and 2000. The national population distribution is included for comparison purposes. In all but four of the age ranges,southeast Pasco is trending toward the national average. In the age ranges where southeast Pasco seems to be diverging from the national average,two were moving away from the national average and then reversed themselves. The other two,15 to 19 and 20 to 24,have consistently been moving in the opposite direction as the national average. The age groups that began trending towards the national average in 1990,after moving away from that average in 1980, Continued on page 14 Table 1Age Range198019902000 Zero to 4 years1,2111,8293,126 5 to 9 years1,2731,8783,150 10 to 14 years1,4811,8333,107 15 to 19 years1,6201,9422,697 20 to 24 years1,2601,6971,912 25 to 34 years2,5804,3366,208 35 to 44 years2,1184,1697,692 45 to 54 years2,2383,5186,707 55 to 64 years4,0285,3686,933 65 to 74 years5,8358,8349,245 75 and older3,5256,3548,854Source: U.S. Census Bureau, 1980 Census of Population and Housing, Table 194; 1990 Census of Population and Housing, PL 94-171 Data, 2000 Census of Population and Housing, SF1Population Table 2Age Range1980 to 19901990 to 20001980 to 2000 Zero to 4 years6181,2971,915 5 to 9 years6051,2721,877 10 to 14 years3521,2741,626 15 to 19 years3227551,077 20 to 24 years437215652 25 to 34 years1,7561,8723,628 35 to 44 years2,0513,5235,574 45 to 54 years1,2803,1894,469 55 to 64 years1,3401,5652,905 65 to 74 years2,9994113,410 75 and older2,8292,5005,329 Population ChangeSource: U.S. Census Bureau, 1980 Census of Population and Housing, Table 194; 1990 Census of Population and Housing, PL 94-171 Data, 2000 Census of Population and Housing, SF1 Table 3Age Range1980 to 19901990 to 20001980 to 2000 Zero to 4 years51.0%70.9%158.1% 5 to 9 years47.5%67.7%147.4% 10 to 14 years23.8%69.5%109.8% 15 to 19 years19.9%38.9%66.5% 20 to 24 years34.7%12.7%51.7% 25 to 34 years68.1%43.2%140.6% 35 to 44 years96.8%84.5%263.2% 45 to 54 years57.2%90.6%199.7% 55 to 64 years33.3%29.2%72.1% 65 to 74 years51.4%4.7%58.4% 75 and older80.3%39.3%151.2% Growth RatesSource: U.S. Census Bureau, 1980 Census of Population and Housing, Table 194; 1990 Census of Population and Housing, PL 94-171 Data, 2000 Census of Population and Housing, SF1
14Demographic Changes in Southeast Pasco County Continued from page 13 are those over 75 and those aged 10 to 14. The 10 to 14 range posted the second lowest growth for the 1980 to 1990 decade,but in the following ten years had the fourth highest growth of all age groups. The opposite is true of the over 75 age range,which had the second highest growth in the 1980's decade,but dropped to the fourth lowest in the 1990's. Absolute growth numbers also show the very small changes in the population in age range 20 to 24,which when combined with large overall population growth rate cause the age group's proportion to fall rapidly. This lack of growth in the midst of an exploding population may be a result of several factors. The 20 to 24 age range is typically the time for college education,which frequently is undertaken away from home. If a young person is not moving away for education,he/she is most likely seeking employment. The commercial activity in the area does not have the strength of nearby cities and thus it would be expected for a young person to move closer to potential employment opportunities. Finally,new housing development in southeast Pasco is geared towards the single family home. Most young people between the ages of 20 and 24 are not in a financial position to purchase a home,even if they have already started a family. Thus,housing opportunities lead this segment of the population to other locations in the metropolitan area. Certainly some of the same causes cited above may contribute to the decline in the proportion of persons aged 15 to 19. In addition,the decrease may also be a reflection of the mathematics of population growth. While the age group did grow from 1980 to 2000,and in fact increased by two-thirds,the growth in all other age categories except two was greater. As a result of being outpaced in growth, the age group's relative share of overall population falls. When looking at population changes over time,it is also important to measure the growth of population cohorts as they move through the age ranges. Analysis of cohorts shows the net effect of population changes while accounting for the passage of time. We now examine age cohorts and follow them,measuring changes in populations,as they age. Table 4 shows the percent changes in cohort groups with each census beginning with the 1980 Census. The striking difference between the analysis by cohort and the straightforward analysis without regard to the population itself aging is seen in the over 75 category. In the straightforward measurement of change,it appeared that the population over 75 was growing rapidly,posting a twenty-year gain of 151.2%. However,the population change measurement by cohort shows steady population loss in the elderly. This population "loss"is a result of combining the populations of those over 65 with those over 75 in 1980,and measuring the difference in this sum versus the 1990 population over 75. The cohort measurement attempts to quantify the net change in population over time and,as applied to the over 75 age range,answer the question,"Are more retirees moving into the southeast Pasco area?"The answer is an unambiguous no. As the population between 65 and 74 ages ten years,that population does not become the entire over 75 population,but merely a part. Assuming no out-migration of the 1980 population,those persons already over 75 in 1980 will remain in the over 75 group in 1990,unless they have died during the decade. Thus in 1990 the over 75 population is the combination of two 1980 cohorts:the 65 to 74 cohort and the over 75 cohort. In 2000,the over 75 cohort is the combination of three 1980 cohorts:the 55 to 64 cohort,the 65 to 74 cohort,and the over 75 cohort. The 33.9% decrease in population for the over 75 cohort in 2000 since 1980 measures the population reduction as a result of death,out-migration and no new influx of over 75 individuals. We find during the twenty years from 1980 to 2000,the over 75 population has not really grown. Rather, other cohorts,namely the 65 to 74 and 55 to 64 cohorts, have been folded into it. If new retirees were moving into the area in sufficient numbers,they would counteract the loss of population through death and the total population would remain steady. Thus,it is safe to conclude that southeast Pasco is no longer primarily a destination for retirees. This raises the question of what age group fuels SE Pasco population growth. All other age cohorts post positive gains throughout the twenty years,indicating that new population age groups are migrating to the area. The 1980 Population Distribution by Age Range0.0% 5.0% 10.0% 15.0% 20.0% 25.0% Zero to 4 years 5 to 9 years10 to 14 years 15 to 19 years 20 to 24 years 25 to 34 years 35 to 44 years 45 to 54 years 55 to 64 years 65 to 74 years 75 and older SE Pasco County 1980 SE Pasco County 1990 SE Pasco County 2000 United States 2000 Chart 1Continued on page 19 Table 4 Age in 1980 Growth Rate 1980 to 1990Age in 1990 Growth Rate 1990 to 2000Age in 2000 Growth Rate 1980 to 2000 Zero to 4 years69.9%10 to 14 years 5 to 9 years43.6%15 to 19 years Zero to 4 years51.4%10 to 14 years4.3%20 to 24 years57.9% 5 to 9 years52.6%15 to 19 yearsincluded below25 to 29 yearsincluded below 10 to 14 years14.6%20 to 24 years70.6%25 to 34 years125.4% 15 to 19 yearsincluded below25 to 29 yearsincluded below35 to 39 yearsincluded below 20 to 24 years50.6%25 to 34 years77.4%35 to 44 years167.1% 25 to 34 years61.6%35 to 44 years60.9%45 to 54 years160.0% 35 to 44 years66.1%45 to 54 years97.1%55 to 64 years227.3% 45 to 54 years139.9%55 to 64 years72.2%65 to 74 years313.1% 55 to 64 years119.3%65 to 74 yearsincluded below75 and older-33.9% 65 to 74 yearsincluded below75 and older-41.7% 75 and older-32.1%
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19 The 2001 USF Economic Development CourseBy Nolan Kimball,Coordinator of Information/Publications of the Center for Economic Development Research I. Background. The International Economic Development Council (IEDC) is the nation's largest economic development association,serving thousands of economic development professionals worldwide. The IEDC was created in May 2001 when the American Economic Development Council (AEDC) and the Council for Urban Economic Development (CUED) voted to merge the two organizations into one Education is an important component of IEDC's member services. The Council accredits 19 economic development courses around the nation. Each course follows a specified curriculum. The courses are offered by organizations affiliated with institutions of higher learning. Courses last for 5 days and are held year round across the United States. Upon successfully completing an economic development course,participants are eligible to attend the Economic Development Institute (EDI),also accredited by the IEDC, which holds courses in Norman,Oklahoma; San Diego, California; and Indianapolis,Indiana. Graduation from EDI and associated professional experience qualify candidates to sit for the Certified Economic Developer (CED) exam. II. The USF Economic Development Course in 2001. The University of South Florida (USF) conducts one of the longest-running economic development courses accredited by the IEDC. The Center for Economic Development Research (CEDR) offered the USF Economic Development Course for the 25th consecutive year from November 4 9,2001. The 2001 USF Economic Development Course was held at the Hilton Garden Inn located in Ybor City,an historic neighborhood in Tampa,Florida. The Ybor Hilton is within easy walking distance to numerous fine restaurants and entertainment facilities. The Course Director was Dr. Kenneth F. Wieand, Director of CEDR. The Course Coordinator was Nolan Kimball,Coordinator of Information/Publications for CEDR. Other personnel involved were Dr. Dennis Colie, Associate Director of CEDR; Dodson Tong,CEDR's Data Manager and Gina Space a CEDR Economist. Tuition for this year's program was $700. Course tuition offset expenses for instruction,course materials,refreshments,two field trips,two dinners and the graduation luncheon. Three sponsoring organizations,the Florida Economic Development Council,Progress Energy and the Florida Association of Counties provided a total of six scholarships for qualified participants. Each participant received a course book containing materials for the instruction modules submitted by the faculty. Additionally,a group photo was taken of all the course participants. CEDR's website http://cedr.coba.usf.edu contains all of the course information including the course registration form. The 2001 USF Economic Development Course was structured on the required core topics established by the IEDC Education Committee. Topics covered were: Marketing Perspectives on Economic Development Community Development Corporate Site Selection FEDC Deal of the Year Business Retention and Expansion Financing Economic Development Projects Entrepreneurship and Small Business Creation Rural Issues in Economic Development Perspectives on Environmental Issues Building and Effective EDO Workforce Development Analyzing the Geography of your Product International Trade and Development Analytical Tools for Economic Development Strategic Planning in Economic Development Continued on page 20 45 to 54 cohort is the fastest growing over twenty years. But an examination by decade shows the ages of the population influx shifts. In the 1980s,the 45 to 54 and the 55 to 64 cohorts dominated growth. However from 1990 to 2000,growth is spread throughout the cohorts,indicating the influx is most likely families,since the cohorts of young children posted high gains. The typical resident of southeast Pasco has certainly changed over the past twenty years. The notion that the population has grown as a result of retirees and the elderly is plainly false. Families and working-age people have moved into the area in great numbers. As a result, population growth has brought more diversity in age ranges. If population trends continue,the future population distribution in southeast Pasco will look much more like the national average. And southeast Pasco will be associated more with those characteristics common to the suburban neighborhoods of Tampa. 1 US Census Bureau,1980 Census of Population,Table 194; US Census Bureau,1990 Census of Population and Housing,PL 94-171 Data; and US Census Bureau,2000 Census of Population and Housing,SF 1 100% Data.Demographic Changes in Southeast Pasco County Continued from page 14
20The 2001 USF Economic Development Course Continued from page 19 Class activities were supplemented by a case study. Field trips highlighted urban redevelopment and environmental issues in development. Of the twenty-eight course instructors,six hold the CED designation. Three of the faculty members are from academia,two from government and seventeen from the private sector. Thirty-four students from nine states participated in the 2001 course. Forty-four percent worked in economic development organizations, 38% worked for city governments,9% worked in state development agencies,6% were employed by utilities and 3% represented workforce development boards. IEDC accredited economic development courses are intended to be rigorous and extensive learning experiences. Attendance is strictly enforced. Each student was asked to rate the faculty on their presentation as well as evaluate other aspects of the Course. On a scale of 1 to 10 with 1 as unacceptable and 10 as outstanding,the average rating for instructors was 8.752 and the average rating for the course was 8.594. The 2002 USF Economic Development Course will be held November 3 8,2002. The course site will be determined by spring 2002. The Relocation of Brooksville Regional HospitalBy Alex A. McPherson,Economist with the Center for Economic Development Research A study recently completed by CEDR analyzed the demographic and economic effect of the potential relocation of the Brooksville Regional Hospital. There is a proposal to relocate the hospital to new facilities to be constructed at a campus approximately 3 miles west of the existing hospital. Demographic effects were analyzed using Census 2000 data to determine the number of residents affected in two ways: those who would be farther from the proposed location,and the difference in number of people within a 3-mile radius of either the existing or proposed sites. The economic impact of construction and other capital expenditures to relocate the hospital to the new campus was analyzed for its contribution to the Hernando County economy. CEDR examined the quantifiable economic effects of these investments, which are measurable in terms of employment,personal income,and output. Concerns have been raised about travel time and distance to the proposed site in emergency situations. If the hospital moves to a new site west of the existing location,the distance midpoint between Brooksville Regional Hospital and the closest hospital to the east,Dade City Hospital,will shift. Using ArcView GIS measurement techniques,the shift in this distance midpoint was determined,resulting in a 15 squaremile wedge in southeast Hernando County which will effectively be farther from the new site than the existing site. The population within this wedge,based on Census 2000 data,is 1,265 Hernando County residents who would be farther from the proposed location than the existing location. The proposed hospital relocation will affect other Hernando County residents. One major reason for seeking the relocation is to allow more of the Hernando County population to be closer to the hospital. Population figures were analyzed within a 3-mile radius of each of the Brooksville Regional Hospital sites (existing and proposed) and the closest hospital to the west,Oak Hill Hospital. Again,using ArcView GIS measurement techniques,together with CACI Demographic Data and Census 2000 Information,the net number of residents affected by the shift was determined. Westward relocation of the hospital by approximately 3 miles would provide closer access to Brooksville Regional Hospital for a net population of 5,428 Hernando County residents within a 3-mile radius of the new location. From an economic perspective,relocation of the hospital to a new site will require major construction expenditures,estimated at $30 million for the hospital and another $3 million for a medical office complex. Capital investment for furnishings and equipment are estimated at $2 million for furnishings and $5 million for equipment. Timing of these expenditures has been presumed to occur in the years 2002,2003,and 2004. CEDR used the Regional Economic Models,Inc. (REMI) software and databases to estimate the effects of these expenditures to the Hernando County economy. All monetary figures are presented in year 2000 dollars. The effects of these expenditures may be measured in terms of output,employment,and personal income. Over the 3-year period of construction,CEDR estimates that $17.068 million of the $40 million in direct expenditures will be spent in Hernando County. Due to the multiplier effect,another $9.323 million will be spent in Hernando County,for a total of $26.391 million in increased output expected to occur in Hernando County. Therefore,the economic impact on Hernando County of the proposed construction is $26.391 million between 2002 and 2004. The local construction industry will benefit the most,with an increase of $21.874 million in economic activity. During the construction phase,employment in Hernando County is estimated to temporarily increase by 79 jobs in 2002, 149 jobs in 2003,and 67 jobs in 2004. Most of the gains in jobs will be in the construction industry. Also,due to the proposed expenditures,it is estimated that an aggregate increase of $7 million in personal income for residents of Hernando County will occur between 2002 and 2004. The full text,data,maps,and tables of the report can be found online at the CEDR website,under "Recent Projects" (http://www.coba.usf.edu/centers/cedr/projects.htm).
21 Update on CEDR's Data CenterBy Dodson Tong,Data Manager with Center for Economic Development Research CEDR's on-line Data Center has just added another database to its web site. This data contains the number of business establishments located within a postal ZIP code area throughout Florida. The database also reports the number of employees by industry. The database can be accessed at http://cedr.coba.usf.edu and "Query CEDR Databases."The Regional and State database section now includes a folder named "ZBP"which enables the user to access this type of data. CEDR also has ZIP code business pattern data for 1997 and 1998. For each year,drop-down menus allow the researcher to specify a ZIP code area by name (ordered alphabetically) or by ZIP code (ordered numerically). Additionally,the researcher can specify a ZIP code and a Standard Industrial Classification (SIC) code for the 1997 data. The 1998 data is organized by North American Industry Classification System (NAICS) codes; therefore, for this year,the researcher may specify a ZIP code and a NAICS code. In the 1997 data,Industrial Divisions are identified by SIC codes ending in -. For example,52 is the code for the Retail Trade Division,which includes all 4-digit SIC codes between 5200 and 5999. Industrial Divisions are "headers"for the SIC codes that follow. The headers contain no data themselves. Industry descriptions accompanied by an asterisk represent partially classified establishments. For example,SIC 5600* represents retail apparel and accessory stores that could not be classified into a 4digit SIC category. In a response to a query,the number of "Query results"indicates the total number of industries (4digit SIC codes) located in the specified postal ZIP code area. The query results will be zero if there are no establishments with the specified SIC code located in the ZIP code area. Otherwise the query returns the number of 4digit SIC's with business establishments in the ZIP code. In the 1998 data,NAICS sectors,representing general categories of economic activities,are identified by NAICS codes ending in -. For example,52 is the code for the Finance & Insurance sector,which includes all 6-digit NAICS codes between 521110 and 525990. These NAICS sectors are "headers"for the NAICS codes that follow and contain no data themselves. In response to a query,the number of "Query results"indicates the total number of establishments by 6-digit NAICS codes that are located in the specified postal ZIP code area. The query results will be zero if there are no establishments with the specified NAICS code located in the ZIP code area. Otherwise the query returns the number of specified NAICS with business establishments in the ZIP 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 the researcher identify and define a local area of interest. ZIP Code Business Patterns maps are now available for 1997 and 1998,which are a graphical representation of the data. In conjunction to this ZIP Code Business Patterns data,maps for 1999 ZIP code boundaries are also made available. In addition to the ZIP code business patterns data,the Regional and State database section continues to make available the following: Cost of Living. This data set provides relative costs of living for Florida's 67 counties and is released annually by the Florida Department of Education. The average cost of living in a given year (1993 to 2000) among Florida's 67 counties is set at 100% and then each Florida county's relative cost of living is expressed relative to 100%. Education Indicators. The indicators in the data 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 organized by 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 1digit 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 unit for the unemployment insurance system). The number of covered employees,total wages of 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 2001. A version with annual data from 1988 to 2000 is also available. Gross Sales. This data series is provided by the Florida Department of Revenue and 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 of Revenue. The Florida Department of Revenue reports gross sales and taxable sales to CEDR by "kind"code. In order to protect the confidentiality of businesses reporting to the Florida Department of Revenue, Continued on page 22
22Update on CEDR's Data Center Continued from page 21 CEDR has aggregated certain kind codes and converted the aggregations to categories. The data set is partitioned by Florida Counties and provides monthly data beginning with 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 permit 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,by county,and by Metropolitan Statistical Area (MSA) for each month of a year from January 1996 to November 2001. The data describe the number of units and aggregate value for which building permits have been issued by single-family,2family,3&4-family,and 5-family units. Local Area Unemployment Statistics (LAUS). This labor force data set is prepared monthly by the Bureau of Labor Statistics (BLS) and describes labor force participation,employment,unemployment,and unemployment rate by county of residence. (Data is also included by Florida 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 of establishments,and ES-202 data. Statewide and Florida counties'data are available. The data can be displayed by month from January 1990 to December 2001. Annual averages are also available. 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 of 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 military payroll. Hence,the REIS data series,which includes farming and nonfarming,military and civilian,proprietorships (i.e. self-employment) and wage and salary employment, are 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-monetary 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 1999. CEDR has also recently received from the Bureau of Economic Analysis (U.S. Dept. of Commerce) State Personal Income,1929 2000. There are tables with annual measures for each of the states of the U.S. Personal income by major source and earning by industry, Wage and salary disbursements by industry, Total full-time and part-time salary employment by industry, State economic profiles, Transfer payments, Farm income and expenses,and Personal tax and non-tax payments. Although the State Personal Income,1929 2000 tables are not available on line,you can go to CEDR's home page and click on "Request Data from CEDR"to e-mail your individualized data need request. Other items that can be found at CEDR's web site are reports of recent studies and publications as well as links to other sites containing data of interest for economic developers. CEDR's on-line data center continues to garner wide interest. In 2001,annual web hits more than doubled,reaching 109,505 (excluding CEDR staff hits) and averaging a monthly count of 9,125. During the most recent period, users remained at the site for an average of 19.1 minutes per visit. Check CEDR's web site at http://cedr.coba.usf.edu for new projects and continuous updated data sources. Census 2000 Data Release ScheduleBy Dodson Tong,Data Manager of the Center for Economic Development Research The Census 2000 has been releasing products at an alarming pace every since data collection efforts ended on the Census deadline date of April 1,2000. Beginning in March 7,2001,the release of the first 100-Percent Data Products was Census 2000 Redistricting Data Summary File for the states. Block, tract,and voting district maps,including population counts for small areas have also been released. From May 15,2001,accompanying selected demographic and racial characteristic data have been published.
23 Census 2000 data also provides new geographic counts at the smallest geographic level called blocks (a block represents about 100 people),and block groups. Maps of these blocks will show census tracts (a tract contains about 4000 people),which includes townships,counties,incorporated places,and American Indian locations),and voting district maps will show outlines of the voting districts with both precincts and wards. The population series report total population,populations by 18 years of age and over,and population broken down by 63 race categories from the 6 single-races including "other"and 57 multi race options that were given for people that choose more than one race. Federal,state and local organizations use census data to determine eligibility to qualify for a particular or multiple federal assistance programs. According to the U.S. General Accounting Office's publication "Using Census Data for Funds Allocations,Letter Report to Congress,B280035 (May 29,1998)",there are currently about 100 federal programs that include the use of employment population data in determining the distribution of federal funds. Eligibility criteria usually are tied to measures of household or family income and economic conditions of particular geographic areas. Census data are also utilized to define metropolitan statistical areas (MSA). Population data and derived data products are also used to formulate the distribution of federal funds. Formulas may include aggregate population data of a state,MSA,county,city,or zip code,as well as the population data for a political subdivision. Formulas may incorporate the number of peo ple over or under a particular age,or indirectly,per capita income or expenditures. The most important use of census population data is for political redistricting. By law,the Census Bureau has to provide population information within one year of the census-day in order for the states to redraw voting and representation districts. Congressional and legislative boundaries will be defined by each state thereafter. City block,census tract,and voting district maps have been made available as PDF files on the Census web site since May 2001. See the list below for a time line on dates of future Census 2000 Data Products for the next couple of years,along with the lowest level of geography for which they are made available in each release. Contact CEDR at 905-5853 or email firstname.lastname@example.org for data availability as well as for any customized project needs in using Census 2000 data. All Census 2000 reports are also available on the Census web site:www.census.gov. Beginning Date100 % Data ProductsLowest Level of Geography Apr. 1,2000Census 2000 survey deadline was due Apr. 1,2001Census Bureau to provide redistricting data to the states Mar. 7,2001Census 2000 Redistricting Data Summary FileBlocks May 15,2001Demographic ProfilePlaces May 15,2001Congressional District Demographic ProfileCongressional Districts of the 106th Congress May 31,2001Census 2000 Housing Unit CountsPlaces June 27,2001Race and Hispanic or Latino Summary filePlaces June 13,2001Summary File 1 (SF 1) StatesBlocks,Census Tract Nov. 16,2001Summary File 1 (SF 1) Advance nationalBlocks,Census Tract May 2002Summary File 1 (SF 1) Final nationalBlocks,Census Tract Dec. 2001Summary File 2 (SF 2) StatesCensus Tracts Mar. 2002Summary File 2 (SF 2) Advance nationalCensus Tracts June 2002Summary File 2 (SF 2) Final nationalCensus Tracts Mar. 7,2001Quick Tables StatesCensus Tracts Nov. 16,2001Quick Tables NationalCensus Tracts Mar. 7,2001Geographic Comparison Tables StatesCensus Tracts Nov. 16,2001Geographic Comparison Tables NationalCensus Tracts Apr. 2002Advance Query FunctionUser defined down to block groups May 2002Summary Population and Housing Characteristics (PHC 1)Places 2003Population and Housing Unit Totals (PHC 3)Places Beginning DateSample Data ProductsLowest Level of Geography Mar 2002Demographic ProfilePlaces Mar 2002Congressional District Demographic ProfileCongressional Districts of the 106th Congress Jun 2002Summary File 3 (SF 3)Census Tracts,Block groups Oct 2002Summary File 4 (SF 4)Census Tracts June 2002Quick TablesCensus Tracts July 2002Geographic Comparison TablesCensus Tracts 2002Pub lic Use Microdata Sample (PUMS) Files 1% sample Super PUMAs of 400,000+ 2003Pub lic Use Microdata Sample (PUMS) Files 5% sampleSuper P UMAs of 100,000+ Jan 2003Advanced Query FunctionUser defined down to Census Tracts 2003 Summary Social,Economic,and Housing Characteristics (PHC 2) Places 2003Congressional District Data Summary FileCensus Tracts within Congressional Districts
24Population Growth in Florida Counties During the 1990s; Regional Amenities and Business Climate400,000 or more. These large urban counties experienced most of the state's new population growth. The 11 counties were home to 10.4 million persons in 2000. During the decade,they grew by 1.84 million residents. In other words, these counties housed 65 % of Florida's population and experienced 61% of the new growth from 1990. The next tier of counties with populations of between 100,000 and 400,000 were home to 28% of residents in 2000 and welcomed 35% of new arrivals during the decade. The least populous countiesthose with under 100,000 population,were home to 6.7% of the state's population in 2000 and experienced 8% of its new population growth. The largest counties continued to experience the greatest absolute growth,while small and medium sized counties gathered increased shares of population growth. Table 1 Florida Counties: Total Population in 2000 Population 2000 Total% of StateChange % of State 1990-2000 Total Florida15,982,3783,044,452 Cities w/population of 400,000 plus10,400,00066%1,840,00061% 100,000-400,0004,468,34528%977,47432% Under 100,0001,066,4316%221,8367% Under 20,000186,5131.20%34,1041.10% Table 1 indicates that small and medium-sized counties experienced higher growth rates than the larger counties. This is true:The population growth rate for counties of 400,000 population and over was 17.7% vs. a 21.5% growth rate for counties between 100,000 and 400,000 population. Population in counties under 20,000 grew by 18.3%. For information on population for each county,see Table A1 on CEDR's web site. Table A1 reports total population in 2000,the absolute change in population during 19902000,the percent change from 1990-2000 and each county's ranking in the state,from 1 to 67. T he Elder l y P opula tion in Flor ida Counties Historically,retired persons come to Florida for the same reasons that tourists visit the state. Warm winters, affordable housing and sandy beaches act as a magnet for tourists and retirees. And,increases in the numbers of both retirees and tourists create demand for new workers in Florida's service sector. Retirees,and the service workers who come to supply their needs as well as to staff tourist businesses,are a major source of population growth. Thus, retirees increase population directly and indirectly through the service sector jobs required to supply their needs. RegionalRetirees &Labor Force Growth: AmenitiesTouristsTrade and ServicesBy Dr. Kenneth Wieand,Director of the Center for Economic Development Research Introduction. Rapid growth in Florida's population during the 1990s was fuelled by the in-migration of two groups. The first group consists of working age persons seeking to fill positions created by the state's expanding businesses,and their dependents. The second group is older persons moving to Florida to enjoy their retirement years. The motivations of the two groups are different. The first group is attracted by the state's positive business climate. The second group, retirees,is attracted by the state's amenities. But,the arrival of retirees impacts job growth and the arrivals of new workers have implications for Florida's retired population. This article describes the growth of these two groups of people in the sixty-seven counties of Florida from 1990 to 2000. We examine the impacts on Florida's workforce and employment during the decade in terms of labor force growth and labor force participation and in terms of employment by industry division. Differences in county economies lead to differences in population growth across the sixty-seven counties. In this article we refer to Tables A1,A2,A3,and A4. These four tables are not reproduced in this article due to their excessive size. To view them,visit CEDR's web site at:http://cedr.coba.usf.edu and click on "Recent Projects,"then select "The Tampa Bay Economy"link,volume 3 #1,to access tables A1 A4. View the performance of your county and compare it to Florida's other 66 counties. If you do not have Internet access but wish to see these tables, call us at 813-905-5854,or write us at: CEDR,USF Downtown Center 1101 Channelside Drive,2nd Floor North Tampa,Florida,33602 There are large variations across Florida counties in employment,labor force participation,industry structure,and the percent of retired persons. The experiences of sparsely populated counties and of densely populated counties have been different in terms of total population, the over 65 population,and in terms of labor force and employment. Rural counties,many located in the interior of the state and in Florida's panhandle,have experienced smaller absolute population growth than have Orlando and the large metropolitan areas located on Florida's Atlantic and Gulf coasts. The article documents the differences in low and high population counties. Population Growth in Florida Counties T otal P opula tion in Flor ida Counties Table 1 summarizes Florida's population growth from 1990 to 2000. The 11 largest counties in Florida have populations of
25Retired persons tend to live in the urban counties on the Atlantic and Gulf coasts. Note,however,that the major metropolitan counties,(Dade,Broward,Hillsborough, Duval,and Orange) have fewer retirees as a percent of county population. This is because retired persons comprise a larger fraction of the populations of smaller coastal metropolitan areas where real estate prices and living costs are lower. Percentages of the elderly are also lower in the primarily rural counties in north Florida and in south central Florida counties. However,some counties in the interior of the state such as Marion,Lake and Highlands counties have proven to be magnets to retirees. Figure 1 reports the distribution of the elderly as a percentage of county population for Florida counties. Certain patterns emerge. The patterns observed in Figure 1 are underscored in Figure 2,which reports changes in percent elderly from 1990 to 2000. Note that urbanized Pinellas and Palm Beach counties have larger than average elderly percentages,but experienced rapid declines in the percent of elderly during the decade. Observe also that the counties in the western panhandle,that have below average elderly population,experienced rapid growth in retirees during the 1990s. Rapidly growing retirement regions are southwest Florida,central Florida,and the western panhandle. The distribution of Florida's over 65-population by county population size differs from the distribution of total population. Table 2 reports elderly population in 2000 and its growth from 1990. The largest counties have a slightly lower proportion of the state's elderly-to-total population. Counties with 100,000 to 400,000 residents compensate for the lower percent in the large counties with 4% greater elderly than total state population. Examining the change in elderly persons during the past decade,one finds more dramatic inter-county differences. The large counties reported 197.237 additional elderly,45.4% of the total increase. Mid-sized counties,however,grew by nearly the same amount,adding 44.8% of the new elders to their populations. Percentage elderly population growth in under 100,000 population counties was nearly equal to that cohort's percent of Florida population in 2000. Table A2 reports over-65 population for all Florida counties and each county's rank from 1 to 67. Those mid-sized counties (population 100,000400,000) that experienced the largest elderly population growth were located in Florida's coastal areas and in mid-Florida counties surrounding Continued on page 26Figure 1 Figure 2
26Population Growth in Florida Counties During the 1990s; Regional Amenities and Business Climate Continued from page 25 Orlando. The growth of the elderly population within the 22 mid-sized counties in 2000 was itself unequal. The six counties with the largest growth accounted for 49% of the 182,516 new residents over-65,and the six counties with the smallest growth accounted for only 12.5% of the increase. Table 3 summarizes population growth and the growth of the elderly population in Florida's mid-sized counties during the past decade. As measured by the over-65 population,there is no evidence that the state as a whole became "grayer"during the decade. Within the state,the percent of population 65 and older rose in some counties and declined in others. The majority of population growth came about as a result of inmigration,as people from the rest of the U.S. and the rest of the world moved to Florida to gain employment. While Florida may deserve its reputation as a retirement haven,under 65 migrants to the state in the 1990s grew at a faster rate than the over 65 population. This caused the fraction of Florida residents who are 65 years or more of age to decline during the decade of the 1990s. Statewide, decennial population census figures indicate that population increased from 12.94 million to 15.98 million,by about 3 million-23.4%. The over 65 population increased from 2,373,227 to 2,807,597 persons,434,370 thousand-an increase of 18.3%. As these figures make clear,Florida's elderly population grew at a slower pace than total population during the 1990s. As a result,the fraction of elderly persons declined statewide by 4.2% during the decade. Workforce and Employment in Florida Counties: 1990-2000 Florida excelled in new business growth during the 1990s due to its positive business climate,as reflected in its low wages and cost of living. The cost of living in major metropolitan areas of the state remains below living costs in many other metropolitan areas of the country. Lower living costs allow firms to pay lower money wages and at the same time remain competitive in the search for new workers. The advantage of low wages gives business the incentive to create new jobs. The first link in the state's business-climate-employment-growth machine therefore connects the business climate with the creation of new job opportunities by the state's businesses. BusinessEmployment ClimateOpportunities Potential employees are attracted to the new jobs because lower living costs offset lower wages. As workers have flocked from other regions of the U.S. and from around the globe to fill the jobs created by Florida's businesses,their arrival has sustained a rising pool of available labor that firms have utilized to increase employment. The second link in the employment chain has been the simultaneous increase in the supply of labor and its employment in Florida's growing business sector. Workforce Growth BusinessEmployment ClimateOpportunities Employment Growth Florida's economy mirrored the strong national U.S. economy during most of the 1990s. Over the latter part of the decade unemployment rates declined state-wide,and employment became a larger fraction of the total workforce. However,the behavior of employment and workforce participation was not the same throughout the state. In particular,the experience was different in small counties than in the state's more urbanized areas. La bor F or ce in Flor ida Counties In 2000,7.49 million of Florida's 15.98 million residents were in the labor force. To be in the labor force an individual has to be either 1) employed or 2) actively seeking employment. Florida's labor force grew by 1,022,507 persons during the decade,an increase of 13.6%. Labor force growth was different in the small and
27 large population counties of the state,however. Table 4 reports labor force by population size of counties. Table 6 Labor Force Participation in Metro Areas over 1,000,000 Population Metropolitan Statistical AreaLabor Force Participation Rates (%) 19902000 Orlando55.954.6 Tampa St. Petersburg Clearwater49.251.5 Jacksonville52.050.8 Ft. Lauderdale PMSA52.948.0 Miami PMSA52.946.8 West Palm Beach-Boca Raton49.445.8 The results of Table 4 are comparable to those of Table 1. The growth in labor force in counties of 100,000 to 400,000 is even greater as a percentage of the total than their percentage of growth in population. Counties of under 100,000,on the other hand,have labor forces that are growing only one-half as rapidly as the 8% increase in population. Given that only part of this discrepancy is explained by an increase in the percent of over-65 persons (22.9% of total growth vs. 15.5% of the state's population growth in 2000),labor force participation is declining in Florida's non-metropolitan counties. W or kf or ce P ar ticipa tion in Flor ida Counties The labor force participation rate is the percent of the population that is active in the labor force. Forty-seven percent of Florida residents were actively engaged in the labor force during 2000,either as employees or as job seekers. Labor force participation rates varied among Florida counties from a high of 60% in Seminole County to a low of 25% in Union County. Although there is significant variation within population groups,labor force participation tends to be higher in more populous counties. If we classify all counties in Florida as metropolitan or non-metropolitan,all counties that had 120,000 residents or more in 2000 either constituted or were part of a metropolitan statistical area (MSA). Santa Rosa County (population 117,743),Nassau County (population 57,663) and Gadsden County (population 45,087) are also parts of the MSA. (The composition of metropolitan areas is determined by commuting patterns.) This dichotomy suggests that one may view 100,000 population as an approximate dividing line between metropolitan and non-metropolitan counties. Table 5 reports participation rates in 2000 and the change in participation rates from 1990-2000 by population size. Table 5 reveals a steady decline in labor force participation rates as county population declines. Participation rates are higher in metropolitan areas than in non-metro areas. They are significantly lower in rural counties with less than 20,000 population. Chart 1 Age and Labor Force: Counties over 100,0000 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 00.10.20.30.188.8.131.52 Labor force participation ratePercent population 65 and over Series3 Linear (Series3) Table 5 indicates that labor force participation declined in most counties during the 1990s. Moreover,labor force participation has fallen more rapidly in non-metropolitan counties. The average labor force participation rate for counties with less than 20,000 population was under 35% in 2000 and represented a 7.2% drop from 1990. Significant differences in labor force participation exist between Florida's MSAs. Most MSAs experienced declines in participation rates during the 1990s. An exception was the Tampa-St. Petersburg-Clearwater metropolitan area. Table 6 reports labor force participation rates for Florida's MSAs with over 1,000,000 population. Some of the disparity in labor force participation rates in larger counties is related to the percent of population over 65. This population cohort has lower than average workforce participation rates. Chart 1 reveals a negative relationship between the over 65 population and labor force participation in counties of 100,000 persons and over. Continued on page 28 Table 4 Labor Force in 2000 and Change from 1990 Labor Force Total% of State Change Change Year 20001990-2000% of State Total Florida7,490,3031,022,307 400,000 plus5,052,672 67.5593,87058.1 100,000-400,0002,202,99727.0387,39037.9 Under 100,000414,6445.541,2474.0 Under 20,00063,0930.8-1,117
Continued on page 3028The data also dispel any thoughts that low labor force participation in rural counties is due to a concentration of retired persons in those counties. The negative relationship between age and labor force participation observed in counties with over 100,000 persons does not hold in counties of less than 100,000. The percent of elderly in the smaller counties is smaller-generally between 5% and 20%. The percent of elderly varies less across rural counties than it does in urban counties. Table A3 reports workforce,workforce growth,and workforce participation,and the ranking by county,for each of Florida's 67 counties. Emplo yment in Flor ida Counties Employment growth in Florida continues to exceed national employment growth. Payroll employment statewide grew 1,526,703,from 5,352,571 to 6,879,274. The increase represents 28.5% growth over the decade. But employment growth was not spread out evenly over the state. Table 7 summarizes payroll employment by county population for 2000 and growth from 1990. Table 8 Payroll Employment: Percent Change from 1990 % Change in Employment 1990 to 2000 Florida Total27.5% Counties in 1990 w/population of: 250,000 plus27.6% 100,000-250,000 29.0% Under 100,00021.2% Under 20,00014.3% Population Growth in Florida Counties During the 1990s; Regional Amenities and Business Climate Continued from page 27 Table 7 Payroll Employment in 2000 and Change from 1990 Employment 2000 Total Percent of Change Percent of State1990-2000State Florida Total6,823,4941,470,923 Cities w/Population of: 400,000 plus4,895,665 71.61,022,89469.5 100,000-250,0001,629,99123.9395,80126.9 Under 100,000297,8384.452,2283.6 Under 20,00044,4300.73,4160.2Source: US Census of Population 2000 and State of Florida ES 202 Program Table 7 shows that over four-fifths of Florida employment occurs within the 18 counties that have 250,000 or more residents. The table also reports that, from 1990 to 2000,non-metropolitan counties with fewer than 100,000 residents produced only 3.6% of new jobs. New jobs generated as a percent of total jobs was below the share of state employment held by these counties in 1990. The fact that employment in nonmetropolitan counties was only 4.4% of the state total, labor force was 5.4% of the state total,and population was 6% of the state total underscores the relative weakness of labor markets in less populous counties. The ten-year trend indicates that this weakness is becoming more pronounced-population grew by 8% in the smaller counties but employment only by 3.6%,from 1990 to 2000. Table 8 reports the percentage growth in employment from 1990 to 2000 by county population size. Table 8 fails to reveal significant variation in employment growth within each group of counties. Among the large counties in the state,Miami-Dade County experienced the slowest employment growth, 13%,over the decade. Orange County grew fastest, adding jobs at close to a 53% rate. Counties with under 100,000 in 1990 grew somewhat more slowly than the state average. As is the case for population and labor force,smaller counties experienced much slower employment growth than did larger counties. The 13% 10-year growth rate in counties that had fewer than 20,000 residents in 2000 was only one-half of the statewide average. Table A4 reports payroll employment in 2000 by industry division (1-digit standard industrial classification) for every county in Florida. Table 9 ranks each county by percent growth employment in each 1-digit industry division. The five counties having the largest number of industries ranked high by employment growth are Wakulla,St. Lucie, Santa Rosa,Flagler,and Seminole. (See Table 9 on page 29) Shift in the Shar e of County Emplo yment Many of the differences in growth rates in employment by county population size appear to stem from different business climates in the large and small counties of Florida. Business climate,by extension, explains differences in labor force and population growth as well. A county's business climate determines the growth and composition of business activity and employment within the county. An overview of the sources of employment growth in a county can be gained with analysis of the shift in the county's share of total national employment. So-called shift-share analysis first computes the number of employees in a county that exceed or fall short of employment changes at the national level. This total "shift in share"is then divided into growth that stems from across-the-board employment changes in a county vs. the nation,and the part of employment
29 Table 9County Rankings in Each of 10 Industries: Ranked by Percent Growth of Employment in Each Industry Between 1990 and 2000CountyPopulationAgriculture Mining and Primary SecondaryTrans Comm Trade FIRE Personal Bus.Govt. RankConstr.Manuf.Manuf. Utilities Svcs. Svcs. Alachua2820521722183819255433 Baker5365433325299146440 Bay455224437454421592265 Bradford65607592136155503463 Brevard2331382151414538455662 Broward925422748243228412834 Calhoun544205450672071401 Charlotte342357141951246172631 Citrus431341127564144322351 Clay362856192047254131324 Collier4621592622102923415 Columbia4272339814354039328 DeSoto27649164615651586650 Dixie665816222466759495560 Duval1421473721263917343864 Escambia338284652434942294167 Flagler4417188313036415114 Franklin58352343663625853 Gadsden2539462910493160646566 Gilchrist5234264336515458936 Glades51464567656242510243 Gulf636767663866171360173 Hamilton646627565556256666311 Hardee205619554648662348617 Hendry8226610515857224946 Hernando39333352944533243022 Highlands113062359556066124657 Hillsborough541373441295014163138 Holmes625366261124763364230 Indian River2251643212112150552052 Jackson5664326417425965545917 Jefferson4659545715646331576221 Lafayette49121655711472327 Lake164217412725131542716 Lee135534409332649402429 Leon2911503640274330282949 Levy38142450671436302125 Liberty671033566345820216723 Madison484031285417333743547 Manatee73510201138195354344 Marion173730162393016441032 Martin212743314739375218556 Miami Dade250605145325154514554 Monroe4748394953463624563337 Nassau3016112462372726171959 Okaloosa3723560588221064761 Okeechobee264455526022858631413 Orange345443044202322191135 Osceola32193613561618452612 Palm Beach15440749402925201841 Pasco1861295335594639313726 Pinellas1526531139215727115755 Polk652584728283434375239 Putnam353284334584032465142 Santa Rosa4024142516101111272718 Sarasota1949612613534848612528 Seminole241512611831161226129 St. Johns3118514825545235333958 St. Lucie10291318434128131519 Sumter50572115363541843605 Suwannee414763263575546384820 Taylor57965453060656265506 Union591486367356467675345 Volusia1236593836235343533648 Wakulla603824242341984 Walton5543958145022351610 Washington61632561197647442
30 Population Growth in Florida Counties During the 1990s; Regional Amenities and Business Climate Continued from page 28 growth that is due to differences in industry structure in the county and the nation. We will refer to the across the board growth as the "competitive shift". The competitive shift reflects the fact that Florida has been more successful than the nation in creating new jobs in every industry. We will refer to the shift in share resulting from differing industry structures in Florida and the U.S.-growth based on concentration in high-growth industries in Florida-as the "industry mix-shift". The industry-mix shift reflects the fact that Florida's employment structure is weighted towards the rapidly growing service and financial sectors and away from more slow-growing basic industries,such as mining and primary manufacturing. A brief description of the equations used to calculate the shift-share amounts is provided in an appendix to this article. CEDR's analysis of county shift in share compares non-farm payroll employment in Florida with the U.S. Of Florida's 1,414,754 non-farm payroll employment increase,889,989 can be explained by the 17% change in U.S. non-farm payroll employment. Florida,however, grew faster than the U.S.,adding 524,765 additional workers. The additional employment was split evenly between greater across-the-board employment gains generated by the state's overall business climate (234,729) and by the state's industrial structure being concentrated in fast growing industries (290,035 new employees). Once again,smaller population counties experienced different growth patterns than did the larger population counties. Table 10 reports employment changes for counties with over 100,000 population,most of which are in MSAs,and for counties with less than 100,000 population,most of which are outside of MSAs. Table 10 reports that the positive shift in share of employment in populous counties,as for the state,is evenly split between all-industry growth in excess of the U.S. average-the competitive shift-and employment growth resulting from a high proportion of high-growth industries-the industry-mix shift. The total shift in share is much smaller in non-metropolitan counties. For counties with fewer than 20,000 residents,the total shift is actually negative and appears to result from competitive disadvantages faced by businesses in the rural counties. Chart 2 shows the correlation of employment growth in major industry divisions in the U.S. and in Florida during the 1990s. The chart trend line,which estimates the quantitative nature of the relationship between Florida and U.S. employment,supports conclusions from the shift-share approach. The trend line intercept is positive,indicating that overall,Florida employment outgrew the nation. The individual industry points show that,in services,Florida outgrew the nation 51% to 45%. Chart 2 Employment By Industry Division:1990-2000-200000 0 200000 400000 600000 800000 1000000 1200000 -200000002000000400000060000008000000100000001200000014000000 U.S. Employment GrowthFlorida Employment GrowthServices Manufacturing Wholesale and Retail Trade Government FIRE Mining and Construction Transport, Utilities, Communications Chart 3 Percent Primary Activities Employment and Population of Florida's Counties: 20000 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0500,0001,000,0001,500,0002,000,0002,500,000 County Population 2000Percent Primary Activity Employment Table 10 Shift-Share Analysis of Florida Counties Employment Growth TotalAttributed to Total ShiftCompetitiv e Industry Mix County Population GrowthU.S. Growth in Share Shift Shift Over 100,000 1,367,524853,434515,100233,273281,827 Under 100,00047,21337,5699,6441,4358,209 Under 20,0004,1996,660-2,461-2,431-30 Analysis of the employment mix of 1-digit SIC industry divisions by population size of Florida counties reveals that the employment base in rural counties is heavily weighted towards mining and construction,primary manufacturing,agriculture,and public enterprises. These industries are often referred to as "primary activities." Chart 3 displays the percentage of primary employment in a scatter diagram. Chart 3 demonstrates that the proportion of employment in primary industries declines as the population increases. Counties in Florida with more than 500,000 residents do not employ over 10% of their workforces in primary production,but the proportion rises to over 60% in some of the state's smallest counties.
31 The concentration of primary industry employment explains much of the difference in the industry-mix shift by county population size. While many primary industry jobs pay good wages,primary industry employment grows less rapidly than the national average. Slow primary industry employment growth is rooted in several factors. One factor is the tendency to move some types of primary jobs to low wage countries. A second factor is the rapid automation in primary industries. Automation replaces workers with new machines and production processes. A third factor is a shift in consumer spending,away from primary goods and from products produced with primary products as inputs,toward services and finished products that have smaller primary product components. The Future of Employment in Florida Counties Florida demographic trends suggest that the state's urban counties can expect continued population growth in the coming decade. Counties that are "built-out"will experience rising real estate prices. Higher real estate costs will generate in-fill development in developed counties,but total population growth will nevertheless be small. Smaller population increases in developed counties does not mean an end to employment growth. Developed counties will attract employees from surrounding counties. Moreover,retired persons who move to surrounding counties where cost of living is lower will be replaced by working age residents. An example of population replacement during the 1990s may have been the relocation of older Pinellas County residents,as the county's over 65 population fell by 14,205. Urban counties that retain developable land will experience more extensive population growth than will built-out counties. County leadership in rapidly growing urban counties must carefully manage population and industry growth. Counties that have less than 100,000 residents are experiencing economic difficulties. Their economies are based upon slow-growing industries. And over time, population increases have grown faster than workforce. Workforce in turn has grown faster than employment. Counties with fewer than 20,000 residents actually showed declines in their labor forces and nearly zero employment growth during the decade of the 90'sa period of strong economic performance in the nation. Florida counties that have smaller populations and are not adjacent to population centers,but that nevertheless wish to increase employment opportunities for county residents must overcome the hurdles imposed by a dearth of fast-growing industries. Several strategies have been proposed. 1. Continue to compete for primary goods producing firms. This is difficult,because the paucity of new jobs means fierce competition at home and abroad. 2. Identify service activities that require competitive wages and that do not require the extensive infrastructure,suppliers and customers found only in urban areas. Small call centers may locate in small cities,given that they can access required employees. Counties with interstate highway access have attracted outlet malls that cater to auto-tourist traffic. Many small counties seek to develop natural resources for tourism. Given the important prerequisite of natural amenities (watercourses,state parks,forests,and beaches) this strategy has the advantage of targeting a rapidly growing industry nationally and within the state. 3. Compete for regional transportation,power,and public sector infrastructure. Examples are airports, power-generating facilities,and state enterprises that can operate within rural infrastructure. Some rural counties have,for example attracted correctional facilities; others are home to state highway maintenance garages. For detailed information for each of Florida's 67 counties,see tables A1 through A4 on CEDR's web site at http://cedr.coba.usf.edu. See the text box on page 1. Appendix:Calculating the Total Shift, the Competitive Shift,and the Industry Mix Shift in shift-share analysis. The formula for the total shift by county is total the difference between 2000 employment in county A and the product of 1) [U.S. employment in 2000/U.S. employment in 1990] and 2) 1990 employment in county A. For example,in Alachua County: This is 7,192 workers over those predicted by national growth. The formula for the competitive shift in employment is the sum over 9 industry divisions of the difference between employment in that industry and growth predicted by the national growth rate in that industry. i is industry i,one of 9 major industry divisions E00iis Employment in industry i in 2000 E90iis Employment in industry i in 1990 Eus00iis Employment in the U.S. in industry i in 2000 Eus90iis Employment in the U.S. in industry i 1990 For example in Alachua County if "industry i" =2 (2 is the SIC for manufacturing),Alachua manufacturing employment was 5,549 in 2000 and 4,782 in 1990. The term for Alachua County's competitive shift in manufacturing is 919: 919 = Continued on page 32
NON-PROFIT ORG. U.S.POSTAGE PAID Tampa,FL Permit No.257 College of Business Administration Center for Economic Development Research 1101 Channelside Drive 2nd Floor North Tampa, FL 33602 University of South Florida Performing this calculation for all 9 SIC industries and summing yields the total competitive shift for Alachua County,which is -4,392. This indicates that Alachua County grew on average more slowly than the U.S. on an industry by industry basis. The industry-mix shift is calculated as the difference between the total shift and the competitive shift. For Alachua County the industry-mix shift is: Industry-mix shift = 7,192 (-4,392) = 11,584 These numbers indicate that Alachua was heavily weighted toward industries that on average grew more rapidly than average during the decade of the 1990s. Indeed Alachua County is the home of Florida s largest university. Because of this,the county is heavily weighted towards services,the most-rapidly growing national industry.
Table A1 Total County Population County Percent County Population Rank Change from Rank Population Rank Year 2000 Year 1990 Change Florida 15,982,378 3,044,452 19.0% Alachua 217,955 20 36,359 23 16.7% 44 Baker 22,259 52 3,773 52 17.0% 43 Bay 148,217 25 21,223 34 14.3% 52 Bradford 26,088 50 3,573 53 13.7% 55 Brevard 476,230 9 77,252 11 16.2% 48 Broward 1,623,018 2 367,530 1 22.6% 28 Calhoun 13,017 62 2,006 62 15.4% 50 Charlotte 141,627 26 30,652 27 21.6% 30 Citrus 118,085 31 24,570 31 20.8% 31 Clay 140,814 27 34,828 25 24.7% 18 Collier 251,377 18 99,278 8 39.5% 4 Columbia 56,513 38 13,900 37 24.6% 19 DeSoto 32,209 48 8,344 43 25.9% 14 Dixie 13,827 58 3,242 54 23.4% 25 Duval 778,879 7 105,908 6 13.6% 56 Escambia 294,410 15 31,612 26 10.7% 63 Flagler 49,832 40 21,131 35 42.4% 2 Franklin 11,057 64 2,090 61 18.9% 39 Gadsden 45,087 42 3,982 51 8.8% 64 Gilchrist 14,437 57 4,770 49 33.0% 7 Glades 10,576 65 2,985 56 28.2% 11 Gulf 13,332 60 1,828 63 13.7% 54 Hamilton 13,327 61 2,397 58 18.0% 41 Hardee 26,938 49 7,439 45 27.6% 13 Hendry 36,210 44 10,437 40 28.8% 10 Hernando 130,802 28 29,687 28 22.7% 27 Highlands 87,366 34 18,934 36 21.7% 29 Hillsborough 998,948 4 164,894 5 16.5% 45 Holmes 18,564 56 2,786 57 15.0% 51 Indian River 112,947 33 22,739 32 20.1% 35 Jackson 46,755 41 5,380 47 11.5% 61 Jefferson 12,902 63 1,606 64 12.4% 57 Lafayette 7,022 66 1,444 67 20.6% 33 Lake 210,528 21 58,424 17 27.8% 12 Lee 440,888 11 105,775 7 24.0% 21 Leon 239,452 19 46,959 20 19.6% 37 Levy 34,450 47 8,527 42 24.8% 16 Liberty 7,021 67 1,452 66 20.7% 32 Madison 18,733 55 2,164 59 11.6% 60 Manatee 264,002 16 52,295 18 19.8% 36 Marion 258,916 17 64,083 15 24.8% 17 Martin 126,731 29 25,831 30 20.4% 34 Miami Dade 2,253,362 1 316,268 2 14.0% 53 Monroe 79,589 35 1,565 65 2.0% 67 Nassau 57,663 37 13,722 38 23.8% 22 Okaloosa 170,498 24 26,722 29 15.7% 49 Okeechobee 35,910 45 6,283 46 17.5% 42 Orange 896,344 6 218,853 4 24.4% 20 Osceola 172,493 23 64,765 14 37.5% 6 Palm Beach 1,131,184 3 267,666 3 23.7% 24 Pasco 344,765 13 63,634 16 18.5% 40 Pinellas 921,482 5 69,823 13 7.6% 66 Polk 483,924 8 78,542 9 16.2% 47 Putnam 70,423 36 5,353 48 7.6% 65 Saint Johns 123,135 30 36,135 24 29.3% 9 Saint Lucie 192,695 22 48,181 19 25.0% 15 Santa Rosa 117,743 32 77,667 10 66.0% 1 Sarasota 325,957 14 39,306 22 12.1% 58 Seminole 365,196 12 42,524 21 11.6% 59 Sumter 53,345 39 21,768 33 40.8% 3 Suwannee 34,844 46 8,064 44 23.1% 26 Taylor 19,256 54 2,145 60 11.1% 62 Union 13,442 59 3,190 55 23.7% 23 Volusia 443,343 10 72,631 12 16.4% 46 Wakulla 22,863 51 8,661 41 37.9% 5 Walton 40,601 43 12,841 39 31.6% 8 Washington 20,973 53 4,054 50 19.3% 38 Source: Prepared by the Center for Economic Development Research 02.02.02 Florida Population: 2000, Population Growth, Percent Change And Ranking of Florida Counties from 1 to 67 CENTER FOR ECONOMIC DEVELOPMENT RESEARCH
Table A2 Total 2000 Rank Total 2000 Rank Total Change Rank Percent Rank Population +65 Population 1990-2000 Change Florida 15,982,378 2,807,597 434,370 18.30% Alachua 217,955 20 20,918 27 3,999 33 23.64% 41 Baker 22,259 52 2,050 58 595 51 40.89% 13 Bay 148,217 25 19,817 30 4,501 31 29.39% 30 Bradford 26,088 50 3,376 50 632 50 23.03% 42 Brevard 476,230 9 94,681 9 27,682 4 41.32% 12 Broward 1,623,018 2 261,109 3 1,407 39 0.54% 65 Calhoun 13,017 62 1,816 62 224 62 14.07% 53 Charlotte 141,627 26 49,167 18 11,416 14 30.24% 26 Citrus 118,085 31 38,010 23 8,524 18 28.91% 32 Clay 140,814 27 13,772 35 4,725 29 52.23% 8 Collier 251,377 18 61,513 16 26,573 5 76.05% 3 Columbia 56,513 38 7,909 39 2,226 35 39.17% 17 DeSoto 32,209 48 6,113 44 1,486 38 32.12% 22 Dixie 13,827 58 2,369 55 828 45 53.73% 6 Duval 778,879 7 81,753 13 9,729 15 13.51% 54 Escambia 294,410 15 39,169 21 7,743 21 24.64% 39 Flagler 49,832 40 14,269 34 6,761 24 90.05% 2 Franklin 11,057 64 1,741 63 148 64 9.29% 62 Gadsden 45,087 42 5,487 47 300 57 5.78% 63 Gilchrist 14,437 57 1,968 60 647 48 48.98% 9 Glades 10,576 65 1,990 59 516 52 35.01% 19 Gulf 13,332 60 2,158 57 399 55 22.68% 43 Hamilton 13,327 61 1,490 64 251 60 20.26% 45 Hardee 26,938 49 3,750 48 785 46 26.48% 34 Hendry 36,210 44 3,641 49 830 44 29.53% 29 Hernando 130,802 28 40,353 20 8,871 17 28.18% 33 Highlands 87,366 34 28,833 26 5,842 26 25.41% 36 Hillsborough 998,948 4 119,673 5 17,364 8 16.97% 49 Holmes 18,564 56 2,749 52 263 58 10.58% 59 Indian River 112,947 33 32,972 25 8,248 19 33.36% 21 Jackson 46,755 41 6,804 41 644 49 10.45% 60 Jefferson 12,902 63 1,865 61 194 63 11.61% 55 Lafayette 7,022 66 869 66 252 59 40.84% 14 Lake 210,528 21 55,603 17 13,434 9 31.86% 23 Lee 440,888 11 112,111 6 28,702 3 34.41% 20 Leon 239,452 19 19,891 29 4,073 32 25.75% 35 Levy 34,450 47 6,172 43 1,234 42 24.99% 37 Liberty 7,021 67 716 67 98 65 15.86% 50 Madison 18,733 55 2,726 53 412 54 17.80% 47 Manatee 264,002 16 65,647 14 6,185 25 10.40% 61 Marion 258,916 17 63,488 15 19,919 6 45.72% 10 Martin 126,731 29 35,786 24 8,038 20 28.97% 31 Miami Dade 2,253,362 1 300,552 1 30,839 2 11.43% 56 Monroe 79,589 35 11,648 38 -771 66 -6.21% 66 Nassau 57,663 37 7,267 40 2,756 34 61.10% 5 Okaloosa 170,498 24 20,656 28 7,211 23 53.63% 7 Okeechobee 35,910 45 5,864 46 1,082 43 22.63% 44 Orange 896,344 6 89,959 11 17,823 7 24.71% 38 Osceola 172,493 23 19,709 31 4,683 30 31.17% 25 Palm Beach 1,131,184 3 262,076 2 51,496 1 24.45% 40 Pasco 344,765 13 92,403 10 2,180 36 2.42% 64 Pinellas 921,482 5 207,563 4 -14,205 67 -6.41% 67 Polk 483,924 8 88,738 12 13,356 10 17.72% 48 Putnam 70,423 36 13,009 36 1,279 41 10.90% 57 Santa Rosa 117,743 32 12,972 37 5,091 28 64.60% 4 Sarasota 325,957 14 102,583 7 12,864 12 14.34% 52 Seminole 365,196 12 38,853 22 8,912 16 29.77% 28 St. Johns 123,135 30 19,579 32 5,642 27 40.48% 16 St. Lucie 192,695 22 43,753 19 11,955 13 37.60% 18 Sumter 53,345 39 14,618 33 7,522 22 106.00% 1 Suwannee 34,844 46 5,905 45 1,368 40 30.15% 27 Taylor 19,256 54 2,708 54 427 53 18.72% 46 Union 13,442 59 1,003 65 240 61 31.45% 24 Volusia 443,343 10 97,811 8 13,044 11 15.39% 51 Wakulla 22,863 51 2,350 56 702 47 42.60% 11 Walton 40,601 43 6,431 42 1,858 37 40.63% 15 Washington 20,973 53 3,293 51 316 56 10.61% 58 SOURCE: Prepared by the Center for Economic Development Research 02.02.02 Florida's Population Over 65 years of Age: Population and Rankings CENTER FOR ECONOMIC DEVELOPMENT RESEARCH
Table A3 Year 2000 Rank Change from Rank Year 2000 Pct Rank Change from Rank Year 1990 Year 1990 Pct Florida 7,490,307 1,022,307 46.87% -3.13% Miami Dade 1,053,924 1 29,947 10 46.77% 17 -6.09% 46 Broward 779,445 2 115,799 1 48.02% 15 -4.84% 37 Hillsborough 564,858 3 112,088 2 56.55% 2 2.26% 6 Palm Beach 517,893 4 87,305 4 45.78% 20 -4.08% 30 Orange 496,692 5 105,964 3 55.41% 3 -2.26% 22 Pinellas 478,889 6 59,160 5 51.97% 7 2.69% 4 Duval 392,748 7 42,030 7 50.42% 10 -1.69% 20 Seminole 220,706 8 52,191 6 60.43% 1 1.83% 10 Brevard 207,695 9 3,560 36 43.61% 25 -7.55% 52 Polk 204,355 10 4,114 31 42.23% 28 -7.17% 50 Lee 181,961 11 27,657 12 41.27% 31 -4.77% 36 Volusia 174,212 12 6,246 27 39.30% 41 -6.01% 45 Sarasota 154,026 13 30,563 9 47.25% 16 2.81% 2 Pasco 140,096 14 31,234 8 40.64% 36 1.91% 7 Leon 131,384 15 18,710 18 54.87% 5 -3.67% 29 Manatee 123,345 16 28,340 11 46.72% 18 1.85% 9 Escambia 120,795 17 -40 58 41.03% 32 -4.95% 38 Alachua 105,370 18 11,813 23 48.34% 14 -3.17% 26 Collier 100,050 19 27,106 15 39.80% 40 -8.16% 57 Marion 99,349 20 16,214 20 38.37% 45 -4.30% 32 Lake 93,768 21 27,486 14 44.54% 22 0.96% 12 Osceola 86,221 22 27,568 13 49.99% 12 -4.46% 34 Okaloosa 82,486 23 16,795 19 48.38% 13 2.69% 3 St. Lucie 78,757 24 6,949 26 40.87% 33 -6.95% 48 Clay 73,268 25 19,493 16 52.03% 6 1.29% 11 Bay 64,938 26 3,983 34 43.81% 24 -4.19% 31 St. Johns 63,233 27 19,270 17 51.35% 8 -1.09% 16 Santa Rosa 53,318 28 14,406 21 45.28% 21 -2.40% 23 Charlotte 50,634 29 9,193 24 35.75% 53 -1.59% 19 Martin 49,480 30 4,204 30 39.04% 42 -5.83% 43 Hernando 49,074 31 13,040 22 37.52% 46 1.88% 8 Indian River 45,001 32 4,074 33 39.84% 39 -5.53% 41 Monroe 44,074 33 3,287 38 55.38% 4 3.10% 1 Citrus 37,698 34 4,798 29 31.92% 59 -3.26% 27 Nassau 29,599 35 7,007 25 51.33% 9 -0.08% 15 Putnam 27,189 36 1,073 45 38.61% 43 -1.53% 17 Highlands 25,962 37 229 54 29.72% 62 -7.89% 54 Columbia 24,239 38 4,100 32 42.89% 26 -4.37% 33 Gadsden 20,014 39 2,024 43 44.39% 23 0.62% 13 Flagler 17,584 40 5,858 28 35.29% 55 -5.57% 42 Jackson 17,439 41 -1,240 67 37.30% 48 -7.85% 53 Walton 16,404 42 3,520 37 40.40% 37 -6.01% 44 Okeechobee 15,362 43 1,703 44 42.78% 27 -3.32% 28 Hendry 15,125 44 2,280 41 41.77% 29 -8.07% 55 Sumter 14,343 45 2,146 42 26.89% 64 -11.74% 65 Levy 13,247 46 2,633 39 38.45% 44 -2.49% 24 Suwannee 13,000 47 1,035 46 37.31% 47 -7.37% 51 Wakulla 11,494 48 3,679 35 50.27% 11 -4.75% 35 Washington 9,708 49 2,326 40 46.29% 19 2.66% 5 Hardee 9,616 50 431 52 35.70% 54 -11.41% 62 Bradford 9,348 51 150 55 35.83% 51 -5.02% 39 Baker 8,989 52 978 47 40.38% 38 -2.95% 25 DeSoto 8,815 53 -855 64 27.37% 63 -13.15% 66 Madison 7,623 54 618 49 40.69% 35 -1.58% 18 Taylor 7,024 55 -1,210 66 36.48% 49 -11.64% 63 Holmes 6,640 56 -81 59 35.77% 52 -6.83% 47 Gulf 4,861 57 -415 62 36.46% 50 -9.40% 58 Calhoun 4,574 58 113 56 35.14% 57 -5.38% 40 Jefferson 4,548 59 -715 63 35.25% 56 -11.34% 61 Franklin 4,510 60 652 48 40.79% 34 -2.24% 21 Gilchrist 4,378 61 377 53 30.32% 61 -11.06% 60 Glades 3,713 62 506 51 35.11% 58 -7.14% 49 Dixie 3,480 63 -290 60 25.17% 65 -10.45% 59 Union 3,314 64 -414 61 24.65% 67 -11.71% 64 Hamilton 3,310 65 -861 65 24.84% 66 -13.32% 67 Lafayette 2,912 66 597 50 41.47% 30 -0.03% 14 Liberty 2,206 67 6 57 31.42% 60 -8.08% 56 Source: Prepared by the Center for Economic Development Research 02.02.02 Florida Labor Force: 2000 and Growth from 1990 Labor Force Labor Force Labor Force Participation Rate CENTER FOR ECONOMIC DEVELOPMENT RESEARCH
Table A4 Mining and ManufacturingManufacturingTrans/ Comm/ Trade FIRE Personal Business Government 2000 Construction Primary Goods Finished Goods Utilities Services Services Population 2000 SIC 1 2000 SIC 2 2000 SIC 3 2000 SIC 4 2000 SIC 5 2000 SIC 6 2000 SIC 7 2000 SIC 8 2000 SIC 9 Florida 15,982,378 396281 198932 283356 395521 1705927 425172 1185955 1588774 443458 Alachua 217,955 4444 1569 3980 3139 23826 5444 11434 55284 7142 Baker 22,259 300 177 104 184 1189 157 167 2314 755 Bay 148,217 4522 1543 1722 2686 18936 3300 7301 14826 5255 Bradford 26,088 431 249 574 215 1217 132 288 1511 1755 Brevard 476,230 10863 2473 22619 6135 44512 6084 21661 51127 13832 Broward 1,623,018 40642 13760 25001 35611 183752 48784 106472 146469 37670 Calhoun 13,017 250 157 55 69 686 91 104 807 1794 Charlotte 141,627 2706 574 538 1149 10871 1395 5545 10457 2803 Citrus 118,085 2394 331 1161 2188 7164 1258 3350 7850 1586 Clay 140,814 2589 583 1161 1207 12342 939 9125 8867 1964 Collier 251,377 12423 1216 1533 2998 26113 6103 18215 21537 4832 Columbia 56,513 1735 885 973 770 5215 435 1244 5114 1969 DeSoto 32,209 356 176 14 158 1461 178 345 2325 1018 Dixie 13,827 154 554 15 97 380 35 106 472 650 Duval 778,879 24654 14483 17052 39191 106285 51410 68262 85318 23649 Escambia 294,410 9206 5166 2574 7368 31907 4937 18812 34437 10435 Flagler 49,832 738 290 1169 321 2956 493 2184 2728 718 Franklin 11,057 138 133 16 144 918 206 212 604 414 Gadsden 45,087 833 1165 567 303 2802 216 294 5079 1230 Gilchrist 14,437 52 101 31 44 382 49 128 702 696 Glades 10,576 78 0 0 82 152 9 115 325 217 Gulf 13,332 68 52 8 241 667 145 84 943 952 Hamilton 13,327 102 1109 58 178 375 32 91 657 1066 Hardee 26,938 386 159 39 192 1137 251 273 1675 947 Hendry 36,210 314 1067 61 346 2114 240 417 1971 1142 Hernando 130,802 2540 529 813 1132 10045 1241 3017 7544 2687 Highlands 87,366 1039 716 518 836 5317 679 2425 6190 1694 Hillsborough 998,948 29258 21346 16566 38411 127772 47108 145797 122020 27274 Holmes 18,564 236 241 64 103 630 47 150 1174 683 Indian River 112,947 2608 756 2428 1353 11114 1981 4668 10514 2844 Jackson 46,755 711 260 503 513 3186 344 473 4060 3268 Jefferson 12,902 139 163 27 108 441 128 192 724 588 Lafayette 7,022 70 5 117 45 202 31 34 343 442 Lake 210,528 5568 2421 2088 3041 17004 3693 5578 15921 4392 Lee 440,888 16557 2666 4515 8451 46756 8777 21911 40210 9457 Leon 239,452 5843 1648 1256 4464 28032 5676 16205 44762 32977 Levy 34,450 895 205 204 367 2177 287 429 1692 783 Liberty 7,021 218 236 87 137 26 41 266 571 Madison 18,733 67 1087 13 238 1123 79 534 1564 765 Manatee 264,002 5494 4713 8315 2843 24160 3212 40607 19044 5520 Marion 258,916 5651 3438 8033 3382 22887 3800 7872 19699 5619 Martin 126,731 4700 1034 2039 2191 13245 2099 7005 13796 2627 Miami Dade 2,253,362 37972 36318 32022 96127 254440 64505 141463 240267 64867 Monroe 79,589 2260 237 214 2493 11716 1757 8099 6342 3342 Nassau 57,663 1462 1635 86 590 3970 469 2787 3030 1557 Okaloosa 170,498 3970 683 2366 3386 20144 4154 14248 14952 8144 Okeechobee 35,910 507 145 35 569 2539 238 452 2632 867 Orange 896,344 28831 11795 25320 39861 136148 33227 187087 115047 25191 Osceola 172,493 3158 524 1188 1269 17152 3103 9372 10787 3631 Palm Beach 1,131,184 30579 10651 20028 20317 117629 33594 86342 117358 27196 Pasco 344,765 6047 1469 1745 2660 21518 3251 7632 22203 5125 Pinellas 921,482 20935 14735 33159 19250 102741 31312 92085 96889 21724 Polk 483,924 13011 11779 7894 10337 51227 8789 19700 39457 12584 Putnam 70,423 1580 2721 432 554 4223 565 1137 4566 2155 Saint Johns 123,135 2114 628 3269 944 11669 1532 6617 9449 2677 Saint Lucie 192,695 3365 1158 1662 3018 12896 2459 3977 13251 4443 Santa Rosa 117,743 2553 708 630 1288 6079 829 2831 7243 2297 Sarasota 325,957 10264 2321 6674 4349 36886 8984 25979 35928 6462 Seminole 365,196 13931 2600 8515 7196 42892 7380 20302 28880 6080 Sumter 53,345 549 300 587 350 2047 248 546 1447 1990 Suwannee 34,844 385 1964 55 397 2283 296 364 2100 850 Taylor 19,256 573 1374 572 124 1174 147 285 1316 801 Union 13,442 84 196 355 200 23 26 543 1970 Volusia 443,343 8404 3479 8033 6464 39270 5872 18877 37533 10055 Wakulla 22,863 290 575 8 216 930 200 263 1050 662 Walton 40,601 664 511 299 483 3365 617 2093 1989 1329 Washington 20,973 829 998 49 347 1208 99 230 1600 785 Source: Prepared by the Center for Economic Development Research 02.02.02 Employment by Industry Division: Florida Counties in 2000 CENTER FOR ECONOMIC DEVELOPMENT RESEARCH