OCS Study 

MMS 2002-025 

Socioeconomic Baseline and 

Projections of the Impact of an 

OCS Onshore Base for Selected 

Florida Panhandle Communities 

Volume II : Technical Description of the MMS 

Florida Panhandle Model 

US. Department of the Interior 

Minerals 7Vlanagement Service 

Gulf of Mexico OCS Region 
OCS Study 

MMS 2002-025 

Socioeconomic Baseline and 

Projections of the Impact of an 

OCS Onshore Base for Selected 

Florida Panhandle Communities 

Volume II : Technical Description of the MMS 

Florida Panhandle Model 

Authors 

Ronald T. Luke 

Eric S . Schubert 

Greg Olsson 

Research and Planning Consultants, Inc. 

Austin, Texas 

and 

F . Larry Leistritz 

North Dakota State University 

Prepared under MMS Contract 

1435-01-96-CT-30821 

by 

Research and Planning Consultants, Inc. 

Austin, Texas 

Published by 

U.S. Department of the Interior 

Minerals Management Service New Orleans 

Gulf of Mexico OCS Region May 2002 
DISCLAIMER 

This model and user's guide were prepared under contract between the Minerals Management 

Service (MMS) and Research and Planning Consultants, Inc. This report has been technically 

reviewed by the MMS, and it has been approved for publication. Approval does not signify that 

the contents necessarily reflect the views and policies of the MMS, nor does mention of trade 

names or commercial products constitute endorsement or recommendation for use. It is, 

however, exempt from review and compliance with the MMS editorial standards. 

REPORT AVAILABILITY 

Extra copies of this model and user guide may be obtained from the Public Information Unit 

(Mail Stop 5034) at the following address : 

U.S. Department of the Interior 

Minerals Management Service 

Gulf of Mexico OCS Region 

Public Information Unit (MS 5034) 

1201 Elmwood Park Boulevard 

New Orleans, Louisiana 70123-2394 

Telephone: (504) 736-2519 or 

1-800-200-GULF 

CITATION 

Suggested citation : 

Luke, R. T., E.S . Schubert, G. Olsson, and F.L. Leistritz. 2002 . Socioeconomic Baseline and 

Projection Model for Selected Florida Panhandle Communities,Volume II : Technical 

Description of the MMS Florida Panhandle Model. U.S . Dept. of the Interior, Minerals 

Management Service, Gulf of Mexico OCS Region, New Orleans, LA. OCS Study MMS 

2002-025 . 87 pp. 

iii 
TABLE OF CONTENTS 

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii 

Overview of Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

Demographic Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 

Economic Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

Labor Market (Economic-Demographic Interaction) Modul . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 

Baseline Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 

Public Services and Fiscal Impact Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 

v 
LIST OF TABLES 

Table Page 

1 .1 Estimated Distribution of the Population of Bay County by Age, Sex, 

and Race in 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3 

1 .2 Estimated Distribution of the Population of Escambia County by Age, 

Sex, and Race in 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4 

1 .3 Estimated Distribution of the Population of Okaloosa County by Age, 

Sex, and Race in 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5 

1 .4 Estimated Distribution of the Population of Santa Rosa County by Age, 

Sex, and Race in 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6 

1 .5 Estimated Distribution of the Population of Walton County by Age, 

Sex, and Race in 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 

2 Survival Rates by Age, Sex, and Race in 1995 . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 

3 Adjustments to Survival Rates from 2000 to 2045 . . . . . . . . . . . . . . . . . . . . . . . . 9 

4.1 Five-Year Fertility Rates by Age, Sex, and Race in Bay County . . . . . . . . . . . . 10 

4.2 Five-Year Fertility Rates by Age, Sex, and Race in Escambia 

County . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 

4.3 Five-Year Fertility Rates by Age, Sex, and Race in Okaloosa County . . . . . . . . 11 

4.4 Five-Year Fertility Rates by Age, Sex, and Race in Santa Rosa County . . . . . . 11 

4.5 Five-Year Fertility Rates by Age, Sex, and Race in Walton County . . . . . . . . . . 12 

5 .1 Inmigration and Outmigration Rates of People Over 65 Years Old 

(Bay County) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 

5 .2 Inmigration and Outmigration Rates of People Over 65 Years Old 

(Escambia County) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 

5 .3 Inmigration and Outmigration Rates of People Over 65 Years Old 

(Okaloosa County) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 

5 .4 Inmigration and Ouhnigration Rates of People Over 65 Years Old 

(Santa Rosa County) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

5 .5 Inmigration and Outmigration Rates of People Over 65 Years Old 

(Walton County) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 

6 Labor Participation in Rates by Age, Sex, and Race . . . . . . . . . . . . . . . . . . . . . . 15 

7 . Industrial Sectors Used in MMS Florida Panhandle Model . . . . . . . . . . . . . . . . 17 

8 .1 Impact Multiplier Matrix for the Fort Walton Beach Area . . . . . . . . . . . . . . . . . 18 

8 .2 Impact Multiplier Matrix for the Panama City Area . . . . . . . . . . . . . . . . . . . . . . 20 

8 .3 Impact Multiplier Matrix for the Pensacola Area . . . . . . . . . . . . . . . . . . . . . . . . 22 

8 .4 Industries Associated with Operation and Maintenance of Offshore Oil and 

Gas Platforms Adjusted for Commuting Patterns of Offshore Workers. . . . . . . 24 

9 Unemployment Rates in the Florida Panhandle (1990-1997) . . . . . . . . . . . . . . . 26 

10 Inmigration into the Florida Panhandle for Selected MSAs (1970-1990) . . . . . . 27 

11 .1 Inmigration and Outmigration Rates of Workers and Their Families 

(Bay County) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 

11 .2 Inmigration and Outmigration Rates of Workers and Their Families 

(Escambia County) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 

vii 
Table 

11 .3 Inmigration and Outmigration Rates of Workers and Their Families 

Page 

(Okaloosa County) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 

11 .4 Inmigration and Outmigration Rates of Workers and Their Families 

(Santa Rosa County) . . . . . . . . . . . 

. . . . . . . 

. . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 

11 .5 Inmigration and Outmigration Rates of Workers and Their Families 

(Walton County) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 

12 Comparison of Census Bureau U.S . Population Projections . . . . . . . . . . . . . . . . 34 

13 Population in the Florida Panhandle Baseline Scenario . . . . . . . . . . . . . . . . . . . 35 

14.1 Output and Employment per $1 Million of Output by Sector in 1994 

(Fort Walton Beach Area) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 

14.2 Output and Employment per $1 Million of Output by Sector in 1994 

(Panama City Area) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 

14.3 Output and Employment per $1 Million of Output by Sector in 1994 

(Pensacola Area) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 

15.1 Comparison of IMPLAN and BEA Employment by Sector in 1994 

(Fort Walton Beach Area) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 

15.2 Comparison of IMPLAN and BEA Employment by Sector in 1994 

(Panama City Area) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 

15.3 Comparison of IMPLAN and BEA Employment by Sector in 1994 

(Pensacola Area) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 

16 Adjustment Factor - Jobs per Worker 1995-2045 . . . . . . . . . . . . . . . . . . . . . . . . 43 

17.1 Baseline Employment by Major Non-Farm Industry for the Florida 

Panhandle - (Fort Walton Beach) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 

17.2 Baseline Employment by Major Non-Farm Industry for the Florida 

Panhandle - (Panama City) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 

17.3 Baseline Employment by Major Non-Farm Industry for the Florida 

Panhandle-(Pensacola) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 

18.1 Baseline Output by Major Non-Farm Industry for the Florida Panhandle 

(Fort Walton Beach) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 

18.2 Baseline Output by Major Non-Farm Industry for the Florida Panhandle 

(Panama City) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 

18.3 Baseline Output by Major Non-Farm Industry for the Florida Panhandle 

(Pensacola) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 

19 Allocation of Economic Migration Between Counties in a Metropolitan 

Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 

20 Annual Migration in the Florida Panhandle Baseline Scenario . . . . . . . . . . . . . . 51 

21 .1 Distribution of Expenditures by Tourists in the Fort Walton Beach Area . . . . . . 53 

21 .2 Distribution of Expenditures by Tourists in the Panama City Area . . . . . . . . . . 54 

21 .3 Distribution of Expenditures by Tourists in the Pensacola Area . . . . . . . . . . . . . 55 

22.1 Projected Expenditures by Tourists in the Fort Walton Beach Area . . . . . . . . . . 56 

22.2 Projected Expenditures by Tourists in the Panama City Area . . . . . . . . . . . . . . . 56 

22.3 Projected Expenditures by Tourists in the Pensacola Area . . . . . . . . . . . . . . . . . 57 

23.1 County Government Revenues of Bay County . . . . . . . . . . . . . . . . . . . . . . . . . . 59 

23.2 County Government Revenues of Escambia County . . . . . . . . . . . . . . . . . . . . . . 59 

viii 
Table Page 

23 .3 County Government Revenues of Okaloosa County . . . . . . . . . . . . . . . . . . . . . . 60 

23 .4 County Government Revenues of Santa Rosa County . . . . . . . . . . . . . . . . . . . . 60 

23 .5 County Government Revenues of Walton County . . . . . . . . . . . . . . . . . . . . . . . 61 

23 .6 County Government Expenditures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 

24.1 Combined Municipal Government Revenues of Bay County . . . . . . . . . . . . . . . 62 

24.2 Combined Municipal Government Revenues of Escambia County . . . . . . . . . . 62 

24.3 Combined Municipal Government Revenues of Okaloosa County . . . . . . . . . . . 63 

24.4 Combined Municipal Government Revenues of Santa Rosa County . . . . . . . . . 63 

24.5 Combined Municipal Government Revenues of Walton County . . . . . . . . . . . . 64 

24.6 Combined Municipal Government Expenditures . . . . . . . . . . . . . . . . . . . . . . . . 64 

25 Population of Florida Panhandle Living in Unincorporated Areasin 1996 . . . . . 65 

26 Per Capita Expenditures for County Governments and for Municipal 

Governments in the Florida Panhandle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 

27 Millage Rates for County Governments and Selected Special Districts 

in the Florida Panhandle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 

28 Municipal Taxation in the Florida Panhandle in 1997 . . . . . . . . . . . . . . . . . . . . . 67 

29 Average Taxable Value of Residential Property in 1997 . . . . . . . . . . . . . . . . . . 68 

30 Value of Taxable Commercial and Industrial Property in 1997 . . . . . . . . . . . . . 68 

31 Per Capita Revenues (direct taxes, fees, and licenses) . . . . . . . . . . . . . . . . . . . . . 69 

32 School District Expenditures per Full-Time Equivalent (FTE) Student . . . . . . . 69 

33 Millage Rates for School Districts in the Florida Panhandle . . . . . . . . . . . . . . . . 70 

34 School District Revenues per Full-Time Equivalent (FTE) by Source . . . . . . . . 70 

35 Baseline Community Service Multipliers for Counties in the Florida 

Panhandle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 

ix 
Technical Description of the MMS Florida Panhandle Model 

Overview of Methodology 

RPC has developed a set of economic-demographic models in which population, labor force, 

output, final demand, unemployment, and economic and retiree migration are all linked in 

five-year periods . This economic-demographic model projects both baseline and impact-related 

economic activity through the interaction of output, labor force, and migration from 1995 

through 2045 . 

For purposes of this study, the RPC team defined the Florida Panhandle as the five counties of 

Escambia, Santa Rosa, Okaloosa, Walton, and Bay. Within the Panhandle are three metropolitan 

areas, Fort Walton Beach, Panama City, and Pensacola. The three areas of the Florida Panhandle 

are defined as follows : 

Fort Walton Beach: Okaloosa and Walton Counties 

Panama City: Bay County 

Pensacola: Escambia and Santa Rosa Counties 

The definitions of the Panama City and Pensacola areas are identical to their metropolitan 

statistical areas (MSAs). The Fort Walton Beach MSA is defined as Okaloosa County. Since 

Walton County is not part of an MSA, RPC had to decide whether to include Walton County as 

part of the Fort Walton Beach area or the Panama City area. A review of data on commuter 

movements across counties indicated that Walton County's workforce had closer ties to Okaloosa 

County than to Bay County, so RPC chose to include Walton County as part of the Fort Walton 

Beach area for this project. (BEA, 1998) 

Discussion of the model will be divided into the following parts: 

Demographic Module 

Economic Module 

Labor Market Module 

Baseline Projections 

Public Services and Fiscal Impact Module 

Demographic Module 

RPC based its demographic module on a cohort-component model that many demographers use. 

The cohort-component module uses information about the existing age, sex, and racial 

composition of the population in an area to project the population of an area over time. The 

Florida Bureau of Economic and Business Research (BEBR) uses this approach to generate 

detailed population projections at the county level. 
RPC used data supplied by BEBR to construct its demographic module. The starting point for 

projecting the population and labor force before economic migration in each county is the 1995 

Age, Sex, Ratio cohorts (persons of the same sex and race born in the same five-year period) for 

each county (Tables l .1-1 .5) . The model applies five-year survival rates for each cohort (Tables 

2 and 3) . The module then applies age- and race-specific fertility rates to the number of women 

in childbearing ages (10-44) to determine births by current residents (Tables 4.1-4 .5). The model 

determines migration for people over 65 years old by applying outmigration rates to the surviving 

population and immigration rates to the population of the U.S . excluding the county in question 

(Tables 5 .1-5 .5) . The demographic module also estimates the labor supply for each period by 

applying labor participation rates by age, sex, and race cohort that the Bureau of Labor Statistics 

publishes (Table 6) . The labor supply comes from the area's pool of people ages 16 through 64. 

The model then compares the area's available labor supply with the projections of labor demand 

(from the economic model) for the county for the relevant period. The supply of and demand for 

labor is balanced through migration of workers (and associated household members). If local 

labor supply exceeds projected demand (after allowing for unemployment), some of the area's 

working age population out-migrates . On the other hand, if the projected labor demand exceeds 

the estimated local labor supply (as is typical in the Florida Panhandle counties), workers are 

assumed to immigrate from other areas . (For a detailed discussion of the rationale and 

procedures associated with this economic-demographic interface, see Leistritz et al. 1990). 

2 
Table 1 .1 

Estimated Distribution of the Population of Bay County 

by Age, Sex, and Race in 1995 

White White Non-White Non-White 

Aye Cohort Male Female Male Female Total 

0-4 4,139 3,982 946 911 9,978 

5-9 4,331 4,189 961 925 10,406 

10-14 4,068 3,798 895 849 9,610 

15-19 3,902 3,604 797 814 9,117 

20-24 4,006 3,749 749 795 9,299 

25-29 4,175 3,981 717 782 9,655 

30-34 5,031 4,806 784 868 11,489 

35-39 5,105 4,985 703 895 11,688 

40-44 4,558 4,527 574 718 10,377 

45-49 4,228 4,211 409 457 9,305 

50-54 3,299 3,349 222 351 7,221 

55-59 2,876 3,147 202 320 6,545 

60-64 2,816 2,991 186 278 6,271 

65-69 2,664 3,088 166 265 6,183 

70-74 2,116 2,578 142 227 5,063 

75-79 1,370 1,770 110 174 3,424 

80-84 744 1,239 65 114 2,162 

85+ 373 870 40 97 1,380 

Total 59,801 60,864 8,668 9,840 139,173 

Source: Florida Bureau of Economic and Business Research, personal communication, June Nogle, 

1996-1999. 

3 
Table 1 .2 

Estimated Distribution of the Population of Escambia County 

by Age, Sex, and Race in 1995 

Age White White Non-White Non-White 

Cohort Male Female Male Female Total 

0-4 7,061 6,795 3,513 3,382 20,751 

5-9 6,776 6,465 3,407 3,296 19,944 

10-14 6,637 6,373 3,224 3,128 19,362 

15-19 7,507 6,798 2,943 2,871 20,119 

20-24 8,636 7,727 2,760 2,637 21,760 

25-29 9,554 8,372 2,577 2,718 23,221 

30-34 9,200 8,581 2,437 2,749 22,967 

35-39 8,662 8,348 2,200 2,806 22,016 

40-44 7,706 7,972 1,800 2,323 19,801 

45-49 7,305 7,577 1,431 1,877 18,190 

50-54 5,808 6,118 1,063 1,473 14,462 

55-59 4,904 5,263 985 1,331 12,483 

60-64 4,469 4,975 812 1,186 11,442 

65-69 4,240 5,107 730 1,050 11,127 

70-74 3,842 4,965 549 893 10,249 

75-79 2,480 3,489 395 629 6,993 

80-84 1,400 2,466 217 506 4,589 

85+ 758 1,931 156 421 3,266 

Total 106,945 109,322 31,199 35,276 282,742 

Source : Florida Bureau of Economic and Business Research, personal communication , June Nogle, 

1996-1999. 

4 
Table 1 .3 

Estimated Distribution of the Population of Okaloosa County 

by Age, Sex, and Race in 1995 

Age White Non-White Non-White 

Cohort Male White Female Male Female Total 

0-4 5,098 4,905 977 940 11,920 

5-9 5,483 5,273 1,118 1,098 12,972 

10-14 5,015 4,797 1,009 946 11,767 

15-19 4,247 4,109 895 872 10,123 

20-24 5,083 4,786 888 863 11,620 

25-29 5,916 5,232 1,021 984 13,153 

30-34 7,192 6,425 1,080 1,042 15,739 

35-39 6,513 5,934 868 986 14,301 

40-44 5,406 5,160 741 919 12,226 

45-49 4,712 4,496 471 656 10,335 

50-54 3,519 3,516 259 403 7,697 

55-59 3,051 3,385 196 311 6,943 

60-64 3,139 3,141 162 250 6,692 

65-69 2,602 3,019 116 197 5,934 

70-74 2,188 2,650 75 127 5,040 

75-79 1,448 1,671 49 59 3,227 

80-84 616 1,058 23 46 1,743 

85+ 347 861 14 53 1,275 

Total 71,575 70,418 9,962 10,752 162,707 

Source : Florida Bureau of Economic and Business Research, personal communication, June Nogle, 1996-1999. 

5 
Table 1 .4 

Estimated Distribution of the Population of Santa Rosa County 

by Age, Sex, and Race in 1995 

Age White Non-White Non-White 

Cohort White Male Female Male Female Total 

0-4 3,259 3,136 258 249 6,902 

5-9 3,604 3,354 278 259 7,495 

10-14 3,310 3,105 265 229 6,909 

15-19 3,021 2,809 243 250 6,323 

20-24 2,819 2,572 284 242 5,917 

25-29 3,502 3,329 301 242 7,374 

30-34 3,973 3,923 297 283 8,476 

35-39 3,887 3,904 233 243 8,267 

40-44 3,424 3,541 208 263 7,436 

45-49 3,071 3,037 158 188 6,454 

50-54 2,499 2,618 105 129 5,351 

55-59 2,143 2,183 87 132 4,545 

60-64 1,879 1,949 72 109 4,009 

65-69 1,725 1,954 58 76 3,813 

70-74 1,347 1,601 41 76 3,065 

75-79 778 965 22 36 1,801 

80-84 430 705 13 40 1,188 

85+ 213 496 16 41 766 

Total 44,884 45,181 2,939 3,087 96,091 

Source : Florida Bureau of Economic and Business Research, personal communication, June Nogle, 1996-1999. 

6 
Table 1 .5 

Estimated Distribution of the Population of Walton County 

by Age, Sex, and Race in 1995 

White White Non-White Non-White 

Age Cohort Male Female Male Female Total 

0-4 857 825 110 106 1,898 

5-9 972 906 108 95 2,081 

10-14 974 903 106 119 2,102 

15-19 1,002 921 139 106 2,168 

20-24 1,021 852 180 90 2,143 

25-29 841 726 208 79 1,854 

30-34 1,184 1,134 221 95 2,634 

35-39 1,137 1,099 190 100 2,526 

40-44 1,017 977 147 99 2,240 

45-49 1,122 1,052 93 88 2,355 

50-54 964 960 59 72 2,055 

55-59 826 929 57 65 1,877 

60-64 917 965 47 70 1,999 

65-69 831 943 44 51 1,869 

70-74 666 763 33 47 1,509 

75-79 418 531 23 35 1,007 

80-84 258 368 16 20 662 

85+ 131 266 12 27 436 

Total 15,138 15,120 1,793 1,364 33,415 

Source : Florida Bureau of Economic and Business Research, personal communication, June Nogle, 1996-1999. 

7 
Table 2 

Survival Rates by Age, Sex, and Race in 1995 

Surviving 

White White Non-White Non-White 

From To Male Female Male Female 

Birth 1-4 0.99455 0.99608 0 .98940 0.99150 

1-4 5-9 0.99379 0.99566 0 .98821 0.99064 

5-9 10-14 0.99838 0.99910 0 .99782 0.99854 

10-14 15-19 0.99628 0.99824 0 .99403 0.99780 

15-19 20-24 0.99369 0.99740 0 .98724 0.99611 

20-24 25-29 0.99189 0.99713 0 .98217 0.99329 

25-29 30-34 0.98989 0.99629 0 .97849 0.98967 

30-34 35-39 0.98686 0.99508 0 .97367 0.98720 

35-39 40-44 0.98342 0.99375 0 .96731 0.98439 

40-44 45-49 0.97910 0.99047 0 .96076 0.98074 

45-49 50-54 0.96990 0.98501 0 .94510 0.97116 

50-54 55-59 0.95332 0.97661 0 .91853 0.95762 

55-59 60-64 0.93138 0.96419 0 .88689 0.93864 

60-64 65-69 0.90589 0.94788 0 .84374 0.90788 

65-69 70-74 0.86749 0.92337 0 .78712 0.87553 

70-74 75-79 0.80893 0.88199 0.72039 0.82700 

75-79 80-84 0.71888 0.81223 0.64231 0.75010 

80-84 85+ 0.50228 0.57733 0.50871 0.58450 

Source : Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

8 
Table 3 

Adjustments to Survival Rates from 2000 to 2045 

Age Years 2000-2005 Years 2010-2015 Year 2020 and Later 

Cohort Male Female Male Female Male Female 

Birth 1 .00138 1 .00112 1 .00263 1 .00213 1 .00388 1 .00314 

1-4 1 .00020 1 .00013 1 .00037 1 .00025 1 .00055 1 .00037 

5-9 1 .00015 1 .00010 1 .00028 1 .00019 1 .00042 1 .00028 

10-14 1 .00030 1 .00020 1 .00058 1 .00037 1 .00086 1 .00055 

15-19 1 .00055 1 .00025 1 .00104 1 .00047 1 .00154 1 .00070 

20-24 1 .00054 1 .00019 1 .00103 1 .00036 1 .00152 1 .00053 

25-29 1 .00020 1 .00012 1 .00038 1 .00023 1 .00057 1 .00034 

30-34 0.99977 1 .00011 0.99956 1 .00022 0.99934 1 .00032 

35-39 0.99973 1 .00040 0.99949 1 .00077 0.99925 1 .00113 

40-44 1 .00055 1 .00093 1 .00105 1 .00177 1 .00155 1 .00261 

45-49 1 .00267 1 .00173 1 .00511 1 .00330 1 .00754 1 .00488 

50-54 1 .00535 1 .00248 1 .01022 1 .00474 1 .01509 1 .00700 

55-59 1 .00837 1 .00310 1 .01598 1 .00592 1 .02359 1 .00873 

60-64 1 .01226 1 .00289 1 .02340 1 .00552 1 .03454 1 .00815 

65-69 1 .01889 1 .00332 1 .03606 1 .00633 1 .05323 1 .00934 

70-74 1 .02861 1 .00729 1 .05462 1 .01391 1 .08063 1 .02053 

75-79 1 .03940 1 .01699 1 .07522 1 .03243 1 .11104 1 .04788 

80-84 1 .03435 1 .03087 1 .06559 1 .05893 1 .09682 1 .08700 

Source: Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

9 
Table 4.1 

Five-Year Fertility Rates by Age, Sex, and Race in Bay County 

Age Cohort White Fertility Non-White Non-White 

Rates Fertility Rates Adjustment 

Factor 

10-14 0 .00139 0.00435 0.90 

15-19 0 .06808 0.11031 0.90 

20-24 0 .14293 0.18274 0.90 

25-29 0 .10837 0.12495 0.90 

30-34 0 .06064 0.06560 0.90 

35-39 0 .01896 0.02889 0.90 

40-44 0 .00250 0.00335 0.90 

Source: Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

Table 4.2 

Five-Year Fertility Rates by Age, Sex, and Race in Escambia County 

Age Cohort White Fertility Non-White Non-White 

Rates Fertility Rates Adjustment 

Factor 

10-14 0 .00064 0.00584 0 .90 

15-19 0 .05117 0.12746 0.90 

20-24 0 .10979 0.20213 0.90 

25-29 0 .10966 0.14216 0.90 

30-34 0 .06331 0.06598 0 .90 

35-39 0.02471 0.02674 0.90 

40-44 0 .00219 0.00548 0.90 

Source: Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

10 
Table 4.3 

Five-Year Fertility Rates by Age, Sex, and Race in Okaloosa County 

Aye Cohort White Fertility Non-White Non-White 

Rates Fertility Rates Adjustment 

Factor 

10-14 0.00059 0.00342 0.90 

15-19 0.05519 0.10166 0.90 

20-24 0.14211 0.17961 0.90 

25-29 0.11388 0.12416 0.90 

30-34 0.06363 0.06239 0.90 

35-39 0.02089 0.02813 0.90 

40-44 0.00394 0.00392 0.90 

Source: Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

Table 4.4 

Five-Year Fertility Rates by Age, Sex, and Race in Santa Rosa County 

Age Cohort White Fertility Non-White Non-White 

Rates Fertility Rates Adjustment 

Factor 

10-14 0.00063 0.00126 0.95 

15-19 0.05936 0.07132 0.95 

20-24 0.13773 0.18213 0.95 

25-29 0.11536 0.13360 0.95 

30-34 0.05956 0.06614 0.95 

35-39 0.02127 0.01444 0.95 

40-44 0.00298 0 .00775 0.95 

Source : Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

11 
Table 4.5 

Five-Year Fertility Rates by Age, Sex, and Race in Walton County 

Age Cohort White Fertility Non-White Non-White 

Rates Fertility Rates Adjustment 

Factor 

10-14 0.00083 0.00672 0.90 

15-19 0.07503 0.09297 0.90 

20-24 0.14136 0.14050 0.90 

25-29 0.08833 0.16024 0.90 

30-34 0.05305 0.07125 0.90 

35-39 0.02134 0.02073 0.90 

40-44 0.00280 0.00951 0.90 

Source : Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

Table 5.1 

Immigration and Outmigration Rates of People Over 65 Years Old 

(Bay County) 

InmiQration Rates Outmigr ation Rates 

Non- Non- Non- NonAge White White White White White White White White 

Cohort Male Female Male Female Male Female Male Female 

65-69 0.00006 0.00006 0.00002 0.00002 0.10939 0.10939 0 .05882 0.05882 

70-74 0.00004 0.00004 0.00002 0.00002 0.08228 0.08228 0 .04159 0.04159 

75-79 0.00004 0.00004 0.00002 0.00002 0.09269 0.09269 0.04193 0.04193 

80-84 0.00003 0.00003 0.00002 0.00002 0.12186 0.12186 0.06361 0.06361 

Source : Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

12 
Table 5.2 

Immigration and Outmigration Rates of People Over 65 Years Old 

(Escambia County) 

Inmigration Rates OutmiQr ation Rates 

Age White White Non- Non- White White Non- NonCohort Male Female White White Male Female White White 

Male Female Male Female 

65-69 0.00010 0.00010 0.00008 0.00008 0 .06565 0.06565 0 .05348 0.05348 

70-74 0.00007 0.00007 0.00005 0.00005 0 .05841 0.05841 0 .03781 0.03781 

75-79 0.00009 0.00009 0.00005 0.00005 0 .06539 0.06539 0 .03812 0.03812 

80-84 0.00007 0.00007 0.00007 0.00007 0 .09734 0.09734 0 .05782 0.05783 

Source : Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

Table 5.3 

Immigration and Outmigration Rates of People Over 65 Years Old 

(Okaloosa County) 

Immigration Rates Outmigration Rates 

Age White White Non- Non- White White Non- NonCohart Male Female White ' White Male Female White White 

: . . . . . . . . . . . . . . . . . . . . . :. . . . . . . .. . . . . . . . . . . . .i . . . . . .. . . . . . . . . . . . . 

Male 

. .~ . . . . . . . . . . . . . . . . .. . . 

Female 

. . . .. . . . . . . . . . . . .. . . . . .i . . . . . . . . . . . . . . . . . . . . . i. . . . . .. . . . . . . . . . . . . 

Male 

. . :.-- .--~-- . . . . . . . . . . . 

' Female 

.~ . . . . . . . . .--- . . . . . . . . .~ 

65-69 

i.. . .. . .. . . .. . .. . .. . .. 

0 .00006 

i. .. . .. . ... . .. . .. . .. 

~ 0.00006 0.00004 0 .00004 0.07448 

. .r . . .. . . .. . .. . .. . .. . ...i . . . . .. . .. . .. . .. . ... . .i . ... . .. . .. . .. . ... . .. .i .. . .. . . . . . . . . . .. . .. . . 

0.07448 

i- . .. . ... . .. . .. . .. . . 

' 0.06952 

..i .. . ... . . . . .. . .. . .. . . 

' 0 .06952 

.i . . .. . .. . . . . . .. . . . . . ..i 

70-74 0 .00005 0.00005 0.00002 0 .00002 0.05885 0.05885 0.04916 0 .04916 

75-79 0 .00004 0.00004 0.00001 0 .00001 0.06928 0.06928 ' 0.04955 0.04955 

80-84 0 .00003 0.00003 0.00002 0 .00001 0.06260 0.06260 0.07517 0.07517 

Source: Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

13 
Table 5.4 

Inmigration and Outmigration Rates of People Over 65 Years Old 

(Santa Rosa County) 

Immigration Rates Outmigr ation Rates 

Age White White Non- Non- White White Non- NonCohort Male Female White White Male Female White White 

Male Female Male Female 

65-69 0 .00004 0.00004 0.00001 0.00001 0.09131 0.09131 0.06952 0.06952 

70-74 0 .00002 0.00002 0.00001 0.00001 0.08313 0.08313 0.04916 0.04916 

75-79 0 .00002 0.00002 0.00001 0.00001 0.06473 0.06473 0.04955 0.04955 

80-84 0 .00002 0.00002 0.00001 0.00001 0.10000 0.10000 0.07517 0.07517 

Source : Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

Table 5.5 

Immigration and Outmigration Rates of People Over 65 Years Old 

(Walton County) 

Immigration Rates OutmiQr ation Rates 

Age White White Non- Non- White White Non- NonCohort Male Female White White Male Female White White 

Male Female Male Female 

65-69 0 .00002 0.00002 0.00001 0.00001 0.05418 0 .05418 0.08021 0 .08021 

70-74 0.00002 0.00002 0.00000 0.00000 0.04638 0.04638 0.05672 0 .05672 

75-79 0 .00001 0.00001 0.00000 0.00000 0.04315 0.04315 0.05718 0 .05718 

80-84 0 .00001 0.00001 0.00000 0.00000 0.05779 0.05779 0.08674 0.08674 

Source: Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999 . 

14 
Table 6 

Labor Participation Rates by Age, Sex, and Race 

(in percent) 

2000 2005 2010-2045 

Age White White Black Black White White Black Black White White Black Black 

Male Female Male Female Male Female Male Female Male Female Male Female 

16-19 56.7 54.7 39.2 39.5 55 .9 54.4 38.0 39 .8 55.6 54.3 37.9 39.9 

20-24 84.2 73 .6 72.2 65.6 84.0 73.8 71 .3 66.7 83.9 73 .8 70.9 66.9 

25-29 93.9 773 87.7 74.9 93 .8 78.5 87.1 75 .0 93.8 78.8 87.0 75 .1 

30-34 94.5 763 85.9 77.5 943 77.5 84.7 78 .4 94.2 77.7 84.5 78 .6 

35-39 93.3 77.8 83.4 79.7 92.8 79.1 81 .9 80.8 92.7 79.4 81.7 81 .0 

40-44 93.0 803 81 .9 78.3 92.2 81 .8 80.3 79 .0 92.0 82.0 80.1 79.1 

45-49 92.0 81 .5 80.0 75.8 913 84.0 783 76.7 91 .2 84.5 78.1 76.9 

50-54 87.6 74.6 72.6 68.4 87.2 77.4 71 .5 69 .7 87.1 77.9 713 69.9 

55-59 80.3 63 .7 65.9 61 .1 80.4 66.9 65.2 64 .3 80.4 67.5 65.1 65 .0 

60-64 553 40.4 41 .8 34.2 553 42.2 40.5 34.6 55.0 42.4 40.2 34.5 

Source: U.S. Bureau of Labor Statistics website, http://www .bls .gov/emplabl .htm 

Economic Module 

The economic module is representative of the economic relationships in the study area. The 

economic module uses an "input / output" approach to estimate the level of output for each 

economic sector and regional employment by sector.' Like other input-output models, the MMS 

Florida Panhandle module assumed that economic activity in the Florida Panhandle is largely 

dependent on the basic industries of the area. The basic industries are those that earn income for 

the region by generating income from goods and services sold to people or institutions outside 

the region . 

In the case of the Florida Panhandle, tourism and the operation of military bases are two basic 

industries . Spending on tourism comes from people who live outside the Florida Panhandle who 

visit the area to enjoy the area's scenic beauty and to fish . The military spends taxpayer dollars in 

the Florida Panhandle generated from all U.S . residents. . When the demands for any of the basic 

sectors increase, then the demands for the sector's outputs increase . For instance, an increase in 

tourism increases spending on hotels, restaurants, and boat rentals. Other sectors within the 

regional economy provide goods and services that are consumed primarily by residents of the 

1 For an introduction on the topic of input/output models and their use in regional economics, see Hoover 

and Giarratani (1984), Leontief (1953), and Miller and Blair (1985). 

15 
Florida Panhandle (e.g ., retail trade and service sectors) . Because these sectors primarily serve a 

local market, they are often referred to as nonbasic sectors. In addition, some firms and/or 

sectors market their products or services primarily to other business and industrial customers 

within the region, rather than exporting (i .e ., selling to customers outside the region) or selling 

directly to final consumers (e.g ., households) . 

The logic of input-output analysis is that an increase in demand for an area's exports or other 

final products (collectively referred to as sales to final demand) results in successive rounds of 

spending and respending, as the firni/sector than initially receives payment for the sale must in 

turn purchase additional inputs (or intermediate products), hire more labor, etc. Input-output 

(I-O) analysis is essentially a technique for tabulating and describing the linkages between 

sectors (groups of similar economic units) within an economy. I-O models thus provide a means 

of measuring the effect of an initial stimulus (i .e ., additional sales to final demand) to a given 

sector on all other sectors of the regional economy (Miller and Blair 1985, Leistritz and Murdock 

1981). 

The economic model used IMPLAN software and data to generate detailed employment, output, 

and input-output matrices for 1994, the year for which most recent data was available when RPC 

built the economic module.' While IMPLAN can generate modeling information for as many as 

528 industries or sectors of the study area, RPC structured the economic module into 23 

industries that represented the broad industrial groupings the Bureau of Economic Analysis uses 

in its employment projections for metropolitan statistical areas and the subsectors involved in the 

impact portion of the model: the tourism industry, the military, commercial fishing, and Outer 

Continental Shelf (OCS) oil and natural gas activity .' (see Table 7). 

The model generates a variety of scenarios described in the User's Guide. RPC developed a set 

of input / output matrices know as Type II multipliers and the associated Leontief-inverse 

matrices for projecting the impacts that changes in OCS, tourism, or military expenditures would 

have on the economy of the three metropolitan areas of the Florida Panhandle over time. The 

Leontief-inverse matrices measure the direct, indirect, and induced effects of these impacts based 

on the size and type of interrelationships among the 23 sectors of the study areas. Type II 

multipliers are closed with respect to households, already incorporates the economic impacts of 

additional spending and any net migration (see Tables 8.1 - 8.3). 

Z In 1998 MMS concluded a contract with the Minnesota Implan Group, the maker of IMPLAN, to provide 

software and data for future modeling projects that the MMS plans to undertake. 

3 The MMS Florida Panhandle model uses two counties, Okaloosa and Walton, to represent the Fort 

Walton Beach area . Okaloosa is the Fort Walton Beach MSA. Therefore, to project employment and population in 

both counties, RPC used BEA projections of the Pensacola Economic Area (which includes the Pensacola MSA, 

the FWB MSA, and Walton County) and subtracted the projection from the Pensacola MSA. 

16 
commuting patterns of offshore workers in the Gulf of Mexico. Gramling and Brabant (1986) 

estimate that 70 percent of offshore workers live more than 100 miles from where they meet to go 

offshore (i .e ., an onshore support base). If an onshore base were located in Panama City or 

Pensacola, these commuting workers and their families would not spend their incomes in the 

Florida Panhandle. As shown in Table 8 .4, RPC adjusted for these commuting workers by 

lowering projected expenditures in SIC category 1389 "Other Oil and Gas Services" by half when 

compared to Table 2.6 in the Final Report.4 

Table 7 

Industrial Sectors Used in MMS Florida Panhandle Model 

Agriculture (excluding Commercial Fishing) 

Commercial Fishing 

Mining (excluding Maintenance and Repairs of Oil and Gas Wells) 

Maintenance and Repair of Oil and Gas Wells 

Construction 

Non-Durable Manufacturing 

Durable Manufacturing 

Transportation, Communications, & Utilities (excluding Air & Water Transportation) 

Water Transportation 

Air Transportation 

Wholesale Trade 

Retail Trade (excluding Eating & Drinking Places) 

Eating & Drinking Places 

Finance, Insurance, and Real Estate 

Services (excluding the six service categories listed below) 

Hotel and Lodging Places 

Equipment Rental and Leasing Services 

Amusement and Recreation Services, N.E .C . 

Engineering, Architectural Services 

Accounting, Auditing and Bookkeeping Services 

Research, Development & Testing Services 

Government (excluding Federal Government - Military) 

Federal Government - Military 

Sources: Minnesota IMPLAN Group, Inc . 1997. 

IMPLAN professional version 1 .1 Software, User's Guide, Analysis Guide, and Data Guide. Stillwater, 

Mn. and RPC 

4 In its impact study on Destin Dome, Chevron (1997) does not appear to adjust for commuting offshore 

workers or assumes that all offshore workers are local. This assumption might be consistent with an effort to show 

the maximum potential economic impact of the project on Mobile, Alabama. 

17 
Table 8.1 

Impact Multiplier Matrix for the Fort Walton Beach Area 

(direct, indirect, and induced effects) 

Sector 1 25 28 48 57 58 133 433 436 437 447 448 

1 1 .021927 2.27E-03 7.42E-04 3.08E-03 3.05E-03 2.19E-02 2.86E-03 1 .77E-03 1 .94E-03 2.14E-03 2.32E-03 2 .31E-03 

25 1 .65E-04 1 .0007778 5.68E-OS 1 .47E-04 2.66E-04 4.77E-04 1 .25E-04 1 .26E-04 1 .43E-04 1 .58E-04 1 .73E-04 2 .02E-04 

28 2.56E-04 2.94E-04 1 .001253 6.32E-04 4.36E-04 2.67E-03 6.28E-04 2.61E-03 6.11E-04 5 .96E-04 3.88E-04 3 .58E-04 

48 1 .47E-02 1 .66E-02 1 .17E-02 1 .015247 1 .96E-02 1 .51E-02 2.51E-02 8.56E-02 3.74E-02 3.56E-02 3.62E-02 3 .61E-02 

57 9.62E-06 1 .10E-OS 3.76E-02 2.38E-OS 1.000016 1 .00E-04 2.36E-OS 9.79E-OS 2.30E-OS 2.24E-OS 1 .46E-OS 1 .35E-OS 

58 2.92E-02 4.22E-02 1 .09E-02 0.029978 4.68E-02 1 .07337 3 .08E-02 2.49E-02 3.19E-02 0.038936 3.49E-02 3.13E-02 

133 7.94E-03 132E-02 6.54E-03 3.92E-02 2.03E-02 1 .05E-02 1 .051725 1 .40E-02 2.81E-02 1 .40E-02 1 .18E-02 1 .08E-02 

433 3 .93E-02 3.80E-02 2.89E-02 S.OIE-02 6.45E-02 6.03E-02 5 .68E-02 1 .171789 0.131819 1 .25E-01 7.03E-02 6 .56E-02 

436 1 .55E-03 6.05E-03 1 .15E-03 1 .80E-03 2.57E-03 2.42E-03 1 .82E-03 2.45E-03 1 .124539 4.19E-03 1 .86E-03 1 .83E-03 

00 437 3 .22E-03 3.00E-03 1 .53E-03 3.35E-03 8.34E-03 337E-03 4.59E-03 4.35E-03 4.86E-03 1 .035498 9.70E-03 4.36E-03 

447 2.43E-02 3.68E-02 1 .27E-02 3.98E-02 3 .89E-02 4.35E-02 5 .21E-02 2.65E-02 3.98E-02 4.85E-02 1 .044537 2 .65E-02 

448 6.11E-02 6.01E-02 2.16E-02 1 .03E-01 9.71E-02 4.23E-02 4.67E-02 7.16E-02 4.35E-02 5 .40E-02 6.67E-02 1 .07774 

454 2.72E-02 2.56E-02 9.49E-03 2.46E-02 4.38E-02 1 .89E-02 2.09E-02 2.17E-02 1 .89E-02 3.39E-02 3.23E-02 3 .54E-02 

456 8 .42E-02 8 .41E-02 3.33E-02 8.52E-02 133E-01 6.19E-02 0.069686 8.32E-02 0.131221 9.52E-02 1 .08E-O1 1 .08E-01 

463 5.26E-02 4.56E-02 5.00E-02 4.92E-02 7.23E-02 4.06E-02 4.57E-02 6.23E-02 9.72E-02 6.82E-02 1 .03E-01 1 .03E-01 

464 1 .44E-01 1 .56E-01 5.89E-02 0.154351 2.29E-01 1 .32E-01 1 .46E-01 1 .73E-01 0.331808 0.231357 0.278185 2.44E-01 

473 1 .13E-03 1 .86E-03 637E-04 5.90E-03 3 .50E-03 2.33E-03 2.89E-03 233E-03 8.11E-03 9.20E-03 4.87E-03 2.93E-03 

488 3.97E-03 3.67E-03 134E-03 3.47E-03 6.40E-03 2.64E-03 2.89E-03 2.91 E-03 2.50E-03 3.11 E-03 3.84E-03 4.75E-03 

506 5.12E-04 7.01E-04 2.31E-03 0.022139 733E-04 1 .90E-03 1 .47E-03 2.43E-03 1 .33E-03 1 .12E-03 1 .47E-03 1.08E-03 

507 2.31E-03 2.29E-03 1 .14E-03 3.78E-03 4.12E-03 4.37E-03 3 .24E-03 3.67E-03 4.53E-03 3.95E-03 6.71E-03 4.80E-03 

509 4.06E-04 6.44E-04 2.57E-04 7.33E-04 6.88E-04 7.24E-04 6.98E-04 1 .09E-03 9.02E-03 1 .10E-03 1 .89E-03 1.20E-03 

510 1 .47E-02 1 .47E-02 8.22E-03 1 .65E-02 2.37E-02 1 .75E-02 1 .72E-02 3.71E-02 3.02E-02 2.97E-02 0.02452 2.41E-02 

519 0.00E+00 O.00E+00 0.00E+00 O.00E+00 O.00E+00 0.00E+00 O.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 O.00E+00 
Table 8.1 (continued) 

Impact Multiplier Matrix for the Fort Walton Beach Area 

(direct, indirect, and induced effects) 

Sector 454 456 463 464 473 488 506 507 509 510 519 

1 5.13E-03 2.05E-03 239E-03 2.92E-03 1.95E-03 4.18E-03 2.63E-03 2.53E-03 2.96E-03 3.33E-03 3.53E-03 

25 2.00E-03 7.71E-OS 8.25E-OS 2.25E-04 1 .61E-04 2.18E-04 230E-04 2.16E-04 2.58E-04 3.06E-04 3.37E-04 

28 5 .21E-04 1 .83E-04 2.10E-04 4.16E-04 3 .10E-04 3 .47E-04 3 .57E-04 3.58E-04 4.06E-04 5.92E-04 4.27E-04 

48 3 .70E-02 3.37E-02 8.01E-02 4.00E-02 2.68E-02 4.12E-02 2 .53E-02 2.51E-02 2.69E-02 0.057824 2.35E-02 

57 1 .96E-OS 6.88E-06 7.89E-06 1 .56E-OS 1 .17E-OS 130E-OS 134E-OS 135E-OS 1 .53E-OS 2.23E-OS 1 .61E-OS 

58 7 .18E-02 1 .40E-02 0.016312 4.49E-02 2.82E-02 3.52E-02 3 .65E-02 3.70E-02 4.22E-02 4.39E-02 4.62E-02 

133 1 .05E-02 5.60E-03 739E-03 1 .79E-02 1 .36E-02 1 .30E-02 1 .26E-02 1 .43E-02 1 .80E-02 1 .55E-02 1 .50E-02 

433 7 .05E-02 339E-02 3.48E-02 6.72E-02 5 .63E-02 5 .79E-02 6.22E-02 6.26E-02 6.87E-02 7.92E-02 7 .10E-02 

436 1.86E-03 7.28E-04 8.00E-04 2.17E-03 1 .48E-03 2.50E-03 2 .09E-03 1 .93E-03 237E-03 3.18E-03 2.88E-03 

437 3.84E-03 3.09E-03 2.44E-03 7 .21E-03 4.85E-03 4.55E-03 6.44E-03 6.48E-03 1 .34E-02 6.67E-03 5 .89E-03 

447 5 .88E-02 1 .18E-02 1 .38E-02 3.69E-02 2.62E-02 2.92E-02 3 .00E-02 3.34E-02 3.56E-02 3.87E-02 3 .91E-02 

448 6.02E-02 3.06E-02 3.36E-02 8 .52E-02 5 .98E-02 7.26E-02 8 .53E-02 7.88E-02 9.59E-02 1 .14E-01 1 .25E-01 

~0 454 1 .027655 1 .41E-02 1 .60E-02 3.68E-02 2.80E-02 333E-02 3 .88E-02 3.74E-02 4.43E-02 5.05E-02 5 .62E-02 

456 0.095459 1 .151266 8 .04E-02 1 .18E-01 1 .00E-01 1 .06E-01 1 .31E-01 1 .14E-01 1 .37E-01 1 .53E-01 1 .68E-01 

463 1 .04E-O1 7.84E-02 1 .152447 1 .16E-01 8.73E-02 1 .26E-01 1 .12E-01 1 .10E-01 1 .00E-01 8.47E-02 8 .96E-02 

464 2.15E-01 1 .29E-01 0.133023 1 .283711 0.332419 0.264122 0362818 0.424157 2.93E-01 2.67E-01 2.87E-01 

473 2.65E-03 2.56E-03 1 .45E-03 3.61E-03 1 .007211 3.12E-03 1 .87E-03 1 .95E-03 3.72E-03 2.11E-03 2.05E-03 

488 3.77E-03 1 .74E-03 1 .76E-03 5.23E-03 3 .75E-03 1 .00453 5.50E-03 5.04E-03 6.12E-03 738E-03 8.24E-03 

506 1 .13E-03 1 .32E-03 2.05E-03 1 .64E-03 1 .00E-03 1 .19E-03 1 .032285 1 .33E-03 4.18E-03 3 .09E-03 8 .63E-04 

507 5.15E-03 6.27E-03 3.86E-03 8 .59E-03 1 .15E-02 4.64E-03 4.93E-02 1 .075543 0.006886 3 .52E-03 3.45E-03 

509 1.61E-03 1 .22E-03 1 .20E-03 1.57E-03 1 .40E-03 1 .88E-03 1 .09E-03 1 .82E-03 1 .011012 7.95E-04 7.51E-04 

510 2.17E-02 2.02E-02 1 .41E-02 2.84E-02 2.81E-02 2.44E-02 2.30E-02 2.26E-02 2.83E-02 1 .028742 2.82E-02 

519 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1 .00E+00 

Sources: RPC. Minnesota IMPLAN Group, Inc . 1997. IMPLAN professional version 1 .1 Software, User's Guide, Analysis Guide, and Data Guide. Stillwater, MN. 
Table 8.2 

Impact Multiplier Matrix for the Panama City Area 

(direct, indirect, and induced effects) 

Sector 1 25 28 48 57 58 133 433 436 437 447 448 

1 1 .018056 131E-03 2.55E-04 1 .52E-03 1 .63E-03 1 .10E-02 139E-03 9.16E-04 1 .04E-03 1 .13E-03 1.25E-03 1 .22E-03 

25 5.49E-OS 1 .000178 9.02E-06 3 .54E-OS 6.25E-OS 1 .13E-04 2.75E-OS 3.02E-OS 3 .53E-05 3 .59E-OS 4.24E-OS 4.73E-OS 

28 2.21E-04 1 .87E-04 1 .00035 3 .21E-04 2.52E-04 1 .45E-03 3.20E-04 130E-03 3 .12E-04 3 .32E-04 2.18E-04 2 .00E-04 

48 2.26E-02 1 .81E-02 7 .06E-03 1 .01551 2.03E-02 1 .92E-02 2.47E-02 8.69E-02 3 .67E-02 3 .80E-02 3.62E-02 3 .65E-02 

57 5.84E-06 4.94E-06 2 .65E-02 8.50E-06 1 .000006 3 .83E-OS 8.46E-06 3.45E-OS 8.27E-06 8.78E-06 5.77E-06 5.30E-06 

58 6.86E-02 6.89E-02 1 .10E-02 0.046558 7.38E-02 1 .117922 4.56E-02 3 .96E-02 5 .01E-02 0.061587 5.54E-02 4.94E-02 

133 8.75E-03 1 .06E-02 3.02E-03 2 .84E-02 1 .55E-02 5 .10E-03 1 .038578 1 .07E-02 2.06E-02 1 .09E-02 8.97E-03 8 .21E-03 

433 6.29E-02 4.52E-02 1 .87E-02 5 .36E-02 7.10E-02 6.68E-02 5 .84E-02 1 .181191 0.134881 1 .39E-01 7.62E-02 7 .11E-02 

N 

0 436 230E-03 6.22E-03 7.02E-04 1 .83E-03 2.64E-03 2.54E-03 1 .76E-03 2.50E-03 1 .118582 4.43E-03 1 .95E-03 1 .88E-03 

437 4.68E-03 3.26E-03 9.84E-04 3.35E-03 831E-03 3.44E-03 4.40E-03 436E-03 4.84E-03 1 .037759 9.46E-03 4.32E-03 

447 5.36E-02 5.64E-02 1 .15E-02 5 .60E-02 5 .75E-02 6.62E-02 7.25E-02 3.90E-02 5 .67E-02 732E-02 1 .064529 3.91E-02 

448 8.80E-02 6.61E-02 1 .47E-02 1 .02E-01 9.92E-02 4.10E-02 437E-02 733E-02 4.71E-02 5 .18E-02 7.02E-02 1 .079291 

454 3.87E-02 2.80E-02 6.47E-03 2.52E-02 4.43E-02 1 .82E-02 1 .94E-02 2.24E-02 2 .05E-02 336E-02 3.35E-02 3.58E-02 

456 1 .22E-01 9.22E-02 2.22E-02 8.70E-02 135E-01 6.07E-02 0.065749 8.57E-02 0.13276 9.44E-02 1 .12E-01 1 .10E-01 

463 7.93E-02 5.05E-02 2.96E-02 5.02E-02 7.45E-02 4.10E-02 4.39E-02 635E-02 9.54E-02 6.91E-02 1 .03E-01 1.04E-01 

464 1 .89E-01 1 .55E-01 3.55E-02 0.142499 2.13E-01 1 .21E-01 1 .27E-01 1.61E-01 0.296639 0.214246 0.25507 2.24E-01 

473 1 .72E-03 2.02E-03 4.16E-04 5.64E-03 3.58E-03 2.51E-03 2.85E-03 2.37E-03 7.80E-03 9.96E-03 4.80E-03 2.95E-03 

488 5.77E-03 4.12E-03 9.44E-04 3.66E-03 6.63E-03 2.57E-03 2.73E-03 3.07E-03 2.80E-03 2.96E-03 4.16E-03 4.94E-03 

506 5.84E-04 5.73E-04 9.68E-04 0.015683 5.70E-04 1 .53E-03 1 .07E-03 1 .77E-03 9.71E-04 8.68E-04 1 .08E-03 7.99E-04 

507 4.62E-03 3.24E-03 9.15E-04 4.55E-03 5.44E-03 6 .07E-03 4.11E-03 4.79E-03 5 .68E-03 5 .33E-03 8.54E-03 6.21E-03 

509 1 .12E-03 1 .27E-03 2.95E-04 1 .33E-03 1 .30E-03 1 .42E-03 1 .26E-03 2.03E-03 1 .58E-02 2 .14E-03 3.41E-03 2.20E-03 

510 1 .99E-02 1 .50E-02 4.67E-03 1 .53E-02 2.23E-02 1 .61E-02 1 .48E-02 3.21E-02 2.58E-02 2 .68E-02 0.022907 2.25E-02 

519 0.00E+00 0.00E+00 O.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 
Table 8.2 (continued) 

Impact Multiplier Matrix for the Panama City Area 

(direct, indirect, and induced effects) 

Sector 454 456 463 464 473 488 506 507 509 510 519 

1 2.70E-03 1.11E-03 1 .21E-03 1 .53E-03 1 .04E-03 2.10E-03 1 .38E-03 1 .37E-03 1 .53E-03 1.77E-03 1 .86E-03 

25 4.44E-04 2.03E-OS 2.08E-OS 5.29E-OS 3.84E-OS 5.08E-OS 5.33E-OS 5.24E-OS 5.77E-OS 7.25E-OS 7.84E-OS 

28 3.12E-04 1 .11E-04 1 .19E-04 2.33E-04 1 .74E-04 1 .97E-04 2.04E-04 2.07E-04 2.33E-04 3.25E-04 2.47E-04 

48 3.83E-02 3.73E-02 8.18E-02 3 .86E-02 2.65E-02 4.17E-02 2.55E-02 2.49E-02 2.86E-02 0.057338 2.42E-02 

57 8.27E-06 2.93E-06 3.15E-06 6.16E-06 4.60E-06 5.22E-06 5.39E-06 5.48E-06 6.18E-06 8 .60E-06 6.55E-06 

58 1 .12E-01 2.44E-02 0.026813 6.87E-02 4.45E-02 5.52E-02 5.73E-02 5.89E-02 6.62E-02 7.03E-02 7.29E-02 

133 8.16E-03 4.65E-03 5.72E-03 131E-02 1 .02E-02 9.81E-03 9.48E-03 1 .07E-02 1 .45E-02 1 .19E-02 1 .14E-02 

433 7.80E-02 4.04E-02 3.90E-02 7.17E-02 6.11E-02 6.31E-02 6.81E-02 6.92E-02 7.60E-02 8 .67E-02 7.81E-02 

436 1 .97E-03 8.34E-04 8.70E-04 2.20E-03 1 .54E-03 2.55E-03 2.13E-03 2.04E-03 2.36E-03 3.26E-03 2.95E-03 

437 4.01E-03 3.37E-03 2.52E-03 6.90E-03 4.73E-03 4.50E-03 6.35E-03 6.29E-03 1 .51E-02 6.65E-03 5.86E-03 

447 8.69E-02 1 .91E-02 2.10E-02 5 .30E-02 3.82E-02 4.28E-02 4.38E-02 4.91E-02 5.22E-02 5.78E-02 5.78E-02 

448 6.32E-02 3.48E-02 3.61E-02 8.64E-02 6.19E-02 7.38E-02 8.61E-02 8.32E-02 9.31E-02 1 .18E-01 1 .27E-01 

454 1 .028805 1 .59E-02 1 .71E-02 3 .72E-02 2.87E-02 3 .36E-02 3 .88E-02 3 .90E-02 4.30E-02 5.19E-02 5.66E-02 

456 0.099699 1 .166747 8.45E-02 1 .19E-01 1 .02E-01 1 .07E-01 132E-O1 1 .19E-O1 1 .35E-01 1 .58E-01 1 .71E-O1 

463 1 .07E-01 8.61E-02 1 .154683 1 .13E-01 8.61E-02 1 .26E-01 1 .13E-01 1 .08E-01 1 .05E-01 8.80E-02 9.18E-02 

464 2.03E-01 1 .30E-01 0.126432 1 .253814 0.298423 0.241711 0.33336 0.375976 2.74E-01 2.50E-01 2.65E-01 

473 2.79E-03 2.81E-03 1 .49E-03 3 .48E-03 1 .007016 3 .15E-03 1 .88E-03 1 .95E-03 4.20E-03 2.19E-03 2.12E-03 

488 4.02E-03 2.03E-03 1 .97E-03 5 .43E-03 3 .96E-03 1 .004683 5 .64E-03 5.44E-03 5 .98E-03 7.76E-03 8.51E-03 

506 8.80E-04 1 .07E-03 1 .50E-03 1 .16E-03 7.25E-04 8.84E-04 1.024924 9.60E-04 3 .75E-03 2.23E-03 6.62E-04 

507 6.91E-03 8.92E-03 5.09E-03 1 .06E-02 1 .45E-02 6.00E-03 6.54E-02 1 .090855 0.010029 4.62E-03 4.51E-03 

509 3 .03E-03 2.44E-03 2.25E-03 2.74E-03 2.50E-03 3.46E-03 2.02E-03 3 .16E-03 1 .024414 1 .50E-03 1 .40E-03 

510 2.05E-02 2.12E-02 138E-02 2.61E-02 2.64E-02 2.30E-02 2.14E-02 2.13E-02 2.72E-02 1 .027143 2.66E-02 

519 0.00E+00 0.00E+00 0.00E+00 O.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 O.00E+00 O.00E+00 1 .00E+00 

Sources: RPC. 

Minnesota IMPLAN Group, Inc. 1997. 

IMPLAN professional version 1 .1 Software, User's Guide, Analysis Guide, and Data Guide. Stillwater, MN. 
Table 8.3 

Impact Multiplier Matrix for the Pensacola Area 

(direct, indirect, and induced effects) 

Sector 1 25 28 48 57 58 133 433 436 437 447 448 

1 1 .042142 3 .66E-03 1 .98E-03 3 .88E-03 4.31E-03 2.30E-02 3.82E-03 2.31E-03 2.95E-03 3.23E-03 3.38E-03 3 .21E-03 

25 5.89E-OS 1 .000275 4.28E-OS 5.76E-OS 9.87E-OS 1 .71E-04 4.98E-OS 4.58E-OS 5.90E-OS 6.22E-OS 6.89E-OS 7.55E-OS 

28 9.47E-04 1 .10E-03 1 .005929 1.83E-03 1.39E-03 7.65E-03 1.91E-03 6.48E-03 1 .59E-03 1 .62E-03 1 .18E-03 1 .07E-03 

48 1 .79E-02 1 .88E-02 2.11E-02 1 .015999 2.03E-02 1 .98E-02 2.69E-02 7.95E-02 3.75E-02 3.67E-02 3.65E-02 3 .64E-02 

57 1 .42E-05 1 .64E-OS 1 .50E-02 2 .73E-OS 1 .000021 1 .14E-04 2 .85E-OS 9.69E-OS 2.38E-OS 2.42E-OS 1 .76E-OS 1 .60E-OS 

58 8.88E-02 1 .16E-01 5 .32E-02 0.07903 1 .21E-01 1 .184163 8 .30E-02 6.21E-02 8.79E-02 0.10307 9 .40E-02 8 .33E-02 

133 6.70E-03 1.17E-02 9.41E-03 3.16E-02 1.68E-02 8.97E-03 1 .045098 1 .08E-02 2.29E-02 1 .17E-02 9.78E-03 8.91E-03 

433 4.68E-02 4.80E-02 5 .61E-02 5 .57E-02 7.18E-02 6.91E-02 6 .47E-02 1 .167543 0.139536 1 .35E-01 7 .78E-02 7 .22E-02 

436 1 .71E-03 635E-03 2.13E-03 1 .92E-03 2.72E-03 2.65E-03 1 .99E-03 2.37E-03 1 .121075 438E-03 2.04E-03 1 .96E-03 

437 3 .68E-03 3.68E-03 2 .93E-03 3 .73E-03 8 .89E-03 3.92E-03 5 .16E-03 4.37E-03 5.46E-03 1 .037744 1 .02E-02 4.71E-03 

447 4.16E-02 5.69E-02 3.33E-02 5 .63E-02 5.70E-02 6.48E-02 7 .69E-02 3 .61E-02 5.77E-02 7.07E-02 1 .064026 3.92E-02 

N 

448 5 .91E-02 6.91E-02 4.44E-02 1 .05E-01 9.95E-02 4.77E-02 4.94E-02 6.91E-02 4.95E-02 5.67E-02 7 .20E-02 1 .080538 

454 2 .54E-02 2.91E-02 1 .93E-02 2.57E-02 4.40E-02 2.10E-02 2.18E-02 2.12E-02 2.14E-02 3.48E-02 3 .40E-02 3 .60E-02 

456 8 .33E-02 9.66E-02 6.69E-02 8 .98E-02 1 .36E-01 6.96E-02 0.074124 8 .12E-02 0.13742 9.96E-02 1 .15E-01 1 .12E-01 

463 5 .88E-02 5.13E-02 8 .71E-02 5 .07E-02 7.25E-02 4.45E-02 4.77E-02 5 .83E-02 9.68E-02 6.91E-02 1 .02E-01 1 .02E-01 

464 1 .48E-01 1 .85E-01 1 .23E-01 0.169384 2.44E-01 1 .52E-01 1 .62E-01 1 .74E-01 0.351285 0.249491 0.296153 2.58E-01 

473 131E-03 2.13E-03 1 .16E-03 5.82E-03 3.65E-03 2.56E-03 3.14E-03 2.24E-03 8.07E-03 9.51E-03 4.90E-03 3.01E-03 

488 233E-03 2.65E-03 1 .74E-03 2.30E-03 4.07E-03 1 .85E-03 1 .90E-03 1 .81E-03 1 .81E-03 2.04E-03 2.62E-03 3.07E-03 

506 8.45E-04 1 .11E-03 5.11E-03 0.027763 1 .08E-03 2.67E-03 2.09E-03 2.93E-03 1 .85E-03 1 .56E-03 1 .98E-03 1 .47E-03 

507 1 .05E-03 9.48E-04 7.61E-04 1 .47E-03 1 .51E-03 1 .66E-03 1 .25E-03 1.24E-03 1 .64E-03 1 .46E-03 236E-03 1 .71E-03 

509 1 .04E-03 1 .67E-03 1 .11E-03 1 .75E-03 1 .67E-03 1 .83E-03 1 .76E-03 2.38E-03 2.04E-02 2.65E-03 4.35E-03 2.80E-03 

510 1 .48E-02 1 .64E-02 1 .50E-02 1 .63E-02 2.33E-02 1 .74E-02 1 .68E-02 2.94E-02 2.72E-02 2.61E-02 0.024393 2.40E-02 

519 O.00E+00 O.00E+00 0.00E+00 O.00E+00 O.00E+00 0.00E+00 0.00E+00 O.00E+00 O.00E+00 0.00E+00 O.00E+00 0.00E+00 
Table 8.3 (continued) 

Impact Multiplier Matrix for the Pensacola Area 

(direct, indirect, and induced effects) 

Sector 454 456 463 464 473 488 506 507 509 510 519 

1 7.13E-03 2.57E-03 2.70E-03 4.16E-03 2.84E-03 5.16E-03 3 .62E-03 3.52E-03 4.02E-03 4.52E-03 4.79E-03 

25 6.88E-04 3.19E-OS 2.77E-OS 8.83E-OS 6.29E-OS 8.11E-OS 8.42E-OS 7.95E-OS 8.83E-OS 1 .11E-04 1 .22E-04 

28 1 .81E-03 5.62E-04 5.78E-04 1 .30E-03 9.61E-04 1 .09E-03 1 .11E-03 1.11E-03 1 .29E-03 1 .83E-03 1 .35E-03 

48 3.93E-02 3.51E-02 8.09E-02 3.81E-02 2.69E-02 4.19E-02 2.54E-02 2.45E-02 3.01E-02 0.063354 2.41E-02 

57 2.70E-OS 8.40E-06 8 .64E-06 1 .94E-OS 1 .44E-OS 1 .63E-OS 1 .66E-OS 1 .65E-OS 1 .93E-OS 2.74E-OS 2.01E-OS 

58 1 .87E-01 4.01E-02 0.039582 1 .16E-01 7.74E-02 9 .31E-02 9.60E-02 9.65E-02 1 .11E-01 1 .15E-01 1 .21E-01 

133 8.97E-03 4.86E-03 5.66E-03 1 .42E-02 1 .11E-02 1 .07E-02 1 .01E-02 1 .12E-02 1 .65E-02 1 .29E-02 1 .23E-02 

433 8.15E-02 3 .91E-02 3.59E-02 7.41E-02 6.29E-02 6.46E-02 6.85E-02 6.79E-02 7.81E-02 8 .87E-02 7.87E-02 

436 2.12E-03 8.51E-04 7.67E-04 237E-03 1 .64E-03 2 .63E-03 2.19E-03 2.03E-03 2.40E-03 338E-03 3 .02E-03 

437 4.48E-03 3 .47E-03 2.44E-03 7.41E-03 5.19E-03 4.93E-03 6.78E-03 6.63E-03 1 .81E-02 7 .28E-03 6.32E-03 

447 8.75E-02 1 .86E-02 1 .84E-02 537E-02 3 .88E-02 4.30E-02 433E-02 4.73E-02 5.23E-02 5 .68E-02 5 .72E-02 

448 6.53E-02 3 .45E-02 3.07E-02 9.12E-02 6.42E-02 7 .51E-02 8.65E-02 8 .00E-02 9.04E-02 1 .16E-01 1 .27E-01 

w 

454 1 .029437 1 .56E-02 1 .45E-02 3.90E-02 2.94E-02 3 .38E-02 3.86E-02 3 .72E-02 4.16E-02 5 .03E-02 5 .61E-02 

456 0.103051 1 .158221 7.70E-02 1 .26E-01 1 .05E-01 1 .09E-01 1 .33E-01 1 .16E-01 134E-01 1 .55E-01 1 .71E-01 

463 1 .07E-01 7.98E-02 1 .147671 1 .10E-01 8.56E-02 1 .24E-01 1 .10E-01 1 .04E-01 1 .08E-01 8 .45E-02 8.91E-02 

464 2.39E-01 1 .43E-01 0.131536 1 .295952 0.345466 0.280011 0.376838 0.424518 3.25E-01 2.83E-01 3 .04E-01 

473 2.95E-03 2.69E-03 1 .44E-03 3.49E-03 1 .007102 3 .23E-03 1 .95E-03 1 .98E-03 4.73E-03 2.27E-03 2.18E-03 

488 2.54E-03 1 .24E-03 9.84E-04 3.53E-03 2.52E-03 1 .002919 3.47E-03 3 .19E-03 3.52E-03 4.65E-03 5 .21E-03 

506 1 .70E-03 1 .80E-03 2.63E-03 2.08E-03 1 .35E-03 1 .64E-03 1 .043208 1 .74E-03 7.62E-03 4.48E-03 1 .24E-03 

507 1 .97E-03 2.28E-03 1 .35E-03 2.80E-03 3.95E-03 1 .67E-03 1 .78E-02 1 .025868 0.003101 131E-03 1 .26E-03 

509 3.95E-03 2.90E-03 2.77E-03 3.39E-03 3.19E-03 4.41E-03 2.49E-03 4.00E-03 1 .036055 1 .93E-03 1 .79E-03 

510 2.25E-02 2.12E-02 1 .48E-02 2.80E-02 2.79E-02 2.50E-02 2.29E-02 2.21E-02 2.92E-02 1 .028129 2.79E-02 

519 0.00E+00 0.00E+00 0.00E+00 O.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 0.00E+00 1 .00E+00 

Sources: RPC. 

Minnesota IMPLAN Group, Inc. 1997. 

IMPLAN professional version 1 .1 Software, User's Guide, Analysis Guide, and Data Guide. Stillwater, MN. 
Table 8.4 

Industries Associated with Operation and Maintenance 

of Offshore Oil and Gas Platforms 

Adjusted for Commuting Patterns of Offshore Workers 

N 

.P 

Code Number Operation and Maintenance Spending 

Typical Florida Panhandle 

Supply Base Percent of Total Supply Base Percent of Total 

132 39 Oil & gas operations X 36.3% 0.0% 

1389 57 Other oil and gas services excl . commuting workers X 9.2% X 17 .5% 

1389 57 Other oil and gas services : commuting offshore workers only 9.2% 17.5% 

2899 209 Chemical, not elsewhere classified X 0.9% 0.0% 

291 210 Petroleum fuel X 4.4% 0.0% 

324 232 Hydraulic cement X 0.7% 0.0% 

3559 331 Special industry machinery, not elsewhere classified X 5.1% 0.0% 

44 436 Water transportation X 4.0% X 7.6% 

45 437 Air transportation X 3 .8% X 7 .2% 

58 454 Eating and drinking places X 1 .7% X 3 .2% 

7359 473 Miscellaneous equipment rent / lease X 1 .4% X 2.7% 

871 506 Environmental and engineering services X 14 .7% X 27.9% 

872 507 Accounting / miscellaneous business services X 4.2% X 8.0% 

873 509 Test / research services X 4.5% X 8.6% 

Total for a typical onshore base 100.0% 

Total for Florida Panhandle onshore base 52.6% 100.0% 

* Assumes no more than 30% of offshore workers are locals 

Sources: RPC 

Chevron, Destine Dome Unit 56, Development and Production Plan, July, 1997. 
The model generates impacts of OCS-related activity on the Florida Panhandle by multiplying a 

final demand vector of projected OCS expenditures that would occur in the Florida Panhandle by 

the Type II multiplier matrix. 

To examine the impact of OCS activity or a decline in tourism or military expenditures within 

the Florida Panhandle, the model multiplies these expenditures by sector by a Type II multiplier 

to generate the change in total output by sector resulting from the scenario inputs chosen. The 

model would generate change in labor demand by multiplying output per sector by output per 

employee by sector. 

Labor Market (Economic-Demographic Interaction) Module 

The Economic-Demographic interface is the component of the model that links the projections of 

required employment from the economic module (labor demand) with projections of people in 

the workforce generated by the demographic module (labor supply) . In the MMS Florida 

Panhandle model, interaction is at the metropolitan area rather than at the county level. For 

instance, in the Pensacola submodel, output is projected for the entire Pensacola area rather than 

for Escambia and Santa Rosa Counties individually . 

The purpose of this module is to ensure that the size of the labor force rises or falls to the level 

that ensures equilibrium in the labor market over time. When the labor force in any given year is 

too small (large) for the underlying labor demand, the model will increase (decrease) the number 

of workers in the area through migration to restore market equilibrium . This type of migration is 

called economic migration, which affects age cohorts under 65 years of age. Economic 

migration differs from the retiree migration discussed above in that the availability of work in the 

area is the primary motivation for economic migration, whereas the state of the job market has 

little or no impact on the movement of retirees . 

The triggering mechanism in the model is the unemployment rate .' If the unemployment rate in a 

given year is less than four percent, then the model will inmigrate workers and their families. 

Four percent figure is based on recent trends in unemployment and makes the economic model 

more consistent with BEBR's population projections . These low unemployment rates are also 

consistent with the area's net inmigration of recent years (Tables 9,10). If the unemployment rate 

in a given year is more than thirteen percent, the model will outmigrate workers and their 

families.' If the unemployment rate in a given year is between four percent and thirteen percent, 

no economic migration occurs . The module distributes economic migrants (1) by the proportion 

for each county within a metropolitan area that is consistent with baseline economic immigration, 

and (2) using the age, sex, and race distribution of economic migration (working-age people and 

their families) developed based on information that BEBR provided (Tables 11 .1 - 11 .5). These 

changes are injected back into the demographic module for use in projecting population and 

labor supply for the next five-year period. 

S This approach was adapted from Murdock and Leish-itz, et al (1979) 

6RPC based the 13 percent for the ouhnigration rate from TAMS. 

25 
26 

Table 9 

Unemployment Rates in the Florida Panhandle 

(1990 - 1997)

County 1990 1991 1992 1993 1994 1995 1996 1997 

Bay 8.9% 7.9% 9.1% 9.2% 8.5% 6.7% 6.0% 6.6% 

Escambia 5.6% 5.7% 5.6% 5.0% 4.7% 4.3% 4.1% 4.2% 

Okaloosa 5.7% 6.1% 6.1% 6.1% 5.2% 4.4% 3.8% 3.6% 

Santa Rosa 5.3% 5.3% 5.3% 4.5% 4.2% 4.1% 3.5% 3.6% 

Walton 6.2% 6.7% 5.3% 5.4% 5.0% 4.4% 4.3% 4.3% 

Florida 6.0% 7.4% 8.2% 7.0% 6.6% 5.5% 5.1% 4.8% 

United States 5.6% 6.8% 7.4% 6.8% 6.1% 5.6% 5.4% 4.9% 

 Source: Florida Department of Labor and Employment Security, personal communication. 
Table 10 

Immigration into the Florida Panhandle for selected MSA's 

(1970 -1990) 

Year Fort Walton Beach: 

Okaloosa County 

only 

Pensacola : 

Escambia and Santa 

Rosa Counties 

Panama City: 

Bay County 

1970 2780 1842 854 

1971 302 2860 1125 

1972 3571 6613 770 

1973 282 641 1245 

1974 -204 596 3922 

1975 5481 5497 1824 

1976 -1537 4071 1843 

1977 897 434 707 

1978 -8 -1532 568 

1979 1115 215 1870 

1980 -1997 -821 76 

1981 1489 2573 1610 

1982 1566 2359 2468 

1983 1476 3082 1355 

1984 2050 3520 4233 

1985 2907 2479 6601 

1986 1684 952 2789 

1987 1795 3385 961 

1988 2200 2764 494 

1989 1697 3319 -30 

1990 1686 1434 -1157 

Source : Bureau of Economic and Business Research, University of Florida, personal communication, 

June Nogle, 1996-1999 . 

27 
Table 11.1 

Inmigration and Outmigration Rates of Workers and Their Families 

(Bay County) 

Distribution of Inmigrating Children 

White 

Aye Male Female 

0-4 8% 8% 

5-9 8% 8% 

10-14 5% 5% 

15-19 12% 12% 

Non-White 

Male Female 

4% 4% 

4% 4% 

6% 6% 

3% 3% 

Distribution of Inmigrating Adults 

White Non-White 

Awe Male Female Male Female 

20-24 5% 5% 1% 1% 

25-29 3% 3% 2% 2% 

30-34 3% 3% 2% 2% 

35-39 4% 4% 2% 2% 

40-44 7% 7% 1% 1% 

45-49 7% 7% 0% 0% 

50-54 3% 3% 0% 0% 

55-59 5% 5% 0% 0% 

60-64 5% 5% 0% 0% 

Sources: RPC. 

Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

28 
Table 11 .2 

Inmigration and Outmigration Rates of Workers and Their Families 

(Escambia County) 

Distribution of Inmigrating Children 

Inmigrating 

White 

Aye Male Female 

0-4 0% 0% 

5-9 0% 0% 

10-14 9% 9% 

15-19 8% 8% 

Non-White 

Male Female 

8% 8% 

8% 8% 

9% 9% 

8% 8% 

Distribution of Inmigrating Adults 

White Non-White 

Awe Male Female Male Female 

15-19 15% 15% 0% 0% 

20-24 9% 9% 0% 0% 

25-29 3% 3% 2% 2% 

30-34 0% 0% 0% 0% 

35-39 0% 0% 1% 1% 

40-44 5% 5% 1% 1% 

45-49 6% 6% 1 % 1 

50-54 3% 3% 0% 0% 

55-59 3% 3% 0% 0% 

60-64 1 % 1 % 0% 0% 

Sources : RPC. 

Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

29 
Table 11 .3 

Inmigration and Outmigration Rates of Workers and Their Families 

(Okaloosa County) 

Distribution of Immigrating Children 

White Non-White 

Aye Male Female Male Female 

0-4 7% 7% 5% 5% 

5-9 10% 10% 5% 5% 

10-14 10% 10% 3% 3% 

15-19 7% 7% 3% 3% 

Distribution of Immigrating Adults 

White Non-White 

Aye Male Female Male Female 

20-24 10% 10% 2% 2% 

25-29 6% 6% 2% 2% 

30-34 6% 6% 1 % 1 

35-39 4% 4% 1% 1% 

40-44 6% 6% 2% 2% 

45-49 4% 4% 0% 0% 

50-54 2% 3% 0% 0% 

55-59 2% 3% 0% 0% 

60-64 1 % 1 % 0% 0% 

Sources: RPC. 

Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

30 
Table 11 .4 

Inmigration and Outmigration Rates of Workers and Their Families 

(Santa Rosa County) 

Distribution of Immigrating Children 

White Non-White 

Awe Male Female Male Female 

0-4 13% 13% 3% 3% 

5-9 12% 12% 3% 3% 

10-14 7% 7% 3% 3% 

15-19 7% 7% 2% 2% 

Distribution of Immigrating Adults 

White Non-White 

Aye Male Female Male Female 

20-24 0% 0% 1 % 1 

25-29 14% 14% 1% 1% 

30-34 5% 5% 1% 1% 

35-39 4% 4% 1% 1% 

40-44 7% 7% 1% 1% 

45-49 4% 4% 1 % 1 

50-54 4% 4% 0% 0% 

55-59 3% 3% 0% 0% 

60-64 3% 3% 0% 0% 

Sources : RPC. 

Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

31 
Table 11.5 

Immigration and Outmigration Rates of Workers and Their Families 

(Walton County) 

Distribution of Immigrating Children 

White Non-White 

Aye Male Female Male Female 

0-4 10% 10% 0% 0% 

5-9 10% 10% 0% 0% 

10-14 10% 10% 0% 0% 

15-19 20% 20% 0% 0% 

Distribution of Immigrating Adults 

White Non-White 

Aye Male Female Male Female 

20-24 0% 0% 0% 0% 

25-29 0% 0% 0% 0% 

30-34 10% 10% 0% 0% 

35-39 0% 0% 0% 0% 

40-44 0% 0% 0% 0% 

45-49 10% 10% 0% 0% 

50-54 10% 10% 0% 0% 

55-59 10% 10% 0% 0% 

60-64 10% 10% 0% 0% 

Sources: RPC. 

Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999. 

32 
Baseline Projections 

The MMS Florida Panhandle Model baseline projections (1995 - 2045) are the levels of 

economic activity and population that would likely occur with a continuation of present activity 

and trends in the area's basic industries prior to any impacts of an onshore support base located 

in the Port of Panama City or the Port of Penascola that would support OCS activity in the 

Eastern Gulf of Mexico. 

Working from a target population projection, RPC calibrated the model's employment, 

population, and final demand projections to be consistent with federal and state employment and 

population projections as well as IMPLAN's output per employee ratios . RPC took the following 

steps to calibrate the baselines for the three metropolitan areas: 

Created a Baseline Population Consistent with BEBR /BEA Population Projections 

In preparing the model's baseline projections, the RPC team gathered historical and projected 

baselines from two sources : Bureau of Economic and Business Research at the University of 

Florida at Gainesville (BEBR) and the U.S . Bureau of Economic Analysis (BEA) . The two 

baselines used two different approaches and two projection periods - the BEBR projections went 

to 2020, while the BEA projections went to 2045 . (See Appendices A and B) 

BEBR uses a cohort-component model to project population without the direct interaction with 

an economic module.' Migration for working-age people and their children are based on 

historical immigration and outmigation rates, rather than projected economic conditions such as 

output and labor demand and supply. 

In 1995, the BEA published projections of employment and earnings from 1995 to 2045 . This set 

of projections is commonly known as the "OBERS" projections, which have been used by the 

Army Corps of Engineers and other agencies and firms working on long-term water development 

projects. In these projections, the BEA started with U.S . Census population projections on a 

national level and used long-term shares in employment by industry group to allocate 

employment and population among states and MSAs across the United States . 

Because local and state government officials use BEBR and BEA figures in making long-term 

planning decisions, RPC calibrated its model so that the population baselines of the model were 

consistent with the population projections of these sources . The BEA and BEBR numbers for the 

Panama City and Fort Walton Beach areas were consistent, after adjusting for more recent U.S . 

Census projections (Table 12) . After consulting with Dr. June Nogle, a demographer at BEBR, 

RPC adjusted the BEA's Pensacola MSA population projections to make them consistent with 

the BEBR's projections. Table 13 shows the baseline population projections of the model . 

7 BEBR (1997), pp. 1-4 describes BEBR's methodology in detail . 

33 
Table 12 

Comparison of Census Bureau U.S. Population Projections 

(millions of people) 

Projections Difference 

Published Published 

Year in 1996 in 1993 People 

2000 274 .63 276.24 -1 .61 -0.58% 

2005 285 .98 288.29 -2.31 -0.80% 

2010 297 .72 300.43 -2.71 -0.90% 

2015 310 .13 313 .12 -2.98 -0.95% 

2025 335 .05 338.34 -3 .29 -0.97% 

2045 381 .71 381 .78 -0.07 -0.02% 

Source: U.S . Bureau of the Census, Population of the United States by Age, Sex and Hispanic Origin, P25-1104 

(November 1993) and P25-1130 (February 1996). 

34 
Table 13 

Population in the Florida Panhandle 

Baseline Scenario 

Area 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 

Fort Walton Beach 196,122 216,005 235,159 254,195 274,218 292,374 311,399 329,090 343,428 355,713 367,255 

Okaloosa County 162,707 178,803 194,199 209,523 225,720 240,693 256,235 270,726 282,836 293,458 303,489 

Walton County 33,415 37,202 40,959 44,672 48,498 51,681 55,165 58,364 60,592 62,255 63,766 

w Panama City 139,173 150,277 162,188 174,575 187,536 198,341 210,054 218,838 227,033 234,538 241,999 

"' Bay County 139,173 150,277 162,188 174,575 187,536 198,341 210,054 218,838 227,033 234,538 241,999 

Pensacola 377,822 410,086 441,204 469,488 502,482 526,239 547,200 567,029 584,973 601,966 618,202 

Escambia County 281,162 296,578 316,849 334,339 352,894 366,819 379,386 390,820 400,920 410,113 418,437 

Santa Rosa County 96,660 113,508 124,354 135,149 149,589 159,420 167,814 176,209 184,053 191,853 199,765 

Total 713,117 776,368 838,550 898,258 964,236 1,016,954 1,068,653 1,114,957 1,155,434 1,192,217 1,227,456 

Sources: RPC. 

Florida Bureau of Business and Economic Research, personal communication, June Nogle, 1996-1999 . 
Adjusted BEA Employment Projections to be Consistent with 1994 IMPLAN Employment Data 

To create a baseline for the time frame involved with development and production of oil and gas 

from Lease Sale 181, RPC combined the sectoral detail of a single year that IMPLAN provides 

with the 50-year time frame of the less-detailed OBERS projections from 1995 to 2045 . Tables 

14.1-14.3 show the 1994 employment and output figures IMPLAN generated for the three study 

areas under this sectoring scheme, and Tables 15 .1-15 .3 show the reconciliation of IMPLAN and 

BEA employment by subsector in 1994 After reconciling the sectors and subsectors, RPC used 

adjustment factors that reconciled the different classification of employment that IMPLAN and 

the BEA used. IMPLAN used number of jobs, and the BEA figures projected the number of 

workers, assuming that a number of people held more than of one job and that the number of jobs 

per worker changed overtime (Table 16). The MMS Florida Panhandle model measures 

employment as the number of people employed. RPC projected subsectors as a constant 

percentage of a sector over time. 

36 
Table 14.1 

Output and Employment per $1 Million of Output by Sector in 1994 

(Fort Walton Beach Area) 

Output Employees per 

Description ($ Millions) Employment $1 Million 

Agriculture 61 .95 1,512 24.408 

Commercial Fishing 3.80 172 45 .257 

Mining 5.94 53 8.927 

Construction 496.95 7,469 15 .030 

Maintenance and Repair Oil & Gas Wells 0.22 22 98.576 

Non-Durable Manufacturing 184.84 2,090 11 .307 

Durable Manufacturing 435 .26 3,397 7.805 

TCPU 315 .56 2,297 7.279 

Water Transportation 16.74 95 5.675 

Air Transportation 28.42 209 7.354 

Wholesale Trade 208.09 2,689 12.922 

Trade 420.70 13,429 31 .921 

Eating & Drinking 272.02 7,997 29.398 

FIRE Excluding Rentals 549.54 2,307 4 .198 

Hotels, Lodging, & Rentals 821 .41 6,469 7 .875 

Services 999.17 22,746 22.765 

Equipment Rental and Leasing 16.01 240 14.987 

Amusement and Recreation Services 32.27 810 25.097 

Engineering, Architectural Services 54.24 881 16.242 

Accounting, Auditing and Bookkeeping 18.60 382 20.543 

Research, Development & Testing Services 71 .38 1,085 15 .200 

Government 583 .45 18,019 30.884 

Federal Government - Military 479.58 15,808 32.962 

Total 6,076.16 110,178 18.133 

Sources: RPC. 

Minnesota IMPLAN Group, Inc. 1997. 

IMPLAN professional version 1 .1 Software, User's Guide, Analysis Guide, and Data Guide. Stillwater, 

MN . 

37 
Table 14.2 

Output and Employment per $1 Million of Output by Sector in 1994 

(Panama City Area) 

Output Employees per 

Description ($ Millions) Employment $1 Million 

Agriculture 12.28 532 43.332 

Commercial Fishing 0.67 34 51 .004 

Mining 2.56 21 8.214 

Construction 468 .37 6,061 12.941 

Maintenance and Repair Oil & Gas Wells 0.12 12 100.231 

Non-Durable Manufacturing 246.20 1,390 5.646 

Durable Manufacturing 239.14 1,658 6.933 

TCPU 322 .10 2,183 6.778 

Water Transportation 66.42 358 5.390 

Air Transportation 21 .27 177 8.323 

Wholesale Trade 234.93 2,764 11 .765 

Trade 351 .64 10,977 31 .216 

Eating & Drinking 241 .59 7,034 29.116 

FIRE Excluding Rentals 448 .68 2,048 4.565 

Hotels, Lodging, & Rentals 584.61 5,695 9.742 

Services 664.98 14,552 21 .883 

Equipment Rental and Leasing 14.88 182 12.230 

Amusement and Recreation Services 48.57 1,192 24.544 

Engineering, Architectural Services 40.50 679 16.764 

Accounting, Auditing and Bookkeeping 22.90 412 17.989 

Research, Development & Testing Services 25 .66 522 20.342 

Government 457 .13 13,334 29.169 

Federal Government - Military 169 .03 5,802 34.325 

Total 4,684 .21 77,619 16.570 

Sources: RPC. 

Minnesota IMPLAN Group, Inc. 1997 . 

MPLAN professional version 1 .1 Software, User's Guide, Analysis Guide, and Data Guide. Srillwater, 

MN. 

38 
Table 14.3 

Output and Employment per $1 Million of Output by Sector in 1994 

(Pensacola Area) 

Output Employees per 

Description ($ Millions) Employment $1 Million 

Agriculture 128.85 2,552 19.806 

Commercial Fishing 2.42 129 53 .388 

Mining 18.19 376 20.670 

Construction 1,162.96 15,786 13 .574 

Maintenance and Repair Oil & Gas Wells 0.27 46 169.142 

Non-Durable Manufacturing 1,529.74 7,766 5 .077 

Durable Manufacturing 499.45 4,318 8.646 

TCPU 904.97 5,392 5.958 

Water Transportation 84.90 468 5 .512 

Air Transportation 142.10 1,071 7.537 

Wholesale Trade 623 .40 7,358 11 .803 

Trade 730.41 22,212 30.410 

Eating & Drinking 369.04 10,988 29.775 

FIRE Excluding Rentals 1,065 .00 4,971 4.668 

Hotels, Lodging, & Rentals 643 .50 4,723 7.340 

Services 2,181 .26 50,180 23.005 

Equipment Rental and Leasing 44.61 536 12.016 

Amusement and Recreation Services 25 .14 735 29.231 

Engineering, Architectural Services 119.21 2,003 16.802 

Accounting, Auditing and Bookkeeping 50.36 995 19.756 

Research, Development & Testing Services 19.95 508 25 .470 

Government 1,189.59 31,142 26.179 

Federal Government - Military 336.43 10,127 30.101 

Total 11,871 .76 184,382 15 .531 

Sources: RPC. 

Minnesota IMPLAN Group, Inc. 1997. 

IMPLAN professional version 1 .1 Software, User's Guide, Analysis Guide, and Data Guide. Stillwater, 

MN. 

39 
Table 15.1 

Comparison of IMPLAN and BEA Employment by Sector in 1994 

(Fort Walton Beach Area) 

(in thousands) 

Sector IMPLAN BEA 

All-Industry Total Jobs 110 .178 101 .368 

Farm, Ag Services, Forestry 1 .684 1 .943 

Mining 0.053 0.114 

Construction 7.491 5 .581 

Manufacturing 5 .487 5 .449 

Durables 3 .397 3 .373 

Nondurables 2.090 2.076 

Transportation, Communications, 2.601 2.599 

and Utilities 

Wholesale Trade 2.689 1 .557 

Retail Trade 21 .426 20.048 

Finance, Insurance and Real Estate 2.307 6.126 

Services 32.613 26.650 

Government 33 .827 31 .301 

Military 15 .808 16.087 

Other Government 18.019 15 .214 

Sources: Minnesota IMPLAN Group, Inc. 1997. IMPLAN professional version 1.1 Software, User's Guide, 

Analysis Guide, and Data Guide. Stillwater, MN. 

RPC. 

U.S . Bureau of Economic Analysis 1998. Regional economic information system 1969-1996. U.S . 

Department of Commerce. Washington, DC. 

40 
Table 15.2 

Comparison of IMPLAN and BEA Employment by Sector in 1994 

(Panama City Area) 

(in thousands) 

Sector IMPLAN BEA 

All-Industry Total Jobs 77.619 77.619 

Farm, Ag Services, Forestry 0.566 1 .068 

Mining 0.021 0.043 

Construction 6.073 5.269 

Manufacturing 3.048 3.111 

Durables 1 .658 1 .692 

Nondurables 1390 1 .419 

Transportation, Communications, and Utilities 2.718 2.605 

Wholesale Trade 2.764 2.573 

Retail Trade 18.011 18.762 

Finance, Insurance and Real Estate 2.048 5 .354 

Services 23 .234 21 .213 

Government 19.136 17.623 

Military 5.802 5.831 

Other Government 13 .334 11 .792 

Sources: Minnesota IMPLAN Group, Inc. 1997. IMPLAN professional version 1 .1 Software, User's Guide, 

Analysis Guide, and Data Guide. Stillwater, MN. 

RPC. 

U.S . Bureau of Economic Analysis 1998. Regional economic information system 1969-1996. 

U.S . Department of Commerce. Washington, DC. 

41 
Table 15.3 

Comparison of IMPLAN and BEA Employment by Sector in 1994 

(Pensacola Area) (in thousands) 

Sector IMPLAN BEA 

All-Industry Total Jobs 184.382 179 .826 

Farm, Ag Services, Forestry 2 .681 3 .161 

Mining 0.376 0.600 

Construction 15 .832 12 .475 

Manufacturing 12 .084 11 .961 

Durables 4.318 4.274 

Nondurables 7.766 7.687 

Transportation, Communications, 6.931 7.940 

and Utilities 

Wholesale Trade 7.358 6.308 

Retail Trade 33 .200 33 .398 

Finance, Insurance and Real Estate 4.971 9.962 

Services 59.680 55 .492 

Government 41 .269 38 .529 

Military 10.127 10.794 

Other Government 31 .142 27.735 

Sources: Minnesota IMPLAN Group, Inc. 1997. IMPLAN professional version 1,1 Software, User's Guide, 

Analysis Guide, and Data Guide . Sriliwater, MN. 

RPC. 

U.S . Bureau of Economic Analysis 1998. Regional economic information system 1969-1996. U.S . 

Department of Commerce. Washington, DC. 

42 
Table 16 

Adjustment Factor - Jobs per Worker 

(1995-2045) 

Adjustment 

Year Factor 

1995 1 .1580 

2000 1 .1598 

2005 1 .1616 

2010 1 .1634 

2015 1 .1652 

2020 1 .1670 

2025 1 .1688 

2030 1 .1706 

2035 1 .1724 

2040 1 .1742 

2045 1 .1760 

Source: Bureau of Economic and Business Research 1995 . 

OBERS Projections, Population Estimates and Projections. University 

Press of Florida, FL. 

Using the Model to Calibrate Employment, Modifying to the Adjusted BEA Employment 

Projections to bring the Model's Baseline Population Figures Close to BEBR /BEA Population 

Projections 

RPC made a final adjustment to make the model's baseline populations within a few percent of 

the BEBR / BEA population baseline in each year of the projection period . The Fort Walton 

Beach and Panama City baselines needed a simple 3 .5 to 4.0 percent decrease from the adjusted 

BEA numbers. The Pensacola baseline required a much larger adjustment that varied over time 

(15 percent around 2000 rising to 19-20 percent by 2030) . 

Divide RPC Baseline Employment Projections Among 23 Sectors 

After RPC calibrated employment so that the model's baseline population would approximate 

BEBR / BEA population projections, RPC subdivided employment among sectors based on the 

reconciliation of IMPLAN and BEA employment by subsector in 1994 (Tables 17 .1-17 .3) . 

Having established baseline employment by sector, RPC used output by worker by sector 

(Tables 14.1-14.3), to convert baseline employment into baseline output (Tables 18 .1-18.3). 

43 
Table 17.1 

Baseline Employment by Major Non-Farm Industry for the Florida Panhandle 

(Fort Walton Beach) (in thousands of workers) 

Sector 

Agriculture 

Commercial Fishing 

Mining 

Construction 

Maintenance and Repair Oil & Gas Wells 

Non-Durable Manufacturing 

Durable Manufacturing 

TCPU 

Water Transportation 

Air Transportation 

Wholesale Trade 

Retail Trade 

Eating & Drinking 

FIRE Excluding Real Estate Rentals 

Hotels, Lodging Places, and Rentals 

Services 

Equipment Rental and Leasing 

Amusement and Recreation Services, N.E .C. 

Engineering, Architectural Services 

Accounting, Auditing and Bookkeeping 

Research, Development & Testing Services 

Government 

Federal Government - Military 

Total 

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 

1 .265 1394 1 .574 1 .634 1 .752 1 .748 1 .743 1 .804 1 .867 1 .931 1 .998 

0.144 0.159 0.179 0.186 0.199 0.199 0.198 0.205 0.212 0.220 0.227 

0.035 0.027 0.027 0.027 0.026 0.026 0.026 0.026 0.026 0.026 0.026 

6.619 7.327 8 .149 8.784 9.285 9.582 9 .885 10 .227 10 .578 10 .941 11 .314 

0.015 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.011 0.011 

1 .686 2.052 2.201 2.279 2 .349 2.424 2 .499 2.560 2.622 2.685 2.749 

2 .740 2.708 2.690 2.685 2 .674 2.585 2 .499 2.541 2.583 2.626 2.668 

1 .941 2.233 2.433 2.571 2 .702 2.766 2 .831 2.918 3.007 3.099 3 .193 

0.080 0.092 0.101 0.106 0.112 0.114 0 .117 0.121 0.124 0.128 0.132 

0.177 0.203 0.221 0.234 0.246 0.252 0 .258 0.265 0.274 0.282 0.291 

2.260 2 .674 3.216 3 .349 3 .474 3 .602 3 .733 3.868 4.007 4.151 4.299 

11 .510 13318 14 .696 15 .914 16 .759 17 .225 17 .698 18 .301 18.922 19.563 20.222 

6.855 7 .931 8.752 9.477 9 .980 10.258 10 .539 10 .899 11 .268 11 .650 12.042 

2.038 2 .401 2.730 3 .023 3 .249 3 .394 3.545 3.687 3.833 3 .985 4.142 

5 .716 6.734 7.654 8 .476 9 .109 9 .518 9.942 10338 10.748 11 .174 11 .614 

20 .097 23 .676 26 .914 29.803 32 .029 33 .465 34 .957 36 .348 37.790 39.288 40.838 

0.212 0.250 0.284 0314 0 .338 0353 0.369 0.384 0.399 0.415 0.431 

0.716 0.843 0.958 1 .061 1 .141 1 .192 1 .245 1 .294 1346 1 .399 1 .454 

0.778 0.917 1 .042 1 .154 1 .241 1 .296 1 .354 1 .408 1 .464 1 .522 1 .582 

0.338 0.398 0.452 0.501 0.538 0.562 0.587 0.610 0.635 0.660 0.686 

0.959 1 .129 1 .284 1 .422 1 .528 1 .596 1 .667 1 .734 1 .803 1 .874 1 .948 

14 .630 16 .697 17 .548 18377 18 .962 19 .203 19 .441 19 .938 20.445 20.964 21 .492 

13 .039 13 .130 13 .045 13 .021 12 .963 12 .934 12 .901 12 .913 12.922 12.931 12 .937 

93 .848 106.305 116.162 124.410 130.667 134.305 138.048 142.400 146.886 151 .524 156.295 

Source: RPC 
Table 17.2 

Baseline Employment by Major Non-Farm Industry for the Florida Panhandle 

(Panama City) 

(in thousands of workers) 

Sector 

Agriculture 

Commercial Fishing 

Mining 

Construction 

Maintenance and Repair Oil & Gas Wells 

Non-Durable Manufacturing 

Durable Manufacturing 

TCPU 

Water Transportation 

Air Transportation 

Wholesale Trade 

Retail Trade 

Eating & Drinking 

FIRE Excluding Real Estate Rentals 

Hotels, Lodging Places, and Rentals 

Services 

Equipment Rental and Leasing 

Amusement and Recreation Services, N.E .C . 

Engineering, Architectural Services 

Accounting, Auditing and Bookkeeping 

Research, Development & Testing Services 

Government 

Federal Government - Military 

Total 

Source: RPC 

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 

0.50 0.48 0.52 0.56 0.60 0.61 0.63 0.64 0.66 0.68 0.70 

0.03 0.03 0.03 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 

5.56 4.94 5 .29 5 .57 5 .83 5.87 5 .96 6.03 6.21 6.40 6.60 

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 

1 .28 1 .35 1 .42 1 .42 1 .42 1 .40 1 .40 1 .40 1 .42 1 .44 1 .46 

1 .52 1 .19 1 .11 1 .03 1 .02 0.97 0.94 0.92 0.92 0.92 0.92 

1 .91 2.17 2 .36 2.50 2.56 2.57 2.60 2.62 2.69 2.76 2.83 

0.31 036 0.39 0.41 0.42 0.42 0.43 0.43 0.44 0.45 0.46 

0.15 0.18 0.19 0.20 0.21 0.21 0.21 0.21 0.22 0.22 0.23 

2.49 2.78 3.03 3.29 3.45 3.55 3.67 3.71 3.82 3.93 4.05 

9.67 9.65 10.56 11 .41 11 .94 12.15 12 .46 12 .62 13 .03 13 .44 13 .86 

6.20 6.19 6 .77 731 7.65 7.79 7.99 8 .09 8.35 8 .61 8.88 

1 .85 1 .98 2.24 2.46 2.64 2.72 2.83 2.89 3.00 3.12 3.24 

5.14 5.52 6.24 6.85 7.33 7.57 7.87 8.03 8.34 8.66 9.00 

13 .14 14 .10 15 .95 17 .51 18 .74 19 .33 20 .11 20 .51 21 .31 22 .14 22 .99 

0.16 0.18 0.20 0.22 0.23 0.24 0.25 0.26 0.27 0.28 0.29 

1 .08 1 .15 1 .31 1 .43 1.53 1 .58 1 .65 1 .68 1 .75 1 .81 1 .88 

0.61 0.66 0.74 0.82 0.87 0.90 0.94 0.96 0.99 1 .03 1 .07 

0.37 0.40 0.45 0.50 0.53 0.55 0.57 0.58 0.60 0.63 0.65 

0.47 0.51 0.57 0.63 0.67 0.69 0.72 0.74 0.76 0.79 0.82 

11 .60 12.16 12 .94 13.58 13 .99 13 .99 14 .11 14 .21 14.58 14 .96 1534 

5.00 4.83 4.81 4.81 4.80 4.75 4.75 4.67 4.67 4.68 4.68 

69.06 70.81 77.15 82.58 86.51 87.93 90.15 91 .25 94.09 97.01 100.03 
Table 17.3 

Baseline Employment by Major Non-Farm Industry for the Florida Panhandle 

( Pensacola) 

(in thousands of workers) 

Sector 

Agriculture 

Commercial Fishing 

Mining 

Construction 

Maintenance and Repair Oil and Gas Wells 

Non-Durable Manufacturing 

Durable Manufacturing 

TCPU 

Water Transportation 

Air Transportation 

Wholesale Trade 

Retail Trade 

Eating & Drinking 

FIRE Excluding Real Estate Rentals 

Hotels, Lodging Places, and Rentals 

Services 

Equipment Rental and Leasing 

Amusement and Recreation Services, N.E .C . 

Engineering, Architectural Services 

Accounting, Auditing and Bookkeeping 

Research, Development & Testing Services 

Government 

Federal Government - Military 

Total 

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 

2.661 3.145 3 .377 3.547 3.691 3 .774 3.858 3.948 4.040 4.134 4.229 

0.135 0.159 0.171 0.179 0.187 0.191 0.195 0.200 0.204 0.209 0.214 

0.322 0310 0.307 0.306 0.311 0.310 0.309 0.310 0.310 0.310 0.310 

17 .462 19 .211 20.539 21 .886 23 .069 23 .522 23 .979 24 .709 25 .458 26.228 27.019 

0.039 0.038 0.038 0.037 0.038 0.038 0.038 0.038 0.038 0.038 0.038 

7.802 8.208 7.674 7.323 7.097 6.740 6.400 6.348 6.297 6.245 6.193 

4.338 4.948 5 .005 4.994 5.070 5 .062 5.053 5 .112 5.172 5.232 5 .293 

5.869 5.821 5 .982 6.192 6.362 6.353 6.341 6.439 6.538 6.637 6.738 

0.509 0.505 0.519 0.537 0.552 0.551 0.550 0.559 0.567 0.576 0.585 

1 .166 1 .156 1 .188 1 .230 1 .264 1 .262 1 .260 1 .279 1 .299 1 .318 1 .338 

7.626 9.483 10.022 10 .640 11 .193 11 .305 11 .415 11 .706 12 .004 12 .308 12 .619 

23.426 26.650 27 .897 29.149 30.409 30.656 30.899 31 .587 32 .289 33 .003 33 .730 

11 .589 13 .183 13 .800 14 .419 15 .043 15 .165 15 .285 15 .626 15 .973 16 .326 16 .686 

5337 6.291 6.848 7330 7.820 8.007 8 .196 8.444 8.698 8.959 9.227 

5.071 5 .977 6.506 6.965 7.430 7.607 7.787 8.022 8.264 8.512 8.767 

53.877 63 .501 69.128 73 .995 78.937 80.822 82.737 85.234 87.800 90.435 93.141 

0.575 0.678 0.738 0.790 0.843 0.863 0.884 0.910 0.938 0.966 0.995 

0.789 0.930 1 .013 1 .084 1 .156 1.184 1 .212 1 .248 1 .286 1325 1 .364 

2.151 2.535 2.759 2.954 3 .151 3.226 3303 3.402 3.505 3.610 3.718 

1 .068 1 .259 1 .371 1 .467 1 .565 1.603 1 .641 1 .690 1 .741 1.793 1 .847 

0.545 0.643 0.700 0.749 0.799 0.818 0.838 0.863 0.889 0.916 0.943 

30.804 37.745 38 .570 39.223 40.196 40.008 39.813 40.426 41 .047 41 .673 42305 

10 .010 10 .966 10 .846 10 .822 10 .987 10 .969 10.950 10 .959 10 .967 10.975 10 .981 

193.173 223342 234.998 245.820 257.169 260.036 262.943 269.060 275.322 281.728 288.279 

Source : RPC 
Table 18.1 

Baseline Output by Major Non-Farm Industry for the Florida Panhandle 

(Fort Walton Beach) 

(in millions of 1994 dollars) 

v 

Sector 

Agriculture 

Commercial Fishing 

Mining 

Construction 

Maintenance and Repair Oil and Gas Wells 

Non-Durable Manufacturing 

Durable Manufacturing 

TCPU 

Water Transportation 

Air Transportation 

Wholesale Trade 

Retail Trade 

Eating & Drinking 

FIRE Excluding Real Estate Rentals 

Hotels, Lodging Places, and Rentals 

Services 

Equipment Rental and Leasing 

Amusement and Recreation Services, N.E .C . 

Engineering, Architectural Services 

Accounting, Auditing and Bookkeeping 

Research, Development & Testing Services 

Government 

Federal Government - Military 

Total 

Source: RPC 

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 

60.02 66.24 74.90 77.87 83 .62 83 .56 83 .47 86.52 89 .66 92.91 96 .26 

3 .68 4.06 4.60 4.78 5 .13 5 .13 5 .12 5 .31 5 .50 5.70 5 .91 

4.57 3 .48 3 .46 3.46 3.45 3 .45 3.44 3 .45 3.46 3.47 3.48 

510.00 565 .41 629.80 679.97 719.85 744.01 768.75 796.51 825.15 854.76 885.25 

0.17 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 

172 .64 210.43 226.14 234.45 242.12 250.15 25837 265.05 271 .86 278.83 285.92 

406.52 402.42 400.44 400.31 399.15 386.60 374.32 381.14 388.03 395.01 402.04 

308.76 355 .72 388.22 410.93 432.50 443.42 454.48 469.20 484.33 499.91 515.88 

16.38 18 .87 20.60 21 .80 22 .95 23 .52 24 .11 24.89 25 .69 26.52 27 .37 

27 .81 32.04 34.97 37 .01 38 .95 39 .94 40.94 42 .26 43 .62 45.03 46 .46 

202.51 239.99 289.08 301.56 313.21 325.27 337.69 350.41 363.55 377.16 391 .20 

417.57 483 .91 534.80 580.01 611 .77 629.74 648.03 671 .15 694.99 719.63 744.99 

270.00 312.90 345.80 375.04 395.57 407.19 419.02 433.97 449.38 46531 481 .71 

562.25 663 .43 755.32 837.69 901 .66 943.54 987.12 1,027.99 1,070.41 1,114.55 1,160.28 

840.41 991.64 1,129 .00 1,252 .12 1,347 .73 1,410 .33 1,475 .47 1,536 .57 1,599 .97 1,665 .94 1,734 .31 

1,022.29 1,206.24 1,373.32 1,523.09 1,63939 1,715.54 1,794.77 1,869.09 1,946.22 2,026.46 2,109.62 

16 .38 19.33 22 .01 24.41 26 .28 27 .50 28.77 29 .96 31 .19 32.48 33 .81 

33 .02 38 .96 4436 49.20 52 .95 55 .41 57 .97 6037 62 .87 65 .46 68 .14 

55 .50 65 .48 74.56 82 .69 89 .00 93 .13 97.44 101 .47 105.66 110.01 114.53 

19 .03 22 .45 25 .56 2835 30 .51 31 .93 33.40 34 .78 36 .22 37.71 39 .26 

73.03 86.17 98.11 108.81 117.12 122.56 128.22 133.53 139.04 144.77 150.71 

548.55 627.05 660.02 692.27 715.43 725.62 735.74 755.73 776.14 797.07 818.38 

458.07 462.00 459.72 459.58 458.25 457.92 457.46 458.58 459.62 460.64 461 .57 

6,029.15 6,878 .38 7,594.91 8,185.52 8,646 .71 8,925 .60 9,214.25 9,538.07 9,872.68 10,219.46 10,577.20 
Table 18.2 

Baseline Output by Major Non-Farm Industry for the Florida Panhandle 

(Panama City) 

(in millions of 1994 dollars) 

00 

Sector 

Agriculture 

Commercial Fishing 

Mining 

Construction 

Maintenance and Repair Oil and Gas Wells 

Non-Durable Manufacturing 

Durable Manufacturing 

TCPU 

Water Transportation 

Air Transportation 

Wholesale Trade 

Retail Trade 

Eating & Drinking 

FIRE Excluding Real Estate Rentals 

Hotels, Lodging Places, and Rentals 

Services 

Equipment Rental and Leasing 

Amusement and Recreation Services, N.E .C . 

Engineering, Architectural Services 

Accounting, Auditing and Bookkeeping 

Research, Development & Testing Services 

Government 

Federal Government - Military 

Total 

Source: RPC 

1995 2000 2005 2010 

13 .29 12.95 13 .99 15 .10 

0.72 0.70 0.76 0.82 

1 .61 1 .52 1 .52 1 .52 

497.84 442.75 475.13 501.08 

0.08 0.07 0.07 0.07 

261 .63 276.90 292.55 293.10 

254.12 198 .96 185.29 172.38 

326.32 371.15 405.07 429.02 

67.29 76.54 83.53 88.47 

21 .54 24.50 26.74 2832 

245.03 274.12 299.17 325.42 

358.66 358.61 392.91 425.28 

246.41 246.37 269.94 292.18 

469.05 504.15 571 .28 628.07 

611 .16 656.88 744.35 818.34 

695.18 747.19 846.69 930.85 

15 .56 16.72 18 .95 20 .83 

50 .77 54.57 61 .84 67 .98 

4234 45 .51 51 .57 56 .70 

23.94 25 .73 29 .16 32 .06 

26.83 28 .83 32 .67 35 .92 

460.39 483.61 515.21 541 .62 

168.70 163.14 162.78 163.09 

4,858.46 5,011 .47 5,481 .19 5,868.23 

2015 

16.15 

0.88 

1 .52 

525.36 

0.07 

292.67 

172 .12 

439.96 

90.73 

29.05 

342.04 

445.71 

306.21 

673.17 

877.12 

997.71 

22.33 

72.87 

60.77 

34.36 

38.50 

558.97 

162 .85 

6,161 .12 

2020 

16.55 

0.90 

1 .51 

529.40 

0.07 

290.35 

164.06 

442.18 

91.18 

29.19 

351.83 

454.20 

312.05 

695.64 

90638 

1,031 .00 

23.07 

75.30 

62.80 

35.51 

39.78 

559.91 

161 .56 

6,274 .41 

2025 2030 2035 

17 .10 17.34 17 .91 

0.93 0.94 0.97 

1 .51 1 .49 1 .49 

537.91 545.18 562.88 

0.07 0.07 0.07 

290.44 289.84 294.65 

157.67 155.24 155.69 

448.10 452.08 464.60 

92.40 93.22 95.81 

29.58 29.85 30.67 

364.90 369.27 380.68 

466.70 473.41 489.19 

320.63 325.24 336.08 

724.85 740.40 770.41 

944.45 964.71 1,003 .81 

1,074 .30 1,097.35 1,141 .82 

24.04 24.56 25.55 

78.46 80.14 8339 

65.43 66.84 69.55 

37.00 37.79 39.33 

41 .46 42.34 44.06 

565.52 570.45 586.17 

161 .61 159.11 159.57 

6,445 .07 6,536.87 6,754.34 

2040 2045 

18 .49 19 .10 

1 .00 1 .04 

1 .49 1 .49 

581 .00 599.72 

0.07 0.07 

299.45 304.35 

156.11 156.53 

477.36 490.47 

98.44 101 .14 

31 .52 32.38 

392.34 404.37 

50536 522.08 

347.19 358.68 

801 .44 833.75 

1,044 .24 1,086 .34 

1,187 .81 1,235 .70 

26.58 27.65 

86.75 90.25 

72.35 75.26 

40.91 42.56 

45 .84 47.68 

602.17 618.62 

160.00 160.43 

6,977 .92 7,209.65 
Table 18.3 

Baseline Output by Major Non-Farm Industry for the Florida Panhandle 

(Pensacola) 

(in millions of 1994 dollars) 

110 

Sector 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 

Agriculture 155.60 184.16 198.07 208.34 217.14 222.37 227.68 233.35 239.15 245.06 251.11 

Commercial Fishing 2.92 3.45 3 .71 3 .91 4.07 4.17 4.27 438 4.48 4.60 4.71 

Mining 18.05 17 .39 17.23 17.22 17 .50 17 .50 17.50 17.54 17.58 17 .62 17 .66 

Construction 1,489 .70 1,641 .45 1,757 .60 1,875 .82 1,980.24 2,022.28 2,064.77 2,130.82 2,198 .83 2,268.81 2,340.79 

Maintenance&Repair Oil & Gas Wells 0.27 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 

Non-Durable Manufacturing 1,779 .63 1,875 .19 1,755 .84 1,678 .29 1,628 .93 1,54939 1,473 .43 1,463 .80 1,454 .12 1,444 .39 1,434.58 

Durable Manufacturing 581 .09 663.75 672.48 671 .99 683.29 683.28 683 .12 692.23 701 .40 710.64 719.93 

TCPU 1,140 .68 1,133 .13 1,166 .25 1,209 .12 1,244 .25 1,244 .23 1,243 .94 1,265.05 1,286 .42 1,308 .04 1,329 .90 

Water Transportation 107.02 106.31 109.42 113 .44 116 .74 116.73 116.71 118.69 120.69 122.72 124.77 

Air Transportation 179.11 177.93 183 .13 189.86 195 .38 195.38 195.33 198.64 202.00 205 .40 208.83 

Wholesale Trade 748 .19 931 .83 986.33 1,048 .80 1,104 .98 1,117 .73 1,130.39 1,161 .00 1,192 .35 1,224 .43 1,257 .26 

Retail Trade 892.06 1,016 .38 1,065 .59 1,115 .13 1,165 .14 1,176 .42 1,187 .57 1,215 .90 1,244.82 1,274 .31 1,304 .37 

Eating & Drinking 450.71 513 .52 538 .39 563 .42 588.68 594.39 600.02 614.33 628.94 643 .84 659.03 

FIRE Excluding Real Estate Rentals 1,324.12 1,563 .09 1,704 .24 1,827 .06 1,952 .09 2,001 .81 2,052 .40 2,117.58 2,184.68 2,253 .72 2,324.72 

Hotels, Lodging Places, & Rentals 800.07 944.46 1,029 .74 1,103 .95 1,179 .50 1,209 .54 1,240.11 1,279.50 1,320.04 1,361 .75 1,404 .65 

Services 2,711.97 3,201 .41 3,490.50 3,742 .05 3,998.13 4,099.95 4,203.57 4,337.08 4,474.50 4,615 .90 4,761 .32 

Equipment Rental and Leasing 55 .46 65 .47 71 .38 76.52 81 .76 83 .84 85 .96 88 .69 91 .50 9439 97 .37 

Amusement&Recreation Srvcs, N.E .C . 31 .26 36.90 40.24 43 .14 46 .09 47.26 48 .46 49.99 51 .58 53 .21 54 .89 

Engineering, Architectural Services 148.22 174.97 190.77 204.52 218.51 224.08 229.74 237.04 244.55 252.28 260.22 

Accounting, Auditing & Bookkeeping 62.62 73 .92 80.59 86.40 92.32 94.67 97.06 100.14 103.31 106.58 109.94 

Research,Development&Testing Srvcs 24.80 29.27 31.92 34.22 36.56 37.49 38.44 39.66 40.91 42.21 43.54 

Government 1,362 .61 1,672 .24 1,711 .43 1,743 .09 1,789 .12 1,783 .50 1,777 .52 1,807 .70 1,838 .26 1,869 .17 1,900 .41 

Federal Government - Military 385.08 422.51 418.56 418.25 425.29 425.28 425.18 426.19 427.17 428.11 429.02 

Total 14,451 .24 16,449.00 17,223 .67 17,974.80 18,765 .98 18,951 .55 19,143 .43 19,599 .57 20,067.56 20,547.44 21,039.27 
Adjusted distribution of migrants within a metropolitan area to make the model's baseline 

population projections consistent with BEBR /BEA at the county level. 

To calibrate the MMS Florida Panhandle model, RPC ran iterations of the model to recreate 

BEBR's and BEA's baseline population projections by area and by counties within an area. 

RPC found that the allocation percentages in Table 19 produced a close approximation of these 

baseline population projections. The model uses these allocations in all of its scenarios. 

Table 19 

Allocation of Economic Migration Between Counties in a Metropolitan Area 

(in percent of total) 

2015- 

Area and Counties 1995 2000 2005 2010 2045 

Fort Walton Beach Area 100% 100% 100% 100% 100% 

Okaloosa County 83% 70% 70% 70% 70% 

Walton County 18% 30% 30% 30% 30% 

Pensacola Area 100% 100% 100% 100% 100% 

Escambia County 60% 65% 55% 45% 40% 

Santa Rosa County 40% 35% 45% 55% 60% 

Panama City Area 100% 100% 100% 100% 100% 

Bay County 100% 100% 100% 100% 100% 

The MMS Florida Panhandle model confirms that all three areas experience economic migration 

in the baseline from 1995 to 2045 (Table 20). 

50 
Table 20 

Annual Migration in the Florida Panhandle 

Baseline Scenario 

Area 1995 2000 2005 2010 2015 2020 2025 2030 

Fort Walton Beach 0 1,677 1,741 1,842 1,914 1,710 1,881 1,938 

Okaloosa County NA 1,174 1,219 1,290 1,340 1,197 1,317 1,356 

Walton County NA 503 522 553 574 513 564 581 

Panama City 0 638 1,075 1,233 1,250 944 1,142 825 

Bay County NA 638 1,075 1,233 1,250 944 1,142 825 

Pensacola 0 0 2,760 2,461 3,311 2,033 1,761 2,077 

Escambia County NA NA 1,518 1,108 1,324 813 704 831 

Santa Rosa NA NA 1,242 1,354 1,987 1,220 1,056 1,246 

Total 0 2,316 5,576 5,536 6,475 4,688 4,784 4,840 

Source: RPC. 

51 
Created a Baseline for Tourism Sector 

Using information derived from the literature review of the tourism industry in the Florida 

Panhandle, RPC used an approach based on a paper to estimate tourist expenditures in the 

Pensacola area to approximate a tourism baseline for the MMS Florida Panhandle model.8 This 

approach identifies a range of local services and retail trade by visitors from outside the Florida 

Panhandle as the model's baseline tourism expenditures . Using survey information on the 

distribution of tourism expenditures for various counties in the Florida Panhandle gathered by the 

Florida Department of Commerce in 1995 along with published figures on taxable sales on rental 

facilities, RPC estimated tourism expenditures in a way that highlights the expenditures of those 

visitors from out of state who would stay at hotels or other transient rentals, because those would 

be the people most at risk for choosing alternative vacation spots outside the Florida Panhandle, 

as opposed to residents of nearby counties who would visit on day trips. 

In dividing the MMS Florida Panhandle model into 23 sectors, RPC combined hotels and motels 

(SIC 65) with real estate (SIC 70) in order to capture the range of rental expenditures subject to 

tourist development taxes. According to Section 125 .0104 of the Florida Statutes: 

It is the intent of the Legislature that every person who rents, leases, or lets for 

consideration any living quarters or accommodations in any hotel, apartment 

hotel, motel, resort motel, apartment motel, rooming house, mobile home park, 

recreational vehicle park, or condominium for a term of six months or less is 

exercising a taxable privilege .' 

A comparison of taxable sales for rental facilities in the Florida Panhandle with preliminary 

estimates of IMPLAN's hotel and motel sector confirmed that taxable sales of rental facilities 

were larger than the expenditures on hotels and motels in some locations. 

Using tax on transient rental facilities, Tables 21 .1 - 21 .3 convert the sales subject to the tax on 

transient rental facilities and the distribution in other sectors . Tables 22 .1 - 22.3 show RPC's 

projections of tourist expenditures (final demand) from 1995 to 2045 . 

° Florida Department of Commerce, 1995; Huth and Stewart, 1995 ; Florida Legislative Committee on 

Intergovernmental Relations, Local Government Financial Information Handbook, September 1998 . 

9 Florida Legislative Committee on Intergovernmental Relations, Local Government Financial Information 

Handbook, September 1998, page 384. 

52 
Table 21 .1 

Distribution of Expenditures by Tourists in the Fort Walton Beach Area 

(in millions of 1995 dollars) 

Percent of Expenditure 

Item N III II I Average Total $ million 

Transportation 5.48 5.50 10.99 32.72 13 .67 1 .90% 9.16 

Gasoline 38.14 31 .95 46.21 55 .36 42.92 5 .98% 28 .74 

Food (Grocery) 59.94 84.52 61 .09 70.38 68 .98 9.60% 46.20 

Food (Restaurant) 110.65 133 .90 159.98 148.19 138 .18 19.24% 92 .55 

Lodging 83 .19 425 .28 383 .21 255 .61 286.82 39.94% 192 .10 

Entertainment 22.02 66.35 87.05 53 .43 57.21 7.97% 38 .32 

Gifts 57.17 49.56 50.32 69.50 56.64 7.89% 37.93 

Other 58.95 69.79 54.95 31 .48 53 .79 7.49% 36.03 

Total 430.06 861 .35 842.81 683 .95 718 .22 100.00% 481 .03 

Sources : RPC. 

Florida Department of Commerce, 1995 . 

Huth and Stewart, 1995 . 

Florida Legislative Committee on Intergovernmental Relations, Local Government Financial Information 

Handbook, September 1998. 

53 
Table 21.2 

Distribution of Expenditures by Tourists in the Panama City Area 

(in millions of 1995 dollars) 

Item 

Transportation 

Gasoline 

Food (Grocery) 

Food 

(Restaurant) 

Lodging 

Entertainment 

Gifts 

Other 

Total 

Sources: RPC. 

Percent of Expenditure 

IV III II I Average Total Million 

8.74 4.37 2.98 5 .47 5 .39 0.79% 3 .22 

55.60 46.85 55.44 57.46 53 .84 7.89% 32 .19 

79.73 55 .91 62.49 76.78 68 .73 10.07% 41 .09 

128.35 145 .11 135.94 129.12 134.63 19.73% 80.49 

191 .98 246.70 346.80 296.51 270.50 39.64% 161 .73 

67.82 85 .62 54.19 63 .25 67 .72 9.92% 40.49 

64.69 40.34 50.44 41 .90 49 .34 7.23% 29.50 

43.63 29.77 14.75 40.73 32 .22 4.72% 19.26 

631 .80 650.30 720.05 705 .75 682 .37 100.00% 407 .97 

Florida Department of Commerce, 1995. 

Huth and Stewart, 1995 . 

Florida Legislative Committee on Intergovernmental Relations, Local Government Financial Information 

Handbook, September 1998 . 

54 
Table 21.3 

Distribution of Expenditures by Tourists in the Pensacola Area 

(in millions of 1995 dollars) 

Item 

Transportation 

Gasoline 

Food (Grocery) 

Food 

(Restaurant) 

Lodging 

Entertainment 

Gifts 

Other 

Total 

Sources: RPC. 

Percent of Expenditure 

IV III II I Avera e Total Million 

15 .38 8.74 3 .59 51 .77 19.87 339% 8.41 

42 .60 40.88 32 .66 53.33 42.37 7.23% 17.93 

35 .55 51 .45 31 .16 78.77 49.23 8.40% 20.84 

99 .64 102.26 151 .69 148.70 125.57 21 .43% 53 .15 

113 .82 154.52 274.88 279.87 205.77 35.11% 87.10 

47 .98 72.62 70.75 48.00 59.84 10 .21% 25 .33 

44.13 34.67 54.25 10.32 35.84 6.12% 15 .17 

47 .27 44.36 64.33 34.35 47.58 8.12% 20.14 

430.99 500.76 679 .72 653.34 586.07 100.00% 248.07 

Florida Department of Commerce, 1995. 

Huth and Stewart, 1995. 

Florida Legislative Committee on Intergovernmental Relations, Local Government Financial Information 

Handbook, September 1998. 

55 
Table 22.1 

Projected Expenditures by Tourists in the Fort Walton Beach Area 

(in millions of 1995 dollars) 

Sector 

Transportation , Communication, Utilities 

Retail Trade Other than Eating & Drinking 

Eating & Drinking 

Hotels & Lodging Places 

Amusement and Recreation Services, N.E .C . 

Total 

Source: RPC. 

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 

9.2 10.8 12 .3 13 .6 14.6 15.2 15 .9 16 .6 17 .2 17.9 18 .6 

148 .9 175.4 199.4 220.8 237.3 248.0 259.0 2693 280.0 291 .1 302.6 

92 .5 109.0 123.9 137.2 147.5 154.1 161 .0 167.4 174.0 180.9 188.1 

192 .1 226.3 257.3 284.9 306.2 319.9 334.1 347.4 361 .2 375.5 390.4 

38.3 45.1 51 .3 56.8 61.1 63 .8 66.7 693 72.1 74.9 77.9 

481.0 566.7 644.2 713 .3 766.6 801 .0 836.7 870.0 904.5 940.4 977.5 

Table 22.2 

Projected Expenditures by Tourists in the Panama City Area 

(in millions of 1995 dollars) 

Sector 

Transportation , Communication, Utilities 

Retail Trade Other than Eating & Drinking 

Eating & Drinking 

Hotels & Lodging Places 

Amusement and Recreation Services, N.E .C 

Total 

Source: RPC. 

1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 

3.2 3.5 3.9 4.3 4.6 4.7 4.9 5.0 5.2 5 .4 5.6 

122.0 131.0 148.2 162.7 174.1 179.6 186.9 190.6 198.0 205 .7 213.6 

80 .5 86.4 97 .7 107.3 114.8 118.5 123 .2 125.7 130.6 135 .6 140.9 

161.7 173.6 196.4 215.5 230.7 238.0 247.6 252.5 262.4 272.5 283.1 

40.5 43.4 49.2 54.0 57.7 59.6 62.0 63.2 65.7 68.2 70.9 

408.0 437.8 495.3 543.7 581 .9 600.4 624.6 637.1 661 .9 687.5 714.1 
Table 22.3 

Projected Expenditures by Tourists in the Pensacola Area 

(in millions of 1995 dollars) 

Sector 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 

Transportation, Communication, Utilities 8.4 9.9 10.8 11 .6 123 12.6 12 .9 13 .3 13 .7 14 .1 14 .5 

Retail Trade Other than Eating & Drinking 74.1 873 95 .1 101.7 108.5 111 .1 113.8 117.2 120.7 124.4 128.1 

Eating & Drinking 53.2 62.6 68.2 73.0 77.9 79.7 81 .6 84.1 86.6 89.2 91 .9 

Hotels & Lodging Places 87.1 102.7 111 .8 119.6 127.6 130.7 133.8 137.8 141 .9 146.2 150.6 

Amusement and Recreation Services, N.E .C . 25.3 29.9 32.5 34.8 37.1 38 .0 38.9 40.1 413 42.5 43.8 

Total 248.1 292.4 318 .3 340.7 363.5 372.1 381 .0 392.5 4043 416.4 428.9 

Source: RPC. 
Public Services and Fiscal Impact Module 

Impact Triggers: Population (total, school age), Households (property valuations), 

Non-government Output (property valuation) 

The module presents a set of representative impacts on local governments . This module converts 

a scenario's impacts on output and population generated in the economic-demographic interface 

into impacts on local public finance and services. The module uses the change in the number of 

households in some of the tax revenue impacts. The models estimates the change in households 

by taking the net impact of the population and dividing by 2.5, which is the average number of 

people per household in the Florida Panhandle (Bureau of Economic Business Research, Florida 

Statistical Abstract 1996). 

Government Bodies 

The module estimates impacts on county and municipal governments . RPC decided to measure 

the impacts on municipalities based on totals of all residents and property within municipalities in 

a given county rather than create a more elaborate model that would require a gravity model to 

specify where economic migrants would locate within a county. The impacts on special districts 

within the counties were deemed to be outside the scope of this project. 

Fiscal Balance 

RPC used data on fiscal year 1996-1997 from the Florida Department of Banking and Finance 

(1998) . Tables 23 .1 - 23 .5 present a detailed breakdown of revenues and expenditures for each of 

the five counties of the Florida Panhandle. Table 23.6 shows the total expenditures per county. 

Tables 24.1 to 24.5 present the breakdown of all municipal expenditures and revenues in each of 

the five counties . Table 24.6 shows the combined municipal expenditures per county . Table 25 

presents the percentage of each county's population that live in unincorporated areas. RPC 

assumed that this percentage stayed constant over time. 

58 
Table 23.1 

County Government Revenues of Bay County 

(FY 1996-1997) 

Revenue Source Dollars Percentage 

Ad Valorem taxes 24,774,514 19.80% 

Other taxes, fees, and licenses 17,324,516 13 .84% 

Federal grants 514,368 0.41 

State and other govt sources 15,244,857 12 .18% 

Charges for services 29,003,403 23 .18% 

Fines and forfeits 1,477,214 1 .18% 

Special assessment and impact fees 149,506 0.12% 

Other miscellaneous revenues 9,038,192 7.22% 

Other sources / Interfund transfers 27,608,135 22 .06% 

Total 125,134,705 100.00% 

Source: Florida Department of Banking and Finance, personal communication, November 1998 . 

Table 23 .2 

County Government Revenues of Escambia County 

(FY 1996-1997) 

Revenue Source Dollars Percentage 

Ad Valorem taxes 50,841,280 23 .19% 

Other taxes, fees, and licenses 46,642,998 21 .28% 

Federal grants 8,430,148 3.85% 

State and other govt sources 35,518,949 16.20% 

Charges for services 40,156,659 18 .32% 

Fines and forfeits 2,402,829 1 .10% 

Special assessment and impact fees 5,462,213 2.49% 

Other miscellaneous revenues 13,733,772 6.26% 

Other sources / Interfund transfers 16,039,504 7.32% 

Total 219,228,352 100 .00% 

Source: Florida Department of Banking and Finance, personal communication, November 1998 . 

59 
Table 23.3 

County Government Revenues of Okaloosa County 

(FY 1996-1997) 

Revenue Tyne Dollars Percentage 

Ad Valorem taxes 21,471,797 14.78% 

Other taxes, fees, and licenses 7,057,170 4.86% 

Federal grants 4,048,773 2.79% 

State and other govt sources 17,632,392 12.14% 

Charges for services 40,900,772 28.16% 

Fines and forfeits 116,272 0.08% 

Special assessment and impact fees 82,200 0.06% 

Other miscellaneous revenues 16,826,100 11 .58% 

Other sources / Interfund transfers 37,108,784 25 .55% 

Total 145,244,260 100.00% 

Source: Florida Department of Banking and Finance, personal communication, November 1998. 

Table 23.4 

County Government Revenues of Santa Rosa County 

(FY 1996-1997) 

Revenue Type Dollars Percentage 

Ad Valorem taxes 20,143,797 25 .85% 

Other taxes, fees, and licenses 12,332,627 15 .83% 

Federal grants 485,989 0.62% 

State and other govt sources 10,720,043 13 .76% 

Charges for services 22,695,672 29 .12% 

Fines and forfeits 1,057,731 1 .36% 

Special assessment and impact fees 840,304 1 .08% 

Other miscellaneous revenues 4,122,335 5.29% 

Other sources / Interfund transfers 5,527,789 7.09% 

Total 77,926,287 100.00% 

Source : Florida Department of Banking and Finance, personal communication, November 1998. 

60 
Table 23.5 

County Government Revenues of Walton County 

(FY 1996-1997) 

Revenue Type Dollars Percentage 

Ad Valorem taxes 14,959,773 33 .57% 

Other taxes, fees, and licenses 7,589,193 17.03% 

Federal grants 3,237,898 7.27% 

State and other govt sources 6,668,814 14.96% 

Charges for services 5,272,988 11 .83% 

Fines and forfeits 708,020 1 .59% 

Special assessment and impact fees 365,313 0.82% 

Other miscellaneous revenues 2,256,359 5 .06% 

Other sources / Interfund transfers 3,506,117 7.87% 

Total 44,564,475 100.00% 

Source: Florida Department of Banking and Finance, personal communication, November 1998. 

Table 23.6 

County Government Expenditures 

(FY 1996-1997) 

Coun Dollars 

Bay 115,386,094 

Escambia 207,906,150 

Okaloosa 142,661,764 

Santa Rosa 76,521,782 

Walton 38,144,204 

Source: Florida Department of Banking and Finance, personal communication, November 1998. 

61 
Table 24.1 

Combined Municipal Government Revenues of Bay County 

(FY 1996-1997) 

Revenue Name Revenue Amount 

Ad Valorem taxes 5,48,046 

Other taxes, fees and licenses 20,694,195 

Federal grants 4,500,556 

State and other government sources 9,564,415 

Charges for services 42,938,793 

Fines and forfeits 1,108,136 

Special Assessments and impact fees 400,922 

Other Miscellaneous Revenues 13,166,022 

Other Sources/Interfund transfers in 12,32,121 

Total 110,183,206 

Source : Florida Department of Banking and Finance, personal communication, November 1998. 

Table 24.2 

Combined Municipal Government Revenues of Escambia County 

(FY 1996-1997) 

Revenue Name Revenue Amount 

Ad Valorem taxes 8,158,937 

Other taxes, fees and licenses 20,053,432 

Federal grants 7,351,718 

State and other government sources 8,176,547 

Charges for services 55,711,991 

Fines and forfeits 637,268 

Special Assessments and impact fees 90,166 

Other Miscellaneous Revenues 49,027,353 

Other Sources/Interfund transfers in 51,265,337 

Total 200,472,749 

Source : Florida Department of Banking and Finance, personal communication, November 1998. 

62 
Table 24.3 

Combined Municipal Government Revenues of Okaloosa County 

(FY 1996-1997) 

Revenue Name Revenue Amount 

Ad Valorem taxes 12,170,540 

Other taxes, fees and licenses 18,931,351 

Federal grants 2,774,656 

State and other government sources 11,513,295 

Charges for services 36,930,130 

Fines and forfeits 940,874 

Special Assessments and impact fees 147,720 

Other Miscellaneous Revenues 19,606,591 

Other Sources/Interfund transfers in 9,418,708 

Total 112,433,865 

Source : Florida Department of Banking and Finance, personal communication, November 1998 . 

Table 24.4 

Combined Municipal Government Revenues of Santa Rosa County 

(FY 1996-1997) 

Revenue Name Revenue Amount 

Ad Valorem taxes 1,050,604 

Other taxes, fees and licenses 1,411,136 

Federal grants 130,002 

State and other government sources 1,654,614 

Charges for services 12,844,891 

Fines and forfeits 320,584 

Special Assessments and impact fees 20,300 

Other Miscellaneous Revenues 4,274,459 

Other Sources/Interfund transfers in 3,867,712 

Total 25,574,302 

Source: Florida Department of Banking and Finance, personal communication, November 1998. 

63 
Table 24.5 

Combined Municipal Government Revenues of Walton County 

(FY 1996-1997) 

Revenue Name Revenue Amount 

Ad Valorem taxes 489,106 

Other taxes, fees and licenses 1,823,892 

Federal grants 1,014,119 

State and other government sources 1,100,140 

Charges for services 4,210,478 

Fines and forfeits 159,043 

Special Assessments and impact fees 0 

Other Miscellaneous Revenues 333,830 

Other Sources/Interfund transfers in 1,544,581 

Total 10,675,189 

Source : Florida Department of Banking and Finance, personal communication, November 1998. 

Table 24.6 

Combined Municipal Government Expenditures 

(FY 1996-1997) 

Court Amount 

Bay 93,953,905 

Escambia 169,567,112 

Okaloosa 94,794,446 

Santa Rosa 25,582,052 

Walton 10,360,776 

Source: Florida Department of Banking and Finance, personal communication, November 1998. 

64 
Table 25 

Population of Florida Panhandle Living in Unincorporated Areas in 1996 

Unincorporated 

County Unincorporated Total as % of Total 

Bay 57,439 142,159 40.40% 

Escambia 223,603 286,301 78 .10% 

Okaloosa 96,665 165,319 58 .47% 

Santa Rosa 84,315 98,491 85 .61% 

Walton 27,112 34,328 78 .98% 

Total 489,134 726,598 67 .32% 

Expenditures: The module uses per capita expenditures to measure the impacts on local 

government expenditures . Table 26 contains per capita expenditures for county governments, as 

well as the average per capita expenditures for municipalities . 

Table 26 

Per Capita Expenditures for County Governments and for Municipal Governments 

in the Florida Panhandle 

County 

Coun Government 

Bay 798 .06 

Escambia 714.12 

Okaloosa 834.09 

Santa Rosa 747.74 

Walton 1,056.80 

Average of All 

Municipal 

Governments 

1,096.84 

2,710.52 

888 .18 

1,785 .96 

1,439 .20 

65 
Revenues: Review of local government revenues by type show that Florida counties receive 

direct and indirect revenues. Direct revenues are taxes that individuals, households, and 

businesses pay in the form of ad valorem (property) taxes, sales taxes, tourist development taxes, 

fees, and licenses . Indirect revenues are grants, intergovernmental transfers, and other funds that 

local governments receive from sources outside the county itself , such as the State of Florida and 

the U.S . Government. 

The MMS model estimates the direct revenue impacts as ad valorem taxes and per capita taxes 

and fees based on changes in population and economic activity in a given scenario . Given that 

county and municipal government budgets are in balance in the long-run, the model also 

estimates the amount of revenue these governments need from indirect sources to balance the 

budget each year in a given scenario (i .e ., direct revenues plus indirect revenues equals 

expenditures). 

Ad Yalorem (Property) Taxes : The MMS model estimates the impact on ad valorem taxes from 

both residential and business sources . The model estimates the change in commercial and 

industrial property valuation for county governments by multiplying the change in 

non-government output by the commercial and industrial valuation in each county per million 

dollars of expenditures in the area. The model estimates the change in residential property 

valuation by multiplying the change in households times the average value of residential property 

in the county. For the impact on municipal governments, the model multiplies the county 

valuation figures by the percent of the county's population that live in incorporated areas. The 

model then estimates ad valorem taxes by multiplying the change in property valuation by the 

millages (unit of taxation based on one tenth of a cent) of the county and municipal governments . 

Table 27 contains the millage rates for county governments . Table 28 presents the taxation and 

average millage for municipalities in each of the five counties in the Florida Panhandle. Table 29 

contains the average taxable value of residential property in 1997. Table 30 contains the value of 

taxable commercial and industrial property in 1997. 

66 
Table 27 

Millage Rates for County Governments and Selected Special Districts 

in the Florida Panhandle 

Coun County County 

Government Government 

Operating Debt Service Total 

Bay 5 .632 0 .000 5.682 

Escambia 8.260 0 .000 8.792 

Okaloosa 4.528 0 .000 4.578 

Santa Rosa 6.972 0 .000 7.022 

Walton 6.740 0 .000 6.790 

Source: Florida Department of Revenue, Florida Property Valuations and Tax Data, December 1997. 

Table 28 

Municipal Taxation in the Florida Panhandle in 1997 

(in millions of dollars) 

County Taxable 

Value of All 

Property 

Taxes Average 

Levied Mi11aQe 

Bay 2,509.6 6.1 2 .4189 

Escambia 1,663.5 8.3 5 .0056 

Okaloosa 3,079 .8 9.1 2 .9512 

Santa Rosa 498.9 1 .050 2 .1058 

Walton 124.8 0.5 4.2585 

Source: Florida Department of Revenue, Florida Property Valuations and Tax Data, December 1997. 

67 
Table 29 

Average Taxable Value of Residential Property in 1997 

Court Taxable Value 

Bay 47,513 

Escambia 36,686 

Okaloosa 62,065 

Santa Rosa 37,869 

Walton 48,689 

Source : Florida Department of Revenue, Florida Property Valuations and Tax Data, December 1997. 

Table 30 

Value of Taxable Commercial and Industrial Property in 1997 

(in millions of dollars) 

County Commercial Industrial Commercial 

and Industrial 

Bay 732.4 90.6 823.0 

Escambia 959.2 250.9 1,210.1 

Okaloosa 755.6 84.6 840.2 

Santa Rosa 225 .2 35.5 260.7 

Walton 173 .3 15.2 188.5 

Source: Florida Department of Revenue, Florida Property Valuations and Tax Data, December 1997. 

Other Taxes, Fees: The model generates other direct taxes, fees, and licenses on a per capita 

basis . As with ad valorem taxes, municipal revenues for a county take into account the 

percentage of people living in incorporated areas in a given county . Table 31 presents data on 

taxes, fees, and licenses. 

68 
Table 31 

Per Capita Revenues 

(direct taxes, fees, and licenses) 

County County 

Government 

Bay 330.64 

Escambia 306.40 

Okaloosa 281 .07 

Santa Rosa 352.62 

Walton 375.97 

Source: RPC model. 

Schools 

Average of All 

Municipal 

rnvarnmantc 

755 .80 

1,221 .29 

808 .34 

1,017.64 

860 .32 

In the Florida Panhandle, each county has one school district . 

Expenditures : The model estimates school district expenditures by multiplying the change in 

school-age population in the chosen scenario by a per capita expenditure per full-time equivalent 

(FTE) for each school district (Table 32). 

Table 32 

School District Expenditures per Full-Time Equivalent (FTE) Student 

(in 1997 dollars) 

County Expenditures per FTE 

Bay 4,952 

Escambia 5,069 

Okaloosa 4,643 

Santa Rosa 4,657 

Walton 5,130 

Source : Florida Department of Education, 

Profiles of Florida School Districts 1996-1997 Financial Data, 

June 1998. 

69 
Revenues : The module estimates two types of revenue impacts for school districts : ad valorem 

and per capita revenue from state and federal sources. Ad valorem taxes multiply the increased 

valuation of residential and business property by school district millage. Table 33 contains 

millage rates of each school district in the Florida Panhandle. State and federal revenues are the 

product of the number of school-age children that the scenario generated times the amount of 

funding per FTE from state and federal sources. Table 34 presents Federal and State revenues 

that each school district received in the 1996-97 school year per full-time equivalent . 

Table 33 

Millage Rates for School Districts in the Florida Panhandle 

County Operating Buds Debt Service Total 

Bay 7.3270 2.0000 9.3270 

Escambia 9.5650 0.0000 9.5650 

Okaloosa 8.5720 0.0000 8.5720 

Santa Rosa 8.8460 0.0000 8.8460 

Walton 9.3540 0.0000 9.3540 

Source : Florida Department of Revenue, Florida Property Valuations and Tax Data, December 

1997 . 

Table 34 

School District Revenues per Full-Time Equivalent (FTE) by Source 

(in 1997 dollars) 

County Federal State Coun Total 

Bay 415 3,375 1,747 5,599 

Escambia 591 3,594 1,423 5,608 

Okaloosa 431 3,224 2,247 5,902 

Santa Rosa 399 3,430 1,549 5,378 

Walton 516 1,960 3,642 6,118 

Source : Florida Department of Education, Profiles of Florida School Districts 1996-1997 

Financial Data, June 1998. 

70 
Public Services 

Changes in local population change the demand on public services such as water usage, roads and 

highways to be maintained, and police and fire protection. The MMS model used community 

service multipliers for the five counties based on information from the Florida Bureau of 

Economic and Business Research and the 1992 Census of Governments to project impacts (Table 

35). The model projects these impacts on a per capita basis. 

Table 35 

Baseline Community Service Multipliers for Counties in the Florida Panhandle 

Santa 

Item Units Bay Escambia Okaloosa Rosa Walton 

Residential Gallons per Person per 88.46 83 .64 81 .90 91 .45 85 .51 

Water Day 

Residential Gallons per Person per 270.02 71 .39 106.33 30.11 71 .60 

Wastewater Day 

Solid Waste Tons per Person per 1 .78 1 .60 1 .41 0.74 0.70 

Year 

Road & Miles per Thousand 5.68 3 .74 4.54 7.09 20.17 

Highway People 

Police Staff per Thousand 2 .70 2.43 1 .95 2 .04 2.22 

People 

Crimes Number per Thousand 66 .76 61 .97 30.60 36 .56 29.60 

People 

Fire Staff per Thousand 1 .08 0.47 1 .04 0 .27 0.70 

People 

Public Staff per Thousand 0.04 0.56 0.03 0.22 0.17 

Welfare People 

Physicians Number per Thousand 1 .74 2.51 1 .70 1 .27 0.44 

People 

Sources: Bureau of Business and Economic Research, 1997 Florida Statistical Abstract ; U.S . Bureau of the 

Census,Compendium of Public Employment, Volume 3, Number 2, 1992. 

71 
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Chevron U.S .A. Production Company. 1997 . Destin dome 56 unit development and production 

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model re-estimation. Filed with the Minerals Management Service, New Orleans, LA. 

Florida Bureau of Economic and Business Research, personal communication, June Nogle, 

1996-1999. 

Florida Department of Banking and Finance, Government expenditures and revenues for counties 

and municipalities, Jeanne Dowdrick, personal communication, November 1998 . 

Florida Department of Commerce, Visitor Profile Reports, 1995 . 

Florida Department of Education, Profiles in Florida School Districts 1996-1997 Financial 

Data, December 1998 . 

Florida Department of Labor and Employment Security, personal communication. 

Florida Department of Revenue, Florida Property Valuations and Tax Data, December 1997 . 

Florida Legislative Committee on Intergovernmental Relations, Local Government Financial 

Information Handbook, September 1998, page 384. 

Gramling R. and S . Brabant. 1986. Boom towns and offshore energy impact assessment: The 

development of a comprehensive model. Sociological Perspectives 29 :177-201 . 

Hoover, E.M. and F. Giarrantani . 1984. An Introduction to Regional Economics, Third edition. 

Alfred A. Knopf, Inc. New York, NY. 

Huth, W.L. and R. Stewart. 1995 . Report on Escambia county tourism economic impact . 

University of West Florida. Pensacola, FL. 

Leistritz, F.L. and S .H . Murdock. 1981 . The Socioeconomic Impact of Resource Development: 

Methods for Assessment. Westview Press, Inc. Boulder, CO. 286 pp. 

Leistritz, F.L., S.H. Murdock, and R.C . Coon. 1990. Developing economic-demographic 

assessment models for substate areas. Impact Assessment Bulletin 8(4) : 49-65. 

72 
Leontief, W. 1953 . Studies in the Structure of the American Economy. Oxford University Press. 

New York, NY. 

Miller, RE. and P.D. Blair. 1985 . Input-Output Analysis : Foundations and extensions . PrenticeHall, Inc. Englewood Cliffs, NJ. 

Minnesota IMPLAN Group, Inc. (MIG). 1997 . IMPLAN professional version 1 .1 Software, 

User's Guide, Analysis Guide, and Data Guide. Stillwater, MN. 

Murdock, S . and F.L. Leistritz. 1979. Energy Development in the Western United States : 

Impact on Rural Areas . New York: Praeger Publishers 

U.S . Bureau of the Census. 1992. Compendium of Public Employment, Volume 3, Number 2. 

U.S . Bureau of the Census, Population of the United States by Age, Sex and Hispanic Origin, 

P25-1104 (November 1993) and P25-1130 (February 1996) 

U.S . Bureau of Economic Analysis (BEA). 1998. Regional economic information system 1969-

1996 . U.S . Department of Commerce. Washington, DC. 

U.S. Bureau of Labor Statistics website, http://www.bls .gov/emplabl .htm 

73 
,,,y The Department of the Interior Mission 

and 

Minerals 

Nation's 

, ,\ The M Management Service ice Mission 

W As a bureau of the Department of the Interior, the Minerals Management Service's (MMS) 

primary responsibilities are to manage the mineral resources located on the Nation's Outer 

Continental Shelf (OCS), collect revenue from the Federal OCS and onshore Federal and Indian 

lands, and distribute those revenues. 

Moreover, in working to meet its responsibilities, the Offshore Minerals Management Program 

administers the OCS competitive leasing program and oversees the safe and environmentally 

sound exploration and production of our Nation's offshore natural gas, oil and other mineral 

resources. The MMS Minerals Revenue Management meets its responsibilities by ensuring the 

efficient, timely and accurate collection and disbursement of revenue from mineral leasing and 

production due to Indian tribes and allottees, States and the U.S . Treasury . 

The MMS strives to fulfill its responsibilities through the general guiding principles of : (1) being 

responsive to the public's concerns and interests by maintaining a dialogue with all potentially 

affected parties and (2) carrying out its programs with an emphasis on working to enhance the 

quality of life for all Americans by lending MMS assistance and expertise to economic 

development and environmental protection . 

o="`~" - As the principal agency, the Department of the Interior has responsibility 

,, for most of our nationally owned public lands and natural resources. This includes fostering 

sound use of our land and water resources ; protecting our fish, wildlife, and biological diversity; 

preserving the environmental and cultural values of our national parks and historical places; 

providing for the enjoyment of life through outdoor recreation . The Department assesses 

our energy and mineral resources and works to ensure that their development is in the best 

interests of all our people by encouraging stewardship and citizen participation in their care. 

The Department also has a major responsibility for American Indian reservation communities 

and for people who live in island territories under U.S . administration .