Linking the Economy and Environment of Florida Keys/Florida Bay

Monroe County Tourist Development Council

NONMARKET ECONOMIC USER VALUES

OF THE FLORIDA KEYS/KEY WEST

October 1997

Vernon R. Leeworthy

Strategic Environmental Assessments Division

Office of Ocean Resources Conservation and Assessment

National Ocean Service

National Oceanic and Atmospheric Administration

U. S. Department of Commerce

J.M. Bowker

Outdoor Recreation and Wilderness Assessment Group

Southern Forest Research Station

USDA-Forest Service

Athens, GA

NAT OI NAL OCEANIC AND ATMOSPHERIC ADMINISTRAT OI N

U.S. DEPARTMENT OF COMMERCE
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Table of Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

List of Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

Overview: Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Environmental Quality and the Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Travel Cost Demand Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

 Key Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Visitor Responses to Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Per Person-trip User Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Annual User Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Asset Value of the Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Uses of Nonmarket Economic Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4

Putting the Cost of Water Quality Improvement and Protection into Perspective . . . . . . .5

Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5

Linking the Economy and Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Nonuse Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Market and Nonmarket Use Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Nonmarket User Values and Environmental Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

Sustainable Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Role of Technologies, Individual Behaviors, and Institutions . . . . . . . . . . . . . . . . . . . . .11

Lag Between Actual Environmental Conditions and User Perceptions - An Opportunity . . . . . . . . . .12

What Kind of Economy? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

Nonmarket Economic User Values: Natural Resource-based Trips . . . . . . . . . . . . . . . . . . . . . . . . .13

Travel Cost Demand Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Sample Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

Estimated Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

Variable Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

Annual User Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

Activity-based Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21

Asset Value of the Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

Uses of Nonmarket Economic Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .23

Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24

Endnotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28
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Preface

This report provides estimates of the nonmarket economic user values for recreating visitors to the Florida

Keys/Key West that participated in natural resource-based activities. It is the fifth in a series that is being

developed as part of the project entitled “Linking the Economy and Environment of the Florida Keys/Florida

Bay.” The overall project objectives are to 1) estimate the market and nonmarket economic values of recreation/tourism uses of the marine resources of the Florida Keys/Florida Bay ecosystem; 2) provide a practical

demonstration of how market and nonmarket economic values of an ecosystem can be considered an integral

component of the economy of a region when formulating sustainable development objectives and policies;

and 3) foster cooperative management processes.

To achieve the above objectives it is necessary to develop information about the users of marine resources,

the way users interact with resources (their recreation activities), the amount and pattern of spending associated with their uses, and users’ assessments of natural resources, facilities and services. It is also important

to develop the necessary tools to analyze the information in practical applications.

The project provided for the design and implementation of a survey of both residents and nonresidents of

Monroe County (visitors) with respect to their recreational activities in the Florida Keys/Florida Bay Area, and

analyses of the data collected to provide the following:

• Estimation of the number of residents and visitors to the Florida Keys and Florida Bay by type of use,

along with estimation of the extent of use by geographic areas (Upper Keys, Middle Keys, Lower Keys,

Key West, and access to Florida Bay through Everglades National Park).

• Development from survey data of profiles of residents and visitors including age, race/ethnicity, sex,

income, education, place of residence, activity participation and spending in the local and regional

economy.

• Estimation of the economic contribution (sales/output, income, employment) of both resident and visitor

uses of the Florida Keys and Florida Bay to the Monroe County economy and the South Florida (Broward,

Dade and Monroe Counties) regional economy.

• Estimation of the net economic user value of marine resources in the Florida Keys and Florida Bay.

• Importance and satisfaction ratings with respect to natural resources, facilities, and services and an

assessment of the importance of water quality and abundance and diversity of sealife as attractions for

visitors to the area.

The project is being conducted through a unique partnership between federal and local agencies and a

private nonprofit organization. Two offices within the National Oceanic and Atmospheric Administration

(NOAA): The Office of Ocean Resources Conservation and Assessment, Strategic Environmental Assessments Division and the Office of Ocean and Coastal Resource Management, Sanctuaries and Reserve

Division, Florida Keys National Marine Sanctuary; The Nature Conservancy, Florida Keys Initiative (TNC);

and The Monroe County Tourist Development Council (TDC) have entered into a cooperative agreement.

These are the “funding partners.”

The actual conduct of the project is done by the “working partners”. NOAA’s Strategic Environmental Assessments Division is the lead working partner and has an interagency agreement with the U.S. Forest Service’s

Southern Forest Research Station, Outdoor Recreation and Wilderness Assessment Group to conduct the

survey of visitors to the Florida Keys and Florida Bay area, and to jointly conduct economic analyses of the

data. The U.S. Forest Service has a cooperative agreement with the University of Georgia’s Environmental

and Resource Assessment Group and the Department of Applied and Agricultural Economics to conduct the

visitor survey and to provide an economist to assist in estimating the economic contribution of both resident

and visitor uses of the Florida Keys and Florida Bay Area. The University of Georgia has a cooperative

agreement with the Bicentennial Volunteers, Inc. to conduct all on-site interviews in the visitor survey. Florida

State University’s Policy Sciences Program, Survey Research Center conducted the survey of residents of

Monroe County under contract to NOAA’s Strategic Environmental Assessments Division.

This report is more technical than the previous fact based reports. The Overview: Summary and Conclusions

is a stand alone section and is directed at nontechnical audiences. The report's key findings and conclusions
iii

are summarized here. The remaining portion of this report provides the theory and methods used to derive

study findings and conclusions. Here we also attempted to lay out a simple conceptual model of the links

between the economy and the environment, showing the theoretical relationships between environmental

quality, use, and market and nonmarket economic values. We also provide our own view of how we think

these concepts relate to this issue of sustainable development in the Florida Keys/Key West. We follow this

with a discussion of what are nonmarket economic use values and how they are measured. We then present

the results from the estimated travel cost models and how we estimated nonmarket user values from these

models as well as how we calculate the asset value of natural resources using these values. We close with a

section summarizing the uses of nonmarket economic values and attempt to put the projected costs of

wastewater and storm water plans into perspective by comparing them with the annual user values and the

asset values of the natural resources of the Florida Keys/Key West.

This report, as well as the other reports in this series, are intended for all people involved in planning, managing or providing natural resources, facilities and services to residents and visitors of the Florida Keys/Key

West. A great deal of information is presented in these reports. Despite the enormous extent of information

available in the reports, the data bases from which these reports were generated are much richer in content.

We encourage users to explore further these rich sources of information by making special requests or

obtaining the data bases and documentation themselves. The visitor data and documentation are already

available on CD-ROM. The resident data and documentation will be available in November 1997.

Other Reports Available

Visitor Profiles: Florida Keys/Key West

Economic Contribution of Recreating Visitors to the Florida Keys/Key West

Importance and Satisfaction Ratings by Recreating Visitors to the Florida Keys/Key West

Technical Appendix: Sampling Methodologies and Estimation Methods Applied to the Florida Keys/Key

West Visitors Survey

A Socioeconomic Analysis of Recreation Activities of Monroe County Residents in the Florida Keys/Key

West

Technical Appendix: Sampling Methodologies and Estimation Methods Applied to the Survey of Residents

of Monroe County

World Wide Web Site

A world wide web site has been established that contains a project background along with all the reports

generated in the project in PDF file format. The site address is

http://www-orca.nos.noaa.gov/projects/econkeys/econkeys.html

The site also provides links to the Monroe County Tourist Development Council site where information can be

obtained on lodging, restaurants, and recreation facilities and services. There is also a link to the Florida

Keys National Marine Sanctuary site. You can also place orders for any of the project reports from this site.

For further information about this project, contact:

Dr. Vernon R. (Bob) Leeworthy

Project Leader

N/ORCA1

1305 East West Highway, SSMC IV, 9th Floor

Silver Spring, MD 20910

telephone (301) 713-3000 ext. 138

fax (301) 713-4384

e-mail: bleeworthy@seamail.nos.noaa.gov
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Table Page

1 Person-trips, Person-days, and Activity Person-days:

 All Recreating Visitors versus Natural Resource-based Visitors ................................................... 15

2 Definitions for Variables in the Travel Cost Demand Models .......................................................... 16

3 Travel Cost Demand Model: Natural Resource Based Trips, June - Nov. 1995 ............................ 17

4 Travel Cost Demand Model: Natural Resource Based Trips, Dec. '95 - May '96 ........................... 17

5 Price Elasticities from the Travel Cost Demand Models ................................................................. 18

6 Relationship between Trips (Visits) and Visitor Age (holding other factors at sample means ......... 19

7 Relationship Between Trips (Visits) and Household Income

 (holding all other factors at sample means) .................................................................................. 20

8 Annual User Values: Natural Resource Based Trips...................................................................... 21

9 Activity Based Annual User Values: Natural Resource Based Trips June - Nov. 1995 .................. 21

10 Activity Based Annual User Values: Natural Resource Based Trips Dec. '95 - Mat '96 ................. 21

11 Activity Based Annual User Values: Natural Resource Based Trips June 1995 - May 1996 ......... 22

12 Asset Value of Natural Resources (Billions $) ................................................................................. 22

13 Monroe County Five-Year Water Quality Plan ................................................................................ 23

List of Tables
v

List of Figures

Figure Page

1 Conceptual Model Linking the Economy and Environment .............................................................. 7

2 Hypothetical Demand Curve ............................................................................................................ 9

3 Changes in Environmental Quality and Changes in Consumer's Surplus

 (Nonmarket User Value) ................................................................................................................. 9

4 Sustainable Use, Environmental Quality and Market and Nonmarket Use Value

 under Existing Technologies, Individual Behaviors and Institutions ............................................. 10

5 Sustainable Development under Changing Technologies, Individual Behaviors and Institutions .. 11

6 Trips by Age June - Nov. 1995 ....................................................................................................... 19

7 Trips by Age Dec. '95 - May '96 ...................................................................................................... 19

8 Trips by Income June - Nov. 1995 .................................................................................................. 19

9 Trips by Income Dec. '95 - May '96 ................................................................................................ 19
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List of Appendices

1 Transformations of the Number of Trips and Travel Costs Consistent

 with this Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30

2 Matrices of Multiple Activity Participation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32

3 Derivation of Person-trips, Person-days, and Activity Person-days

 for Natural Resource-based Visitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37

4 Creation of the Continuous Household Income Variable . . . . . . . . . . . . . . . . . . .38
1

Overview: Summary and Conclusions

The objective of this review is to provide the results of the study and their implications with a minimum of

technical detail. Those who wish to simply review the results and understand their general application need

not read the remaining portion of the report. All the main conclusions and important study measurements are

summarized here. For those that want to learn more about the underlying theory and methods used, we

encourage you to read on. For more technical audiences, we have attempted to provide as much of the

technical details as is practical in the main body of the report and in report appendices. The results of this

report are limited to visitors (nonresidents of Monroe County) to the Florida Keys/Key West that

engaged in at least one natural resource-based activity on their visit to the area. This group of visitors

accounted for 72 percent of the total annual person-trips made by recreating visitors to the Florida Keys/Key

West. In addition, they accounted for 85 percent of the total annual person-days and 95 percent of the total

annual activity-person days.

It is unfortunate but true that the nature of nonmarket economic valuation is such that the methods used to

estimate such values are more technical and the underlying economic theory on which nonmarket economic

valuation is based is not as easy to understand compared with market values such as sales/output, income,

employment, and tax revenues. These market economic values are more familiar as we are exposed constantly to reports on the status of our economy, through newspapers, television, radio, magazines and other

media, as it relates to these measures. With every report of either upturns or downturns in any one these

measures, the inferences are that we are either better off or worse off.

 For at least 100 years, economists have recognized the deficiency of market economic values as indicators

of social or economic welfare or well-being. The reason is that market economic values do not fully account

for the values of resources not traded in markets. We have polluted our air and water because the lost values

from polluting these resources are not included in the costs of producing products. We harvest our renewable

natural resources such as fish, wildlife, and timber beyond their abilities to replenish themselves because

many of these resources are held in common and they too go unpriced in markets. But property rights aren’t

the only problem. Many wetlands are privately owned. However, all the public benefits that wetlands generate (e.g. habitat for fish and wildlife, water recharge to aquifers, storm and flood protection, etc.) cannot be

captured by the wetland owner i.e., there is no way the owner can figure out what or who to charge and

collect the charges. Therefore, the public benefits of wetlands are not considered by developers in a free

market economy. Consequently, most of the nation’s wetlands have disappeared and are continuing to

disappear despite the “no net loss” policy fostered during the Bush Administration.

There are real economic values associated with the environment and natural resources. Economics now

includes a separate discipline of environmental and natural resource economics. One of the main thrusts of

this field of economics is how to measure the nonmarket economic values of the environment and natural

resources. In recent years, the field of environmental and natural resource economics has expanded to

include ecological economics. Ecological economics attempts to integrate the methods of ecology and

economics. Terms such as ecosystem management, sustainability and sustainable development are the

subjects of ecological economics.

We attempted to bring together the concepts from environmental and natural resource economics and

ecological economics to show the relationships between environmental quality and tourist/recreational use of

the Florida Keys/Key West and how environmental quality and tourist/recreational use are related to market

and nonmarket economic values. We present a simple conceptual model of how the economy and environment are linked (See Figure 1, pg 7). We also provide our own definition of sustainable development as we

see it relating to practical application in the Florida Keys/Key West. The inset box on page 2 contains a

summary of the lessons on the relationship between environmental quality and the economy.
2

Travel Cost Demand Model Results

Travel cost demand models were estimated for both the summer and winter seasons. The models relate the

number of trips (visits) per year to the Florida Keys/Key West to travel costs (price) and other socioeconomic

factors such as age, household income, race/ethnicity, years experience visiting the Keys, forgone earnings to

make the trip, length of stay, and whether the visitor visited other sites. Travel cost demand models allow for

the estimation of how visitors would respond to price increases and their nonmarket economic user values per

person-trip.

Key Definitions

• Price elasticities. For estimated travel cost models, price elasticities provide a prediction of the

percentage change in the number of trips (visits) for a given percentage change in price, holding all

other factors constant. Elastic demands mean a more than proportional reduction in the number of

trips for a given change in price, while inelastic demands mean a less than proportional reduction in

the number of trips for a given change in price.

• Consumer’s Surplus or Nonmarket Economic User Value. The value a consumer receives from a

good or service over and above what they have to pay to consume the good or service.

• Asset Value of the Resources. This represents the price one would be willing to pay for the resources today based on the flow of annual user values that the resources could generate into the

indefinite future.

Visitor Responses to Prices. Price elasticities provide a prediction of the percentage change in the number

of trips (visits) for a given percentage change in price, holding all other factors constant. Significant differences were found between summer and winter season visitors. Significant differences were also identified for

Hispanic visitors during the summer season and day trip visitors during the winter season. The details of

these findings are summarized below.

Environmental Quality and the Economy

• Sustainable development in the Florida Keys/Key West is dependent on maintaining or increasing

the natural capital stock of the area. The natural capital stock is represented by the quality of the

environment and abundance & diversity of the natural resources of the area.

• In the long-run, market and nonmarket economic values will decline if environmental quality declines.

• Market economic values (sales/output, income, employment, tax revenues) are not good leading

indicators of the long term health of the natural resource dependent portion of the economy, because market economic values can increase in the short-run if natural capital is sacrificed.

• Theoretically, nonmarket economic values (consumer’s surplus, see definition on page 2) are a

better leading indicator of the long term health of the natural resource dependent portion of the

economy, but suffer the same problem, in that, nonmarket values can continue to increase in the

short-run if natural capital is sacrificed.

• Levels of sustainable use are a function of technologies, individual behaviors and institutions.

• Economic opportunities can be expanded by investments in technologies, changes in individual

behaviors, and changes in institutions that alter the relationship between environmental quality and

use.

• Environmental indicators can be better leading indicators of the long term health of the natural

resource dependent economy.
3

• Price elasticities for visitors to the Florida Keys/Key West vary with the level of prices and become more

“elastic” as the level of prices rise. In other words, a higher percentage response in trips for a given

percentage change in prices.

• Generally, visitor demands are price “inelastic,” which means a less than proportional change in trips

(visits) for a given change in price. This means that price increases will result in increases in total revenue.

• Generally, winter season visitors are more responsive to prices than summer season visitors.

• During the summer season, Hispanic visitors had more elastic demands than all other visitors. At overall

sample mean levels of travel cost, Hispanic’s price elasticity was -1.15 versus -0.30 for all other visitors.

This would mean that for a 10 percent increase in price, Hispanics would reduce their number of trips

(visits) by 11.5 percent, while other visitors would reduce their visits only 3.0 percent. However, Hispanic

visitors generally come from South Florida and have, on average, lower costs. At the Hispanic group

mean level of travel costs, Hispanic visitor’s price elasticity was equal to -0.30. And, at the all other

visitors group mean level of travel cost, the price elasticity for all other visitors was -0.34.

• During the winter season, no difference in price elasticities were found for Hispanic visitors. However,

they were a smaller proportion of winter season visitors.

• During the winter season, day trip visitors had more elastic demands than all other visitors. At overall

sample mean levels of travel cost, day trip visitors price elasticity was -0.71 versus -0.44 for all other

visitors. However, as with the Hispanic visitors during the summer, winter season day trip visitors primarily come from South Florida and have, on average, lower costs. At day trip visitors’ group mean level of

travel costs, day trip visitor’s price elasticity was -0.30. And, at the all other visitor’s group mean level of

travel costs, the price elasticity for all other visitors was -0.46.

Per Person-trip User Values. Separate travel cost demand models were estimated for summer and winter

season visitors that participated in natural resource-based activities. The travel cost demand models were

then used to derive estimates of the nonmarket economic user values on a per person-trip basis. The estimated models yielded not only different values by season but also significantly different values for Hispanic

visitors during the summer season and day trip visitors from South Florida during the winter season. The

estimated values are summarized below.

Summer Season (June - November, 1995)

• Hispanic visitors had a per person-trip user value of about $201 versus $790 for all other summer season

visitors.

• The weighted average per person-trip user value for all summer season visitors was about $740.

Winter Season (December 1995 - May 1996)

• Hispanic visitors did not have significantly different per person-trip values during the winter season.

However, Hispanic visitors were a much smaller proportion of winter season visitors.

• Day trip visitors from South Florida had a per person-trip user value of about $289 versus $594 for all

other winter season visitors.

• The weighted average per person-trip user value for all winter season visitors was about $561.

Annual Weighted Average

• The average annual weighted per person-trip user value was about $654.

Annual User Value

Annual user values were estimated by multiplying estimates of the total number of person-trips, by group, by

the per person-trip user values, by group, presented above. Estimates of the total number of person-trips, by

group, can be found in Table 1 of the main body of this report. The estimated annual user values are summarized by season and group below.

Summer Season. Hispanic visitors had a total user value of about $16 million while all other visitors had a
4

value of about $689 million for a total summer season value of about $705 million.

Winter Season. Day trip visitors had a total user value of about $28 million, while all other visitors had a

value of about $471 million for a total winter season value of about $499 million.

Total Annual Value. All visitors that engaged in natural resource-based activities had a total annual user

value of about $1.2 billion.

Activity-based User Values. Visits to the Florida Keys are a complex mix of recreation activities and most

often visitors do not think of any one activity as being either the main activity on the visit or the main reason

for making the visit. However, for many purposes activity-based user values are desired. The authors

generated estimates of user values by activity and season using the distribution of estimated person-days by

activity and season. This method produces a first approximation for the estimated values by activity because

the method assumes a constant per activity-day value for each activity i.e., that the value of a snorkeling day

is the same as a scuba diving day or a fishing day. Using this method, the estimated value of snorkeling is

different from scuba diving or fishing simply by the relative amount of days visitors spent doing each activity.

The method also allows for differentiating the proportion of value assigned to non natural resource-based

activities (e.g. swimming in outdoor pools, visiting historic areas, or visiting museums) and therefore provides

a conservative estimate of the proportion of value assigned to the natural resources of the area.

• The constant per person-day values were about $97 for the summer season and about $77 for the winter

season with an annual weighted average of about $87.

• Natural resource-based activities accounted for about 76 percent of the activity-days and total annual

user value of $1.2 billion, or about $910 million, while non natural resource-based activities accounted for

about 24 percent of the $1.2 billion, or about $294 million.

• Beach activities accounted for about $233 million in annual user value, viewing nature and wildlife about

$224 million, fishing about $171 million, snorkeling about $156 million, and scuba diving about $49

million.

Asset Value of the Resources

The natural resources of the Florida Keys/Key West are represented by the environment as a tourist destination and abundance & diversity of specific usable resources. The asset value of the resources of an area

represents the price one would be willing to pay for the resources today based on the flow of annual user

values that the resources would generate into the indefinite future. This value can be approximated using a

couple of conservative assumptions. First, the annual values (net of inflation) remain constant in all future

years. This means that combination of total person-trips and value per person-trip do not change in the

future. Second, the interest rate that converts future dollars to current dollars (net of inflation) i.e. the real

discount rate, ranges between 3 and 5 percent. Based on the findings in this study, the following asset values

are estimated.

• The total asset value of all natural resource-based trips is $24.1 billion at a 5 percent interest rate and

$40.2 billion at a 3 percent interest rate.

• Using only the natural resource-based activity component of value, the asset value of the resource is

$18.2 billion at a 5 percent interest rate and $30.4 billion at a 3 percent interest rate.

Uses of Nonmarket Economic

Values

Nonmarket economic use values

now have a long tradition of use in

benefit-cost analyses for public

projects that either benefit or have

adverse impacts on natural resources. We expect that the

greatest benefits will be had in

Uses of Nonmarket Economic Values

• Natural Resource Damage Assessment

• Restoration of Natural Resources

• Public Investments in facilities and land acquisition

• Public Investments in environmental protection

• Investments in education and enforcement efforts

• Green Accounting
5

applying the results in evaluations of many public projects that will be required to fully implement the FKNMS

Management Plan and the Monroe County Comprehensive Plan and Development Regulations (Florida

Department of Community Affairs, 1996). Other uses include natural resource damage assessments, restoration of natural resources, investments in education and enforcement efforts, and “green accounting”.

Nonmarket economic user values found in Leeworthy (1991) have been used in several damage assessment

cases brought against owners of vessels that have grounded on and damaged the coral reefs in the Florida

Keys. Millions of dollars have been recovered and these funds are being used to help restore the corals in

the area. Attempts are underway by the U.S. Department of Commerce, Bureau of Economic Analysis to

expand the Nation’s economic accounts to include the nonmarket economic values of the Nation’s natural

resources (Green Accounting). The values estimated in this project could provide important information to

this effort.

Putting the Cost of Water Quality Improvement and Protection into Perspective

 The Monroe County Comprehensive Plan and Land Development Regulations includes estimates of the fiveyear costs of implementation of all aspects of the water quality protection plan. The total costs of this plan

range from about $251 million to $283 million. However, implementation of all aspects of the water quality

plan could push costs to as high as $500 million.

The above costs do not include annual operating costs. On the other side, the large up-front investment costs

would be expected to have some useful life (usually 20 to 30 years). But even if we accept the $500 million

estimate, it still pales in comparison to the $1.2 billion in nonmarket use values we estimated for one year.

This conclusion still holds if we limit the comparison to only the natural resource-based activity component of

the annual nonmarket economic user value ($910 million). In addition, the five-year investment estimated for

wastewater and stormwater protection is only about 2.7 percent of the conservatively estimated natural

resource-based component of the asset value of the area’s natural resources at a 5 percent discount rate and

only 1.6 percent of the natural resource-based component of the asset value of the area’s natural resources

at a 3 percent discount rate. Although this simple comparison does not meet the rigorous requirements of a

benefit-cost analysis, it does suggest that the costs of water quality protection are a relatively small proportion

of the total nonmarket economic user value of the resources they are designed to protect. In addition, the

investment would have looked even more favorable if the other components of nonmarket values (e.g.

economic rents, nonuse values, and passive use values) could have been included. Given the area’s recognition internationally as a biosphere reserve, the nonuse and passive use values are potentially large, possibly equal to or exceeding the use values of the area.

Limitations

As with most studies there are many limitations. Even though we explain the definitions of all types of

nonmarket economic values, in this study, we were only able to estimate the nonmarket economic user values

related to tourist/recreational uses of the natural resources of the Florida Keys/Key West. Nonmarket values

such as “economic rents” to producers and nonuse or “passive use” values were not estimated as part of this

project. These values may be very important components of the total economic value of the area’s natural

resources, but they must be left for future research. We were also not able to estimate relationships between

environmental quality or the abundance & diversity of natural resources and tourist recreational uses in order

to estimate the “marginal benefits” of investments to improve or protect the environment. We cannot answer

the question “What are the benefits to recreating visitors of investments that reduce nutrient loads by either

10, 20, 50 or even 100 percent ? We can however estimate the total value of the resources that are negatively impacted by nutrients and place this against the total cost of protection efforts. This would not be a

legitimate comparison in a formal benefit-cost analysis, but would allow decision-makers to formulate benchmarks for deciding the level of investment. For example, would it be reasonable to spend an amount equal to

3 percent of the total value of the resource if the investments reduced nutrients 50 percent ? Other studies

and other experiences might be drawn upon to aid in this decision making. What we have provided is simply

one piece, albeit an important piece, of the puzzle.
6

The travel cost models that we presented are only a small subset of what was estimated. The models incorporate many judgments. Instead of presenting many different models incorporating many different modeling

choices and judgments, we chose to present what ,in our judgment, were the best results based on our own

extensive experience and the latest findings in our scientific journals. We have carefully documented each of

the modeling choices and judgments made and have made the data and documentation available to other

researchers. Rather than a limitation, we believe this to be a strength of our work.

A major limitation to all economic valuation work is our inability to forecast very far beyond current baselines.

When calculating the asset value of the resources, the formula used required the assumption that the total

annual user value in real terms (net of inflation) and the real discount rate (interest rate net of inflation) were

constant into perpetuity. Assuming that the quality of the resources of the area do not decline and increased

use does not result in negative reactions because of crowding, we called these conservative assumptions

(i.e., assumptions that would produce the smallest values). We would predict that the demand for trips (visits)

to the Florida Keys will increase in the future, and because of increasing scarcity of good recreation opportunities in the future (i.e., that demand will increase relative to total supply) nonmarket economic user values

per person-trip will also increase. The combination of both would mean increases in the total annual user

value in future years versus our assumption of constant annual value. This is simply a limitation of economic

science and a limitation that will not likely be overcome by future research.
7

Linking the Economy and

Environment

There are many factors that

determine the demand for recreation/tourism in the Florida Keys/

Key West. The overall economic

conditions of the U.S. and other

countries (close to 20 percent of

the recreating visitors to the

Florida Keys/Key West are foreign

visitors, see Leeworthy and Wiley,

1996a) play a major role, as do

exchange rates, energy costs, the

relative prices of goods and

services provided in the Keys, and

various socioeconomic factors

(e.g. household income, age,

race/ethnicity, years of experience

visiting the Keys, etc.). Other

determinants of demand include

the nature of supply in terms of

the quantity and quality of facilities

and services and the quality of the

environment and the abundance

and diversity of natural resources.

Figure 1 is a simple conceptual

model that shows the link between

the economy and the environment. In this model, actual

conditions with respect to the

quantity and quality of facilities &

services, the quality of the environment and abundance &

diversity of natural resources, and

the degree of crowdedness are

important factors in determining

peoples’ perceptions of these

conditions.

How peoples’ perceptions are

actually determined is complex.

Other important factors are

peoples’ frames of reference and

their experiences. For example,

people from highly developed

places of residences with no prior

experience with tropical or coral

reef environments will have quite

different perceptions of environmental quality than people from

less developed places and

extensive experience with visiting

tropical or coral reef environments. Peoples’ perceptions

determine their behaviors and

thus their demands and their

nonmarket economic values.

Nonmarket economic values then

determine the market economic

values. The level of demand for

recreation/tourism uses may then

have a feedback effect on the

actual conditions of facilities &

services and environmental

quality, the abundance & diversity

of natural resources, and the

degree of crowdedness.

Nonuse Values. Note that, in our

simple conceptual model,

nonmarket economic values are

divided into nonmarket user

values and nonuse or “passive

use” values. Nonuse values, by

definition, involve no direct uses of

the environment by the individuals

expressing their values. Part of

the population may not currently

be users, but may be willing to pay

an amount to have the option of

visiting sometime in the future.

This willingness to pay would be

like purchasing an insurance

policy to preserve actual conditions for possible future visitation.

Economists call this “option value”.

Another nonuse value is called

“bequest value”. Some people

that have never visited the Florida

Keys/Key West and may never

plan to make a visit, may still be

willing to pay an amount to ensure

that their children or grandchildren

have the opportunity to visit

sometime in the future. Still

others, who also have never

visited or plan to visit the area,

may also be willing to pay an

amount simply to ensure the

existence of the place, in a certain

condition. Economists call this

“existence value”.

Nonuse values are increasingly

being referred to as “passive use

values”. The reason is that to

have value for something requires

knowledge of what is being

valued. People learn about

natural resources and their

conditions through a variety of

media including books, magazines, newsletters, videos, television shows, etc. Through this

passive use (e.g. reading a book

or watching a television show),

people develop real economic

values for resources. Thus the

term “passive use value”.

Market and Nonmarket Use

Values. When people think of the

economy, they usually think of it in

terms of market economic values

such as spending, sales/output,

income, employment, and tax

revenues generated. But as

described in our simple conceptual

model, the market economic

values are driven by nonmarket

user values. But what are

nonmarket user values and how

are they measured ?

Nonmarket values can be broken

down into two categories according to the group receiving the

value. Generally, they have been

referred to as consumer’ surplus

and producer’s surplus. In

economic welfare theory,

consumer’s surplus and a special

sub-category of producer’s

surplus, economic rents, have

relevance in the context of economic efficiency arguments. The

concepts are related to efficiency

because they represent a net gain

from the utilization of a certain

amount of society’s available

resources. They are the values

that are incorporated in benefitcost analyses which attempt to

determine what is the best use of

society’s available resources i.e.,

which uses yield the greatest net

gain.

Economic rents are a return on

investment above “normal” returns

on investment. Normal meaning

the amount that could be earned

elsewhere. In an open and

competitive environment, economic rents will not exist for long

as new entrants will be attracted to

the relatively high rates of return
8

• Quantity and Quality of 

 Facilites & Services

• Crowdedness

• Quality of Environment

• Abundance and Diversity

 of Natural Resources

• Quantity and Quality of Facilites & Services

• Quality of Environment

• Abundance and Diversity of Natural Resources

• Crowdedness

• Number of Trips (visits)

• Number of Person-Days

Demand

• Net User Values of Natural 

 Resources

• Economic Rents to Producers

Value

• Books, Magazines, Newsletters, 

 Videos, Television Shows, etc

Demand

• Option Value

• Bequest Value

• Existence Value

Value

• Number of Trips (visits)

• Number of Person-Days

Demand

• Spending

• Sales/Output

• Income

• Employnment

• Tax Revenues

Value

• User Values + Nonuse Values

• Asset Values of Resource

Figure 1. Conceptual Model Linking the Economy and Environment

Asset Values of Resources Asset Values of Resources

Actual Conditions

Perceptions

Nonmarket User Values

Market Values

Total Value

• Market + Nonmarket

Actual Conditions

Nonuse and/or Passive Use Values

Total Nonmarket Value
9

on investment. Economic rents

can however persist when, for

example, governments pass

regulations that limit entry. If, for

example, the government passes

a regulation that prohibits further

dredging of wetlands to build or

expand marinas, this may limit the

supply of marinas relative to the

demand for marinas resulting in

returns on investment to existing

marinas above the normal rate of

return. These higher than normal

rates of return on investment

increase the asset value of

existing marinas. They therefore

could command a higher price to a

potential buyer.

When economists estimate

consumer’s surpluses for outdoor

recreation/tourism, they have used

the term nonmarket economic

user values or sometimes simply

use values. Consumer’s surplus

is an amount of value that a

consumer of a good or service

receives over and above what

they pay to get the good or

service. Figure 2 illustrates the

concept of consumer’s surplus.

Figure 2 shows a hypothetical

demand curve. A demand curve

shows the maximum amount a

consumer would be willing to pay

for each quantity of the good or

service per time period. The

demand curves shows the relationship between prices and

quantities holding all other factors

that determine demand constant

(e.g. income, taste & preferences,

and environmental quality).

At price, P1

, the consumer would

buy Q1 units/time period. That is,

P1 is the maximum the consumer

would be willing to pay in order to

get Q1 units/time period. The

consumer pays an amount equal

to P1*Q1 which is equal to the area

P1

 AQ1Q6. This area is equal to

the total sales or revenue from the

consumer’s purchase of Q1 units/

time period at price, P1. Similarly,

the consumer would be willing to

pay P2

 for Q2, P3 for Q3, etc. But

what the consumer is willing to

pay and what they have to pay are

determined by both the demand

and supply of goods and services.

If the market demand and supply

are such that the consumer gets to

purchase Q1 units/time period at

P1, the consumer only pays the

amount equal to the area

P1AQ1Q6. But according to the

demand curve, the consumer’s

total willingness to pay for Q1

would be equal to the area

Q6P6AQ1. The area P1P6A is the

consumer’s surplus or the amount

of value the consumer receives

from the amount of goods and

services consumed over and

above what the consumer was

required to pay.

Nonmarket User Values and

Environmental Quality.

Nonmarket user values for visits to

the Florida Keys/Key West are

determined, in part, by environmental quality. Increases in

environmental quality would

increase the demand for recreation/tourism. This would result in

an upward shift in the demand

curve and a resulting increase in

both use and consumer’s surplus

(nonmarket economic use value).

Figure 3 illustrates this effect.

In Figure 3, D1 is the demand for

recreation/tourism at the original

Q6

Demand

Price

$/Unit

(Q) Units/Time Period

Figure 2. Hypothetical Demand Curve

Q5 Q4 Q3 Q2 Q1

P6

P5

P4

P3

P2

P1

A

B

C

D

E

F

Price

$/Unit

(Q) Units/Time Period

Figure 3. Changes in Environmental Quality and Changes in Consumer’s Surplus 

 (Nonmarket User Value)

Q1 Q2

P*

P1

D1 (Environmental Quality, EQ1)

D2 (Environmental Quality, EQ2)

P**

Consumer’s

Surplus

Added Consumer’s Surplus from 

increase in Environmental Quality

A

B

0
10

environmental quality (EQ1). At

price, P1, visitors make Q1 visits to

the Florida Keys/Key West.

Visitors spend an amount equal to

the area 0P1AQ1 and receive a

consumer’s surplus equal to the

area P1P*A. When environmental

quality increases to EQ2, the

demand for recreation/tourism

increases to D2. At price, P1,

visitors will now make Q2 visits,

spend an amount equal to the

area 0P1BQ2, and receive a

consumer’s surplus equal to the

area P1P**B. Market spending

increases by the amount Q1ABQ2

and consumer’s surplus increases

by the amount equal to the area

AP*P**B. Thus both the market

and nonmarket economic use

values increase with increases in

environmental quality.

Sustainable Development

One of the overall objectives of

this project was to provide a

practical demonstration of how

market and nonmarket economic

values of an ecosystem could be

considered an integral component

of an economy when formulating

sustainable development projects

and policies. Defining sustainable

development in a manner that has

practical application has been, and

continues to be, a challenge.

Defining sustainable development

for the planet, for a nation, for a

county, and for a particular reef

area may all involve significantly

different considerations. But one

thing that seems to be common

among all definitions of sustainable development and one that

has practical value for application

to the Florida Keys/Key West is

the idea of increasing or maintaining the natural capital stock. By

this we mean the environmental

quality and abundance & diversity

of natural resources as presented

in our simple conceptual model of

how the economy and environment are linked.

Figure 4 shows the relationships

between environmental quality,

use, and market and nonmarket

economic values under existing

technologies, individual behaviors,

and institutions. The top part of

Figure 4 shows the relationship

between environmental quality

and level of use. This relationship

remains constant up to the sustainable level of use depicted as

point A. Up to this point, wastes

from use activities, harvest of

resources, and other use related

damages are such that the natural

stock of capital remains intact.

That is, the environment is able to

assimilate the amount of wastes

without declining environmental

quality. Fish harvests do not

exceed the population’s ability to

replenish itself. Damages to other

resources like seagrasses and

corals are at a rate not exceeding

the replenishment rates of these

resources.

The bottom portion of Figure 4

shows the relationship between

market and nonmarket economic

values and use for different

amounts of environmental quality

A Sustainable Use

EQ1

EQ1

Quality

of 

Environment

Use

Use A

Sustainable Use

EQ1

Value

Figure 4. Sustainable Use, Environmental Quality and Market and Nonmarket Use Value 

 under Existing Technologies, Individual Behaviors and Institutions

Market

Nonmarket

Market (EQ1)

Nonmarket (EQ1)
11

under existing technologies,

individual behaviors, and institutions. Note that up to the level of

sustainable use both market and

nonmarket economic values are

increasing. In the short-run, it is

possible that market and

nonmarket economic values can

continue to increase with use

levels above the sustainable level.

For this to occur requires the

short-run sacrifice of natural

capital for short-run gain. But as

Figure 4 shows, this situation

cannot persist (is not sustainable).

Using up the natural capital stock

will eventually lead to downturns in

both market and nonmarket

economic values.

Role of Technologies, Individual

Behaviors, and Institutions. The

relationships between environmental quality, use, and market

and nonmarket economic values,

as depicted in Figure 4, were

defined under existing technologies, individual behaviors, and

institutions. Changes in any of

these could shift these relationships. Different technologies

(advanced wastewater treatment)

and individual behaviors (prop

scaring and buoyancy control) can

change the relationship between

environmental quality and use.

Changes in technologies and

individual behaviors can expand

the market and increase the

sustainable level of use. This

essentially is the idea behind

sustainable development and what

differentiates it from simple

economic growth. Sustainable

development recognizes the

inherent limits of the natural

environment but also recognizes that it’s not simply how

much or often something is

done but how it is done that

determines economic value. It

may be true, for example, that

given currently applied technologies and individual behaviors,

Monroe County’s economic growth

is limited (cannot increase tourist

visitation or number of residents

without decreasing environmental

quality). However, a sustainable

development plan would change

how things are done, by encouraging investment in redevelopment

and changes in individual behaviors that would expand economic

opportunities without declines in

environmental quality (would be

sustainable).

Recent changes have been made

to the institutions in the Florida

Keys/Key West. The Florida Keys

National Marine Sanctuary

(FKNMS) was created along with

the Sanctuary Advisory Council

representing a broad cross-section

of stakeholders. This change in

institutions is an attempt at

integrated coastal management.

The recently approved FKNMS

Management Plan contains many

management strategies, most of

which have been organized into

action plans. This set of institutional changes can be viewed as

an attempt to shift the relationship

between environmental quality

and use and thus raise the longterm sustainable level of use

through investments in new

technologies (advanced wastewater treatment, mooring buoys,

channel markers) and changes in

individual behaviors through

education and enforcement

efforts. Figure 5 illustrates the role

A

Sustainable Use

EQ1

B

Sustainable Use

EQ2

EQ1

EQ2

Quality

of 

Environment

Use

Use

Market (EQ1)

Nonmarket (EQ1)

A

Sustainable

Use

EQ1

B

Sustainable

Use

EQ2

Value

Market (EQ1)

Nonmarket (EQ1)

Market (EQ2)

Nonmarket (EQ2)

Figure 5. Sustainable Development under Changing Technologies, Individual Behaiviors 

 and Institutions

Market (EQ2)

Nonmarket (EQ2)
12

of changing technologies, individual behaviors, and institutions

in raising the levels of sustainable

use thus expanding economic

opportunities.

Lag Between Actual Environmental Conditions and User

Perceptions - An Opportunity

In our simple conceptual model of

the link between the environment

and the economy, a crucial link

was between actual environmental

conditions and people’s perceptions of those conditions. People

do not always have complete and/

or accurate information about

environmental conditions. This is

made worse by either great

uncertainty of actual environmental conditions or disagreement

among scientists over the state of

environmental conditions. These

problems aside, there usually is a

time period between actual

changes in environmental conditions occur and when they are

perceived by users (lag period).

This lag period presents an

opportunity to correct the problems causing the adverse environmental conditions before they

have negative impacts on

nonmarket and market economic

values.

During the lag period, cash flow to

businesses and governments may

be relatively high and thus provide

the wherewithal and thus opportunity to make the necessary

investments in new technologies,

change individual behaviors, and

make any necessary changes in

institutions to improve actual

environmental conditions. If one

waits until perceptions of adverse

environmental conditions catch-up

with actual environmental conditions, the risk is that nonmarket

and market economic values will

already be on the decline and

remove the necessary cash flow to

effectively correct the problems.

This could lead to the spiraling

down in both nonmarket and

market economic values.

Another potential adverse impact

is that it may become more costly

to attract visitors, so this warrants

further discussion. Usually the

most sensitive and active users

recognize the changes in environmental conditions first. Sometimes the majority of users are

unaware of adverse changes in

environmental conditions because

they either have had no or little

previous experience with the

resources of the area or their

frames of reference (i.e. where

they live or other places they have

visited) are such that the resources in the Keys are by comparison much better or worse. A

key element in assessing this

situation is the distribution of the

most sensitive and active users

relative to those with little experience or frames of reference of

places with lower environmental

quality than the Keys.

First time visitors to the Florida

Keys/Key West made up 32

percent of the summer season

visitors and 40 percent of the

winter season visitors. Overall, 42

percent of visitors had visited the

Florida Keys/Key West at least

five years ago. In Leeworthy and

Wiley (1996b), it was found that

visitors who had visited the Keys

at least five years prior to the

current visit had significantly lower

satisfaction scores for eight of

eleven natural resource attributes,

facilities, and services they were

asked to rate (e.g. clear water,

amount of living coral on the reefs,

opportunity to view large wildlife,

uncrowded conditions, shoreline

access, parks and specially

protected areas, and value for the

price).

Changes in perceptions of the

quality of the natural resource

attributes, facilities, and services

of the area by the most experienced users may result first in

declines in nonmarket economic

use values which may result in

declines in use (e.g. trips or visits).

Reductions in the number of trips

(visits) will lead to reductions in

market economic values (e.g.

sales/output, income, employment, and tax revenues) and thus

negative impacts on the local

economy. To off-set this negative

impact, local government and

business may be forced into a

costly strategy of continually

attracting first-time visitors.

What Kind of Economy ?

The relationships between the

environment and the economy

discussed above are predicated

on the assumed desire for a

certain type of economy. There

are many different types of

economies in the U.S. New York

City currently has a vibrant

economy based on the usual

measures (e.g. sales/output,

income, employment). In addition,

other social indicators of wellbeing such as crime are on the

decline. But the economy of New

York City is not based on quality

snorkeling, scuba diving, or fishing

experiences nor on the opportunities to view and study an abundance and diversity of wildlife and

nature.

Monroe County has faced and will

continue to face the choice of what

kind of economy it wants. Many

development choices will not be

consistent with maintaining the

kinds of opportunities that currently exist. There might be just

as many jobs and just as much

income to be made by changing to

a non-environmentally based

economy, but it would indeed be a

totally different place.

Given the recent changes in the

Monroe County Comprehensive

Plan and Land Development

Regulations and the FKNMS

Management Plan, Monroe

County is moving towards an

economy based on a high quality
13

Environmental Quality and the Economy

• Sustainable development involves maintaining or increasing the

natural capital stock of the area. The natural capital stock is

represented by the quality of the environment and abundance

and diversity of the natural resources of the area.

• In the long-run, market and nonmarket economic values will

decline if environmental quality declines.

• Market economic values (sales/output, income, employment, tax

revenues) are not good leading indicators of the long term

health of the natural resource dependent portion of the

economy because market economic values can increase in the

shortrun if natural capital is sacrificed..

• Theoretically, nonmarket economic values are a better leading

economic indicator of the long term health of the natural

resource dependent portion of the economy than market

economic values, but suffer from the same problem, in that,

nonmarket values can continue to increase in the short-run if

natural capital is sacrificed.

• Levels of sustainable use are a function of technologies, individual behaviors and institutions.

• Economic opportunities can be expanded by investments in

technologies, changes in individual behaviors, and changes in

institutions that alter the relationship between environmental

quality and use.

• Environmental indicators can be better leading indicators of the

long term health of the natural resource dependent economy.

environment and high quality

natural resource-based activities.

But all of the efforts outlined in

both referenced plans will have

costs as well as benefits. Some of

these costs may seem quite high.

Further, costs are usually well

defined, while the benefits are

usually stated only qualitatively.

This imbalance in information can

lead to less than optimal investments.

In English et al., 1996, we presented estimates of the market

economic values associated with

recreating tourists to the Florida

Keys/Key West. Here we add

estimates of the nonmarket user

values of the natural resources of

the area. In the last section of this

report, we attempt to put some of

the projected costs of implementing the water quality and growth

management plans into perspective by relating them to the annual

user values generated by recreating visitors that participate in

natural resource-based activities

and the asset values of the

resources the programs are

designed to protect.

Nonmarket Economic User

Values: Natural Resource-based

Trips

Travel Cost Demand Models.

The travel cost method (TCM) is

one of the most popular means of

nonmarket valuation used over the

past 30 years. The theoretical

basis for TCM derives from the

basic notion of economic utility

maximization subject to budget

and time constraints. The method

is predicated on a number of

assumptions, foremost of which is

that individuals perceive and

respond to changes in the travelrelated component of cost of a trip

or visit to a recreation site in the

same way they would respond to a

change in admission price (Freeman, 1993). In its various forms

(see Fletcher, Adamowicz, &

Graham-Tomasi, 1990: Smith,

1989: or Ward & Loomis, 1986),

TCM has generally been preferred

to estimate economic values

derived from site use over other

nonmarket methods because of its

behavioral basis.

The most frequently used TCM

empirical approaches are the

zonal approach and the individual

approach. The zonal approach

(ZTCM) was the first developed

and is still widely used (English &

Bowker, 1996; Hellerstein, 1991;

Richards et al., 1990). It is based

on establishing a relationship

between per capita participation

rates at a site from various geographic origin zones and the costs

incurred in travel from the origin

zone to the given site. The

individual travel cost model (ITCM)

is conceptually similar to the zonal

model, however, the travel cost/

trip relationship is based solely on

individual observations. Another

important distinction between the

ZTCM and the ITCM is that with

the ZTCM one estimates the total

population of users using the

model. The ITCM requires an

independent estimate of the

number of users. Examples of

ITCM applications in recreation

include Adamowicz, Fletcher, &

Graham-Tomasi (1989); Creel &

Loomis (1990); Englin &

Shonkwiler (1995); and Leeworthy

(1991).
14

Currently, ITCM seems preferred

over ZTCM for reasons such as

(a) statistical efficiency, (b) theoretical consistency in modeling

individual behavior, (c) avoiding

arbitrary zone definitions, and (d)

increasing heterogeneity among

populations within zones. In

addition, statistical methods are

now available for dealing with the

integer nature of individual trip

demand and zero truncation

common to choice-based samples

(Creel & Loomis, 1990 and Yen &

Adamowicz, 1993) and endogenous stratification commonly

associated with on-site survey

data collection (Shaw, 1988).

Both of these problems have been

shown to effect a bias in regression-based demand models which

do not take them explicitly into

account (Hellerstein, 1992).

Another distinction in applying the

ITCM is the choice between

multiple-site models and single

site models. The most popular

multiple-site choice model is the

random utility model (RUM).

RUMs focus on site choice and

therefore substitution across sites.

RUMs also allow for the inclusion

of site attributes in the model.

Attribute values are derived from

these models by deducing their

value based on people’s choices

among sites with different attributes.

Although RUMs have several

desirable properties, the use of

this type of model for estimating

nonmarket user values was

rejected for several reasons. First,

trips to the Florida Keys/Key West

are characterized by visitors

engaging in a variety of activities,

and most often, none of the

activities is considered either the

main activity or the main reason

for visiting the area (see Appendix

2 for a set of activity matrices

showing the complex patterns of

activities participated in by visitors). RUMs are usually activity

specific in order to relate specific

site attributes to specific activities.

Second, the Florida Keys/Key

West is a unique place. It is the

only place in the U.S. that can be

accessed by automobile with an

expansive tract of coral reefs

within a short distance from shore.

Specifying a reasonable set of

alternative sites for the site

selection choice set is problematic. Third, most of the trips to the

area are longer than one day.

RUMs do not handle multiple-day

trips very well. But probably the

most important reason has to do

with the nature of the nonmarket

user values estimated from a

RUM.

 The RUM is focused on site

substitution. The main reason for

using this type of model is that it

addresses the biases inherent in

ignoring substitution across sites.

For example, if an oil spill occurs

on a beach, users can usually go

to another beach thereby mitigating losses in nonmarket economic

user values. A RUM model would

account for this whereas the

simple single site model would

not. Values derived from a single

site model would have an upward

bias because they would not

account for this type of substitution. However, when addressing

the long-run sustainability of an

environment, the RUM would over

adjusts for substitution when

applied to a resource characterized by the unique resources of

the Florida Keys/Key West. Even

the single site model is not completely able to capture long-run

values. We are very limited in our

abilities to project such values

very far into the future. In our

judgment, the single site ITCM is

more appropriate for estimating

the nonmarket economic use

values for the Florida Keys/Key

West.

Sample Data. The data used for

estimating travel cost demand

models came from a sub-sample

of the CUSTOMER Survey

conducted during the July-August,

1995 period and during the

January-April, 1996 period. The

July-August, 1995 period data

were used to estimate models for

the June-November, 1995 season

(summer) and the January-April,

1996 period data were used to

estimate models for the December

1995 - May 1996 season (winter).

[See Leeworthy (1996) for a

complete description of the

sampling methods and for estimates of the total population of

users (person-trips and persondays) for each season.]

Leeworthy and Wiley (1996a)

gives detailed profiles of the

visitors and Leeworthy and Wiley

(1996b) provides importancesatisfaction ratings for 25 natural

resource attributes, facilities, and

services.

The sub-sample used for estimating the travel cost demand models

only included those who participated in natural resource-based

activities. This included all waterrelated activities (except swimming in a pool) and viewing wildlife

or other nature study from land.

Complete information was provided by 1,608 respondents during

the summer season and by 2,427

respondents during the winter

season. Table 1 provides a

summary comparison of all

recreating visitors with the subpopulation of those visitors that

participated in natural resourcebased activities for person-trips,

person-days and activity persondays, by season.

Natural resource-based trips

accounted for 72.5 percent of the

total person-trips (81.3% summer

and 65.0% winter) made to the

Florida Keys/Key West by recreating visitors during the year June

1995 - May 1996. However,

natural resource-based visitors

accounted for much larger shares

of the person-days. They accounted for 85.4 percent (90.4%

summer and 82.5% winter) of the
15

total annual number of persondays.

The third measure in Table 1

requires further explanation.

Activity person-days are used in

this report to provide a first order

approximation of activity-based

user values by distributing the total

estimated value for each season

by the distribution of days across

activities. Activity person-days are

derived by adding up days of

recreation activity reported and

estimated for 39 different activities

(see Leeworthy, 1996 Chapter 4).

Since any part of a day was

counted as a whole day, visitors

could have participated in multiple

days of activities in a single day of

visitation in the Keys. So activity

person-days could potentially

exceed the total number of

person-days in the Keys. This

was true for the summer season.

However, during the winter

season, activity person-days were

less than total person-days in the

Keys. The reason for this is that

the winter season is characterized

by seasonal visitors that are on

extensive stays. Many do not

participate in outdoor recreation

activities every day they are in the

Keys.

Estimated Models. As mentioned

above, the on-site sampling format

leaves us with a sample that is

zero truncated because only

participants are sampled and

might be endogenously stratified

because the probability of being

chosen in an on-site sample is not

independent of the frequency of

usage.1

 To overcome estimation

biases caused by these two

problems, we employ a truncated

negative binomial regression

model which accounts for the

truncation problem while retaining

the property of an integer-based

trip distribution.2

 Within this

structure individual trip demand is

generally specified as:

(1) Ln (TRIPSi

) = β0 + βtc TCi +

βsb SBi + βm Mi + β SEi + μi

where, for the ith individual Ln

(TRIPS) is the natural log of the

quantity of recreation trips, TC is

the travel cost per trip, M is

income, SB is a substitute variable, SE represents a vector

including other relevant socioeconomic and site attributes, the β’s

are regression parameters and

exp (μ) is assumed to follow a

gamma distribution with mean 1.0

and variance α (Greene, 1995).

Seasonal Models. In Leeworthy

and Wiley (1996a), it was shown

that there are statistically significant differences between visitors

by season. This also proved true

with respect to the estimated

travel cost models. So we estimated separate models for the

summer and winter seasons.

These will be discussed below.

Variable Definitions. Table 2

provides definitions of all the

variables used in estimating the

final models presented here. 3

Several of the variables in Table 2

require further explanation. We

followed Bowker, English &

Donovan (1996) and defined the

dependent variable as a persontrip. Hence a family of four visiting

the Keys once per year would

account for four person-trips as

would an individual visiting the

Keys four different times in one

year. However, given the same

origins and travel modes, the price

per person-trip would differ as the

single visit cost for the family of

four would be apportioned to four

person-trips. While intuitively

appealing, this construction of the

dependent variable is practical for

situations where group travel by

car is common as in the Florida

Keys. As well, it helps to avoid the

empirical malady of low dispersion

of the dependent variable (Ward &

Loomis, 1986) which was espeTable 1. Person-trips, Person-days, and Activity Person-days: All Recreating

 Visitors versus Natural Resource-based Visitors

 June - Nov. 1995

Activity1

Person-trips Person-days Person-days

(Thousands) (Thousands) (Thousands)

All Recreating Visitors 1,172.0 4,889.4 7,613.8

Natural Resource-based Visitors 952.9 4,421.7 7,307.5

Percent Natural Resource-based 81.3 90.4 96.0

 Dec. '95 - May '96

Activity1

Person-trips Person-days Person-days

(Thousands) (Thousands) (Thousands)

All Recreating Visitors 1,368.5 8,409.2 6,972.8

Natural Resource-based Visitors 889.7 6,939.3 6,481.5

Percent Natural Resource-based 65.0 82.5 93.0

June 1995 - May 1996

Activity1

Person-trips Person-days Person-days

(Thousands) (Thousands) (Thousands)

All Recreating Visitors 2,540.5 13,298.4 14,586.6

Natural Resource-based Visitors 1,842.6 11,361.0 13,789.0

Percent Natural Resource-based 72.5 85.4 94.5

1. Activity person-days include double-counting since a visitor can do multiple

 activities in a day. During the winter season, activity person-days are less

 than total person-days in the Keys because of seasonal visitors that do not

 participate in outdoor activities on every day of their visit.
16

cially true for the winter season

sample. Appendix 1 reports the

details of how NTRIPS was

constructed.

Travel Costs. TC2PPTH is the

travel cost per person-trip scaled

in hundreds of dollars. 4 We call

this the marginal mileage cost

because we only assign a portion

of the total mileage costs to trips

where the Keys was not the

primary destination of the trip. We

also differentiated between modes

of travel used in calculating travel

costs. Mileage costs were equal

to $0.14/mile for those that used

auto mode only and $0.30/mile for

those that used multiple modes.

These per mile costs were calculated using information obtained

from an expenditure mail back

survey from the Auto, Air & Cruise

Ship Survey component of the

project (See Leeworthy and Wiley,

1996a). Appendix 1 details how

the travel cost variables were

constructed. An important point

here is that travel costs and trips

were constructed to be internally

consistent and thus yield proper

measurements for estimating the

relationships between the annual

number of person-trips and the

travel cost per person-trip.

Time costs. The inclusion of time

costs, both in-transit and on-site is

subject to considerable debate.

Theoretically, Freeman (1993)

demonstrates that both kinds of

time costs should be included.

However, he points out a number

of problems which continue to

plague applied researchers. One

is the inability of a large portion of

the population to easily substitute

between working increased hours

at their normal (or overtime) wage

rate and leisure time. Another is

the possibility of utility or disutility

resulting from work, travel, or onsite time, hence rendering the full

wage rate a potentially poor

measure of the shadow cost of

time. He points out that while

most surveys elicit a pretax

income measure, a more realistic

wage rate would be derived from

after tax income. McConnell

(1992) states that judgments

about time and the cost of time

have been dominated by theoretical considerations rather than

empirical results and that a

measure of the cost of time may

be considered “good” when it

yields an “appropriate” measure of

consumer’s surplus. Interestingly,

overall about 85 percent of those

surveyed reported that they lost no

opportunity to earn income during

their visit(s) to the Keys. Hence,

we chose to avoid the common

but arbitrary practice of factoring a

percentage of the household wage

rate into mileage costs opting

instead for a binary variable

(DTIME) to indicate the group for

which income was forgone to

partake of the trip. We also

constructed an interaction variable

between DTIME and TC2PPTH.

Neither variable was statistically

significant in any model specifications.

Length of trip. It is important in

travel cost modeling to control for

the length of trip. Trips to the

Florida Keys/Key West are

characterized by a wide range of

trip lengths. Again, there are

significant differences between the

length of trips during the summer

and winter seasons. DTRIP is the

average number of days per trip

calculated as the annual number

of trips to the Keys divided by the

annual number of days in the

Keys. This is different from the

length of the trip on which the

interview took place but not

significantly different. Summer

season visitors average trip length

was 4.9 days, while winter season

trips were on average 7.6 days.

We also constructed a binary

variable for single day trips since

these trips might be significantly

different (DDAY). We also conTable 2. Definitions for Variables in the Travel Cost Demand Models

Variable Definition

TC2PPTH Travel cost per person-trip in hundreds of dollars. Based on round-trip distance from

 home to the interview site for those where the Keys was the primary destination of the

 trip and from either a temporary residence or last place visited to the interview site

 for those on multiple destination trips. Mileage cost is the round-trip distance times

 $0.14/mile for auto only mode and $0.30/mile for multiple modes. 

HISPANIC Binary variable representing ethnicity (1=Hispanic, 0=non Hispanic)

HISPINT Hispanic-travel cost interaction (HISPANIC * TC2PPTH)

DDAY Binary variable for single day trips (1=day trip, 0=multiple day trip)

INTDAT Single day trip-travel cost interaction (DDAY * TC2PPTH)

DSUB1 Binary substitute variable (1=would travel to alternative site, 0=no alternative site)

YRKEYS Number of years experience visiting the Florida Keys/Key West

DTRIP Average number of days per trip

DTIME Binary variable (1=income forgone by taking trip, 0=no income forgone)

AGEH Age of person interviewed divided by ten.

AGESQ Age of person interviewed squared in hundreds of years.

INC Household income (in 10,000 of $)

INCSQ Household income (in 10,000 of $) squared

α Overdispersion parameter. A significant parameter indicates the presence of

overdispersion and that the negative binomial is the preferred model.

NTRIPS The annual number of person-trips to the Florida Keys/Key West for natural resource

 based activities (uncorrected).
17

structed an interaction variable

between DDAY and TC2PPTH

(INTDAY). Both DDAY and

INTDAY were statistically significant in the winter season model

but were not statistically significant

in the summer season model.

Substitution. As discussed in the

previous section of this report,

ignoring substitution can lead to

biases. We include a binary

variable (DSUB1) which indicates

whether the visitor would travel to

an alternative site. We also

constructed an interaction term

(DSUB1 * TC2PPTH) but this term

was not statistically significant in

any specifications of the models.

Race/Ethnicity. For race/ethnicity,

we originally constructed separate

binary variables for each racial/

ethnic classification. However,

only the binary variable for Hispanics (HISPANIC) entered any of

the models as statistically significant. We also constructed an

interaction term between HISPANIC and TC2PPTH (HISPINT).

HISPANIC was statistically

significant in both season models,

but HISPINT was only significant

in the summer season model.

This is an important finding and

will be discussed further below.

Other Socioeconomic Factors.

Age, household income, and years

of experience visiting the Keys

were also included in the travel

cost models. Age and household

income were included along with

their squared values. This yields

parabolic relationships between

the number of trips and each of

these variables. However, we

found exactly opposite relationships for the summer and winter

season models. This will be

discussed further below. It is

important to note that these

variables have been scaled down.

This is necessary when estimating

count data models that have

difficult time reaching convergence

in maximum likelihood estimation.

the hypothesis of no over dispersion was rejected based on a

Wald test equivalent to the asymptotic t-ratio on the estimated

dispersion parameter, α (Yen &

Adamowicz, 1993). Table 3

summarizes the results for the

summer season model and Table

4 summarizes the results for the

winter season model. All variables

included in both models were

statistically significant with high

levels of confidence as expressed

in the column labeled, P[ |z| ≥z].

This column contains the significance level for the test of whether

the estimated coefficient is significantly different from zero. A value

Also, the original household

income variable was obtained in

the survey using income categories or ranges of income and many

visitors did not provide their

income. We estimated household

income for those that did not

provide income with household

income models and converted the

values to a continuous variable

using the mid point of each

interval. The details for this can

be found in Appendix 4.

Results. Truncated Poisson and

truncated negative binomial (TNB)

models were estimated. 5 Only the

TNB models are reported because

Table 3. Travel Cost Demand Model: Natural Resource Based Trips, June - Nov. 1995

Variable (X) Coefficient (b) Standard Error (s.e.) z=b/s.e. P[|z| >=z] Mean X

Constant 1.457700 0.2150100 6.780 0.00000 1.0000

TC2PPTH -0.125840 0.0089661 -14.035 0.00000 2.4170

HISPINT -0.348920 0.0706860 -4.936 0.00000 0.0896

HISPANIC 1.516800 0.1410400 10.754 0.00000 0.1393

DSUB1 -0.528870 0.1000500 -5.286 0.00000 0.2108

YRKEYS 0.043377 0.0037677 11.513 0.00000 11.0500

DTRIP -0.052022 0.0037986 -13.695 0.00000 4.9140

AGEH -0.477380 0.0962590 -4.959 0.00000 3.9400

AGESQ 0.045937 0.0140180 3.277 0.00105 16.8900

INC 0.129450 0.0349040 3.709 0.00021 6.0300

INCSQ -0.005515 0.0016542 -3.334 0.00086 51.4600

 α 5.341700 0.9305100 5.741 0.00000

NTRIPS (mean) 9.531100

Log likelihood function -4291.697

Restricted log likelihood -15881.04

χ 2 23178.69

Degrees of freedom 1

Significance level 0.0000

N=1608

Table 4. Travel Cost Demand Model: Natural Resource Based Trips, Dec. '95 - May '96

Variable (X) Coefficient (β) Standard Error (s.e.) z=b/s.e. P[|z| >=z] Mean X

Constant -1.868900 0.3242300 -5.764 0.00000 1.000

TC2PPTH -0.167510 0.0108620 -15.422 0.00000 2.601

INTDAY -0.106390 0.0539390 -1.972 0.04856 0.084

DDAY 0.917130 0.1010600 9.075 0.00000 0.076

DSUB1 -0.521880 0.0547040 -9.540 0.00000 0.430

YRKEYS 0.048495 0.0025115 19.310 0.00000 9.290

DTRIP -0.007492 0.0031228 -2.399 0.01643 7.604

HISPANIC 1.201500 0.1602100 7.500 0.00000 0.050

AGEH 1.158100 0.1506700 7.686 0.00000 4.349

AGESQ -0.135980 0.0171170 -7.944 0.00000 20.960

INC - 0 . 0 5 5 6 1 5 0.0199510 -2.788 0.00531 6.512

INCSQ 0.002734 0.0009947 2.749 0.00599 61.390

α 3.312800 0.4564100 7.258 0.00000

NTRIPS (mean) 4.2662

Log likelihood function -4528.41

Restricted log likelihood -10041.70

χ 2 11026.58

Degrees of freedom 1

Significance level 0.0000

N=2427
18

of 0.05 would indicate significance

at the 5 percent level or that we

are confident at the 95 percent

level. The lowest significance

across both models was for

INTDAY in the winter season at

0.04856 significance or about the

95 percent confidence level. So

these are considered to be strong

results. Below we discuss the

interpretation of these results.

Price Elasticities. The coefficient

on TC2PPTH is negative indicating a downward sloping demand

curve as presented in section one

of this report. The price elasticity

of demand measures how the

demand for trips change with

changes in prices, holding all other

factors constant. Specifically,

price elasticities measure the

percentage change in the number

of trips for a percentage change in

price.

For the summer season, we were

able to identify significantly

different price elasticities for

Hispanics, while for the winter

season there were significantly

different price elasticities for

visitors on single day trips. Table

5 summarizes the results for both

seasons. The footnotes in Table 5

describe how price elasticities are

calculated using the estimated

coefficients in Tables 3 and 4 and

the mean travel costs. The results

indicate that, during the summer

season, Hispanic visitors are more

sensitive to price changes than all

other visitors. At the overall

sample mean travel cost, demand

for trips is elastic. For a 10

percent increase in price, Hispanics would reduce their number of

trips by 11.5 percent. This represents a more than proportional

reduction in trips for a given

change in price. During the

summer, the price elasticity of

demand for all other visitors was

only -0.30, meaning that for a 10

percent increase in prices all other

visitors would reduce their number

of trips by only 3.0 percent. We

say that this is an inelastic price

response or a less than proportional response. However, in the

model specification estimated,

price elasticities are variable and

depend on the level of prices, that

is, price elasticities vary with the

level of price. Using separate

group means for travel cost,

Hispanics have inelastic demands.

At the group mean travel cost for

Hispanics ($0.643 in hundreds of

dollars or $64.30), the price

elasticity is only -0.31. Thus, for a

10 percent increase in the average

cost to Hispanics at the current

level of costs to Hispanics, they

would reduce their trips only 3.1

percent. For all other visitors at

their mean travel cost, a ten

percent increase in prices would

result in a 3.4 percent reduction in

the number of trips. Using separate group means reduces the

differences in price responses

between Hispanic and all other

visitors. However, an important

point is that if prices that Hispanics

experience were to rise to the

overall sample mean levels,

Hispanics would reduce their trips

to the Florida Keys by a much

higher percentage than other

visitors.

During the winter season, we did

not find a significant difference in

the price elasticity of demand for

Hispanics (HISPINT was not

statistically significant). However,

we did find that visitors on day

trips did have significantly different

price elasticities. Although dayvisitors had more highly elastic

demands than all other visitors,

day visitor demands are still

inelastic. At the overall sample

mean, for a ten percent increase

in prices, visitors on day trips

would reduce their number of trips

by 7.1 percent, but at their group

mean travel cost would only

reduce their trips by 3.0 percent.

All other visitors would reduce

their trips 4.4 percent for a 10

percent increase in travel costs at

the overall sample mean travel

cost and 4.6 percent for a 10

percent increase in costs at the

Table 5. Price Elasticities from the Travel Cost Demand Models

Price Elasticities 1

At Sample Mean At Group Mean

Season/Group Travel Cost 2 Travel Cost 3

June - Nov. 1995

 Hispanics -1.15 -0.31

 All Others -0.30 -0.34

Dec. '95 - May '96 

 Day Trippers -0.71 -0.30

 All Others -0.44 -0.46

1. Price elasticities are calculated using the formula Btc * TC, where 

 Btc is the coeffcient ont the travel cost variable (TC2PPTH) and TC

 is the travel cost (TC2PPTH). Elasticities vary with the level of travel

 cost. For Hispanics, Btc is equal to the coefficient on TC2PPTH

 plus the coefficient on HISPINT. For Day trippers, Btc is equal to the

 coefficient on TC2PPTH plus the coefficient on INTDAY.

2. For June - Nov. 1995, the overall sample mean for TC2PPTH was

 $2.417. For Dec. '95 - May '96, the overall sample mean for TC2PPTH

 was $2.60.

3. For June- Nov. 1995, the sample mean for TC2PPTH for Hispanics

 was $0.643 and was $2.704 for All Other Visitors. For Dec. '95 -

 May '96, the sample mean for TC2PPTH for Day trippers was $1.101

 and was $2.724 for All Other Visitors.
19

group mean travel cost. Generally, winter season visitors are

more sensitive to price increases

than summer season visitors.

The importance of price elasticities

for businesses has to do with

projecting total sales revenues in

response to different prices.

Inelastic demands mean that, for

price increases, total revenue will

increase. That is, the increase in

prices will more than offset the

decrease in demand. However,

for decreases in price, total

revenue will decrease because

demand will not increase enough

to offset the decrease in price.

Except for Hispanics during the

summer season at sample mean

levels of price, demand appears to

be price inelastic for the Florida

Keys/Key West.

Relationship between Age and

Trips. The estimated relationships

between age and the number of

trips were parabolic for both

seasons but with opposite relationships by season. For the summer

season, the number of trips first

decreases with increasing age,

reaching a minimum at age 52,

then increases with increases in

age beyond age 52 (see Table 6

and Figure 6). For the winter

season, the number of trips first

increases with age, reaching a

maximum at age 43, then decreases with increases in age

beyond age 43 (see Table 6 and

Figure 7).

Relationship between Household

Income and Trips. The estimated

relationships between household

income and the number of trips

were also parabolic for both

seasons and were also characterized by opposite relationships by

season. For the summer season,

number of trips first increases with

increases in household income,

reaches a maximum at $117,368,

then decreases with increases in

income beyond $117,368 (see

Table 7 and Figure 8). For the

winter season, number of trips first

decrease with increases in household income, reaches a minimum

at $101,714, then increases with

income beyond $101,714 (see

Table 7 and Figure 9).

Relationship between Race/

Ethnicity and Trips. Above we

discussed the difference price

responsiveness for Hispanics

during the summer season.

Hispanics also, on average, take

more trips than all other visitors

during both the summer and

winter seasons. Hispanics are,

however, a much smaller proportion of total visitors during the

winter season. During the summer season, Hispanic visitors

make, on average, 14.46 more

trips than all other visitors, holding

other factors constant. During the

winter season, Hispanic visitors

make, on average, 5.12 more trips

than all other visitors, holding

other factors constant.

Figure 7. Trips by Age: Dec. ‘95 - May ‘96

TRIPS

(Visits)

AGE

0.3

2.2

5.6

16 43 90

Figure 6. Trips by Age: June - Nov. 1995

TRIPS

(Visits)

AGE

0.0

8.3

15.1

16.2

0 52 100 16

Figure 8. Trips by Income: June - Nov. 1995

TRIPS

(Visits)

INC

6.6

8.5

12.4

$10,000 $117,368 $200,000

Figure 9. Trips by Income: Dec. ‘95 - May ‘96

TRIPS

(Visits)

INC

3.9

$10,000 $101,714 $200,000

4.9

5.1

Table 6. Relationship between Trips (Visits) and Visitor

 Age (holding other factors at sample means)

June - Nov. 1995 Dec. '95 - May '96

Trips Age Trips Age

15.1 16 2.2 16

11.6 25 3.7 25

9.5 35 5.2 35

8.5 45 5.6 4 3

8.3 5 2 5.5 45

9.0 65 3.7 60

9.7 70 2.8 65

11.9 80 2.0 70

16.2 90 0.8 80
20

Relationship between Other

Factors and Trips. The marginal

effects are reported here. Marginal effects are the changes in

the number of trips for a one unit

change in a factor. Marginal

effects are calculated as the mean

number of NTRIPS times the

estimated coefficient on a selected

factor. For the summer season,

increases in the average length of

trip by one day results in a reduction in the number of trips of 0.50

trips. For the winter season,

increases in the average length of

trip by one day results in a reduction in the number of trips by 0.03

trips. During the winter season,

day trip visitors make, on average,

3.9 more trips than all other

visitors. For those that have

substitute sites (DSUB1=1) the

results were different by season.

During the summer, those with

substitute sites take, on average,

5.0 less trips than those that do

not have substitute sites. During

the winter season, those with

substitute sites take, on average,

only about 2.23 less trips than

those that do not have substitute

sites. The relationship between

years of experience visiting the

Keys and number of trips was

significantly different between

seasons. During the summer

season, a one year increase in

experience results in about 0.41

more trips. During the winter

season, the estimate is 0.21.

Annual User Values

From the estimated travel cost

models, we can estimate the per

person-trip nonmarket economic

user values. The formula is simply

1/βtc or one divided by the absolute value of the estimated travel

cost coefficient6. To obtain estimates of total annual user values,

we multiply the estimated per

person-trip values by the total

number of person-trips made to

the Florida Keys/Key West by

those on trips where they participated in natural resource-based

activities. These calculations were

made by season then added

across seasons to get an annual

total.

User Values Per Person-trip. As

discussed in the previous section

on estimated price elasticities, we

found different price responsiveness for Hispanics during the

summer season and visitors on

day trips, during the winter season. The differences in estimated

price coefficients also has implications for estimated user values as

well. According to the formula

above, the higher the absolute

value of the travel cost coefficient,

the lower the per person-trip use

value. During the summer season, Hispanics had an estimated

per person-trip use value of

$201.30 versus $790.61 for all

other visitors. The weighted

average per person-trip value for

all visitors, during the summer,

was $740.52. During the winter

season, those on day trips had a

per person-trip value of $289.16

versus $594.47, with a weighted

average for all visitors of $561.19.

The summer season per persontrip values were significantly

higher than the winter season

values. This is true even though

trips are of significantly longer

length during the winter. Part of

this difference is probably due to

the greater share of activity in

water related activities during the

summer, especially snorkeling and

scuba diving. The overall

weighted annual average for per

person-trip user values was

$653.94. Table 8 summarizes

these results7

.

Annual User Values. Table 1 of

this report contains the estimated

total number of person-trips to the

Florida Keys/Key West made by

those that participated in natural

resource-based activities. Table 8

provides a breakdown of these

estimates by group and season so

we can calculate total annual user

values using our estimates of per

person-trip values by group and

season. Table 8 shows that

summer season visits were worth

almost $706 million, while winter

season visits were worth about

$499 million for a total annual

value of about $1.2 billion.

Total Market and Nonmarket

Value. Referring back to our

simple conceptual model linking

the economy and environment, the

Table 7. Relationship between Trips (Visits) and Household

 Income (holding all other factors at sample means)

June - Nov. 1995 Dec. '95 - May '96

 Household Household

Trips Income Trips Income

6.6 $10,000 4.9 $10,000

7.3 $20,000 4.7 $20,000

8.1 $30,000 4.5 $30,000

8.9 $40,000 4.3 $40,000

9.6 $50,000 4.2 $50,000

10.3 $60,000 4.1 $60,000

10.9 $70,000 4.0 $70,000

11.5 $80,000 3.95 $80,000

11.9 $90,000 3.92 $90,000

12.2 $100,000 3.91 $100,000

12.4 $117,368 3.9 $101,714

11.7 $150,000 4.2 $150,000

8.5 $200,000 5.1 $200,000
21

estimated annual nonmarket

economic use value of $1.2 billion

would be added to the market

economic values estimated at

about $1.33 billion for Monroe

County or $2.94 billion for all of

South Florida to obtain total

market and nonmarket economic

use values. The total would be

about $2.5 billion if we count only

the market output in Monroe

County and over $4.1 billion if we

count the total market output to

South Florida (Broward, Dade,

and Monroe Counties). Details on

the market economic values were

reported in English et al. (1996).

However it is important to note

that only the nonmarket economic

value portion of total economic

value is appropriate to use in

benefit-cost analyses.

Activity-based Values. Traditionally, values for outdoor recreation

have been reported on an activity

basis. As was discussed earlier

and presented in Appendix 1 of

this report, trips to the Florida

Keys are typically characterized by

visitors participating in multiple

activities. It is also true that not all

the estimated value of even

natural resource-based trips is

attributable to the natural resources of the area. Therefore,

we have provided a break-down of

the annual value by activity for

each season. We call this a first

approximation set of estimates

because the estimates are based

only on the distribution of activity

days, that is, the total user value

for a season is apportioned to

different activities according to the

percent distribution of person-days

across activities. The reason that

this will only be considered a first

order approximation is that it

results in a constant per personday value across activities. Thus,

a snorkeling person-day has the

same value, for a given season,

as a scuba diving day, a fishing

day, etc. Total values differ for an

activity simply on the basis of the

relative amount of days visitors

Table 8. Annual User Values: Natural Resource Based Trips

June - November 1995

 User Value Annual

Group Per Person-trip ($) Person-trips User Value ($)

Hispanics $201.30 81,001 $16,305,501

All Others $790.61 871,955 $689,376,343

 Total $740.52 952,956 $705,681,844

Dec. '95 - May '96

 User Value Annual

Group Per Person-trip ($) Person-trips User Value ($)

Day trippers $289.16 96,972 $28,040,424

All Others $594.47 792,679 $471,223,885

 Total $561.19 889,651 $499,264,309

June '95 - May '96

 User Value Annual

Group Per Person-trip ($) Person-trips User Value ($)

All Natural Resource $653.94 1,842,607 $1,204,946,153

Table 9. Activity Based Annual User Values: Natural Resource Based

 Trips, June - Nov. 1995

Annual

Person-days % of User Value

Activity (Thousands) Days (Millions $)

All Snorkeling 1,261.0 17.26 121.801

All Scuba Diving 408.8 5.59 39.448

All Fishing 1,055.1 14.44 101.900

Personal Watercraft Use 238.0 3.26 23.005

Sailing 80.6 1.10 7.763

Other Boating 190.6 2.61 18.418

Viewing Nature & Wildlife 1,031.8 14.12 99.642

All Beach Activities 1,321.9 18.09 127.658

Windsurfing 8.0 0.10 0.706

Natural Resource-based sub-total 5,595.8 76.57 540.341

Swimming in Outdoor Pools 1,169.7 16.01 112.980

Visiting Historic Areas 369.5 5.06 35.708

Visiting Museums 172.5 2.36 16.654

Non Natural Resource-based sub-total 1,711.7 23.43 165.341

Total 7,307.5 100.00 705.682

Table 10. Activity Based Annual User Values: Natural Resource Based

 Trips, Dec. '95 - May '96

% Annual

Person-days of User Value

Activity (Thousands) Days (Millions $)

All Snorkeling 441.5 6.81 34.000

All Scuba Diving 126.0 1.94 9.686

All Fishing 894.7 13.80 68.898

Personal Watercraft Use 140.4 2.17 10.834

Sailing 137.1 2.12 10.584

Other Boating 70.1 1.08 5.392

Viewing Nature & Wildlife 1,613.4 24.89 124.267

All Beach Activities 1,366.7 21.09 105.295

Windsurfing 16.4 0.25 1.248

Natural Resource-based sub-total 4,806.3 74.15 370.204

Swimming in Outdoor Pools 1,056.1 16.30 81.380

Visiting Historic Areas 409.3 6.31 31.504

Visiting Museums 209.8 3.24 16.176

Non Natural Resource-based sub-total 1,675.2 25.85 129.060

Total 6,481.5 100.00 499.264
22

Table 12 includes the asset values

calculated using the above

formula under alternative discount

rates. A range of real discount

rates (not including inflation since

the returns or annual user values

are not increased by projected

inflation rates) from 3 to 5 percent

is used. The total asset values for

natural resource-based trips

ranged from $24.1 billion to $40.2

billion. Including only the natural

resource-based activity portion of

total trip value yields a range of

asset values of $18.2 billion to

$30.4 billion. The asset value of

the resources represent the price

one would be willing to pay today

based on the flow of annual user

values the resource would generate into perpetuity (the indefinite

future). Indeed the resources of

the Florida Keys/Key West would

appear to be of extremely high

value even under the conservative

assumptions used here.

Uses of Nonmarket Economic

Values

Nonmarket use values now have a

long tradition of uses in benefitcost analyses for public projects

that either benefit or have adverse

impacts on natural resources.

Nonmarket values are now widely

used in natural resource damage

assessment cases. The most

widely known case in recent times

was the oil spill in Alaska by the

Exxon Valdez. Exxon settled out

of court for $2.5 billion and this

spent doing those activities. Table

9 shows these calculations for the

summer season, Table 10 for the

winter season, and Table 11

provides an annual total by adding

the values derived in Tables 9 and

10.

Natural resource-based activity

values are estimated to account

for about 77 percent of the summer season values and 74 percent

of the winter season values with a

weighted annual average of about

76 percent. The natural resourcebased component of total annual

value was estimated at over $910

million. The constant per personday values were about $97 for the

summer and about $77 for the

winter, with a weighted annual

average of about $87.

Asset Value of the Resources

The annual user values estimated

and presented above are flows of

benefits for a particular period of

time attributable to the areas

assets. The natural resourcebased component of the value

would be attributed to the asset

represented by the environment

and abundance & diversity of the

natural resources of the area. The

value of an asset is defined as

follows:

R0 V=

(1+n)t0 + + . . . +

R1

(1+n)t1

Rk

(1+n)tk

where,

V = value of the asset

R = return to the asset

n = discount rate

k = number of time periods

t = time periods (years)

0, 1, 2, . . ., k

If the return, R, flows for a large

number of periods (k→∞), then the

formula above can be simplified

where the returns are constant

into the future (R1 = R2 = Rk) to

the following:

 V = R

 n

Table 12. Asset Value of Natural Resources (Billions $)

1,2

Value at Various Discount Rates

3% 4% 5%

Natural resource-based component only $30.4 $22.8 $18.2

Non Natural resource-based component $9.8 $7.4 $5.9

Total natural resource-based trips $40.2 $30.2 $24.1

1. Asset value is calculated using conservative assumptions. First, the number of

 person-trips and person-days are assumed to remain constant in the future.

 Second, the real value (adjusted for inflation) per person-trip or per person-day

 is also assumed to remain constant in the future. Finally, the appropriate

 discount rate (interest rate adjusted for inflation) ranges between 3 to 5

 percent.

2. The formula for calculating the asset value is the annual user value divided

 by the discount rate. This yields an estimate of the net present value of

 an annual series of constant values into perpetuity.

Table 11. Activity Based Annual User Values: Natural Resource Based

 Trips, June 1995 - May 1996

Annual

Person-days User Value

Activity (Thousands) (Millions $) Percent

All Snorkeling 1,702.5 155.801 12.93

All Scuba Diving 534.8 49.134 4.08

All Fishing 1,949.8 170.798 14.17

Personal Watercraft Use 378.4 33.839 2.81

Sailing 217.7 18.347 1.52

Other Boating 260.7 23.810 1.98

Viewing Nature & Wildlife 2,645.2 223.909 18.58

All Beach Activities 2,688.6 232.953 19.33

Windsurfing 24.4 1.954 0.16

Natural Resource-based sub-total 10,402.1 910.545 75.57

Swimming in Outdoor Pools 2,225.8 194.360 16.13

Visiting Historic Areas 778.8 67.211 5.58

Visiting Museums 382.3 32.830 2.72

Non Natural Resource-based sub-total 3,386.9 294.401 24.43

Total 13,789.0 1204.946 100.00
23

was an amount less than the

nonmarket value losses estimated.

Nonmarket user values found in

Leeworthy (1991) have been used

in several damage assessment

cases brought against owners of

vessels that have grounded on

and damaged the coral reefs in

the Florida Keys. Millions of

dollars have been recovered and

are being used to help restore the

corals in the area.

But damage assessments and

restoration focus on events that

have happened in the past.

Sustainable development also

focuses on past events to the

extent they can increase the

natural stock of capital (e.g

restoration and redevelopment

projects). We expect that the

greatest benefits will be had in

applying the results in evaluations

of the many public projects that

will be required to fully implement

the FKNMS Management Plan

and the Monroe County Comprehensive Plan and Land Development Regulations (Florida Department of Community Affairs, 1996).

Below we attempt to simply put

into perspective the estimated

costs for implementing the Monroe

County Five Year Work Program.

We do this by simply comparing

the projected five year costs with

the annual user values and the

corresponding asset values of the

natural resources the investments

proposed are designed to protect.

The Monroe County Five Year

Plan calls for the development of a

water quality plan (Table 13). The

total costs for these items range

from $251 million to $283 million.

The report goes on to mention full

implementation of all aspects of

the water quality plans could push

costs to as high as $500 million.

The above costs do not include

annual operating costs. On the

other side, the large up-front

investment costs would be expected to have some useful life

(usually 20 to 30 years). But even

if we accept the $500 million

estimate, it still pales in comparison to the estimated $1.2 billion in

nonmarket user values for one

year. Even if we only count the

natural resource-based activity

component of $910 million per

year, the $500 million seems

reasonable. So just one years

worth of annual user values

exceed the five-year investment in

wastewater and stormwater

protection efforts.

If we compared the $500 million

investment with the asset values

of the natural resources they are

designed to protect, the $500

million would only be about 2.7

percent of the natural resourcebased component of the asset

value at the 5 percent discount

rate and only 1.6 percent of the

natural resource-based component of the asset value at the 3

percent discount rate. Referring

back to our discussion in the

beginning of this report on the role

of technology in sustainable

development, we showed how

both market and nonmarket

economic values would increase

with investments in environmental

protection. The above comparisons were made assuming

constant nonmarket values. If the

investments in water quality

protection succeed, then this

would expand the market increasing the sustainable level of use

and the market and nonmarket

economic values making this

investment seem even more

favorable.

Limitations

As with most studies there are

many limitations. Even though we

explain the definitions of all types

of nonmarket economic values, in

this study, we were only able to

estimate the nonmarket economic

user values related to tourist/

recreational uses of the natural

resources of the Florida Keys/Key

West. Nonmarket values such as

Uses of Nonmarket Economic Values

• Natural Resource Damage Assessment

• Restoration of Natural Resources

• Public Investment in facilities and land acquisition

• Public Investment in environmental protection

• Investments in education and enforcement efforts

• Green Accounting

Table 13. Monroe County Five-Year Water Quality Plan

Estimated

Cost (millions $)

1. Preparation of a wastewater master plan $ 1

2. Preparation of a stormwater master plan $0.5

3. An environmental carrying capacity study $ 1

4. Design and construction of the Marathon Wastewater Facility $ 5 8

5. Cesspit identification and elimination $25.7 - $48

6. Implementation of the Stormwater Master Plan $ 4 0

7. Implementation of Wastewater Master Plan $125 - $135

Total $251.2 - $283

Source: An Economic Impact Statement under Chapter 120.54, F.A.C. for Rule Establishing

 F.A.C. 28-20, Part II: Amendment to the Monroe County Proposed Rule 28-20.1000.

 March 27, 1996. Florida Department of Community Affairs, Tallahassee, Florida.
24

“economic rents” to producers and

nonuse or “passive use” values

were not estimated as part of this

project. These values may be

very important components of the

total economic value of the area’s

natural resources, but they must

be left for future research. We

were also not able to estimate

relationships between environmental quality or the abundance &

diversity of natural resources and

tourist recreational uses in order to

estimate the “marginal benefits” of

investments to improve or protect

the environment. We cannot

answer the question “What are the

benefits to recreating visitors of

investments that reduce nutrient

loads by either 10, 20, 50 or even

100 percent ? We can however

estimate the total value of the

resources that are negatively

impacted by nutrients and place

this against the total cost of

protection efforts. This would not

be a legitimate comparison in a

formal benefit-cost analysis, but

would allow decision-makers to

formulate benchmarks for deciding

the level of investment. For

example, would it be reasonable

to spend an amount equal to 3

percent of the total value of the

resource if the investments

reduced nutrients 50 percent ?

Other studies and other experiences might be drawn upon to aid

in this decision making. What we

have provided is simply one piece,

albeit an important piece, of the

puzzle.

The travel cost models that we

presented are only a small sub-set

of what was estimated. The

models incorporate many judgments. Instead of presenting

many different models incorporating many different modeling

choices and judgments, we chose

to present what ,in our judgment,

were the best results based on our

own extensive experience and the

latest findings in our scientific

journals. We have carefully

documented each of the modeling

choices and judgments made and

have made the data and documentation available to other

researchers. Rather than a

limitation, we believe this to be a

strength of our work.

A major limitation to all economic

valuation work is our inability to

forecast very far beyond current

baselines. When calculating the

asset value of the resources, the

formula used required the assumption that the total annual user

value in real terms (net of inflation)

and the real discount rate (interest

rate net of inflation) were constant

into perpetuity. We called these

conservative assumptions (i.e.,

assumptions that would produce

the smallest values). Assuming

that the quality of the resources of

the area do not decline and

increased use does not result in

negative reactions because of

crowding, we would predict that

the demand for trips (visits) to the

Florida Keys will increase in the

future, and because of increasing

scarcity of good recreation opportunities in the future (i.e., that

demand will increase relative to

total supply) nonmarket economic

user values per person-trip will

also increase. The combination of

both would mean increases in the

total annual user value in future

years versus our assumption of

constant annual value. This is

simply a limitation of economic

science and a limitation that will

not likely be overcome by future

research.
25

Endnotes

1. Zero truncation occurs in the

CUSTOMER Survey because only

visitors to the Florida Keys/Key

West are included in the sample,

that is, those that did not visit the

area are excluded. Endogenous

stratification might occur because

those that make multiple trips to

the Florida Keys/Key West might

have a higher probability of being

selected for an interview. In the

CUSTOMER Survey, visitors on

longer trips, and who were not

leaving the Florida Keys at the

time of the interview, have a

higher probability of sample

selection. This would offset the

endogenous stratification based

on the number of trips. Because

of these offsetting effects, it is not

clear that endogenous stratification bias exists in the CUSTOMER

Survey sample so only the truncation problem is addressed in the

statistical methods employed here.

2. People cannot take fractions of

a trip. It is possible to make from

0 to 366 trips per year (1996 was

a leap year and contained 366

days). Count data models like the

poisson and negative binomial

models employ maximum likelihood statistical methods to

integer-based distribution.

3. A variety of models including

continuous models based on

linear, semi-log, lin-log and log-log

functional forms were estimated.

Details of these models can be

obtained from the author on

request.

4. Two different travel cost

variables were constructed from

the survey information. Construction of both of these variables is

detailed in Appendix 1. There was

little difference in the model results

between using the different travel

cost variables.

5. Models were estimated using

LIMDEP Version 7 econometric

software.

6. Although estimates of βtc are

unbiased in estimating demand

and price elasticities, they are not

unbiased in estimating consumer’s

surplus (See Bockstael and

Strand, 1987 and Zellner and

Park, 1979). We multiply our

estimates by the correlation factor

of 1/(1+(t)-2), where t is the t-value

on the travel cost coefficient

(TC2PPTH). For price interaction

terms, HISPINT and INTDAY, we

multiply both the correction factor

for (TC2PPTH) and the correction

factor for the interaction terms.

7. 90 percent confidence intervals

were estimated for the per persontrip user values. Variances were

calculated via the delta method

(Yen and Adamowicz, 1993). For

the summer season, the 90

percent confidence interval for

Hispanics was $82.08 - $320.51

and for all other visitors was

$697.66 - $883.55. For the winter

season, the 90 percent confidence

interval for day trip visitors was

$-327.51 - $905.81 and for all

other visitors $530.87 - $658.07.

The 90 percent confidence

intervals were quite large for

Hispanics and especially day trip

visitors. For Hispanics, the

estimated values were still significantly lower than for all other

visitors (i.e. the 90 percent confidence intervals do not overlap).

For day trip visitors in the winter,

the confidence interval is extremely wide, including the value

of zero and overlaps with the

confidence interval of all other

visitors. Although the day trip

visitor value is less reliable, we

use it in calculating total annual

user value since we do expect day

trip visitors to have lower values.
26

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185-93
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29

Appendix
30

Appendix 1

Transformations of the Number of Trips

and

Travel Costs Consistent with this Transformation

The number of annual trips to the Florida Keys for the majority of visitors in both seasons are bunched around

one trip per year. This bunching-up around one trip presents econometric problems. The continuous trips

models produce extremely inelastic demand functions, while the count data models, especially the truncated

negative binomial model, fails to converge or reach the function minimum.

In addition to the above problem, travel costs are defined as round-trip mileage times a cost per mile. The

share of this cost attributable to the randomly selected person for the interview (person 16 years or older) is a

straight forward calculation when combined with information on the number of people the person interviewed

was paying for on the trip or the number of people he or she was sharing expenses. Travel cost per person

per trip is the total travel cost per trip divided by either the number of people the person interviewed was

paying for or the number of people that were sharing expenses. However, the number of trips this travel cost

is related to is more complicated.

For purposes of illustration, assume a family of three are traveling together. The family makes two trips per

year to the Florida Keys. The travel cost per person per trip is simply the total travel cost per trip divided by

three. The number of trips taken at this travel cost per trip, measured in person-trips, is not two, but two times

three, or six person-trips. For a group that is sharing expenses, say a group of three friends that are sharing

the costs, the travel cost is the total travel cost per trip divided by three, but the number of person-trips

associated with this cost is simply the number of trips taken by the randomly selected person for the interview,

not the number of people in the groups times the number of annual trips.

The dependent variable transformed in the manner described above is NTRIPS. The corresponding travel

cost variables are TC1PPTH and TC2PPTH. Both travel cost variables have been scaled to hundreds of

dollars to resolve convergence problems in the count data models, thus the suffix “H”. The suffix “PPT”

stands for per person per trip. TC1 is based on the total round-trip mileage from the respondents home to the

interview site. For those that visited multiple sites and for which the Keys was not the primary destination of

the trip, only the mileage from a temporary residence or the distance the Keys interview site was from the

respondents primary destination was used. We call this the marginal mileage and TC2 is the marginal roundtrip mileage times the costs per mile.

Mileage and Travel Cost Calculations

Method of Calculating Mileage: PC Program Prophesy Plus was used to calculate highway mileage from

zip code to zip code. We obtained the zip code of the survey respondents home as well as the interview site.

For foreign visitors and air or multiple mode travelers we followed steps detailed below.

Version 1. Separate Auto, Air, and Boat mileage for multiple mode travelers. Variable name HMILES1.

Auto Mode Only: From home to interview site using Prophesy Plus zip code to zip code.

All Air Mode: From home to closest airport. Road miles using Prophesy Plus (HAIRPORT). From home

airport to destination airport. Straight line distance airport to airport (AIRMILES). From destination airport to

interview site using Prophesy Plus (DAIRPORT).

Boat Mode Only: From home to interview site using auto mileage from Prophesy zip code to zip code.

Cruise Ship: From home to Key West using auto mileage from Prophesy zip code to zip code.

Version 2. Multiple Destination Trips - Variable (HMILES2)
31

Here we gathered information on whether the person was on a multiple destination trip and if so, how many

days or hours they planned to spend at destinations other than the Florida Keys. We also asked if the Florida

Keys was their primary destination and if not, the distance to their primary destination. We also asked if they

traveled from a temporary residence outside the Florida Keys and if so, the distance from the temporary

residence to the interview site.

This information allowed us to impose several rules for deciding the proportion of mileage on the trip attributed to the Florida Keys. Below is the logic employed in creating variable HMILES2.

If Fl Keys PRIMARY DESTINATION, then HMILES2=HMILES1 above.

If Fl Keys NOT PRIMARY DESTINATION, and traveled from a temporary residence outside the Keys, then

HMILES2=miles from the temporary residence (TEMPMI).

If Fl Keys NOT PRIMARY DESTINATION, and NO temporary residence then HMILES2= HMILES1 * (DAYS/

(DAYS + DYVISIT))

 where, DAYS = length of stay in Florida Keys measured in days and

 DYVISIT = days on the trip at other destinations

Calculation of Cost Per Mile

We could have used AAA estimates as have been done in other studies. However, we had access to our own

separate survey using the expenditure mail back survey from the Auto, Air and Cruise Ship Survey documented in Leeworthy (1996) and Leeworthy and Wiley (1996a). Mileage was done a little differently from this

sample since we did not have the details on the trip itinerary nor all the details on multiple mode use. For

auto visitors, we used Prophesy Plus the distance from their home zip code to Key Largo (where they were

intercepted) plus the mileage to the mid-point to the furthest region south they visited. For air visitors (Marathon and Key West airports), we used the same procedures described above for air miles and road miles from

their home to the airport. For foreign visitors, we assumed they all flew to Miami and drove from there.

Travel Costs Per Mile included the following elements:

Rental Auto (Q14A)

Gas and Oil - auto/RV (Q15A)

Repair and service - auto/RV (Q16A)

Parking fees and tolls (Q17A)

Taxi fare (Q18A)

Airline fares

a) package tours (Q21A)

b) any other airline fare (Q22A)

Bus Fare

a) package tour (Q19A)

b) any other bus fare (Q20A)

Travel Cost Per Mile = (Q14A+Q15A+Q16A+Q17A+Q18A+Q19A+Q20A+Q21A+Q22A)/(Hmiles2 * 2)

We then estimate these costs for two groups. We first eliminate all cruise ship passengers since they are

generally not in the CUSTOMER Survey samples. The first group includes those that had airfares. Their

average costs per mile were 29.85 cents. The second group includes those with no airfares (auto use only).

Their average costs were 14.32 cents per mile. We rounded the estimates to $0.14/mile and $0.30/mile,

respectively and then calculated travel costs using the following logic statements. Basically what was done

was to assign those that used multiple modes of travel the $0.30/mile and those that used only the auto mode

$0.14/mile.
32

If AIRMILES GT 0 then TC1=(HMILES1*2*.30);

IF AIRMILES EQ 0 THEN TC1=(HMILES1*2*.14);

IF (HMILES2 LT HMILES1) AND TEMPMI GT 0 THEN TC2=(HMILES2*2*.14);

IF (HMILES2 LT HMILES1) AND TEMPMI=. AND AIRMILES GT 0 THEN TC2=(HMILES2*2*.30);

IF (HMILES2 LT HMILES1) AND TEMPMI=. AND AIRMILES=0 THEN TC2=(HMILES2*2*.14);

IF (HMILES2 EQ HMILES1) AND AIRMILES GT 0 THEN TC2=(HMILES2*2*.30);

IF (HMILES2 EQ HMILES1) AND AIRMILES EQ 0 THEN TC2=(HMILES2*2*.14);

These last two statements put the travel costs on a per person-trip basis and scaled them to hundreds of

dollars to avoid convergence problems with the count data models.

TC1PPTH=(TC1/PEOPLE2)/100;

TC2PPTH=(TC2/PEOPLE2)/100;
33

Appendix 2. Matrices of Multiple Activity Participation

Attached are two matrices that summarize the extent of Multiple activity participation from the summer and winter CUSTOMER samples. The matrices include raw scores, that is, no weighting has been

employed. There were 1,781 observations for the summer sample and 2,809 for the winter sample. Aggregate activities have been formed that eliminate double-counting. Thirteen (13) activities are reported in the

attached two matrices.

SNORK

100 A Snorkeling from charter/party boat (pay operation)

101 A Snorkeling from Rental boat

102 A Snorkeling from private boat

10 A Snorkeling from shore

SCUBA

200 A Scuba diving from charter/party boat (pay operation)

201 A Scuba diving from rental boat

202 A Scuba diving from private boat

11 A Scuba diving from shore

FISH

400 A Fishing from charter boat (pay operation six persons or less) - offshore

401 A Fishing from party or head boat (charge per person) - off shore

402 A Fishing from rental boat - offshore

403 A Fishing from private boat - offshore

404 A Fishing from Charter/party boat (pay operation) - flats or back country

405 A Fishing from rental boat - flats or back country

406 A Fishing from private boat - flats or back country

407 A Other fishing from charter boat (pay operation six persons or less)

408 A Other Fishing from party or head boat (charge per person)

409 A Other fishing from rental boat

410 A Other fishing from private boat

14 A Fishing from shore (beach, bank, pier, bridge, jetty, dock)

BNATURE

500 A Glass bottom boat rides (pay operation)

501 A Back country boating excursions (pay operation/guided service/NOT FISHING)

502 A Viewing nature and wildlife from private or rental boat

LNATURE

19 A Wildlife observation or wildlife photography

20 A Other nature study and observation
34

Appendix 2. Matrices of Multiple Activity Participation (cont.)

BEACH

15 A Swimming at Beaches (not in pool)

44 A All Beach Activities (other than swimming)

PWCRAFT

600 A Personal watercraft - rental

601 A Personal watercraft - private

SAIL

700 A Sailing charter/party boat (pay operation)

701 A Sailing rental boat

702 A Sailing private boat

BOATOTH

800 A Other activities from charter/party (pay operation)

801 A Other activities from rental boat

802 A Other activities from private boat

This includes activities such as water skiing, parasailing, banana boat rides, canoeing, kayaking not included

in any of the other activities above.

ATTRACT

29 A Visiting historic areas, sites, buildings or memorials

30 Attending special events (fairs, festivals, ceremonies, etc.)

31 Attending outdoor concerts, plays or other outdoor performances

32 Attending indoor concerts, plays, performances or events

33 Sight-seeing tours and tourist attractions (paid)

34 Sight-seeing (not paid tours)

35 Reading roadside exhibits or markers

36 A Visiting a museum, educational facility or information center

37 Attending outdoor sports events (sailing or boat races; spectator at fishing tournament)

CAMP

23 Camping in developed campgrounds

24 Camping in primitive campgrounds

OUTSPORT

38 Golf

39 Tennis outdoors

40 Participation in other outdoor sports and games

ACT16A

16 A Swimming in Outdoor pool
35

Appendix 2. Matrices of Multiple Activity Participation (cont.)

How to Interpret Activity Matrices

The matrices are each 13 by 13 activity matrices. They were formed by running cross-tabulations between each of the activities.

The upper left-hand corner has the total sample size for the season (1,781 for the summer and 2,809 for

the winter). Reading across the top line over each variable name are the number of people in the sample that

participated in the activity. The first column, on the left, repeats the variable names and the number of

participants, but also includes the percent of the entire sample that participated in the activity. Thus, 712

people out of the 2,809 sampled in the winter season participated in Snorkeling (25.35 percent of the sample).

The matrices are 13 by 13 matrices and thus the diagonal values would all be equal to 100 percent. This

matrix would also be symmetrical with respect to the information contained in the cells above the diagonal.

There are three values in each cell above the diagonal. The top value is the number in the sample that

participated in both activities. For example, during the winter season, 82 of the 2,809 sampled participated in

snorkeling and scuba diving. The second number in the cell is read from left to right. Using our same example, 11.52 percent of all those sampled that did snorkeling also did scuba diving (82 divided by 712).

Reading further across the SNORK row, we find that 34.41 percent of those that participated in snorkeling

also participated in fishing. The third number in each cell is the percent of the entire sample that did both

activities. Again using our same example, 2.92 percent of the entire sample did both snorkeling and scuba

diving (82 divided by 2,809).

The values below the diagonal correspond to the second value in the cells above the diagonal. Reading

the bottom row (left to right) 32.23 percent of those that participated in swimming in an outdoor pool also

participated in snorkeling. 8.24 percent of those that participated in swimming in an outdoor pool also participated in scuba diving.

The attached activity matrices show that trips to the Florida Keys are a complex mix of activities.
36

Summer CUSTOMER Florida Keys

 1,781 942 326 623 395 153 906 181 69 119 882 290 101 501

SNORK SCUBA FISH BNATURE LNATURE BEACH PWCRAFT SAIL BOATOTH ATTRACT CAMP OUTSPORT ACT16A

52.89 213 366 255 107 525 121 51 78 475 218 58 296

SNORK 22.61 38.85 27.07 11.36 55.73 12.85 5.41 8.28 50.42 23.14 6.16 31.42

942 11.96 20.55 14.32 6.01 29.48 6.79 2.86 4.38 26.67 12.24 3.26 16.62

18.30 123 75 28 108 43 10 28 137 72 13 95

SCUBA 65.34 37.73 23.01 8.59 33.13 13.19 3.07 8.59 42.02 22.09 3.99 29.14

326 6.91 4.21 1.57 6.06 2.41 0.56 1.57 7.69 4.04 0.73 5.33

34.98 142 59 311 61 19 43 228 165 39 163

FISH 58.75 19.74 22.79 4.47 49.92 9.79 3.05 6.90 36.60 26.48 6.26 26.16

623 7.97 3.31 7.46 3.43 1.07 2.41 12.80 9.26 2.19 9.15

22.18 83 230 65 26 45 239 75 27 128

BNATURE 64.56 18.99 35.95 21.01 58.23 16.46 6.58 11.39 60.51 18.99 6.84 32.41

395 4.66 12.91 3.65 1.46 2.53 13.42 4.21 1.52 7.19

8.59 102 15 10 6 91 33 7 44

LNATURE 69.93 18.30 38.56 54.25 66.67 9.8 6.54 3.92 59.48 21.57 4.58 28.76

153 5.73 0.84 0.56 0.34 5.11 1.85 0.39 2.47

50.87 101 49 75 534 187 80 333

BEACH 57.95 11.92 34.33 25.39 11.26 11.15 5.41 8.28 58.94 20.64 8.83 36.75

906 5.67 2.75 4.21 29.98 10.5 4.49 18.7

10.16 18 32 90 27 17 65

PWCRAFT 66.85 23.76 33.7 35.91 8.29 55.80 9.94 17.68 49.72 14.92 9.39 35.91

181 1.01 1.80 5.05 1.52 0.95 3.65

3.87 13 51 8 7 23

SAIL 73.91 14.49 27.54 37.68 14.49 71.01 26.09 18.84 73.91 11.59 10.14 33.33

69 0.73 2.86 0.45 0.39 1.29

6.68 69 14 10 53

BOATOTH 65.55 23.53 36.13 37.82 5.04 63.03 26.89 10.92 57.98 11.76 8.40 44.54

119 3.87 0.79 0.56 2.98

49.52 156 69 308

ATTRACT 53.85 15.53 25.85 27.10 10.32 60.54 10.2 5.78 7.82 17.69 7.82 34.92

882 8.76 3.87 17.29

16.28 11 53

CAMP 75.17 24.83 56.90 25.86 11.38 64.48 9.31 2.76 4.83 53.79 3.79 18.28

290 0.62 2.98

5.67 51

OUTSPORT 57.43 12.87 38.61 26.73 6.93 79.21 16.83 6.93 9.90 68.32 10.89 50.50

101 2.86

28.13

ACT16A 59.08 18.96 32.53 25.55 8.78 66.47 12.97 4.59 28.13 61.48 10.58 10.18

501

Winter CUSTOMER Florida Keys

 2,809 712 210 810 574 250 1,502 179 128 121 1,970 730 199 692

SNORK SCUBA FISH BNATURE LNATURE BEACH PWCRAFT SAIL BOATOTH ATTRACT CAMP OUTSPORT ACT16A

25.35 82 245 188 73 476 75 62 51 457 245 64 223

SNORK 11.52 34.41 26.40 10.25 66.85 10.53 8.71 7.16 64.19 34.41 8.99 31.32

712 2.92 8.72 6.69 2.60 16.95 2.67 2.21 1.82 16.27 8.72 2.28 7.94

7.48 44 32 11 79 23 10 12 89 33 11 57

SCUBA 39.05 20.95 15.24 5.24 37.62 10.95 4.76 5.71 42.38 15.71 5.24 27.14

210 1.57 1.14 0.39 2.81 0.82 0.36 0.43 3.17 1.17 0.39 2.03

28.84 177 74 395 49 32 43 464 267 74 194

FISH 30.25 5.43 21.85 9.14 48.77 6.05 3.95 5.31 57.28 32.96 9.14 23.95

810 6.30 2.63 14.06 1.74 1.14 1.53 16.52 9.51 2.63 6.91

20.43 76 315 57 35 45 421 153 51 151

BNATURE 32.75 5.57 30.84 13.24 54.88 9.93 6.10 7.84 73.34 26.66 8.89 26.31

574 2.71 11.21 2.03 1.25 1.60 14.99 5.45 1.82 5.38

8.90 144 14 9 13 176 102 16 48

LNATURE 29.20 4.40 29.60 30.40 57.60 5.60 3.60 5.20 70.40 40.80 6.40 19.20

250 5.13 0.50 0.32 0.46 6.27 3.63 0.57 1.71

53.47 113 78 66 1,119 481 137 489

BEACH 31.69 5.26 26.30 20.97 9.59 7.52 5.19 4.39 74.50 32.02 9.12 32.56

 1,502 4.02 2.78 2.35 39.84 17.12 4.88 17.41

6.39 13 24 119 59 18 61

PWCRAFT 41.90 12.85 27.37 31.84 7.82 63.13 7.26 13.41 66.48 32.96 10.06 34.08

179 0.46 0.85 4.24 2.10 0.64 2.17

4.56 14 93 38 17 51

SAIL 48.44 7.81 25.00 27.34 7.03 60.94 10.16 10.94 72.66 29.69 13.28 39.84

128 0.50 3.31 1.35 0.61 1.82

4.31 80 25 8 32

BOATOTH 42.15 9.92 35.54 37.19 10.74 54.55 19.83 11.57 66.12 20.66 6.61 26.45

121 2.85 0.89 0.28 1.14

70.13 498 164 525

ATTRACT 23.20 4.52 23.55 21.37 8.93 56.80 6.04 4.72 4.06 25.28 8.32 26.65

 1,970 17.73 5.84 18.69

25.99 69 189

CAMP 33.56 4.52 36.58 20.96 13.97 65.89 8.08 5.21 3.42 68.22 9.45 25.89

730 2.46 6.73

7.08 86

OUTSPORT 32.16 5.53 37.19 25.63 8.04 68.84 9.05 8.54 4.02 82.41 34.67 43.22

199 3.06

24.64

ACT16A 32.23 8.24 28.03 21.82 6.94 70.66 8.82 7.37 4.62 75.87 27.31 12.43

692

Appendix 2. Matrices of Multiple Activity Participation (cont.)
37

Appendix 3. Derivation of Person-trips, Person-days, and Activity Person-days for Natual Resource-based Visitors

December 1995 - May 1996

Participation Rates (%)

Upper Middle Lower Key

Activity Keys Keys Keys West Total

Swimming in Outdoor Pools 6.45 7.56 2.54 12.77 26.48 

Visiting Historic Areas 3.49 5.29 2.27 22.05 31.19 

Visiting Museums 3.11 3.59 0.89 13.77 19.94 

Natual Resource Based - - - - 65.01

Person-trips

Upper Middle Lower Key

Activity Keys Keys Keys West Total

Swimming in Outdoor Pools 57,382 67,258 22,597 113,608 235,580 

Visiting Historic Areas 31,049 47,063 20,195 196,168 277,482 

Visiting Museums 27,668 31,938 7,918 122,505 177,396 

Natual Resource Based - - - - 889,651

Average Days Per Person-trip

Upper Middle Lower Key

Activity Keys Keys Keys West Total

Swimming in Outdoor Pools 2.75 6.97 5.18 2.75 -

Visiting Historic Areas 1.10 1.51 1.36 1.41 -

Visiting Museums 1.05 1.22 1.04 1.09 -

Total Person-days

Upper Middle Lower Key

Activity Keys Keys Keys West Total

Swimming in Outdoor Pools 157,800 468,788 117,052 312,422 1,056,062

Visiting Historic Areas 34,154 71,065 27,465 276,597 409,281 

Visiting Museums 29,051 38,964 8,235 133,530 209,780 

Non-natual Resource Based 221,005 578,817 152,752 722,599 1,675,723

June - November 1995

Participation Rates (%)

Upper Middle Lower Key

Activity Keys Keys Keys West Total

Swimming in Outdoor Pools 7.95 9.44 1.39 13.21 29.64 

Visiting Historic Areas 3.03 2.98 1.73 15.22 22.41 

Visiting Museums 2.11 2.44 1.10 8.20 13.14 

Natual Resource Based - - - - 81.31

Person-trips

Upper Middle Lower Key

Activity Keys Keys Keys West Total

Swimming in Outdoor Pools 75,760 89,959 13,246 125,885 282,456 

Visiting Historic Areas 28,875 28,398 16,486 145,040 213,557 

Visiting Museums 20,107 23,252 10,483 78,142 125,218 

Natual Resource Based - - - - 952,956

Average Days Per Person-trip

Upper Middle Lower Key

Activity Keys Keys Keys West Total

Swimming in Outdoor Pools 7.37 2.27 2.13 3.01 -

Visiting Historic Areas 1.64 1.35 1.64 1.77 -

Visiting Museums 1.48 1.10 1.71 1.27 -

Total Person-days

Upper Middle Lower Key

Activity Keys Keys Keys West Total

Swimming in Outdoor Pools 558,351 204,207 28,214 378,914 1,169,686

Visiting Historic Areas 47,355 38,337 27,037 256,721 369,450 

Visiting Museums 29,758 25,577 17,926 99,240 172,501 

Non-natual Resource Based 635,464 268,121 73,177 734,875 1,711,637
38

Appendix 4: Creation of the Continuous Household income Variable

Predicted Income

Because a significant proportion of visitors did not answer the household income question, before creating a

continuous variable for income, household income was predicted for those with missing values. This was

done by modeling household income as a function of age, household type, race/ethnicity, education, employment status, sex, and whether a visitor was a foreign or domestic visitor. Separate models were estimated for

the summer (Table A.4.1) and winter (Table A.4.2) seasons. The models were estimated using the demographics files. These files contain, as separate observations, each person in each recreation group. The

models were estimated using only those individuals age 16 or older, thus corresponding to the same population that was selected as respondents in the CUSTOMER Survey.

The Poisson model was chosen to predict household income because variable HHINCOME is in discrete

categories 1-15. The sample mean and variance are very close lending further support for the use of the

Poisson model. LIMDEP Version 7 was used to estimate the models. As with most project results, there

were significant differences between the summer and winter season samples. AGESQ was not significant

and DOMESTIC was positive, whereas AGESQ was negative and significant and DOMESTIC was negative

and significant in the winter model. The signs on AGE and AGESQ are consistent with the life-cycle hypothesis where household income first increases with age, reaches an maximum, then declines in the retirement

years. SEX was not significant in either season, nor was whether employment status was homemaker

(HOMEMAK), so these variables were dropped from the models.

The estimated Poisson models were then used to predict household income for respondents to the CUSTOMER Survey that did not answer household income. The equations predict the household income category (1-15). Actually, the predictions take on values between categories, so they must be assigned to the

closest category.

Once the household income categories were assigned, the continuous version of household income was

created. This was done in the usual way by assigning a value equal to the mid-point of the income category

interval. For the category 15, greater than $150,000, the value of $200,000 was assigned. The created

continuous variables were then scaled to $10,000 dollars in order to avoid convergence problems when

estimating the travel cost demand models with the negative binomial model.

Below we include the SAS program for the summer season data which defines the variables used in the

income prediction models and documents how the above described transformations were done.

LIBNAME SUM ‘F:\KEYS\LF\SUM’;

OPTIONS PS=54 LS=72;

DATA SUM.LFSUM21;SET SUM.LFSUM20;

IF HHDESCRI=1 THEN SINGNC=1;ELSE SINGNC=0;IF HHDESCRI=. THEN SINGNC=.;

IF HHDESCRI=2 THEN SINGC=1;ELSE SINGC=0;IF HHDESCRI=. THEN SINGC=.;

IF HHDESCRI=3 THEN TWOADNC=1;ELSE TWOADNC=0;IF HHDESCRI=. THEN TWOADNC=.;

IF HHDESCRI=4 THEN TWOADC=1;ELSE TWOADC=0;IF HHDESCRI=. THEN TWOADC=.;

IF HHDESCRI=5 THEN MTWOADNC=1;ELSE MTWOADNC=0;IF HHDESCRI=. THEN MTWOADNC=.;

IF HHDESCRI=6 THEN MTWOADC=1;ELSE MTWOADC=0;IF HHDESCRI=. THEN MTWOADC=.;

IF EMPIN1=1 THEN UNEMP=1;ELSE UNEMP=0;IF EMPIN1=. THEN UNEMP=.;

IF EMPIN1=2 THEN FULLTIME=1;ELSE FULLTIME=0;IF EMPIN1=. THEN FULLTIME=.;

IF EMPIN1=3 THEN PARTTIME=1;ELSE PARTTIME=0;IF EMPIN1=. THEN PARTTIME=.;

IF EMPIN1=4 THEN RETIRED=1;ELSE RETIRED=0;IF EMPIN1=. THEN RETIRED=.;

IF EMPIN1=5 THEN STUDENT=1;ELSE STUDENT=0;IF EMPIN1=. THEN STUDENT=.;

IF EMPIN1=6 THEN HOMEMAK=1;ELSE HOMEMAK=0;IF EMPIN1=. THEN HOMEMAK=.;

IF EMPIN1=7 THEN EMPNONE=1;ELSE EMPNONE=0;IF EMPIN1=. THEN EMPNONE=.;

IF RACEIN1=1 OR RACEIN1=2 THEN INDASIAN=1;ELSE INDASIAN=0;

IF RACEIN1=. THEN INDASIAN=.;

IF RACEIN1=3 THEN BLACK=1;ELSE BLACK=0;

IF RACEIN1=. THEN BLACK=.;
39

IF RACEIN1=4 THEN HISPANIC=1;ELSE HISPANIC=0;

IF RACEIN1=. THEN HISPANIC=.;

IF RACEIN1=5 THEN WHITE=1;ELSE WHITE=0;

IF RACEIN1=. THEN WHITE=.;

IF RACEIN1=6 THEN OTHRRACE=1;ELSE OTHRRACE=0;

IF RACEIN1=. THEN OTHRRACE=.;

IF SEXIN1=1 THEN MALE=1;ELSE MALE=0;

IF SEXIN1=. THEN MALE=.;

IF COUNTRY=1 THEN DOMESTIC=1;ELSE DOMESTIC=0;

AGESQ=AGEIN1**2;

IF HHINCOME LT 1 THEN

PREDINC=1.5545 + .0041626 * AGEIN1 + .062476 * EDUCIN1 - .18658 * HISPANIC

 - .24346 * BLACK + .15914 * SINGC + .34038 * TWOADNC + .39354 * TWOADC

 + .24051 * MTWOADNC + .28578 * MTWOADC

 - .10999 * UNEMP - .11185 * RETIRED + .055323 * STUDENT

 + .04199 * DOMESTIC;

IF HHINCOME GE 1 THEN PREDINC=.;

IF HHINCOME LT 1 AND PREDINC GT 0 THEN PREDINC2=EXP(PREDINC);

ELSE PREDINC2=.;

IF PREDINC2 GT 0 AND (PREDINC2 GE 0 AND PREDINC2 LE 1.5)

 THEN PREDINC3=1;

IF PREDINC2 GT 0 AND (PREDINC2 GT 1.5 AND PREDINC2 LE 2.5)

 THEN PREDINC3=2;

IF PREDINC2 GT 0 AND (PREDINC2 GT 2.5 AND PREDINC2 LE 3.5)

 THEN PREDINC3=3;

IF PREDINC2 GT 0 AND (PREDINC2 GT 3.5 AND PREDINC2 LE 4.5)

 THEN PREDINC3=4;

IF PREDINC2 GT 0 AND (PREDINC2 GT 4.5 AND PREDINC2 LE 5.5)

 THEN PREDINC3=5;

IF PREDINC2 GT 0 AND (PREDINC2 GT 5.5 AND PREDINC2 LE 6.5)

 THEN PREDINC3=6;

IF PREDINC2 GT 0 AND (PREDINC2 GT 6.5 AND PREDINC2 LE 7.5)

 THEN PREDINC3=7;

IF PREDINC2 GT 0 AND (PREDINC2 GT 7.5 AND PREDINC2 LE 8.5)

 THEN PREDINC3=8;

IF PREDINC2 GT 0 AND (PREDINC2 GT 8.5 AND PREDINC2 LE 9.5)

 THEN PREDINC3=9;

IF PREDINC2 GT 0 AND (PREDINC2 GT 9.5 AND PREDINC2 LE 10.5)

 THEN PREDINC3=10;

IF PREDINC2 GT 0 AND (PREDINC2 GT 10.5 AND PREDINC2 LE 11.5)

 THEN PREDINC3=11;

IF PREDINC2 GT 0 AND (PREDINC2 GT 11.5 AND PREDINC2 LE 12.5)

 THEN PREDINC3=12;

IF PREDINC2 GT 0 AND (PREDINC2 GT 12.5 AND PREDINC2 LE 13.5)

 THEN PREDINC3=13;

IF PREDINC2 GT 0 AND (PREDINC2 GT 13.5 AND PREDINC2 LE 14.5)

 THEN PREDINC3=14;

IF PREDINC2 GT 0 AND (PREDINC2 GT 14.5) THEN PREDINC3=15;

IF PREDINC2 LT 0 THEN PREDINC3=.;

IF HHINCOME GE 1 THEN HHINCOM2=HHINCOME;

ELSE HHINCOM2=PREDINC3;

IF HHINCOM2=1 THEN INC=.25;

IF HHINCOM2=2 THEN INC=.75;

IF HHINCOM2=3 THEN INC=1.25;

IF HHINCOM2=4 THEN INC=1.75;

IF HHINCOM2=5 THEN INC=2.25;

IF HHINCOM2=6 THEN INC=2.75;
40

IF HHINCOM2=7 THEN INC=3.25;

IF HHINCOM2=8 THEN INC=3.75;

IF HHINCOM2=9 THEN INC=4.25;

IF HHINCOM2=10 THEN INC=4.75;

IF HHINCOM2=11 THEN INC=5.5;

IF HHINCOM2=12 THEN INC=6.75;

IF HHINCOM2=13 THEN INC=8.75;

IF HHINCOM2=14 THEN INC=12.5;

IF HHINCOM2=15 THEN INC=20.0;

IF HHINCOM2 LT 1 THEN INC=.;

INCSQ=INC**2;

AGEH=AGEIN1/10;

AGESQH=AGESQ/100;

LABEL PREDINC2=’PREDICTED HOUSEHOLD INCOME CATEGORY 1-15'

 PREDINC=’LOG PREDICTED HOUSEHOLD INCOME CATEGORY’

 PREDINC3=’PREDICTED HOUSEHOLD INCOME CATEGORY 1-15'

 HHINCOM2=’HOUSEHOLD INCOME-PREDICTED MISSING 1-15'

 INC=’HOUSEHOLD INCOME-PRED MISSING $10,000'

 INCSQ=’HOUSEHOLD INCOME SQUARED-$10,000'

 AGESQ=’AGE SQUARED’

 AGEH=’AGE DIVIDED BY 10'

 AGESQH=’AGESQ DIVIDED BY 100'

 HISPANIC=’RACE/ETHNICITY HISPANIC’

 BLACK=’RACE/ETHNICITY BLACK’

 INDASIAN=’RACE/ETHNICITY INDIAN-ASIAN-PAC ISL’

 WHITE=’RACE/ETHNICITY WHITE’

 OTHRRACE=’RACE/ETHNICITY OTHER RACE’

 SINGNC=’SINGLE ADULT NO CHILDREN’

 SINGC=’SINGLE ADULT WITH CHILDREN’

 TWOADNC=’TWO ADULTS NO CHILDREN’

 TWOADC=’TWO ADULTS WITH CHILDREN’

 MTWOADNC=’MORE THAN TWO ADULTS NO CHILDREN’

 MTWOADC=’MORE THEN TWO ADULTS WITH CHILDREN’

 UNEMP=’UNEMPLOYED’

 FULLTIME=’EMPLOYED FULL TIME’

 PARTTIME=’EMPLOYED PART TIME’

 RETIRED=’EMPLOYMENT STATUS RETIRED’

 STUDENT=’EMPLOYMENT STATUS STUDENT’

 HOMEMAK=’EMPLOYMENT STATUS HOMEMAKER’

 EMPNONE=’EMPLOYMENT STATUS NONE’

 MALE=’SEX IS MALE’

 DOMESTIC=’1=US RESIDENT 0=FOREIGN’;

RUN;

TITLE ‘SUMMER CUSTOMER’;

TITLE2 ‘PREDICTED HOUSEHOLD INCOME FOR MISSING’;

DATA TEMP1;SET SUM.LFSUM21;

IF PREDINC2 GT 0;

RUN;

PROC UNIVARIATE FREQ DATA=TEMP1;

VAR PREDINC PREDINC2 PREDINC3 HHINCOM2 INC;

RUN;

TITLE2 ‘ORIGINAL INCOME VERSUS THAT WITH PREDICTED’;

PROC UNIVARIATE FREQ DATA=SUM.LFSUM21;

VAR HHINCOME HHINCOM2 INC;

RUN;
41

Appendix 4. Derivation of Continuous Household Income Variable (Continued).

Winter

Standard

Variable Coefficient (β) Error z=b/s.e. P[|z|≥ z] Mean of X

Contant 1.3412 0.052696 25.452 0.00000 1.000

EDUC 0.04853 0.0040274 12.05 0.00000 4.398

HISPANIC -0.1959 0.021308 -9.193 0.00000 0.0612

BLACK -0.12794 0.046521 -2.75 0.00596 0.0113

SINGC 0.19488 0.030779 6.331 0.00000 0.03773

TWOADNC 0.34599 0.018859 18.346 0.00000 0.4560

TWOADC 0.402853 0.019743 20.404 0.00000 0.2339

MTWOADNC 0.33172 0.02156 15.386 0.00000 0.1281

MTWOADC 0.34984 0.27759 12.603 0.00000 0.04434

UNEMP -0.15246 0.050823 -3.000 0.00270 0.01002

RETIRED -0.13868 0.017933 -7.734 0.00000 0.1473

STUDENT -0.12901 0.024148 -5.343 0.00000 0.0744

DOMESTIC -0.02723 0.014916 -1.826 0.06789 0.8992

AGE 0.020692 0.0021534 9.609 0.00000 42.44

AGESQ -0.00018073 0.000023788 -7.597 0.00000 2018.0

HHINCOME (Mean) 10.3 S.D. 3.6

Log Likelihood Function -12429.36

Restricted log likelihood -13148.79

χ2=1438.848

Degrees of Freedom 14

Significance level 0.00000

R2

-p=0.1535

R2

-d=0.2020

N=4,691

Summer

Standard

Variable Coefficient (β) Error z=b/s.e. P[|z|≥ z] Mean of X

Contant 1.5545 0.041104 37.819 0.00000 1.000

EDUC 0.062476 0.0049107 12.722 0.00000 4.176

HISPANIC -0.18658 0.015986 -11.672 0.00000 0.1785

BLACK -0.24346 0.048366 5.034 0.00000 0.01849

SINGC 0.15914 0.032601 4.881 0.00000 0.05820

TWOADNC 0.34038 0.022973 14.817 0.00000 0.2980

TWOADC 0.39354 0.022504 17.487 0.00000 0.3762

MTWOADNC 0.24051 0.029657 8.11 0.00000 0.07517

MTWOADC 0.28578 0.028205 10.132 0.00000 0.09306

UNEMP -0.10999 0.046741 -2.353 0.01862 0.01728

RETIRED -0.11185 0.029206 -3.829 0.00013 0.05123

STUDENT 0.555323 0.024054 2.3 0.02145 0.08002

DOMESTIC 0.04199 0.019647 2.137 0.03258 0.9112

AGE 0.0041626 0.00055119 7.552 0.00000 38.10

HHINCOME (Mean) 9.9 S.D. 3.4

Log Likelihood Function -8459.707

Restricted log likelihood -8946.233

χ2=973.0523

Degrees of Freedom 13

Significance level 0.00000

R2

-p=0.1906

N=3,299