BENEFITS FROM IMPROVEMENTS

I N

CHESAPEAKE BAY wATER QUALITY

Volume III

of

BENEFIT ANALYSIS USING

INDIRECT OR IMPUTED MARKET METHODS

(Budget Period II).

DEPARTMENT OF AGRICULTURAL AND RESOURCE ECONOMICS

SYMONS HALL

UNIVERSITY OF MARYLAND

COLLEGE PARK 20742
BENEFITS FROM IMPROVEMENTS

I N

CHESAPEAKE BAY WATER QUALITY

Volume II I

of

BENEFIT ANALYSIS USING

INDIRECT OR IMPUTED MARKET METHODS -

(Budget Period II)

Prepared ● nd Edited by

Nancy E. Bockstael, Kenneth E. McConnell ● nd Ivar E. Strand

University of Maryland

Sections Contributed by

Douglas Larson ● nd Bruce Madariaga

University of Maryland

Final Draft - April 1988

Principal Investigators

Nancy E. Bockstael ● nd Kenneth E. McConnell

EPA Contract No. CR-811043-O1-O

Project Officer

Dr. Peter Caulkins
“The information in this document haa been funded wholly or

in part by the United States Environmental Protection Agent y

under Contract No. 811043-01-0. It haa been subject to the

Agency*. peer and administrative review, and it has . been

approved for publication as an EPA document. Mention of

trade names or commercial products does not constitute

endorsement or recommendation for use. ”
Acknowledgements

First and foremost, we wish to thank Dr. Peter Caulkins, our

project off icer, who has exhibited patience,wisdom, and

appreciation.

Douglas Orr and Firuzeh Arsenjani contributed to the analysis

in this volume. Special thanks are given to Marion Story for

her commitment and technical assistance.

All opinions and remaining errors are the sole responsibility

of the editors. This effort waa funded by USEPA Cooperative

Agreement number CR-81 1043-01-0.
Forward

This is the second of two reports constituting the final report for budget

period II of Cooperative Agreement #811043-01-0, which was initiated and

supported by the Environmental Protection Agency. From the beginning, this

cooperative agreement haa dealt with improving methods of measuring the

benefits of environmental improvements. Budget period I of the agreement

produced two documents which considered theoretical, conceptual and

methodological issues involved. in using hedonic models (Vol. I) and

recreational demand models (Vol. 11) evaluating environmental improvements.

The second budget period’s work has extended the work of the first,

especially in the area of recreational demand models. Volume I of budget

period II's final report looks at the theoretical issues of measuring the

benefits of quality changes, the conceptual issues surrounding perceptions of

water quality and methodological issues related to estimating models with

sample selection problems.

The report which follows is the second part of budget period II's final

report. This report provides information on the recreational activities which

take place on the Bay, as well as the monetary values people place on these

activities. While not commissioned with the intent of helping in the process of

revising the Bay plans, we hope that the discussions in this report will do

just that.
Executive Summary

For more than ten years, the Chesapeake Bay has been the focus of an

impressive amount of research and an array of environmental programs. The

Chesapeake Bay Program, a cooperative effort by the federal government,

Maryland, Virginia, Pennsylvania, and the District of Columbia, represents a

coordinated commitment to enhancement of the water quality of the Chesapeake

Bay.

Large commitments of money have been made to clean u p the Chesapeake

Bay. Yet there is little understanding of the nature and extent of the

benefits which are derived from these massive commitments. Raising this issue

does not imply that either the programs are misguided or need to be justified

on some benefit-cost criterion, for many believe that the cleanup process is an

expression of a fundamental morality that despoiling our surroundings is

wrong. But understanding more precisely how people benefit from cleaner

water in the Bay can help in allocating resources to clean up the water, for

funds must be allocated temporally, spatially and functionally. Perhaps

knowledge about the benefits from water quality improvements can help with

those decisions.

Even though the returns from the Program derive from human benefits--

human use and human health--the specific objectives and implementation

strategies are designed to affect chemical and biological characteristics of the

Bay. The connection between human benefits on the one hand and reductions

of nutrients and toxic materials” on the other remains implicit. Perhaps the

clearest link is between human use and fisheries and wildlife management.

Here the vehicle for linking the strategy and the goal is at least understandable, even if the details of this linkage remain indistinct. In other

cases, however, we are left confused as to how the policies impact on humans

and how we would ever measure the success of these policies in terms of their

achievements.

Thie report attempts to focus attention on the human use of the

Chesapeake Bay. It describes something about the nature and level of that

use. It also considers what we know and what we do not know about the

relationship between chemical and biological characteristics of the Bay and

human use. This relationship must be understood in order to address the

more complex measurement of human benefits.

One objective of the report is to provide estimates of Values of

Chesapeake Bay recreational activities and willingness-to-pay estimates of

improvements in water quality associated with these activities. Available data

has been used together with what is known about estimating environmental

benefits. While Chapters 3, 4, 5 and 6 reflect our beat efforts at this task, it

should be kept in mind that benefit estimates have an elusive nature. A

number of different studies have been assembled, and an array of methods

and specifications has been used to provide as much information as is

currently available on the topic.

iv
The benefit estimates themselves do not represent this report’s most

important contribution, however. We seek to describe, model and to some

extent explain recreational uses of the Chesapeake. We may have serious

doubts about the precision of willingness-h-pay estimates, but we still learned

a great deal about the factors which matter to people in using the Bay, the

obstacles to their increased enjoyment

implications of improving the Bay.

Specific information contained in this

● Maryland boaters, beach users and

of the” Bay and the distributional

report includes:

sportfishermen alter their behavior

in response to poor water quality, as scientifically measured.

● Demographic factors, such es income and location of residence,

influence observed use of the Bay.

● Cent ingent valuation experiments (hypothetical responses) reveal an “

annual willingness to pay in increased taxes of over $100 million for

improvements in Chesapeake Bay water quality.

● Observed behavior of Maryland western shore beach users reveal an

annual willingness to pay for 20 percent improvements in water quality

of between $2 million and $26 million.

● Many of the gains from water quality improvement are concentrated in

the area of heaviest use around Annapolis, Maryland.

The estimates give magnitudes for the annual benefits to residents of the

Baltimore-Waehington area of improving water quality in the Bay in the range

of from $10 million to over $100 million. There are numerous sources of error

and random elements in these estimates, and several activities and populations

have been omitted. But based on these numbers, it seems plausible to

estimate that the annual returns to cleaning up the Chesapeake are at least of

this order of magnitude.

The long-run annual benefits will be higher than these estimates, however.

Firat, as people learn that the Bay has become cleaner, they will adjust their

preferences toward Bay recreation. This is especially true of people who do

not currently use the Bay and are largely excluded from the analysis.

Second, the population and income of the area have grown since 1984, and

both are likely to grow more, increasing the demand for and value of

improvements in water quality. Finally, we have ignored the value (both use

and existence value) which households outaide the Baltimore-Washington area

may have for the Bay. The Chesapeake Bay is a nationally prominent

resource. Its improved health is of value to many who will never use it.

In conclusion, we hope this volume will provide a stimulus to decision

makers to refocus attention on human uses of the Bay. Human uses and the

protection of human health have always been the central theme of clean water

legislation, but because of difficulties in relating these to specific standards,

they have often dropped from sight in the formation of the actual programs.

We hope to shed some

related to the behavior

light on ways in which Bay cleanup policies might be

and preferences of actual and potential users of the Bay.

v
Table of Contents

Chapter Page

1. Enviromental Programs for the Chesapeske Bay . . . . . . . . . . 1

The State of the Bay’s Water Quality . . . . . . . . . . . . . 1

The Current Environmental Programs . . . . . . . . . . . . . . 2

The Role of This Report.. . . . . . . . . . . . . . . . ..4

2. The Role of Human Use Activities in Defining Goals and Strategies

for the Chesapeake Bay...... . . . . . . . . . . . . . ...7

Systematic Evidence of the Link Between Perceptions

and Behavior. . . . . . . . . . . . . . . . . . . . . . . . . 8

Some General Attitudinal Patterns . . . . . . . . . . . . 10

Two Propositions about Water Quality and Behavior . . . . 12

Results of Focus Group Experiences . . . . . . . . . . . . . . 17

Summary of Focus Group Experience . . . . . . . . . . . . 22

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 23

3. Recreational Uae of the Bay and Willingness topsy Estimates

for Improvement to the Bay as a Whole . . . . . . . . . . .

Recreational Use of the Bay . . . . . . . . . . . . . .

Aggregate Willingness to Pay . . . . . . . . . . . . . .

Analysis of Willingness to Pay Responses . . . . . .

Results of Analysis by Subgroups of Respondents . .

Regional Comparisons . . . . . . . . . . . . . . . .

Existence Value . . . . . . . . . . . . . . . . . . . .

Existence Value Experiment . . . . . . . . . . . . .

Interpretations of Results . . . . . . . . . . . . .

Conclusions . . . . . . . . . . . . . . . . . . . .

. . . 25

. . . 26

. . . . 29

. . . 30

. . . 32

. . . 36

. . . 38

. . . 39

. . - 40

. . * 41

4. Effect of Chesapeake Bay Water Quality on Beach Use . . . . . . . 42

Tbe SurveyandtheData . . . . . . . . . . . . . . . . . . . 44

The Data. . . . . . . . . . . . . . . . . . . . . . . . . 45

The Varying Parameter Model . . . . . . . . . . . . . . . . . 47

The Varying Parameter Model Estimates . . . . . . . . . . 50

Estimated Benefit Changes . . . . . . . . . . . . . . . . 53

Discrete/Continuous Choice Model . . . . . . . . . . . . . . . 59

The Choice Among Sites . . . . . . . . . . . . . . . . . . 59

Estimation of Discrete Choices Among Beaches . . .- . . . . 64

The Number of Trips Decision . . . . . . . . . . . . . . . 67

Estimated Benefit Changes . . . . . . . . . . . . . . . . 68

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 70

5. Recreational Boating and the Benefits of Improved Water Quality . 71

A Profile of Boaters and Boat Owners . . . . . . . . . . . . . 71

The BoatOwnerSurvey . . . . . . . . . . . . . . . . . . 71

Boaters and Boat Owner Characteristic . . . . . . . . . . 71

The Importance of Water Quality to Boaters . . . . . . . . 77

The Behavior of Boat Owners Who Trailer Their Boats . . . . . 77

TheGeneralModel . . . . . . . . . . . . . . . . . . . . 77

The Data. . . . . . . . . . . . . . . . . . . . . . . . . 80

TheEstimatedModel . . . . . . . . . . . . . . . . . . . 81
Modelling the Behavior of Boat Owners Who Do Not Trailer

Their Boats . . . . . . . . . . . . . . . . . . . . . . . . . 83

Calculating Estimates of the Benefits of Water Quality . . . . 85

Improvements for the Trailered Boat Sample . . . . . . . . 85

6. The Benefits for Recreational Fishing: Striped Bass . . . . . . . 90

A Description of the Data . . . . . . . . . . . . . . . . . . 92

TheBasicModel . . . . . . . . . . . . . . . . . . . . . . . 95

Empirical Results . . . . . . . . . . . . . . . . . . . . . . 96

7. Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . 100

Demand for Chesapeake Bay Recreational Activities . . . . . . 100

Estimates of Benefits from Water Quality Improvements . . . . 102

Caveats . . . . . . . . . . . . . . . . . . . . . . . . . 103

Estimates . . . . . . . . . . . . . . . . . . . . . . . . 105

Reference. . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

Appendices

The Random Digit Dialing Telephone Survey Procedures . . . . . 121

Telephone Survey Instrument . . . . . . . . . . . . . . . . . 125

The User Survey and Sampling Procedure . . . . . . . . . . . . 139

User Survey Instrument. . . . . . . . . . . . . . . . . . . . 145

v ii
Tables

Numbers

2.1

2.2

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

3.10

3.11

4.1

4.2

4.3

4.4

Page

Parameter Estimates and Standard Errors for Weibull

Distribution . . . . . . . . . . . . . . . . . . . . . . . . ..13

Effect of Perceptions on Use . . . . . . . . . . . . . . . . . . 14

Participation Rate in Chesapeake Bay Activities by Activity

and Area, 1984 . . . . . . . . . . . . . . . . . . . . . . . . . 27

Joint Participation Rates in Chesapeake Bay Activitiae

By Activity and Region, 1984 . .

Percent of People Willing to Pay

Quality Acceptable for Swimming,

Logistic Model Estimates Related

● ☛☛☛☛✎ ✎ ✎ ✎ ✎ ✎ ✎ ✎ ✎ ✎ ✎ 28’

Additional Taxes for Water

by Amount of Tax . . . . . . . . . 30

to the Probability

a Respondent Will Accept a Tax Increase . . . . . . . . . . . . . 33

Estimates of Utility Parameters by Income Group . . . . . . . . . 34

Expected Value of Willingness to Pay for Water Acceptable

for Swimming, by Income Group and User Group, 1984 . . . . . . . 34

Estimated Willingness to Pay for Acceptable Water Quality

by Participation and Racial Composition of Household, 1984 . . . 35

Estimated Aggregate Willingness to Pay for Water Quality

Acceptable for Swimming, by Classification and Scenarios, 1984 . 36

Logistic Model Estimates Related to the Probability a

Respondent Will Accept a Tax Increase to Improve Chesapeake

Bay Water Quality, by Geographic Area . . . . . . . . . . . . . . 37

Estimated Willingness to Pay for Acceptable Water Quality

by Region, Participation, and Racial Composition of Household,

1984 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Summary Results of Contingent Valuation Experiment

on Existence Value . . . . . . . . . . . . . . . . . . . . . . . 40

Average Values of Regression Variables for Visitors, by Beach . . 51

Tobit Estimates for Beach Demand Model, by Beach . . . .

Annual Benefits per Beach User froa a 20 Percent Decrease

in Pollutant, by Beach, 1984 . . . . . . . . . . . . . .

Annual Benefits per Beach User from a 10 Percent Decrease

in Pollutant, by Beach, 1984 . . . . . . . . . . . . . .

.... 52

. . . . 56

. . . . 57
4.5

4.6

4.7

4 . 8

4 . 9

4.1 0

5 . 1

5 . 2

5 . 3

5 . 4

5 . 5

5 . 6

5 . 7

5 . 8

5 . 9

5.1 0

5.11

5.1 2

Annual Losses per Beach User

in Pollutant, by Beach, 1984

Aggregate Benefits/Losses to

Bay Water Quality, by Beach,

Logit

Log it

Local

from a 20 Percent Increase . . . . . . . . . . . . . . . . . . 58

Users from Changes in Chesapeake

1984... . . . . . . . . .

Regression for Selection Among Sites . . . . . . .

Analysis for Selection Between State Parks and

Beaches. . . . . . . . . .

Ordinary Least Squares Regression

or Trips . . . . . . . . . . . .

Comparison of Benefits Based on a

and-Discrete/Continuous

Average Characteristics

in Baltimore/Washington

Average Characteristics

in Baltimore/Washington

Choice .

● ✎ ✎ ✎ ✎ ● ✎ ✎ ✎ ✎ ✎ ✎

for “Choice Occasions”

. . . . . . . . . . . .

Varying Parameter Model

. . . . . . . . . . . .

of Boaters and Non-Boaters

SMSA, 1984 . . . . . . . . . . .

. . . . 60

. . . . 66

. . . . 66

. . . . 68

. . . . 70

. . . . . 73

of Boat Owners and Non-Boat Owners

SMSA, 1984 . . . . . . . . . . . . . . 73

Characteristics of Boats and Boat Owners from

Boat Owners’ Survey, 1983 . . . . . . . . . . . . . . . . . . . 74

Numbers of Trailered Boats and Boats Kept in the Water

By Residence, 1983 . . . . . . . .

Percent of Boaters Who Fish or Swim

Boat Owners’ Survey, 1983 . . . . .

Factors Cited Most Important in the

. . . . . . . . . . . . . . . 76

While Boating,

. . . . . . . . . . . . . . . 77

Selection of a Boating

Area in Maryland - Percent Response from 718 Boat OwnersWhoTrailerTheirBoats. . . . . . . . . . . . . . . . . . . . . 78

Factors Cited Most Important in the Selection of a Boating

Area in Maryland - Responses from 788 Boat Owners Who Keep

Their Boats in Marinas . . . . . . . . . . . . . . . . . . . . . 78

Estimated Tobit Demand Coefficients:

Estimation Results from Second Stage

Estimated Demand for Boating Tripe -

Estimated Demand for Boating Trips -

Maryland Counties, 1983 . . 82

. . . . . . . . . . . . . . 83

Boats Kept in Marinaa . . . 84

Boats Kept in Marinas.

Fishing Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Par Boater Annual Benefits from a 10% Decrease in Pollutant

by Geographical Area . . . . . . . . . . . . . . . . . . . . . . 87

ix
5.1 3

5.1 4

6 . 1

6 . 2

6 . 3

6 . 4

7 , 1

7.2

Per Boater Annual Benefits from a 20% Decrease in Pollutant

by Geographical Area.... . . . . . . . . . . . . . . . . ..88

Per Boater Annual Losses from a 20% Increase in pollutant

by Geographical Area . . . . . . . . . . . . . . . . . . . . . . 89

Characteristics of Striped Bass Fishermen and Other

Fishermen/Hunters in the Sample . . . . . . . . . . . . . . . . . 91

Sample Distribution Number of Fishermen, Days of Striped

Bass Fishing in 1980, and Catch Rate, by Regions . . . . . . . . 93

Tobit Estimation of the Demand for Striped Bass Fishing . . . . . 97

Aggregate Consumers’' Surplus for Striped Bass Fishing:

Effect of Changing Catch Rates . . . . . . . . . . . . . . . . . 98

Aggregate Benefits for Three Water-related Activities

from a “20%” Improvement in the Chesapeake Bay’s

Water Quality, 1987 dollars . . . . . . . . . . . . . . . . . . . 106

Aggregate Benefits from Water Quality ImprovementsContingent Valuation, 1967 dollars . . . . . . . . . . . . . . . 107

x
PigUrea

Numbers Page

2 . 1 The Sampling Region for the Telephone Survey and Location of

the Beaches Used in the Intercept Survey . . . . . . . . . . . . 9

,2.2 Annual Net Change in Swimming Habits 1970-1964 . . . . . . . . 15

2.3 Cumulative Net Change in Swimming Habits 1970-1964 . . . . . . 16

xi
Environmental

Chapter 1

Programs for the Chesapeake Bay

Over the past ten years, the Chesapeake Bay has been the focus of an

impressive amount of research and the beneficiary of a great many

environmental programs. Concentrated efforts began in 1976 when the

Congress directed the U. S. Environmental Protection Agency to conduct a

five-year study of the Bay’s resources and water quality. The study, which

focused on three major problems of the Bay--nutrient overenrichment, toxic

substances? and the decline of submerged aquatic vegetation--prompted action.

In 1983 the three surrounding states, the District of Columbia, and the federal

government signed a pact, the Chesapeake Bay Agreement of 1983, committing

them to improve and protect water quality of the Chesapeake Bay through

coordinated environmental enhancement activities. In late 1987 a new

agreement was signed.

The State of the Bay's Water Quality

Concern over the Chesapeake Bay stems from declines in direct and

indirect measures of the quality of the Bay’s waters. The most apparent

measures are related to the productivity of the Bay. Reduction in fish

landings, combined with an awareness of the increasing loads of pollutants and

their consequences, led scientists to assess the Bay’s water quality.

The use of the term “quality“ in assessing the Bay connotes a set of

standards goals, or ideals with which the current conditions of the Bay can

be compared. The quality of the water depends on one’s standards, and the

relevant standards depend on intended uses and frame of reference. For

example, if the most desired use of the water were for transportation, then

the Bay’s current water quality would be quite satisfactory. At the other

extreme, if one’s standards are derived relative to the state of the Chesapeake

Bay three centuries ago, its current quality is clearly too low.

Since the thrust of the Chesapeake Bay program cornea from observed

declines in ecosystem productivity, it is useful to summarize the nature of

those declines. Summary measures give the status of the Bay as a whole, but

mask considerable differences in quality between the upper and lower Bay and

among the various river systems and inlets of the Bay. The following

measures suggest the nature of the thinking that led to the conclusion that

the Chesapeake Bay waa declining in quality.

There are two kinds of evidence of the historical decline in the Bay’s

water quality. First there are scientific measures which are indicators of

impairment of the Bay as a functioning ecosystem. A common measure of water

quality is the level of dissolved oxygen in the water. This is oxygen available

to various plant and animal life. Its absence can eliminte higher forms of life

from ecosystems. Studies have shown that the extent of water with little or

no dissolved oxygen has increased by 15-fold in the last 30 years.

1
.

Another indicator of water quality is the level of nutrients (nitrogen and

phosphorus, mainly). These nutrients, while not harmful per se, enhance algal

growth, whose decay increases the demand for oxygen. The increase of

nutrients in the Bay’s waters is an indirect consequence of population growth,

changing technology and industrial and agricultural expansion in the area.

The decline in submerged aquatic vegetation (SAV) is another indicator of

the decline in the Bay’s water quality. The decline in SAV is connected with

turbidity and growth of epiphytes and phytoplankton, by excessive

nutrification. The loss of SAV means less suitable habitat for spawning finfish

and shellfish. There are of course many other indirect measures of the

declining health of the Bay. They all reinforce the notion that human factors

are destroying the traditional ecological linkages of the Bay.

There are other signs of declining water quality more cogent to the lay

public. Landings of well-known anadromous species such as rockfish and

shad have dropped precipitously in the past several decades. Oyster harvest

and oyster reproduction have also declined in the past decade. There is some

ambiguity in the use of landings as a measure of water quality, of course. A

considerable increase in effort devoted to harvesting fish has happened to

coincide with the increase of effluents over time. Further, natural phenomena

such as hurricane Agnes (1972) induce cyclical variations in finfish and

shellfish reproduction. Nevertheless, there can be little doubt that the quality

of the Chesapeake Bay’s waters has declined, both in terms of the ecological

health of the estuary and the benefits to humans of its use.

The Current Environmental Programs

The foundation of the Chesapeake Bay Program is the Clean Water Act, the

ongoing federal environmental legislation dealing with water. Under the Clean

Water Act, appropriations have been made available annually to the Chesapeake

Bay Program, providing both its operating budget and its grant funds. The

relationship between the federal legislation and Chesapeake Bay activities goes

beyond funding, of course, since the Clean Water Act establishes the

guidelines by which states then set specific water quality standards. The

Water Quality Standards Handbook is the most recent document which contains

the guidelines prepared by EPA to assist states in implementing 1983 revisions

of the water quality regulations.

The Handbook defines acceptable approaches by which water quality baaed

effluent limitations may be determined. Whether the pollutant specific or

biomonitoring approach is taken, however, states must adopt criteria which are

sufficient to protect the “designated uses" of a water body. Determination of

designted uses requires an “attainability analysis,” i.e. physical, chemical and

biological studies to identify the suitable potential uses of the water and to

determine whether these uses have been impaired. There is, throughout, a

clear sense of the central position which human use activities should play in

the setting of standards and the overriding obligation states should feel

toward the protection of

recreational uses of aquatic

human health where ‘people are involved in

resources.

2
The first plan of the Chesapeake Bay Commission waa the Chesapeake Bay

Restoration and Protection Plan of September 1985. This is currently the

central document describing the goals of the Chesapeake Bay Program and the

means by which these goals are being achieved. The general goals as stated

in the plan are to

“Improve and protect the water quality and living resources of the

Chesapeake Bay estuarine system (in order) to restore and maintain

the Bay’s ecological integrity, productivity, and beneficial uses and

to protect public health.” 1

The goals of the Restoration and Protection Plan are boad, and include both

ecological and human health, as well as productive use by humans.

large, however,

By and

there is no clear connection between the goals of the Bay

Program which emphasize human health and human use and the means by

which humans benefit from implementation.

To accomplish the broad goals, specific objectives and implementation

strategies have been developed. Many of these strategies are designed to

reduce or control nutrients. Major strategies to control point sources of

nutrienta include plane to provide grants to design, construct, operate and

maintain sewage treatment facilities, and plans to support phosphorous removal

projects at treatment plants. Plans to support nitrogen removal at treatment

plants have not been proposed, except for an experimental project conducted

by the State of Maryland in the Patuxent estuary.

The primary stratagy established to control non-point sources of nutrients

to the Bay has been to subsidize the implementation of “Best Management

Practices” (BMPs) to reduce runoff from urban, forested, and in particular,

agricultural lands. Mar yland, Virginia, and Pennsylvania have instituted costsharing programs to promote agricultural BMPs. Among the agricultural BMPs

supported through cost-sharing have been: strip cropping, buffer stripping,

terrace and diversion construction, animal waste system installation, and

reduced tillage planting. Some of these practices are employed to reduce

sediment, pesticide, and herbicide runoff, as well as nutrient runoff. A

secondary strategy to control non-point source pollution is to control urban

runoff, in particular combined sewer overflows. Tactics to control combined

sewer overflow include revamping of sewer systems and building holding

ponds. The state of Maryland has also enacted legislation banning the use of

phosphate detergents and controlling development along the Bay’s shoreline.

The Chesapeake Bay Restoration and Protection Plan hae enacted a series

of additional policies to reduce or control the level of toxic materials in the

Bay, Among these policies are programs to support pretreatment plans to

reduce the discharge of metals and organics from sewage treatment plants

resulting from industrial sources, to fund dechlorination processes to reduce

chlorine discharges into critical finfish and shellfish areas, and to implement

oil spill response plans.

1 Chesapeake Executive Council, Chesapeake Bay Restoration and Protection

Plan U. S. Environmental Protection Agency, Sept. 1985.

3
Lastly, the Chesapeake Bay Restoration and Protection plan has instituted

a series of policies designed directly to “provide for the restoration and

protection of living resources and their habitats and ecological relationships” 2

in the Chesapeake. Among these policies were programs to develop

comprehensive fisheries management plans, expand oyster repletion activities,

improve waterfowl and wildlife habitat, and re-establish submerged aquatic

vegetation.

There is no “clear connection between the implementation strategies

mentioned above and the goals of the Chesapeake Bay Program. The goals of

the program are couched in terms of human benefits--human health and human

use--but the specific objectives and implementation strategies are designed to

affect chemical and biological characteristics of the Bay. The connection

between human benefits on the one hand and reductions of nutrients and toxic

materials on the other remains implicit, unsubstantiated and unarticulated.

Perhaps the clearest connection is between human use and fisheries and

wildlife management. Here the vehicle for linking the strategy and the goal is

at least understandable? even if the details of this linkage remain indistinct.

In other cases, however, we are left confused as to how the policies impact on

humans and how we would ever measure the success of these policies in terms

of their achievement of the Program’s goals. In implementation, the focus on

human use seems to have been lost.

The Role of This Report

This report attempts to focus attention on the human use of the

Chesapeake Bay. The report describes something about the nature and level

of that use. It also considers what we know and what we do not know about

the relationship between chemical and biological characteristic of the Bay and

human use. This relationship must be understood in order to grapple with

the more complex measurement of human benefits.

Large commitments of money have been made to clean up the Chesapeake

Bay. Yet there is little understanding of the nature and extent of the

benefits which are derived from these massive commitments. How do people

gain from the cleanup? Asking this question does not imply that either the

programs are misguided or need to be justified on some benefit-cost criterion,

for many believe that the cleanup process is an expression of a fundamental

morality that despoiling our surroundings is wrong. Whataver the motivation

for environment. al improvement, we believe that understanding more precisely

how people benefit from cleaner water in the Bay can help in allocating

resources to clean up the water. Moral imperative are of limited usefulness

in the tactics of cleaning up the Bay, Even with commitments for a cleanup of

the Bay, one must allocate those funds temporally, spatially and functionally.

Perhaps knowledge about the benefits from water quality improvements can

2 Chesapeake Bay Restoration and Protection Plan, Chapter 2, page 1,

help with those decisions.
One of our objectives is to provide some initial estimates of values of

Chesapeake Bay recreational activities and willingness-to-pay estimates of

improvements in water quality associated with these activities. We have used

available data together with what we know about estimating environmental

benefits (see Bockstael, Hanemann and Strand, 1985; and McConnell, Bockstael

and Strand, 1987) to determine these “ball park” willingness-b-pay figures.

While Chapters 3, 4, 5 and 6 reflect our best efforts at this task, it should be

kept in mind that benefit estimates have an elusive nature. Much has been

written about the imprecision of non-market benefit estimation, and we will

have more to say on the question in this report. The usual difficulties are

compounded by generally sketchy data. No new surveys were conducted for

this study, and only one of the surveys used in the subsequent chapters was

designed with benefit estimation in mind. Nonetheless we have put together a

number of different studies and have used an array of methods and

specifications to provide as much information as is currently available on the

topic. We provide estimates of aggregate benefits for a variety of recreational

activities. We have also tried to provide information on the relative

magnitudes of benefits which are likely to accrue to different groups of users

and to improvements in different geographical areas.

The benefit estimates themselves do not represent this report’s most

important contributions however. We seek to describe, model and to some

extent explain recreational uses of the Chesapeake. The report represents an

attempt to begin to understand the preferences and behavior of individuals

toward the Bay. Models of behavior are essential to benefit estimation. Even

in the face of huge uncertainty over benefit estimates, the underlying

behavioral models can provide useful and reliable information. We may have

serious doubts about the precision of willingness-to-pay estimates, but we can

still learn a great deal about the factors which matter to people in using the

Bay, the obstacles to their increased enjoyment of the Bay and the

distributional implications of improving the Bay.

The Restoration and Protection Plan is an interim plan, “the first iteration

of the planning effort implemented in response to this commitment. ” As such

it is a first move in the direction of Chesapeake Bay improvements but it is

sub ject to revision and fine-tuning as goals of environmental improvement

become clearer and information about problems, technology and costs becomes

more sophisticated.

What we hope this volume will provide is a stimulus to decision makers to

refocus attention on human uses of the Bay, as the goals and the strategies

for achieving these goals are fine-tuned in the coming year. Human uses and

the protection of human health have always been the central theme of clean

water legislation, but because of difficulties in relating these to specific

standards, they have often dropped from sight in the formation of the actual

programs, We hope to shed some light on ways in which Bay cleanup policies

might be related to the behavior and preferences of actual and potential users

of the Bay.
This report on the Chesapeake Bay is part of a larger EPA Cooperative

Agreement. The initial agreement dealt with improving methods of measuring

the benefits of environmental improvement. and did not deal with the

Chesapeake Bay. This report provides information on the recreational

activities which take place on the Bay, as well as the monetary values people

place on these activities. While not commissioned with the intent of helping in

the process of revising the Bay plans, we hope that the discussions in this

report will do just that.
Chapter 2

The Role of Human Use Activities in Defining Goals and Strategies

for the Chesapeake Bay

According to the EPA’s Water Quality Standards Handbook, “States must

adopt water quality criteria sufficient to protect designated uses. ” In the

process of developing standards, if water body assessments are called for,

they must “characterize present uses, uses impaired or precluded, and the

reasons why uses are impaired or precluded. ”

The definition of designated uses which must be protected remains a

murky issue. Underlying much of the document is the implicit assumption that

chemical, physical and biological parameters can be used to define uses. On

the other hand there is some acknowledgement of the reality that human

activity does not necessarily align itself ‘with physical and chemical

properties:

water

“The basis of this policy is that the States and EPA have

an obligation to do as much as possible to protect the health

of the public even though it may not make sense to encourage

use of a stream for swimming or wading because of physical

conditions. In certain instances, particularly urban areas,

people will use whatever water bodies are available for

recreation. ”

At the heart of the dilemma is the disparity between the goals which are

couched in terms of human uses and the targets of policy actions which are

callibrated in ambient pollution levels. There is no one-to-one mapping

between human use and scientific measurement. Failure to come to terms with

their relationship has lead to something of a schizophrenia about human

activity and scientific measurement in the Water Quality Standards. This

schizophrenia is not unlike that found in the recreational demand literature

which typically seeks to value environmental amenities by relating behavior to

changes in ambient pollution levels without explaining how people perceive

pollution.

The connection between human activity and scientific measures of ambient

water quality is further obscured by the considerable ambiguity one finds in

both these discussions about the ways in which individuals gain from water

quality improvements. In the EPA Water Quality Standards Handbook, we find

repeatad reference to protecting “uses,” i.e. recreational activities, and at the

same time a sense of obligation to protect human health. One might argue

that these two concepts are coincidental, that we are interested in the health

of humans as they participate in recreational activities in the Bay. In terms

of pollutants which the individual can see (or smell or learn about in some

less direct way), an individual’s criteria for using the Bay are likely to exceed

those minimum standards required to avoid health risks. On the other hand,

the individual is totally unaware of health risks stemming from pollutants

which cannot be easily detected. Thus many recreation decisions are not

directly guided by quality characteristics associated with health standards.

7
Whether modelling recreational decisions or developing standards, we face

a dilemma when we try to link water quality and human behavior. The

obvious way to measure water quality is through chemical and physical

readings perhaps supplemented by assessments of the biological resources.

But water quality improvement is undertaken to enhance society’s welfare

which is recognized as deriving, in large part, from human use. Does human

use respond to the changes in the chemical and physical composition of the

water which physical and biological sciences measure? Are factors which

affect their health the sole factors which matter to people? Can people

perceive changes in these measures?

These questions not only plague benefit measurement, they are central to

environmental policy making. If gods are fundamentally human oriented and

standards are scientifically based, then the disparity between the two must be

resolved for environmental regulation to achieve its potential. In what follows

we present evidence about the human side of the problem.

First we present descriptive information on the variation in household

perceptions of the Bay based on two surveys. These survey results do not

reveal anything about the formation of perceptions however. To gain some

insight into this process, we use the focus group approach, The material

discussed in an earlier volume of this report is summarized in this chapter,

and insights from our focus group experience are offered which are specific

to water quality in the Chesapeake. From these various sources, we draw

some implications for environmental policy.

Systematic Evidence of the Link Between Percept.ions and Behavior

Evidence on what people think of the water quality of the Chesapeake and

how they behave toward the Bay comes from two surveys: an on-site survey

of beach users and a telephone survey (Figure 2.1). Our telephone survey

was conducted May 1, 1984 to September 1, 1984, Research Triangle Institute

(RTI) collected data for the University of Maryland on recreational use and

perceptions of the Chesapeake Bay using a random telephone survey. The

telephone survey was planned and executed jointly with an on-site survey of

beach users at western shore Chesapeake beaches. The 1,044 households with

completed interviews were residents of the Baltimore and Washington SMSA’s.

Demographic, attitudinal and use data were obtained. Chapter 3 reports on

the analysis of use patterns and activities derived from the telephone survey.

It also provides estimates of willingness to pay for Bay improvements.

In this chapter the attitudinal information obtained from the telephone

survey is examined. This survey allows inferences to be made about the

impact of perceptions on decisions to use the Bay. It also facilitates

expansion of sample patterns of behavior and perceptions to the population.

The phone survey provides information about broad perceptions of the

Bay, but without details about regional variation in quality. Specific regional

information comes from the user survey, which gathers data about patterns of

use and perceptions for 408 users

the Chesapeake. The user survey

volume.

o f

is

twelve beaches on the western shore of

described in detail in Chapter 4 of this

8
IWW98

.9

The Sampl

?

Figure 2.1

ing Region for the Telephone Survey ( v) and the Beaches ( ● )

Used in the Intercept Survey
Some General Attitudinal Patterns

Several important patterns emerged from the telephone sample. Fortythree percent of the households responded that they had used (31%) or

intended to use (12%) the Chesapeake Bay for recreation in 1984. For the

users, boating (73% of users), sightseeing (69%), beach use (66%)1, swimming

(64%), and fishing (56%) were quite popular activities. Hunting (5%) and other

uses (20%) were not as prevalent responses. The percent of the Bay users

w h o visited eastern shore beaches (44%) was nearly the same as the

percentage who visited western shore beaches (47%). Most of the users (81%)

also visited ocean beaches in 1984.

Among those who did not use the Bay, reasons for non-use included:

Not interested in water-related recreation (40% of non-users)

Too busy to use (25%)

Takes too long to get there (20%)

Unable to use for health reasons (6%)

Water quality unacceptable (5%)

Costs too much (5%)

Too crowded (3%)

Too many jellyfish (3%)

Other (31%)

Personal preferences and the scarcity of households’ leisure time were

important considerations. Trip costs and poor water quality were not cited as

often (5% of the time) but were still recognized as reasons.

The fact that only 5 percent of our telephone sample stated that the

Chesapeake Bay water quality was responsible for their nonparticipation may

diminish one’s assessment of the role of water quality to Chesapeake citizens.

For one thing, water quality in the Bay is not homogeneous--it varies

substantially and respondents in our sample recognized the differences.

Suppose respondents living in Annapolis believe Annapolis’ water to be

unsuitable for swimming but water at Pt. Lookout to be suitable. These

individuals may respond that time was the prohibiting factor. It takes nearly

three hours to travel from Annapolis to Pt. Lookout. From another

perspective, people who do not visit the Bay because of time constraints may

know little about the Bay’s water quality and will not cite water quality as a

problem.

A number of other questions were included in both surveys to learn more

about perceptions of water quality. For example, we asked telephone

respondents,

“Do you consider the water quality in the Chesapeake

acceptable or unacceptable for swimming and/or other

activities?”

Only 43 percent of the telephone respondents answered

Alternatively stated, 57 percent of a random sample from the

Washington SMSA’s found the Bay water quality unacceptable

and/or other water activities.

10

to be

water

“acceptable,”

Baltimore and

for swimming
A similar question was asked in the user survey concerning specific

western shore beaches with which the respondent was familiar. Even here

there was negative reaction to the water quality, especially at certain beaches.

We discovered, in fact, that some households found the Bay’s water quality

unacceptable? but nonetheless used it. There are several explanations for this

apparent inconsistency, It is possible that households may find the water

unacceptable for certain kinds of activities (swimming) but not for others

(beach use or boating). Households may find the water quality acceptable for

activities with short duration or during certain seasons of the year when the

Bay appears cleaner. Finally, as mentioned earlier, the question abstracts

entirely from the natural heterogeneity of the Bay. Some areas may be

unacceptable to just about everyone while others may appear clean to the

majority.

Some insight was provided by the user survey into the specific factors

considered important in visiting a Chesapeake public beach. We asked

individuals to rank each of five factors on a scale of one to five, with five

being the most important to them. The weighted averages and medians were:

Mean Median

Presence of floating debris or oil 4.32 5

Presence of odors 3.44 4

Presence of jellyfish 3.41 3

Presence of cloudy water 1.97 2

Presence of seaweed and other 1.85 1

aquatic plants

These numbers indicate that floating debris and oil is the major quality

criterion, with odors and jellyfish being the next most important.

The question was re-analyzed by considering the differential responses of

users who came into contact with the water (swimmers and waders) and those

who did not (sunbathers, etc.). The contact users cited odor as the most

important or the second most important criterion 56 percent of the time,

whereas non-users cited it as highly important only 16 percent of the time.

On the other hand, the presence of jellyfish was considered to rank as the

first or second factor for the non-contact users 84 percent of the time but

only 37 percent for individuals who were in contact with the water. These

results are somewhat difficult to interpret because we cannot determine cause

and effect. Logically, those moat bothered by jellyfish are likely to refrain

from entering the water. However, those who go into the water are more

likely than those who don’t to detect unpleasant odors.

In any event, of the five factors deemed important and perceivable to

beach users, three are characteristics which could be linked with water

quality. It is interesting, for the purpose of keeping our perspective, that a

natural factor (jellyfish ) ranks among the unpleasant features of the Bay.

11
Two Propositions about Water Quality and Behavior

To investigate the relation between current water quality and the use of

the Bay, we examined two simple propositions.

Proposition 1: The percentage of respondents at a particular beach who

find the water quality unacceptable is related to water

quality as measured by scientific water quality readings

at the site.

Proposition 2: An individual’s use of the Bay is related to his/her

assessment of whether the water quality is acceptable.

Affirmation of Proposition. 1 implies a positive relationship between

individual behavior and the objective measures of water quality upon which

environmental policy is based. Proposition 2, if true, indicates that people are

consistent in matching their behavior to their perceptions of water quality.

Both propositions are important in making the connection between

environmental improvements and behavior-based benefit measures.

The Bay is a well studied ecosystem and has been the focus of much

attention by the U. S. Environmental Protection Agency and the states of

Maryland and Virginia (e.g. U. S. EPA, 1983). Also, Maryland counties on the

western shore sample water at the beaches on a monthly basis in compliance

with Maryland health requirements. These various sources provide objective

measures of water quality at the Chesapeake beaches, and allow examination of

the relationship between users’ perceptions and objective measures at the

beaches visited.

As mentioned earlier, the user survey instrument contained a question

asking respondents to judge whether specific beaches on the western shore

were acceptable or unacceptable for swimming or other water related activities.

To answer, the respondent was not required to have used the beach but only

to be familiar with it. The water quality at Sandy Point State Park was

familiar to the largest percentage of people (63 percent), whereas only one

person knew about the water quality at Camp Merrick. The percentage of

those familiar with a beach who found the water quality at that beach

acceptable varied from 94 percent at Rocky Point State Park to 12 percent at

a Baltimore Park, a beach used primarily by picnickers.

As a guide to the sample’s responsiveness to water quality, the percentage

of people not finding the water quality acceptable (PCNA) at a beach was

regressed on the moot probable fecal coliform count (FCC) for that beach.

The fecal coliform counts were collected at the beaches during the swimming

season by county officials. Unfortunately, the FCC measurement was available

for only nine of our twelve beaches.

One might argue that individuals would have no way of perceiving fecal

coliform. However, a high FCC might manifest itself in odors or may be

correlated with other factors which cause visible changes in the water. Of

course, periodically high counts could cause a beach to be occasionally closed

by the health officials, a practice that could “brand” the water quality at

certain beaches. Since there were five examples of beach closures, the

12
estimation of a relationship between PCNA

test of the ability of individual to perceive

To assure that obvious restrictions on

and FCC should serve as a small

factors correlated with FCC.

the PCNA and FCC variables were

not violated by our functional forms a Weibull distribution was assumed:

(2.1) PCNA = l - exp[-(FCC/~)O),

where ● is the shape parameter and d is the scale parameter. Using a

non-linear least squares routine, we obtained parameter estimates of 2,537 and

.49 for J and o respectively with ratios of parameter values to standard

errors greater than two in both cases (see Table 2.1). These results support

Proposition I. There is an apparent connection between objective measures of

water quality at a beach and households’ perceptions that water quality at the

beach is acceptable.

Table 2.1

Parameter Estimates and Standard Errors

for Weibull Distribution

Coefficient ● d

Estimate .495 2,537.

Standard Error .095 923.

The value of the shape parameter suggests that the percent of beach

users who find water quality unacceptable is concave in the water quality

variable. (A sufficient condition for concavity is ● < 1, 6 > O.) To find the

fecal coliform level for a given level of acceptance, equation (1) is inverted

and estimated coefficients inserted

For an

coliform

coliform

quality.

The

FCC = = 2,537 *(-ln(PCNA))a*Oa.

acceptance rate of 90 percent, the estimated maximum median fecal

count is in the order of 25 fecal coliform per 100 ml. At fecal

counts of 200, 75 percent of the users are estimated to accept the

At counts of 1,200, this estimated ratio drops to 50 percent.

second proposition was tested using the telephone survey response.

Households were- asked whether anyone in their household had changed

(stopped or started) swimming patterns in the Chesapeake because of water

quality. Two hundred seven of the 1,044 telephone respondents stated they

had stopped, and 26 stated they had started. Of those who stopped, 75

13
percent believed the water quality was unacceptable. In comparison, 53

percent of those who did not change thought the water quality was

unacceptable. Finally, the water quality was believed unacceptable by 42

percent of those who started to swim.

We regressed the individual binary response as to whether or not they

stopped swimming against their acceptance of the water quality. The following

logistic probability model was estimated:

P = {1 + exp[a. + alWQP]

where

[

O if

P =

1 if

WQP = water

(1 if

the household stopped using the Bay

the household did not stop

quality percept ion

acceptable, O otherwise)

A maximum likelihood estimation approach produced the effects reported in

Table 2.2. Water quality perception appeared to have a positive statistically

significant impact on whether the household continued swimming, indicating

some relationship between users’ perceptions of water quality and their use of

the water. This result provides support for Proposition 2 that behavior is

related to perceptions. Nonetheless, some people who consider water quality

unacceptable are still observed to swim.

Table 2.2

Effect of Perceptions on Use

Effect Estimate (al ) Standard Error

Intercept 1.54 .1?

Water Quality Perception .57 .13

Sample Size = 503

Additional insight into the first proposition can be gained from an analysis

of temporal changes in household habits of using the Chesapeake. It is the

consensus among scientists that the water quality of the Chesapeake fell

substantially over the period 1950-1980 (EPA, 1983). The living resources of

the Bay have been used

aquatic vegetation and

resources whose dramatic

as a primary indicator of this decline. Submerged

anadromous fish

decrease over this

14

stocks are among those living

period has been cited. If we can

.
show that contemporaneous with the decline in objective measures of water

quality, individuals were more likely to quit using the Bay, we have additional

indirect evidence of the link between behavior and water quality,

Responses from the telephone survey were used to develop a time series

on the percentage of households who changed their swimming participation.

The procedure was fairly complicated since the size of the population eligible

to change its behavior varied from year to year. That is, consideration had

to be made for how long the household had lived in the Chesapeake region

and whether they had previously stopped swimming. For example, households

responding that they stopped swimming in 1979 clearly were not eligible to

stop again in 1982.

The time series is shown in Figure 2.2. Approximately one percent of the

eligible households stopped swimming each year in the early 1970’s. This

increased to around five percent per year in the early 1980’s. The trend is

definitely one of increasing non-participation in swimming over the time in

which it is believed that declines in water quality were occurring. Although

the overall pattern is a diminishing one, there appears to be a possible

modification of the trend near the end of the period.

.

Figure 2.2

Annual Net Change in Swimming Habits

1970 -1984

(+)

(-)

Net

Challgc

in

Swiming

Habits

o“ ‘-

.01. ‘

. oz. .

.03‘“

.04 “b

.05., -

m “

.07““

L

----- ----- ----- ----

1970 ] 975 19R0

15
If one is bold. enough to assume no reversals in habit for a particular

household, the individual percentages can be combined to show the cumulative

effect of water quality change on a given 1970 population Of households

(Figure 2.3). With this assumption, 30 percent of households that had been in

the area in 1970 would have had a ❑ ember who ceased swimming by 1984.

for a Popu

.1

(+1

c

(-l

.1

TOTAL

CHANGE

IN .1

Sh’IWl13X

HABITS .1

.4

Cumulative Net

ation in Residence in

Figure 2.3

Change in Swimming Habits

1970 and Remaining in Resdence untiI 1984

.- -0 -..-- $=7”-+ -

%

\

\AI

\

\

.--0 ---— --—-

-+<---

‘+=W

16
The decline is interesting for a number of reasons, but mostly because it

shows the potential stock of individuals who could be enticed to return to the

Bay if water quality were improved. Benefits from water quality improvements

will derive from increased desirability of current recreational trips, more (of

these higher quality) trips taken by current users, and finally trips taken by

those who are currently non-users but are enticed into the activity by higher

quality. The above analysis suggests that water quality improvements could,

over a sufficiently long time, attract a large number of new (or returning)

users. To the extent that demand analysis is based on current use and fails

to predict accurately this potentially large number of new entrants, benefit

estimates will be understated.

Results of Some Focus Group Experiences

The above evidence about the relationship among (a) use of the Bay, (b)

subjective perceptions of the Bay’s water quality, and (c) objective measures

of specific attributes of water quality in the Bay suggests in general that

changes in water quality have an effect on behavior. In subsequent chapters

we show how economists can use information about changes - in behavior

induced by quality changes to assess the economic gains from water quality

improvement.

Good theory, convincing benefit measurements, and effective environmental

policy do not require that individuals act knowingly and mechanically in

response to changes in ambient quality. In fact, casual observations suggest

that many people have only vague notions about environmental quality, and

act unconsciously in response to changes in quality. However, much can be

gained by understanding better the link between perceptions and behavioral

changes. How are perceptions formed? Which aspects of water quality,

objectively measured, matter most to people? These and other questions about

the formation of perceptions require some insight into individual motives.

Traditional research methods have not been very helpful in obtaining

these insights. In contrast, focus groups (Reynolds and Johnson; Caldor;

Desvousges and Smith) have been found to be a useful means of investigating

the existence and formation of subjective perceptions on environmental issues

and marketing questions. Focus groups are group interviews conducted in the

form of informal discussion sessions under the guidance of a neutral

moderator. Participants are encouraged to talk at will and describe personal

experiences, anecdotes, and acquired knowledge. The moderator merely

encourage participation by all, mediates arguments and spurs conversation

and thought through questions carefully designed to give direction to the

discussion. By encouraging participants to reveal thought processes and

levels of awareness, their motives begin to emerge.

For this study we conducted two focus groups of a quite different nature.

The groups were made up of 10 to 15 individuals who had some common

association with one another. Each session lasted

In each group there were Chesapeake Bay users

groups were chosen so as to be heavily weighted

the Bay.

17

about one and a half hours.

and nonusers; however, the

towards people familiar with
One focus group consisted of students at the University of Maryland in

College Park, who were members of a wildlife conservation group. Many of

these students had taken environmentally related courses. A number of these

students had grown up in close proximity to the Chesapeake Bay, as active

users of the Bay.

The second group consisted of residents of neighborhoods along the

western shore of the Chesapeake in the vicinity of Plum Point, Maryland.

Their participation was solicited through an announcement in a local

newspaper. Many, although not all, of the individuals were retired, and all

lived near the shore year round. Their backgrounds and education were

quite varied.

These groups were polar in several respects. The college group was

young, formally educated in many scientific and environmental matters, and

tended to be active users of the Bay. The Plum Point group was older, often

retired, not necessarily fluent in scientific and technical matters, and often

somewhat passive in their use of the Bay. The groups shared the

characteristics of having no small children and not being actively engaged in

building careers.

In each case, the moderator presented a formal introduction indicating the

general purpose of the gathering and the underlying research. The

introduction was notably vague so as not to bias subsequent responses. For

the remainder of the session the moderator rained questions but did not

attempt to confine individuals’ responses. All individual were asked to

respond to moat of the questions so

people.

Examples of the types of questions

What does water quality mean

as to avoid dominance by one or two

raised were the following:

to you?

How do you know when the water quality is poor?

What activities do you pursue on the Chesapeake?

Has the water quality gotten worse over time?

What do you think is the moat serious cause of pollution in the Bay?

Is water quality different in different parts of the Bay?

Initially, we had several questions in mind

groups could help us answer. These included the

1.

2.

3.

4.

What sources of information do people use

of water quality in the Chesapeake Bay?

which we

following:

in forming

hoped the focus

their perceptions

What factors affect their interpretation of this information (e.g. past

experience, attitudes), i.e. what is their standard based upon?

In what way does the water quality of the Chesapeake affect people;

i.e., in what sense do they lose when water quality deteriorates?

Do their perceptions affect their behavior and how quickly can

behavior be expected to change in response to environment changes?

18
Our focus

Question 1.

group results suggest the following answers to these questions.

Appearance was by far the most frequently mentioned signal of water

quality deterioration. Whether or not the individual had been exposed to

scientific information on the subject, he was most likely to report that how

the water looked, felt and smelled were his most important indicators. Even

individuals who no longer used the Bay or certain sections of the Bay based

their decisions on the water’s appearance during their last visit.

Without exception, individuals used clarity of the water as an indicator.

The degree of transparent y of” the water was taken for granted to be a

measure of quality. A few individuals, particularly those living on the Bay all

year, noted seasonal differences in clarity, but still used this as their first

quality indicator. Other factors which signalled poor water quality were the

nature of the bottom, discoloration of the shoreline, froth on the water?

floating debris (including man-made) and dead fish. Smell was a clear signal

of poor water quality, but odors were not common, and visual appearance was

used as a more discriminating indicator.

The second most common signal of pollution was “guilt by association.”

Individuals frequently stated that they deduced that water quality would be

poor in sections close to activities which they reasoned would generate

pollution. Such activities included sewage treatment plants, housing and

industrial developments, marinas and other heavy concentrations of boats, and

farms (particularly with livestock). These deductions took place in both

groups but were of a slightly different nature. The college-age group was

relatively more concerned with agricultural operation and with contamination

from boat sewage. The older group seemed to consider development -- with

or without sewage treatment -- of greatest concern. Some of this difference

can be accounted for by the spatial location and familiarity of the two groups.

The Plum Point group knew local conditions well but were relatively immobile

and had limited experience with the rest of the Bay. In contrast, the college

students were heavy boat users and therefore extremely mobile. They tended

to have personal experience along large portions of the Bay and its

tributaries. Graphic examples of manure pond overflows, run-off from pig

farms, etc., were offered. Residents of the Plum Point area would have little

exposure to agricultural runoffs more common to the upper Bay.

Television, radio and newspapers were the next most common external

source of information. Rarely waa specific information about local Bay

conditions gleaned from the media. Instead, these sources create a general

awareness of environmental problems. In large part the inferences about

activities which create pollution were baaed on information gathered from

these secondary sources.

It was clear that at least some individuals were privy to more objective

and scientific information than that available in the public media, although the

distinction between types of information sources was not always made clear.

Many of the college students had taken courses and subscribed to scientific

journals. They were able to draw more sophisticated deductions about links

19
between water quality and surrounding land uses. Also, these individuals

tended to be distrustful of information obtained from the media.

The final signal of pollution also depended on deduction. Individuals

noticed changes in the amount and diversity of wildlife in and around the Bay

and concluded that these changes were the result of water quality changes.

Specifically mentioned were crabs, turtles, and ducks. One individual

associated a decline in the diversity of finfish species with water quality

deterioration, Another individual argued that increases in fish prices were

due to pollution.

Question 2.

Different individuals seemed to interpret similar information in different

ways. Factors which affected their interpretation included their past

experience and general attitudes. When individuals react to the appearance of

something, they must, by definition, be comparing it to a standard. More

often than not, the standard used by these groups was past experience,

although occasionally individuals seemed to be operating with a less personal

standard such as pictures of clean water in mountain lakes. Frequently

individuals compared the appearance of the water to what they (accurately or

not) remembered experiencing as a child. With the exception of one individual

(of the older group), everyone remembered the Bay water being cleaner when

they were children. This was true of the 18- and 19-year-olds as well as the

50- and 60-year-olds. When questioned, individuals admitted that both

maturity and publicity had raised their level of consciousness about water

quality but still insisted that water quality was poorer now than when they

were children. Also, the college studenta noticed some improvement over the

last few years -- in terms of fewer dead fish and birds, less heavy oil

present, and the cleanup of dumps along the tributaries — although they

thought the water was dirtier now than ten years ago.

Individuals’ interpretationa

their general attitudes -- level

and confidence in technology.

distrust for political processes

they believed everything was

of information were also clear] y affected by

of trust in political and entrepreneurial forces

Among the college students, some indicated

and commercial enterprises to the extent that

polluted, whether or not they could see it,

These individuals stated that they would need hard scientific evidence to be

convinced that improvements had been made. At the other extreme, notably in

the older group, a few individuals indicated a trust in the scientific

community, regulator y processes and technology, fueling that the populace

would be protected from unsafe conditions through cleanup activities. Some

indicated resignation to the trade-off between the environment and

development. In all cases attitudes affected how individuals interpreted the

same sensory and media information.

Question 3.

Individuals perceived themselves to be affected by water quality in a

number of ways. It was clear from the discussions that both groups were

apprehensive about going into water they perceived to be dirty. A distinction

20
was made between wading and swimming, as many indicated that they did not

dare submerge their heads in water they thought to be polluted. Also many

wore shoes when wading to avoid contact with the bottom. While only a few

actually mentioned bacteria and potential illness, most seemed to have health

and safety factors in mind.

It is difficult to separate this apprehension from the general

unpleasantness associated wit h unattractive water. No doubt we are

conditioned to link clarity with cleanliness and cleanliness with health.

Nonetheless, it seems that even if the individuals were convinced there were

no health risks, they would consider themselves hurt by water quality

deterioration. Many mentioned the unpleasant feel of the bottom and the

sticky film that swimmers feel on their skin after bathing. Others mentioned

that clear water allowed them activities such as seeing living organisms in the

water, activities which were precluded by murky water. Still others who

never entered the water but only walked along it reported the experience

more pleasurable when the water looked cleaner. In fact, some gave up the

activity when the water looked dirty.

What is interesting however is that no one mentioned toxics or heavy

metals. Some were aware of the term nutrients, and most connected this with

turbidity, algal blooms and slimy bottoms. Others emphasized oil spills.

Particularly in the older group, individuals expected that pollution could be

seen. Those individuals seemed most conscious of health risks, yet indicated

they felt safe going swimming on days when the water looked clear. Few

indicated apprehension about health effects from unseen pollutants.

Lasses also accrued to individuals through perceived reductions in angler

and duck hunting success. Many complained of a decline in the quality of

fishing, crabbing and duck hunting. Others complained of the reduced

variety of finfish available in the Bay. Among the college students were some

who professed a concern for the wildlife in situ. That is, some individuals

indicated reduced enjoyment of non-consumptive wildlife uses.

Individuals also indicated a fear of eating fish and shellfish caught in

polluted waters. For many individuals low catch rates were irrelevant because

they did not dare eat fish caught in local waters.

Interestingly, one individual who did not use the Bay for any recreational

activity indicated that he really did not care what happened to the water

quality in the Chesapeake. His only concern was the quality of his drinking

water. Here is a real world example of the concept of “weak complementarity.”

Weak complementarily is said to characterize an individual’s preferences if he

does not care about the quality of a resource that he does not use.

Question 4,

Earlier in this chapter, survey results were shown to support the

empirical relation between behavior and perceptions. However, frequently

inconsistent behavior was observed--some individuals perceived the water

quality to be poor but continued to use it. The focus groups shed some light

on these anomalies.
With only a few exceptions,

familiar with . the Bay and were

Also, with only a few exceptions,

the water quality of the Bay. In

up all forms of recreational use,

some activities altogether,

the individuals in both focus groups were

recreational users of one sort or another.

the individuals were deeply concerned about

most circumstances individuals had not given

but they had reduced that use and curtailed

We can model an individual’s behavior as changing in three ways in

response to perceived changes in water quality: he can alter the choice of

whether to participate in an activity, he can alter the sites at which he

recreates, and he can alter the frequency of recreation.

The two groups revealed different types of reactions to water quality

deterioration. The younger, more mobile group stopped going to certain

places which they perceived to be worse. The older, less mobile, group

stopped participating in certain activities which they perceived to be sensitive

to water quality, particularly swimming. They also curtailed their fish and

crab catch to avoid eating contaminated fish. In both groups, there appeared

to be a frequency dimension to individuals’ reactions as well. Many who

found the water quality too poor for swimming generally indicated they would

go in on especially hot days or on days when the water looked especially

clear. The latter suggests that the degree of intra-seasonal variation in

pollution and other causes of turbidity will affect the frequency of

participation in a recreational activity. Of course only those who live near

the shore can assess the water clarity before incurring the coats of the

recreational trip.

While there is no firm evidence for this, many individuals seemed to

participate more in recreational activities than they believed wise. In many

cases, it was because they had been participating in these activities for years

and resisted giving them up and because they perceived no suitable

alternative. In contrast, some individuals had curtailed certain activities

because of bad experiences and indicated that it would take very convincing

evidence to bring them back. All of this suggests that the response of

behavior to perceptions may be significant but may also be a delayed

response.

Summary of Focus Group Experience

In summary, we can construct a set of hypotheses about perception

formation. The- list

10

2.

3.

4.

Individual

specifically

Individuals

(man-made)

Individuals

Individuala

would include

associate the quality of the water with its appearance --

its clarity and color.

associate the quality with the amount of floating

debris and dead organisms.

associate quality with angler success rates.

deduce quality from surrounding land and water uses.

22
5. Individuals infer things about the quality of water from general

publicity about the environment and/or technology change.

6. Individuals learn specifics about quality from scientific publications,

educational experiences.

The more exposure individuals had to information of the sort included in (5)

and (6) , the more likely were they to deduce things about water quality from

surrounding activities. Nonetheless, items (1) and (2) in the above list

dominated, irrespective of age, education, etc.

Individuals perceived themselves to be harmed by poor

through a number of routes:

1. The individual’s recreational experience is degraded

appearances floating debris, etc.

2. The individual fears health and safety risks.

water quality

by unpleasant

3. The individual believes poor water quality reduces catch rates and

variety of species.

4. The individual fears eating fish from areas with poor water quality.

It is worthy of note that both unpleasant appearance and poor fishing

conditions harm individual but also serve M signals of poor water quality.

These signals carry with them suspicions of further losses in the form of

health risks from contact with the water or from eating contaminated fish.

Conclusions

In this chapter we set out to explore the relationship between human

activities and the water quality of the Bay. This relationship is important for

the Chesapeake Bay Program for several reasons. First, human use of the Bay

is the ultimate goal of devoting resources to improving the quality of the Bay.

Gaining some sense that people change their use of the Bay with changes in

water quality suggests that Bay clean-up strategies can have significant

value. Second, economists’ benefit measures of improvements in water quality

are based primarily on changes in behavior. Knowing that households have

some sense of water quality and are affected by this sense of water quality

when deciding how to allocate their scarce time and resources gives support

to the methodology of benefit measurement.

We have explored the relationships between perceptions and human

activities in two ways. From two surveys, a phone survey of households and

an on-site survey of beach users, the relationship between objective measures

of quality and perceptions of quality and behavior has been examined. The

telephone survey supports the relationships in several ways. Households that

perceive water quality as unacceptable are more likely to quit using the Bay.

The telephone survey also shows an implicit but positive correlation between

the likelihood of quitting and the Bay’s -

provided support for the perceptions

water quality. The user survey also

link. This survey shows positive

23
correlation between measures of fecal coliform at each of nine beaches and the

proportion of households that found each beach unacceptable.

The focus groups provide insight into how people judge the quality of

water and why they change their behavior in response to quality changes. A

variety of sensible motives contribute to behavior changes. People smell, feel

and see the water and its surroundings. They react when they learn about

changes in water quality from newspapers television and other media.

Of particular importance to policy makers is the clear signal that

individuals suffer from water quality deterioration in more than one way.

Many regulation are implicitly based on health standards, yet health effects

are only part of the story. Irrespective of health risks, individuals were

uniformly adament in arguing that recreation in water perceived to be dirty is

less enjoyable. This dimension is not totally independent of health concerns,

however, since dirty water was additionally considered to be a signal for

health risks. The word “risk” is a key one. Whether or not a given state of

water quality does in fact present a health risk, the individual suffers from

the uncertainty associated with not being able to assess the risk himself.

While we do not go into this problem in great depth in this study, it is

important to keep a few things in mind. Uncertainty is ceteris paribus

undesirable, and there are two sources of uncertainty involved here. One is

the uncertainty associated with not knowing what is in the water and whether

it is potentially harmful. The second is the uncertainty associated with the

actual onset of adverse health consequences if indeed the water was

potentially harmful.

The losses described above pertain to water use that involves contact.

There are still more ways in which individuals perceive themselves to be

harmed by poor water quality. The enjoyment associated with any activity

within sight of the Bay is claimed to be diminished if the water appears dirty.

Finally, to the extent that poor water quality reduces fish abundance and

species variability y, sportfishermen see themselves harmed. Finally, even if

fish catches aren’t reduced, perceived poor water quality suggests health

risks associated with eating fish catch.

Together the -two sources of information provide support for the

inferences which we draw in the following chapters. Individuals are aware of

water quality, change their behavior in response to water quality changes,

and derive benefits when the quality of the Bay is improved.

24
Chapter 3

Recreational Use of the Bay and Willingness

for Improvement to the Bay se a

to Pay Estimates

Whole

A variety of methods have been used to analyze the welfare effects of

water quality improvements. In the introduction to this chapter, a brief

description of the two basic approaches is offered to prepare the reader for

the methods used in this and following chapters. A more thorough examination

of one of the methods? contingent market valuation? is offered in Cummings)

Brookshire and Schulze. Bockstael, Hanemann and Strand supply a thorough

examination of the other method, indirect market valuation,

The indirect market approach uses individual behavior in related markets

to infer values of non-marketed goods. For the case in question, water

quality, the researcher observes the demand for goods that are related to

water quality, such as recreational tripe. The usefulness of the approach

depends on the responsiveness of behavior toward water quality. If

individuals value good water qualit y, the y will be drawn to goods or activities

associated with high quality water end will be willing to travel farther and

incur greater costs for this improved experience. Their behavior can be

observed, and from this, values deduced. One drawback to this approach is

that assumptions regarding behavior must be made in order to assess values.

This results in untestable restrictions on behavior implicitly or explicitly

imposed in the modelling process.

Contingent market analysis involves the establishment, in the interviewee’s

mind, of a fictitious or hypothetical market circumstance. The interviewee is

asked to respond to the circumstance in a hypothetical manner. By

establishing a scenario to explain the respondent’s answers, the researcher is

able to deduce characteristics of the respondents preferences.

The “average” willingness to pay or sell is the predominant value obtained

in most contingent valuation exercises. A question or series of questions is

designed to elicit the respondents (hypothetical) bid for or against the policy

in question. The approach can be directed very specifically to the good or

quality change to be valued, and thus, in theory, elicit the amount of money

needed to keep the individual at the same level of satisfaction before and

after an event. The questions can be quite specific and may therefore define

precisely the event or policy to be assessed. The disadvantage of the

approach is its hypothetical nature. Rarely is it possible to test the validity

of the response. through observations on behavior. In addition, the specific

valuation problem may be so remote from the respondent's market valuation

experiences as to leave him unable to respond reliably.

Contingent valuation has been deemed a useful technique (see Cummings,

Brookshire and Schulze) provided it is applied to problems which are closely

associated with common market valuation experiences. Car-son and Mitchell

present evidence of stable contingent valuation estimates for national benefits

of clean freshwater in the U. S.

25
It seems reasonable to attempt some contingent valuation for the

Chesapeake Bay problem as long as caution is exercised in the interpretation

of the results. For us, derivation of the contingent values thus obtained is

not intended as an end unto itself, but rather information to support the

results of additional analyses.

Recreational Use of the Bay

During the summer of 1984, a telephone survey of over 1,000 households

in the Washington, D. C. and Baltimore Statistical Metropolitan Sample Areas

(SMSA’s) was conducted. A description of procedures can be found in

Appendix A. Appendix B is a copy of the survey instrument. One objective

of the survey was to provide a complete inventory of beach use by residents

in the Baltimore/Washington SMSA’s ( see Figure 2.1 ) , which include the District

of Columbia, several counties and incorporated cities in Northern Virginia and

much of central and southern Maryland. Restricting the geographical area in

this way biases the sample of individuals toward urban residents. However,

this area includes a large percentage of the population surrounding the Bay.

In the subsequent discussion, the percentage figures reflect the sample

response rates corrected by sampling weights to define unbiased estimates.

The projected total number of households purported below to participate in

various activities are estimated as the product of these weighted response

rates and the approximately two million households residing in the

Baltimore/Washington SMSA (the 1980 Census reported 1,876,144 households).

On the basis of the telephone survey, 43 percent of the region’s

households are estimated to have used or intended to use the Bay for some

recreational activity in 1984. Participation rates varied across the region (see

Table 3.1) with Anne Arundel County having the highest percentage use (69%)

and the District of Columbia the lowest (21%). Of the remaining areas,

Northern Virginia had the next lowest participation rate (37%) and Montgomery

County the next highest rate of participation (48%).

The households used the Bay for a variety of recreational activities.

Swimming/beach use was the most popular, with a projected 740,000 households

participating. The next most popular activity was boating which attracted a

projected 620,000 households. Sightseeing (estimated 586,000 households) and

fishing (estimated 477)000 households) were also very popular. The projected

number of households who used the Bay in conjunction with hunting totalled

only about 45,000. There were an estimated 170,000 households that reported

other uses of the Bay.

As one might expect, households often participate in more than one

activity. For the major use activities of swimming, fishing and boating, Table

3.2 shows the percentage of- respondents who participated in one activity or

more. Roughly speaking, about one-third of the households participated in all

three activities, one-third participated in two of the three activities, and the

remaining one-third participated in a single activity. This distinction has

importance for benefit estimation; if any one household’s participation were

limited to only one activity, independent behavioral studies of each activity

26
Table 3.1

Participation Rate” in Chesapeake Bay Activities

By Activity and Area, 1984

Prince George’s

Northern District of Montgomery end Charles Anne Arundel

Virginiab Columbia county Counties County Baltimorec Othersd

% Participation in

CB Activity (1984) 22 9 37 34 60 36 36

% Participate or’

Intend to Participate 37 21 48 46 69 45 42

% Participate CB

: Fishing 12 8 16 18 33 19 21

% Participate CB

Swiming 10 R 24 21 33 23 23

% Participate CB

Boating 17 8 26 24 4B 24 33

% Participate CB

Hunting 1 0 1 0 5 2 3

aWeighted. bIncludes

c Includes

dIncludea

percentage, representing a random sample of Baltimore-Washington, D.C. SMSA's

Fairfax, Arlington, Prince William and Loudon counties and the cities of Alexandria, Fairfax and Falls Church.

Baltimore City and portions of Howard and Baltimore counties.

Carroll and Harford counties and portions of Howard and Baltimore counties.
Table 3.2

Joint Participation Ratesa in Chesapeake Bay Activities

By Activity and Region, 1984

One Activity Two Activities Three Activities

Fishing I Fishing Swimming

Fishing I Swimingb I Boating Swimming Boating Boating Fishing Swimming Boating

Overall 3 14 10 9 9 20 34

Northern Virginia 3 15 11 6 9 15 38

District of Columbia 10 IQ 15 5 15 54

@ Montgomery County 1 22 12 8 9 22 26

Prince George’s and

Charles counties 8 17 7 7 11 19 31

Anne Arundel County 3 3 10 7 10 34 33

Baltimore City

and County 3 13 9 12 6 20 37

Other Maryland 3 15 4 20 18 40

● Weighted percentages, representing a random ample of the Baltimore-Washington, D.C. SMSA's.

bSwimming includes beach use.
could be aggregated to provide the basis of a total benefit estimation for

improved water quality. Multiple participation and the interdependence among

activities prevents straightforward addition of benefits calculated in demand

studies of individual activities.

While it may be necessary eventually to undertake a comprehensive benefit

analysis of all Bay activities? there is enough current information to shed some

light on the value of the recreational use of the Bay. Independent studies

are useful, if for no other reason than to establish “conditional” relationships

between activities and key factors. This may facilitate future studies by

isolating key factors for which information is critical. Moreover, by analyzing

a series of partial systems, bounds may be established on the total potential

benefits.

Aggregate Willingness to Pay

This portion of the chapter employs the contingent valuation technique to

value improvements in water quality in the Chesapeake Bay. The hypothetical

circumstance posed to survey resondents involves the alteration of the Bay’s

water quality from its current condition to an improved condition which, in

the respondent’s view, is acceptable for swimming. . Because individuals’

perceptions of water quality are not easily linked to objective measures (see

Chapter 2) and because individuals do not easily understand these scientific

measures, the hypothetical circumstance was framed in terms of the

respondent’s acceptability. This limits the specific application of the results,

since there is no simple way to determine at what point clean-up efforts raise

the water quality to an acceptable level for everyone. However, the evidence

presented in Chapter 2 offers some guidance as well as some historical

perspective.

The households responding to the contingent valuation experiment are a

subset of the telephone survey of the Baltimore-Washington SMSA’s. Each of

the randomly selected households was asked:

“Do you consider the water quality in the Chesapeake to be

acceptable or unacceptable for swimming and/or other water

activities?”

Of the 959 respondents, over one-half (57 percent) found the water quality

unacceptable. ‘l’hose who responded that it was unacceptable were asked:

“Would you be willing to pay ($A) in extra state or federal

taxes per year if the water quality were improved so that you

found it acceptable to swim in the Chesapeake?”

The amount of money ($A) was varied randomly from $5 to $50 over the

sample. The percentage of respondents who answered “yes” is shown in Table -

3.3.

29
Table 3.3

Percent of People Willing to Pay Additional Taxes

for Acceptable Water Quality for Swimming, by Amount of Tax

Amount of Tax Increase $5 $10 $15 $20 $25 $30 $35 $40 $45 $50

Percent of

Respondents Willing

To Pay Tax Increase 64 66 63 70 58 46 57 47 47 53

If sample sizes were big enough, monotonically decreasing percentages

over the entire range would likely be revealed. Nonetheless, the percentages

are in general declining as the amount of the tax increases. Of those who

were presented a tax of $5, $10, $15, or $20, an average of 66 percent agreed

(hypothetically) to accept the tax burden in exchange for acceptable water

quality. Of those presented a tax of $25, $30, $35, or $40, the average

percentage dropped to 52 percent.

Analysis of Willingness to Pay Responses

Hanemann (1984) describes a method for analyzing a central tendency in

willingness to pay from questions with “yes” or “no” answers. Let the

respondent derive utility from the nonmarket good, water quality, and from

money income (y) which can be used to purchase marketed goods. Also let a

vector (x) of individual characteristics affect his utility. Utility is given by

u 1(1 ,y;x) when the water quality is acceptable and U. (O,y;x) when

The functions u,, and u. are not known, and thus are considered

to the researcher; That is

(3.1)

where vj

mean zero.

uj(j, y;x)

independently

When offered

the tax providing

(3.2)

swimmable

that

it is not.

stochastic

= v(jjy; x) + Vj j = 0,1

and identically distributed random variables with

water at a tax of $A, the individual will accept

v(l, y-A; x) + I/l ● v(o, y;x) + U.

and decline otherwise. In this framework, the individual’s response becomes a

30
random variable with probability density

Po = Pr{accept tax in exchange for swimmable water}

= Pr{v(1, y-A; x) - v(0, y;x) ● Vo - V1}

P1 = Pr{not accept tax in exchange for swimmable water} = 1 - po.

Define q = vo - VI and let Fn(*) be the cumulative distribution function of q.

Then the probability of accepting the tax in exchange for swimmable water

equals Fq(AV) where LV is the difference between the deterministic portions of

the utility function in the two states (see equation (3.2)}.

At this point any of a number of utility functions, individua l

characteristics, and density functions to complete the analysis could be

chosen. Like Hanemann, we chose a logistic cumulation distribution function.

Also, we chose a linear function (see Sellar, Chavas and Stoll for the

limitations of this form) for v(0), such that

(3.3) v(j, y) = aj + j9 y, p>o

where the arguments of x have been temporarily suppressed. The difference,

Av, is (al - aO) - PA, which gives a probability model of the form

(3.4) F(q) = n F(-aO + al - /YAi) ~ [1 - F(-ao + a, - #A~)]

its, itso

where So is the set of individuals refusing to pay the tax, and S1 its the set

accepting the tax.

Conceptually, we sought the value A for which

(3.5) u(1, y-A; x) = u(0, y;x).

Combining equations (3.2) and (3.3) produces the result that when (3.5) holds,

A is defined as the following

A = [(a, - aO)(-h) l/8”

31
Since T is random, so is A. To evaluate A we chose to take its expectation,

assuming ao~ a I ~ and # to be constants, which yields

(3.6) E[A] = (a, - aO)/~.

Thus, (al - aO)/@ is the expected (or average) tax that would make an

individual just indifferent to paying the tax in exchange for acceptable water

quality and not paying the tax but forgoing good water quality. Now we need

estimates of the parameters a ~, ao~ and # to get a value for E[A].

Results of Analysis. BY Subgroups of Respondents

In developing the theory it was admitted that individual characteristics

(designated by the vector x) were likely to affect the utility function which in

turn would affect the parameters in (3.6). Some of these characteristics are

strictly idiosyncratic and not worth trying to model, but others may be

associated with identifiable subgroups of the population. Three means of

subdividing the population suggest themselves--by household income, by race

and by Bay user/nonuser. In the sample obtained in 1984 there was sufficient

correlation between race and income to make the separate treatment of these

infeasible. Additionally, it was difficult to subdivide the population by income

because income appears in the data set as a continuous variable and

arbitrarily dividing it into ranges did not prove useful.

After some preliminary logit analysis, a modification of the model shown in

(3.4) was estimated. One modification entailed making the (*1 - =0) depend on

whether someone in the household had used or intended to use the

Chesapeake Bay in 1984. A variable (D ~ ) was included to reflect use. This

approach allows us to test whether users value the change in the Bay’s water

quality more than non-users, ceteria paribus. The other modification involved

making the bid coefficient, 8, depend on the racial classification. Because

there is a wide disparity in income between whites and non-whites (average of

$40,000 annually vs. average of $25,000), the marginal utility of income, which

# represents, maY be different for the two groups. Use of a binary variable

(D2) in conjunction with the tax variable permitted an examination of the

effect of race on the marginal utility of income.

The results of the estimation are reported in Table 3.4. The amount of

the tax significantly reduced the probability that a respondent would agree to

pay the annual tax increase. Also significant were the use/intercept

interaction variable and the tax/race interaction variable. Both users and

whitea were more likely to accept the tax increase.

32

,
Table 3.4

Logistic Model Estimates Related to the Probability a Respondent

Will Accept a Tax Increase

Estimated

Variablea

Coefficient Standard Error t-rat io

Constant (al - aO) .385 .222 1.73

D1 “ Constant 1.084 .202 4.77

Amount of Tax - .04 3 .009 - 5.3?

D2 “ tax .035 .007 “ 4.7 8

Chi-squared = 47.10

aD1,D2 represent binary variables for the use of the Bay and white racial

characteristics, respectively.

The above results are difficult to interpret because of the high correlation

between race and income. It should not be assumed that whites, ceteris

paribus, have a higher willingness to pay for water quality. There is

insufficient data, however, to test the separate effects of income and race. To

determine whether willingness to pay changes by income classes, the analysis

was reworked and estimates for the expected value of A were obtained for five

arbitrarily defined income classes ($0-$20,000; $20,100-$50,000; $50,000-$80,000;

over $80,000; income not reported ). We thus assumed the utility function (3.3)

was linear in income only within the ranges described above. Additionally we

allowed the (al - a.) estimate to vary depending on whether an individual was

a user or non-user during 1984.

The results are shown in Tables 3.5 and 3.6. The coefficients are of the

proper sign although. their statistical significance is not overwhelming. Income

classes, however, do appear to be an important determinant of the willingness

to pay. The results in Table 3.4 suggest that willingness to pay at first rises

with income and then falls with the highest willingness to pay coming from the

middle income group ($20,000-$50,000).

Returning to the stronger results of Table 3.4, but bearing in mind the

correlation between race and income, the expected willingness to pay of a

randomly chosen individual in each of four subgroups is computed and

presented in Table 3.7. The values are divided on the basis of use and racial

composition of the household. In addition, standard errors for the calculations

are shown. They are computed on a first-order approximation basis (Kendall

and Stuartj pages 228-332) assuming independence of coefficients. A problem

arises with the estimate of expected willingness to pay by white users of the

Bay. The expected value is substantially out. of the range of the tax increase

asked in the survey. Because it is computed as a ratio of estimated

coefficients, there is nothing to guarantee the value will lie within the range

of values used in the questionnaire. However, predictions which fall outside

33
Table 3.5

Estimates of Utility Parameters by Income Group

. ai - a.

Sample Likelihood

Incom e Class Users Non-users i Size Rat io

$0 - $20,000 1.282

(1.89)”

$20,000- $50,000 1.652

(2.96)

$50,000 - $80,000 1.695

(.98)

Over $80,000 1.157

(2.81 )

Income not .533

reported (1.53)

.833

(1.72)

.968

(2.04)

1.471

(2.80 )

.543

(1.24)

.200

(.42)

.028 99 19.35

(1.73)

.012 200 11.05

(.81 )

.017 101 42.33 .

(.95)

.013 22 5’?.79

(.90 )

.016 93 9.68

(1.11)

W-statistic in parenthesis

Table 3.6

Expected Value of Willingness to Pay

for Acceptable Water Quality for Swimming by Income Group and User Group,

1984.

Expected Value of Willingness to Pay

Income Class Average for All Average for Users Average for Non-users

o - $20,000 $ 38.54 $ 415.94 $29.85

$20,000-$50,000 108.60 134.25 78.48

$50,000-$60,000 95.16 101.20 88.08

over $80,000 66.’44 89.00 41.77

not reported 22.26 32.64 12.25

34
Table 3.7

Estimated Willingness to Pay for Acceptable Water Quality

by Participation and Racial Composition of Household

1984.

Expected Willingness to Pay

Participation Status Racial Compos it ion

White Non-Whit e

User $183.63 $34.16

(55.12)a (10.40)

Non-User $48.13 $ 8.95

(10.25) (2.53)

a Standard deviation in parentheses

the range are less reliable. The results suggest that a wider range of tax

increases would have yielded more confidence in the estimates’ accuracy.

Individual willingness to pay bids for water quality improvements appear to

have a larger range (i.e. take on larger values) than we anticipated when

constructing the survey.

In Table 3.8 the average willingness to pay for each subpopulation is

combined with estimates of the subpopulation size to project total willingness

to pay figures. The values are based on the telephone sample estimate that

57 percent of the population find Chesapeake Bay water quality unacceptable

and on the sample percentages of white users (27%), white non-users (35%),

non-white users (16%) and non-white non-users (21%).

Expected values as well as optimistic and pessimistic values are shown.

The optimistic (pessimistic) value is derived using the expected value of

willingness to pay plus (minus) one standard deviation. On the basis of these

estimates, we could argue a reasonable range of willingness to pay values of

$60 million to slightly over $100 million. Care must be exercised when

considering the standard deviation, as it is computed as an approximation and

is not associated with the normal distribution. The values shown, however,

represent an “order of magnitude” contingent valuation estimate of willingness

to pay for improved water quality.

35
Table 3.8

Estimated Aggregatea Willingness to Pay for Water Quality Acceptables

for Swimming, by Classification and Scenarios

1984

Scenario

Wingness to Pay “Average*’ b

“Optimistic"c “Pessimistic"d

---- ---- --- Thousand $ ----------

White 55,838 72,595 39,081

Non-white 6,164 8,020 4,271

Non-user

White 19,505 23,641 15,409

Non-whit e 2,105 2,720 1,514

Aggregate 83,612 106,976 60,275

a

Baltimore-Washington SMSA population =.

bBased on expected willingness to pay

cBased on expected willingness to pay plus one standard deviation

dBased on expected willingness to pay minus one standard deviation

Regional Comparisons

Stretching the data somewhat further, one can also examine geographical

patterns of responses. The logistic model wae re-estimated using sub-samples

grouped by region: the Southeast region (Prince George’s County, Charles

County, Anne Arundel County and the District of Columbia), the Western region

(Northern Virginia, Montgomery County) and the Northern region (Baltimore

36
City and County, Howard County, and Harford County). The sub-samples

represent groups, each of which exhibits reasonable internal homogeneity, for

which we have at least one-hundred and fifty responses. Even with these

conditions, however, the statistical results are less significant than the earlier

ones because of the smaller sample size.

The results suggest regional similarities and differences (Table 3.9). Some

consistency is evident as signs on all coefficients are the same for all regions.

Thus an increase in the hypothetical tax decreases the probability of

acceptance of the tax associated with water quality improvement. Additionally

the effect of participation and race on willingness to pay for the improvement

is consistent across all regions.

Table 3.9

Logistic Model Estimates Related to the Probability a

Will Accept a Tax Increase to Improve Chesapeake Bay

by Geographic Area

Respondent

Water Quality,

Variable Southeast ● West b Northc

Constant .334

(.94)d

D1 “ constant .78

(2.36)

Amount of Tax -.050

(3.33)

D2 . Tax .041

(3.15 )

Chi-squared f o r

Likelihood ratio 36.5

.71

(.46)

1.02

(2.49)

-.070

(3.04)

.060

(3.00)

37.2

.12

(.30)

1.67

(4.77)

-.023

(1.77)

.015

(1.36)

48.6

aDist. of Columbia and Counties of Prince George’s, Charles and Anne Arundel

bNort hem Virginia and Montgomery County

cBaltimore City and Count iee of Baitimore, Harford and Howard dt-ratio in parentheses

There are, however, systematic differences across regions. Users from the

Northern region are willing to pay on average substantially more than those

from the southeast or western regions. The figures for nonusers are less

disparate across regions, with those for the West region somewhat larger. The

estimated willingness to pay figures are presented in Table 3.10.

37
Estimated Willingness to

Participation, and

Table 3.10

Pay for Acceptable Water Quality by Region,

Racial Composition of Household, 1984.

Region

Household Characteristic Southeast West North

White, User $124 $133 $224

Non-White, User 22 25 77

Non-White, Non-user 7 10 5

White, Non-user 3’? 55 15

Existence Value

In the preceding contingent valuation experiment we present non-zero

willingness to pay estimates for non-users as well as users. There are a

number of reasons why non-users may be willing to pay for improved water

quality. One of these reasons has been labelled existence value by

non-market benefit analysts (Krutilla) and stems from early experiences

applying benefit cost analysis to water resources projects. Individuals who

never use a resource either directly or indirectly and never intend to uae it

may still be willing to pay to improve ita quality or assure its existence.

Formal studies of existence value are limited, but some empirical evidence

exists. Fisher and Raucher (1984) suggest that nonuse benefita (including

both option value and existence value) are some fraction of the use value of

water quality changes. Other research (e.g., Walsh et al., 1985; Schulze et al.,

1983 ) suggests that existence value may be greater than use value, and

sometimes substantially so.

Existence value is a frequently cited concept in the literature, and several

studies have attempted to derive explicit estimates of existence value

associated with water quality (Mitchell and Carson, 1981; Cronin, 1982; Walsh et

al., 1978; Desvousges et al., 1983). Nonetheless, no consensus exists on the

models which underlie the measurement. Behaviorally based methods of

welfare measurement are unsatisfactory because, by definition, existence value

is unconnected with behavior. Suspicion surrounds contingent valuation

estimates of existence value because these estimates are even less susceptible

to proof or disproof than contingent valuation estimates of use values. Even

more to the point, the success of a contingent valuation approach depends on

well defined questions.

existence value, properly

Without a clear idea of the -

focused questions are difficult to

38

motivations behind

define.
The Existence Value Experiment

In this section we present some preliminary results of an experiment

designed to shed some light on the motives behind existence value for the

Chesapeake Bay. The sample frame was derived from the phone survey

described above. The households contacted randomly by phone were asked if

they would complete an additional mail survey. Of the 1,044 contacted, 741

agreed to fill out and return a brief mail questionnaire regarding water

quality in the Chesapeake Bay, and of these 741 households, 282 actually

returned the questionnaires. Because only about 70 percent of those

contacted agreed to receive the mail questionnaire, and only 38 percent of

those who agreed actually returned these questionnaires, these results should

not be taken as representative of the population sampled but as useful for

gaining preliminary y insigh ts into willingness to pay motives.

The 282 respondents were grouped as users or non-users. Users were

defined as all respondents who currently use the Bay or thought they might

do so in the future. Respondents who felt certain that they would not use

the Bay for recreation at any time in the future were defined as non-users.

Non-users accounted for 16.3 percent of the respondents.

Respondents were asked to consider a series of situations concerning

public beaches surrounding the Chesapeake Bay. They were asked to assume

that water quality at these beaches had fallen below a level acceptable for

swimming, They were told that a project could be undertaken that would

clean the beaches so that a water quality level acceptable for swimming was

achieved and maintained. The respondents were then asked the following

question under four scenarios:

“Would you prefer that the clean-up project be undertaken?”

Scenario 1, No additional information.

Scenario 2. Access to the beaches by the public is permanently denied

so that even if clean, the beaches will not be used.

Scenario 3. If the project is undertaken, taxes would be raised so much

that nearly everyone prefers that the project is not

undertaken. These taxes would be paid by individuals other

than the respondent.

Scenario 4. If the project is not undertaken, funds would instead be

used to improve hospital services in selected communities

surrounding the Bay. Of all the people who care, half want

the beaches cleaned, and half want improved hospital

services, The respondent himself would never need to visit

any of the improved hospitals.

The proportion of “yes” responses for users and non-users under each

scenario is given in Table 3.11.

39
Summary Results of

on

Table 3.11

Contingent Valuation Experiment

Existence Value

Scenario Proportion of Standard Proportion of Standard

Number Yes Responses: Error of Yes Responses: Error of

Users ● Difference Non-usersb Difference

1 .96 .83

2 .70 .032 .69 . .088

3 .71 .032 .67 .088

4 .49 .035 .37 .091

aThe number of users in the sample of respondents is 236.

bThe number of nonusers in the sample of respondents is 46.

cThis is the standard error of the difference between the proportion in

Scenario 1 and the proportion in the given scenario.

Interpretation of the Results

In order to interpret the responses reported in Table 3.11, it is necessary

first to consider the ‘potential motives for existence value. Two broad motives

may be discerned: intrinsic and altruistic. Existence value based on intrinsic

motives stems from a concern about the state of the world. Concern about the

order of things may cause people to suffer simply by learning about pollution

incidents. What has been called the “environmental ethic” is closely linked

with the intrinsic motive.

Of concern here is the second of the two motives: altruism. People can

gain value from the enhanced wellbeing of others (individualistic altruism).

An extensive discussion of these altruistic motivations can be found in

Madariaga and McConnell (1987).

Responses to the question under Scenario 1 are used as a control to be

compared with responses under Scenarios 2 through 4, where Scenario 1 is

purposely ambiguous about ‘project costs.

preferred that the project be undertaken

non-user responses of “yes” as evidence

high number of non-users giving positive

results of previous studies that have found

As expected, moat respondents

under Scenario 1. Interpreting

of existence value, the relatively

responses is consistent with the

evidence of existence value.

40
With access to beaches denied under Scenario 2, the number of “yes”

responses to the question predictably declined. Since the number of non-user

responses of ‘*yes” declined when access was denied, it appears that existence

value, at least to some individuals, is related to others’ use. Thus, altruism

may , be one motive that underlies existence value. However, even with access

denied, most respondenta preferred that the project be undertaken. This may

reflect the presence of a number of motivational including an environmental

ethic. Finally, it is interesting to note the closeness of user and non-user

group responses under Scenario 2. Since with access denied there can be no

users, “yes” responses from the user group will also indicate positive

existence value. In this scenario, the proportion of users and non-users

exhibiting existence value was nearly identical.

Scenario 3 is similar to Scenario 2 in that both attempt to eliminate

altruistic motives. In this scenario, the Bay can be used after the cleanup,

but other individuals will be forced to pay more than what the improved water

quality is worth to them. The similarity of proportions in Scenarios 2 and 3

supports the notion that the Chesapeake resource is valued for its own sake.

In Scenario 2, about 70 percent of the people support the project despite the

fact that there is no use and hence no direct use value. In Scenario 3,

roughly the same proportion supports the project even though there is no net

value to the users.

Under Scenario 4 the number of “yes” responses fell dramatically

compared with the responses under Scenario 1. Since less than half of the

non-users preferred that the cleanup project be undertaken, it appears that

the improved hospital services are on average at least as valuable as clean

water in the Bay.

The individuals were instructed that they would not need the services of

the hospital, themselves, so it is tempting to label their value for the

improved hospital services as existence value. However, the entire value of

the hospital services may be due to altruistic motives while individuals appear

to have motives beyond altruism for Chesapeake water quality improvements.

Conclusions

The underlying motives for existence value matter to the proper design

and interpretation of contingent valuation experiments. The preliminary

results concerning existence value associated with the Chesapeake Bay suggest

some ambiguity about its motivation. People are willing to pay for water

quality improvements in the Bay, but how much they are willing to pay

depends on the specific nature of the opportunities foregone by doing so.

Among other considerations, these suggest attention should be paid to the

methods for financing the cleanup of the Bay.
Chapter 4

The Effect of Chesapeake Bay Water Quality on Beach Use

The previous chapter contains a range of benefits from improved

Chesapeake Bay water quality, based on a contingent valuation experiment.

Although there is substantial evidence to suggest that the responses to the

hypothetical questions were not random but rather associated with households’

use of the Bay and racial/income strata, problems still exist with the approach.

Follow-up questioning revealed households did not consider alternative uses of

tax increases (e.g. improvements in other public goods such as hospitals,

roads, etc.). Moreover, the subjective nature of the water quality measurement used in the contingent valuation question does not lend itself easily to

policy analysis, based as it is on objective (scientific) measures of water

quality. Finally, the values represent aggregate values, indistinguishable on

the basis of type of recreation or geographic location of pollution. Knowledge

of user group and geographical impacts of programs can provide a depth and

richness of understanding important in the political process.

The remaining chapters are devoted to providing analyses of the observed

behavior of households based on data gathered in previous studies which are

specific to different recreational activities. The analyses use cross-sectional

information on households to model beach use, boating and recreational

fishing. Once demand functions are estimated, benefits from access and from

changes in water quality are assessed for each of these activities.

This chapter contains a cross-sectional analysis of beach use on the

western shore of Maryland. It draws from the random telephone survey of

the Baltimore-Washington SMSA’s and a stratified random survey of twelve

public beaches on Maryland’s western shore. As such, the analysis is not

comprehensive of all beach use in Maryland but rather the use of the public

areas in one portion of the Bay by the citizens in the surrounding environs

of the two large metropolitan areas closest to the Bay.

A number of approaches to estimating recreational response to environmental quality changes have evolved. Many of these depend first on the

estimation of demand for recreational activities which are closely linked to the

environmental resource in question. The recreational demand models currently

in use have grown out of the union of neoclassical demand theory and the

travel cost model proposed by Hotelling and employed extensively by recreational economists for the paat several decades. The principal contribution of

the travel cost model is found in the use of the travel cost to the recreational

sita as the principal component in constructing a “price” for the recreational

commodity. The simple travel cost model can be derived from a neoclassical

utility maximization framework, as can more complex models which incorporate

added dimensions to the problem (see Bockstael, Strand and Hanemann, 1986).

One particularly important modification of the simple model is the

introduction of quality characteristics of recreational sites (see Volume I of

42
this report for the theory). If the recreational demand model is to be used to

estimate the value of environmental quality improvements then individuals’

behavioral responses to changes in quality must be modelled. This requires

observing behavior in the context of differing levels of environmental quality,

which can generally be done only by examining recreation behavior at a given

point in time over a group of sites which vary in quality.

The procedure can lead to a number of specific methods of analysis (see

Kling, Bockstael and Strand, 1985), each imposing a different set of

restrictions/assumptions on recreational behavior. While there is no concensus

regarding the “correct” model, the two most prominent. models in the literature

can be categorized as the (modified) neoclassical model and the discrete choice

model. The neoclassical model has the form of the traditional demand system,

with quantities being a function of prices and water quality. The model is

modified in some way to facilitate the inclusion of water quality parameters

which do not tend to vary for a given site over the population, Additionally,

demands are generally treated independently. A common approach is the

varying parameter model (VPM), as put forth by Smitht Desvousges and

McGivney (1986). Here, independent single-site models of recreational trip

demand are estimated, and the estimates of the intercept and price coefficient

are correlated with the site’s water quality. Then,

in water quality change the intercept and slope of

influencing quantity consumed and the welfare

activities.

The discrete choice model (DCM) has also taken

in policy analysis, changes

the demand curve, thereby

derived from recreational

many forma (e. g., Caulkins;

Morey and Rowe) but the form employed by Bockatael, Hanemann and Strand is

representative. In this model, the individual is viewed as having a number of

choice occasions upon which to select a site. The selection of site is discrete

in the sense that only one site is chosen per choice occasion. Site

characteristics such as travel cost, water quality and facilities are used to

explain the choice of a site on any given occasion. A composite “value”

reflecting the desirability of available choices is computed from the discrete

choice estimation, and this is used with other factors to estimate the number

of choice occasions.

Although both models are behaviorally based, there are advantages and

disadvantages associated with both. The varying parameter model starts from

the assumption that the demand functions for trips to sites are interior

solutions to a utility maximization process. The discrete choice model,

however, starts from the viewpoint that, on any given occasion, an individual

chooses among a finite set of alternative sites. Neither approach is perfectly

satisfactory. In the DC model, the link between the number of choice

occasions and the site selection per choice occasion is ad hoc. With the VP

model, the demand for any one site does not adequately reflect the alternatives. Additionally, the fact that individuals do not visit all sites is inconsistent with the implicit theory and must be handled econometrically. Kling

has employed Monte Carlo studies to examine the performance of these models.

Not surprisingly, her results suggest that the VP model excels when most

recreationalists tend to visit almost all alternative sites in a season, and the

DC model excels when most tend to visit one or only a few sites in a season.

43
Most recreational data sets will be characterized by something between the two

extremes, however neither model has an obvious advantage, and no tractable

model is perfectly consistent with this situation.

In this chapter both the varying parameter and discrete choice models are

applied to western shore beach use in Maryland. In subsequent chapters only

the varying parameters model will be applied since neither the boating nor

fishing data can support the data intensive discrete choice model. The data

on beach use at western shore beaches is relatively rich, however. Both

types of models will utilize this same data set of Chesapeake Bay beach users

in the subsequent analysis. The results will be a range of values which

suggest orders of magnitude for welfare measures of hypothetical changes in

water quality.

The Survey and the Data

This section is devoted to a description of the survey of Chesapeake

Beach Use conducted in 1984. Unlike the data used in analyses of boating

and fishing in Chapters 5 and 6, the data used in this chapter were collected

during an earlier budget period of this cooperative agreement. Great care

was taken with the sampling frame to improve confidence in the results.

Because the survey itself is important to the project, the content and

procedures are described extensively in Appendix C. A copy of the survey

instrument can be found in Appendix D.

From May 26, 1984 to August 19, 1984, Research Triangle Institute (RTI)

interviewed individuals on the western shore beaches in Maryland. The study

population consisted of all residents of the Baltimore and Washington, D. C.,

SMSA’S, age 14 or older, that used these beaches for recreation in 1984. More

specifically, the population was limited to recreational users of the following 12

beaches:

1.

2.

3.

4.

6.

7.

8.

9.

10.

11.

12.

Beach

Sandy Point

Point Lookout

Fort Smallwood

Miami

5. Rocky Point

Elm’ s Beach

Bay Ridge

Kurtz

Breezy Point

Rod & Reel

Morgantown

North Beach

Strata

Geographic Size

north

south

north

north

north

south

south

north

south

south

south

south

large

large

small

small

small

small

large

small

small

small

small

small

44
Four hundred and eight individuals were interviewed at the beach to

learn of their recreational patterns and perception of water quality at these

beaches. These individuals were randomly selected from sample beaches and

days. The sampling design can be described as a two-stage stratified sample

in which a probability y sample of beaches and days was selected , and a random

systematic sample of persons was interviewed at each sample site (day-beach

combination) .

The User Intercept Survey Questionnaire was designed to record and

collect the following:

✎ Frequency of visits made to beaches on the western shore of the

Chesapeake

● Activities that the respondent (and his/her family) participated in

when visiting beaches

● Activities not participated in and the reason why they were not

● Cast related to a typical trip to each beach that the respondent had

visited since January 1, 1984

● The respondent’s perception of the quality of the beach and the

beach facilities at each beach with which he/she was familiar

✎ Factors that influenced a respondent’s decision to visit or not visit a

beach

● The respondent’s willingness to continue to visit the sample site if

costs related to the use of the beach were to rise.

In addition, a series of demographic questions was included to enable analysts

to establish profiles of beach users.

The Data

Household Trips

Respondents were asked, on-site, how many trips they had taken in 1984

prior to the interview and how many they intended to take during the rest of

1984. Follow-up telephone interviews at the end of the seaeon obtained

complete 1984 trip information for 251 of the 408 beach users interviewed.

For the remaining households, information was obtained solely on-site.

To assure consistency in our trip measurement, the end of the season

information was compared with in-season response so that a correction factor

could be applied to households with only “in-season” information.” Using data

from the largest beach (Sand y Point), a regression of end-of-season trips (xe )

on reported plus intended trips during the season (xr ) yielded:

45
(4.1 ) Xe

 = .686 + .632xr +

(1..40) (35.00)

where the t-statistics are in parentheses.

t Ra . .89 (n = 148)

Equation (4.1) was used to predict

total trips to a site from on-site information for households that did not

receive follow-up inteviews. The combination of a fairly small constant term

and a coefficient on Xr which is less than one suggests that households tend

to report intentions in excess of trips later realized.

Access Costs and Time Costs

Previous studies (e.g. Bockstael, Hanemann, and Strand, 1986) have

considered travel costs to include the person’s (or household’s) monetary

costs of travel as well as the opportunity costs of their time. Distance to a

site, transformed into transportation costs is a feature common to all visitors.

However, those individuals who forego income in order to take time to recreate

incur monetary expenses in excess of transportation costs. For these

individualist these costs can be measured as the foregone wage rate times the

time spent accessing the activity.

For households without employed persona or with employed persons with

fixed work schedules, there is no direct loss of income incurred when

recreation is undertaken. The opportunity cost of recreation time for these

individuals is the value of foregone alternative activities. Unfortunately,

opportunity costs will vary over individuals in ways which are not observable.

The only observable factor related to the total opportunity cost of the

recreation experience will be the time spent traveling and recreating. Even

this measurement is troublesome, however, since the on-site portion of this

time also measures the amount of the recreational good consumed. To avoid

many of these complications we employ only round-trip travel time as a

surrogate for opportunity costs in these cases.

In addition to these access costs, most western shore

admittance fee which must be added to the other costs of

site. Often the fee will vary depending on the day of the

party.

Water Quality

beaches have an

traveling to the

week and size of

The Chessie System environmental quality data, maintained

Chesapeake Bay program, were’ used to construct the water quality

by EPA’s

measures.

Turbidity, bacteria counts, total suspended solids, total nitrogen, total

phosphorous, and total chlorophyll A are among the potential indicators of

water quality to which beach demand might be sensitive, Since these ,

exhibited a high degree of collinearity, two variables were extracted from the

data set to use in this analysis: total nitrogen and total phosphorus. A good

case can be made for using these variables. Studies of the Bay conducted by

the U. S. Environmental Protection Agency indicate that perhaps the most

significant problem facing Bay restoration and protection efforts is nutrient

46
over-enrichment of Bay waters. Excessive nutrient levels may be the partial

cause of decreased submerged aquatic vegetation, which in turn has adverse

effects on the food chain and on the habitat for many fish species. Further,

over-enrichment leads to lower dissolved oxygen levels which have additional

adverse effects on fish stocks, degrading the appearance of the water as well.

High collinearity y between nitrogen and phosphorus readings prevented

separate inclusion of both variables in the analysis. The product of nitrogen

and phosphorus was used to avoid this problem and to capture the interactive

nature of these nutrients.

In each case, mean monthly water quality levels from April through

September 19?7 were calculated for areas of twelve counties contiguous to the

Bay, The summer months were chosen because they represent the peak of

recreational activity. Complete data over regions of the Bay were available for

only some years, 1977 being the closest to the survey year. The relative

water quality readings across the Bay are unlikely to be considerably

different between the two years, even if the absolute readings are different.

Additionally, individuals‘ decisions are unlikely to be related solely to water

quality in the current year, but will be based on a cumulative learning

process which includes

obvious correct choice,

one year are unclear.

Other Variables

past observations as well. Consequently, there is no

and the errors associated with using data from any

Additional factors are known to influence recreation activity, including the

ownership of certain types of household capital equipment. Boats, recreational

vehicles and swimming pools are the types of capital equipment which may

affect the use of beaches on the western shore. Some of these beaches have

boat-launch facilities, some camp sites, while others offer good swimming

possibilities. Years living in the area, previous recreational history, family

size and participation are some other factors which may

The Varying Parameter Model

To formalize the model of behavior. the individual is

be important.

assumed to maximize a

constrained utility function which is a function of number of trips taken to

each of n quality-differentiated sites, the quality characteristics of each site,

and a Hicksian good. Thus

(4.2) max u(x, q,z) s.t. px + z = y

where x is an n-dimensional

corresponding vector of costs of

qij, i = 1,...,n and j = 1,...,m,

characteristic at the it h site, z

simplify notation in this section,

vector of trips to the n sites, p is a

accessing the sites, q is a matrix of variables

where

is the

we will

47

qij is the level of the jt h quality

Hic sian good, and y is income. To

assume that there is only one quality
characteristic, and thus m = 1, and q represents the vector of values of the

one quality characteristic across sites.

Problem (4.2) defines n demand functions, each of which may be a function

of all n prices, n quality levels, and income:

(4.3) xi = gi (P, q,y) i = l ,...,n.

This model cannot be estimated with cross-section data. Imagine having

observations on S individuals who visit site i. Own price (pl ) and substitute

prices ( Pk, k s i) will typically vary across individuals if they come to site i

from different geographical areas. However, there will be no variation in the

quality characteristic at site i (q i ) across the S individuals, nor will there by

any variation across individual in the characteristics of other sites (q k, k =

i). With no variation in the q i‘s across observations, their coefficients cannot

be estimated, and nothing can be learned about behavioral response to quality

changes.

There are several methods for resolving the dilemma presented above.

Some of them build on the model presented in (4.3) (these are described in

Kling, Bockstael and Strand, 1985), while others rely on discrete choice models

(see Bockstael, Hanemann and Strand, 1986). The method used in this section,

the varying parameters model, falls in the former category and follows similar

methods applied by Vaughan and Russell (1982); Smith, Desvousges and

McGivney (1983); and Smith and Desvousges (1985).

One way to motivate the varying parameters model is to consider a simple

linear form such as:

n

(4.4) x{ ‘~of + Z ~lfkpk ‘@21y+ c1 i = l ,.. .,n,

k=1

but to further assume that the parameters in site demand functions are

deterministic functions of the quality characteristics. For example, the IPs

might be linear functions of the q’s:

@o i = 70 + Ylqi + ~ 72jqj9 j=i

(4.5) ~lfk = aok + alkqf + E ‘Zkjqj$ j=i

k = l ,.. .,n,

48
The model in (4.4) and (4.5)

across sites (i.e., variations in

in own-site attributes (qi )

Specifically, the above model

following form:

implies that variations in demand parameters

the ISO i ‘s, pi i ‘s, etc. ) correspond to variations

and subatitute-site attributes (qj, j ~ i).

implies a demand for trips to site i of the

(4.6) xj = (70 + Ylqt + ~7, jqj) + : (a

Ok + al~qf + ‘a2kjqj)Pk k=l

Even though the model can be collapsed into one expression as

the estimation procedure usually involves two steps: the regression

to each site on prices and income (e.g., n separate regressions)

in {4.6),

of trips

and the

regression of the coefficients from the– first n- regressions on the quality

characteristics of the sites. The second step requires the application of

generalized least squares because of the properties of the error structure

implicit in the estimation of (4.5) which must use estimated parameters (I? 's) in

place of the true 8$s.

The first-stage estimation procedure is further complicated by the need to

correct for a censored sample bias. Moat consumer demand problems analyzed

with household data encounter this problem. A random sample of households

will reveal a certain (often substantial) number of households that do not

consume the good in question and thus have zero as the value of their

dependent variable. In the sample, there will therefore be many observations

concentrated around zero. Neither omitting the zero observations, nor

including them in an OLS regression, will produce unbiased estimates.

Tobin analyzed this problem in 1958 and produced the first of several

approaches to handling the problem. His approach applied to the first stage

of our varying parameters model characterizes the problem in the following

way:

While z may be distributed as a normal, x will not be. The estimation of the

/?’s therefore requires maximum likelihood techniques, where the likelihood

function is given by

(4.8)

49
where z? is the right-hand side of (4.4), u is the standard deviation of e, and

+ and + are, respectively, the density function and the distribution function

of the standard normal.

The Varying Parameter Model Estimates

Of the twelve mentioned beaches, there was sufficient data to estimate

demand functions for only nine. For two beaches, Kurtz Pleasure Beach and

North Beach, there were less than 20 respondents. Additionally, Breezy Point

had only 24 observations, and over 10 percent of these were more than

one-day trips. We were, however, able to separate the Chesapeake Beach

location into two sites, the Chesapeake Beach proper and the Rod and Reel

Club beach. Thus there were ten sites initially considered in the analysis.

The arithmetic means of the variables used in the model are shown in

Table 4.1. The average is taken over persons actually visiting the site. The

largest average number of trips per user occurs at Rocky Point, whereas the

smallest occurs at Porter’s New Beach. Point Lookout requires on average the

greatest monetary and time expenditures for access, whereas Fort Smallwood

and Rock y Point have the least average monetary costs per user. Table 4.1

also reports, for each beach, the percentage of users owning certain types of

recreational equipment. The percentage of beach users owning boats ranged

from a low of 12 percent at Bay Ridge to a high of 19 percent at Porter’s New

Beach and Miami Beach. The range was larger for recreational vehicle

ownership, with as much as a quarter to a third of users at Chesapeake

Beach, Rod and Reel, Bay Ridge, Point Lookout and Morgantown being

recreational vehicle owners.

There are a number of methods for incorporating substitute site

information into recreational demand models (see Bockstael, Hanemann and

Kling, 1986), none of which is completely satisfactory. The approach taken

here is to identify for each site and each individual one substitute beach.

Average access costs and time costs for a substitute beach are included in

Table 4.1. For each individual and each beach, the designated substitute

beach is the least cost alternative.

The initial set of regressions was run using equation (4.4) as the

behavioral model and a tobit estimation procedure as the statistical basis. In

some cases, multicollinearity among the cost and time variables required

eliminating one or both of the substitute cost variables. In the case of

Morgantown, the small number of observations gave such poor results that the

site was dropped from the model. The results reported in Table 4.2 were

generated using the LIMDEP statistical package and an IBM 4341 computer.

As indicated earlier, the total sample of beach users is 408 individuals.

The tobit estimation for any beach j includes both users of beach j (non-limit

observations) and individuals who were in the beach sample but did not use

beach j. The number of observations in each tobit estimation differs from

beach to beach however, because some individuals had missing cost data for

some beaches.

50
Table 4.1

Average Values of Regression Variables for Visitors by Beach

Substitute Beach Ownership

Access Access Access Access Recreation Swimming

Tripe coats Time coats Time Boat Vehicle Pool

Beach (#/yr) ($) (hr) ($) (hr) (%) (%) (%)

Sandy Point 8.06

Fort Smallwood 5.83

Chesapeake Beach 2.87

U-I

Rod & Reel 6.42

Bay Ridge 7.33

Point Lookout 3.85

Rocky Point 10.20

Porter’ s New Beach 2.92

Miami 5.16

Morgantown 7.71

15.98

8.66

18.79

22.80

15.75

36.42

9.07

9.50

11.81

26.09

1.29

1.11

1.45

1*58

1.14

2.73

.93

.72

.86

1.22

12.09

6.38

11.30

13.61

10.84

13.10

6.43

5.81

4.82

9.80

.84

.69

.84

.87

.84

1.03

.55

.49

.59

1.02

15

14

17

13

12

17

13

19

19

17

18

20

28

33

28

30

12

11

17

35

18

14

17

25

12

12

20

11

17

09
Table 4.2

Tobit Estimates for Beach Demand Model, by Beach”

Beach Substitute Beach Ownership

Nonlimit/

Access Access Access Access Rec. Swim. Limit

Beach Constant Costs Time costs Time Boat Veh. Pool a Observations

Sandy Point 8.17

(2.83)b

.16

(.05)

10.44

(--2. 18)

10.29

(2.04)

-3.96

(-1.89)

-.70

( .31)

3.49

(-2.72)

-2.20

(--1.45)

-6.96

( 1.16)

-.35

(-4.07)

-.53

(-2.86)

“.1O

(-.84)

-.47

( 1.45)

.18

(-2. 19)

-.29

(-2.21)

-.05

(--5.62)

.09

(-1.35)

-.78

(-4.90)

-4.85

(-3.61)

.24

(2.86)

2.47

(1.15)

14.85

(57.59)

243/ 139

41/198

22/201

87/66

46/272

Fort Smallwood -4.24

(-2.58)

.34

(1.14)

9.52

(11.61)

-1.51

(-1.28)

.29

(1.25)

9.72

(5.47)

Rod & Reel

3.55

(1.36)

12.41

(19.00)

Rocky Point -5.63

(-2.38)

6.16

(10.00)

Chesapeake Beach

VI ?Q

Porter’s New

Beach

-1.19

(-1.76)

.19

(1.80)

3.23

(2.58)

-1.28

(-1.28)

.31 1.54

(1.10) (1.32)

4.55 2.19

(5.41) (1.69)

3.43

(5. 15)

25/118

82/262

50/121

61/292

Point Lookout. -1.72

( 4.72)

.12

(3.35)

Miami -1.27

(-1.18)

7.42

(10. OG)

Day Ridge -9.63

(-3.50)

.83

(3.19)

7.40 -6.19

(1.96) ( -1.00)

7.55 -5.67

(1.50) (-1.13)

l8.06

(17.56)

aNo coefficients were significantly different from zero for Morgantown site. bt–ratios in parentheses
The estimated coefficients on own-price (travel cost) were all of the

expected sign) and most were statistically significant from zero. Beaches for

which a reasonably large on-site sample was obtained yielded the most

significant estimates. Small sample effects of multicollinearity among the price

and time variables likely caused the large standard errors for Miami, Rod and

Reel, etc. In some instances, the multicollinearity was sufficiently troublesome

that only the own-price and own-time variables were considered. Obtaining

results for as many beaches as possible was critical because the sample size

in the second-stage estimation equals the number of beaches in the first

stage.

The results of the second-stage estimation, i.e. the estimation of equation

(4.5), were obtained from a weighted least squares procedure in which the

weights were l/uP,j, the inverse of the standard error of the own-price

coefficient for each beach. The estimated equations are:

(4.10) #lj = -.0308- .00020 TNPj (-.04) (-2.22)

#oJ = -2.66- .0016 TNPj

(-1.10) (-.001)

where TNP is the water quality variable defined earlier, and the values in

parentheses are t-ratios.

The results show no significant relationship between water quality and the

intercepts of the beach-use demand equations but a significant relationship

between water quality and the coefficients on travel cost. The poorer the

water quality (i.e. the higher the level of TNP), the larger the negative

response of beach users to travel costs. This results in a pivoting inward of

the demand curve as water quality deteriorates and a pivoting outward with

improvements. The results are in accordance with the proposition that poor

water quality lowers beach users willingness to pay for access to beaches.

Estimated Benefit Changes

The analysis above describes the behavioral response of the average

western shore beach user to the change in water quality. From this

information and information on the number of users of the beaches, we are

able to determine some estimates of benefits of hypothetical improvements in

water quality to the average user of each beach. We are also able to expand

to the total population of beach users.

Three hypothetical changes in the environmental variables are considered,

a 10 percent and a 20 percent decrease (environmental improvement) in the

environmental (pollution) variables, and a 20 percent increase (environmental

degradation). Since we will want to assess the effects of the change, we will

want to calculate consumer surplus before and after the change. The formula

53
for individual i's consumer surplus from site j is given by the following when

the demand function is linear

(4.11) Csij = (x@/(-21?jl),

where xi j is its demand for trips to j and @j 1 is the coefficient on cost of

access in the jth site demand function. For a given hypothetical change in

water quality at beach j, the weighted average change in consumer surplus

over the sample is calculated:

Nj (4.12) ACS = E [(xlj(qj) )z/(-2Pjl (q])) - (xij(@)a/(-28j, (q~))l*k\/Nj 1=1

where k, is the weight, q! and q~ are the levels of water quality before and

after the change, and the notation xl j (qj ) and #j ~ (qj ) implies that both

demand and the coefficient on travel cost are functions of the level of water

quality. Nj is the size of the sample of beach users used to estimate the

tobit equation for beach j. The sample includes all 408 observations minus

those for which information about beach j was unavailable.

Calculating consumer surplus for hypothetical environmental circumstances

(equation 4.12) thus requires values for X“ = x(q”), P“(qo), x1 = x(q1 ), and Pl

= P(q’)- The first step in assessing the hypothetical changes is to use the

results of model (4.10) to predict P j i (q~ ) , that is to predict the new travel

cost coefficient given the hypothetical change in water quality. The

coefficients P j 1 (q~) and IYd ~ (q]) are then used to determine values for demand,

i.e. x? and x]; - - -

Prediction of the demand for trips

function was initially estimated using a

underlying the Tobit,

x* = @*z + c

is complicated because the demand

Tobit procedure. Recall the model

t- N(0, UJ)

where values of x% < 0 are censored and observed as zeroes, so that

x = /?02 + z when I?’Z + s > 0

and

x = 0 otherwise.

Given the underlying model, the systematic portion of (4.4) cannot be used as

the expected value 10CUIY of x, The Tobit predicting equation given below

adjusts for the censored nature of the dependent variable:

54
(4.13) ~

where z is a vector of explanatory variables and o

cumulative distribution functions for the standard

first term represents the conditional expectation

person participates times the probability that the

and * are the density

normal, respectively,

of trips given that

person participates.

second term corrects for non-normality because of potential truncation.

There are two ways of obtaining the “before” and “after” x’s for

and

The

the

The

.*.’

the

consumer surplus functions. One way is to use the predicting equation (4.13)

to calculate both i“ and 51 values. The second method is to accept the

observed x as X“ and then to adjust this x by f t - ii” to reflect the

hypothetical change in water quality to obtain the estimated x 1. (See

Bockstael and Strand, 1986, for details of the two approaches. )

Because there is no clear theoretical reason to choose one approach over

the other, we calculate the results both ways.

(4.11) to calculate the change in average consumer

A calculates trip values as

(4. 14)

and

(4.15)

where the z ~ ~ are the explanatory variables in the

Both methods - use formula

surplus. However, Method

jth beach’s regression (see

Table 4.2). Method B calculates the demand for trips in the following way:

x? j

and

x# j

where 21 ~ and R? j

Tables 4.3 -

= observed value of x { j

are defined in (4. 14) and (4. 15).

4.5 summarize the average beach users benefits and losses

from the hypothetical changes in the nitrogen and phosphorus concentrations

in the Chesapeake Bay. The first and fourth columns in each table represent

the base line average consumer surplus over the entire sample of beach users

55
Table 4.3

Annual Benefits per Beach User from a 20 Percent Decrease

in Pollutant, by Beach

1984

Calculation Method Aa Calculation Method Bb

Consumer Surplus Consumer Surplus

Beach Before After Benefits Before After Benefits

Sandy Point

Fort Smallwood

Chesapeake Beach

Rod & Reel Club

Porter’s New Beach

Rocky Point

Point Lookout

Bay Ridge

Miami Beach

133.94

.82

36.32

10.32

5.9 5

80.38

15.86

178.18

5.3 8

169.03

5.1 7

43.88

16.19

8.4 5

89.53

22.61

204.76

‘1O.27

35.09

4.35

7.56

5.87

2.50

9.15

6.75

26.58

4.89

342.04

57.69

57.89

259.81

12.20

179.65

315.27

171.64

220.68

379.33

73.13

60.77

284.08

12.34

191.02

415.06

178.98

304.99

37.06

15.44

2.88

24.27

1.14

11.34

99.79

7.34

84.31

● With Method A, the average consumer surplus for a change in quality at beach

j is taken over a sample which includes all beech users whether or not they

visited beach j.

bWith Method B, the average consumer surplus for a change in quality at beach

j is taken over a sample which includes only users of beach j.
Table 4.4

Annual Benefits per Beach User from a 10 Percent Decrease

in Pollutant, by Beach

1984

Calculation Method Aa Calculation Method Bb

Consumer Surplus Consumer Surplus

Beach Before After Benefits Before After Benefits

Sandy Point

Fort Smallwood

Chesapeake Beach

Rod & Reel Club

Porter’s New Beach

Rocky Point

Point Lookout

Bay Ridge

Miami Beach

133.94

.82

36.32

10.32

5.95

80.38

15.86

178.18

5.3 8

150.39

1.50

39.96

13.00

7.12

84.82

18.73

191.08

7.34

16.45

.68

3.64

2.68

1.17

4.44

2.87

12.90

1.96

342.04

57.69

57.88

259.81

12.20

179.65

315.27

171.46

220.68

363.35

69.28

61.11

277.73

13.55

186.63

363.61

176.55

261.16

21.31

11.59

3.22

17.92

1.35

6.9 8

48.34

5.0 9

40.48

aWith Method A, the average consumer surplus for a

j is taken over a sample which includes all beach

visited beach j.

bWith

j is

Method B, the average consumer surplus for a

change in quality at beach

users whether or not they

change in quality at beach

taken over a sample-which includes only users of beach j.

57
Table 4.5

Annual Losses per Beach User from a 2.0 Percent Increase

in Pollutant, by Beach

1984

Calculation Method Aa Calculation Method Bb

Consumer Surplus Consumer Surplus

Beach Before After Losses Before After Losses

Sandy Point

Fort Smallwood

Chesapeake Beach

Rod & Reel Club

Porter’s New Beach

Rocky Point

Point Lookout

Bay Ridge

Miami Beach

133.94

.82

36.32

10.32

5.95

80.38

15.86

178.18

5.3 8

106.54 (27.40)

.29 (.53)

29.81 (6.51)

6.25 (4.07)

4.05 (1.90)

72.26 (8.12)

11.92 (3.94)

154.56 (23.62)

3.06 (2.32)

342.04

57.69

57.88

259.81

12.20

179.65

315.27

171.64

220.68

311.26

47.63

55.27

239.35

11.24

166.81

253.41

164.55

172.41

(30.78)

(10.06)

(2.62)

(20.46)

(.96)

(12.84)

(61.86)

(7.09)

(48.27)

‘With Method A, the average consumer surplus for a change in quality at beach

j is taken over a sample which includes all beach users whether or not they”

visited beach j.

b

With Method B, the average consumer surplus for a change in quality at beach

j is taken over a sample which includes only users of beach j.

58
for use of each beach, calculated using each of the two methods mentioned

above. The second and fifth columns show the average consumer surplus per

beach user in the sample following a water quality change at each beach. The

third and sixth columns represent the change in surplus for the average

beach user associated with a water quality change at each beach.

The method of calculation makes a good deal of difference for some

beaches, especially Point Lookout, Miami Beach and Rod and Reel. Recalling

the econometric results in Table 4.2, the estimated demand equations for these

three are price inelastic relative to other beaches; that is, the absolute values

of their price coefficients are quite small. When demand is very inelastic, big

differences are likely between the mean consumer surplus and the consumer

surplus associated with the mean number of tripe (see Bockstael and Strand).

The average consumer surplus values are expanded to the entire

Baltimore-Washington SMSA’s in Table 4,6. This was accomplished by knowing

that the 1980 number of regional households was 1,977,000 (census of the U. S.,

1980), by determining from a contemporaneous phone survey that 47 percent

of the regional population used western shore beaches” and by knowing the

percentage of western shore beach users who used each beach. Large

aggregate benefits are associated with Sandy Point (in both methods of

calculation) because of the very large number of households that visit that

beach. Whereas 21 percent of the population used western shore beaches,

over half used Sandy Point. When expanding to households, Sandy Point has

nearly twice as many users as any other beach.

The Discrete/Continuous Choice Model

The utility maximizing model in (4.2) and the resulting demand functions in

(4.3) are an apt description of the individual’s decision problem only if he

chooses positive values for all x i (i.e., if he is at interior solutions in all the

markets). It is not an adequate description if corner solutions arise (i.e., x i = o). The discrete choice model is appropriate when an individual chooses one

from a finite set of alternatives, by comparing the available alternatives. The

discrete choice model presented here is amended to include a component which

describes the demand for trips as well as the discrete choice among trips on

any choice occasion.

The Choice Among Sites

The first part of the model involves the estimation of the household’s

choice among sites. It will be important here to capture those elements which

vary over sites. McFadden (1976) provides a utility theoretic framework for

employing the multinominal logit model which is applicable to a discrete choice

problem of this sort. For further discussion of its application to recreation

demand, see Bockstael, Hanemann and Strand (1986).

59
Table 4.6

Aggregate Benefits/Losses to Users from Changes in Chesapeake Bay

Water Quality, by Beach

1984

Calculation Method A Calculation Method B

Change Change

Improvement Degradation Improvement Degradation

Beach 20% 10% 20% 20% 10% , 20%

Sandy Point

Fort Smallwood

Chesapeake Beach

Rod & Reel Club

Porter’s New Beach

Rocky Point

Point Lookout

Bay Ridge

Miami Beach

. . . . . . . . . . . Thousands$. . . . . . . . . . . .

14,064

1,744

3,038

2,356

1,006

3,673

2,708

10,667

1,963

6,602

275

1,462

1,075

468

1,781

1,153

5,176

788

(11,001)

(212)

(2,612)

(1,632)

(750)

(3,258)

(1,577)

(9,484)

(931)

9,967

1,576

1,316

52

923

12,484

823

3,975

4,704

651

329

626

24

449

5,375

397

1,674

(8,009)

(781)

(597)

(1,089)

(40)

(824)

(7,520)

(710)

(2,255)

60
Suppose we call ~ a latent variable denoting the level of indirect utility

associated with the ith alternative. The observed variable Yi has the property

that

Yi = 1 if Vf = max(Vf, V*$ . . . ,V#)

Yi = 0 otherwise.

Indirect utility associated with the ith alternative is some function of zi, a

vector of attributes of the ith alternative so that @l = Vi(zi) + Ci. The

random component is generally attributed to the systematic? but unmeasurable,

variation in tastes and omitted variables. Thus, each household has a level of

error which, in a sense, remains with it over time. If the t‘s are

independently and identical y distributed with type I extreme value

distribution (Weibull), then it is well known that

Prob (Yi=l I Z) = ~ , z eJ

j=l

(see Maddala 1983; McFadden, 1973; Domencich and McFadden, 1975). The

likelihood function for the sample is

L = [ 1

~Vi gi

;—

i=l ~ evj

J

where gi = 1 if i is chosen, gi = O otherwise.

The multinominal logit has a property which in some circumstances is useful

but in others is unrealistic. The model presented above implicitly assumes

independence of irrelevant alternatives, i.e. the relative odds of choosing any

pair of alternatives remains constant no matter what happens in the remainder

of the choice set. Thus, this model allows for no specific pattern of

correlation among the errors associated with the alternatives; it denies--and

in fact is violated by--any particular similarities within groups of alternatives.

McFadden (1978) has shown that a more general nested logit model

specifically incorporating varying correlations among the errors associated

with the alternatives can also be derived from a stochastic utility maximization

framework (see also Maddala, 1983). If the z’s have a generalized extreme

value distribution then a pattern of correlation among the choices can be

allowed. McFadden defines a probabilistic choice model

VN evlGi(ev’, . . ..e )

P. = 1 G(evl , . . . . evN )

61
here G1 is the patial of G with respect to the i argument th and G(ev~. +.evN)

has certain properties which imply that

F(c1 ,..,zn ) = exp{-G(e-’l ,... ,e-’n)}

is a multivariate extreme Vi

s Ze then the model

described above. However

G(Y) =

value distribution. When G(evl,. ..,evN) is defined

reduces to the ordinary multinornial logit (MNL)

when

l-drn

m~larn[&

eVi/(1-6m)

1

where there are M subsets of the N alternatives and O ● ~m < 1, then a

general pattern of dependence among the alternatives is allowed. The

parameters, ~m, can be interpreted as an index of the similarity within groups.

Suppose we were to classify the alternatives into these M groups where ~.

denotes the set of alternatives in group m, and we were interested in the

probability of choosing some alternative i. Then

n

where

Otherwise

and

e eVi/(l-d~)

z

evjm/(1-6m)

P(il~) = I j~

\ o

l-din ~ evj~( l-din) ~[ jG~ 1

P(~) =

n!lanik~sne%/(1-4~) l-dn 1

The above GEV model is useful in many applied discrete choice problems.

Frequently, alternatives group themselves in

ability. If they do, it is both convenient and

model. It is appropriate because the results

62

obvious patterns of substitutappropriate to estimate the GEV

of an ordinary MNL will violate
the independence of irrelevant alternatives asumption if such a pattern

actually exists. It is convenient because it reduces the number of alternatives

included at each stage.

Let us make the estimation process explicit. In the problem at hand,

individuals are choosing among ten beaches. Two of these beaches are

qualitatively different. They are state parks, larger and providing more

services than the local beaches. Now we can view the choice problem as a

two-level nested one: the choice between state park or local beach (m = 1, 2)

and the choice among beaches within each group. Consider a redefinition of

vi:

v = 0’2. + #“wm im lm

where the Z’s denote attributes associated with all sites and the W’s are

attributes associated with the state park and local beach choice. Also let us

assume that ~m is identical within all groups and equal to d

Now define a variable, Im~ in the following way:

(4.16) Im = in ( i~%e 1

● ’Zim/(1-6) .

Then the probabilities above can be rewritten as

(4. 17)

and

y’Wm + (1-6)Im

(4.18 ) Pm= N=

~ e’#-Wj + ‘- “- “

j=l

(1-~)Ij -

The variable Im is sometimes termed an

and serves as an index of the relative

subgroup m.

inclusive value (see McFadden, 1978) .

value of the alternatives included in

As expressed in (4.17) and (4.18), the probabilities of interest can be

estimated using MNL proced urea. First, the Pi I m are estimated with M

independent applications of the multinominal logit. Note that at this stage e is

not recoverable, but can be estimated only up to a scale factor of 1-6. From

63
the results of (4.17),

incorporated as variables

and the 6 are estimated.

A 6 outside the unit

the inclusive prices (4.16) are calculated and

in the second level of estimation (4.18). Here the +)s

interval is inconsistent with the underlying utility

theoretic model and suggests misspecification (see McFadden). The parameter

6 is an index of similarity of alternatives within groups not present across

groups. A value of one for 15 indicates that alternatives within a group are

perfect substitutes. Thus, all relevant choice involves choice among groups.

A value of zero for ~ implies there is no special similarity of alternatives

within groups and thus no particular gain from using a nested GEV model.

Two-step estimation, i.e. the estimation of (4.17) and (4.18) independently,

is not necessarily efficient. Amemiya (1973) explores this property of the

model and presents a correction factor. However, even Amemiya suggests that

the cost in computational complexity is probably not worth the gains. We

consider McFadden’s estimation method adequate and use it to estimate a GEV

model in the next section.

Estimation of the Discrete Choice Among Beaches

The two-tiered discrete choice model considers the individual choosing

between two categories of sites (state park and local beach sites) and then

choosing among beaches within the desired category. The state park beaches

are located at Sandy Point (adjacent to

Lookout (at the mouth of the Potomac),

to include Fort Smallwood, Bay Ridge,

Morgantown Beach, Porter’s New Beach,

and Chesapeake Beach.

In estimating the model, however,

the Chesapeake Bay Bridge) and Point

whereas the local beaches are defined

Kurtz’s Pleasure Beach, Miami Beach,

Rod and Reel Club Beach, North Beach

the decision among sites within each

category is dealt with first. In assessing the available sites within a

category on a choice occasion, the household chooses on the basis of certain

household attributes in combination with specific site characteristics. These

are denoted Zj i and are defined for one model as:

access costs in $ to site i, calculated using distance (d i) from the

household’s origin to the site1 (Z1 i = 1.088 + .049*d1 - .000074d! )

plus the entrance fee plus the wages lost from traveling if the

individual had directly foregone income to visit the site;

access time (in minutes) to site i, calculated using distance from the

household’a origin to

water pollution index

the sites (Z; i = .7 + .02d1 ) ;

for site i (see description page 18);

1 Exact formula was determined by regressing reported costs against distance. 2 Exact formula was determined by ‘regressing- reported travel time against

distance.

64
z., =

Z~i =

Z*, =

the availability of recreational vehicle facilities at site i (O if not

available, 1 if available) times whether the household owned a

recreational vehicle (O if not owned, 1 if owned);

the availability of fishing facilities at site i times whether the

household owned fishing equipment;

the availability of boat launch facilities at site i times whether the

household owned a boat.

The first stage of the estimation is reported in Table 4.7. The results

indicate that relatively large monetary and time costs negatively influence the

probability of choosing a beach. Water pollution also has a negative influence

as does fishing facilities. Presumably, fishing activity draws the household

members away from the beach. Boat facilities and recreational vehicle facilities

improve the probability that someone owning a boat or RV will attend beaches

with facilities for that equipment.

The second tier of the discrete decision involves whether individuals

select a state park (with many activities) or a local beach. The factors

hypothesized to be important in deciding to visit a state park were thought to

be the years the household had visited western shore beaches (WI), whether

the intercepted household had more than one family member in the party ( W2 ),

the size of the group intercepted (w, ) and the inclusive value (Im) derived

from the first-stage estimation. People with a larger history of beach use in

the area would be more likely to learn of the smaller beaches and hence be

less likely to use the state parks. On the other hand, the state parks usually

offer a greater variety of activities, and thus families and large parties might

be more likely to attend them.

The results of the estimations are presented in Table 4.8. The hypotheses

about the choice between state parks and local beaches were not rejected.

Signs of coefficients were as expected and coefficients statistically significant.

The estimated coefficient on the inclusive value term is .152 yielding an

estimate of .848 for d. This is significantly different from zero suggesting

that there are gains from using the nested model.. There is considerably more

similarity among beaches within the two categories than across the categories.

Had the nested model not been used, the independence of irrelevant

alternative assumption would certainly have been violated. The estimate of d

is also significantly different from one, suggesting that beaches within groups,

although similar, are not perfect substitutes,

65
Table 4.7

Logit Regression for Selection Among Sites

Variable

Access Access Water Recreational Fishing Boat

cost Time Pollution Vehicle Activity Facilities

Estimate Z1i Z2i z,{ z, i Z5i Z6i

Coefficient -.072 -*75 -.00037 1.06 -2.09 1.14

(t-statistic) (-5.30) (-8.63) (-4.00) (1.93) (-5.28) (2.10)

Chi-squared = 311.2

Table 4.8

Logit Analysis for Selection Between State Parks and Local

Variable

Inclusive Years Attending Family Party

Estimate Value Western Shore Beaches Members Size

Im w, W2 w,

Coefficient .152 -.019 .261 .024

(t-statistic) (9.26) (-8.34) (4.85) ( 12. 79)

Chi-squared = 28.07

66
The Number of Trips Decision

There is no operational model that generally treats utility maximization

with non-negativity constraints (Bockstael et al., 1986, Chapter 8),. There

exists no utility theoretic means of linking the discrete choice model of site

choice on each choice occasion to a continuous choice model of demand for

trips (i.e., demand for choice occasions. )

When one decides to use a nonclassical continuous demand function for the

demand for recreational trips (irrespective of site), a problem immediately

arises in determining the appropriate choice of explanatory variables. Since

costs and quality vary across sites and since individuals are observed

choosing more than one site in a season, which site’s price and quality should

be included?

The approach taken here is to consider the number of trips to western

shore beaches as a function of a number of explanatory variables and an

inclusive value type variable calculated from the second stage of the discrete

choice model. This was originally suggested by Hanemann (1978) and was used

in Bockstael et al. (1986). Thus when water quality changes, the inclusive

value changes and influences the number of trips. In this sense, “the discrete

and continuous decisions are linked , although not in a utility theoretic way.

The discrete/continuous choice model has the advantage of emphasizing the

substitutability of sites but does appear to underestimate the response of

demand for trips to changes in cost and quality at one or more sites.

Based on the results of the discrete choice estimations, a new inclusive

value (In ) which includes the factors in the choice among sites and the choice

between state parks and local beaches is calculated. This value, along with

the individual’s income (INC, income or full income if at interior in the labor

market ), discretional y time available (DT, if at corner in the labor market) and

the number of trips to western shore beaches in the previous year (xt-1 ), is

used to estimate the 1984 total number of trips per household to western

shore beaches (xt).

The higher an individual’s inclusive value, the more attractive are his

beach alternatives (e.g. good beaches are cheaper to get to) and the more

trips he is likely to take. Additionally, beach use habits (as reflected in

previous trips) would likely lead to more trips. Whether income and

discretionary time positively or negatively affect the number of trips depends

on whether a day trip to western shore beaches is a normal (positive effect)

or inferior (negative effect) good.

The results of an ordinary least squares regression are given in Table

4.9. The expected signs occur, and the results indicate a western shore trip

is an inferior good, both with respect to income and time. The predictive

powers of the equation are especially good considering the cross-sectional

nature of the data.

67
Table 4.9

ordinary Least Squares Regression for “Choice Occasions” or Trips

Variable

Inclusive Lagged Discretionary

constant Value Trips Income Time

Estimate IN Xt- 1 INC DT

Coefficient .442 4.06 .516 -.028 -.981

(t-statistic) (2.92) (8.70) (-2.79) (-4.80)

R2 = .35

F-value (4,253) = 36.30

Estimated Benefit Changes

The ultimate purpose of the modelling effort is to estimate the benefits

associated with improvements in water quality. Formulas for deriving welfare

measures in the context of discrete choice models of random utility

maximization have been developed by Hanemann (1982, 1984). It is generally

the compensating and/or equivalent variation of the quality change which is

taken as a useful measure of benefits. Selecting the compensating variation

(C), this measure can be defined by the following expression:

v(p 0 q0, y) = V(po, q’, y - c)

where again v is the indirect utility function, p and q are vectors of site

prices and qualities, and y is income.

The compensating variation is now defined by

V(po, qo, y;c) = V(po, ql, y - C; z),

where c is random, and as a result C is now a random variable. Depending on

how one chooses to take account of this randomness, three different measures

of compensating variation can be defined. In the case of GEV models the

median value of C coincides with the C which equates the expected values of

the indirect utility functions (Hanemann, 1978). It is this measure which we

calculate in the subsequent illustration.

In our problem, using the previous notation,

‘[:l$*;N] = [,h/’(1-’)4’-4 + [J~J,;’’(’-a)]d-d

68
where J S is the set of state park sites and J 1 is the set of local beach sites;

where v = .,’Zi~ + *“W~; Zi~ are factors which vary over sites; and Wm are

factors which vary between state park and local beach sites. Thus

[ 1 Y’WS + (PQI~

Gev’, . . ..evN = it

where

1, = in

e’Zfs/(a-f5)’

Ze . i&s

Then the expected value of the indirect utility function equals

V(w”, zo, y) = lnG(ev~, evfi) + k,

<

. . . .

where k is a constant.

Now consider a change in quality which causes W“ and Z“ to change to W1

and z 1. The compensating variation measure (C’) defined above is given by

V(wo, zo, y) = V(wl, zl, y+)”

or

in G(ev~(y’zO’wO), . . .,evB(y’zo’we)]

= In G[evi(y<’, zltwl),. a., evh(-” Sz’$wl)]

There is no closed-form solution for compensating variation in this case, but

Hanemann (1982) shows that the compensating variation per choice occasion of

this change can be approximated as:

(14)

69
where the set J includes the two cases: state and local beaches, Y is the

element of the e vector which serves as the price coefficient, and v ‘j = $“wj +

(1-&)I’j. To expand to annual welfare change, this value is multiplied by he

number of choice occasions estimated in the continuous choice model.

These equations were used to estimate the benefits from hypothetical

water quality changes. To be consistent with the varying parameters model

estimates, we considered a 20 percent reduction and a 20 percent increase in

water pollution. The values associated with the changes are $1.08 per trip

and $4,70 per household user of western shore beaches. Given that 20

percent of the households used western shore beaches (about 401,000

households), the total gains from a 20 percent improvement in water quality

were estimated to be nearly $2 million annually. The estimated loss for a 20

percent degradation was approximately the same.

Discussion

Reiterating, the purpose for our work was to offer benefit estimates based

on different methods so as to provide a range of reasonable values. The two

models derive from two different conceptualizations of the recreationalists'

decisions. The continuous, neoclassical model (represented here by the

varying parameters model) is strictly correct only if interior solutions

characterize demand for each site, with all individuals attending all sites,

Another drawback of this model is that, because of the econometric functions

estimated, total benefits cannot legitimately be added across sites. This sort

of aggregation provides upwardly biased results.

The discrete/continuous choice model, on the other hand, begins by

emphasizing the corner-solution nature of the decision on each choice

occasion. Thus, the substitutability among sites receives special attention.

The decision about number of trips per season is not well integrated into the

estimation process. These models tend to provide low estimates of aggregate

benefits because the effect of water quality improvements on demand for trips

is not well accounted for by the ad hoc inclusive value variables in the trips

equation.

The estimated benefit change resulting from changes in Chesapeake Bay

water quality at the western shore beaches is presented in Table 4.10 for the

two models. - Predictably, the

change.

Comparison of Benefits

varying parameter

Table 4.10

Based on a Varying

model offers the largest

Parameter Model

and Discrete/Continuous Choice

Change

Model 20 Percent Improvement 20 Percent Degradation

---- -- Varying Parameter - -(in thousands) -- ---- -- -

upper bound $26,160 - $25,839

Discrete/Centinuous Choice $ 1,885 - $1,884

70
Chapter 5

Recreational Boating and the Benefits of Improved Water Quality

Boating is an especially important part of any study of Chesapeake Bay

recreation. As the second most popular recreational activity in our telephone

sample of Bay users (second only to beach use) , 34 percent of the area’s

households participated in boating activities on the Chesapeake in 1984. This

represents nearly three-quarters of the houeeholdo who used the Bay for

recreational activities. Its importance is further supported by the large

number of registered boats in Maryland (as many as 134,000 in 1981) and by

the fact that of the approximately 15 million Person-trips taken on boats in

Maryland waters in 1979, 90 percent (or 13.5 million) were taken on estuarine

waters of the Bay or its tributaries (Harmon and Associates, 1983).

In this chapter we examine the behavior of Chesapeake boatetrs and

estimate the value to boaters from improved water quality. Since no new

survey could be initiated for this purpose, the data upon which the analysis

rests are drawn from a 1983 boat owners survey which waa made available by

the Sea Grant Program and the Department of Recreation at the University of

Maryland.

A Profile of Boaters and Boat Owners

The Boat Owners Survey

In 1983 a survey of boaters was sponsored by the University of Maryland

Sea Grant Program and the Maryland Coastal Zone Management Program. It

consisted of a mail survey of 2515 registered boat owners in Maryland. The

design of the sample provided equal representation to owners of boats kept in

slips and owners who trailered their boats. The questionnaire, which sought a

variety of information about the household and ita boating activities, achieved

a response rate of approximately 70 percent.

The boat owners' survey provides different but complementary information

to the telephone survey conducted by RTI and described in Chapter 3, as it

samples a different population and uses a different sampling scheme. The Sea

Grant survey draws only from the population of registered boat owners. From

the telephone survey, which is a random sample of the population, we can

identify not only those who own boats but also those who uae the Bay for

boating whether they own a boat or not. The telephone survey provides

information about non-boaters, as well. It does not, however, provide detailed

information about boating behavior. Consequently, it is the boat owner

survey which will provide most of the data for analysis.

Boaters and Boat Owner Characteristics

Information from both th boat e owner

survey helps describe boating in Maryland.

71

survey and the random telephone

For example, a comparison of
those households in the telephone survey who reported that they used the

Bay for boating with those who did not revealed no significant differences in

family size or years lived in the area but did suggest considerable differences

in income and race (ace Table 5.1). The average of boaters' incomes waa

significantly higher than the non-boaters’ average, and a significantly higher

percentage of boaters were white. Interestingly, this significant difference in

income appears in the Prince Georges/Anne Arundel/Calvert Counties area

(southeastern region) but not in Northern Virginia/Montgomery Count y

(western region) or in the Baltimore area (northern region). Likewise, when

broken down by subarea the difference in racial composition appears evident

in the southeastern and western subareas but not in the northern subarea.

Some information can be extracted from the telephone survey about boat

ownership as well. Once again average family size and years in the area were

not significantl different over the two y groups, but average income and the

racial composition of the sample were (see Table 5.2).

The design of the boat owners survey permits a distinction to be drawn

between individuala who trailer their boats and those who keep their boats in

the water during the season (either at marinas, docks or moorings). The

distinction between individual in these groups is important to establish, since

the decisions they face are quite different and their behavior must be

analyzed separately. Additionally, we shall see that profiles of both boats and

owners differ somewhat between the two groups. The sampling design was

stratified to contact approximately equal numbers from the two groups. Of

those who returned questionaires, 718 trailered their boats and 788 kept their

boata in the water during the season.

Some interesting features of these two groups are presented in Table 6.3.

By far the most common type of boat in the trailered boat sample is ●

runabout. For obvious reasons, the sample of trailered boats contains very

few with cabins (4 percent). It also contains few sailboats (only 6 percent),

but this is not a representative figure, since sailboats which do not use

auxiliary motors are not required to register in Maryland and thus would not

be part of the population sampled. Of boats kept in the water, runabouts (at

33 percent) remain the single moat common class and sailboats (at 31 percent)

represent a close second. Once again sailboats with no auxiliary power are

likely to be under-repreaented in the sample. However, this distortion will

affect the trailered group more, as the boats kept in the water are larger and

more likely to have auxiliary engines. Combining cabin cruisers and cruising

sailboats, half the boats in the non-trailered sample are cruising boats

presumably outfitted for more than one-day trips.

Aa the difference in the type of boat i n the two groups suggests, the

average size and the average value of boats kept in the water are

significantly larger than the averages for trailered boats. Additionally, the

average income of boat owners in the two groups is significantly different,

with trailered boat owners having on average lower incomes.

Returning to the telephone survey which provides data on boaters and not

just boat owners, we can learn something about the geographical distribution
Table 5.1

Average Characteristics of Boaters and Non-Boaters

in BaMmore/Washington SMSA, 1984

from random telephone survey

Boaters Non-boaters

Average Family Size 3.31 3.47

Average Years Lived in Area 29 26

Average Household IncomeS

$46,858 $37,063

By Area:

Northern Va. /Montgomery Cty., Md. $50,576 $41,471

Prince George’s,Anne Arundel,

Calvert Ctya. , Md. ● 50, 2s8 28,083

Baitimore County 41,824 38,211

Percent Whitea 74%

By Area:

Northern Va./Montgomery Cty. , Md. ● 97%”

Prince Georges, Anne Arundel,

Calvert Ctya. , Md.a 64%

Baltimore County

aMeans of two samples are significantly different at 99% level

source : Telephone Survey, Research Triangle Institute, 1984

Table 5.2

Average Characteristics of Boat Owners and Non-Boat Owners

in Balttmore/Washington SMSA, 1984

Boat owners Non-boat Owners

Average Family Size 3.67 3.27

Average Years Lived in Area 28.6 28.2

Average Household Incomea $56,511 $40,931

Percent Whitea 94 81

aMeans of two samples are significantly different at 99% level.

Source: Telephone Survey, Research Triangle Institute, 1984.

73
Table 5.3

Characteristics of Boats and Boat Owners

from Boat Owners’ Survey, 1983

Trailered Boats Boats Kept in Water

Total number responding to

question of where boat kept

By boat type

Runabout

Cabin Cruiser

Cruising Sail

Day Sail

Workboat

Houseboat

Rowboat

Other

NA

Used boats for swimming

at least sometimes

Used boats for swimming

usually or always

Used boats for swimming

always

Used boats for fishing

at least sometimes

Used boats for fishing

usually or always

Used boats for fishing

always

Average Income of Ownerb

Average Current Boat Valueb

Average Boat Lengthb

718

445 (63%)

23 (3%)

4 (l%)

32 (5%)

124 (17%)

1 (<1%)

47 (7%)

35 (5%)

7

360 (51%)a

133 (19%)a

41 (6%)a

656 (94%)a

502 (72%)a

302 (43%)a

$38,000

$14,000

16 feet

788

251 (33%)

182 (24%)

199 (26%)

38 (5%)

38 (5%)

13 (2%)

25 (3%)

22 (3%)

20

555 (73%)a

235 (16%)a

41 (5%)a

582 (76%)a

290 (38%)a

107 (14%)’

$51,000

$25,000

23 feet

aNumbers in parentheses are percent of those answering question in each

stratified sample who gave this response. bMeans of two stratified samples are significantly different at 99 percent level.

Source: Maryland Boat Owners Survey, 1983

74
of households interested in this activity. The geographical distribution of

boaters within the state tells us something about the importance of thie

activity to different population subgroups. From the telephone survey it is

clear that boating is an important activity to Maryland residents throughout

the state; it is the first or second moat popular recreational activity on the

Bay in each of the geographical ● ubareaa of the study. However, as might be

expected, boaters are moat commonly residents of counties contiguous to the

Bay. A good example is the large proportion of Anne Arundel residents who

participate in boating. Extrapolating from our survey suggests that almost

half of the households in Anne Arundel County had at least one member who

went boating on the Bay in 1984.

Table 5.4 contains residence data gleaned from the boat owners survey for

boat owners, by trailered and nontrailered classes. The values shown in this

table indicate the distribution of residence counties among those who

responded to this question in the boat owners survey. Of those who reported

residence, 64 percent of each group lived in counties contiguous to the Bay.

Approximately 60 percent of the respondents came from the four most

populated counties in the state - Baltimore, Anne Arundel, Montgomery and

Prince Georges. A final interesting feature of the sample is that about 5

percent of the respondents lived out-of-state even though they registered

their boats in Maryland.

The fact that boating is often the vehicle for other Bay recreational

activities makes the analysis of boating both important and complex. The

incidence of multiple activities is critical because participation in the

complementary activities of swimming and fishing may make boaters more

sensitive to water quality. Additionally, the overlap of activities complicates

benefit estimation when benefits are aggregated over activities.

From the boat owners’ survey we can learn something about the

importance of these multiple activity trips. Table 5.5 reports frequencies of

responses to questions regarding these complementary activities. One striking

feature of these answers is that fishing is extremely important to those

registered boat owners who trailer their boats. In fact almost all (94 percent)

of the trailered boat owners who responded to the question indicated that

they fished at least occasionally from their boats, and almost half (43 percent)

claimed to fish on every trip. Fishing was less important among the

non-trailered boat owners, although three-quarters of them indicated that they

fished from their boats at least Occasionally. About the same percentage of

this group indicated they sometimes ueed their boats for swimming. Fewer,

about half, of the trailered boat owners sometimes used their boats for

swimming.

75
Table 5.4

Number of Trailered Boats and Boats Kept in the Water,

By Residence, 1983.

Trailered Boats Boats Kept in Water

Residence

Baltimore 162 (24%)a

Anne Arundel 99 (15%)

Montgomery 64 (9%)

Prince George’s 71 (11%)

Calvert 17 (3%)

St. Mary’s 25 (4%)

Charles 23 (3%)

Lower Western Shore -

Total

Cecil

Harford

Kent

Upper Bay -

Total

Dorcester

Queen Anne

Somerset

Talbot

Wicomico

Eastern Shore -

Total

Carol ine

Worcester

Carrel

Allegheny 0

Frederick

Garrett 0?

Howard

Washington

Other - Total

Pennsylvania

Virginia

District of Columbia

Not Identified

TOTAL

65 (10%)

12 (2%)

27 (4%)

7 (1%)

46 (7%)

8 (1%)

13 (2X)

5 (1%)

9 (1%)

21 (3%)

56 (8%)

7 (1%)

10 (1%)

11 (2%)

16 (2%)

19 (3%)

20 (3%)

83 (12%)

13 (2%)

10 (1%)

6 (l%)

43

718

144 (20%)

184 ( 25%)

60 (8%)

28 (4%)

14 (2%)

25 (3%)

7 (l%)

46 (6%)

11 (1%)

20 (3%)

5 (1%)

36 (5%)

9 (1%)

12 (2%)

2 (<1%)

30 (4%)

9 (l%)

62 (8%)

2 (<1%)

9 (l%)

4 (1%)

3 (<1%)

7 (1%)

5 (1%)

14 (2%)

6 (1%)

50 (8%)

67 (9%)

36 (5%)

18 (2%)

57

788

aNumbers in parentheses represent percent of those answering questions in each

stratified sample who gave this response.

Source: Maryland Boat Owners Survey, 1983

76
Table 5.5

Percent of Boaters Who Fish or Swim While Boating

Boat Owners’ Survey, 1983

At Least

Occasionally Usually Always Occasionallya

Trailered boats:

Fish while boating 22% 29% 43% 94%

Swim while boating 33% 13% 6% 52x

Non-trailered boats:

Fish while boating 38% 24% 14% 76%

Swim while boating 42% 25% 5% 72%

● Total of other columns

Source: Maryland Boat Owners Survey, 1883

The importance of Water Quality to Boaters

It is useful to consider the qualitative evidence that exists to support the

notion that water quality does in fact matter to boaters. Some evidence to

this effeot can be found in the 1983 boat owners survey. Tables 5.6 and 5.7

present a compilation of responses to a series of questions about factors

important in the selection of boating areas. As can be aeon from these tables,

boat owners who trailered their boats considered water quality to be the

moat important factor in choosing boating areas Water quality waa considered

“very important” or at least “moderately important” by the non-trailered boat

owners more often than any other factor except water depth. The latter is

often a physical constraint for the larger boats found in marinas. Comparing

the two subgroups, i.e. those who considered water quality “moderately” or

“very” important and those who did not, it is interesting to note that the

former had on average significantly higher incomes and more valuable boats.

The Behavior of Boat Owners Who Trailer Their boats

The General Model

We are interested in modelling two types of decisions that owners of

trailered boatS make in a season. One of these is the commonly modelled

economic decision of how many trips the individual takes. The second haa to

do with the location to which the boat owner takes his boat. This subgroup

of boat owners is far more flexible in the short run than those who keep their

boats in the water during the season, because on a day to day basis they can

77
Table 5.6 Factors Cited Most Important

in the Selection of a Boating Area in Maryland, 1983

Percent Response from 718 Boat Owners Who Trailer Their Boats

Factor Not Slightly Moderately Very No

Important Important Important Important Response

Water

Quality 11.1 9.3 33.0 42.2 3.1

Water

Depth 16.3 13.2 29.5 36.4 3.3

Natural

Beauty 15.7 17.5 34.0 28.1 4 . 6

Easy

Access 13.8 20.1 35.7 26.7 3.8

Lack of

Congestion 19.5 16.7 32.0 27.4 4.3

Source: Maryland Boat Owners Survey, 1983

Table 5.7 Factors Cited Moat Important

in the Selection of a Boating Area in Maryland, 1983

Responses from 788 Boat Owners Who Keep Their Boats in Marinas

Factor % Not % Slightly % Moderately % Very % No

Important Important Important Important Response

Water

Depth 7.7 8.9 24.4 54.9 4.1

Water.

Quality 9 . 3 10.0 35.7 39.7 5 . 3

Natural

Beauty 9.8 13.3 41.0 31.2 4.7

Protected

Anchorage 23.5 15.1 31.5 25.1 4.8

Lack of

Congestion 19.9 17.4 37.2 20.4 5.1

Source: Maryland Boat Owners Survey, 1983

78
alter the boating area by trailering to different launch sites. Of course, the

farther they must trailer the boat, the more costly (in both time and money)

will be the trip. Responses to the survey indicate that factors besides costs

are important in choosing boating area. Specifically, water quality was

considered the moat important factor in this choice, Thus we analyze the

demand for trips to different sites with measurably different water quality.

This gives us some basis to deduce how demand might change if water quality

were to change at different sites.

In order to accomplish this, a model of multiple site demand is designed to

accommodate differing qualities across sites but to require no more data than is

available from the boat owners’ survey and Chesapeake Bay water quality

data. Given these limitations and a desire for consistency of analysis across

recreational activities, the varying parameters model presented as the first

method of analysis in Chapter 4 is used.

The model takes the following form. The demand for trips to each site j

is estimated as a function of the coat to individual i of accessing the site

(C i j ), substitute site ace-a costs (s i j ), and other exogenous variables

associated with the individual (s i ):

(5.1) x i j

j = fj(cij, sij, zi; ?j) for all i,

where f j is the demand function for the jt h site, #j is the vector of

parameters in each of the site demand functions to be estimated.

Equation (5.1), which is the first stage of the varying parameters models,

can not be estimated using ordinary least squares methods. The sample upon

which the estimation is baaed includes a large number of zero values for the

dependent variable. As described earlier in this volume, ordinary least

squares applied to censored samples will produce biased estimators. As

described in the last chapter, Tobit estimation procedures are used to correct

for the problem.

The second stage of the model relates the set of P parameters in the site

demand functions to the site water quality characteristics (a vector w 1). In

this way the demand

site’s characteristics.

(5.2)

for a site is implicitly modelled as a function ‘of the

The ● econd stage model is of the form:

~k j = gk(wj ) for all j,

where k indexes the ● pecific # coefficient within the demand equations and j

indexes the site demand. Again the application of ordinary least squares is

not optimal. The above model likely suffers from heteroskedasticity (see

Desvousges, Smith, and McGivney, 19S3) which will produce Inefficient

estimators. To correot for the expected heteroskedasticity, the entire equation

can be multiplied by the reciprocal of the

estimated Coefficient Thus, if uk j is the

coefficient dk j from the Tobit estimation Of

79

standard error of the respective

standard error of the estimated

the demand for site j, then the
second stage model corrected for heteroskedasticity can be written

(5.3) $kj/ekj = gk(W@kj )

for each of the k=1,...,K @ coefficients from the first stage.

The Data

The arguments of equation (5.1) which were determined to be relevant for

the analysis include the coat of access to the site, the coat of access to a

substitute site, and the value of the boat. The latter variable waa chosen as

a surrogate for two very important factors. Boat value and income are highly

correlated , so boat value serves as a surrogate for income level, a variable

which is normally rather difficult to obtain with accuracy. Boat value alao will

be highly correlated with boat sise which haa an effect on the site choice.

Since boat length and income are correlated, including them separately in the

equation produces unresolvable multicollinearity.

The coat of access variable is the roundtrip cost, including time coat.

Costs vary across boaters for several reasons. Obviously the county of origin

influences costs, for the further the county is from the launch site, the

higher the coat of travel. The cost variables include time costs, computed as

one-half the individual’s average hourly wage (income/2080) time. the distance

traveled divided by 40 miles per hour. Finally the money cost of trailering a

boat depends on the size of the individual’s boat. The coat per mile of

trailering a boat was estimated, using coat and boat length information from

the data set, as -.78 + .08 *(boat length). The final coat variable includes the

toll for the Bay Bridge, when relevant.

To calculate the substitute cost, the above formula waa applied to the

closest cite not chosen. While a vector of costs to all alternative sites might

be considered preferable, there are several practical problems with including

such a vector in the regression, espicially severe multicollinearity.

Not all observations on boat owners who trailered their boats were ueed

because of heterogeneity of respondents and incomplete data. Observations

were deleted if the individual did not use his boat for any trip. in 1983 or if

he did not report launch sites or location of residence. Additionally, those

who reporeted their residence to be a distant state, precluding day trips to

the Bay, were deleted. Finally, to make the sample relatively homogeneous,

sailboats were excluded from the analysis. The latter accounted ultimately for

only seventeen deletions. The final sample included 408 observations.

The above information. waa obtained from the boat owners’ survey.

However, this data source dose not include any information about either

perceived or measurable water quality at various sites. Once again the

Chessie system provided the environmental data, and the environmental quality

variable used was the product of nitrogen and phosphorus, as in Chapter 3.

80
The Estimated Model

For the first stage of the model, the parameters in the following functional

form are estimated:

(5.4)

where x i j is

alternative to

trips to site j, c1 j is cost to site j, s1j is cost to next best

site j, and b i is boat value, all for individual i.

The first stage results for each of the twelve sites are presented in Table

5.8, The results are remarkably consistent across sites. The own price

coefficients are all negative and significantly different from zero at the 99%

level of confidence. Substitute price coefficients are universally positive and

significantly different from zero for eight of the twelve sites. The coefficient

on boat value is significantly different from zero for seven of the sites and in

each of theaa cases haa a positive sign, suggesting that wealthier people

and/or people with bigger boats take more boating trips, ceteris paribus.

Demand is relatively inelastic with respect to substitute price and boat

value (i.e. a 1 percent change in either of these causes a less than 1 percent

change in the demand for trips), However, the demand for trips to a site is

quite elastic with respect to the cost of accessing the site, with own price

elasticity ranging from -1.5 to -7.0.

In the second stage, there are as many equations as there are parameter

from the first stage which we wish to allow to vary with the environmental

factors. Since we have no particular a priori information as to what

parameters might vary, we can model each as a function of the environmental

variables and allow the test statistics to determine the outcome. o

The second stage model is given by

(5.5)

and

own

and the results are presented in Table 5.9. The product of nitrogen

phosphorous serves as the environmental variable. The regression of the

coat coefficients from the linear first stage model on these environmental

variables produced good results. The coefficient is significantly different

from zero and negative. The negative sign suggests that the demand curve

becomes less steep with increasing levels of pollutant.

Neither the constant term nor the coefficient on boat value yielded

significant second stage results. Even though the coefficient of substitute

price waa associated with a significant negative coefficient on the

environmental variable, allowing this coefficient to vary had no appreciable

effect on the welfare results. As a consequence, in the remainder of the

analysis we allow only the coefficient on own price to vary wtth environmental

quality. The results suggest that an increase in pollution would tend to have

the effect of pivoting inward the demand for trips to the site.

81
TABLE 5.8

Estimated Tobit Demand Coefficients: Maryland Counties 1983

Estimated Coefficients (Variable)a non-

?, L B, Bo

limit

county (cost/ (substitute (boat value (constant) observatrip) cost/trip) 1000’s) tions

Anne Arundel

Baltimore

Calvert

Cecil

Charles

Dorchester

Harford

Kent

-.13

(8.75)b

-.43

(9.21)

-.14

(4.14)

-.22

(4.84)

-.34 .

(8.41)

-.09

(2.86)

-.15

(5.55)

-.25

(4.94)

-.27

(6.17)

-.11

(6.40)

-.12

(4.76)

-.15

.03

(3.42)

1.29

(5.94)

-2.21

(1.61)

142

75

44

17

38

30

36

28

36

67

24

26

-.02

(1.13)

1.78

(4.01)

-1.94

(.77)

.08 1.84

(13.70) (3.45)

-27.14

(7.21)

.04

(1.54)

2.12

(3.09)

-16.44

(3.87)

.07

(3.77)

2.75

(6.79)

.49

(• 19)

.08 .66

(2.69) (.78)

-34.38

(6.68)

1.51

(1.67)

-12.21

(3.74)

.05

(2.63)

. 1 1

(3.57)

.14

(.14 )

-18.25

(3.45)

Queen Anne’s

St. Marys

Somerset

Wicomico

.07

(2.88) ● 12

(. 19)

-3.83

(1.03)

● 05 1.25

(3.12) (2.94)

-9.46

(3.31)

-.03

(.58)

2.81

(3.13)

-37.20

(6.64)

.05

(1.58)

1.02

(1.71)

-7.03

(6. 93) (2.02)

aEach equation is estimated with the 496 boaters who trailer their boats. bAbsolute values of t-statistics in parentheses
Table 5.9

Estimation Results from Second Stage

Regression of trip coat coefficient -.0887 -.000102

(4.29)a (3.54)

Regression of substitute cost coefficient .0682

(7.47)

-.000016

(1.78)

Regression of constant -19.414 .007338

(4.14) (1.93)

● absolute values of t-statistics in parentheses

Modelling the Behavior of Boat Owners Who Do Not Trailer Their Boats

Boat owners who keep their boats in the water during the season are far

more restricted in the short run as to the quality of the water that they can

easily enjoy. To alter the area in which they boat they must take long trips

in their boats or they must change mooring arrangement. We do not have

access to information that would allow us to model either of these decisions.

None of our data allows us to observe these individual making trade-offs

between water quality and other goods or money. As a consequence we can

not deduce from observations on their behavior how they might value

improvements in water quality.

What we can do however is to learn something about their demand for

boating and their use of the Chesapeake, which in itself is useful information

for the policy maker. In this section we estimate the demand for boating

trip. by boaters who keep their boats in marinas. Since we are interested in

the short run decision of how many tripe to take, the relevant cost variable is

the variable coat of a trip. Given the information available, we can

approximate the money and time costs of travel to the marina. Explanatory

variables which may shift the demand function include income and the size (or

value) of the boat. -

The boats in the marina subgroup are somewhat

thing, about half are sail boats and half are motor

might wish to teat whether the demand functions

significantly different.

heterogeneous. For one

boats. Consequently we

for the two groups are

The results of thin analysis are reportad in Table 5.10. The coefficient on

coot is negative, as expected, and significantly different from zero. The

coefficient on boat value is positive, indicating that, all else equal, boaters

with more expensive boats take more trips. Income appears not to affect

systematically the demand for trips.

83
Table 5.10

Estimated Demand for Boating Trip.

Boats Kept in Marinas

Explanation Variable Coefficient t-statistic

Cost -1.046 -3.93

Boat Value ($1, 000) .357 1.94

Income ($1,000) .148 .66

Sailboat Indexa -23.596 -2.27

Cost Sailboat Index .615 2.16

Constant 63.363 6.66

R2 = .109

# of Observations = 240

● Variable equals 1 if boat is sailboat, O otherwise

Both the constant term and the cost coefficient

individuals who own sail boats. The demand function

is given by

shift significantly for

for non-sail boat trips

TRPS = 63.36- 1.05 coat + .36 boat value + .15 income

where boat value and income are in thousands of dollars. The sail boat

demand for trips is given by

TRPS = 39.77- .43 cost + .35 boat value

At the same cost and boat value, sailboat owner.

their demand for trips appears to be rim-e inelastic.

+ .15 income.

demand fewer trips, and

Because of the eventual need to aqgregate behavior over recreational

activities, it would also be useful to know whether those boaters who ● penal a

large portion of their time fishing have significantly different demands from

those who do not. These results are presented in Table 15.11. It appears

that the two groups demands are different. For any given cost and boat

value, fishermen demand more trips and their demand tends to be more elastic.

Fishermen}. demand is given by

84
TRPS = 73.61- 1.32 coat + .35 boat value + .15 income

and demand by non-fishermen is

TRPS = 43.00- .51 cost + .35 boat value + .15 income.

Table 5.11

Estimated Demand for Boating Trips, Fishing Behavior

Boats Kept in Marinas

Explanatory Variable Coefficient t-statistic

Trip Coat -.506 -3.86

Boat Value ($1,000) .350 1.90

Income ($1,000)

Fishing Indexa

● 148 .88

30.535 2.8s

Coat Fishing Index -.815 -1.82

Constant 43.085 3.81

R2 = .116

● Variable equals 1 if boater fishes on boating tripe "usually" or "always,"

O otherwise.

calculating Estimates of the Benefits of Water Quality

Improvements for the Trailered Boat Samle

Because we have bean able to estimate the demand for boating trips by

boaters who trailer their boats to different areas as functions of costs and

the water quality in those areas, we can estimate welfare gains and losses

from water quality changes to this group. Unfortunately no observable

behavior of the boaters who keep their boats in the water allows us to deduce

anything about the value they place on improved water quality.

As in the previous chapter, three different changes in the environmental

variables are considered. In one caae we impose a 20 percent decrease

(environmental improvement) in the environmental (pollution) variables.

Subsequent experiments include a 10 percent decrease and a 20 percent

increase (environmental degradation).

85
Additionally, two different calculation procedures are provided, identical to

those used With the varying parameters model in Chapter 4. The two methods

of calculating “before” and “after” trip demands yield two sets of average

consumer surplus estimate. before and after the environment change and,

consequently, two sets of benefit (or leas) estimates due to the environmental

change. (A description of these methods can be found in Chapter 4.) The

estimates are reported in Tables 5.12? 5.13, and 5.14 for the 10 percent

improvement 20 percent improvement and 20 percent deterioration in the

water quality variable, respectively. The average consumer surplus figures

are per boater ( trailered boats, only) per season for the designated site.

Thus the first entry in Table 5.12 is an estimate of the value of access to

sites in Anne Arundel County per boater in the sample.

In examining the consumer surplus figures, it is well to keep in mind that

these benefit estimates are affected by the probability that a boater will go to

a particular site, the number of trips taken, given that the boater goes to the

site, as well as the size of the own-price coefficient. The figures in columns

3 and 6 are estimates per boater of how the value of access to a site changes

with changes in the environmental variable. These surpluses are not additive

across sites. That is, if we want to consider the effects of a ten-percent

decrease in TNP throughout the Chesapeake Bay, we cannot add surplus

changes across sites. Each estimate of surplus per boater by site assumes

that the water quality at other sites remains fixed. The bias which would be

created by simple aggregation across sites depends on price and quality

elasticities and is of unknown size.

What do all these calculation say about the value of reductions in the

nitrogen/phosphorus variable? We can estimate the aggregate benefits of

changes in TNP from Tables 5.12, 5.13, and 5.14 by expanding these estimates

from the sample to the population of boat owners who trailer their boats.

Consider St. Mary’s County. We have two estimates of the increase in ● Urplus

associated with a 10 percent decrease in TNP. These suggest a range of

between $1 and $5 per boater per season. If there are about 80,000 boaters

who trailer in Maryland (about the number estimated by Harmon and Associates

for 1983), we would estimate a change in total surplus in the range of $575,000

to $1,400,000 annually for a 20 percent reduction in TNP at the sites that

Anne Arundel County comprises. This calculation assumes that the original

sample from which the benefit estimate. were derived is representative of the

boater population as a whole.

The results in these tables eeem at least plausible. For example,

surpluses appear highest for western shore waters, those moat easily accessed

by the concentration of population in the state. The surplus figures for any

given site are not especially large, but this is to be expected since when

boaters have the ability to substitute relatively cheaply among sites, very

high surpluses at any one site would violate some prior expectations on the

size of benefits. While the magnitudes of returna from changes in TNP are 

not extremely large on a per-boater basis, in the aggregate they are quite

substantial

86
Table 5.12

Per Boater Annual Benefits from a 10% Decrease in Pollutant

in each Geographical Area

Calculation Method A Calculation Method B

County with

Water Quality Consumer Surplus Consumer Surplus Consumer Surplus Consumer Surplus

Change Before Change After Change Benefits Before Change After Change Benefits

Anne Arundel

Baltimore

Calvert

Ceci 1

5’

Charles

Dorcester

Harford

Kent

Queen Anne’s

St. Mary’s

Somerset

Wicomico

$30.01

15.07

17.50

4.80

38.79

1.61

3.45

24.08

26.17

14.80

7.17

7.87

$33.30

17.38

18.60

5.43

46.05

1.78

3.89

27.09

28.99

16.03

7.60

8.60

$3.29

2.32

1.11

.63

7.26

.17

.45

3.00

2.81

1.23

.44

.73

$119.05

49.83

108.57

18.55

38.34

75.72

47.24

73.71

51.74

139.22

99.18

32.82

$127.46

54.95

111.38

19.51

43.40

78.15

49.63

76.86

53.98

“143.71

101.95

34.53

$8.41

5.12

2.81

.96

5.05

2.43

2.38

3.15

2.24

4.4 9

2.77

1.71
Table 5.13

Per Boater Annual Benefits from a 20% Decrease in Pollutant

in each Geographical Area

Calculation Method A Calculation Method B

Count y with

Water Quality Consumer Surplus Consumer Surplus Consumer Surplus Consumer Surplus

Change Before Change After Change Benefits Before Change After Change Benefits

Anne Arundel

Baltimore

Calvert

Cecil

s

Charles

Dorcester

Harford

Kent

Queen Anne’s

St. Mary's

Somerset

Wicomico

$30.01

15.07

17.50

4.80

38.79

1.61

3.45

24.08

26.17

14.80

7.17

7.87

$37.17

20.33

19.79

6.17

55.54

1.98

4.41

30.51

32.18

17.40

8.08

9.46

$7.16

5.26

2.30

1.38

16.75

.37

.97

6.42

6.00

2.61

.91

1.59

$119.05

49.83

108.57

18.55

38.34

75.72

47.24

73.71

51.74

139.22

99.18

32.82

$137.05

61.20

114.35

20.60

50.13

80.74

52.27

80.38

56.51

148.54

104.88

36.44

$18.01

11.37

5.78

2.06

11.79

5.03

5.03

6.67

4.76

9.32

5.70

3.62
Table 5.14

Per Boater Annual

in

Losses from a 20% Increase in Pollutant

each Geographical Area

Calculation Method A Calculation Method B

County with

Water Quality Consumer Surplus Consumer Surplus Consumer Surplus Consumer Surplus

Change Before Change After Change Losses Before Change After Change Losses

Anne Arundel

Baltimore

Calvert

Cecil

z

Charles

Dorcester

Harford

Kent

Queen Anne’s

St. Mary’s

Somerset

Wicomico

$30.01

15.07

17.50

4.80

38.79

1.61

3.45

24.08

26.17

14.80

7.17

7.87

$24.76

11.68

15.51

3.80

28.78

1.33

2.74

19.12

21.48

12.70

6.40

6.68

$5.24

3.38

1.99

1.00

10.01

.28

.71

4.96

4.70

2.09

.77

1.19

$119.05

49.83

108.57

18.55

38.34

75.72

47.24

73.71

51.74

139.22

99.18

32.82

$105.07

41.96

103.98

16.93

31.51

71.30

43.12

68.34

47.93

131.10

94.08

29.88

$13.98

7.87

5.19

1.62

6.83

4.42

4.13

5.37

3.81

8.12

5.10

2.94
Chapter 6

The Benefits for Recreational Fishing: Striped Bass

This chapter provides come preliminary estimates of the increase in

benefits to sport anglers from increases in water quality. We use a portion of

the 1980 National Survey of Fishing, Hunting and Wildlife Related Recreation,

referred to as USFWS data, to estimate the demand for and value of fishing

for striped bass in Maryland. This survey, while not designed for these

purposes, is the only data aet currently available which enables us to

investigate the recreational fishing of the Chesapeake. Striped bass is the

only specie. important to the Chesapeake Bay recreational fishery for which

there is sufficiently detailed-catch information to link water quality changes to

the benefits of sportfishing.

The link between improved water quality and changes in recreational

fishing demand depends on the ecological connection between water quality

and catch rates and the behavioral connection betwean catch rates and fishing

activities. Descriptive and analytic studies of the Bay have focused on the

impacts of water pollution on the density and productivity of fish stocks.

Lower dissolved oxygen, declines in SAV, and increases in water toxicants all

appear to have an impact on fish stocks. Further, where records are kept for

commercial fisheries, there haa been a substantial decline in landings per unit

effort, especially for those species which spawn in the Bay or ita tributaries

It is plausible to expect considerable benefits to recreational fishermen

from improvements in water quality. The number of recreational anglers is

quite large, baaed on information from the primary sources of data on

saltwater recreational fishing in Maryland. Estimates of saltwater fishing

participation in Maryland during 1980 range from 539,000 anglers over 16

years of age taking 4.1 million trips to somewhat over 800,000 anglers of all

age. taking 2.7 million trips (U. S. Fish and Wildlife Service and Bureau of

Census; U. S. National Marine Fisheries Service; William et al.). According to

NMFS and State of Maryland data, each saltwater angler took approximately

three trips, while USFWS estimates approximately 7.6 trip. and 9.0 days fished

per angler.

Data on striped bass fishing are somewhat more difficult to obtain.

According to the Maryland Department of Natural Resources, roughly 203,000 of

the saltwater trips were for stiped bass. Our analyais of the USFWS data

indicates that 239,000 anglers (over 16 years of age) fished for striped bass

in Maryland and Sussex County, Delaware, fishing for approximately 2.1 million

days, or roughly 8.8 days per angler. Estimates of the striped baas

recreational catch in Maryland range from 211,000 to 377,000 fish, a total

weight of 200 to 474 metric tons. The USFWS data are not well suited for

estimating aggregate catch, because the survey used waa designed primarily

for other purpose., even though catches are self-reported by respondents for

come saltwater species, notably striped bass.

90
Table 6.1 provides some descriptive information about the sample of

anglers which was analyzed in this portion of the study. The sample was

partitioned into two groups baaed on whether the individual fished for striped

bass or not. Individuals in the two subsamples are very similar in the amount

of fishing and hunting done and in their exprience, income, age, education,

and other demographic makeup, Striped baea fishermen, on average, showed a

slightly higher propensity to own a boat and to allocate more money to

hunting and fishing activities, though these differences are not significantly

different from zero due to the high within-subsample variation.

Table 6.1

Characteristics of Striped Bass Fishermen

and Other Fishermen and/or Hunters in the Sample

Striped Baaa Non-Striped Baaa

Fishermen Fishermen

Number of Individual in Sample

Average

Average

Percent

Average

Average

Average

Percent

Percent

Percent

Average

Average

Average

Average

Percent

Percent

Number of Days Fishing, Striped Baas

Number of Days Fishing, All Species

Who Also Hunted

Number of Days, Hunting

Years of Fishing Experience

Age When First Fished

Owning Inboard Boat

Owing Outboard Boat

owning Other Boat

Household Income

Fishing/Hunting Budget in 1980

Agea

Years of Schooling

Working in Job or Business

from Urban Areas

184

11 daya

28 daya

41%

17 daya

24 years

10

19%

42%

17%

$28,300

$982

38

13 years

70%

44%

576

0 days

2’7 daye

37%

15 days

24 years

12

7%

28%

12%

$27,600

$588

38

13 years

73%

38%

aThe sample is for individuals 16 yeara of age and over.

91
A Description of the Data

The 1980 National Survey of Fishing, Hunting, and Wildlife-Associated

Recreation was the source data for analysis. Of the available data sets on

Chesapeake Bay sportfishing, the portions of this survey relating to saltwater

recreational fishing in Maryland, and by Maryland residents, offered the best

prospects for modelling the effects of water quality improvements. This data

set oontained the essential variables for estimating recreational fishing demand

functions, including information on (a) trips taken by destination; (b) costs

incurred by recreationists for goods and services used in recreation; (c)

household income; and (d) catch rates reported by anglers for certain

species.

The survey consisted of two parts. The first was a telephone screening

of households, predominantly by telephone interviews, to collect demographic

characteristics and to determine the hunting, fishing, and non-consumptive

recreation activities of household members during 1980. The second part was

a detailed questionnaire administered (typically in person) to selected

individuals who indicated they had hunted or fished in 1880, collecting

information on activities and expenditures. Of the 30,300 fishermen and

hunters and 6,000 non-consumptive users interviewed nationwide, 760 pursued

some or all of these activities in Maryland. These 760 individuals were the

subject of this analysis.

Of the 760 who hunted, fished or Participate in non-consumptive

wildlife-related activities in Maryland, 456 indicated they participated in some

form of saltwater fishing. Catch rate estimates were only obtained for a

limited number of saltwater and estuarine species, with striped baas the only

recorded species relevant to Maryland. One hundred eighty-four individuals

indicated they fished for striped baas in 1960.

The survey waa designed to provide estimates of recreation activities and

expenditures at the state level, and states were divided into large subregions

for purpose. of identifying trip destinations. Maryland was divided into four

such regions, three of which border the Chesapeake and were the location of

striped bass fishing. Broadly defined, the four areas are: the Southeastern

Chesapeake region, Northern Chesapeake, Southwestern Chesapeake, and

Northwestern Maryland. Significant numbers of Maryland residents also fished

for striped baaa in Sussex County, Delaware. Of the 184 striped bass

fishermen in the sample, 16 reported fishing in Delaware, 46 indicated they

fished for striped baaa in the Northern Chesapeake, 59 fished in the

Southeastern Chesapeake region, and 86 in the Southwestern Chesapeake

(Table 6.2).

The data aet includes days fished for ● trtped bass and other species,

rather than number of trips by specie., the latter being the preferable

measure for travel cost models. The survey did, however, include the total

number of trips to each region. Aggregating over all areas to get total trips

and all species to get total days

about 4.1 million trips and fished

of 1.17 days/trip. Thus, the two

one another.

fished, it was determined that anglers took

about 4.8 million days, yielding an average

measures may not be bad approximations of

92
Table 6.2

Sample Distribution Number of Fishermen, Days of Striped Bass Fishing

in 1880, and Catch Rate, By Regions

Number Mean Striped Mean Catch Rate

Region Who Visitad Baas Days Fish/Day

Sussex DB 16 6.8 4.4

Northern Chesapeakea 46 9.6 4.9

Southeastern Chesapeakeb 59 11.3 3.3

Southwestern Chesapeake 88 8.8 2.8

‘Baltimore City and Bait imore, Carroll , Cecil , Harford, Kent , and Queen Anne’s

counties.

bCarol ine, Dorcester, Somerset, Talbot, Wicomico, and Worcester count ies.

cAnne Arundel, Calvert, Charles, Howard, Montgomery, Prince George’s, and St.

Mary’s counties.

While detailed information was collected on costs of travel, lodging, food,

fees, and other expenses incurred during recreation trip., these costs were

not area specific; instead total expenditures over all saltwater fishing trips to

all areas (regardless of species sought) were collected for eaoh cost category.

The variable cost of trips to a single area could be determined only by

prorating total variable costs according to distance travelled. The method

ueed in this analysis waa to determine the total miles travelled by the

individual for all saltwater fishing trip. in 1980, aa the ● m of products of

round trip miles travelled to (the usual fishing location in) each area and the

number of trips taken to each area, The fraction of total variable fishing

expenses prorated for eaoh trip to each site waa the round trip miles

travelled to the site divided by total miles travelled. The money coat of a

trip to each site waa this fraction times the reported total variable costs for

saltwater fishing. Espressed as a formula,

where MC i j is the money coat of a trip by individual i to area j, Mi j is the

round trip miles travelled by individual i to area j, 01 j is the number of trips ‘

individual i takes to area j, VC 1 is individual i’. reported saltwater fishing

variable costs, and there are n areaa.

The coat of time spent in recreation

demand. The ● urvey data were not ideal

93

is also an important determinant

for determining this coat because

of

no
information was collected on the time spent in travel or at the site on each

trip. However, miles traveled is a reasonably good proxy for time spent in

travel. The procedure used here waa to assume an average rate of speed

during travel of 40 miles per hour and that the annual household income

divided by the number of hours in the average full-time work year (2,040)

was a suitable approximation for the wage rate. Then, the value of time

travelled was determined as the product of the amount of time spent in travel

and 40 percent of the wage rate. Expressed as a formula,

[1Y*

=Mtj-m

where TC i j is the time coat for individual i travelling to

household of person i, and M i I is, again, round-trip miles.

area j, y i is the

Of course, this is

a rather arbitrary formulation for time cost based on a series of restrictive

assumptions, but preferable ways of treating the value of time were not possible given the available data. The full price of a trip is then calculated as

the sum of the time and money prices for each individual TP1 = M C1 + TCi.

In the survey, respondents were asked to estimate their average catch

rate per day for selected species. Unfortunately, there was a lag of up to a

year or more between the time the fishing trip was taken and the time the

questionnaire was answered. There is evidence (e.g. Deuel, Hiett and Worrall)

that fishermen do not accurately remember numbers of fish caught or their

sizes well beyond a period of a few months. A comparison of the USFWS data

and data collected by the State of Maryland suggests that the USFWS data

might contain an upward bias in reported catch rates. The sample and

population average catch rates were both somewhat over three striped bass

per day, which is considerably higher than the State of Maryland data which

suggests a catch rate for the came period of one striped bass per day. When

the sample catch rates were extrapolated to estimate total 1980 catch, the

estimate was an order of magnitude or more larger than the published

estimates noted in the introduction, although some of this difference may be

attributable to difference. in estimates baaed on total trips versus total days.

The fact that sample catch rates do not predict aggregate catch well does not

invalidate their uae as quality indicators, however. As indicators of the

quality factors which signal individuals' fishing decisions, sample catch rates

may perform quite well.

The survey data contained a categorical variable measure of household

income. A second measure was also calculated: total budget for fishing and

hunting recreation, the sum of all fishing and hunting-related expenditures in

1980. If the individual has a weakly separable utility function and determines

first the total amount of income to allocate to hunting and fising recreation,

94
the fishing and hunting budget to a more relevant income constraint than

overall household income. The fishing/hunting budget measures, however, is

subject to potential errors Of measurement, both from faulty recall by

respondents and from year-to-year fluctuations due to purchase of major

durable goods.

The Basic Model

For a variety of reasons, the model we estimate for recreational fishing is

different from the recreational beach uee and boating model. In the beach

use and boating estimations , trip data existed for a number of

quality-differentiated sites. In the fishing data, trips are available only by

region. There are only four of these regions, and each is large so there can

easily be as much variation within any region M there in among the regions.

Further, 164 of the 184 striped bass anglers in the sample visited only one

region. Rather than estimating four demand curves, we have estimated a

single equation where the dependent variable is the sum of the trips to all

sites.

The handling of the quality variables differs al- For recreational

boating, w. ueed a varying Parameter model because the quality variable,

scientific measures of water quality, varied across sites but not across

individuals. The quality variable in recreational fishting, catch rate, varies

across individuals. Consequently, we need not use a varying parameter

model. Instead we uee the observation on the reported catch in arena where

the individual took his trips. The data set includes many individuals who did

not fish for striped bass. For these individual, costs and catch rates were

inferred.

The fishing model estimated waa

(6.1)

where xi is the number of days taken by the ith individual, TCi is the

individual’. full coat (in dollars per trip) of striped bass fishing, CR1 is the

catch rate (fish per day), IB 1 and OB i are (0,1) variables denoting availability

of an inboard or outboard boat for fishing, respectively; and BD1 is the

individual’. fishing/hunting budget in dollar. per year.

No eubatitute sites were specified in the model because the regions were

so broadly defined that they might not in fact act as substitutes for each

other. There is probably extensive substitution among sites within each

region that cannot be captured at all given the level of aggregation we face;

and the sample data indicates that only about 10 percent of respondents

visited more than one region. Instead, the price and catch rate for

Participant who visited more than one site were calculated as the mean of

price. and catch rate. at each region visited, weighted by the day. fished.

Only slightly more than one-quarter of the respondents who either hunted

or fished in Maryland reported having fished for striped bass. This level of

95
non-participation implies a serious censored variables problem. There are

several ways of handling this problem in recreational demand models. We

explore these approaches in Chapter 4 of Volume I of this report. For the

current task of estimating fisheries demand models, we choose the following

simple Tobit formulation:

(6.2) x= (

#’z + e j?l’z+u>o

o J5?’Z +840

where z is the vector of explanatory variable.. The Tobit model imposes come

rather extreme restrictions on individual behavior that more general sample

selection models avoid. But for preliminary results, we accept these

restrictions for the sake of simplicity.

Determining the relevant price and catch rate for non-participants was

problematic. For these individuals, it was not known which of the four

price-quality combinations were moat relevant to their decision to go/not go

striped bass fishing. In the application we used the minimum price to access

a striped bass “site” and its corresponding catch rate.

Welfare measures are calculated, in principle, the “ same way as for the

varying parameter. model. That is, the benefits of an increase in catch rates

are given by the change in consumer’a ● mplua which, for the linear model

above, is

(6.3) ,cs=?M!z!LK@El

-2;1 -2;,

where 81 is the own-price coefficient, and x is the individual’s trip level.

Empirical Results

The model in equations (6.1) and (6.2) was estimated using the maximum

likelihood method of LIMDBP. Table 6.3 gives the results which will be used

for preliminary benefit estimation, along with the sample means of the

variables. The results in Table 6.3 are for a model in which actual catch

rates reported were used for participant, and a predicted catch rate was

used for non-participants. We also estimated a model in which predicted catch

rates were used for every individual In the latter estimation, the coefficient

estimates remained basically unchanged, but the standard error on the catch

rate coefficient increaseddresulting in a t-statistic of about 1.3.

The coefficient estimates all have intuitively correct signs, and they are

different from zero at better than the 5 percent significance level Having an “

inboard motorboat seems to induce more striped bass tripa than having an

outboard motorboat. The own-price elasticity for Participant is about minus

one, while the catch rata elasticity for participant is about .10,

96
Table 6.3

Tobit Estimation of the Demand for Striped Bass Fishing

Explanatory Coefficient Mean of

Variable Estimate t-statistic Variable

Constant (C) -10.6 -5.79 1.00

Own price (’IT) -.336 -7.52 $27.2

Catch rate (CR) ● 337 2.13 3.2 fish/day

Inboard Motor ( IB) 12.65 4.49 .10

Outboard Motor (OB) 6.66 3.47 .31

Budget (FHB) 1.40 3.04 . 70($000)

~a = 18.3

N = 760

We can use the estimated coefficients in Table 6.3 to estimate welfare

effects of increases in catch rates. As in Chapters 4 and 5, two estimates of

consumer surplus are provided. Method A employs predicted trips plus

changes in predictions whereas Method B uses actual trips plus changes in

predictions.

It is rather eaay to expand sample results to the population, since the

Fish and Wildlife Survey includes sample weight or sample expansion factors. These weights account for the fact that different population strata are

sampled disproportionately. Consumer's ● rplua for the population is simply

the weighted sum of the surpluses of the sample observation:

(6.4) Cs= : -(xf)a ft

1 2$1

where a is the sample size and f 1 is the expansion factor.

Table 6.4 gives

the estimate of the

in 1880, baaed on

substantially, with

predicted.

the estimates of aggregate surplus. The first column is

value of access to striped baas fishing as

1980 prices. The actual and predicted

the actual being more than three time.

97

it was perceived

estimates differ

larger than the
Table 6.4

Aggregate Consumers' Surplus for Striped Bass Fishing:

Effect of Changing Catch Rates, 1960

(Population of Maryland Hunters and Fishermen)

Aggregate

Consumer Surplus Change with Change in Catch Rate

Surplus

for Access 20% Decrease 10% Increase 20% Increase

. ..*.. . . Thousands of Dollars . . . . . . . . .

Predicted (Method A) 14,652 -572 314 1,501

Actual (Method B) 54,196 -422 231 4s1

The second, third and fourth columns in Table 6.4 give the net impact of

a 20 percent reduction, 10 percent increase and 20 percent increase,

respectively, in the striped baa. catch rate compared with the level perceived

in 1960. Here the actual and predicted results are closer, especially for the

10 percent changes.

The numbers that are most interesting for environmental policy on the

Chesapeake are found in the third and fourth columns. These figures are

rough estimate. of the dollar amount people who currently fish or hunt in

Maryland might gain annually from improving striped baas fishing.

There are a number of complicating factors which cannot be integrated

into our preliminary calculations of benefit estimates. First, consider how long

it would take for environmental policy to produce a ● ubdantid, sustainable

increase in catch rate. Reduction in effluents for one year will have only a

small effect. To improve ambient water quality enough to bring about better

striped bass reproduction and survival could take many years.

The second question relates to the role of expectations regarding catch.

Aaide from the likely bias and high noise in the catch rate estimate, what

respondents report is the ox post realisation of catch rates, while their

decisions regarding whether, when, and how frequently to go are baaed on

expectation about the catch rate, ex ante. Consequently, while recalled ex

post catch rate is the best quality variable we could obtain for striped baas

96
fishing, we need to be skeptical about its implications for the relationship

between days taken and expected catch rate.

The second question concerning these benefit estimates is whether, given

sufficiently improved ambient water quality, the catch rates are sustainable.

The answer is no. Better catch rates induce more fishing and hence more

harvest. Since there is some evidence that overharvesting is partly

responsible for the decline in fish populations to begin with, it is likely that

healthier stocks will induce more harvesting. The long run equilibrium will

result in higher than current benefits, but smaller than the benefits which

implicitly assume that the increase in fishing effort will have no long run

effects on fish stocks.

Last, it is worth remembering that the benefit estimates are baaed on a

sample of households that hunted or fished in Maryland in 1980. If there are

people who currently do not hunt or fish, but would go striped bass fishing

if the fishing improved sufficiently, then the annual benefit estimates are an

underestimate.

99
chapter 7

Conclusions

Restoration of Chesapeake Bay water qulity rewires substantial

resources on the part of public agencies, private firms and households. There

are many choices to be made in implementing programs to clean up the Bay.

This report haa described acme of the activities which would benefit from the

enhancement of Bay water quality. Chapters 2 and 3 described ways in which

people think about the Bay and benefit from better water quality. Chapters 4

through 6 contain descriptions of come recreational activities which would gain

from improved water quality. All of these chapters provide estimated

willingness to pay from potential improvements.

In deriving benefits, sometimes we 1000 eight of the informational content

of the models behind the benefit estimates, the estimated demand functions

themselves. Chapters 2 through 6 contain substantial new information about

the structure of demand for recreational activities associated with the

Chesapeake Bay. In nearly every instance where sufficient data were

available, recreators responded to travel and time costs in a manner consistent

with our theoretical model. They were also observed to be responsive to even

the crudest of water quality measures. Additionally, demographic variables

such as income, race, and boat ownership were observed to influence behavior.

As we turn to the benefit estimates, the reader is reminded not to consider

the “bottom-he” benefit figures as the only value of this report.

Demand for Chesapeake Bay Recreational Activities

The data and modelling exercises described in Chapters 3 through 6

provide a good picture of the recreational use of the Chesapeake Bay.

Chapter 3 includes an overall picture of Chesapeake recreational activities

derived from a random sample of all households in the Baltimore/Washington

SMSA’s (BWSMSA). This survey revealed that a full 43 percent of the BWSMSA

population used the Bay or intended to use it for recreation in 19S4.

Geographical distribution of users showed Anne Arundel County reaidenta (69

percent) moat likely to be Bay users and District of Columbia residents (21

percent) to be least likely (ace Table 3.1). The moat common recreational

activities were fishing, swimming and boating, with about a third of the Bay

users participating in all three activities. Of these activities, swimming was

enjoyed by more people than either of the other two, with 77 percent of users

participating. In the remaining chapters, each of these activities was looked

at in greater detail using specific surveys of subsamples of the population.

In Chaptar 4, wa provide two types of demand models for western shore

beach uae activity. Each draw. on an on-site sample of beach users at

western shore beaches in the summer of 19S4. The varying parameter model

is a modification of traditional demand models where the demands for tripe to

each cite are treated largely independently, but the difference in parameters

across sites are attributed in part to site characteristic. The discrete choice

model explains the choice among cites directly, as a function of site

characteristics, but does not handle the total number of trips well. Each type

of model gives a good description of one aspect of the recreational decision.

100
From the results in Chapter 4 it is char that both money and time

access costs are extremely important in determining demand for trips to any

given beach, as are the costs of accessing alternative beaches. Whether or

not an individual owns a boat or recreational vehicle also affects demand for a

subset of beaches, those which have facilities for these capital goods. Demand

functions for trips to a site tend to pivot inward, becoming more elastic, with

declining water quality.

The results of the nested multinominal logit or discrete choice model of

beach use suggested acme similar and acme additional characteristics of

demand for this activity. Once again, money and time costs of access were

important this time in explaining the choice among sites. Additionally, the

availability of boating and recreational vehicle facilities increased the

likelihood of a boat or recreational vehicle owner to choose a site. An

interesting hypothesis was tested regarding the differential substitutability

among local beaches and among state beaches vis-a-vis the substitutability

between local and state beaches. Beach users seemed to consider local

beaches closer substitutes for one another than for state beaches. Individual

with larger parties or families were more likely to attend state beaches where

a variety of activities were available. The longer an individual had attended

western shore beaches, the more likely he was to use local rather than state

beaches.

Chapter 5 provides a rather extensive profile of boaters and boat owners

derived from a survey of boaters sponsored by Maryland Sea Grant and

Maryland Coastal Zone Management and from the BWSMSA telephone survey.

The boater survey subsample includes registered boat owners in Maryland.

The profile includes an analysis of characteristics which distinguish boat

owners from others and looks at these distinguishing characteristics b y

geographical area. Average household income, for example, is higher for boat

owners than non-owners, but this difference is only ● tatiatically significant in

Prince Georges, Anne Arundel and Calvert counties.

Considering the boats themselves, a different profile characterizes those

which are kept in the water all season (in marinas, moored, etc. ) than

characterizes boats which are trailered. As would be expected, trailered boats

are significantly smaller and less valuable, they are more likely to be

runabouts or workboats and their owners are likely to have less income than

the owners of boats kept in the water. Almost all trailered boats were used

for fishing at leastt Occasionally. About three-quarters of the non-trailered

boats were used for swimming at leastt Occasionally.

Table 5.4 summarizes the boat owners survey by county of residence,

revealing more about the geographical distribution of Bay users. Residents of

Baltimore and Anne Arundel counties accounted for 39 pecent of the trailered

boats and 45 percent of the non-trailered boats with Prince Georges County

and Montgomery County residents accounting for another 20 percent of

trailered and 12 percent of non-trailered boats.

The last of the descriptive information suggests the importance of water

quality to boaters. Water quality was considered either moderately or very

important in the selection of a boating area by 75 percent of the trailered

boat owners and by 76 percent of the non-trailered boat owners,

101
A varying parameters model similar to the one used in Chapter 4 revealed

that trailered boat owners’ demand for trips from launch sites was affected by

access costs to the launch site and costs of accessing alternative sites. In

general, the demand for trips to any given site was Positively affected by the

value of the boat; i.e., individuals with more valuable boats took more trips.

The demand function fo r any given site tended to pivot inward and become

more elastic as water quality declined.

Owners of boats kept in the water do not choose launch sites when they

take a trip, and consequently we have no way of knowing where they boat.

As a result we cannot model their decisions in responnse to varying water

quality. For these individuals? simple demand functions were estimated.

Factors which significantly affected their demand for boating trip. included

the coat of a trip (negatively) and the value of the boat (positively).

Additionally it was determined that sailboat owners tend to take fewer trips

and their demand for trips is more price inelastic.c. Finally, boat owners who

fish while boating tend to demand more trips and their demand tend. to be

more price elastic.

In Chapter 6 information about ● portfishing on the Bay is presented.

Estimates of sportfishing activity vary by data source and range from 539,000

to 900,000 anglers in 19S0 and from 2.7 million to 4.1 million trips for that

came year. The two prominent sources of information on sportfishing are the

U. S. Fish and Wildlife Hunting and Fishing Survey and the U. S. National

Marine Fisheries Survey.

Our analysis in this chapter concentrated on ● tripod bass fishing since

this was the only species important to Chesapeake recreational fishing for

which ● ufflciently detailed data existed. One source (U. S. Fish and Wildlife)

reports that in 1960, 239,000 anglers fished for striped bass in Maryland and

Sussex County, Delaware and fished 2.1 million days in total. Table 6.1

presents come descriptive statistics of striped bass fishermen and other

Chesapeake Bay fishermen.

In the analytical section of Chapter 6, demand for sportfishing trips was

modelled as a function of the individual’. trip costs, catch rates, his annual

fishing/hunting budget and indices of types of boat ownership. All variables

affected the demand for trip. in the expected direction, with owners of

inboard motorboat likely to take more trips than those with outboard

(presumably smaller) motorboat.

l?~ of Benefits from Water Quality Improvements

While the analysis of the demand for recreational activities is worthwhile

in ita own right, more information about the size of rewards from Bay

restoration can be obtained, There are several reasons for computing

aggregate willingness to pay rather than simply providing descriptive

measures such as recreational use days. Obviously such measures cannot be

compared to the costs of restoration; they cannot even be added across

activities. A day of fishing is different from a day of swimming, and changes

in water quality have different effects on the benefits derived from the two

activities. Further, as we observed in Chapter 3, there is some willingness to

pay for clean water by people who do not use the Bay. If we limit ourselves

to descriptive measures such as user days, we ignore the returns to people

102
who value cleaner water but do not uae it. Consequently, we have taken a

first step toward the logical, albeit venturesome, task of estimating the

aggregate benefits of improving the Bay’s water quality.

Caveats

The aggregation of benefits across activities and for the population at

large is venturesome because it is so filled with known difficulties. We can

take a systematic view of these potential errors by recalling the links between

environmental policies designed to reduce effluent pollution and the benefits

of environmental improvements. Policies influence effluents directly through

regulations and indirectly through changes in incentives. Reductions in

effluents will eventually improve the ambient water quality. Improvements in

ambient quality when perceived by individuals eventually lead to changes in

behavior toward the Bay, implying benefits. Further, when non-users perceive improvements in the ambient water quality, they too will be better off.

There is potential for errors in the measurement of each link in this process.

The analysis of the previous chapters has concentrated on the connection

between ambient quality and economic benefits. It rests, however, on the

relationship between environmental policy, effluents, and ambient quality. The

considerable debate regarding the connection between effluents and ambient

quality suggests the potential for honest differences of opinion on the nature

of the ecological links. Similar uncertainty over the behavioral and perception

links exists.

While a complete catalog of the sources of potential error would take an

entire chapter, we describe broadly what we think the major difficulties are.

If the problems inherent in explaining the link between policy and ambient

quality are ignored, the foremost uncertainty is between ambient quality and

behavior. Recall briefly how this link waa estimated. For boating and beach

uae we used a varying parameters model to estimate the relationship between

the product of total phosphorus and nitrogen readings in 1977 and trips in

19S4. There is clearly substantial room for error in this relationship.

First, since people cannot perceive nitrogen and phosphorus, we must

assume that the nitrogen and phosphorus are approximate measures of the

ambient quality. It is not unreasonable to expect such a relationship to hold

in principle. Chapter 2 describes ways in which individuals form perceptions

of water quality. Some of the deductive and media-baaed means by which

individuals form quality perceptions may be directly related to effluent

discharges. Others, such as stimulants of sensory perceptions, may be highly

correlated with, or even caused by, nitrogen and phosphorus levels. Previous

studies which have attempted to link behavior to individual ambient water

quality indicator. (e.g. Binckley and Hanemann) have detected a

correspondence. Chapter 2 describes acme evidence which supports this ,

hypothesized link derived from our telephone survey of the BWSMSA and the

field survey of western shore beaches. Through the telephone a significant

relationship waa detected between a household perception of the water quality

in the Bay and ita likelihood to quit using the Bay. Additionally, a significant

relationship appeared between objective measures of the Bay's water quality

over time and the proportion of households who atopped using the Bay for

recreation because they perceived the Bay’s water quality to be unacceptable.

Finally, the user (field ) survey showed a positive correlation between

103
meaaures of fecal coliform at each of nine beaches and the proportion of

households that found each beach unacceptable.

A further difficulty is the seven years which separate the

nitrogen/phosphorus readings and the recreational behavior, since 1977 was

the latest year for which complete information was available. While this is

clearly a source of potential error, there are a few reasons why it might not

be as bad as it seems. For one thing, the relative levels across different

regions of the Bay may have remained approximately constant even if absolute

levels have changed. Additionally, it is not clear what year or combinations of

years would be correct in signaling the recreational behavior stimulated by

water quality because behavior is probably largely affected by prior

experiences.

Since we are really explaining choices among sites of different quality,

our behavioral models depend more on the relative levels of ambient water

quality rather than on absolute levels; and if relative levels have remained

fairly constant, our behavioral models are likely to be quite good. Extracting

benefit measures from these models, however, must be done with caution since

the absolute levels of nitrogen and phosphorus readings used may not be

trustworthy y.

For recreational fishing the problem is in some waya a little simpler.

Here we use the catch rate experienced by the individual for 1980, the year

the trips were taken. There is of course a complex and uncertain chain of

relationships between improvements in ambient quality and growth in the

density of fish stocks. There is further uncertainty in the connection

between fish stocks and catch rates. These are largely, although not

completely, problems of biology and are not addressed here, but nonetheless

remain as imperfectly understood links in the system.

Restricting our comments entirely to the behavioral realm does not

eliminate these uncertainties and potential sources of modelling error. In what

sense is the catch rate in the year the trip. were taken a good measure of

quality? Fishermen may value higher catch rates but their demand (behavior)

for tripa this year may be baaed on catch rates experienced in previous

years. When the quality of the good is uncertain to the consumer, there may

be one eat of quality indicators that stimulate demand and another which

affect the benefits derived from consumption. Further, there is no guarantee

that catch rate is the only (or moat important) variable which determines the

enjoyment of trips to catch fish. For example, catching one five-pound

striped baaa may be batter than catching two two-pound stripers.

In addition to the severe difficulties in inferring the relationships

between ambient quality, there are two other significant sources of error in

computing aggregate benefits. First, there is the problem of sampling and

non-sampling error associated with the measurement of the number of trips

per participant and the number of participant in each activity, M well as

measurements of exogenous variables such as costs per trip. The boating

survey is a good example of non-sampling error for trips This survey waa a

mail survey, so in a sense the respondents are volunteers. The return rate

was 70 percent. We have no way of knowing whether those who competed

their questionnaire. were representative of the boating population as a whole

or if there is a built-in sample selection bias.

104
We have also used only segments of the total population in our analysis

of benefits. The boaters were limited to those who trailer their boats, the

fishermen to those who fish for striped bass and the beach users to those

who use public-accees western shore beaches. In the boating and fishing

analysis we have excluded non-Maryland households. In the contingent

valuation and beach uae analysis, only 20 percent of Virginia’s population wae

included and about 80 percent of Maryland’s households. In every instance, a

major portion of users is excluded so any estimates derived will be lower

bounda.

Another source of error in aggregating benefits across activities is

aggregation bias. This comes in two forms: simple doublecounting and

conceptual aggregation bias. Doublecounting occurs because a ● ubatantial

number of boaters also fish, and many fishermen have boats. The conceptual

aggregation biaa occurs because of the jointness of choice among sites for a

given activity and among activities. For example, the choice of visiting Sandy

Point versus Point Lookout may depend in part on water quality. Enhancing

water quality at both sites may only increase attendance at one sites making

the addition of benefits across sites incorrect. A discussion of this problem is

offered in Chapter 3, but both forms of aggregation bias are treated in detail

in Chapter 5 of the conceptual volume of this report.

Finally, we must remember that we have only three activities: boating,

fishing, and swimming. There are many other recreational and commercial uses

of the Bay whose value is enhanced by cleaner water. For example, our

analysis of fishing rovers only striped bass; fishing for species besides

striped bass (e.g. crabbing) is widespread and not covered by our analysis.

And our analysis of the effect of changes in water quality covers only

trailered boats, not boats at marinas. Many other, especially more casual,

activities are omitted. We have limited our analysis to boating, fishing, and

swimming because we could obtain data of adequate quality only for these

activities

Estimates

With these difficulties firmly in mind, we are prepared to hazard some

judgments on the magnitude of the aggregate benefits of improving the Bay’s

water quality. Cur approach is to present low, middle and high benefits for

the beach use (Chapter 4), boating (Chapter 5), and fishing (Chapter 6) and

qualitatively compare those benefits with the total benefits derived from

Chapter 3. Comparing the ranges of these independent sources of benefits

will help us to form a judgment, but nothing more, of the magnitude of

aggregate benefits.

Chapters 4 through 6 give benefit estimates for activities conditioned on

the computational method and the proportionate change in ambient quality and

catch rate. We adopt the convention of analyzing a 20 percent reduction in

nitrogen and phosphorus for boating and beach use and a 20 percent increase

in the catch rata for striped bass fishing. These changes should be

interpreted loosely as considerable improvements in the quality of the Bay

without attaching much significance to the absolute change in ambient

readings which would be implied. In particular, one should not interpret the

estimated effect of nitrogen and phosphorus as an “all else equal” effect. The

change in nitrogen and phosphorus is a proxy for changes in moat ambient

105
determinantts of water quality so that the implicit assumption is that a range

of ambient factors may be improving. Further, to counteract the problem of

aggregating across sites for a given activity, we select as a pessimistic

estimate the lowest estimate of the benefits of improving the quality by 20

percent at the one moat important site.

Table 7.1 summarizes some of the estimates of aggregate benefita for our

groups of boaters, sportfishermen and beach users, translated into 1987

dollars. The variation from pessimistic to optimistic is provided by two

sources: variation induced by the method of calculating benefits (i.e., using

actual trips versus predicted trips) and variation caused by choosing one site

rather than the sum over all sites. Recall that because each site's benefits

are calculated assuming other sites' quality remains unchanged aggregating

these measures over sites will produce an upwardly biased aggregate benefit.

The pessimistic estimates for beach use and for boating are the lower of the

two estimates of the benefits for a 20 percent improvement in water quality

from Sand y Point for beach uae and Anne Arundel County for boating. One

site was chosen as a lower bound because wtth only one site all (upward)

aggregation bias is eliminated. The average estimates for beach use and

boating are the lower of the two calculation methods for sums across all sites.

The optimistic estimates are the higher of the two calculation methods for the

sums across all sites. For striped bass fishing, the pessimistic estimate is the

lower of the two methods of calculation. The sites have already been

aggregated for the fishing case, and as we show in Chapter 5 of the

accompanying volume, the nature of the aggregation bias in this case is not

obvious. The optimistic estimate is the higher of the two calculation arithmetic

methods and the average is the mean of the pessimistic and optimistic.

Table 7.1

Aggregate Benefits for Three Water-related Activities from a

“20%” Improvement in the Chesapeake Bay’s Water Quality

in 1967 dollars

Benefit Estimate

Activity Pessimistic “Average” (Mimistic

($ Thousand)

Public Western Shore Beach Usea 16, 853 34,658 44,960

Boating with Trailered Boatb 4,717 8,129

Striped Bass Sportfishingc 1,366 2,071

● From Table 4.6. Pessimistic estimate is the Method B value for Sandy Point,

the average is the sum of Method B values over all ten sites, and the

optimistic is the sum of Method A values over all sites.

bFrom Table 5.13. All per boater estimates expanded to 60,000 boaters “

trailering boats. Pessimistic estimate is the low value (Method A) for Anne

Arundel County, the average estimate in the sum of low values (Method A)

across all counties and the optimistic value is the sum of high values (Method

B) across all counties.

cFrom Table 6.4. Pessimistic value is the value using Method B, the “average”

value is the average of the pessimistic and optimistic value, the optimistic

value is the value using Method A.

106
The aggregate meaaurea of willingness to pay for water quality

improvements are revealing for several reasons. First, regardless of which

benefit measure we use (pessimistic, average, or optimistic), the returns to

beach use are the greatest. This ia primarily because a larger proportion of

the population engages in come beach-going during the year than boating Or

fishing. Additionally, this group may be more sensitive to changes in water

quality than the boating-fishing group.

A second interesting implication of the results, although not obvious from

looking at Table 7.1, is the importance of regional variation in water quality.

If we were able to clean up the water around Anne Arundel County only, we

would still go a long way towards satisf ying some of the human needs for

using the Bay. While we realize that confining a water qualtty improvement

program to a particular locality may not be technically or ecological y feasible,

any clean-up strategies which result in significant improvements in this region

of the Bay will yield substantial benefits.

A comparison of the behaviorally based measures of benefits presented in

Table 7.1 with benefit estimates derived from contingent valuation (ace Table

7.2) is interesting even though the valuation questions driving the two

analyses are different. All of the estimates in Table 7,1 are partial estimates

in that they account for only one activity and involve only ● ubaeta of the

population. Table 7.2 presents contingent valuation produced benefit estimates

associated with a broader but less precise hypothetical improvement:

improving water quality to an “acceptable” level. The subset of the

population includes those in the BWSMSA who found water quality unacceptable

for swimming or related uses.

Table 7.2

Aggregates Benefits from Water Quality ImprovementsContingent Valuation

in 1984 dollars

Willingness to Pay for Improved Water Qualityb

Group Pessimisticc Averagec Optimisticc

($ Thousand)

User 47,254 67,582 87,870

Non-User

Total

18,446

65,700

23.556

91,137

28.733

116,603

● Population is the Washington, D. C. and Baltimore SMSA's bWillingness to acceept tax increase to raise Chesapeake Bay Water quality from

a level unacceptable for swimming end/or other related activities to a level

acceptable for swimming. cThe average will ingness to pay plus or minus one standard error in est imate.

See Table 3.8.

107
The numbers in Tables 701 and 7.2 give likely magnitudes for the annual

benefits of improving water quality in the Bay. The numbers suggest a range

of from $10 million to over $100 million, We know that there are numerous

random elements in all estimates. Further, we know that several activities and

populations have been omitted. But based on these numbers, it seems

plausible to estimate that the annual returns to cleaning up the Chesapeake

are at least of this order of magnitude. We have only the evidence presented

herein to make this judgment.

In conclusion, we recapitulate the premise. Society haa undertaken an

investment program. The nature of the program is the cleanup of the

Chesapeake Bay. The costs of the program include such things as sewage

treatment plants, funding of government programs to regulate and monitor

agricultural effluents, installation of industrial waste disposal systems,

restrictions on housing development, etc. The annual returns on the

investment program are measured by what people are willing to pay for the

improved services of the Bay. TMa is the dividend yielded by the public’s

investment program.

For several reasons, we think that the long-run benefits are higher than

the figures Tables 7.1 and 7.2 indicate. First, as people learn that the Bay

haa become clamor, they will adjust their preferences toward Bay recreation.

This is especially true of people who do not currently use the Bay and are

largely excluded from the analyaia. Second, the population and income of the

area have grown since 1984, and both are likely to grow more, increasing the

demand for and value of improvements in water quality. Finally, we have

ignored the value (both uae and existence value) which households outaide the

BWSMSA may have for the Bay. The Chesapeake Bay is a nationally prominent

resource. Its improved health is of value to many who will never use it.

10s
REFERENCES

Bell, F. W. 1968. “The Pope and the Price of Fish. " American Economic

Review 58142-147.

Binkley, Clark S. and W. Michael Hanemann. 1978. The Recreation Benefits of

Water Quality Improvements: An Analysis of Day Trips in an Urban

Setting. Washington, D.C. E. P.A.-600/5-78-010.

Bishop, R.C. and T.A. Heberlein. “Simulated Markets, Hypothetical Markets and

Travel Cost Analysis: Alternative Method. of Estimating Outdoor

Recreation Demand.” Working paper no. 187.

Bishop, R. C. and K. Sample. 1886. “Estimating the Value of Variations in

Anglers’ Success Rate.: An Application of the Multiple-Site Travel Cost

Method.” Marine Resource Economics 2:1:55-74

Blackburn, A.J. 1870. “An Alternative Approach to Aggregation and Estimation

in the Non-Linear Model.” Ch. 9 in The Damand for Travel: Theory and

Measurement. Edited by R. E. Quandt. D.C. Heath, Lexington.

Bockstael, Nancy, W. Michael Hanemann and Catherine Kling. 1887. “Modeling

Recreational Demand in a Multiple Site Framework.” Water Resources

Research. Forthcoming.

Bockstael, Nancy, W. Michael Banemann and Ivar Strand. 18SS. Measuring the

Benefits of Water Quality Improvments Using reation Demand Models.

Report presented to the B. P.A. under Cooperative Agreement

CR-81 143-01-1, Volume II.

Bockstael, N. E., and K. E. McConnell. 1981. “Theory and Estimation of the

Household Production Function for Wildlife Recreation.” Journal of

Environmental Economics and Managem● nt 8:188-214.

Bockstael, N. E., and K. E. McConnell. 1983. “Welfare Measurement in the

Household Production Framework.” American Economic Review 73:806-814.

Bockstael, N. B., and J. J. Opaluch. 1983. "Discrete Modelling of Supply

Response under Uncertainty: The Case of the Fishery." Journal of

Environmental Economics and Management 10:125-137.

Bockstael, N. B., I. B. Strand, and W. M. Hanemann. 1987. “Time and The

Recreational Demand Model.” American Journal of Agricultural Economics

69213-302

Bockstael, N. E.

Regression

and I. E. Strand. 1987. “The Effect of Common Sources of

Error on Benefit Estimates.” Land Economics 63:11-20.

109
Bockstael, N. E., I. E. Strand, and A. Graefe. Economic Analysis of Artificial

Reefs: A Pilot Study of Selected Valuation Methodologies. Technical

Report #6. Artificial Reef Development Center, Sportfishing Institute.

1986.

Brookshire, D.S., L.S. Eubanks and A. Randall. . 1983. “Estimating Option

Prices and Existing Values for Wildlife Resources. ” Land Economics

58:1-15.

Brown, Gard nor and Robert Mendelsohn. 1984. “The Hedonic Travel Cost

Method.” Review of Economics and Statistics 66:427-33.

Brown, W. G., C. Sorhus, B. Chow-Yang, S. Richards. 1883. “Using Individual

Observations to Estimate Recreation Demand Functions." “ American Journal

of Agricultural Economics 65:154-157.

Brown, W. G., C. N. Sorhus, and D. A. Meyer. 1882. “Estimation of Oregon

Sport-Caught Salmon and Steelhead Values Via the Travel Cost Method.”

Mimeo, ASSA Meetings, 25 ppa.

Brown, W. G., A. Singh and E. N. Castle. “Net Economic Value of the Oregon

Salmon-Steelhead Sport Fishery." Journal Wildlife Management

29(2):266-279.

Burt, Oscar and Durward Brewer. 1872. “Estimation of Net Social Benefits

From Outdoor Recreation.” Econometrica 39:813.

Carson, R. T. and R C. Mitchell 188s0 “The Value of Clean Water: The

Public’. Willingness to Pay for Beatable, Fishable, and Swimmable Quality

Water.” Discussion paper No. QE85-08 revised, (Washington, D. C.,

Resources for the Future).

Castle, Emery N. and W. G. Brown. 1884. “The Economic Value of a

Recreational Resource: A Case Study of the Oregon Salmon-Steelhead

Sport Fishery. Paper presented before the Committee on the Economics

of Water Resource* Development of the Western Agricultural Economics

Research Council, San Francisco, California.

Caulkins, Peter, Richard Bishop and Nicolass Bouwes. 1982. “The Travel Coat

Model for Lake Recreation A Comparison of Two Methods for

Incorporating Site Quality and Substitution Effects.” Mimeo, ABA annual

meetings,

Caulkins, Peter P., Richard C. Bishop and Nicolass W. Bouwes, Sr. 1888. “The

Travel Cost Model for Lake Recreation: A Comparison of Two Methods for

Incorporating Site Quality and Substitution Effects.” Amer. J. Air. Econ.

68(2):291-297.

Caulkins, Peter, Richard Bishop and Nicolass Bouwes. 1985. “Omitted

Cross-Price Variable Biases in the Linear Travel Coat Model: Correcting

Common Misperceptions. ” Land Economics 61:182-85.

110
&aario, Frank J. 1969. “Operations R esearch in Outdoor Recreation. ”

Journal of Leisure Research 1:33-52.

Ce sario, Frank J. 1973. “A Generalized Trip Distribution Model.” Journal of

Regional1 Science 13:233-48.

Cesario, Frank J. 1974. “More on the Generalized Trip Distribution Model, ”

Journal of Regional Science 14:389-97

Cesario, Frank J. and Jack Knetach. 1976. “A Recreation Site Demand and

Benefit Estimation Model. ” Regional Studies 10:97-104.

Christensen, L. R., Jorgenson, D.W. and Lawrence Lau. 1975. “Transcendental

Logarithmic Utility Functions.” American Economic Review 65:367-83.

Cicchetti, Charles, Anthony Fisher and V. Kerry Smith. 1976. “An Econometric

Evaluation of a Generalized Consumer Surplus Measure: The Mineral King

Controversy.” Econometrica 44:1268-76.

Cicchetti, C.J., J.J. Seneca and Paul Davidson. 1869. The Demand and Supply

of Outdoor Recreation: An Econometric Analysis. Rutgers. 1869.

Clawson, MartOn. 1859. Methods for Measuring the Demand for and Value of

Outdoor Recreation. Resources for the Future, Inc., Reprint no. 10.

Clawson, Marion and Jaok Knetach. 1866.

Resources for the Future, Washington D.C.

Cox, D.R. 1861. “Tests of Separate Families of Hypotheses." Proceedings of

the Fourth Berkeley Symposiurn on Mathematical Statistics and Probability.

Vol 1. University of California Press.

Cronin, F. J. 1882. “Valuing Non-Market Goods Through Contingent Markets. ”

Pac. Northw. Lab., Richland, Wash.

Cropper, M. L., L.B. Deck, K.B. McConnell and T.T. Phipps. 1886. “Should the

Rosen Model be Used to Value Environmental Amenities?” Papar

presented at the conference on the Economics of Chesapeake Bay

Management 11, Annapolis, MD.

Cummings, R G., D. S. Brookshire and W. D. Schulze. 1984. valuing

Environmental Goods: A State of the Arts Assessment of the Contingent

Valuation Method. Draft report to EPA. Universities of New Mexico and

Wyoming. “

Daubert, J. T. and R A.

Instream Flows: A

63(4):666-676.

Young. 1981. “Recreational Demands for Maintaining

Contingent Vaulation Approach.” Amer. J. Agr. Econ.

Deaton, A.S. 1974, “The Analysis of Consumer Demand in the United Kingdom,

1800-1970.” Econometrics 42:341-67.

111
Deaton, Henry. 1978. “Specification and Testing in Applied Demand Analysis. ”

Economic Journal 88:524-36.

Desvousges, W. H., V. K. Smith and M. P. McGivney. 1983. A Cornparison of

Alternative Approaches for Estimation of Recreation and Related Benefits

o f Water Quality Improvements. Washington, D. C.: Environmental

Protection Agency, Office of Policy Analysis.

Deuel, D. G. 1978. “National Marine Recreational Fishery Statistics Program. ”

Paper presented at Canadian Sport Fisheries Conference, New Brunswick,

Canada.

Domencich, Thomas and Daniel McFadden. 1975. Urban Travel Demand: A

Behavioral Analysis. North-Holland, Amsterdam.

Dwyer, J. F. and M. D. Bowes. 1978. "Concepts of Value for Marine

Recreational Fishing.” American Journal of Agricultural Economics 60(5):

1008-10120

Feenberg, Daniel and Edwin Mills. 1980. Measurinzg the Benefits of Water

Pollution Abatement. New York, Academic Press.

Fisher, Ann and William H. Desvousges. 1881. “Alternative Contingent Market

Approaches to Valuation of Recreation and Related Benefits of Improved

Water Quality.” Paper presented at the Eastern Economics Association

Meetings, Washington D.C.

Fisher, Am and Robert Raucher. 1982. “Comparison of Alternative Methods of

Evaluating the Intrinsic Benefits of Improved Water Qualiy." Paper

presented at ASSA meetings, New York City.

Fisher, F. and K. Shell. 1871. “Taste and Quality Change in the Pure Theory

of the True Cost-of-Living Index.” in Price Indexes and Quality Change.

Edited by Zvi Griliches. Harvard University Press, Massachusetts.

Freeman, A. Myrick III. 1979. The Benefites of Environmental Improvement:

Theory and Practice. Johns Hopkins Press for Resources for the Future,

Inc., Baltimore. 1979.

Freeman, A. Myrick III. 1882. and Water Pollution Control: A Benefit-Coat

Assessment. John Wiley and Sons.

Freund, R. J. and R. R. Wilson. 1974. “An Example of a Gravity Model to

Estimate Recreation Travel.” Journal of Leisure Research 6:241-56.

German, W.M. 1976. “Tricks with Utility Function.” in Essays in Econmic

Analysis: Proceddings of the 1975 AUTE Conference, Sheffeld. Edited by

M.J. Artis and A.R. Nobay. Cambridge University Press, Cambridge.

112
.

Greenley, Douglas A., Richard G. Walsh and Robert A. Young. 1981. “Option

Value: Empirical Evidence from a Case Study of Recreation and Water

Quality.” Quartelv J. Econ. 96(4):657-673.

Gum, R.L. and W.E. Martin. 1975. “Problems and Solutions in Estimating the

Demand for and Value of Rural Outdoor Recreation. ” American Journal of

Agricultural Economics 57:558-66.

Hanemann, W. Michael. 1980. “Quality Changes, Consumer’s Surplus, and

Hedonic Price Indices.” California Experiment Station working paper

number 116.

Hanemann, W. Michael. 1981. “Quality and Demand Analysis.” Ch. 3 in New

Directions in Econometric Modeling and Forecasting in U.S. Agriculture.

Edited by Gordon Rausser, Elsevier/North-Holland, New York.

Hanemann, W. Michael. 1882. “Applied Welfare Analysis with Qualitative

Response Models.” California Agricultural Experiment Station working

paper no. 241.

Hanemann, W. Michael. 1984a. “Multiple Site Demand: A Review of Existing

Models and New Development”, Chapter 9 in Measurinzg the Benefits of

Water Quality Improvements Using rea n Demand els. Draft

Report presented to E.P.A. under Cooperative Agreement CR-80043-01-0,

VOL II.

Hanemann, W. Michael. 1984b. “Discrete/Continuous Models of Consumer

Demand.” Econometrics 52:541-61.

Hanemann, W. M . 1978. “A Methodological and Empirical Study of Recreation

Benefits from Water Quality Improvement.” Ph. D. dissertation,

Department of Economics, Harvard University.

Hanemann, W. M. 1984. “Welfare Evaluations with Market Data.” American

Journal of Agricultural Economics. 66:332-41.

Hanemann, W. M. 1984. “Welfare Evaluation in Contingent Valuation

Experiment with Discrete Responses." American Journal of Agricultural

Economics. VOL 66(3):332-341.

Harmon, R. J. and Associates. 1883. Recreational Resources at Risk. Taak 2

Working Paper. Washington, DC.

Harrison, D. and D. Rubinfeld. 1878. “Hedonic Housing Prices and the Demand

for Clean Air.

w

Journal of Environmental Economics and Management

5:81-102.

Hausman, J.A. 1886. “Exact Consumer's Surplus and Deadweight Loss.”

American Economic Review 71:662-76.

113
Heintz, H.T. Jr., A. Hershaft and G.C.

and Water Pollution. A Report to

MD.

Hiett, R. L. and J. W. Worrall. 1977.

Horak. 1976. National Damages of Air

the EPA. Enviro Control, Inc. Rockville,

“Marine Recreational Fisheries Ability to

Estimate Catch and to Recall Catch and Effort over Time.” Human

Sciences Research, Inc., Report HSR-RR-77/18, Washington, D.C.

Horowitz, Joel. 1983. “Statistical Comparison of Non-nested Probabilistic

Discrete Choice Models. ” Transportation Science 17:319-50.

Hotelling, H. 1948. Letter to the National Park Service in “Economics of

Outdoor Recreation” - The Prewitt Report.

Huppert, D.D. 1983. NMFS Guideline. on Economic Valuation of Marine

Recreational Fishing. NOAA Technical Memorandum

NOAA-TM-NMFS-SWFC-32, Department of

Johnston, J. 1972. Econometric Methods.

York.

Just, Richard, Darrell Hueth and Andrew

Commerce, Washington, D.C.

Second Edition McGraw-Hill, New

Schmits. 1982. Applied Welfare

ECO1’10miCS and Public Policy. Englewood Cliffs,N.J. Prentice-Hall Inc.

Just, R E., and D. L. Hueth. 1979. “Multimarket Welfare Measurement."

American Economic Review 69947-64.

Kealy, Mary Jo, and Richard C. Bishop. 1983. “Theoretical and Empirical

Specification Issues in Travel Cost Demand Studies.w Unpublished.

Kendall, M. and Alan Stuart. 1977. The Advanced Theory of Statistics. 4th

Edition, (Vol. 1), New York: MacMillan.

Kling, Catherine. 1986. ‘A Simulation Approach to Comparing Multiple Site

Recreation Demand Model. Using Chesapeake Bay Survey Data.” Paper

presented at the Conference on the Economics of the Chesapeake Bay

Management II, Annapolis, MD.

Kling, Catherine, Nancy Bockstael and Ivar Strand. 1985. “An Evaluation of

Multiple Site Model Development.” Paper presented at the AEA meetings,

New York, N.Y.

Kling, Catherine. 1886. Benefits of En

Dissertation, University of Maryland.

Krutilla, J. V. 1967. “Conservation Revisited.” American Economic Review.

57( Sept. ):777-86

Lewbel, Arthur. 1975. “A Unified Approach to Incorporating Demographic or

Other Effects into Demand Systems.” Review of Economic Studies 42.

114
Loomis, J. 1986. “An Examination of the Variation in Empirical Estimates of

Site Specific Marginal Values for Recreational Fishermen.” Working paper,

Division of Environmental Studies, University of California, Davis.

Maddala, G. S. 1983. Limited Dependent and Qualitative Variables in

Econometrics. Cambridge University Press New York.

Maddala, G. S. 1977. Econometric McGraw-Hill, Inc., N. Y. 516 pp.

Maler, Karl-Goran. 1974. Environmental Economics: A Theoretical Inq uiry,

Baltimore, Johns Hopkins University Press for Resources for the Future.

Mathews, S. B. and G. S. Brown. 1970. “Economic Evaluation of the 1967

Sport Salmon Fisheries of Washington*” Washington Department of

Fisheries, Technical Report #2.

McConnell, K. E. and N. E. Bockstael. 1984. “Aggregation in Recreation

Economics: Issues of Estimation and Benefit Measurement.” Northeastern

Journal of Agricultural and Resource Economics 13:181-186.

McConnell, K. E. and I. E. Strand. 1981. ‘Measuring the Cost of Time in

Recreation Demand Analysis.” American Journal of Agricultural Economics

63( Feb):153-156.

McConnellt K. B. 1986. Identification of preference. in Hedonic Models.

Report presented to Environmental Protection Agency under Cooperative

Agreement CR-811043-O1-O, Vol I.

McConnell, K. E. 1879. “Values of Marine Recreational Fishing: Measurement

and Impact of Measurement." American Journal of Agricultural Economics

61(5):921-923.

McConnellr K. E. and T. P. Smith. 1979. “Marine Recreational Fishing in Rhode

Island,” University of Rhode Island Marine Memorandum #62.

McFadden, Daniel.

Behavior.” in

McFadden, D.

In Karlquist,

Theory and

company.

1874. “Conditional Logit Analysis of Qualitative Choice

Frontiers in Econometrics. Edited by P. Zarembka.

1978. “Modelling the Choice of Residential Location. ”

Lundquist, Snickars, and Weibull (eds) Spatial Interaction

Planning Models. New York North-Holland Publishing

Mendelsohn, Robert. 1984. “Estimating the Structural Equations of Implicit

Markets and Household Production Functions.” Review of Economics and

Statistics 66:673-77.

Meyer, P. A. 1982. Net Economic Values for Salmon and Steelhead from the

Columbia River Sustem. NOM Technical Memorandum NMFS F/NWR-3, U.

S. Department of Commerce, Washington, D.C.

115
Miller, Jon. 1982. "Valuation Methods for Fish and Wildlife Associated

Recreation: An Empirical Comparison of the Travel Coat and Contingent

Valuation Method-.” Paper presented at the ASSA meetings, New York,

N.Y.

Mitchell, R C. and Richard T. Carson. 1984. “National Fresh Water Quality

Benefits: Findings from a New National Contingent Valuation Survey.”

Presented at the annual AERE meeting, Cornell University.

Morey, Edward. 1981. “The Demand for Site-Specific Recreational Activities: A

Charactaristics Approach. ” Journal of Environmental Economics and

Management 8:345-71.

Morey, Edward. 1984a. “Characteristics, Consumer Surplus and New Activities:

A Proposed Ski Area.” Paper presented at the ASSA meetings, Dallas,

TX.

Morey, Edward. 1984b. “The Choice of Ski Areas: Estimation of a Generalized

CBS Preference Ordering with Characteristics.” Review of Economics and

Statistics 66:584-90.

Muellbauer, J. “Household Composition, Engel Curvea and Welfare Comparisons

Between Households A Duality Approach.” European

5(1974):103-22.

Economic Review

Norton, V. J., T. P. Smith, and I. E. Strand. 1889. Stripers: The Economic

Value of the Atlantic Coast Commercial and Recreational Striped Bass

Fisheries. Maryland Sea Grant Publication #UM-SG-TS-83-12.

Pacific Fisheries Management Council. 1886. Review of 1985 Ocean Salmon

Fisheries. Portland, Oregon.

Parks, RS. 1969, "Systems of Demand Equations: An Empirical Comparison of

Alternative Functional Forma.” Econometrics 37:629-65

Pesaran, M.H. and A.S. Deaton. 1978. “Testing Non-nested Non-linear

Regression Models.” Econometricca 46:677-94.

Pindyck, Robert and Daniel Rubinfeld. 1981. Econometric Models and Economic

Forecasts. New York, McGraw-Hill Book Company.

Pow, Robert. 1971. “Additive Utility Functions and Linear Engel Curves. ”

Studies 38401-14.

Pollak, Robert and Terence Wales. 19790 “Estimation of the Linear

Expenditure S ystem." Econometrics 37:611-29.

Pollak, Robert and Terence Wales. 1881. "Demographic Variables in Demand

Analysis." Econometrics 4*1533-51.

116
Reiling, S. D., K.C. Gibbs and H.H. Stoevner. 1973. Economic Benefits from an

Improvement in Water Quality. Washington,D.C., U.S. EPA.

Ribaudo, Marc O. and Donald J. Epp. 1983. “The Importance of Sample

Discrimination in Using the Travel Cost Method to Estimate the Benefits of

Improved Water Quality.” Paper presented at the annual AAEA meeting,

Purdue, Indiana.

Rosen, Sherwin. 1974. “Hedonic Prices and Implicit Markets: Product

Differentiation in Pure Competition.” Journal of Political Economy

82:34-55.

Rowe, Robert, Edward Morey, Arthur Ross and W. Douglass Shaw. 1985.

Valuing Marine Recreation Fishing on the Pacific Coast. Energy and

Resurce Consultants, Report prepared for National Marine Fisheries

Service, NOAA.

Russell, C. S., and W. A. Vaughan. 1982. “The National Recreational Fishing

Benefits of Water Pollution Control.” Journal of Environmental Economics

and Management 9:328-354.

Samples, K. C., and R. C. Bishop. (Undated). “Estimating Social Value. of

Sport Caught Fish A Suggested Approach.” Draft, 12 pps.

Samuelson, Paul A. 1947. “Some Implications of Linearity,” Review of

Economic Studies 15:88-90.

Saxonhouse, G.R. 1977. "Regressions from Samples Having Different

Characteri-tics.” Review of Economics and Statistics 59294-7.

Schulse, W. D., D. S. Brookshire, B. G. Walther, M. K. McFarland, M. A. Thayer,

R. L. Whitworth, S. Ben-David, W. Maim, and J. Molenar. 19S3. “The

Economic Benefits of Preserving Visibility in the National Parklands of the

southwest.” Natural Resources Journal 23:149-173.

Sellar, Christine, John Stoll and Jean-Paul Chavas. 196s. “Validation of

Empirical Measures of Welfare Change: A Comparison of Nonmarket

Techniques. ” Land Economics 61:156-75.

Sellar, C., J. P. Chavas and J. R. Stoll. 1966. “Specification of the Logit

Model: The Case of Valuation of Nonmarket Goods.” Journal ‘of

Environmental Economics and Managment Vol. 13:382-390.

Smith, V. Kerry and William H. Deavousge.

Cost Model and Water Quality Benefits:

Economic Journal 62371-81.

1985. “The Generalized Travel

A Reconsideration.” Southern

Smith, V. K., W. H. Desvousges and Ann Fisher. 1986. “A Comparison of Direct

and Indirect Methods for Estimating Environmental Benefits.” Amer. J.

Agr. Econ. 68(2):280-290.

117
Smith, V. Kerry, William HO Desvousges and M.P. McGivney. 1983. "Estimating

Water Quality Benefits: An Econometric Analysis. ” Southern Economic

-50:422-37”

Smith, V. K., W. H. Desvousges and M. P. McGivney. 1983. “The Opportunity

Cost of Travel Time in Recreation Demand. Models.” Land Economics.

59:3:259-78.

Smith, V. Kerry and Yoshiaki Kaoru. 1985. “The Hedonic Travel Cast Model: A

View from the Trenches.” Mimeo.

Stevens, J. B. 1966. “Recreation Benefits from Water Pollution Control. ”

Water Resources Research 2:167-182.

Stevens, Joe B. 1866. "Angler Success as a Quality Determinant of Sport

Fishery Recreational Values. ” Transactions of the American Fisheries

Society. 95:357-362.

Stoevener, H. H. 1863. “An Economic Evaluation of Water Pollution Control

Alternatives: A Progress Report.” Water Resources and Economic

Development of the West, No. 12, Committee on the Economics of Water

Resources Development of the Western Agricultural Economic. Research

Council, Salt Lake City, 47-59.

Stoevener, H. H. 1864. “Water uee Relationships as Affected by Water Quality

on the Yaquina Bay.” New Horizons for Resources Research: Issues and

Methodology, Western Resources Conference, University of Colorado Press,

Boulder, 87-86.

Stone, J.R.M. 1854. The M easurement of Consumers' Expenditure and

Behaviour in the United Kingdom, 1920-1938, Vol L Cambridge University

Press, Cambridge.

Strand, I. E. and N. E. Bockstael. 1985. “The Effect of Common Sources of

Regression Error on Benefit Estimation.” Working paper, University of

Maryland.

Sutherland, R.J. 1881. “A Regional Approach to Estimating Recreation Benefits

of Improved Water Quality. " Journal of Environmental Economics and

Management 9:229-47.

Thayer, Mark. 1981. “Contingent Valuation Techniques for Assessing

Environmental Impacts: Further Evidence." Journal of Environmental

Economic● and Management 8:27-44.

Theil, Henry. 1976. Theory and Measurement of Consumer Demand. Vols 1 and

2. Amsterdam.

Tobin, James.

Variables.”

1985. “Estimation of Relationships for Limited Dependent

Econometrics 26:24-36.

118
U. S. EPA. 1983.

Regulation. and

U. S. Department of

Water Quality Standards Handbook. office of Water

Standards. Washington, DC.

Commerce. 1984. “Marine Recreational Fishery Statistics

Survey, Atlantic and Gulf Coasts, 1979 (Revised) - 1980, Current Fishery

Statistics 8322.” Washington, DC.

U, S. Department of the Interior. “1980 National Survey of

and Wildlife-Associated Recreation.” U. S. Fish and

Washington, DC. Undated.

Vaug ban, W. J. and C. S. Russell. 1982. “Valuing a

Application of a Systematic Varying Parameter Model.”

58:450-62.

Fishing, Hunting,

Wildlife Service.

Fishing Day: An

Land Economics

Vaughan, W. J. and C. S. Russell. 1982. Fresh Water Recretional Fishing: The

National Benefits of Water Pollution Control. Washington D. C.:RFF, Inc.

Vaughan, W. J., C. S. Russell, L. P. Gianessi and L. A. Nielsen. “Measuring and

Predicting Water Quality in Recreation Related Terms." J.” Env. Mgmt.

15:363-380.

Violette, Daniel. 188s. “A Model to Estimate the Economic Impacts on

Recreational Fishing in the Adirondacks from Current Levels of

Acidification.” Paper presented at the AERE Workshop on Recreation

Demand Modeling, Boulder, CO.

Wales, Terence J. 1971. “A Generalised Linear Expenditure Model of the

Demand for Non-Durable Goods in Canada.” Canadian Journal of Eonomics

4:471-84.

Wales, Terence and A. D. Woodland. 1883. “Estimation of Consumer Demand

S ystems with Binding Non-Negativity Constraints." Journal of

Econometrics 21:263-85.

Walsh, Richard G. Undated. ‘*An Extension of the Contingent Valuation

Approach to Estimate the Effect of Instream Flow on Recreational

Demand.” Unpublished.

Walsh, R G., D. A. Greenley, R. A. Young, J. R.McKean, and A. A. Pinto. 1978.

“Option Values and Recreational Benefits of Improved Water Quality: A

Case Study of the South Platte River Basin, Colorado.” Rep.

EPA-600/5-78-100. U. S. Environmental Protection Agency, Research

Triangle Park, N . C.

Walsh, R G., L. D. Sanders, and J. B. Loomis. 1985. Wild and Scenic River

Economics: Recreation Use and Preservation Values. American Wilderness

Alliance, Englewood, Colorado.

119
Wegge, Thomas, W. Michael Hanemann and Ivar Strand. 1985. “An Economic

Assessment of Marine Recreational Fishing in Southern California.”

Report submitted to National Marine Fisheries Service Southwest Regional

Office.

Wennergren, E. B. and D. B. Nielson. 1970. ‘ “Probability Estimates of

Recreation Demands.” Journal of Leisure Research 2:112-22.

Williams, J. B., T. P. Smith, M. J. Speir, and S. Early. 1983. “1980 Maryland

Saltwater Sport Fishing Survey. ” Maryland Department of Natural

Resources Tidewater Administration Report No. TA-CRD-83-1. Annapolis,

MD.

Willig, R.D. 1976. “Consumer’s Surplus without Apology.” American Economic

Review 66:589-97.

Willig, R. D. 1978. “Increment Consumer's Surplus and Hedonic Price

Adjustments." Journal of Economic Theory 17:227-253.

Wilman, Elizabeth A. 1884. External Costa of Coastal Beach Pollution.

Resources for the Future, Washington, DC.

Wilman, Elizabeth. 1985. “Valuing Quality Changes in Recreation Resources. w

Paper presented at the AERE Workshop on Recreation Demand Modeling,

Boulder, CO.

Zellner, A . and Soo-Bin Park. 1879. “Minimum Expected Lose (MELO)

Estimators for Functions of Parameters and Structural Coeffiecients of

Econometric Models.” Journal of the American Statistical Association.

74:365:185-193.

120
REFERENCES

Bell, F. W. 1968. “The Pope and the Price of Fish. " American Economic

Review 58142-147.

Binkley, Clark S. and W. Michael Hanemann. 1978. The Recreation Benefits of

Water Quality Improvements: An Analysis of Day Trips in an Urban

Setting. Washington, D.C. E. P.A.-600/5-78-010.

Bishop, R.C. and T.A. Heberlein. “Simulated Markets, Hypothetical Markets and

Travel Cost Analysis: Alternative Method. of Estimating Outdoor

Recreation Demand.” Working paper no. 187.

Bishop, R. C. and K. Sample. 1886. “Estimating the Value of Variations in

Anglers’ Success Rate.: An Application of the Multiple-Site Travel Cost

Method.” Marine Resource Economics 2:1:55-74

Blackburn, A.J. 1870. “An Alternative Approach to Aggregation and Estimation

in the Non-Linear Model.” Ch. 9 in The Damand for Travel: Theory and

Measurement. Edited by R. E. Quandt. D.C. Heath, Lexington.

Bockstael, Nancy, W. Michael Hanemann and Catherine Kling. 1887. “Modeling

Recreational Demand in a Multiple Site Framework.” Water Resources

Research. Forthcoming.

Bockstael, Nancy, W. Michael Banemann and Ivar Strand. 18SS. Measuring the

Benefits of Water Quality Improvments Using reation Demand Models.

Report presented to the B. P.A. under Cooperative Agreement

CR-81 143-01-1, Volume II.

Bockstael, N. E., and K. E. McConnell. 1981. “Theory and Estimation of the

Household Production Function for Wildlife Recreation.” Journal of

Environmental Economics and Managem● nt 8:188-214.

Bockstael, N. E., and K. E. McConnell. 1983. “Welfare Measurement in the

Household Production Framework.” American Economic Review 73:806-814.

Bockstael, N. B., and J. J. Opaluch. 1983. "Discrete Modelling of Supply

Response under Uncertainty: The Case of the Fishery." Journal of

Environmental Economics and Management 10:125-137.

Bockstael, N. B., I. B. Strand, and W. M. Hanemann. 1987. “Time and The

Recreational Demand Model.” American Journal of Agricultural Economics

69213-302

Bockstael, N. E.

Regression

and I. E. Strand. 1987. “The Effect of Common Sources of

Error on Benefit Estimates.” Land Economics 63:11-20.

109
Bockstael, N. E., I. E. Strand, and A. Graefe. Economic Analysis of Artificial

Reefs: A Pilot Study of Selected Valuation Methodologies. Technical

Report #6. Artificial Reef Development Center, Sportfishing Institute.

1986.

Brookshire, D.S., L.S. Eubanks and A. Randall. . 1983. “Estimating Option

Prices and Existing Values for Wildlife Resources. ” Land Economics

58:1-15.

Brown, Gard nor and Robert Mendelsohn. 1984. “The Hedonic Travel Cost

Method.” Review of Economics and Statistics 66:427-33.

Brown, W. G., C. Sorhus, B. Chow-Yang, S. Richards. 1883. “Using Individual

Observations to Estimate Recreation Demand Functions." “ American Journal

of Agricultural Economics 65:154-157.

Brown, W. G., C. N. Sorhus, and D. A. Meyer. 1882. “Estimation of Oregon

Sport-Caught Salmon and Steelhead Values Via the Travel Cost Method.”

Mimeo, ASSA Meetings, 25 ppa.

Brown, W. G., A. Singh and E. N. Castle. “Net Economic Value of the Oregon

Salmon-Steelhead Sport Fishery." Journal Wildlife Management

29(2):266-279.

Burt, Oscar and Durward Brewer. 1872. “Estimation of Net Social Benefits

From Outdoor Recreation.” Econometrica 39:813.

Carson, R. T. and R C. Mitchell 188s0 “The Value of Clean Water: The

Public’. Willingness to Pay for Beatable, Fishable, and Swimmable Quality

Water.” Discussion paper No. QE85-08 revised, (Washington, D. C.,

Resources for the Future).

Castle, Emery N. and W. G. Brown. 1884. “The Economic Value of a

Recreational Resource: A Case Study of the Oregon Salmon-Steelhead

Sport Fishery. Paper presented before the Committee on the Economics

of Water Resource* Development of the Western Agricultural Economics

Research Council, San Francisco, California.

Caulkins, Peter, Richard Bishop and Nicolass Bouwes. 1982. “The Travel Coat

Model for Lake Recreation A Comparison of Two Methods for

Incorporating Site Quality and Substitution Effects.” Mimeo, ABA annual

meetings,

Caulkins, Peter P., Richard C. Bishop and Nicolass W. Bouwes, Sr. 1888. “The

Travel Cost Model for Lake Recreation: A Comparison of Two Methods for

Incorporating Site Quality and Substitution Effects.” Amer. J. Air. Econ.

68(2):291-297.

Caulkins, Peter, Richard Bishop and Nicolass Bouwes. 1985. “Omitted

Cross-Price Variable Biases in the Linear Travel Coat Model: Correcting

Common Misperceptions. ” Land Economics 61:182-85.

110
&aario, Frank J. 1969. “Operations R esearch in Outdoor Recreation. ”

Journal of Leisure Research 1:33-52.

Ce sario, Frank J. 1973. “A Generalized Trip Distribution Model.” Journal of

Regional1 Science 13:233-48.

Cesario, Frank J. 1974. “More on the Generalized Trip Distribution Model, ”

Journal of Regional Science 14:389-97

Cesario, Frank J. and Jack Knetach. 1976. “A Recreation Site Demand and

Benefit Estimation Model. ” Regional Studies 10:97-104.

Christensen, L. R., Jorgenson, D.W. and Lawrence Lau. 1975. “Transcendental

Logarithmic Utility Functions.” American Economic Review 65:367-83.

Cicchetti, Charles, Anthony Fisher and V. Kerry Smith. 1976. “An Econometric

Evaluation of a Generalized Consumer Surplus Measure: The Mineral King

Controversy.” Econometrica 44:1268-76.

Cicchetti, C.J., J.J. Seneca and Paul Davidson. 1869. The Demand and Supply

of Outdoor Recreation: An Econometric Analysis. Rutgers. 1869.

Clawson, MartOn. 1859. Methods for Measuring the Demand for and Value of

Outdoor Recreation. Resources for the Future, Inc., Reprint no. 10.

Clawson, Marion and Jaok Knetach. 1866.

Resources for the Future, Washington D.C.

Cox, D.R. 1861. “Tests of Separate Families of Hypotheses." Proceedings of

the Fourth Berkeley Symposiurn on Mathematical Statistics and Probability.

Vol 1. University of California Press.

Cronin, F. J. 1882. “Valuing Non-Market Goods Through Contingent Markets. ”

Pac. Northw. Lab., Richland, Wash.

Cropper, M. L., L.B. Deck, K.B. McConnell and T.T. Phipps. 1886. “Should the

Rosen Model be Used to Value Environmental Amenities?” Papar

presented at the conference on the Economics of Chesapeake Bay

Management 11, Annapolis, MD.

Cummings, R G., D. S. Brookshire and W. D. Schulze. 1984. valuing

Environmental Goods: A State of the Arts Assessment of the Contingent

Valuation Method. Draft report to EPA. Universities of New Mexico and

Wyoming. “

Daubert, J. T. and R A.

Instream Flows: A

63(4):666-676.

Young. 1981. “Recreational Demands for Maintaining

Contingent Vaulation Approach.” Amer. J. Agr. Econ.

Deaton, A.S. 1974, “The Analysis of Consumer Demand in the United Kingdom,

1800-1970.” Econometrics 42:341-67.

111
Deaton, Henry. 1978. “Specification and Testing in Applied Demand Analysis. ”

Economic Journal 88:524-36.

Desvousges, W. H., V. K. Smith and M. P. McGivney. 1983. A Cornparison of

Alternative Approaches for Estimation of Recreation and Related Benefits

o f Water Quality Improvements. Washington, D. C.: Environmental

Protection Agency, Office of Policy Analysis.

Deuel, D. G. 1978. “National Marine Recreational Fishery Statistics Program. ”

Paper presented at Canadian Sport Fisheries Conference, New Brunswick,

Canada.

Domencich, Thomas and Daniel McFadden. 1975. Urban Travel Demand: A

Behavioral Analysis. North-Holland, Amsterdam.

Dwyer, J. F. and M. D. Bowes. 1978. "Concepts of Value for Marine

Recreational Fishing.” American Journal of Agricultural Economics 60(5):

1008-10120

Feenberg, Daniel and Edwin Mills. 1980. Measurinzg the Benefits of Water

Pollution Abatement. New York, Academic Press.

Fisher, Ann and William H. Desvousges. 1881. “Alternative Contingent Market

Approaches to Valuation of Recreation and Related Benefits of Improved

Water Quality.” Paper presented at the Eastern Economics Association

Meetings, Washington D.C.

Fisher, Am and Robert Raucher. 1982. “Comparison of Alternative Methods of

Evaluating the Intrinsic Benefits of Improved Water Qualiy." Paper

presented at ASSA meetings, New York City.

Fisher, F. and K. Shell. 1871. “Taste and Quality Change in the Pure Theory

of the True Cost-of-Living Index.” in Price Indexes and Quality Change.

Edited by Zvi Griliches. Harvard University Press, Massachusetts.

Freeman, A. Myrick III. 1979. The Benefites of Environmental Improvement:

Theory and Practice. Johns Hopkins Press for Resources for the Future,

Inc., Baltimore. 1979.

Freeman, A. Myrick III. 1882. and Water Pollution Control: A Benefit-Coat

Assessment. John Wiley and Sons.

Freund, R. J. and R. R. Wilson. 1974. “An Example of a Gravity Model to

Estimate Recreation Travel.” Journal of Leisure Research 6:241-56.

German, W.M. 1976. “Tricks with Utility Function.” in Essays in Econmic

Analysis: Proceddings of the 1975 AUTE Conference, Sheffeld. Edited by

M.J. Artis and A.R. Nobay. Cambridge University Press, Cambridge.

112
.

Greenley, Douglas A., Richard G. Walsh and Robert A. Young. 1981. “Option

Value: Empirical Evidence from a Case Study of Recreation and Water

Quality.” Quartelv J. Econ. 96(4):657-673.

Gum, R.L. and W.E. Martin. 1975. “Problems and Solutions in Estimating the

Demand for and Value of Rural Outdoor Recreation. ” American Journal of

Agricultural Economics 57:558-66.

Hanemann, W. Michael. 1980. “Quality Changes, Consumer’s Surplus, and

Hedonic Price Indices.” California Experiment Station working paper

number 116.

Hanemann, W. Michael. 1981. “Quality and Demand Analysis.” Ch. 3 in New

Directions in Econometric Modeling and Forecasting in U.S. Agriculture.

Edited by Gordon Rausser, Elsevier/North-Holland, New York.

Hanemann, W. Michael. 1882. “Applied Welfare Analysis with Qualitative

Response Models.” California Agricultural Experiment Station working

paper no. 241.

Hanemann, W. Michael. 1984a. “Multiple Site Demand: A Review of Existing

Models and New Development”, Chapter 9 in Measurinzg the Benefits of

Water Quality Improvements Using rea n Demand els. Draft

Report presented to E.P.A. under Cooperative Agreement CR-80043-01-0,

VOL II.

Hanemann, W. Michael. 1984b. “Discrete/Continuous Models of Consumer

Demand.” Econometrics 52:541-61.

Hanemann, W. M . 1978. “A Methodological and Empirical Study of Recreation

Benefits from Water Quality Improvement.” Ph. D. dissertation,

Department of Economics, Harvard University.

Hanemann, W. M. 1984. “Welfare Evaluations with Market Data.” American

Journal of Agricultural Economics. 66:332-41.

Hanemann, W. M. 1984. “Welfare Evaluation in Contingent Valuation

Experiment with Discrete Responses." American Journal of Agricultural

Economics. VOL 66(3):332-341.

Harmon, R. J. and Associates. 1883. Recreational Resources at Risk. Taak 2

Working Paper. Washington, DC.

Harrison, D. and D. Rubinfeld. 1878. “Hedonic Housing Prices and the Demand

for Clean Air.

w

Journal of Environmental Economics and Management

5:81-102.

Hausman, J.A. 1886. “Exact Consumer's Surplus and Deadweight Loss.”

American Economic Review 71:662-76.

113
Heintz, H.T. Jr., A. Hershaft and G.C.

and Water Pollution. A Report to

MD.

Hiett, R. L. and J. W. Worrall. 1977.

Horak. 1976. National Damages of Air

the EPA. Enviro Control, Inc. Rockville,

“Marine Recreational Fisheries Ability to

Estimate Catch and to Recall Catch and Effort over Time.” Human

Sciences Research, Inc., Report HSR-RR-77/18, Washington, D.C.

Horowitz, Joel. 1983. “Statistical Comparison of Non-nested Probabilistic

Discrete Choice Models. ” Transportation Science 17:319-50.

Hotelling, H. 1948. Letter to the National Park Service in “Economics of

Outdoor Recreation” - The Prewitt Report.

Huppert, D.D. 1983. NMFS Guideline. on Economic Valuation of Marine

Recreational Fishing. NOAA Technical Memorandum

NOAA-TM-NMFS-SWFC-32, Department of

Johnston, J. 1972. Econometric Methods.

York.

Just, Richard, Darrell Hueth and Andrew

Commerce, Washington, D.C.

Second Edition McGraw-Hill, New

Schmits. 1982. Applied Welfare

ECO1’10miCS and Public Policy. Englewood Cliffs,N.J. Prentice-Hall Inc.

Just, R E., and D. L. Hueth. 1979. “Multimarket Welfare Measurement."

American Economic Review 69947-64.

Kealy, Mary Jo, and Richard C. Bishop. 1983. “Theoretical and Empirical

Specification Issues in Travel Cost Demand Studies.w Unpublished.

Kendall, M. and Alan Stuart. 1977. The Advanced Theory of Statistics. 4th

Edition, (Vol. 1), New York: MacMillan.

Kling, Catherine. 1986. ‘A Simulation Approach to Comparing Multiple Site

Recreation Demand Model. Using Chesapeake Bay Survey Data.” Paper

presented at the Conference on the Economics of the Chesapeake Bay

Management II, Annapolis, MD.

Kling, Catherine, Nancy Bockstael and Ivar Strand. 1985. “An Evaluation of

Multiple Site Model Development.” Paper presented at the AEA meetings,

New York, N.Y.

Kling, Catherine. 1886. Benefits of En

Dissertation, University of Maryland.

Krutilla, J. V. 1967. “Conservation Revisited.” American Economic Review.

57( Sept. ):777-86

Lewbel, Arthur. 1975. “A Unified Approach to Incorporating Demographic or

Other Effects into Demand Systems.” Review of Economic Studies 42.

114
Loomis, J. 1986. “An Examination of the Variation in Empirical Estimates of

Site Specific Marginal Values for Recreational Fishermen.” Working paper,

Division of Environmental Studies, University of California, Davis.

Maddala, G. S. 1983. Limited Dependent and Qualitative Variables in

Econometrics. Cambridge University Press New York.

Maddala, G. S. 1977. Econometric McGraw-Hill, Inc., N. Y. 516 pp.

Maler, Karl-Goran. 1974. Environmental Economics: A Theoretical Inq uiry,

Baltimore, Johns Hopkins University Press for Resources for the Future.

Mathews, S. B. and G. S. Brown. 1970. “Economic Evaluation of the 1967

Sport Salmon Fisheries of Washington*” Washington Department of

Fisheries, Technical Report #2.

McConnell, K. E. and N. E. Bockstael. 1984. “Aggregation in Recreation

Economics: Issues of Estimation and Benefit Measurement.” Northeastern

Journal of Agricultural and Resource Economics 13:181-186.

McConnell, K. E. and I. E. Strand. 1981. ‘Measuring the Cost of Time in

Recreation Demand Analysis.” American Journal of Agricultural Economics

63( Feb):153-156.

McConnellt K. B. 1986. Identification of preference. in Hedonic Models.

Report presented to Environmental Protection Agency under Cooperative

Agreement CR-811043-O1-O, Vol I.

McConnell, K. E. 1879. “Values of Marine Recreational Fishing: Measurement

and Impact of Measurement." American Journal of Agricultural Economics

61(5):921-923.

McConnellr K. E. and T. P. Smith. 1979. “Marine Recreational Fishing in Rhode

Island,” University of Rhode Island Marine Memorandum #62.

McFadden, Daniel.

Behavior.” in

McFadden, D.

In Karlquist,

Theory and

company.

1874. “Conditional Logit Analysis of Qualitative Choice

Frontiers in Econometrics. Edited by P. Zarembka.

1978. “Modelling the Choice of Residential Location. ”

Lundquist, Snickars, and Weibull (eds) Spatial Interaction

Planning Models. New York North-Holland Publishing

Mendelsohn, Robert. 1984. “Estimating the Structural Equations of Implicit

Markets and Household Production Functions.” Review of Economics and

Statistics 66:673-77.

Meyer, P. A. 1982. Net Economic Values for Salmon and Steelhead from the

Columbia River Sustem. NOM Technical Memorandum NMFS F/NWR-3, U.

S. Department of Commerce, Washington, D.C.

115
Miller, Jon. 1982. "Valuation Methods for Fish and Wildlife Associated

Recreation: An Empirical Comparison of the Travel Coat and Contingent

Valuation Method-.” Paper presented at the ASSA meetings, New York,

N.Y.

Mitchell, R C. and Richard T. Carson. 1984. “National Fresh Water Quality

Benefits: Findings from a New National Contingent Valuation Survey.”

Presented at the annual AERE meeting, Cornell University.

Morey, Edward. 1981. “The Demand for Site-Specific Recreational Activities: A

Charactaristics Approach. ” Journal of Environmental Economics and

Management 8:345-71.

Morey, Edward. 1984a. “Characteristics, Consumer Surplus and New Activities:

A Proposed Ski Area.” Paper presented at the ASSA meetings, Dallas,

TX.

Morey, Edward. 1984b. “The Choice of Ski Areas: Estimation of a Generalized

CBS Preference Ordering with Characteristics.” Review of Economics and

Statistics 66:584-90.

Muellbauer, J. “Household Composition, Engel Curvea and Welfare Comparisons

Between Households A Duality Approach.” European

5(1974):103-22.

Economic Review

Norton, V. J., T. P. Smith, and I. E. Strand. 1889. Stripers: The Economic

Value of the Atlantic Coast Commercial and Recreational Striped Bass

Fisheries. Maryland Sea Grant Publication #UM-SG-TS-83-12.

Pacific Fisheries Management Council. 1886. Review of 1985 Ocean Salmon

Fisheries. Portland, Oregon.

Parks, RS. 1969, "Systems of Demand Equations: An Empirical Comparison of

Alternative Functional Forma.” Econometrics 37:629-65

Pesaran, M.H. and A.S. Deaton. 1978. “Testing Non-nested Non-linear

Regression Models.” Econometricca 46:677-94.

Pindyck, Robert and Daniel Rubinfeld. 1981. Econometric Models and Economic

Forecasts. New York, McGraw-Hill Book Company.

Pow, Robert. 1971. “Additive Utility Functions and Linear Engel Curves. ”

Studies 38401-14.

Pollak, Robert and Terence Wales. 19790 “Estimation of the Linear

Expenditure S ystem." Econometrics 37:611-29.

Pollak, Robert and Terence Wales. 1881. "Demographic Variables in Demand

Analysis." Econometrics 4*1533-51.

116
Reiling, S. D., K.C. Gibbs and H.H. Stoevner. 1973. Economic Benefits from an

Improvement in Water Quality. Washington,D.C., U.S. EPA.

Ribaudo, Marc O. and Donald J. Epp. 1983. “The Importance of Sample

Discrimination in Using the Travel Cost Method to Estimate the Benefits of

Improved Water Quality.” Paper presented at the annual AAEA meeting,

Purdue, Indiana.

Rosen, Sherwin. 1974. “Hedonic Prices and Implicit Markets: Product

Differentiation in Pure Competition.” Journal of Political Economy

82:34-55.

Rowe, Robert, Edward Morey, Arthur Ross and W. Douglass Shaw. 1985.

Valuing Marine Recreation Fishing on the Pacific Coast. Energy and

Resurce Consultants, Report prepared for National Marine Fisheries

Service, NOAA.

Russell, C. S., and W. A. Vaughan. 1982. “The National Recreational Fishing

Benefits of Water Pollution Control.” Journal of Environmental Economics

and Management 9:328-354.

Samples, K. C., and R. C. Bishop. (Undated). “Estimating Social Value. of

Sport Caught Fish A Suggested Approach.” Draft, 12 pps.

Samuelson, Paul A. 1947. “Some Implications of Linearity,” Review of

Economic Studies 15:88-90.

Saxonhouse, G.R. 1977. "Regressions from Samples Having Different

Characteri-tics.” Review of Economics and Statistics 59294-7.

Schulse, W. D., D. S. Brookshire, B. G. Walther, M. K. McFarland, M. A. Thayer,

R. L. Whitworth, S. Ben-David, W. Maim, and J. Molenar. 19S3. “The

Economic Benefits of Preserving Visibility in the National Parklands of the

southwest.” Natural Resources Journal 23:149-173.

Sellar, Christine, John Stoll and Jean-Paul Chavas. 196s. “Validation of

Empirical Measures of Welfare Change: A Comparison of Nonmarket

Techniques. ” Land Economics 61:156-75.

Sellar, C., J. P. Chavas and J. R. Stoll. 1966. “Specification of the Logit

Model: The Case of Valuation of Nonmarket Goods.” Journal ‘of

Environmental Economics and Managment Vol. 13:382-390.

Smith, V. Kerry and William H. Deavousge.

Cost Model and Water Quality Benefits:

Economic Journal 62371-81.

1985. “The Generalized Travel

A Reconsideration.” Southern

Smith, V. K., W. H. Desvousges and Ann Fisher. 1986. “A Comparison of Direct

and Indirect Methods for Estimating Environmental Benefits.” Amer. J.

Agr. Econ. 68(2):280-290.

117
Smith, V. Kerry, William HO Desvousges and M.P. McGivney. 1983. "Estimating

Water Quality Benefits: An Econometric Analysis. ” Southern Economic

-50:422-37”

Smith, V. K., W. H. Desvousges and M. P. McGivney. 1983. “The Opportunity

Cost of Travel Time in Recreation Demand. Models.” Land Economics.

59:3:259-78.

Smith, V. Kerry and Yoshiaki Kaoru. 1985. “The Hedonic Travel Cast Model: A

View from the Trenches.” Mimeo.

Stevens, J. B. 1966. “Recreation Benefits from Water Pollution Control. ”

Water Resources Research 2:167-182.

Stevens, Joe B. 1866. "Angler Success as a Quality Determinant of Sport

Fishery Recreational Values. ” Transactions of the American Fisheries

Society. 95:357-362.

Stoevener, H. H. 1863. “An Economic Evaluation of Water Pollution Control

Alternatives: A Progress Report.” Water Resources and Economic

Development of the West, No. 12, Committee on the Economics of Water

Resources Development of the Western Agricultural Economic. Research

Council, Salt Lake City, 47-59.

Stoevener, H. H. 1864. “Water uee Relationships as Affected by Water Quality

on the Yaquina Bay.” New Horizons for Resources Research: Issues and

Methodology, Western Resources Conference, University of Colorado Press,

Boulder, 87-86.

Stone, J.R.M. 1854. The M easurement of Consumers' Expenditure and

Behaviour in the United Kingdom, 1920-1938, Vol L Cambridge University

Press, Cambridge.

Strand, I. E. and N. E. Bockstael. 1985. “The Effect of Common Sources of

Regression Error on Benefit Estimation.” Working paper, University of

Maryland.

Sutherland, R.J. 1881. “A Regional Approach to Estimating Recreation Benefits

of Improved Water Quality. " Journal of Environmental Economics and

Management 9:229-47.

Thayer, Mark. 1981. “Contingent Valuation Techniques for Assessing

Environmental Impacts: Further Evidence." Journal of Environmental

Economic● and Management 8:27-44.

Theil, Henry. 1976. Theory and Measurement of Consumer Demand. Vols 1 and

2. Amsterdam.

Tobin, James.

Variables.”

1985. “Estimation of Relationships for Limited Dependent

Econometrics 26:24-36.

118
U. S. EPA. 1983.

Regulation. and

U. S. Department of

Water Quality Standards Handbook. office of Water

Standards. Washington, DC.

Commerce. 1984. “Marine Recreational Fishery Statistics

Survey, Atlantic and Gulf Coasts, 1979 (Revised) - 1980, Current Fishery

Statistics 8322.” Washington, DC.

U, S. Department of the Interior. “1980 National Survey of

and Wildlife-Associated Recreation.” U. S. Fish and

Washington, DC. Undated.

Vaug ban, W. J. and C. S. Russell. 1982. “Valuing a

Application of a Systematic Varying Parameter Model.”

58:450-62.

Fishing, Hunting,

Wildlife Service.

Fishing Day: An

Land Economics

Vaughan, W. J. and C. S. Russell. 1982. Fresh Water Recretional Fishing: The

National Benefits of Water Pollution Control. Washington D. C.:RFF, Inc.

Vaughan, W. J., C. S. Russell, L. P. Gianessi and L. A. Nielsen. “Measuring and

Predicting Water Quality in Recreation Related Terms." J.” Env. Mgmt.

15:363-380.

Violette, Daniel. 188s. “A Model to Estimate the Economic Impacts on

Recreational Fishing in the Adirondacks from Current Levels of

Acidification.” Paper presented at the AERE Workshop on Recreation

Demand Modeling, Boulder, CO.

Wales, Terence J. 1971. “A Generalised Linear Expenditure Model of the

Demand for Non-Durable Goods in Canada.” Canadian Journal of Eonomics

4:471-84.

Wales, Terence and A. D. Woodland. 1883. “Estimation of Consumer Demand

S ystems with Binding Non-Negativity Constraints." Journal of

Econometrics 21:263-85.

Walsh, Richard G. Undated. ‘*An Extension of the Contingent Valuation

Approach to Estimate the Effect of Instream Flow on Recreational

Demand.” Unpublished.

Walsh, R G., D. A. Greenley, R. A. Young, J. R.McKean, and A. A. Pinto. 1978.

“Option Values and Recreational Benefits of Improved Water Quality: A

Case Study of the South Platte River Basin, Colorado.” Rep.

EPA-600/5-78-100. U. S. Environmental Protection Agency, Research

Triangle Park, N . C.

Walsh, R G., L. D. Sanders, and J. B. Loomis. 1985. Wild and Scenic River

Economics: Recreation Use and Preservation Values. American Wilderness

Alliance, Englewood, Colorado.

119
Wegge, Thomas, W. Michael Hanemann and Ivar Strand. 1985. “An Economic

Assessment of Marine Recreational Fishing in Southern California.”

Report submitted to National Marine Fisheries Service Southwest Regional

Office.

Wennergren, E. B. and D. B. Nielson. 1970. ‘ “Probability Estimates of

Recreation Demands.” Journal of Leisure Research 2:112-22.

Williams, J. B., T. P. Smith, M. J. Speir, and S. Early. 1983. “1980 Maryland

Saltwater Sport Fishing Survey. ” Maryland Department of Natural

Resources Tidewater Administration Report No. TA-CRD-83-1. Annapolis,

MD.

Willig, R.D. 1976. “Consumer’s Surplus without Apology.” American Economic

Review 66:589-97.

Willig, R. D. 1978. “Increment Consumer's Surplus and Hedonic Price

Adjustments." Journal of Economic Theory 17:227-253.

Wilman, Elizabeth A. 1884. External Costa of Coastal Beach Pollution.

Resources for the Future, Washington, DC.

Wilman, Elizabeth. 1985. “Valuing Quality Changes in Recreation Resources. w

Paper presented at the AERE Workshop on Recreation Demand Modeling,

Boulder, CO.

Zellner, A . and Soo-Bin Park. 1879. “Minimum Expected Lose (MELO)

Estimators for Functions of Parameters and Structural Coeffiecients of

Econometric Models.” Journal of the American Statistical Association.

74:365:185-193.

120
Appendix A

The Random Digit Dialing Survey Telephone Procedures

For the Random Digit Dialing Survey, three instruments (copies included in

the Appendix) were developed: a two-page screening form, an 1 l-page,

28-item questionnaire, and a Record of Calls sheet.

1. Screening Form

The screening form was intended to determine the eligibility of the

location served by a randomly generated telephone number. The number was

printed on a label affixed to the top of the screening form. Pay phones and

phones used only by businesses were not considered eligible, since people

answering ouch telephones would be eligible at their residence phone.. In

addition, if the household served by the phone waa not located within the

counties/cities making up the selected SMSA’s then that phone (residence) was

not eligible. Once an eligible phone (residence) waa identified, a member of

the household who waa 18 years of age or older waa required. If all

residents were under 18, the screening waa completed with ● member of the

household who waa 14 or older.

The screening form waa composed of five sections: an Identification

sections consisting of an area code, telephone number, and five-digit case

identification number, all printed on the aforementioned label; ● brief

introduction to be read by the interviewer which explained the study; a

screening section which waa used to eliminate pay phones, businesses without

living accommodations. and residences not located in certain specific SMSA's; a

screening status section to record the screening eligibility of the location;

a questionnaire status section to record whether or not a questionnaire

completed with an eligible person.

2. Questionnaire

The Random Digit Dialing Survey Questionnaire was intended to determine

the following:

Uae or intent to use the Chesapeake Bay for recreation during 1984;

Reasons for nonuse;

Activities that the respondent (and his/her family) participated in

while visiting beaches;

Reasons the respondent or other members of his/her family do not go

in the water during visits to the western shore beaches;

Changes in swimming participation in the Chesapeake brought about

by change. in the water quality;

The respondent’s perception of the water quality in the

The value respondents place on the Bay and how they

improvement should be made and financed.

Chesapeake;

visualize that

121
As in the User Intercept Survey, a series of demographic questions

which will enable analysts to establish profiles of beach users and nonuser

were included in the questionnaire.

The Random Digit Dialing Survey Questionnaire waa also divided into

sections. The first, Identification, had space provided for recording the case

ID number from the screening forms the telephone interviewer’s initials, and

the date the interview was completed. The second section, as in the User

Intercept Survey, was a lengthier introduction to be read by the telephone

interviewer, which went into greater detail regarding the purpose of the

survey and contained statement informing the respondent of the voluntary

nature of his/her participation in the study and assurances of the

confidentiality of the data collected. The third section, Recreational Uae of

the Chesapeake Bay, sought specific responses which would: (1) enable

analysts to determine if and how the beaches were used and (2) what the

overall perception of the water quality waa. Thie waa followed by a fourth

and final section consisting of some 18 demographic questions.

Data Collection Methods

Two field interviewers were trained in Baltimore for the data collection of

the Ueer Intercept Survey on May 25, 1884. A Field Interviewer Manual was

developed (which is available upon request) and included quemtion-by-question

specification, probing techniques, confidentiality procedures, refusal

conversion strategies, and other measure● necessary to assure the collection

of standardized, quality data during the course of the field survey process.

Alao covered in the manual were: background information, assignment

information, sampling procedures and administrative procedures.

The final day of field work on the ueer survey was August 16, 1984. The

confirmation portion of the user survey was completed on September 1. The

following represents the response rates for the field work:

Table A.1

Response Rates for Beach User Survey

Sample Successfully Eligible Eligible Individuals

Individual screened Individuals Interviewed

463 414 408

( 100%) (98.79%) ( 100%) (88.55%)

122
Of the 468 individuals screened, 60 were not administered questionnaires for

the folowing reasons:

Ineligible because of residence . . . . . . . . . . . 39

Refused screening . . . . . . . . . . . . . . . ...7

Language barrier-screening . . . . . . . . . . . . 6

Other . . . . . . . . . . . . . . . . . . . . . ...2

Refused questionnaire . . . . . . . . . . . . . ...3

Language -barrier - questionnaire . . . . . . . . . . 3

Regarding the confirmation portion of the ueer survey, 340 of the people

interviewed gave telephone numbers or come other piece of information

through which contact could be made to conduct a confirmation/intention

interview. Approximately 240 (71 percent) of these individuals were

successfully contacted during the time period allowed.

Training of telephone interviewers for the Random Digit Dialing Survey

started on July 23. A total of 11 telephone interviewer. were hired with three

of these spending the majority of their time making confirmation/intention

calls to participants in the User Intercept Survey.

As in the User Intercept Surveyt each interviewer received a copy of a

Telephone Interviewer Manual specifically developed for this phase of the

project, as well as copies of the Random Digit Dialing instruments. The

Telephone Interviewer Manual (available upon request) included question-byquestion specification, probing techniques, confidentiality porcedures, refusal

conversion ● trategiea, and other measures necessary to assure the collection

of standardized, quality data during the course of the telephone ● nvey

process.

Approximately 192 telephone interview. were completed with western shore

beach users. The remainder consisted of approximately 804 nonusers and 48

intended users. The following two tables represent questionnaire completions

per strata and final totals for screening and questionnaire status codes.

123
Table A.2

Questionnaire Completions Per Strata

Stratum Cases Cases Quest. Quest. Tota l Quest .

Number Avail. Assigned Complete Partia l Quest. Needed Diff.

1 1,230 1,060 155 10 165 138 + 27

2 1,100 1,000 225 7 232 220 + 12

3 408 408 70 0 70 “ 77 - 7

4 1,014 1,014 96 4 100 112 - 12

5 820 820 171 6 177 158 + 19

6 1,560 1,560 2 9 3 7 300 295 + 5_

Totals 6,132 5,962 1,010 34 1,044 1,000 -44

Table A.3

Final Telephone Result Totals for Screening and Questionnaire Status Codes

Eligible Identified/Screener Completed . . . . . . . . . . . . Nota Working Telephone Number . . . . . . . . . . . . . . . .

Pay Telephone . . . . . . . . . . . . . . . . . . . . . . . .

Business Telephone . . . . . . . . . . . . . . . . . . . . . .

No Answer After Repeated Calls . . . . . . . . . . . . . . . . Telephone Not Located in Bait./Wash. SMSA . . . . . . . . . . No Eligible Respondent Available After Repeated Calls . . . . Refused to Answer Screening Questions . . . . . . . . . . . . Language Barrier O.... . . . . . . . . . . . . . . . . . .

Other . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Questionnaire Status Codes

Questionnaire Completed . . . . . . . . . . . . . . . . . . .

Questionnaire Partially Completed . . . . . . . . . . . . . .

Language Barrier . ...’ . . . . . . . . . . . . . . . . . . . 0

Questionnaire Refused . . . . . . . . . . . . . . . . . . . .

Other . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1,108

2,866

13

843

11

10

203

9

3

1,010

34

63

1

124
Appendix B

Telephone Survey instrument

125
t

A. IDENTIFICATION

TsuPllGNs NUMBER (_)--- CASE ID NUMBER I I I

B. INTRODUCTION

I m callia- frsa the Rescmch

conduct ● telephone ltl~e~ ● bout the

fiad out if I’ve contacted tho proper

First,

Triansls Institute near Raleigh, North Carolioe to

Chesapeake Soy for the UmivcrSaty of ~ryl~ad. TO

typo of place, X need to s$k ● few siaple questions.

c. SCRESNIlfG

1. Is this talcphona amber (RSAD ~ 7-DIGIT NUQZR ASOVS) ~a ● rea code

(READ 3-DIGIT AR&Acom mm)?

Nom (RANG UP AND DIAL NUPfSSR AGAIN)

Yes ~

2. Is this ● pay phoao?

3. ● .

b.

e.

d.

4. ● .

b.

s. ● .

b.

so~

78s ~ (GGT06)

Is this talopkoae locstod in ● privsto rcsidogce or s buoiaoss?

?rivato roaidoue m (GDT060)

Duoiaoss m

Aro thoro ● y livias scctittoso ● t this pl~c. of buoiaoss?

No ~ (GDm6)

T*S m

Do tho people living there us. this phoao for their calls?

No ~ (GOTG6)

Y*S E

Whoa caa I speak to oso of tko POOP1* who lIVOS ● t this buxiaoss

locstioa ond uses this tolcpkoas for parsoaol calls?

AN* :

(CALL-u NSCSS2ABYT0 conPuTs scRtsNING)

ACO you ● -r of * hwoobdd somicod by tkis tolophono?

~m

T*S El (m TO So )

-will ● aabor of *ho-hold bo ● ilable to talk to a?

AllsuR:

(CALL SACKIFZcsssAsY To CO?IPLSTS SCRSESING)

Io tkis rosidoace located or B- Otkor place?

Horylaad

Virginia

Wasbiagton, DC

SW othor plscc

ia Horylaad, Virginia, Washiattoa, DC

~ (@TGb)

El (GOTOb)

~ (GOT07)

~ (OOT06)

Io whet cousty is this residmco locsted?

NmER : (RSCORDMWERAND CODS SSLW

HARYLAND

(AOM Audel, hltiaorr includin~ cxty, Carroll,

Charles,Hsrford, Howard, fioat~onory,

Prince Goorses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ig (GOTO 7)

VIRGINIA

(Arlio@on, Fairfsx, Loudoa, ● od Prisce Uillias.

Also iocludo Alexmdri~ cicY, Fairfw city sad

~~lla Church city . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ~ (@To7)

(1)

(2)

(s)

(4)

(s)

(6)

(?)
6.

7.

1’s sorry but we ~ro not intorostod in talkiat to people (at Pay te18phoaes/at

business telephones/in the ● rea vhere YOU live). Tbsnk You for ● svermg ay

questioas ● nd couAd I hove your 0000 in case my supervisor WJOCS co check oy

work?

IIAla (CmLSTS SCUSNING STATUS IN PART D ONLY)

● ✎ ~is telephone is located ● t s resideace in ● ● rea where us ● re interested

ia talkin-s to people. Are you under 18 years of ● g e

● ge?

uader 18 l= (GOTOb)

over 18 ~ (GOTOC)

b. Whoa will I be ● bLo to talk to someoae over 10 years

or over 18 yesrs of

of ● ge who lives ia

this household? ANSUER :

(CALL MCX IF NSCSSSARY TO VSRIFT ANSUSRS AND COWLS= QUEST IONNAIM. IF

tiRSSIDSNTSARSUNDSR 18PROCEEDWTNANTRiSIDSNT l~~-o~.)

c . Hew moay tolephoaee with differeat atmbors, aoc ● xtensions, service this

household? i-j (8-9)

d. HeY I hove Your w ia case- supervisor uaats to check my work?

(PROCSSD TO QUSSTIONWAIRS SW SMJRII

m AND DE SCRSSNING STATUS m QmIONMIRS STATUS -WESTI-AI~

ADflINISl%TIONJ

D. SCRESMING STA~ COWS

Eligible IdestifiedtScroeaor C~letod ,..**...*...*****.***...m

Hot aUorkimS Telophoae H*r . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . m

Pay Telopboae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ~

Business Telopheao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . m

llo Aaour After Sopooted Cello . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . m

Telephoae Sot Located IaBolt./Vash. S.IY.S.A. . . . . . . . . . . . . . . . . . ~

No Xli#blo Roepeadout Amilablo After Ropo#tod Coils . . . . . . . ..~

Rofuoed To Anowr Scrooaiag Quostioao , . . . . . . . . . . . . . . . . . . . . . . . . m

Lsasuogo Sorrior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . m

Oth*r . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . m l

E. gUSSTIONIIAIRI STATUS CoDss

Quostie-oire~lotod (NoMil follomp) ....................m

Quostioaaoire Ceqleted Oloil follo-p) .......................E

QUOstioMOirO Partially CO@etd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . m

L4aguage Sorrier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..IK

Quostioaasire Refused . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I=

Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -. . . . . . ~

(lo-l:.

(1:-;:
CHESAEEW BAY TELEPHONE SURVEY QUESTIONNAIRE

Conducted by Research Triangle Institute

for the University of Maryland

A. IDENTIFICATION

CASE ID 11111 I

Interviewer Initials

Date Interview Completed

&‘1Date

B. INTRODUCTION

As I said ● arlier, researchers ● t the University of Maryland ● re currently studying citizens’ use of the Chesapeake Bay. I will ● sk you some

questions regarding your recreational use of the Chesapeake Bay, particularly ● t the beaches. I ● lso have to ● sk some questions which will

enable the researchers to ● stablish profiles of typical users ● nd nonusers of the Bay. There is no direct benefit from taking part in this

study ● nd you have the right to refuse to ● aswer ● ay or ● ll of the questions or discontinue your participation at aay time. The information

that you provide will be combiaed with that provided by other people who

participate in the survey to ● ssure complete coafideatiality ● nd your

same will aot be released or revealed to ● nyone other than authorized

project staff. The results of this survey may be helpful in ● ffectively

● llocating mosey to cleaniag up the Bay.
c. RECREATIONAL USE OF THE CHESAPEAKE BAY

1. Have you or ● ny members of your household used the Chesapeake Bay

for recreation in 1984?

No . . . . . .~ (m’fo Q. 2.)

Yes . . . . .~ (GO TO Q. 4.)

2. Do you or any members of your household intend to use the Chesapeake

Bay for recreation during the rest of 1984?

No. . . . . . ~ (GO TO Q.3. )

Yes . . . . .~ (GO TO Q.4)

3. What ● re the reasons you ● nd members of your household have not used

and do not intend to use the Chesapeake Bay for recreation during

1984? (CODE ALL RESPONSES GIVEN INTO-THE CA~-GORIES BELOW

CODE IF GIVEN

● ✎

b.

c.

d.

● ✎

f.

8“

h.

i.

(GO TOQ. 8.)

Not interested in water related recreation . . . . .

Unable for health reasons . . . . . . . . . . . . .

Coststoomuch. . . . . . . . . . . . . . . . . . .

Takes too mxh time to get there (too far to

travel). . . . . . . . . . . . . . . . . . . . . . .

Unacceptable water quality . . . . . . . . . . . . .

Toomanyjellyfish. . . . . . . . . . . . . . . . .

Too crowded . . . . . . . . . . . . . . . . . . . .

Have not had ● chance (too busy) . . . . . . . . . .

Other . . . . . . . . . . . . . . . . . . . . . . .
.

4. Whst ● ctivities did or will you (and/or members of your household)

participate in while using the Chesapeake? (READ EACH OF THE FOLLOWiNG~-I~ICATE PARTICIPATION FOR EACIi AcTIVIm.)

NO

a. Fishing. . . . . . . . . . . . . . . . . . 0 . s o ~1

b. Swinming . . . . . . . . . . . . . . . . . “ “.” IQJ

c. Boating. . . . . . . . . . . . . . . . .* ● o so “ El

—

d. Hunting. . . . . . . . . . . . . . . . ● ● “CO ● &l

—

● . Beach Activities . . . . . . . . s . . . c ● . ● “ ‘_!_!

f. Sightseeing. . . . . . . . . . . . . ● ● o= ● ● ● ~

g“ Other. . . . . . . . . . . . . . . . . .: . . . . m

5. This next question ● lso pertains to YOU and *Dbers Of

household. Durin$ 1984 did ● ny of you or will ● ny of you...

THE FOLLOWING. ) —

NO

● . Visit beaches. on the Eastern Shore of the

Chesapeake, for ● xample shores close to

Casbrid~e, Salisbury or Chestertown? . . . . . . . . m

b. Visit beaches on the ocean, such ● s

Ocean City? . . . . . . . . . . . . . . . .* ● ● .~

c. Go swirmning from ● boat in the Chesapeake? . . . . ~

d. Go swiamin$ in public or private swiming pools? . m

e. Visit beaches on the Western Shore of the

Chesapeake, for ● xample beaches near Baltimore,

Annapolis, Prince Frederick or Lexington Park? . . . ~

(IF YES TO PART e., ASK f, IF NO TO PART ● , GO TO QUESTION 8.)

f. Durin8 visits to Western Shore beaches did

or will ● nyone ● ttend but not so

inthewater foranyreason? . . . . . . . . . . . . 1a

(IF YES TO PART f, GO TO Q. 6. IF NO TO PART f, GO TO Q. 7.)

(;

(

(~

(

(

(

(

(

(

(

(

(

(
6. What ● re the reasons

visits to the Western

you or others

Shore beaches?

do not go in the water during

Do (READ EACH PART AND CODE NO OR YES.)

NO

a. You or they believe the water is

dirty/polluted. . . . . . . . . . . . . . . . . . m

b. You or they believe there ● re too many

jellyfish . . . . . . . . , . . . . . . . . . . . “m

c. You or they have some other reason . . . . . . . . m

7. Can you tell me which Western Shore beaches you (and your family)

have visited in 1984 or plan to visit during the rest of this year?

(CHECK NO OR YES FOR EACH BEACH LISTED. )

8.

b.

c.

d.

e.

f.

8“

h.

i.

j“

k.

1.

m.

n.

o.

?“

q“

r.

VISITED

BEACH YEs

Sandy PointSt. Park.... . . . . . . . . . . . .;

FortSezllwood. . . . . . . . . . . . . . . . . . . JxI

BsyBridgeBeach. . . . . . . . . . . . . . . . . .~

HerringtonHarbor . . . . . . . . . . . . . . ...l~

Kurtz Pleasure Beach... . . . . . . . . . . . . . 13

Campllerrick . . . . . . . . . . . . . . . . . . .~

Breezy Point Beach . . . . . . . . . . . . . . . . . . m

Chesapeake Beach. . . . . . . . . . . . . . . . . .=

North Beach . . . . . . . . . . . . . . . . . . . ● !XI

RodandReelDock . . . . . . . . . . . . . . . . . ~

Point LookoutSt. Park... . . . . . . . . . . . . Ig

Elm’sBesch . . . . . . . . . . . . . . . . . . . . .l~

lforgantownBeach. . . . . . . . . . . . . . . . . . l=

Hiami Beach (Baltimore) . . . . . . . . . . . . . . ~

RockyPointPark. . . . . . . . . . . . . . . . . .=

Conrad’sRuthVilla . . . . . . . . . . . . . . . . ~

PorterNewPark . . . . . . . . . . . . . . . . . .m

Other (SPECIFY)

m
8. Have you (or members of your family who live with your) ● ver changed

your swinsuing participation in the Chesapeake because of changes in

the Bay’s water quality?

No. . . . . . . El (GO TOQ. 9.) .

Yes (stopped). . ~

Yes (started). . ~

a. In what year did you (or members of your family) last change

your swimming habits in the Chesapeake because of changes in

the Bay’s

9. We would like

quality in the

water quality?

Year. . . . . .

to find out how

Chesapeake Bay.

(46

● ✎ Do you consider the water ● cceptable or unacceptable

● ctivities.

m (4

people currently perceive the water

quality in the Chesapeake to be

for swiminS ● rid/or other water

Acceptable . . . . . . . ~

Unacceptable . . . . . ● m

b. Do you believe the water quality varies ● t different beaches

● ions the Western Shore of the Chesapeake?

No. . . . . . . . . . . El (GO ?0 CHECKPOINT I.)

Yes . . . ;. . . . . . 2a

c. (IF YES, SAY:) In genersl, which statement best describes your

beliefs?

The water quality is better North of Annapolis . . ~ {:

The water quality is better South of Annapolis . . ~

INTERVIEWER CHECKPOINT I

REFER TO QUESTION 9.A. .

WAS m WA~R QuA.LI~ IN ~ CHESAPEm RATED AS UNACCEPTABLE?

No . . . . . . . . . ~ (00 TO Q. 11. )

Yes. . . . . . . . . ~ (GO TO CHECKPOINT TABLE.)

(4

(5
CHECKPOINT TABLE

ENTER ~ LAST DIGIT OF THE CASE ID NUMBER HEW.

CIRCLE AND

IF THE LAST DIGIT IN USE THIS AHOUNT IN

THE CASE ID NUMBER IS QUESTION 10

1 . . . . . . . . . . . . . . . . . . . . . . . .. $5.00

2. . . . . . . . . . . . . . . . . . . ● . . . . .$10.00

3. . . . . . . . . . . . . . . . . . . . . . . . .$15.00

4. . . . . . . . . . . . . . . . . . . . . . . . .$20.00

5. . . . . . . . . . . . . . . . . . . . . . . . .$25.00

6. . . . . . . . . . . . . . . . . . . . . . . . .$30.00

. $35.00 “

L ::: : : : : : :: : : : : : : : :: : : : : .$40.(30

9. . . . . . . . . . . ● . ● ● . ● . . . . . . . . .$45.00

0. . . . . ● . . . . . . . . . . . ● O ● , . . ● .$50.00

10. You indicated that in your opinion the water quslity in the Chesapeake is unacceptable for swimins”. Uo”Uld you be willing to pay

(AMOUNT FROH CP TABLE) in ● xtra state or federal taxes per year, if

the water quslity were improved so that you found it ● cceptable to

swiB in the Chesapeake?

No. . . .“. . . JzEi

Yes . . . . . . ● zl

Don’t know . . . . m

D. BACKGROUND INFORMATION

11. The next few questions ● re ● bout you ● nd your household. How many

of ● ach of the following types of people live in your household?

(READ EACH OF TJE FOLLOWING AND ENTER TIE NUMBER OF EACH TYPE. )

8. Adults (age 18 ● nd older) . . . . . . . . . . . m

b. Children between the ● ges of 14 ● nd 18 . . . . . I I

c . Children underageof14 . . . . . . . . . . . u

12. What best describes your status in the household?

a. Grandparent. . . . . . . . . . . . . . . . . . . l=

b. Psreat . . . . . . . . . . . . . . . . . . . . . ~

c. Child. . . . , . . . . . . . . . . . . . . . . “m

d. Other relative . . . . . . . . . . . . . . . . .~

e . I live ● lone or with unrelated individuals . . . l=
13. How many years have you (and your family) lived in either Maryland,

Virginia, or Washington, DC?

Number of years . . Ill

14. Do you or any other members of your household

INGj... ‘ -

● ✎

b.

c.

d.

‘e.

f.

15. Are

~

a boat?. . . . . . . . . . . . “ ‘z

a boat trailer? . . . . . . . . . ~

fishing tackle (rod, reel,

tackle box, etc.)? . . . . . . . T

a recreational vehicle (RV)?. . .~

● swiming pool? . . . . . . . .=

Other recreational items (SPECIFY)

you one of the principal wale ● arners in your household, ● wage

● arner but not the principal wa8e earner, or ● re you ● homemaker, ●

student or retired?

● . One of the principal wa~e ● arners in the family . . . . . .~

b. A wage ● arner but not the principal wage earner . . . . . . ~1

—

c. Homemaker. . . . . . . . . . . . . . . . . . . . . . . . . IQ

d. Retired. . . . . . . . . . . . . . . . . . . . . . . . . . Ig

● . Student. . . . . . . . . . . . . . . . . . . . . . . . . .~

f. Other (SPECIFY)

ITI

INTERVIEWER NOTE: ASX QUESTIONS 16 ~OUGH 19 IF CODE 1 OR 2 IS -D IN

QUESTION 15 OTHERWISE GO TO QUESTION 20.

16. How many hours do you usually work per week?

Hours . . . . . . IT

17. How many paid vacation days will you have ● ltogether in 1984, including those you’ve ● lready taken?

(l- 2

(3-4

(5-6

,-, (:)

(~-.l

/- - Vacation Days . . ‘rl ,-. -
18. For ● typical recreational outing, if you did not go, could you work

● t some ptyins job instead?

No . . . . . . . ~

Yes. . . . . . . ~

19. If you could have worked, what hourly wage might you have been paid

specifically for the hours you worked?

a.

b.

c.

d.

● .

f.

8“

h.

i.

J*

20. Are

$3.35/hour . . . . . . . . . ~

$3.36 - $5.00/hour . . . . . ~

$5.00-$7.50 . . . . . ..m

$7.50 - $10.00/hour . . . ● m

$10.00- $15.00/hour . . . .~

$15.00 - $20.00/hour . . . . ,= “

$20.00 - $2S.00/hour . . . . ‘~

Over $2S.00/hour . . . . . . ~

Don’tkmow. . . . . . . . . m .

Refuse. . . . . . . . . . .~

there ● ny (other) major wage ● arners in your family?

No . . . . . . . ~(W TOQ. 25. )

Yes . . . . . . . ml

The next few questions ● re ● bout the other major wage ● arner.

21.

22.

23.

How

Hw

For

many hours does he/she usually work per week?

Hours . . . . . . II

(

(

/

may paid vacation days will he/she have ● ltogether in 1984?

Vacation Days . . I !

the typical recreational outing, if he/she did not go could

he/she work-at some paying job inste~d?

No . . . . . . .~

Yes’.......!=
24. If &e/she could have worked, what hourly rate would he/she have been

paid specifically for the hours worked?

● ✎

b.

c.

d.

e.

f.

8“

h.

i.

jt

$3.35/hour . . . . . . ...&

—

$3.36 - $5.00/hour . . . . .=

$5.00 - $7.50 . . . . . . m

$7.50 - $10.00/hour . . . .=

$10.00 - $15.00/hour . . . .=

$15.00 - $20.00/hour . . . . ~

$20.00 - $25.00/hour . . . . ~

Over $25.00/hour . . . . . ● m

Don’tknow . . . . . . . . . 1~

Refuse. . . . . . . . . . . IT}

25. We need ● n ● stimate of your household’s income for ● ll of. 1984. I

will read ● series of income categories. Please stop BS when I read

the cate80ry which best describes the total smouat of income ● ll

members of your household will receive during 1984.

INTERVIEWER ~CICPOINT II

ENTER TNELAST DIGIT OF TIE CASE ID NUPIBERIERE.

IF TIE LAST DIGIT START READING THE

IS ANSUER CHOICES AT

1,3, s,7,9 . . . . . . . . . . . . . . . . . . . . . .a.les~h;c~oo

2,4, 6,8,0 . . . . . . . . . . . . . . . . . . . . . . l.over$loo,ooo

AND DESCEND

● ✎ less then $5,000 . . . ~, g. $50,000 to $60,000 . . ~

b. $S,ooo to $10,000 . .~ h. $60,000 to $70,000 . ● m

c. $10,000 to $20,000 . . ~i i. $70,000 to $80,000 , .~

d. $20,000 to $30,000 . . ~ j. $80,000 to $90,000 . . != -

● . $30,000 to $40,000 . . ~ k.’ $90,000 to $100,000. . ~

f. $40,000 to $50,000 . . ~ 1. Over $100,000 . . . . ,ZL

Don’tknow . . . . . . . ~

Refused . . . . . . . .=
26. CODE SEX

female or

BASED

male?

Female . .

Hale . . .

ON NAHE, PREVIOUS ANSfiRs/MS OR ‘SK: ‘

re 

you

. . . I l l

27. Which racial group

White . . .

Black . . .

Oriental .

.

.

.

do you identify

. .

. .

. .

.

.

.

.

.

●

.

.

.

.

.

.

.

.

.

.

.

.

.

.

●

.

.

.

.

.

.

.

.

.

Other (SPECI~)

.

.

●

.

.

.

Refused. . . . . . . . . . . . . . ● ● “

Don’tknow . . . . . . . . . . . . . . .

~COLS. 28-73 = blank ~

i COLS. 74-80 = CASE 02i
.

28. ● . This is the last question. We would like to send short questionnaires about the Chesapeake Bay to people through the mail.

We would include ● postage paid ● nvelope to return the completed

questionnaire, so it would not cost anything to mail it back to

us.

Would you be willing to receive and complete such a

questionnaire?

No. . . . . .~ (GO TO C.)

Yes . . . . .~ (GO TO b.)

b. What is your mailing ● ddress?

(VERIFY NAME)

(P.O. Box/Street number ● nd name)

City State Zip

ENTZRCASE ID NUMBER

lit I I I

c. Thank you for takin~ time to ● nswer our questions. Your responses will be very helpful in determining the status of swiuming

● nd other ● ctivities on the Chesapeake Bay.

IF YES TO 28a. ALSO SAY: When the questionnaire comes throu8h

the mail, please c~lete ● nd return it ● s quickly ● s possible.
Appendix C

The User Survey and Sampling

This section is devoted to a description of the sampling procedures used

in a survey of Chesapeake Beach Use conducted in 1984. Unlike the data

used in analyses of boating and fishing in Chapters 5 and 6, the data used in

this chapter were collected during an earlier budget period of this cooperative

agreement. Great care was taken with the sampling frame to improve

confidence in the results. Because the survey itself is important to the

project, we describe the content and procedures extensively. Copies of the

survey instruments can be found in Appendix C.

From May 26, 1984 to August 19, 1984, Research Triangle Institute (RTI)

interviewed individual on the western shore beaches in Maryland. The study

population consisted of all residents of

SMSA's, age 14 or older, that used these

specifically, the population waa limited to

beaches:

the Baltimore and Washington, D. C.,

beaches for recreation in 1984. More

recreational users of the following 12

10

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

sandy Point

Point Lookout

Fort Smallwood

Miami

Rocky Point

Elm's Beach

Bay Ridge

Kurtz

Breezy Point

Rod & Reel

Morgantown

North Beech

Strata

Geographic Size

north.

south

north

north

north

south

south

north

south

south

south

south

large

large

small

small

small

small

large

small

small

small

ml 1

Oman

Four hundred and eight individuals were interviewed at the beach to

learn of their recreational patterns and perception of water quality at these

beaches. These individual were randomly selected from sample beaches and

days. The sampling design can be described as a two-stage stratified sample

in which a probability sample of beaches and days was

systematic sample of persons was interviewed at each

combination).

selected, and a random

sample site (day-beach

i The discussion of the sample is composed of selected excerpts from Devore,

McDonald, Myers and Williams, Chesapeake Bay Beach Use Survey, Research

Triangle Institute, 1984.

139
A sample of at least 300 completed interviews was intended so that relative

sampling errors would be approximately 11 percent or less for estimating

proportions of .30 or larger. This assumes that a design effect of

approximately 1.5 might result because of clustering (a beach-day unit

constitutes a cluster of individuals) and unequal. weighting. A self-weighting

sample was sought but difficult to obtain because of errors in size measures

(projected number of users for a beach-day unit).

The number of sample primary sampling units (PSU) waa baaed on the

requirement to obtain at least 300 completed interview. such that each PS U

would involve approximately one-half day of interviewing for a two-person

team. To match this design, each PSU was randomly designated a. a morning

or afternoon interview period, beginning at 1000 and 1300 hours, respectively.

Hence, an average of eight or nine completed interview. waa expected for each

of the 36 PSU’s (see Table 4.1 for sample allocation).

Stratification and Sample Selection

Primary sampling unit. (PSU’s) consisted of beach-day units (1,204 PSU’s = 66 days x 14 locations). The 14 locations consisted of 11 beaches plus three

beach locations at Sandy Point — partititioned into three segments because of

its relatively large size and usage. Beaches were stratified into north (Sand y

Point and beaches north) and couth (the prior listing of beaches indicates the

stratification). The beaches wore further stratified by size (expected weekly

usage). Large is defined as greater than 7,000 visitors per week in north

beaches and greater than 3,000 in the south beaches. Additionally, days were

stratified into weekdays, Saturdays, and Sundays (and holidays). The strata,

their population and sample PSU counts are indicated in Table Cl.

Table C. 1

Population Counts and PSU Allocation by Stratums

Anticipated Number of Beach Number of PSU's (beach-day

Visitors May 26 to units) Allocated to Bach

August 19, 1964 (thousands) Stratus

Sat Sun Weekday Total Sat Sun Weekday Total

North/large 77 110 106 293 3 5 5 13

North/small 676 4 94 243 3a 42 4 13

south/large 37 43 44 124 2 2 22 6

South/small 20 24 17 61 22 2 2 6

Totals 201 261 259 721 10 13 13 36

1 Population counts are baaed on cite interviews prior to the survey, and PSU 

allocation is proportional to number of visitors with the constraint that

greater or equal to two per cell are selected.

2One fewer resulted in these cells because of random subsampling needed in

the latter part of the survey period. (A slightly larger number of

interviews than expected were obtained early in the survey.)

140
The selection of PSU’s within strata involved equal probability y for days

and probability proportional to size (expected number of visitors) for beaches.

Within selected PSU’s, individual were selected on site with equal

probabilities. Approximately eight or nine interviews were needed from each

PSU. The procedure used to satisfy this ob jective waa for the interviewers to

estimate the number of beach users just prior to starting time for

interviewing. By comparing this estimated count to a table prepared

specifically for this survey, the interviewers obtained a sampling interval and

a random starting point. By using the interval and starting point in a

pro-designated pattern for the entire site, a valid systematic sample of users

was obtained.

Sampling Weights

Whenever observation units (in this caae the individual users) enter the

sample with different probabilities, weights must be used with the

observation to obtain unbiased estimates for the study population. Because

of the complexities in an intercept-type survey, selection probabilities are

often not known. In the present survey, however, PSU selection probabilities

are known, and final-stage selection of individual can be reasonably estimated

(even though a systematic sample of visitors at the site is taken), their

chance of selection will vary with the amount of time spent at the beach

during the sample day as well as number of visits to this and other stud y

beaches during the survey period. A sampling weight for the jth individual

is calculated aa follows:

where

Pi

d

n

s,

Pi j

fi j

and

f; j

1 ‘s L the "selection probability" (expected number of hits) d ‘

+ ‘ for PSU (i);

the number of daya during the survey period for the particular

type of day being sampled (d = 13 for Saturdays, for example);

number of PSU’s being selected for the stratum (12 strata);

size measure for PSU (i) = the expected number of visitors for

that type of day (S+ = Zsf);

sampling rate of users within sample PSU (i); and

factors to adjust for number of houra user spent at the beach

on the sample day and number of trips to the study beaches

during the summer, respectively.

141
Note that fij and fij are baaed in part on intentions. In fact, f; j is

particularly uncertain if the interview is being conducted early in the season.

TO verify the accuracy of these intentions both to obtain selection multiplicity

and to estimate related statistics, intention-based questions were verified b y

telephone at the end of the season for interviews taken early in the survey.

Screening and Confirmation

A screening form waa designed to identify as eligible respondents only

those people living in certain counties of Virginia or Maryland, or Washington,

Do C. In addition, the screening process ruled out as ineligible any person

who was under 14 years of age; however, if the selected sample individual was

over 14 but less than 18 years of age and in the company of someone he/she

lived with who was over 18 years of age, the interviewer deferred to the

older individual.

The User Intercept Survey Questionnaire was designed to record and

collect the following:

* Frequency of visits made to beaches on the western shore of the

Chesapeake

● Activities that the respondent (and his/her family) participated in

when visiting beaches

* Activities not participated in and the reason why they were n ot

. Coat related to a typical trip to each beach that the respondent had

visited since January 1, 1984

● The respondent’s perception of the quality of the beach and the

beach facilities ● t each beach wtth which he/she was familiar

. Factors that influence a respondent’s decision to visit or not visit a

beach

. The respondent’s willingness to continue to viait the sample site if

coots related to the use of the beach were to rise.

In addition, a series of demographic questions to enable analysts to

establish profiles of beach users were also included in the questionnaire.

A Confirmation/Intention Contact sheet (used in conjunction wtth a Record

of Calls sheet) was developed not only to confirm the number of visits to

sample beaches reported in the Intercept Survey Questionnaire, but also to

ascertain the number of visits the respondents intended to make at any

sample beach for the rest of 1984. The confirmation/intention contacts were

only made with those respondents who had provided adequate information to

contact them by telephone.

142
Data Collection Procedures

The data collection schedule originally called for a total of 36 visits to the

12 sample beaches, with an expected yield of 300 or more completed interviews.

This schedule was later revised and the subsequent number of total visits wae

reduced. Table C.2 shows the original data collection schedule. Those

sampling days which were eliminated have been denoted by an asterisk. The

resulting number of visits are summarized as follows (AM indicates

interviewing beginning in the morning at 10 o’clock, and PM indicates

interviewing beginning at 1 o’clock in the afternoon):

Beach Beech AM PM Total

er Name Visits Vimits Visits

1 Sandy Point 5 6 11

2 Point Lookout 1 1 2

3 Fort Smallwood 1 0 1

4 Miami o 3 3

6 Elms o 0 0

7 Bay Ridge o 3 3

8 Kurtz Pleasure Beach o 1 1

9 Breezy Point o 1 1

10 Rod and Reel Dock 1 0 1

11 Morgantown 1 1 2

12 North Beach 1 0 1

TOTALS 12 18 30

For the User Intercept Survey, each field interviewer waa given a

Uatin# of the names, addresses, and ID numbers for each PSU (beach) in the

assignment, along with area maps with the beaches marked. In addition, each

interviewer wae given a sketch of each beach. Other materials included were

the table. to determine sampling rates and listing forma for counting and

selecting sample individuals. The interviewers always worked as a team,

splitting up only to interview eligible respondents.

The field interviewers were asked to review their materials and

determine the moat efficient route of travel to reach each beach. Upon arrival

at a beach, they had first to check that they had correctly identified and

located the precise boundaries of the area. Once this waa verified, the

interviewers estimated the number of sample individual on the beach , spending no more than 30 minutes in doing ao. When the estimate was

determined, they looked up that number in the table of sampling intervals.

Marking the estimate and sampling interval at the top of the listing form, they

next consulted their list of random numbers to determine the number of the

first person to be interviewed and marked that in the space provided on the

listing form. They circled the number of the randomly selected start person,

counted the proper interval, and circled the last interval number as the next

selected person. This activity continued until they had gone through the

entire list interviewing the selected individuals.

143
Table C.2

Chesapeake Bay Beach Use Study Data Collection Schedule

Beach Beach Sample Beach Beach Sample

Date Name Number Time Date Name Number Time

May 26

27

28

29

30

31

Pt. Lookout

Morgantown

Miami

Miami

2

11

4

4

1300

1000 9

1300

1300

July 8

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

Kurtz 8

7

5

4

1

1

10

12

1

2

7

3

5

8

2

1

1

1300

1300

1300

1300

Bay Ridge

Rocky Pt.

June 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

Miami

Sandy Pt. (NofS) 1 1000

sandy Pt. (SofS)

sandy Pt. (SofS)

Rod & Reel

1300

1300

1000

Sandy Pt. (NofS) 1

Morgantowm

Sandy Pt. (NofS)

Bay Ridge

11

1

7

1000

1300

1300

1300

North Beach

Sandy Pt. (SofS)

1000*

1300

Sandy Pt. (NofS) 1 1300 Aug 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

Pt. Lookout 1000*

Rocky Pt.

North Beach

Sandy Pt. (E)

Rocky Pt.

1000

1000

1000

1300

Bay Ridge

Ft. Smallwood

1300

1000*

Rocky Pt. 1000

1300*

1000

Kurtz

Breezy Pt. Pt. Lookout

sandy Pt. (SofS)

1300

July 1 1000 Sandy Pt. (E) 1300*

2

3

4

5

6

7

sandy Pt. (SofS) 1000

Sandy Pt. (E) 1 1300

18

19 Sandy Pt. (NofS) 1 1000*

%riginally scheduled but subsequently eliminated.
Appendix D

User Survey instrument

145
-- —--- .-Auk da. -A.=-*A -4* 4wL\ w&A

SCREENING FORM

I. ASSIGNMENT INFORMATION:

CASE ID #:~ D..e:l~l /u](z-” Ti.e:l~l

Month Date

(5-8)

SITE #:m Weather: S~PC~ Cm ,~

INTERVIE~R NAME (9)

~II.

Hello, my name is . Researchers ● t the University of Maryland are currently studying citizens’ use of the Chesapeake Bay

and they are looking for certain types of people to ansuer ● questionnaire. I

would like to ● sk you 4 questions

we are looking for. First. . .

III. ELIGIBILITY:

A. Do you currently live in

to determine if you ● re the type of person

(20)

Maryland, . . . . . . . . . . . . . . . . . ~ (Go to B)

Virginia, . . . . . . . . . . . . . . . . . ~ (GotoB)

Washington, D.C, or . . . . . . . ~ (To to C)

Some other place? . . . . . . . . . ~ (GotoF)

B. What county do you live in? (ENTER, COHP~. AND CODE)

HARYLAND

(Anne Arundel, Baltimore including city,

Carroll, Chsrles, Harford, Howard, (21)

tfontgomery, Prince George’s) . . . . . . . . . . . . . . . . . . . . . . . a

VIRGINIA

(Arlington, Fairfax, Loudoun,

Prince William) . . . . . . . . . . . . . . . . . . ● . . . . . . . . . . . . . . . . . m

SOME COUNTY NOT LISTED ● . . . . . ● . . . . . . . . . . . ● . . . . . . . . . . . m

c . How old ● re you? (12)

Less than 14years of age. . . . . . . . . . . . . . . . . . m

Over 14, but less than 18 years of age . . . . . 2u

18 years ofage or older. . . . . . . . . . . . . . . . . . . El

D. Did you cone here . ..with other people including

someone you live with who is (13)

18 years of ● ge or older? . . . . . . 1c1

. ..other people but none you

live with who is ~ears

of age or older? . . . . . . . . . . . . . . . m

. ..by your self? . . . . . . . . . . . . . . . . . . m

(Go to C)

(Go to C)

(Go toF)

(Go to F)

(Go to D)

(Go to G)

(Go to E)

(Go to G)

(Go to G)

E. I would like to talk to the person you live with who as 18 years of

age or older and is here with you today. Point that person out to

me or tell me how and where to find him or her. (WHEN YOU LOCATE

THE PERSON, DETERMINE ELIGIBILITY BY VERIFYING OR REASKING ANY OR

ALL OF THE QUESTIONS IN PART III. BEFORE YOU BEGIN n QWSTIONS

READ THE INTRODUCTION TO ~ NEW PERSON.)
F. (CODE 2 or 3 IN Iv BELOW AND SAY:)

You (do not live in the area/belong to the age group) that we wish

to survey on this study. Will you give me your name ● nd telephone

number in case my supervisor wants to check on my work?

NM TELEPHONE ( )

G. (CODE 1 IN IV BELOW AND SAY:)

You are the type of person we want to interview for this study. The

questions will take 15 to 20 minutes. Can we start now? (AITER

HEETING OBJECTIONS SAY:) Before I ask the questions I need to keep

a record of the people I speak to. What is your name, address, and

telephone number? What time did you enter the beach? What time

will you leave?

NAME TELEPHONE ( )

ADDRESS TI~ ~-’”’~

IV. RECORD OF ELIGIBILITY (Screening Status)

TI~ LEAVING BEACH:*

(18-21)

v .

Eligible . . . . . . . . . . . .

Ineligible because of

Residence . . . . . . . . . . . .

A8e . . . . . . . . . . . . . . . . . .

Refused Screening . . . .

Language Barrier . . . . .

Other (Specify) . . . . . .

RECORD OF INTERVIEW STATUS

VI. NOTES :

●

Completed . . . . . . . . . . .

Partial/Breakoff. . . .

Refused . . . . . . . . . . . . .

Lsngusge Barrier . . . .

Other:(Specify) . . . . .

~ (INTERVIEW AND/OR COMPLETE PART V)

~ (STOP. DO NOT INTERVIEW. U

~ %=O?%D%&iNk%%TACT. )

I

~ (uCO~ DETAILS

IN NOTES SECTION BEUMf)

m

(22-23)

ml (EDIT CASE AND SHIP TO RTI)

(24-25)

COLS. 26-72 = blank

,COLS. 73-80 = SITE/CASE 1
CHESAPEAKE BAY BEACH USE SURVEY

Conducted by Research Triangle Institute

for the University of Maryland

A. IDENTIFICATION

SITE NO. ~T

Field Interviewer Name

Date Interview Completed

—w

/ /

tfonth Year

B. INTRODUCTION

AS I said ● arlier, researchers ● t the University of Harylaad ● re curreatly studyin8 cltizene’ use of the Chesapeake Bay. I will ● sk you SODS

questions retarding your recreational use of the Chesapeake Bay, particularly ● t the beaches. I ● lso have to ● sk soM questions which will

● nable the researchers to establish profiles of typical users of the Bay.

l’here is no direct benefit fr~ ta&~S part in this study ● nd you have

the right to refuse to ● nswer ● ny or ● ll of the questions or discontinue

your participation ● t ● ny time. The information that you provide will be

combined with that provided by other people who participate in the survey

to ● ssure complete confidentiality ● nd your name will not be released or

revealed to anyone other than ● uthorized project staff. It may be

necessary to recontact you later to verify the number of times you

visited beaches during the season. The results of this survey may be

helpful in ● ffectively ● llocating money to cleaaing up the Bay.

c . BEACH Li,iILIZATION

10 How many of the followin~ types of people ● re in your party today?

.

Adults (age 18 ● ad older). . . . . . . . . . W ( :-2 )

TeenaSers ● ~e 14 years to 18 . . . . . . . .111 (3-4 )

Children under age14 . . . . . . . . . . . ( S-6 )

2. Are you at the beach today with your family? By family I mean people who

● re related to you by blood, marriage, or adoption, ● nd who live with

you .

No . . . . . . . . . al (7)

Yes . . . . . . . . .(Z1
3. [SAHPLE SITE NAME] is situated on the Western Shore of the Chesapeake

Bay. 1 1983, have you (and members of your family who

live with you ““’e ‘~LLOWING)

a.

b.

c.

d.

g ~

Visited beaches on the Eastern

shore of the Chesapeake, for

example, shores close to

Cambridge, Salisbury, or Easton? . , . . .m...m(~)

Visited beaches on the ocean,

such asOceanCity?. . . . . . . . . . ..~ Q (9)

gone sw-ing from ● boat ia

the Chesapeake? . . . . . . . . . . . . . ● m — m (lo)

gone swisuing in public or

private swi-ing pools? . . . . . . . . . . ~ ~ ~ (21)

THIS SPACE INTENTIONALLY BLANK
000, do you (and *ra

&A.

YES (Aok Q.s 4A. ●

& a.)

of your fodly * live with You) Parti

Eou Uny dults 6B. lfou uay childre

participate in & teoaagers part

(MXVI=) clpato in (ACTIVI

b b

8.

b.

c.

d.

● .

f .

svimisg or wad-g? . . . . .m . . . . . . . . ..m. (~2.4. ”””1J-.4.” P ““”””””””~,

hooch ● ctivitioomabathiac, picnicking,

shollins? . . . . . . . . . . m< . . . . . . . ..m. (~ ’). ”””” ~)”” ““””””””g,

beach ● ctivitiosplaygroaad? . . . . .

boociw? . . . . . .

wstor8M*? . . . . .

Other Activitioo?

(Specify)

. . . . m......,...m.Q2).”..”P~-24) -”-g J

. . . . m..,....’..m”(~’).....P#-2#)”” . . ..” Q)” Q)

. . . . m.......”..”.(s~j....”P ~-J41.”..””.... P3s-s6 )

m..........m.~’).....P,-J$l-”-. ”Q)

. . . . m..........”.(ft)..”.”P~A4,”......*.”P4S4U )

. . . . m..........m.~p)...”.P

#-49,. ”.”” ”.”’Q)
.

who liVO wi* YOU) dom’t go into tha WaCOK, *ich of * follouiot st

5A. Iiw May sdults S8. HOW meoy ch

do not go into & t88Mc@CS

No (co to next ~ (Ask Q.s 5A. + the water bocaum go mto the

reeeoa) & n.) (s&AsON)? becmsc (WA

+ *

● ✎

b.

e.

d.

● .

f .

Don’t bow how t o

Swti . . . . . . . . . . ..m..........m(~z).....

No bstbhouse facilities

stbesch . . . . . . . . ..m..........m.(~7).....

Person’s health

prccludos it . . . . . . .-m.””””.”.”.m.(e~).....

Youor t~holiew

w8terisdirty/pollutod . ..~ . . . . . . . . ..~.(87).....

Too meayjollyfixa . . . .

.m*.........m.(~).*...

Other r8asom

(Smcm) . . . . . . . . ..m..........m.(u).....

P ..~m 53-54)”””””””” (Ss-si)

m. . . . . . . . ImF) ““TG%?) m

P

.JT-l

#3-64)”””””””” (Ss-ds)

. . . . . . . . . .

P

. . . . . .

66-63)” “ “ “

II 1

“77Z7T)

1~~ . 7 2-$0 = B SITU CUE 011

w-)”””””””””” P74-)

!T-6)”””””””””” P

- 9-10)
6. HOW many years have you (and members of your family who live with you)

been coming to the beaches on the Western Shore?

Numberofyears. . . . . . . . . . . . . . I I I (12-12)

7. Have you (or anyone in your family who live with you) changed your

swiomiing habits in the Chesapeake because of changes in the Bay’s water

quality?

No. . . . . . . . . .~ (GO TO Q.8)

Yes (stopped). . . . . ~

Yes (started). . . . -m (13 )

A. In what year did you (or members of your family) last chan8e your

swiauing habits in the Chesapeake because of changes in the Bay’s

water qblity?

Year . . . . . . m

The next few auestions deal with the

( 14-15)

frequency with which you (and your

family) visit %eaches ● nd the cost related to-visits -de to ● ach beach.

HAND RESPONDENT CARD A.

THIS SPACE INTENTIONALLY BLANX

-.
Q.SA. HOW May trips did you Q.8B. J&w Nay trzps

Q.8. Chock i f

M WSIIZD

(sad your fomily) uko (and your famil

visited te (DEK8) ia 1983? to (BEACH) la

● ✎

b.

c.

d.

● .

f .

t.

h.

i.

1.

k.

1.

● .

a.

0.

P.

q,

r.

Soady Point St. Park

~Ort hlluood . . .

Soy Ridge Such . .

Eorringtoa Ihrbor .

Kurtz Pleamue Soach

~ Morriek . . . .

Broozy Point Doack .

Chesapooko Smch . .

Berth Soack . . . .

Mmdholhck .

. . . . . m a . . . . . . . . . . . . m 0748) . . . . . . . . . . m .(1

. . . . . m a . . . . . . . . . . . . . (22-23 ] . . . . . . . . . . m CD .(24

. . . . . Q). (mu) . . . . . . . . . . m . (27-28 ) . . . . . . . . . . . C!3 (2

. . . . . ~.(32)#. o....... m .(ss-s3) . . . . . . . . . . m! . Q

. . . . . m a . . . . . . . . . . . m . 674) . . . . . . . ...”ml . 

. . . . . lTJ. (4z) . . . . . . . . . .m. (42-43 ) . . . . . . . . . . m l .U4

. . . . .~ . (46) . . . . . . . . ..-m .tiza) . . . . . . ..o. m . 

. .. 0 .~. (sz) . . . . . . ..oi m l

.’(28-ss) . . . . . . . . . . m . (

. . . . . ~. (m . . . . . . . . . .m .(2z4t) . . . . . . . . . . m . 

. . . . .~. (cl) . . . . . . . . . . .f#A#s)m . . . . . . . . . . l m l . (s

?Oiot kokout St. ?mk . . ..~. (ue) . . . . . . . . . ..[-rl. $7-88 ) . . . . . . . . . . D .U9

( COLS . 71-80 = WSITE/CX

UB’ssoack. . . . . . . . . @ . (J) . . . . . . . . . . m . ( 24) . . . . . . . . . . .( m

Borgootouo Sooth . . . . . . .m. (u) . . ..- . . . . ..m. ( 7-4 ) . . . . . . . . . . m .(

lYio@ Sooth (Boltl,oro) . . . ~.trz) . . . . . . . . . . m. 02-13 ) . . . . . . . ...”ml . (2

Rotky ?oht Pack . .

Couad’ s Mb villa

?ortcr M ?srk . .

Other (SPSCII’T)

. . . . . m a . . . . . . . . . . . ml . (27-18 ) . . . . . . . . . . m l

. (

. . . . . ~.(tl) . .:....... m l

.(2s-23) . . . . . . . . . . ml . (2

. . . . . ~ . (26) . . . . . . . . . . . (2748 )........ _ m . . m. (

—.” .~. (31) . . . . . . . ..om . (32-33 ) . . . . . . . . . . m .(Z
9. Oo YOU (md Pur f~ily) Plan ‘o ‘h W triP# to W of ~

s-r?

Ro. . . . . . . . . .

Om’tkoou. . . . . .

?8s . . . . . . . . .

boachos listad oa MJ card durias thO ro=siador of

m

m 1

(00 m 0.10)

m

(38 )

A. UUch of these bc~eks do You Plaa to visit this sar? Pleas. give mo tko letter *C SPPOJZS besxde the

of ● ach beach (=U (~) ma BCACH KMTIONED AND ~ ASK Q.9D FOR EACX ~.)

Q.9B. Row mony trips do you (and your

9.9A. Check if Plon f~ily) intend to to- to (BEWf)

SEAai to risit Skis s-r?

● ✎

b.

c.

d.

● .

f .

8.

h.

1.

j.

k.

1.

● .

n.

o.

P.

Q.

r.

Ssady PoiatSt. Park.... . . . . ..m . (37) . . . . . . . . . . . . . m . (38-3$)

Fort&llwod. . . . . . . . . . . . .m. up ) . . . . . . . . . . . . m .UI-42)

BoyRidmBoMk . . . . . . . . . . ..m . (43) . . . . . . . . . . . . . In . (44-4s )

Eorriagtoohrbor . . . . . . . . . . .m. (mu) . : . . . . . . . . . . . m l

. (4?-48)

xuru Plumrosooch.... . . . . ..~. (as) . . . . . . . . . . . . . m . (s041)

WIlorrick . . . . . . . . . . . . ..~. ($z) . . . . . . . . . . . . . m .(s=4)

BroosyPointBo8ch. . . . . . . . . . .m. (as) . . . . . . . . . . . . . . m .(34-s7)

chos8poako Botch . . . . . . . . . . ..m. (i) . . . . . . . . . . . ..m. (S&so)

morthBeack . . . . . . . . . . . . ..~. (~1) . . . . . . . . . . . . . .(62-43) m

RodMdRoolDock . . . . . . . . . . p.(u4) . . . . . . . . . . . ..lm. (4s-4u)

Point Lookout St. Park . . . . . . . . .m. (s?) . . . . . . . . . . . . . m ● W-49 )

Elm’8Doach . . . . . . . . . . . . ..~ . (70) . . . . . . . . . . . . . ml . (72-72)

[cOLS. 73-S0 = SITE/CME 031

.

llorgouw &ock . . . . . . . . . . ..~. (Z) . . . . . . . .. S... m .(2-3)

Hi* web (Bsltinro) . . . ● . . .. ~ . (4) . . . . . . . . . . . . . ml.( S-6 )

Rocky Point Pork . . . . . . . . . . ..~ . (7) . . . . . . . . . . . . . m .( 4-9 )

Conrad’s Ruth Vills . . . . . . . . ..~. (w), . . . . .. o..... ml. (IZ-zz)

Portor Nou Park . . . . . . . . . . ..~ . (33) . . . . . . . . . . . . . ml . (,24-1s )

Other (SPECI~)

. . . . .~. w) . . . . . . . . . . . . . I I i . (J7-J8)
INTERVIE~ NOTE: ASK QUESTIONS 1OA-H SEPARATELY FOR EACli BEACH CHECKED IN

QUESTIONS 8 AND 9A.

10. Now think ● bout the cost of a typical (family) trip to (BEACH) in 1984.

A.

B.

c .

D.

E.

F.

G.

H*

What would you say the total ● ntrance fee ~*, including

parking, would be for you (and your family) ● t (BEACH)? (ENTER

MOUNT IN COLUMN A BESIDE APPROPRIATE BEACHI.

What is the typical coat for you (and your family) to travel to

(BEA~) ? (m AHOUNT IN COLUMN B BESIDE APPROPRIATE BEACH NAME).

How much will it, cost you (and your family) ~ ~ in other

expenditures such ● s food, hotel or cqing fees, ● nd entertainment

● t (w=)? (ENTER MOUNT IN COLUHN C BESIDE APPROPRIATE MACH

NAME) .

ADD AMOUNTS ENTERED IN COLUMS A-C AND ENTER TOTAL IN COLUI’@ D.

How much travel time does it typically take you (and your family) to

make ● round-trip froa your home to (BEWE)? (BNTER TI~ IN COLUMN

E BESIDE BEACN NAKE) .

How maay miles is it round-trip from your borne to (BEACN)? (ENTER

NUKBER OF MILES II? COLUIQl F BESIDE REACH NAME).

How much time do you (and your family) typically spend on the beach

~ ~ when you visit (BEACH)? PROBE FOR BEST ESTIHATE OF ~

NWBER OF HOURS AND ENTER NUMBER IN COL1.ll@l G BESIDE BEACE NAME.

What is the ● verage number of days that you ● nd your family spend on

● typical trip to (BEACH)? (ENTER NU15BER IN COLUMN H BESIDE BEACH

NAHE) .

.
TASLS 1

QUSSTIOH 10

(Coaeinuod) .

Soach

k.

1.

● .

A

● .

?.

q.

?oiat Lookout St.

Park

Elm’ s Soach

l!orgaatouo 2ach

mod Sootk

(Bolt-se )

Socky toiat Park

Co8rad’ s Bnth Villa

?ortar Nau Park

?. Other (sPscIrT)

T

A

Iatrou Tr~i

Feo coots

P,w

ww

u

Total

E

R-t

Tin

P18-19

P4S-46

P8-19:

P4s481

P8-1s 1

P4S-46 )

1

I I

?z’iFI

P$481

r G H

Tiao Da

R-T 00 I

nilos Beach

M-72 = bi~~

CCM: 73-80 = SIT2/CASE 0

1 1

Ww, la

COLS. 73-s0 = SmlCAss 1

Wp,G
- RESPONDENT CARD B

11.

12.

Plesse read the Water quality characteristics listed on this card and

rank them ● ccording to how important they ● re to you (and your family)

when deciding whether or not to visit ● beach. Please rank them on a

scale of 1 to 5, with

a. Presence of

b. Presence of

c. Presence of

d. Presence of

● . Presence of

1 being least important and 5 being most important.

cloudy water . . . . . . . ..’.

floating debris or oil . . . . .

odors . . . . . . . . . . . . .

jellyfish . . . . . . . . . . ._

seaweed ● nd other

● quaticplants. . . . . . . . . . . . . . ._

RESPONDENT CARD c

(ss)

(56)

(s7)

(58)

(Sfi)

Each sumer jellyfish, ● lso called sea nettles, ● ppear in the waters of

the Chesapeake Bay. &ok ● t the statements on this card ● nd tell me

which one describes your (and your fmly’s) behavior after jellyfish

● ppear.

● . Stop going to the beach when

jellyfish appear . . . . . . . . . ...=. m

b. Still go to the beach, but less often . . . ~ (60)

c. Continue to 80 to the beach, but

don’t go into the Water . . . . . . . . . . ~

d. Don’t considex presence of

jellyfish atoll . . . . . . . . . . . . . . m

COLS. 61-72 = blank

COLS. 73-80 = SITE/CASE 121
THIS PAGB INTENTIONALLY BLANK

-.
13* A. HAND ltES~NDENT CARD A. We would li&e to find out how beach users

perceive the water quality, the quality of the beach facilities, the

beach quality, ● nd crowding ● t the beaches on the Chesapeake Bay.

Please give me the letter that appears beside the name of ● ach beach

on this card that you (and your family) ● re familiar with. By

“familiar,” I mean that you know something ● bout the beach ● ither by

having been there or you have heard ● bout it through some other

source. Please include this beach in the ones that you mention.

(CHECK COLUMN A FOR EACH BEACH MENTIONED.)

ASK QUESTIONS 13B-E SEPAMTELY FOR EACH BEACH MENTIONED.

B.

c.

D.

E.

Do you consi’der the water quality ● t (BEACE) ● cceptable or unacceptable for sw~ing ● rid/or other water ● ctivities? (CHECK APPROPRIATE CODE IN COL~ B BESIDE BEACN NAME).

What about the beach facilities, such ● s space, bathhouses, tables,

swkin$ ~rds, etc. ● t (BEACH). Would you rste it ● s ● cceptable

or unacceptable? (C13ECE APPROPRIATE CODE IN COLU?Ql C BESIDE BEACH

NAME) .

Thimk ● bout the beach itself ● t (BEACH). Do you feel that the

quality of the beach is ● cceptable or unacceptable? (CHECK

APPROPRIATE CODE IN COLU?M D BESIDE BEACH NAME).

Do you feel that the size of the crowd ● t (BEACH) is ● cceptable or

unacceptable? (~~ APPROPRIATE CODE IN COLUZW E BESIDE BEACH

NAtlE) .
Beach

sandy point

st. Pmrk . .

Fort 2ullwoo

Bay Ridge

Seach . . . . Berriagton

Harbor . . .

Kurtr Pl*asux

Seark . . . . x Marrick

Breu7 Point

‘“tiarh . . .

,. Chesapeake

Death . . . . IIortk Besch

Rod ARml

“tick . . . .

;. Point Lookout

St. Park . .

,. Eh’

s Beark

L ?lorgtatotm

Bmch . . .

}. Rockp Point

Park . . . .

p. Conrad’s Rud

Vills . . .

q. Porter Mew

Park . . . .

r. Otker

(SPECIFY)

al

tmlisr w)“ w)“ P2 .

Ill PM) “ P22) “ P2 .

24) RS1 “ PM) “ P41) “ Pa) “

‘ Sz)P “

‘w)“

‘w)“ ‘ 66) P “ . R

. . R)“ .

FR)“

.m. “ (28)

ulit~

naccemable

m(s)

m(7)

. m (22)

. m (17)

. 

~ (221

. 

m (2?)

. m (U]

. ~ (371

. ~ (4s:

. 

~ (4?:

. ~ (s2 :

. 

~ (s7:

,.m(62

..~(s?

..m(s

..~(7

..g Clz

..~(27

TAlu 2

~IOIIS 12A-E

.~(3)

.~(8)

. ~ (23)

. ~ (18)

. ~ (23)

. ~ (?8) I .~ ($3)

. . ~ (381

. .m (4s1

. .m (481

. .mw

. .~ (s8;

. .

~ (63:

..m08:

..~(e

..m(r3

Bed

:cemabh

a.

m.

m.

m.

,m.

.m.

.m.

.m.

.m.

.(I!*

.m.

.m.

.m.

.m.

.K!.

.m.

.Iz.

.m.

.~(4) .~.

.~(9) .m.

.m(J4) .m. . m (29) .U1.

. m (s4) .Cn.

. ~ (w) .m. . mJ (s4) .m. . ~ (22) .m.

. ~ (44)

. 

~ (48)

. ~ (s4)

. 

~ W)

. ~ (64)

.~(lw)

EE

.R(4)

.~(9)

.~(24)

. ~(xl)

p(s) . ~ (:3)

. m(:s)

. ~ (20

. 

~ (2s

. 

~ (30

. ~ (3s

. m (40

. ~ (4s

. ~ (s0

. ~ (ss

. ~(60

. ~(ss ! . ~(70
INTERVIEWER CHECl@OINT 1

CHECK ~ RESPONSES TO QUESTION 8B FOR TIE _ OF TRIPS ~ RESPONDENT

NAS MADE TO TEE SAMPLE SITE IN 1984 AND QUESTION 9B FOR TIE NUMBER OF’

TRIPS THE RESPONDENT PLAN~ T—~~TNE SAMPLE SITE DURING TIE REMINDER

NERE.

OF ——— THIS 

+

S-. ADD THE NUMBER ENTERED FOR BOTliimS-~~

TOTAL N7JHBER OF 1984 TRIPS =

CIECK COLUMN D IN TABLE 1 FOR THE TOTAL COST OF A TYPICAL 1984 TRIP TO THE

SAMPLE SITE . ENTER THE AHOUNTNERE+

—— —.— ——

— .

TOTAL COST OF TTPICAL 1984 TRIP $

INSERT THE TOTAL NUMBER OF 1984 TRIPS AND TNE TYPICAL 1984 COST PER TRIP

IN ~ APPROPRIATE SPACES MEN QUESTION 14 IS -.

ENTER TNELAST DIGIT OF TBE CASE ID NUHBER=

use $ amount below iB

If last digit iS Question 14

0 . . . . . . . . . . . . . . . . . . ● $50.00

. g45.oo

:: : : : : : : : : : : : : : : : : : .$40.00

3. . . . ● . . ● ● . . . . ● ● ● ● . .$3s.00

4. . . . . . . . . . ● . . ● ● . . . . $30.00

5. . . . . . . . . . ● .* . . ● ● . . $25.00

6 . . . . . . . . . . . . . . . . . .. $20.00

7. ● . . . . . . . . . . . . . . . . . $15.00

8. . . . . . . . . . . . . . . . . . . $lO.OO

9.. . . . ● ● . . . . ● . . ● ● . . . $s.0 0

I

14. According to your responses to previous questions, you (and your family)

take ● bout JTOTALNUHBEROF 1984 TRIPS) to this site per year ● t an

approximate cost of (IVTAL COST OF TYPICAL 1984 TRIP~ per day. If your

costs per day were to rise by (USE INITIAL AMOUNT FROM TABLE ABOVE),

would you still visit this site? Keep ia mind that the costs of visiting

other sites on the Chesapeake or participating in other ● ctivities would

remsin the ssme.

NO. . . . . . . .

4

TEs . . . . . . .

IF NO, DECREASE TIE DOLLAR

AtlOUNT IN ~5.00 Increments

UNTIL A “YES” ANSUER IS GIVEN.

UIENAYES ANSUER IS GIVEN,

RECORD DOM AMOUNT BELOW.

. .

(21)

. . ;

IF YES, INCREASE TIE DOLLAR

AtlOUNT IN $5.00 INCREI’WiTS

UNTIL A “NO” ANSUER IS GIVEN.

~ A “NO” ANSUER IS GIVEN,

RECORD DOLLAR AMOUNT OF LAST

‘TM” RESPONSE.

14. DOLLARS

(22-25)

,.
INTERVIEWER CHECKPOINT 2

ENTER TIE LAST DIGIT OF TKE CASE ID NUMBER =

Use $ amount below in

If last digit is Questions 15 and 16

1 . . . . . . . . . . . . . . . . . . . $ 5.00

2. . . . . . . . . . . . . . . . . . . $10.00

3. . . . . . . . . . . . . . . . . . . $15.0 0

4. . . . . . . . . . . . . . . . . . .$20.00

5. . . . . . . . . . ● . . . . . . . . $25.00

6.. . . . . . . . . . . . . . . . . ● $30.00

. $35.00

:: : : : : : : : : : ; : : : : : : : .$40.00

9. . . . . . . . . ● . . . . . . . . . $45.00

0. . . . . . . . . . . . . . . . . . . $50.00

15. Jellyfish ● re frequently identified ● s ● nuisance to swimers. Would YOU

FROM CP 2) per yeas ia ● xtra

be ● laminated ● s ● nuisance

(and your f~ly)- be willing to pay (AMOUNT

state or federal taxes if jellyfish could

without ● ny ● dverse ecological ● ffects?

No. . . . . . . . . mm

Yes . . . . . . . . ● m

Don’t Know . . . . ● m

(26-2?)

INTERVIEWER CMmCPoINT 3

REPER TO COLUMN B IN TABLE 2 (QUESTIONS 13A-D)

UAS ~ UATER QUALITY OF TKICS SITE RATED AS UNACCEPTABLE?

No. . . . . . . . . . ~1 + (GO TOQ.17)

Yes . . . . . . . . ● m ” (ASK Q. 16) (28)

16. You indicated that the water quality ● t this site is unacceptable for

swirning. Would you be willing to pay (AHOUNT FROH CP 2) in extra state

or federal taxes if the water quality were improved so that” you found

it ● cceptable to swim here?

No. . . . . . . . . . ~

Yes . . . . . . . . . m

Don’t fiOW . . . . . . . m (29-30)
17. The next few questions ● re ● bout you ● nd your household. Ifow many of

each of the following types of people live in your household? (READ MCH

OF TIE FOLLOWING AND ENTER = ~oF UCH TYPE).

a. Adults (age 18 and older)

b. Children between the ages

c. Children under ● ge 14 . .

. . . . . . . . . . . ml (32-32)

of14and 18... m (33-$4)

. . . . . . . . . . m (3s-25)

18. ONIXltXRDD. Which of the relationships listed on this card best describes your status in your household?

● . Grandparent . . . . . . . . . ● m

b. . Husband. . . . . . . . . . . . a

c. Wife . . . . . . . . . . . . ● l

d. Child. . . . . . . . . . . . ● m

● . Other Relative . . . . . . . ● m

f. I live ● lone or with

unrelated individuals . : . . ■ EJ

(37)

19. How meny years have You (and your fsmily) lived in either Maryland,

Virgini~o; Washington; D.C;? - --

Number of years . . . . . . . . . . m

20. Do you or ● ny other ~ber of your household own

● ✎

b.

c.

d.

e.

f .

g YEs

● boat?. . . . . . . . ● m**m *

● boat trailer? . . . . ● cm=mzl *

fishing tackle (rod,

reel, tackle box,

etc.)? . . . . . . . . ● m**m *

● recreational

vehicle (RV)? . . . . . ● m. ● lz!m

● swiaming pool?. . . . .m*”m*

Other recreational

ite=s (SPECIFY)

m. — ● m.

(38-39)

(READ TIE FOLLOWING)

JzEl

● EEI

● EKI

. .

● ✎

.,

. .

. .

m
21. Are you one of the principal wage earners

earner but not the priacipal wage ● arner,

student or retired?

8. one of the principal wage earners

in the family

b. A wage ● arner

wage ● arner .

c. Homemaker . .

d. Retired . . .

e. Student . . .

● ✎ ✎ ✎ ✎ ✎ ✎ ✎ ✎ ✎ ✎

but not the principal

. . . . . . ..6. .

. . . . . . . . . . .

. ...* . . . . . .

. . . . . . . . . . .

in

or

. .

. .

. .

your household, ● wage

● re you ● homemaker, a

. . . . . . . ~

. . . . . . ● EI

. . . . . . . ~ (s2)

.**.. . . . ● m

. . . . . .000~

f. Other (SPECIH)

● ● IMJ

INTERVIEWER NOTE: ASK QUESTIONS 22 ~OUOE 24 IF CODE 1

QUESTION 21. OTHERWISE GO TO QUESTION 26.

22.

23.

24.

How many hours do you uauelly work per week?

Hours. . . . . . . . . . In

How many paid vacstioa days will you have ● lto@er

those you’ve ● lready taken?

Vacation Daya . . . . . . m

OR 2 IS HARIWl IN

(53-54)

in 1984, including

(5s-56)

For your typical trip to the beach, if you hadn’t gone to the beach,

could you have worked ● t some job instead?

No . . . . . . . . . . ● m

Ye8. . . . . . . . . .. ● m

(57 )
25. —- If You could hwe worked. Wht hourly

spe~ifically for the hours ‘you Worked? -

8.

b.

c.

d.

● .

f .

8“

h.

i.

j-

$3.35/hour . . .

$3.36-$5.00/hour

$5.00-$7.50/hour

.

.

.

$7.50-$10.00/hour

$10.00-$15.00/hour

$15.00-$20.00/hour

$20.00-$2S.00/hour

Over $25.00/hour .

Don’tKnow . . . .

Refuse . . . . . .

26. Are there ● ny (other) ujor

wage night you hsve been paid

No. . . . . . . . . ● EI

Yes . . . . . . . . ● l

The next few questions ● re about the

todsy .

(58-5S)

. ...* . ml

. . . . . . m

. . . . . ● ml

. . . . . ● EI

. . . . . ● UKI

● ...0● EEI

. . . . . ● m

● *... ● m

. . . . . ● m

. ..** ● m

wage ● arners in your fsaily with you today?

(00 TO Q.31)

(ASK QS . 27-30] (60 )

other msjor wa~e ● arner who is with you

27. How msny hours does he/she ususlly work per week?

Hours . . . . . . . . m

28. How meny paid vacation days will he/she heve ● ltogether in 1984?

Vacation Days . . . . m

(51-?72)

(83-64 )

29. For the typical trip to the beach, if he/she hadn’t gone to the beach,

could he/she have worked ● t some payin~ job instead?

No. . . . . . . . . ● EI

Yes . . . . . . . . . ~

(65)
30. If he/she could have

specifically for the

● ✎

b.

c.

d.

● .

f .

~“

h.

i.

Jo

$3.35/hour

worked, what hourly rate would he/she have been paid

hours worked? -

. . . . . . . . . . ~

$3.36-$5.00/hour . . . . . . . ~

$5.00-$7.50/hour . . . . . . ● “m

$7.50-$10.00/hour . . . . . ● m

$10.00-$15.00/hour . . . . . . ~

$15.00-$20.00/hour . . . . . ● m

$20.00-$25.00/hour . . . . . ● m

Over $25.00/hour . . . . . . cm

Don’tKnow . . . . . . . . . ● m

Refuse. . . . . . . . . . . . m

31. HAND CARDE. Which one of the categories on this

your (family’s) inco~ during 1984? Please give

● ppears beside the catetory?

● . . . . . . . . . ● EEI t“ “ “ ““ ●

b . . . . . . . . . -m h. .+ . . .

c . . . . . . . . . ● m i. . . . . .

d. . . . . . . . . ● m j****..

e. . . . . . . . . . m k..... .

f. . . . . . . . . . 06 a

1. . . . . .

DIC. . . . . . . . . ._ lm

RF. . . . . . . . . . m

32. CODE SEX BY OBSERVATION.

Female. . . . . . . . . am

Hale. . . . . . . . . . m

(66-$7)

card best describes

me the letter that

.,..~

. . . . ~

. ..0~

~

(68-69 )

. . . .

. . . . ml

. . . . ml

(?0 )
f

33. CODE RACE BY OBSERVATION.

White . . . . . . . . . . ~

Black . . . . . . . . . ● m

Oriental . . . . . . . . . ~

Other (SPECIFY)

. . . . ~“

(72)

ICOLS. 72-80 = Y SITE/CASE 141

Thank you for participating in our survey of beach users. Your responses will

be helpful to us ● nd hopefully to the State ● nd Federal govenwents in determining the statua of swiming and other ● ctivities on Chesapeake Bay.
TELEPHONE CONFIWTION QUESTIONS

FOR EACH BEA~ LIS~ ASK 34. A. AND B. BEFORE GOING TO NEX’I

34. A. During 1984 how many times have you (or members

live with you) visited (ENTERED NAME OF BEACH)?

RECORD ANSWER IN COLUt!N A AND ASK:

B. During the remainder of 1984 how many times will

BEACH .

of your family who

you (or members of

Your .;amily who live with You) visit (ENTER NAME OF BEACH)?

● ✎

b.

c.

d.

● .

f.

8“

h.

i.

jk.

1.

m.

n*

o.

P“

q“

r .

RECOti ANSWER IN COLti B AND GOTO NEXT BEACH.

BEACH

Sandy Point St. Park

Fort Smallwood . . .

Bay Ridge Beach . .

Herrington Harbor .

Kyrtz Pleasure Beach

Csmp Merrick . .

Breezy Point Beach

Chesapeake Beach .

North Beach . . .

Rod ● nd Reel Dock

.

.

.

.

.

. . . .

. . . .

..*.

.*..

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

Point Lookout St. Park . . .

Elm’sBeach . . . . . . . .

Morgantown Beach . . . . . .

?!iami Beach (Baltimore) . .

Rocky Point Park . . . . . .

Conrad’s Ruth Villa . . . .

Porter New Park . . . . . .

Other (SPECIFY)

34.A

VISITS

ITI

TI

iii

I I I

I I 1

cm

I I I

( I I

m

(l-2) “ “

(s-6) ● “

(9-lo) ● ●

(13-14) “ ●

(1?-18)” ●

(21-22) “ “

(25-28) “ ●

(29-30) “ w

(33-34)” “

(37-38)” ●

(41-42) w ●

(45-46) “ “

(49-50)” “

(53-54) “ “

(57-58) “ w

(61-62)’ “

(65-66)* ●

(69-70)

34.B

PLANNED

VISITS

m

-j

1 i 1

\ll

I I 1

I I J

[ I I

m

m

(3-4)

(7-8)

(11-12)

(25-16)

(19- 20)

(23-24 )

(27-28 )

(31-32 )

(3S-36 )

(39-40 )

(43-44 )

(47-48 )

(51-s2 )

(55-56 )

(59-60 )

(6S-64) 

(67-68 )

(71-72)

ICOLS. 73-80 SITE/CAsE 151

AFTER PART r SAY: Thank you for taking part in the survey ● nd talkin8 to

me today. ‘The information you-have provided will be very helpful in detemininS use of the Chesapeake.
CHESAPEAKE BAY BEACH USE SURVEY

TELEPHONE CONFIRMATION

SCRIPT AND INTRODUCTION

I am calling to speak with ( INSERT NAHE ). IF NOT THERE,

FIND OUT WHEN HE/SHE WILL BE AVAILABLE. WHEN AVAILABLE SAY:

Muy name is (INSERT NAME ) ● nd I'm calling for the University of Maryland.

On (INSERT DAY AND DATE you were interviewed ● t (INSERT THE NAME OF THE BEACH

WHERE INTERVIEW TOOK PLACE) by ● lady working for the University on ● survey

about the Chesapeake Bay. Do you remember that interview? (IF YES PROCEED.

IF NO, TRY TO REFRESH THE RESPONDENT’S MEMORY BY TELLING HIM OR HER THE TYPE

OF QUESTIONS ASKED.. IF STILL NO, TERMINATE THE CALL).

I’m calling today to confirm some of the information you reported the day

you were interviewed. I want to know which beaches you have visited or plan

to visit on the Western Shore of the Chesapeake Bay. I will read through ●

list of beaches ● nd ● sk ● bout each one individually. First,

GO TO QUESTION 34.A.