BENEFITS F RO M 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 20742BENEFITS FRO M IMPROVEMENTS I N CHESAPEAKE BAY WATER QUALITY

Volume II I of BENEFIT ANALYSIS USING INDIRECT OR IMPUTED MARKET METHODS
- (Budget Period II)

Prepare d ● n d Edited by Nancy E. Bockstael, Kenneth E. McConnell ● n d Ivar E. Stran d University of Marylan d

Sections Contributed b y Douglas Larson ● nd Bruce Madariaga University of Marylan d

Final Draft - April 1988

Principal Investigator s Nancy E. Bockstael ● nd Kenneth E. McConnell

EPA Contract No. CR-811043-O1-O

Project Office r Dr. Peter Caulkins“The information i n this document haa been funded wholl y or in part by the Unite d States Environmental Protection Agent y under Contract No. 811043-01-0. It haa bee n subject to the Agency*. peer and administrative review, and i t has . been approved for publication as a n EP A document. Mentio n of trade name s o r commercial products does not constitute endorsement or recommendation for use. ”Acknowledgements

First and foremost, we wish to thank Dr. Pete r Caulkins, our projec t of f icer, who has exhibited patience , wisdom , and appreciation. Douglas Orr and Firuzeh Arsenjani contributed to the analysis in this volume. Specia l thanks are given to Mario n Story for her commitment and technical assistance.
All opinions and remaining errors are the sole responsibility of th e editors. This effort waa funded by USEPA Cooperative Agreement number CR-81 1043-01-0.Forward

This is the second o f tw o 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 o f the agreement produced two documents which considered theoretical, conceptual and methodological issue s 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. Volum e I of budget period II's final report looks at the theoretica l issues of measuring the benefits o f quality changes, th e 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 peopl e place on these activities. While not commissioned with the inten t o f helping in the proces s of revising the Bay plans, we hope that the discussions in this report will do just that.Executive Summary

For more than te n 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 mad e to clea n 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-cos t 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 peopl e benefit from cleaner water in the Bay can help in allocating resources to clean up the water, for fund s must b e allocated temporally, spatially and functionally. Perhaps knowledge about th e benefits from water quality improvements can help with those decisions. Even though the returns from the Program derive from huma n benefits-- human use and human health--the specific objectives and implementation strategies are designed t o 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 remain s implicit . Perhaps the clearest link is between human use an d fisheries an d wildlife management. Here the vehicle for linking the strategy and the goal is a t least understandable, even if the details of this linkage remain indistinct. In other cases, however, we are left confuse d as to how th e policies impact on humans and how we woul d eve r measure the success of these policies in term s o f 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 als o 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 o f the report is to provide estimates o f Values of Chesapeake Bay recreational activities and willingness-to-pay estimates of improvement s 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 effort s 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 informatio n as is currently available on the topic.

ivThe benefit estimates themselves do not represent this report’s most important contribution, however. We see k 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 grea t deal about th e factors which matte r to people in usin g the Bay, the obstacles to their increased enjoyment implications of improving the Bay. Specific information contained in this

● 

### M aryland 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 ingen t valuat ion experiments (hypothetical responses) reveal an “ annual wil l ingness to pay in increased taxes of over $10 0 mill ion 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 betwee n $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 are a o f improving water quality in the Bay in the range of from $10 million to over $100 million. There are numerous sources of error and rando m 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 o f the area have grown sinc e 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 thes e to specific standards, they have often dropped from sight in the formatio n o f 

### 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.

vTable o f Contents

Chapter Page

1. Enviromen tal Program s 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 o f Human Use Activities in Defining Goal s 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 o f 

### Focu s Group Experiences . . . . . . . . . . . . . . 17 Summary of Focus Group Experience . . . . . . . . . . . . 22 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 23

3. Recreational Uae of the Ba y and Willingness tops y Estimates fo r Improvement to the Bay as a Whole . . . . . . . . . . . Recreational Us e o f the Bay . . . . . . . . . . . . . . Aggregate Willingness to Pay . . . . . . . . . . . . . . Analysis 

### of Willingness to Pay Responses . . . . . . Results o f 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 o f Chesapeake Bay Water Quality on Beach Use . . . . . . . 42 Tbe SurveyandtheData . . . . . . . . . . . . . . . . . . . 44 The Data. . . . . . . . . . . . . . . . . . . . . . . . . 45 The Varying Parameter Model . . . . . . . . . . . . . . . . . 47 The Varyin g Parameter Mode l Estimates . . . . . . . . . . 50 Estimated Benefit Changes . . . . . . . . . . . . . . . . 53 Discrete/Continuous Choice Model . . . . . . . . . . . . . . . 59 Th e Choice Among Sites . . . . . . . . . . . . . . . . . . 59 Estimation of Discrete Choices Amon g 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 . . . . . . . . . . . . . . . . . . . 81Modelling the Behavior of Boa t Owners Who Do Not Trailer Their Boats . . . . . . . . . . . . . . . . . . . . . . . . . 83 Calculating Estimates of the Benefits of Water Quality . . . . 85 Improvements for th e 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 i iTables

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 fo r Weibull Distribution . . . . . . . . . . . . . . . . . . . . . . . . ..13
Effect of Perceptions on Use . . . . . . . . . . . . . . . . . . 14 Participation Rat e 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 fo r Swimming, Logistic Mode l Estimates Related ● ☛☛☛☛✎ ✎ ✎ ✎ ✎ ✎ ✎ ✎ ✎ ✎ ✎ 28’
Additiona l Taxes for Water by Amount of Tax . . . . . . . . . 30 to the Probability
a Respondent Will Accept a Ta x 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 fo r 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
Estimate d 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

. . . . 574.5

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
5.1 1

5.1 2 Annual Losses per Beach User in Pollutant, by Beach, 1984 Aggregate Benefits/Losse s 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 Benefit s Based on a and-Discrete/Con t inuous 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 Boa t Owners and Non-Boat Owners SMSA, 1984 . . . . . . . . . . . . . . 73
Characteristics of Boats and Boat Owners from Boat Owners’ Survey, 1983 . . . . . . . . . . . . . . . . . . . 74
Numbers o f Trailered Boats and Boats Kept in the Water By Residence, 1983 . . . . . . . . Percent of Boaters Who Fish o r Swim Boat Owners’ Survey, 1983 . . . . . Factors Cited Most Important in the . . . . . . . . . . . . . . . 76 While Boating,

. . . . . . . . . . . . . . . 77 Selection of a Boating Area in Maryland - Percent Respons e from 718 Boat Owners WhoTrailerTheirBoats. . . . . . . . . . . . . . . . . . . . . 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
Estimate d Tobit Demand Coefficients:
Estimation Result s from Second Stage
Estimated Demand for Boating Tripe -
Estimate d Demand for Boatin g Trips - Maryland Counties, 1983 . . 82



### . . . . .
. . . . . . . . . 83 Boats Kept in Marinaa . . . 84

Boat s Kept in Marinas. Fishing Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Par Boate r Annual Benefits from a 10 % Decreas e in Pollutant by Geographical Area . . . . . . . . . . . . . . . . . . . . . . 87

i x5.1 3

5.1 4

6 . 1

6 . 2

6 . 3
6 . 4

7 , 1

7.2 Per Boater Annual Benefit s 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: Effec t 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 Qualit y ImprovementsContingent Valuation, 1967 dollars . . . . . . . . . . . . . . . 107

xPigUrea

Numbers Page

2 . 1 Th e Sampling Region for the Telephone Survey and Location of th e Beaches Used in the Intercept Survey . . . . . . . . . . . . 9
,2.2 Annual Net Change i n Swimming Habits 1970-1964 . . . . . . . . 15

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

x iEnvironmental 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. Concentrate d effort s began in 1976 when the Congress directed the U. S . Environmental Protection Agency to conduct a five-year study of th e Bay’s resources and water quality. The study, which focused on three major problems of the Bay-
- nutrien t 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 o f 1983, committing them to improve and protect water quality o f the Chesapeake Bay through coordinated environmental enhancement activities. In late 1987 a new agreemen t was signed.

The Stat e of th e Bay's Water Quality Concern over the Chesapeake Bay stems from declines in direct and indirect measures of the quality o f the Bay’ s waters . The most apparent measures are related to the productivity of the Bay. Reduction in fish landings, combined with an awarenes s o f the increasin g loads o f pollutants and their consequences, led scientists t o assess the Bay’s water quality.
The us e of the ter m “quali t y “ in assessing the Bay connotes a set of standards goals, o r ideals with which the current condition s o f 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 fo r 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 stat e o f 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 i n ecosystem productivity, i t is useful to summarize the natur e of those declines. Summary measures give th e status of the Ba y 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 natur e o f 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 impairmen t o f the Ba y as a functionin g ecos ystem . A common measure of water quality is the level o f dissolved oxygen in the water. This is oxygen available to various plant and animal life. Its absence ca n eliminte highe r forms of life from ecosystems. Studies have shown that the exten t o f water with little or no dissolved oxygen has increased by 15-fold in the last 30 years.

1.

Another indicator of water qualit y 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 th e 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 vegetatio n (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 SA V means less suitable habitat for spawning finfish and shellfish. There ar e o f 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 th e lay public. Landing s o f well-know n anadromous species suc h 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 o f landing s as a measure of wate r quality, of course. A considerable increase in effort devoted to harvesting fish has happened to coincide with the increas e o f effluents over time. Further , natural phenomena suc h as hurricane Agnes (1972) induce cyclical variations i n finfish and shellfish reproduction. Nevertheless, there can be little doubt that the quality of the Chesapeake Bay’s waters has declined, both i n terms of the ecological health of the estuary and the benefits t o humans o f its use.

The Current Environmental Programs The foundation of the Chesapeake Bay Progra m is the Clean Water Act, the ongoing federal environmental legislation dealing with water. Under the Clean Wate r 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, o f 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 t o assist states in implementing 1983 revisions of the water quality regulations.

Th e Handbook defines acceptable approaches by which water quality baaed effluent limitations may be determined. Whether the pollutant specific or biomonitoring approac h is taken, however , states must adopt criteria which are sufficient to protect the “designate d 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 sens e o f the central position which human use activities should play in th e setting o f standards and the overriding obligation states should feel towar d the protection of recreational uses of aquatic human health where ‘people are involved in resources.

2The first plan of the Chesapeake Bay Commission waa the Chesapeake Bay Restoration and Protection Pla n o f September 1985. Thi s is currently the central documen t describing the goals of the Chesapeake Bay Program and the means by which thes e goals are being achieved. The genera l goals as stated in the plan are 

### to “Improve and protect the water quality and living resource s o f the Chesapeake Ba y 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 ar e boad, and include both ecological and huma n health, as wel l as productive use b y humans. large, however, By and there is no clear connection between th e goals of the Bay Program which emphasize human health and human use and the means by which humans benefit from implementation. To accomplish the broa d goals, specific objectives and implementation strategies have been developed. Many of these strategies are designe d to reduc e o r control nutrients. Major strategie s to control point sources of nutrienta include plane to provide grants to design, construct, operate and maintain sewage treatment facilities, an d plans to suppor t phosphorous removal projects at treatment plants. Plan s to support nitrogen removal at treatment plants have not been proposed, except for an experimental project conducted by the State o f Maryland in the Patuxent estuary. The primar y stratagy established to control non-point sources of nutrients to the Ba y has been to subsidize the implementation o f “Best Management Practices” (BMPs) to reduce runoff from urban, forested, and in particular, agricultural lands. Ma r yland, Virginia, and Pennsylvania have institute d costsharing programs to promote agricultura l BMPs. Among the agricultural BMPs supported through cost-sharing have been: strip cropping, buffer stripping, terrace and diversion construction, animal waste system installation, and reduce d 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 revampin g o f sewer systems and building holding ponds. Th e state of Marylan d has also enacted legislation banning the us e 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 o f toxic materials in the Bay, 

### Among these policies are programs to support pretreatment plans to reduce the discharge of metals an d 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.

3Lastly, the Chesapeake Bay Restoration and Protection plan has instituted a series of policies designed directly to “provide for the restoration and protectio n of living resources and their habitats and ecological relationships ” 2 in the Chesapeake. Among these policies were programs to develop comprehensive fisheries managemen t plans, expand oyster repletion activities, improv e waterfowl and wildlife habitat, and re-establish submerged aquatic vegetation.
There is n o “clear connection between the implementation strategies mentioned above and th e 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 reduction s o f nutrients and toxic materials on the other remains implicit, unsubstantiated and unarticulated. Perhaps the clearest connection is between huma n use and fisheries and wildlife management. Here the vehicle for linking the strategy and the goa l is at least understandable? even i f 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 o f 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 o f the Chesapeake Bay. The report describes something about the nature an d level of that use. It also considers what we know an d 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 an d 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-cos t criterion, for many believe that the cleanup process is an expression of a fundamental morality that despoiling our surrounding s 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 ar e of limited usefulness in th e tactics o f 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 Restoratio n and Protectio n Plan, Chapter 2 , pag e 1, help wit h those decisions.One of our objectives i s to provide some initia l estimates of values of Chesapeake Bay recreational activities and willingness-to-pay estimate s of improvements in water quality associated with these activities. We have used available data together with what we know about estimating environmental benefits (se e Bockstael, Hanemann and Strand, 1985; and McConnell , Bockstael an d Strand, 1987 ) to determine thes e “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 o f non-market benefit estimation, and we will have more t o say on the question in this report. Th e usual difficulties ar e compounded by generally sketchy data. No new surveys were conducted for this study, and only on e 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 arra y of methods and specification s to provid e as much information as is currently available on the topic. We provide estimate s o f 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.

Th e benefit estimate s themselves do not represent this report’s most important contributions however. We seek to describe, model an d to some extent explain recreational uses of the Chesapeake. The report represents an attemp t to begin to understand the preferences and behavior of individuals toward the Bay. Model s of behavior are essential to benefit estimation. Even in the fac e 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 o f 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 effor t implemented in response to this commitment. ” As such it is a first move in the directio n o f Chesapeake Bay improvements but it is sub ject to revision and fine-tuning as goal s o f 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 o f 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 hav e always been the central theme of clean water legislation, but becaus e o f difficulties in relating thes e to specific standards, they have often dropped from sight in the formatio n o f 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 Ba y is part of a larger EPA Cooperative Agreement. The initial agreement dealt with improving methods of measuring the benefit s o f 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 wel l as the monetar y values people place on these activities. While no t commissioned with the intent of helping in the proces s o f revising th e Bay plans, we hope that th e discussions in this report will do jus t that.Chapter 2

Th e Role of Huma n Use Activitie s in Definin g Goals and Strategies fo r the Chesapeake Bay

According to the EPA’s Wate r Quality Standards Handbook, “States must adopt water quality criteria sufficient to protect designated uses. ” In the process of developin g standards, 

### if water body assessments are called for, they mus t “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 som e 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 possibl e 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 certai n 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 th e targets of policy actions which are callibrated in ambient pollution levels. Ther e 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 on e finds in bot h these discussions about the ways in which individuals gain fro m 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 fo r 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 characteristic s associated with health standards.

7Whethe r modelling recreational decisions or developing standards, we face a dilemma when we tr y to link water quality and human behavior. The obvious wa y 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, i n 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 god s are fundamentally human oriented and standards are scientifically based, then the disparity between the two must be resolved for environmental regulatio n to achiev e 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 volum e o f this report is summarized in thi s 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 Evidenc e of the Link Between Percept.ions and Behavior Evidence on what people thin k o f the water qualit y o f the Chesapeake and how they behave toward the Ba y 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 o f 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 estimate s o f willingness to pay for Bay improvements. In this chapter the attitudinal information obtained fro m the telephone surve y is examined. This surve y allows inferences to be made about the impact of perceptions on decisions to use the Bay. It also facilitates expansio n o f 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 informatio n comes from the user survey, which gathers data about patterns of us e 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

8IWW98 

# .9

Th e Sampl

? Figure 2. 1 ing Region for the Telephone Survey ( v) and the Beaches ( 

# ● ) Used in the Intercep t SurveySome 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 us e (66%)1, swimming (64%), and fishing (56%) were quite popular activities. Hunting (5%) and other uses (20%) were not as prevalent responses. The percen t 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 % o f non-users) Too busy t o use (25%) Takes too long to get there (20%) Unable t o 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 o f households’ leisure time were important considerations. Trip costs and poor water quality were not cited as often (5 % o f 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 wate r 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. Lookou t t o 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 Washingto n SMSA’s found the Bay water quality unacceptable and/or other water activities. 10 to be water

“acceptable,” Baltimore and for swimmingA similar question was asked in the user survey concerning specific western shore beaches with which the respondent was familiar. Even here ther e was negative reaction to the water quality, especiall y at certain beaches. We discovered, in fact, that some households found th e 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 th e water quality acceptable for activities with short duration or during certain season s o f 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 specifi c factors considered important in visiting a Chesapeake publi c beach. We asked individuals to rank each of five factors on a scal e o f one t o five, with five being the most importan t 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 o f jellyfis h 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 wa s re-analyzed by considering th e 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 th e time, whereas non-users cite d it as highly important only 16 percen t o f the time. On the other hand, the presence of jellyfish was considere d to rank as the first or second factor for the non-contact users 84 percen t o f 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 fiv e factor s deemed important and perceivable to beach users, thre e 

### are characteristics whic h could be linked with water quality. It is interesting, for the purpos e o f keeping our perspective, that a natural factor (jellyfish ) ranks among the unpleasant feature s of the Bay.

11Two Prop o sitions about Wate r Quality and Behavior To investigate the relation between current water quality and the use of th e Bay, we examined two simple propositions. Proposition 1: Th e 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 us e o f 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 an d has been the focus of much attention by the U. S. Environmental Protection Agency and th e 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 monthl y basis in compliance with Maryland health requirements. These variou s sources provide objective measures of water quality at the Chesapeake beaches, and allo w examin a tion of the relationship between users’ perceptions and objective measures at the beaches visited. As mentioned earlier, the user survey instrument contained a question asking respondent s 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 onl y one person knew about the water quality at Camp Merrick . The percentage of those familiar with a beac h who found the water quality at that beach acceptable varied from 94 percent at Rocky Point State Park t o 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 acceptabl e (PCNA) at a beach was regressed on the moot probabl e feca l coliform count (FCC) for that beach. The feca l coliform counts were collected at the beaches during the swimming season by county of f icials. 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 colif orm. However, a high 

### FC C 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 certai n beaches . Since there were five examples of beach closures, the 12estimatio n o f 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 functiona l forms a Weibull distribution was assumed:

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

wher e ● is the shape parameter and d is the scale parameter. Using a non-linear least squares routine, we obtained paramete r estimates o f 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 objectiv e measures of water quality at a beach and households’ perceptions that water quality at the beac h is acceptable.

Table 2.1 Paramete r Estimates and Standard Errors fo r Weibull Distribution
Coefficient ● d
Estimate .495 2,537.

S t andard Error .095 923.

The value of the shape parameter suggests that the percent of beach users who find water quality unacceptabl e is concave in the water quality variable. (A sufficient condition for concavity i s ● < 1, 6 > O.) To find the feca l coliform level for a give n level o f 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 coun t is in the order of 25 feca l coliform per 100 ml. 

### At fecal counts o f 200, 75 percent of the users are estimated to accept the At counts o f 1,200, this estimated rati o 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 becaus e o f water quality. Two hundred seve n o f the 1,044 telephone respondents stated they had stopped, and 26 stated they had started. Of those who stopped, 75
13percent 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 th e 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’ perception s o f 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 o f Perceptions on Use
Effect Estimate (a l ) 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. I t 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 bee n used aquatic vegetation and resources whose dramatic as a primary indicator o f 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 objectiv e measures of water quality, individuals were more likely to quit using the Bay, we have additional indirec t 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 i n 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 

### o f the eligible households stopped swimming each year in the early 1970’s. This increase d to aroun d 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 i t is believed that declines in water quality were occurring. Although the overall pattern is a diminishing one, there appear s to be a possible modification of the trend near the end of the period.

.

Figure 2.2 Annua l 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

15If one i s bold. enoug h to assume no reversals in habit for a particular household, th e individual percentages can b e 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 i n 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 i n Figur e 2.3 Change in Swimming Habi ts 1970 and Remaining in Res dence untiI 1984

.-

- 0 -. . -- 

## $=7”-+

- %

\ \ A I
\

\

.-- 0

---— --—- -+<--- 

# ‘+=W

16The decline is interesting fo r a number of reasons, bu t mostly because it shows the potential stock of individuals who could be entice d to return to the Bay if water quality were improved. Benefits from water quality improvements will deriv e fro m increased desirability of current recreationa l 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 numbe r of new (or returning) users. To the extent that demand analysis is based on current use an d fails to predict accurately this potentially large number of new entrants, benefit estimates will be understated.

Results of Som e Focus Group Experiences The above evidence about the relationship among (a) use of the Bay, (b) subjective perceptions of the Bay’s wate r 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 abou t changes - in behavior induced by quality changes t o assess the economi c 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 t o 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, objectivel y 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 mean s o f 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 guidanc e o f 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 designe d to give direction to the discussion. By encouraging participants to reveal thought processes and level s o f awareness, their motives begin to emerge. For this study we conducted two focus group s o f a quite different nature. The groups were made up of 10 to 15 individuals who had some common association with on e 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 withOne focus group consisted of students at the University of Maryland in College Park, who were members of a wildlife conservation group. Man y of these students had taken environmentally related courses. A numbe r 

### o f these students had grown up in close proximity to the Chesapeak e 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 o f the Bay. The groups shared the characteristic s o f having n o small children and not being actively engaged in building careers.
In each case, the moderato r presented a formal introduction indicating the general purpose of the gathering and the underlying research. The introductio n 

### was notably vague so as not to bias subsequent responses. For the remainder of the session th e moderator rained questions but did not attempt to confine individuals ’ responses . All individual wer e asked to respon d to moat of the questions so people. Examples of th e types of questions
Wha t does water quality mean as to avoid dominance by one or two

raise d 

### wer e 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 caus e o f 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 affec t 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 b e expected to change in respons e to environment changes?

18Our focus

Question 1. group results suggest the following answers to these questions.

Appearanc e 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 wate r looked, felt and smelled wer e his most important indicators. Even individuals wh o n o 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 wate r 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 use d this as their first quality indicator. Other factors which signalled poor water quality were the nature of th e bottom, discoloration of th e shoreline, froth on the water? floating debris (including man-made) and dead fish. Smell was a clea r signal of poor water quality, but odors were not common, and visual appearance was use d as a more discriminating indicator.
The secon d most common signal of pollutio n 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 concentration s o f 

### boats, and farms (particularly with livestock). These deductions took place in both groups but were o f a slightly different nature. The college-age group was relatively more concerned with agricultural operation and with contamination from boat sewage. The older grou p seemed to consider development -- with or without sewage treatment -- o f 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. I n 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 example s o f manure pond overflows, run-off from pig farms, etc., were offered. Residents of the Plum Point area would have little exposur e to agricultura l runoffs more common to the upper Bay. Television, radio and newspapers were the next most common external sourc e o f 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 source s 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 19between 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 depende d on deduction. Individuals noticed changes i n 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 du e 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 tha n 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 mountai n lakes. Frequently individuals compared the appearance o f the water to what they (accurately or not) remembered experiencin g as a child. With the exceptio n o f one individual (of the older group), everyone remembered the Bay water being cleaner when they were children. This was true of the 18- an d 19-year-olds as wel l as the 50- and 60-year-olds . When questioned, individuals admitted that both maturity and publicity had raised their leve l o f 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 las t fe w 

### year s

- - in term s o f fewer dead fish and birds, less heavy oil present, and the cleanup o f 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 o f information were also clear] y affected by of trust i n political and entrepreneurial forces Among the college students, some indicated and commercial enterprise s to the extent that polluted, whether or not they could see it, These individual s 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 al l cases attitude s affected how individuals interpreted the same sensory and media information.

Question 3. Individuals perceived themselves to be affected by water quality in a numbe r o f ways. It was clear from the discussions that both groups were apprehensive about going into water they perceived to be dirty. A distinction
20was made between wading an d 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. N o doubt we are conditione d to link clarity with cleanliness and cleanliness with health. Nonetheless, i t 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 unpleasan t feel o f 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 mentione d toxics or heavy metals. Some were awar e o f 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, ye t indicated they felt safe going swimming on days when the water looked clear. Few indicated apprehension about health effect s 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 concer n was the qualit y o f his drinking water. Here is a real world exampl e o f the concept of “wea k complementarit y.” Weak complementaril y is said to characterize an individual’s preferences if he does not care about the quality of a resource that h e does not use.

Question 4, Earlie r 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 recreationa l use, some activities altogether, the individual s 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 whethe r to participate in a n activity , he can alter th e sites at which he recreates, and he can alter the frequenc y o f recreation. The two groups revealed different types o f 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 participatin g in certain activities which they perceived to be sensitive to water quality, particularly swimming. The y 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 o n days when the water looked especially clear. The latter suggests that the degree o f intra-seasonal variation in pollution and other causes of turbidity wil l affec t 

### the frequency of participation in a recreational activity. Of course only those who live near the shore ca n assess the water clarity before incurring th e coats of the recreational trip.
While ther e 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 o f this suggests that the response of behavior to perceptions may be significant but may also be a delayed response.

Summar y of Focu s Group Experience In summary, we can construct a set o f hypotheses about perception formation. The - list
10

2.

3.
4. Individual specifically
Individuals (man-made)
Individuals
Individuala would include
associate the qualit y o f the water with its appearance -- its clarity and color.

associate the quality with the amount of floating debris and dea d organisms. associate quality with angler success rates. deduce quality from surrounding land and water uses.

225. 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 the y 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 numbe r 

### o f routes:
1. The individual’s recreational experience is degraded appearances floatin g debris, etc.
2. The individua l fears health and safety risks. water quality

by unpleasant

3. The individual believes poor water quality reduces catch rates and variet y of species. 4. The individual fears eating fish from areas with poo r water quality.

I t is worthy of note that both unpleasant appearance and poor fishing conditions harm individual but also serve M signals of poo r water quality. These signals carry with them suspicion s o f furthe r losses in the form of healt h risks from contact with the water or from eatin g contaminated fish.

Conclusions In this chapter we set ou t to explore the relationship between human activities and the water quality of th e Bay. This relationshi p is important for the Chesapeak e Bay Program for several reasons. First, huma n use 

### o f the Bay is the ultimate goal of devoting resources to improving the qualit y o f 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 sens e o f water quality and are affected by this sens e o f 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 i n 

### tw o 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 i n 

### severa l ways. Households that perceive water quality as unacceptable are more likely to quit using the Bay. Th e telephone survey also shows an implicit but positive correlation between th e likelihood o f quitting and the Bay’s - provided support for the perceptions water quality. The user survey also link. This survey shows positive

23correlation 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 th e quality of wate r 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 fro m wate r 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 o f 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 awar e of water quality, change their behavior in response to water quality changes, and derive benefits when the quality of the Bay is improved.

24Chapter 3 Recreational Use of the Bay and Willingness for Improvement to th e Bay se a to Pay Estimates Whole

A variet y o f methods have been used to anal yze the welfare effects of water quality improvements. In the introduction to this chapter, a brief description of the two basic approaches is offered to prepar e 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 an d Schulze. Bockstael, Hanemann and Strand supply a thorough examination of the othe r method, indirect market valuation, The indirect market approach uses individual behavior in related markets to infer value s o f non-markete d goods. For the case in question, water quality, the researcher observes the demand for goods that are related to water quality, suc h as recreational tripe. The usefulnes s 

### of the approach depends on the responsivenes s 

### 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 greate r 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 t o assess values. This results in untestable restrictions on behavior implicitly or explicitly impose d in th e modelling process. Contingent marke t analysis involves the establishment, in the interviewee’s mind, of a fictitious or hypothetical market circumstance. The interviewee is aske d 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 question s is designed to elicit the respondent s (hypothetical) bi d for o r against the policy in question. The approach can be directed very specifically to the good or quality change to b e 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 th e 

### even t o r policy to be assessed. The disadvantage of the approach is its hypothetical nature. Rarely is it possible to test the validity o f the response. through observations on behavior. In addition, the specific valuation problem may be so remote from th e respondent's market valuation experience s as to leave him unable to respond reliably. Contingent valuation has been deemed a useful technique (see Cummings, Brookshire an d 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 clea n freshwater in the U. S.

25It 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 intende d as an en d unto itself, but rather information to support the results of additional analyses.

Recreational Use of the Bay During the summer of 1984, a telephone surve y o f 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 cop y o f the survey instrument. One objective of the survey was to provid e a complete inventor y 

### o f b each use by residents in the Baltimore/Washington SMSA’s ( see Figur e 

### 2. 1 ) , which include the District o f Columbia, several counties and incorporated cities in Northern Virginia and much o f 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 projecte d 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 millio n household s 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 t o use the Bay for some recreational activity in 1984. Participatio n rates varie d across the region (see Table 3.1) with Ann e Arundel County having the highest percentage use (69%) and the Distric t o f 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 numbe r 

### of households who used the Bay in conjunction with huntin g totalled only about 45,000. There were an estimated 170,000 households that reported othe r uses 

### o f 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-thir d 

### o f the households participated in all three activities, one-third participated in two o f 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 studie s 

### o f eac h activity

26Table 3.1 Participation Rate ” in Chesapeake Bay Activities By Activit y an d Area , 1984
Prince George’s Northern District of Montgomery en d Charle s Anne Arundel Virginia b Columbia county Counties County Baltimore c Others d

% Participation in

C B Activit y (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

a Weighted

. b Includes

c Includes d Includea percentage, representing a random sample of Baltimore-Washington, D.C. SMSA's Fairfax, Arlington , Princ e William an d Loudon counties and th e cities of Alexandria, Fairfax and Falls Church. Baltimore City and portions o f Howard and Baltimore counties. Carroll and Harford counties and portions of Howard and Baltimore counties.Table 3.2 Joint Participatio n Rates a in Chesapeake Bay Activities By Activit y 

### and Region, 1984

One Activity Two Activities Three Activities Fishing 

# I Fishing Swimming Fishing I Swiming b I Boating Swimmin g Boating Boating Fishin g Swimming Boating

Overall 3 14 10 9 9 20 34 Northern Virginia 3 15 11 6 9 15 38 Distric t 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
Ann e Arundel County 3 3 10 7 10 34 33 Baltimore City

an d Count y 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. b

Swimming includes beach use.could be aggregated to provide the basi s 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 i t may be necessary eventually to undertake a comprehensive benefit analysis of all Bay activities? there is enough current information to she d some light on th e value 

### of the recreational use of the Bay. Independent studies ar e 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 th e total potential benefits.

Aggregate Willingnes s 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 surve y 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’ perception s 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. Thi s limits the specific application of the results, since there is no simple wa y to determine at what point clean-up efforts raise the water quality to an acceptabl e 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 ar e 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 pa y ($A) in extra state or federal taxes per year if the water quality were improved so that you found it acceptable to swim in the Chesapeake?”
The amount of money ($A) was varied randomly from $5 to $50 over the sample. The percentage of respondents who answered “yes” is shown in Table - 3.3.

29Table 3.3 Percent of People Willing to Pay Additional Taxes for Acceptabl e Water Quality for Swimming, by Amount of Tax

Amount of Tax Increase $5 $1 0 $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 sampl e sizes were big enough, monotonically decreasing percentages over the entir e 

### rang e would likely be revealed. Nonetheless, the percentages are in genera l declining as the amoun t 

### of the tax increases. Of those who were presente d a ta x 

### o f $5, $10 , $15, or $20, an averag e of 6 6 percent agreed (hypothetically) to accept the tax burden in exchang e fo r acceptabl e water quality. O f those presented a tax of $25, $30, $35, or $40, the average percentage dropped to 52 percent.

Analysis o f Willingness to Pa y Responses Hanemann (1984) describes a method for analyzing a central tendency in willingness to pa y fro m questions with “yes” or “no” answers. Let the respondent derive utility from th e nonmarket good, water quality, and from money income (y) which can be use d to purchase marketed goods. Als o let a vector (x ) 

### o f 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 v j mea n zero. u j (j, y;x)

independently

When offered the tax providing

(3.2) swimmable that i t is not. stochastic

= v(jjy; x) + Vj 

### j = 0,1

and identically distributed random variables with

water at a tax o f 

### $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

30random variable with probabilit y density

Po = Pr{accept tax in exchange for swimmable water}

= Pr{v(1, y-A; x ) - v(0, y;x) ● Vo - V 1 }

P 1 = Pr{not accept tax in exchange for swimmable water} = 1 - p

o .

Define q = vo - V I and let Fn(*) be the cumulative distribution function of q. Then the probabilit y o f accepting the tax in exchange for swimmable water equals Fq(AV) where LV is the difference between th e deterministic portions of the utility function in the two states (see equation (3.2)}.
At this point any of a numbe r o f utility functions, individua l characteristics, and density functions to complete the analysis could be chosen. Lik e Hanemann, w e chose a logistic cumulation distribution function. Also, we chose a linear function (se e Sellar, Chavas an d Stoll for the limitations o f this form) for v(0), such that

(3.3) v(j, y) = aj + j9 y, p>o

where the argument s of x have been temporarily suppressed. The difference, Av, is (a l - 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 refusin g to pay the tax, and S 1 its the set accepting th e 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 th e result that when (3.5) holds, A is defined as the following

A = [(a, - aO)(-h) l/8”

31Since T is random, s o is A. To evaluate A we chose t o tak e its expectation, assuming ao~ a I ~ and # to b e 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 fo r E[A].

Result s o f Analysis. B Y Subgroups of Respondents In developing the theory it was admitted that individual characteristics (designated by the vector x) were likely t o 

### affec t 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 subgroup s o f th e population . Three means of subdividing the population suggest themselves-
- by household income, by race and by Ba y user/nonuser . In the sample obtained in 1984 there was sufficient correlation between race and income to make th e separate treatmen t o f these infeasible. Additionally, it was difficult to subdivide the population by income because income appears in the data se t as a continuous variable and arbitrarily dividing it into ranges did not prove useful.
Afte r some preliminar y 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 th e household had used or intended to use the Chesapeake Bay in 1984. A variable (D ~ ) was included to reflect use. This approach allows us t o test whether users value the change in the Bay’s water quality more than non-users, ceteri a 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. Us e o f a binary variable (D 2 ) in conjunction with the tax variable permitted an examination o f the effec t o f 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

,Tabl e 3.4 Logistic Model Estimates Relate d to the Probability a Respondent Will Accept a Tax Increase
Estimated Variable a Coefficient Standard Error t-rat io

Constant (al - aO) .385 .222 1.73
D 1 “ Constan t 1.08 4 .20 2 4.7 7 Amount of Tax - .04 3 .00 9 - 5.3 ? D 2 “ ta x .03 5 .007 “ 4.7 8 Chi-squared = 47.10

a

D 1 , D 2 represent binary variables for the use of the Bay and white racial characteristics, respectively.

The above results are difficul t to interpret becaus e of the high correlation between race and income. It should not b e assumed that whites, ceteris paribus, have a higher willingness to pay for water quality. There is insufficien t data, however, to test the separat e effect s of income and race. To determine whether willingnes s to pay changes by income classes, the analysis was reworked and estimates for the expected valu e o f A were obtained for five arbitrarily defined incom e 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)
wa s linear in income only within the ranges described above. Additionally we allowed the (al - a.) estimate to vary d epending 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 ar e o f 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 i n Table 3.4 suggest that willingness to pay at first rises with income and the n falls with the highest willingness to pay coming from the middle income group ($20,000-$50,000).
Returnin g to the stronger results of Table 3.4, but bearing in mind the correlation between race an d income , the expected willingness to pay of a randomly chosen individual in each of fou r 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 Stuart j pages 228-332 ) assuming independenc e o f coefficients. A problem arises with the estimate o f 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 rati o o f 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

33Tabl e 3.5 Estimates of Utility Parameters by Income Group

. a i - 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)

Ove r $80,00 0 1.157 (2.81 )

Income not .533 reported (1.53) .833 (1.72)

.968 (2.04)

1.47 1 (2.80 )

.543 (1.24)

.200 (.42) .028 99 19.35 (1.73)

.01 2 200 11.05 (.81 )

.01 7 101 42.33 . (.95)

.01 3 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 Clas s Average for All Average for Users Average for Non-users

o - $20,000 $ 38.54 $ 415.9 4 $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

34Table 3.7 Estimated Willingness to Pay for Acceptabl e Water 

### Quality by Participation and Racial Composition of Household 1984.

Expected Willingness to Pa y Participation Status Racia l 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 willingnes s 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 eac h subpopulation is combined with estimate s o f th e 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 o f white users (27%), white non-users (35%), non-white users (16%) and non-white non-users (21%). Expected values as wel l as optimistic and pessimistic values are shown. The optimisti c (pessimistic) valu e is derived using the expected valu e 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 i t is computed as an approximation and is not associated with the normal distribution. The values shown, however, represent an “orde r o f magnitude ” contingent valuation estimate of willingness to pay for improved water quality.

35Table 3.8 Estimated Aggregate a Willingness to Pay fo r Water Quality Acceptables for Swimming, by Classification and Scenarios 1984

Scenario Wingnes s 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

Aggregat e 83,612 106,976 60,275

a Baltimore-Washington SMSA population =. b Based on expected willingness to pay c Based on expected willingness to pay plus one standard deviation d Based on expected willingness to pay minus on e standard deviation

Regiona l Comparisons Stretching the data somewhat further, one can also examine geographical patterns of responses. The logistic model wa e re-estimated using sub-samples grouped by region: the Southeast regio n (Prince George’s County, Charles County , Ann e Arundel County and the District o f Columbia), the Western region (Northern Virginia, Montgomery County) and the Northern region (Baltimore

36City and County , Howard County, an d Harford County). The sub-samples represen t 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 becaus e o f 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 fo r 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 i s consistent acros s 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 Wate r Quality,

Variable Southeast ● Wes t b North c

Constant .334 (.94) d

D 1 “ constant .78 (2.36)

Amount of Tax -.050 (3.33)

D 2 . 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

a Dist. of Columbia and Counties of Prince George’s, Charles and Anne Arundel b Nort hem Virginia and Montgomery County c Bal t imore City and Count iee of Bai t imore, Harford and Howard d t-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 ar e less disparate across regions, with those for the Wes t region somewhat larger. The estimated willingness to pay figures are presented in Table 3.10.

37Estimated Willingness to Participation, and Tabl e 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. On e o f these reasons has been labelled existence value by non-market benefit analysts (Krutilla) an d 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 assur e its existence. Formal studies of existence value are limited, but some empirical evidence exists. Fisher an d 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 substantiall y so.
Existence valu e 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 o f use values. Eve n more t o 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 Existenc e 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 Chesapeak e Bay. The sample fram e was derived from the phone survey described above. The households contacted randomly by phone were asked if they would complete an additional mail survey. O f 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. Becaus e 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-user s accounted for 16.3 percent of the respondents. Respondents wer e asked to consider a series o f situations concerning public beaches surrounding the Chesapeake Bay. They wer e asked to assume that water quality at thes e beaches had fallen below a level acceptabl e 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 an y o f the improved hospitals. The proportion of “yes” responses for users and non-users under each scenario is given in Table 3.11.

39Summary 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 User s ● Difference Non-users b Difference
1 .96 .83

2 .70 .032 .69 . .088
3 .71 .032 .67 .088
4 .49 .035 .37 .091

a The numbe r o f users in the sampl e o f respondent s is 236. b The numbe r o f nonusers in the sampl e o f respondents is 46. c This 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 th e 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 suffe r simply by learning about pollution incidents. Wha t has been called the “environmental ethic ” is closely linked with the intrinsic motive.

Of concern her e is the secon d o f the two motives: altruism. People can gain value from the enhanced wellbein g o f others (individualistic altruism). An extensive discussion of these altruistic motivations can be foun d in Madariaga and McConnell (1987). Responses to the question under Scenario 1 are used as a control to be compared with responses unde r Scenarios 2 through 4, where Scenario 1 is purposely ambiguous about ‘projec t 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 unde r Scenario 1. Interpreting
o f existence value, the relatively responses is consistent with the evidence of existence value.

40With access to beaches denied under Scenario 

### 2 , th e 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 b e no users, “yes” responses from the user group will also indicate positive existence value. In this scenario, the proportio n o f 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 similarit y o f 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 n o 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” response s fel l dramatically compared with the responses under Scenario 1. Since less than half o f 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 valu e matter to the proper design and interpretation of contingent valuation experiments. The preliminary results concernin g existenc e 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 natur e o f the opportunities foregone by doing so. Among other considerations, these suggest attention should be paid to the methods for financing the cleanu p o f 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 polic y analysis, based as it is on objective (scientific) measures of water quality. Finally, the values represent aggregate values, indistinguishable on the basis of typ e o f 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 devote d to providin g analyses o f the observed behavior of households based on data gathered in previous studies which are specific to different recreational activities. The analyse s use cross-sectional information on households to model beach use, boating and recreational fishing. Once demand functions are estimated, benefits fro m access and from changes in water quality are assessed for each of these activities. This chapte r contains a cross-sectional analysi s o f 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 surve y o f twelve public beaches on Maryland’s western shore. As such, the analysis is not comprehensive o f all beach use in Maryland but rather the use of the public areas in one portio n o f the Bay by the citizens in the surrounding environs o f 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 ou t o f the union of neoclassical demand theory and the travel cost model proposed b y Hotelling and employed extensively by recreational economists for the paat several decades. The principal contribution of the trave l cost mode l is foun d in th e use o f the travel cost to the recreational sit a as the principal component in constructing a “price” for the recreational commodity. The simple travel cost mode l ca n be derived from a neoclassical utility maximization framework, as can more complex models which incorporate added dimensions to th e problem (see Bockstael, Strand an d Hanemann, 1986). One particularly important modification of the simple model is the introduction of quality characteristics of recreational sites (see Volume I of

42this report for the theory). If the recreational demand model is to be used to estimate the value o f environmental quality improvements then individuals’ behavioral response s to changes in quality must b e modelled. This requires observing behavior in the context of differin g levels of environmenta l 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 n o 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 wa y 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 Smith t 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 slop e of influencing quantity consumed and the welfare activities.
The discrete choice model (DCM) has also taken in polic y analysis, changes the demand curve, thereby derived from recreational

many form a (e. g., Caulkins; Morey and Rowe) but the form employed b y Bockatael, Hanemann and Strand is representative. I n this model, the individua l 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 start s from the assumption that the demand functions for trip s 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 VPmodel, the demand for any one site does not adequately reflect the alternatives. Additionally, the fact that individuals do not visit 

### al l sites is inconsistent with the implicit theory and must be handled econometrically. Kling has employed Monte Carlo studie s to examine the performanc e o f these models. Not surprisingly, her results suggest that the VP model excels when most recreationalists tend to visit almost all alternative sites i n 

### a season, and the DC model excels when most tend to visit one or onl y a fe w sites in a season.

43Most recreational data sets will be characterized by something between th e two extremes, however neither model has an obviou s 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 choic e 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 o f hypothetica l changes in water quality.

The Survey and the Data Thi s section is devoted to a description of the survey of Chesapeake Beach Use conducted in 1984. Unlike the data used in analyse s o f boating and fishing in Chapters 5 an d 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 itsel f 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 Institut e (RTI) interviewed individuals on the western shore beaches i n 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 o f the following 12 beaches:

1. 2. 3. 4.
6. 7. 8. 9. 10. 11. 12. Beach Sandy Point Point Lookout For t Sma ll wood 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 smal l small small small large small small smal l smal l small

44Four hundred and eigh t 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 o f beaches and days was selecte d , and a random systematic sample of persons was interviewed a t each sample site (day-beach combination ) . The User Intercept Survey Questionnaire was designed to record and collect the following:

✎ Frequency of visits mad e 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 the y were not

● Cast related to a typical tri p to each beach that the respondent had visited since January 1, 1984

● The respondent’s perception of the qualit y o f 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 continu e to visit the sample site if cost s related to the us e o f 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 trip s the y had taken in 1984 prior to the interview and how many they intended t o 

### tak e during the rest of 1984. Follow-up telephone interviews at the end of the seaeon obtained complete 1984 trip information for 251 o f the 408 beach users interviewed. For th e remaining households, informatio n 

### was obtained solely on-site.

T o assure consistency in our trip measurement, the end of the season information was compared wit h in-season response so that a correction factor could be applie d to households with only “in-season” information.” Using data from the largest beach (Sand y Point), a regression of end-of-seaso n trips (x e ) on reported plus intended trips during th e season (x
r ) yielded:

45(4.1 ) X e = .686 + .632x r + (1..40) (35.00)

where the t-statistics are in parentheses. t Ra . .89 (n = 148)

Equation (4.1) was used to predict total trip s to a site from on-site information for households that did not receive follow-u p inteviews. The combination of a fairly small constant term and a coefficient o n X r 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 opportunit y 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 direc t loss of income incurred when recreation is undertaken. The opportunity cost of recreation time for these individual s is the valu e o f foregone alternative activities. Unfortunately, opportunit y costs will vary ove r individuals i n ways which are not observable. The only observable factor relate d to the total opportunity cost of the recreation experience will be the time spent traveling and recreating. Even this measuremen t 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 adde d t o the other costs of site. Often the fee will vary depending on the day of the party.

Water Quality beaches have an travelin g to the week and size of

Th e 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 thes e 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

46over-enrichment of Bay waters. Excessive nutrient levels may be the partial
caus e o f decreased submerged aquatic vegetation, which in turn has adverse effects on the food chain and on the habitat for many fish species. Further, over-enrichmen t 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 inclusio n 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 som e 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, individual s ‘ decisions are unlikel y to be related solely to water qualit y i n the current year, but will b e based on a cumulative learning process which includes obvious correct choice, one year are unclear.

Other Variables past observations as well. Consequently, ther e is no and the errors associated with using data from any

Additional factors are know n to influence recreation activity, including the ownershi p o f certain types of household capital equipment. Boats, recreational vehicles and swimming pools are the type s o f capital equipment whic h may affect the us e o f 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 individua l is be important.

assumed to maximize a constrained utility function which is a function of number of trips take n to each o f n quality-differentiated sites, the qualit y

characteristics o f eac h site, and a Hicksian good. Thus

(4.2) max u(x, q,z) s.t. px + z = y

wher e x is an n-dimensional corresponding vecto r o f costs of q ij , i = 1,...,n and j = 1,...,m, characteristic at the i t h site, z simplify notation in this section, vecto r o f trips to the n sites, p is a accessing the sites, q is a matrix of variables

where is the we will

47 q ij is the level of th e j t h quality Hic sian good , and y i s income. To assume that there is only one qualitycharacteristic, and thus m = 1, and q represents the vector of values of the one quality characteristic acros s sites. Problem (4.2) defines n demand functions, each of which may be a function of al l n prices, n quality levels, and income:

(4.3) x i = g i (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 (p l ) and substitute prices ( P k , 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 ar e severa l methods for resolving th e 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 (se e Bockstael, Hanemann and Strand, 1986). The method used i n this section, the varying parameters model, falls in the former category and follow s similar methods applied by Vaughan and Russell (1982); Smith , Desvousges and McGivney (1983); and Smith an d Desvousges (1985).

One 

### wa y to motivate the varying parameters mode l is to conside r 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,

48The model in (4.4) and (4.5) across site s (i.e., variation s in in own-site attribute s (q i ) 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 (q j , 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 int o on e expression as the estimation procedure usually involves two steps: the regression to each site on prices and income (e.g. , n separat e regressions) in {4.6), of trips and the regressio n o f the coefficients from the – first n - regressions 

### on the quality characteristic s o f the sites. The second step requires the application of generalized leas t squares because o f the propertie s o f the error structure implicit in the estimatio n o f (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 correc t for a censored sampl e bias. Moa t consumer demand problems analyzed with household data encounter this problem. A rando m sample o f households wil l revea l 

### a certain (often substantial) number of households that do not consume the good in question and thus have zer o 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 functio n is given by

(4.8)

49where z? is the right-hand side o f (4.4), u is the standard deviation of e, and
+ and + are, respectively, the density function and the distribution function of the standar d normal.

The Varying Parameter Model Estimates Of the twelve mentioned beaches, there was sufficient data to estimate demand function s for only nine. For two beaches , Kurtz Pleasure Beach and

North Beach, there were less tha n 20 respondents. Additionally , Breezy Point had onl y 24 observations, and over 10 percent of these wer e more than one-day trips. We were, however, able to separate the Chesapeake Beach locatio n into two sites, the Chesapeake Beach proper and the Rod an d Reel Club beach. Thus there were ten sites initially considered in the analysis.

The arithmetic means of the variables used in the model are show n in Tabl e 4.1. The average is taken 

### ove r persons actually visiting the site. The largest averag e number of trips per user occurs at Rocky Point, whereas the smallest occurs at Porter’s Ne w Beach. Point Lookout requires on average the greatest monetary and time expenditures for access, whereas For t Smallwood and Rock y Point have the least average monetar y costs pe r 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 o f 12 percent at Bay Ridge to a hig h o f 19 percen t 

### a t Porter’s New Beach and Miami Beach. The rang e was larger for recreational vehicle ownership, with as much as a quarte r to a thir d o f 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), non e 

### of which is completely satisfactory. The approach taken here is to identify for each site and each individual one substitute beach. Averag e access costs and tim e costs for a substitut e beach are included in Table 4.1. For each individual and each beach, the designated substitute beach is the least cost alternative.
The initial se t o f regressions was run using equation (4.4) as the behavioral model and a tobit estimation procedur e as the statistical basis . In some cases, multicollinearity among the cost and time variables required eliminating one or bot h o f the substitute cost variables. In the case of Morgantown, the small number of observations gave such poo r results that the sit e was dropped from the model. The results reported in Table 4.2 were generate d using th e LIMDEP statistical package and a n IBM 4341 computer. As indicated earlier, the total sample of beach users is 408 individuals. The tobit estimation for any beach j includes both user s o f beach j (non-limit observations) and individuals wh o 

### wer e 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.

50Tabl e 4.1


### Averag e Value s of Regression Variables 

### fo r Visitors by Beach

Substitute Beach Ownership

Access Access Access Access 

### Recreat ion Swimming Tripe coats Time coats Time Boat Vehicle Pool Beach (#/yr) ($) (hr) ($) (hr) (%) (%) (%)

Sand y 

### Point 8.06

For t Smallwood 5.83 

### Chesapeake Beac h 2.87 U-I Rod & 

### Reel 6.42

Ba y 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
09Table 4.2 Tobit Estimates for Beac h Deman d Model, b y Beach”
Beach Substit ute Beach Ownership Nonl imit/ Access Acces s Access Access Rec. Swim. Limit Beach Constant Costs Time 

### costs Time Boat Veh. Pool a Observations
Sand y Poin t 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) -4.8 5 (-3.61 ) .24 (2.86) 2.47 (1.15) 14.85 (57.59 ) 243/ 139

41/19 8

22/201

87/6 6

46/272 For t Smallwoo d -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 )
6.1 6 (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 . -1.7 2 ( 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)

a No coefficient s were significantly differen t from zero for Morgantown site. b t–ratios in parenthesesThe estimated coefficients on own-price (travel cost) were all o f the expecte d 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-tim e variables were considered. Obtaining result s 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/u P , j , the inverse of the standard error of the own-price coefficient fo r each beach. The estimated equations are:

(4.10) #lj = -.0308
- .00020 TNP j (-.04) (-2.22)

#oJ = 

### -2.66
- .0016 TNP j (-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 th e beach-use demand equations but a significant relationship between water quality and the coefficients on trave l cost . The poorer the water quality (i.e. the higher the level of TNP), the larger the negative respons e o f 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 willingnes s to pay for access to beaches.

Estimated Benefi t 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 use r o f each beach. We are also able to expand to the total populatio n 

### of beach users. Three hypothetical changes in the environmental variables are considered, a 10 percent and a 20 percen t decrease (environmental improvement) in the environmental (pollution) variables, and a 20 percent increase (environmental degradation). Since we will wan t to assess th e effect s of the change, we will want to calculate consumer surplus before and after the change. The formula



### 53for individua l i's consumer surplus from site j is given by the following when the demand function is linear

(4.11) 

### Cs ij = 

### (x@/(-21?jl),

wher e x i j i s its demand for trips to j and @j 1 is the coefficient on cost of access in th e j th 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:

N j (4.12) ACS = E [(xlj(qj) )z/(-2Pj l (q]) ) - (xij(@)a/(-28j, (q~))l*k\/Nj 1=1

where k, is the weight, q! and q~ ar e the levels of water quality before and after the change, and the notation x l j (q j ) and #j ~ (q j ) implies that both demand and the coefficient on travel cost are functions o f the level of water quality. N j is the siz e 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 fo r X“ = x(q”), P“(qo), x 1 = x(q 1 ), and Pl

= P(q’)- The first step i n assessing the hypothetical changes is to use the result s o f model (4.10) to predict P j i 

### (q~ ) , tha t is to predict the new travel cost coefficient give n 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 th e Tobit,

x* = @*z + c is complicated because the demand Tobit procedure. Recall the model

t- N(0, UJ)

where value s of x% < 0 ar e 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 1 0CUI Y of x, The Tobit predicting equation given below adjusts for the censored nature of the dependen t 

### variable:

54(4.13) ~

where z i s 

### a vector of explanatory variables and o cumulative distribution functions for the standard first term represents the conditional expectation perso n participate s 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 consume r 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 detail s o f the two approaches. )
Because ther e is no clear theoretical reason to choose one approach over the other, we calculate the results both ways. (4 . 11) to calculate the change i n averag e 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

j th 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 th e average beach users benefits and losses from the hypothetical changes in th e nitrogen an d phosphorus concentrations in the Chesapeake Bay. The first and fourth columns in each table represent the base lin e average consumer surplus over the entire sampl e o f beach users

55Table 4.3 Annual Benefits per Beach User from a 20 Percent Decrease in Pollutant, by Beach 1984

Calculation Metho d A a Calculation Metho d B b

Consume r Surplus Consumer Surplus Beach Before After Benefits Before 

### After Benefits

Sandy Point

For t Smallwood Chesapeake Beach
Rod & Reel Club
Porter’s New Beach Rocky Point
Point Lookout Bay Ridge
Miami Beach 133.9 4

.8 2

36.3 2

10.3 2

5.9 5

80.3 8

15.8 6

178.1 8

5.3 8 169.0 3

5.1 7

43.8 8

16.1 9

8.4 5

89.5 3

22.6 1

204.7 6
‘1O.2 7 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

15.4 4

2.88
24.27

1.14

11.34
99.79

7.34

84.3 1

● With Method A
, the 

### averag e consumer surplus for a change in quality at beach

j is taken over a sample whic h includes all beech 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 include s only users of beach j.Table 4.4 Annual Benefits per Beach User from a 10 Percent Decrease in Pollutant, by Beach 1984

Calculation Metho d A a Calculation Method B b
Consumer Surplus Consumer Surplus Beach Before After Benefits Before After Benefits

Sandy Point

For t Smallwood Chesapeake Beach
Rod & Reel Club
Porter’s New Beach Rocky Point
Point Lookout Bay Ridge
Miami Beach 133.9 4

.8 2

36.3 2

10.3 2

5.9 5

80.3 8

15.8 6

178.1 8
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

11.5 9

3.2 2
17.92

1.3 5

6.9 8

48.3 4

5.0 9

40.4 8

a With Method A, the average consumer surplus for a

j is taken over a sample which includes all beach visited beach j. b

With j is Method B, the average consumer surplus for a chang e in quality at beach users whether or not they

change in quality at beach taken over a sample-which include s only users of beach j.



### 57Table 4.5 Annual Losses per Beach User from a 2.0 Percent Increase in Pollutant, by Beach 1984

Calculatio n Metho d A a Calculation Method B b
Consumer Surplus Consumer Surplus Beach Before After Losses Before After Losses

Sandy Point

For t 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

15.8 6

178.1 8

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 whethe r 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 include s only users of beach j.



### 58for use of each beach, calculated using each of th e 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 o f 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 numbe r o f tripe (see Bockstael and Strand). The average consumer surplus values are expanded to the entire Baltimore-Washingto n SMSA’s in Table 4,6. Thi s was accomplished by knowing that the 1980 numbe r o f regional households was 1,977,000 (census of the U. S., 1980), by determining from a contemporaneous phone survey that 47 percent o f 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 method s of calculation) becaus e o f the very large number of households tha t visit that beach. Whereas 21 percent of the population used western shor e 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 descriptio n o f 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 a n 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 deman d fo r trips as well as the discrete choic e among trips on any choice occasion.

The Choic e Among Sites The first part of the model involves the estimation of the household’s choic e 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 multinomina l logit model which is applicabl e t o a discrete choice proble m o f this sort. For further discussion of its application to recreation demand, see Bockstael, Hanemann and Strand (1986).

59Table 4.6 Aggregate Benefits/Losses to Users from Changes in Chesapeake Bay Water Quality, by Beach 1984

Calculation Method A Calculation Metho d B Change Change Improvement Degradation Improvement Degradation
Beach 20% 10% 20% 20% 10% , 20%

Sandy Point


### For t 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)



### 60Suppose we call ~ a latent variable denoting the level of indirect utilit y associated with the ith alternative. The observed variabl e Y i has the property that Y i = 1 if Vf = max(Vf, V*$

. . . ,V#)
Y i 

### = 0 otherwise. Indirect utility associated with the ith alternative is some functio n 

### of z i ,

a 

### vecto r of attributes of the ith alternative so that @l = V i (z i ) + 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 distributio n (Weibull), then it is well known that

Pro b (Y i =l I Z ) = ~ , z eJ j=l

(se e Maddala 1983; McFadden, 1973 ; Domencich and McFadden, 1975). The likelihood function for the sample is

L = [ 1 ~Vi gi ;— i=l ~ evj J

where g i = 1 if i is chosen , gi = O otherwise.
The multinomina l 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, thi s model allows for no specific patter n of correlation among the errors associated with the alternatives; it denies--and in fact is violated by--any particular similarities within group s o f 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 patter n 

### o f correlatio n among the choices can be allowed. McFadden defines a probabilistic choic e model

VN evlGi(ev’,

. . ..e 

### ) P. =

1 G(evl , . . . . evN )

61here G 1 is th e patial of G with respect to the i argument th and G(ev~. +.evN) has certain properties which imply that

F(c1 ,..,z n ) = exp{-G(e-’l ,... ,e-’n)}

is a multivariate extreme Vi s Ze then the model described above. However

G(Y) = valu e 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~sn e%/(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 violatethe independence of irrelevant alternatives asumption if such a pattern actuall y exists . It is convenient because it reduces the number o f alternatives included at each stage.

Le t us make the estimation process explicit. In the problem at hand, individuals are choosing among ten beaches. Tw o o f these beaches are qualitatively different. They are stat e 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
v i:



### 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 an d 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 variabl e I m 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

th e probabilities o f interest can b e As expressed in (4.17) and (4.18), estimated using MNL proced urea. First , the Pi I m are estimated wit h 

### M independent application s o f the multinomina l logit. Note that at this stage e is not recoverable, but can be estimated only up to a scal e factor of 1-6. From

63the 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 th e underlying utility theoretic model and suggest s misspecification (see McFadden). Th e 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 o f alternatives within groups and thus no particular gain from using a nested GEV model. Two-step estimation, i.e. the estimation o f (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.

Estimatio n o f 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 choosin g 

### amon g beaches within the desired category. The state park beaches are located at Sandy Point (adjacen t to Lookout (at the mouth of the Potomac), to include For t 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 Ree l Club Beach, North Beach

the decision among sites within each category is dealt with first. I n assessin g th e 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 denote d Z j 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 th e site 1 (Z 1 i = 1.088 + .049*d 1 - .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, calculate d using distance from the household’a origin to water pollution index the site s (Z; i = .7 + .02d 1 ) ; for site i (see description page 18);

1 Exact formula wa s determined by regressing reported costs agains t 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 availabilit y o f fishing facilities at site i times whethe r the household owned fishing equipment; the availability of boat launch facilities at site i times whethe r 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 fishin g 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 ( W 2 ), the size of the group intercepted (w, ) and the inclusive valu e (I m ) 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 result s o f the estimations are presented in Table 4.8. The hypotheses about the choice between stat e 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 amon g beaches within the two categories tha n acros s the categories. Had the nested model not been used, the independence o f 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,

65Tabl e 4.7 Logit Regression for Selection Among Sites

Variable

Access Access Water Recreational Fishing Boat

cost Time Pollution Vehicle Activity Facilities Estimate Z 1i Z 2 i z,{ z , i Z 5i Z 6i

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 Shor e Beache s Members Size I m w, W 2 

### w,

Coefficient .152 -.019 .261 .024 (t-statistic) (9.26) (-8.34) (4.85) ( 12. 79)

Chi-squared = 28.07

66The Number o f Trips Decision There is no operational model that generally treats utility maximization with non-negativity constraints (Bockstael e t al., 1986, Chapte r 8),. There exists no utility theoretic means of linking the discrete choice model of site choice on each choic e occasion to a continuous choic e 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 o f the discrete choice model. This was originally suggested b y Hanemann (1978) and was used i n Bockstael et al. (1986). Thus when water qualit y changes, the inclusive value changes and influences the number of trips. In this sense, “the discrete and continuous decisions are linke d , although not i n 

### a utility theoretic way. The discrete/continuous choice model has the advantag e o f emphasizing the substitutability of site s 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 result s o f the discrete choice estimations, a new inclusive value (In ) which includes the factors in the choice among sites and the choice between state parks an d local beaches is calculated. This value, along with the individual’s incom e (INC, income or full income i f 

### a t interior in the labor market ), discretional y time availabl e (DT, if at corner in the labor market) and the number of trips to western shore beaches in the previous yea r (x t-1 ), is used to estimate th e 

### 198 4 total number of trips per household to western shore beaches (x t ). 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 numbe r 

### of trips depends on whether a day tri p to western shore beache s is a normal (positive effect) or inferior (negative effect ) good.
The result s 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 power s o f th e equatio n ar e especially good considering the cross-sectional nature of the data.

67Table 4.9 ordinary Least Squares Regression for “Choice Occasions” or Trips Variable Inclusive Lagged Discretionary constant Value Trips Income Time Estimate I N X t - 1 INC DT Coefficient .442 4.06 .516 -.028 -.981 (t-statistic) (2.92) (8.70) (-2.79) (-4.80) R 2 = .35 F-value (4,253 ) = 36.30

Estimated Benefit Changes The ultimate purpose of th e modelling effort i s 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 b y 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 q 0
, y ) = V(po, q’, y - c)
where again v is the indirec t utility function, p an d q are vectors of site prices and qualities, and y is income. The compensating variation is no w define d by
V(po, qo, y;c) = V(po, ql, y - 

### C; z),

where c i s random, and as a result C is now a random variable. Depending on how on e chooses to take account of this randomness, three different measures of compensating variation can be defined. In th e case of GEV models the median value of C coincides with the C which equates the expected values of the indirec t utility function s (Hanemann, 

### 1978). It i s 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

68where J S is the se t o f 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 W m

are factor s 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 t o W 1 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), . . .,e vB(y ’ zo ’ we) ]

= In 

### G[evi(y<’, zltwl),. 

# a., evh(-” Sz’$wl)]

Ther e is no closed-form solution for compensating variation in this case, but Hanemann (1982) shows that the compensating variation per choic e occasio n of this chang e can b e approximated as:

(14 )

69where the set J includes the two cases: state and local beaches , Y is the elemen t o f 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 estimat e 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 estimate d to be nearly $2 million annually. The estimate d loss for a 20 percent degradation was approximately the same.
Discussion Reiterating, the purpose for our work was to offer benefit estimates based o n different methods s o as to provide a range of reasonable values. The two models derive from two different conceptualizations o f th e recreationalists' decisions. The continuous, neoclassical model (represented here by the varying parameters model) is strictly correct only if interior solutions characteriz e deman d for each site, with all individuals attending all sites, Another drawbac k 

### 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 numbe r o f trips per season is not well integrate d into the estimation process. These models tend to provide low estimate s o f 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 o f Benefits varying parameter

Table 4.10 Based on a Varying model offers the largest

Parameter Model 

### and Discrete/Continuous Choice Change Model 2 0 Percent Improvement 20 Percent Degradation

---- -- Varying Parameter - -(in thousands) -- ---- -- - upper bound $26,160 - $25,839 Discrete/Cent inuous Choice $ 1,885 - $1,884



### 70

### Chapter 5


### Recreational Boating and the Benefits o f Improved Wate r Quality



### Boating is an especially important part o f any study of Chesapeake Bay recreation. As the secon d most 

### popular recreational activity in our telephone sample of Ba y 

### users (second onl y to beac h use) , 3 4 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 recreationa l activities. Its importance is further supported by the large number of registere d boats in Maryland (as many as 134,00 0 

### i n 1981) and by the fact that of the approximately 15 million Person-trips taken o n boats in 

### Marylan d waters in 1979, 90 percent (or 13. 5 million ) 

### wer e taken on estuarine waters of the Bay or its tributaries (Harmon and Associates, 1983).
In thi s 

### chapter we examine the behavior of Chesapeake boatetrs and estimat e 

### the value t o boaters from improve d wate r quality. Since no new survey could be initiated for this purpose, the data upon which the analysis rests ar e 

### drawn from a 1983 boat owners survey which waa made available by the Se a Gran t Program and the Departmen t o f 

### Recreation at th e Universit y 

### of Maryland.

A Profile of Boaters 

### and Boat Owners Th e Boa t Owners Survey In 1983 a survey of boaters was sponsored by the University of Maryland Se a Grant Program and the Maryland Coastal Zone Management Program. It consisted of a mail survey of 2515 registered boat owners i n 

### Maryland. The design of th e sampl e 

### provided equal representation t o 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 7 0 percent.


### The boa t owners' survey provides different but complementary information to the telephone survey conducted b y RTI and described in Chapter 3, as it samples a different population and uses a differen t 

### samplin g scheme. The Sea Gran t 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 no t only those who own boats but also those who uae the Bay for boating whether they own a boat o r not . The telephone survey provides informatio n 

### abou t non-boaters, as well. It does not, however, provide detailed information about boating behavior. Consequently , it is the boat owner surve y which will provide most of the data fo r analysis.



### Boaters and Boat Owner Characteristics Information from both th e boat 

### owner survey helps describe boating in Maryland.

71 

### survey and the random telephone For example, a compariso n ofthose households in th e 

### telephone survey wh o reported that they used the Bay fo r boating with those who did no t revealed no significant differences in famil y size or years 

### lived in the area but did suggest considerable differences i n income and race (ace Table 5.1). Th e 

### averag e of boaters' incomes waa significantly highe r 

### than th e non-boaters ’ average, and a significantly higher 

### percentag e o f boaters 

### were white. Interestingly, this significant difference in income appear s in the Princ e Georges/Anne Arundel/Calvert Counties 

### area (southeastern region) bu t 

### not i n Northern Virginia/Montgomer y 

### Count y (western region) or in the Baltimore area 

### (northern region). Likewise, when broken dow n by subarea the difference in racial composition appears evident in th e southeastern an d wester n subareas bu t 

### not in 

### th e norther n subarea. 

### Some information can be extracted from the telephone survey about boat ownership as well. Once agai n averag e famil y size and years in the area were 

### no t significantl y 

### different over the two groups, but average income and the racial composition of the sample were (see Table 5.2). The desig n o f 

### the boa t owners surve y permits a distinction to be drawn between individuala who trailer their boats and those who keep thei r boats in the water during the season (either a t marinas, docks or moorings). The distinction between individual in these group s is important to establish , since the decisions they face are quite different and their behavio r 

### must be analyzed separately. Additionally, we shall see that profile s o f 

### both boats and owners diffe r somewhat between the tw o groups . The sampling design was stratified to contact approximatel y equa l numbers from the tw o groups. Of those who returne d questionaires, 71 8 trailered thei r boats 

### and 788 kept their boat a 

### in th e wate r during th e season. Some interestin g features of thes e two groups are presente d 

### i n Table 6.3. By far th e most 

### common typ e o f boat in 

### th e trailered boat sampl e is ● 

### runabout . For obvious reasons, 

### the sample of trailered boats contains very fe w with cabins (4 percent). It also contains fe w 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. O f boats kept in the water, 

### runabouts (at 33 percent) remain th e single moat commo n class and sailboats (at 31 percent) represent a close second. Once agai n sailboats with no auxiliary power are likely to be under-repreaented in the sample. However , this distortion will affect the trailered grou p 

### more , as th e boats kept in the water ar e 

### larger 

### and more likel y to hav e auxiliary engines . Combining cabi n cruisers an d cruising sailboats , half the boats in the non-trailered sample are 

### cruisin g boats presumably outfitted for more tha n one-day trips. Aa the difference in the type of boat i n 

### the two 

### groups suggests, the averag e size and th e averag e value o f boats kept in the water are significantly larger than th e averages fo r trailered boats. Additionally, the average income of boat owner s i n 

### the tw o groups is significantl y different, wit h trailered boat owners having o n 

### averag e lowe r 

### incomes.
Returnin g to the telephon e survey which provides data o n boaters and not just 

### boat owners, we can learn somethin g 

### about 

### the geographical distribution

### Table 5.1 Averag e Characteristics o f Boaters and Non-Boaters in BaMmore/Washingto n SMSA, 1984 from random telephon e survey Boaters Non-boaters Averag e 

### Family Size 3.31 3.47 Averag e Years Lived in Area 29 26 Average Household Income S 

### $46,858 $37,063 By Area: Norther n 

### Va. /Montgomery Cty. , Md. $50,576 $41,471 Prince George’s,Anne Arundel, Calvert Ctya. , Md. ● 

### 50, 2s8 28,083 Bai t imore County 41,824 38,211 Percen t White a 74% By Area: Northern Va. /Montgomery Cty. , Md. ● 

### 97%” Princ e Georges, Anne Arundel, Calvert Ctya. , Md. a 64% Balt imore County

a Means of two samples are significantly different at 99% level source : Telephon e Survey, Research Triangle Institute, 1984

Table 5.2 Averag e Characteristics of Boat Owners and Non-Boat Owners in Balttmore/Washingto n SMSA, 1984
Boa t 

### owners Non-boat Owners Averag e Family Size 3.67 3.27 Averag e Years Lived in Area 28.6 28.2 Average Household Income a $56,511 $40,931 Percen t White a 94 81

a

Means of two samples ar e significantly different at 99% level. Source: Telephon e Survey, Research Triangle Inst itute, 1984.



### 73Table 5.3 Characteristics of Boats and Boat Owners from Boat Owners’ Survey, 1983

Trailered Boats Boats Kept in Water Total number responding to questio n 

### of wher e boat kept

B y 

### boat type
Runabout Cabin Cruiser Cruising Sail Day Sail Workboat Houseboat Rowboat Other NA

Use d boats for swimming at least sometimes


### Used boats for swimming usually o r always
Used boat s 

### fo r swimming always 

### Used boats for fishing at least sometimes Used boats for fishing usually or always Used boats for fishing always Average Income of Owner b
Averag e Current 

### Boat Value b
Average Boat Length b 71 8



### 445 (63%)

23 (3%)

4 (l%) 

### 32 (5%) 124 (17%) 1 (<1%) 47 (7%) 35 (5%)

7

360 (51%) a

13 3 (19%) a



### 4 1 (6%) a

65 6 (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

55 5 (73%) a

23 5 (16%) a

4 1 (5%) a

582 (76%) a

290 (38%) a

107 (14%)’ $51,000 $25,000
23 feet

a Numbers in parentheses are percen t o f those answering questio n in 

### each

stratified sample who gave this response. b Means 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 boater s within the state tell s us something about the importance of thie activity to different populatio n subgroups. From th e telephone survey i t is clear that boating is an important 

### activity t o Maryland residents throughout the state; i t is th e first o r second moat popular recreational activity on the Bay 

### in each of the geographica l ● 

### ubareaa of the study. However , as might be expected, boaters are moat commonly residents of counties contiguous to the Bay. A good exampl e is the large proportion of Ann e Arundel residents who participat e in boating. Extrapolating fro m our survey suggests that almost half of the households in Ann e Arunde l County had a t least one member who went boating on th e Bay i n 1984.
Table 5. 4 contains residenc e 

### data gleaned from the boat owners surve y for boa t owners, 

### by trailered and nontrailered classes. Th e values shown in this 

### table indicate the distribution o f residence counties among those who responded to this question in the boa t owners survey. Of those who reported residence, 64 percent o f 

### each group lived in counties contiguous to the Bay. Approximately 60 percent of th e respondents came from the fou r most populate d counties in th e state - Baltimore, Ann e Arundel, Montgomery and Princ e Georges. A final interestin g 

### feature of the sampl e is that about 5 percent of the respondents lived out-of-state eve n though they registered their boats in Maryland. 

### The fact that boating is ofte n 

### the vehicle for other Bay recreational activitie s makes th e analysis o f boating both important an d complex . The incidence of multiple activities is critical because participation in the complementary activities o f 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 lear n something about the importance o f 

### these multiple activity trips . Table 5.5 reports frequencies of 

### responses to questions regarding these complementary activities. One striking feature of thes e answers is that fishin g is extremely important to those registered boat owners who trailer their boats. In fac t almost al l 

### (94 percent) of th e trailered boat owners who responded to th e question indicated that they fished a t least occasionally from thei r boats, an d almost hal f 

### (43 percent) claimed to fish on every trip . Fishin g was less 

### important among the non-trailered boa t owners, although three-quarters of them indicate d 

### that they fishe d from thei r 

### boats 

### a t leas t Occasionally. About th e same percentage of this group indicated the y sometimes ueed thei r boats for swimming. Fewer, abou t half, of the trailered boa t owners sometimes used thei r boats for swimming.

75Table 5.4 Number of Trailered Boats and Boats Kept in the Water, By Residence, 1983.

Trailered Boats Boats Kept in Water
Residence
Baltimore 16 2 (24%) a Ann e 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 

### Easter n Shore - Total
Carol ine Worcester Carrel Allegheny 0 

### Frederick Garrett 0? 

### Howard Washington Othe r - Total Pennsylvania Virginia Distric t of Columbia Not Identified
TOTAL 6 5 (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%)
6 7 (9%) 36 (5%) 18 (2%) 57
788

a

Numbers in parentheses represent percent of those answering questions in each stratified sample who gave this response.

Source: Maryland Boat Owners Survey, 1983

76Table 5.5

Percent of Boaters Wh o Fish or Swim While Boating Boa t Owners’ 

### Survey, 1983

At 

### Least Occasionally Usua l ly Always Occasionall y a 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
I t is useful t o consider the qualitative evidence tha t exists to support the notion that water qualit y does in fact matter t o boaters . 

### Some evidence to thi s effeot can b e 

### found in the 198 3 boat owner s survey . Tables 5.6 and 5.7 present a compilation of response s to a series of questions abou t factors 

### important in th e selection of boatin g areas. As can b e aeo n 

### fro m these tables, boa t owners wh o trailered thei r boats considere d wate r quality to be the moat important factor in choosing boatin g areas Wate r quality wa a considered “very important” or a t least “moderately important” by th e non-trailered boat owners more often than any other factor except water depth. The latte r is often a physical constraint for the large r boats found i n marinas. Comparing the tw o subgroups, i.e . those who considered water quality “moderately” or “very” important and those who did not, i t is interesting to note that the former had on average significantly highe r incomes an d mor e 

### valuabl e boats.

The Behavior of Boat Owners Who Trailer Their boats Th e Genera l Model 

### We ar e interested in modelling tw o types of decisions tha t owners of trailere d boatS make in a season . One of these is the commonly modelled economi c 

### decision of how man y trips the individua l takes. The second haa to do with the location to which the boat owne r takes his boat. This subgroup of boa t 

### owners is fa r 

### more flexible in the short run than those who keep their boats in 

### the wate r during th e season, because on a day to da y basis they can

77Table 5.6 Factors Cited Most Important in the Selection of a Boating Area in Maryland, 1983
Percent Response from 718 

### Boat Owners Wh o 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 Congest ion 19.5 16.7 32.0 27.4 4.3

Source : 

### Maryland Boa t 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 Wh o Kee p Thei r Boats i n 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 Congest ion 19.9 17.4 37.2 20.4 5.1 

### Source: Maryland Boat Owners Survey, 1983

78

### alter the boating area b y 

### trailering to different launc h 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 considere d the moat important factor in this choice, Thus we analyze the demand for trip s to differen t sites wit h measurably different wate r quality. Thi s gives us som e basis t o deduce how demand migh t 

### change i f wate r quality were t o change at different sites.
In orde r to accomplis h this, a model of multiple site deman d is designe d to accommodate differing qualities across sites but to require no more data tha n is available from the boat owners ’ survey and Chesapeake Bay water quality data. Give n these limitations and a desire fo r consistency o f analysis across recreationa l activities, the varying parameters mode l presented as the first method o f analysis in Chapter 4 is used.
The model takes 

### the followin g form. 

### The demand for trips to each site j is

estimate d as a functio n o f 

### 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 individua l (s i ):

(5.1) x i j j = fj(c ij , s 

### ij , z i ; ?j) for all i,

where f j is the demand function for the j t h 

### site, #j is th e vector of parameters in each of th e site demand functions to be estimated. Equation (5.1), whic h is th e first stage of th e varying parameters models, can not be estimated using ordinary least squares methods. The sample upon which the estimatio n is baaed includes a large number of zer o values for the dependen t variable. As 

### described earlier in this volume, ordinar y least squares applied to censored samples wil l produce biased estimators . As described in the last chapter , Tobit estimation procedures are used to correct for the problem.

Th e second stage of the mode l relates th e set of P parameters in th e site 

### demand functions to th e site water qualit y characteristics (a vector w 1 ). In thi s way the demand site’ s characteristics.

(5.2) for a sit e is implicitl y modelled as a function ‘of the Th e ● 

### econd stage mode l is o f 

### the form:

~k j = g k (w j 

### ) fo r 

### all j,

where k indexes th e 

## ● pecific # coefficien t within the demand equations an d j indexes th e site demand. Again the application o f ordinary least square s is not optimal. The above model likely suffers from heteroskedasticit y (see Desvousges, Smith, an d McGivney, 19S3) which will produce Inefficient estimators. T o 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 fro m 

### th e Tobit estimatio n Of



### 79 standard erro r o f 

### th e respective standard erro r 

### of the estimated the demand fo r site j, then the

### secon d stag e model 

### corrected fo r heteroskedasticity can be written

(5.3) $kj/ekj = 

### gk(W@kj )

for each of th e k=1,...,K @ coefficients from the first stage.

The Data Th e arguments 

### of equatio n (5.1) whic h wer e determine d to be relevan t for th e analysis include the coat o f access to the site, the coat o f access to a substitut e site, and the valu e of th e boat . The latte r 

### variable waa chosen as a surrogate for two very important factors. Boat value and income are highly correlate d 

### , so boat valu e serves as a surrogate for income level, a variable whic h is normally rather difficult to obtain with accuracy. Boat value ala o will be highly correlated with boat sise which haa an effect on the sit e 

### choice. Since boat length and income are correlated, including them separately in the equation produces unresolvable multicollinearity.
The coat o f access variable is the roundtri p cost, including time coat. Costs var y across boaters fo r several reasons. Obviously th e county of origin influence s costs, for the further the count y 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 wag e (income/2080) time. th e distance traveled divided by 40 miles per hour. Finally the money cost of trailering a boat depends on th e size of the individual’ s boat. The coat per mile of trailering a boat was 

### estimated, using coat and boat length information from th e data set, as -.78 + .0 8 *(boat length). Th e final coat variable includes the toll for the Ba y Bridge, whe n relevant. To calculate the substitute cost, the above formula waa applied to the closest cite no t chosen. While a vecto r o f costs t o 

### all alternativ e sites might be considered preferable, there ar e several practical problems with including such a vector in the regression , espiciall y sever e multicollinearity.
Not al l observations on boat 

### owners wh o trailered thei r boats were ueed becaus e o f 

### heterogeneity of respondents and incomplete data. Observations were delete d if the individual did not use his boat for any trip . in 1983 or if he di d not 

### report launc h sites or location of residence. Additionally, those who reporeted their residence to be a distant state, precluding da y trips to the Bay, were deleted. Finally, to make the sample relativel y homogeneous, sailboats were excluded from th e analysis. The latter accounted ultimately for onl y seventeen deletions. The final sampl e included 40 8 observations.
The above information. waa obtained from th e boat 

### owners’ survey. However, this dat a source dose not include any information about either perceived o r measurable water quality a t various sites . Onc e agai n 

### the Chessie system provided th e environmental data, 

### and the environmental quality variable use d was the product o f nitrogen and phosphorus , as i n Chapter 3.

80The Estimated Model For th e first stage of the model, the parameters in the following functional form ar e estimated:


### (5.4)

where x i j is alternative to trips to site j, c 1 j is cost to site j, s 1j is cost to next best sit e j, and b i is boat value, all for indivi dual i.
stage results for each of the twelve sites are presented in Tabl e 

### The first 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 significantl y 

### different from zero for eight of the twelve sites . The coefficient on boat valu e is significantly different 

### from zer o for seven of the 

### sites 

### an d in each of thea a cases haa a positive sign, suggesting that wealthier people and/o r 

### people with bigge r boats 

### take more boating trips, ceteris paribus.

Deman d is relatively inelastic with respect to substitute price and boat value (i.e. a 1 percent change in either 

### of thes e causes a less than 1 percent change in the demand for trips) , 

### However, the demand for trips to a site is quit e elastic with respect to the cost of accessing the site, with own price elasticity rangin g 

### from -1.5 to -7.0.

In the second stage, there are as many equations as there ar e 

### parameter from th e 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

Th e second stage model is given by

(5.5)

and own 

### and th e results are presented in Table 5.9. The product of nitrogen phosphorou s serves as the environmenta l 

### 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 negativ e sign 

### suggests that the demand curve becomes less steep with increasing levels of pollutant.
Neithe r the constant term nor the coefficient on boat value yielded significan t second stage results. Even though the coefficient of substitute price waa associated with a significant negative coefficient on the environmental variable, allowing this coefficien t to vary had no appreciable effect on the welfar e results. As a consequence, in the remainder o f 

### the analysis w e allow only th e coefficien t on ow n price to vary wtth environmental quality. The result s 

### sugges t that a n increase i n 

### pollution would tend to have the effect of 

### pivoting inward 

### the demand for trips to the site.

81TABLE 5.8
Estimated Tobit Demand Coefficients: Maryland Counties 1983

Estimated Coefficient s (Variable) a non- ?, L B, Bo limit county (cost/ (substitute (boat value (constant) observatrip) cost/trip) 

### 1000’s) tions



### Ann e 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) .1 4 (.14 ) 

### -18.25 (3.45) Quee n 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 (2.02 ) (6. 93)

a Each equation is estimated with the 49 6 

### boaters who trailer their boats. b Absolute values of t-statistics in parenthesesTable 5.9 Estimation Results from 

### Second Stage

Regression o f trip coat coefficient 

### -.0887 -.000102 (4.29) a (3.54) Regressio n of substitute cost coefficient 

### .0682 (7.47 ) 

### -.000016 (1.78) Regression of constant -19.414 .007338 (4.14) (1.93)
● 

### absolut e values of t-statistics i n parentheses

Modelling th e Behavior of 

### Boat Owners Who Do Not 

### Trailer Their Boats Boat owner s 

### who keep their boats in the water during the season are far 

### more restricte d in the short ru n as to th e quality of the water tha t they can easily enjoy. To alte r 

### the area in whic h they boat they must tak e long trips i n 

### thei r boats or they must change mooring arrangement. We do not have access to information that would allo w us to model either of thes e decisions. Non e o f 

### our dat a allows us to observe these individual making trade-offs between water quality and othe r goods o r 

### 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 howeve r is to learn something about their demand for boating and their use of the Chesapeake, which i n itsel f is 

### useful information fo r 

### the policy maker. In thi s 

### section we estimate the demand for boating trip. b y boaters who keep thei r boats in marinas. Since we are interested in the short run decision of how many tripe to take, the relevant cost variable is the variabl e coat 

### of a 

### trip. Given the information available, we can approximate the money and time costs o f 

### travel to the marina. Explanatory variables which may shift the demand function include income and the size (or value ) o f 

### th e boat. -


### Th e boats in the marina subgroup are somewhat thing, about half are sai l boats and half ar e motor 

### might wis h to teat whether the demand functions significantl y different. heterogeneous. Fo r one 

### boats. 

### Consequently we for 

### the two groups are

Th e results o f 

### thi n analysis are reporta d in Table 5.10. Th e coefficient on coo t is negative, as expected, and significantly different fro m zero. The coefficient o n boat 

### valu e is positive , 

### indicating that, all else 

### equal, boaters with more expensiv e boats take mor e trips. Income appears not t o affect 

### systematically the demand for trips.

83Table 5.10 

### Estimated Deman d for Boating Trip. Boats Kept i n Marinas

Explanation Variable Coefficient t-sta t istic


### Cost -1.046 -3.93 Boat Value ($1 , 

### 000) .357 1.94 Income ($1,000) .148 .66
Sailboa t Index a -23.596 -2.27
Cost Sailboat Index .615 2.16
Constant 63.363 6.66
R 2 = .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 valu e + .15 income
where boat value and income are in thousands of dollars. Th e sail boat 

### demand fo r trips is give n by

TRPS = 39.77
- .4 3 cost + .35 boat 

### value
At the sam e cost and boat value, sailboat owner. their demand fo r trips appears to be rim- e 

### inelastic. + .15 income.
deman d fewe r 

### trips, and

Because of the eventual need t o aqgregate behavior over recreational activities, it would also be useful to know whether those boaters who 

## ● penal a large portio n of their time fishing hav e significantly different demands from those who do not . 

### These results ar e presented in Table 15.11. I t appears that the two groups demands are different . 

### For an y give n cost 

### and boat value, fishermen demand mor e trip s and their deman d 

### tends to be more elastic. Fishermen}. deman d is given by

84

### TRPS = 73.61
- 1.32 coat + .35 boa t value + .15 income
and deman d by non-fisherme n is

TRPS = 43.00
- .51 cost + .35 boat value + .15 income.

Table 5.11
Estimated Demand fo r 

### Boatin g Trips, Fishing Behavior Boats Kept i n Marinas 

### Explanator y Variable Coefficient t-statistic 

### Trip Coat -.506 -3.86 Boat Value ($1,000) .350 1.90 Income ($1,000) 

### Fishin g Index a ● 148 .88


### 30.535 2.8s Coat Fishin g Index -.815 -1.82 Constant 

### 43.085 3.81

R 2 = .116
● 

### Variabl e equals 

### 1 if boater fishes on boating trip e "usually" o r "always,"

O otherwise.

calculatin g Estimates of the Benefits of Water Quality Improvements fo r the Trail ered Boa t Samle Because we have bean abl e to estimate the demand for boating trips by boaters who trailer thei r boats to differen t areas as functions o f costs 

### and the wate r quality in those areas, we ca n estimate welfar e gains and losses fro m wate r quality 

### change s to this group. Unfortunately no observable behavior of th e 

### boaters who keep thei r boats in th e water allows 

### us to deduce anything about the value they place on improved water quality.

As in th e 

### previous chapter, three differen t changes in the environmental variables are considered. In one caae we impose a 20 percent decrease (environmental improvement) in the environmental (pollution) variables. Subsequen t experiments include a 10 percen t decreas e and a 2 0 percent increase (environmental degradation).



### 85Additionally, two 

### different calculation procedures are provided, identical to those used With the varying parameters model in Chapte r 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 benefi t (o r leas) estimate s 

### 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 improvemen t 2 0 percent improvement and 2 0 percen t deterioration in the water qualit y variable, respectively. The average consumer surplus figures are per boater ( trailere d boats, only) per season 

### for the designate d site. Thus the first entry in Table 5.1 2 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, i t is well to keep in mind that these benefi t 

### 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 ar e estimates per boater of how the valu e of 

### access t o 

### a site changes with changes in the environmental variable. Thes e 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 change s across sites. Each estimate of 

### surplus per boater by site assumes tha t 

### 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 an d is of unknown size.
What do al l these calculation say about the value of reductions in the nitrogen/phosphorus variable? We can estimate the aggregat e 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 boa t owners who trailer their boats. Consider St. Mary’s County. We hav e two estimates of the increase 

### i n 

## ● 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 (abou t the 

### number estimated by Harmon an d 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 Ann e Arundel County comprises. This calculation assumes that the original sample from which the benefit estimate. were derived is representative of the boater populatio n as a whole.
Th e results in these tables eeem 

### at least plausible. For example, surpluses appear highest for western shore waters, those moat easily accessed by the concentratio n of population in the state. The surplus figures fo r 

### any 

### given site are no t especially large, but this is to be expected since when boaters have 

### the ability to substitute relatively cheaply among sites, very hig h surpluses at any one site would violate some prior expectations on the size o f 

### benefits. While th e magnitudes of returna from changes in TNP are not extremel y large on a per-boater basis, in the aggregate they are quite substantial

86Table 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 Qual ity Consumer Surplus Consumer Surplus Consumer Surplus Consumer Surplus Change Before Change After Change Benefits Before Change After Change Benefits

Ann e 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% Decreas e in Pollutant in each Geographical Area



### Calculatio n Method A Calculation Method B Count y with Wate r Qualit y 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 Recreationa l Fishing: Striped Bass

This chapter provides come preliminary estimates of th e increase in benefits t o sport 

### anglers fro m increases in water quality. W e use a 

### portio n of th e 

### 1980 National Survey of Fishing, Hunting and Wildlife Related Recreation, referred t o 

### as USFWS data, to estimate the demand fo r 

### and value of fishing for stripe d bass in Maryland. This survey, while no t 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 

### onl y specie . important to th e 

### Chesapeake Bay recreational fishery fo r 

### which there is sufficientl y 

### detailed-catch information to link water quality changes to th e 

### benefits o f sportfishing.

The 

### link between improved water quality and changes in recreational fishing demand depends on the ecological connection between water quality and catc h rates and the behavioral connection betwean catch rates an d fishing activities. Descriptive and analytic studies of the Bay have focused on the impacts of water 

### pollution on the density and productivit y o f 

### fish stocks. Lower dissolved oxygen, declines in SAV, and increases in wate r toxicants all appear to hav e 

### an impact on fish stocks. Further, wher e records are kept for commercia l fisheries, there haa been a substantial decline i n landings pe r unit effort, especially 

### for thos e species whic h spawn in the Bay or ita tributaries

It is plausible to expect considerabl e benefits to recreational 

### fishermen from improvements in water quality. The number of recreational anglers is quite large, baaed on information from the primary sources o f 

### data on saltwater recreational fishing in Maryland. Estimates of saltwater fishing participation in Maryland durin g 1980 range from 539,000 anglers over 16 years of age taking 4.1 millio n trips t o somewhat over 800,000 anglers of all age. taking 2.7 millio n trips 

### (U. S . Fish and Wildlife Service and Bureau of Census; U. S. National Marin e Fisheries Service; William et al.). Accordin g to NMFS and State of Maryland data, each saltwater angler took approximately thre e trips, whil e 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 Departmen t o f 

### Natural Resources, roughly 203,000 of the saltwater trips were for stiped bass . Our analyais o f 

### the USFWS data indicates that 239,000 anglers (over 1 6 years of age ) fished for stripe d bass in Maryland and Sussex County, Delaware, fishing for approximately 2. 1 million days, or roughl y 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 47 4 metric tons. The USFWS data ar e not wel l suited for estimatin g aggregate catch , because th e survey used waa designe d primarily for other purpose., even though catches are self-reported by respondents for come saltwater species, notably striped bass.

90Tabl e 6.1 provides some descriptive informatio n 

### about the sample of anglers whic h was analyzed in this portio n o f 

### th e study . The sampl e was partitione d into tw o group s 

### baaed on whether the individual fished for striped bass o r not. Individuals in the tw o subsamples are very similar in the amount of fishing and hunting done and in their exprience, income, age, education, and othe r demographic makeup, Striped baea fishermen, on average, showed a slightly higher propensity to own a boat and to allocate more money to hunting and fishin g activities, thoug h these differences are not significantly different from zero due to th e 

### hig h within-subsample variation.

Table 6.1 Characteristics of Stripe d 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 Als o Hunted Number of Days, Hunting Years of Fishing Experience Age When Firs t Fished Owning Inboard Boat Owing Outboard Boat owning Other Boat Househol d Income Fishing/Hunting Budget in 1980 Age a
Years of Schooling Working in Jo b o r 

### Business from Urba n Areas 184 11 daya 28 daya 41% 17 daya 24 years 10 19% 42% 17% $28,300 $982 38 1 3 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%

a The 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 Recreatio n was the source dat a for analysis. 

### Of the available data sets on Chesapeake Bay 

### sportfishing, the portions o f 

### this 

### surve y relating to saltwater recreational fishing in Maryland, an d b y 

### Marylan d residents, offered the best prospects fo r modelling the effects of water quality improvements. This data set oontained th e essential variables for estimating recreationa l fishing demand functions, including information on (a) trip s take n b y 

### destination; (b ) costs 

### incurred b y recreationists fo r 

### goods and services used in recreation; (c) household income; and (d) catch rates reported by anglers for certain species.
The surve y 

### consisted o f two parts. 

### The first was a telephone screening of households, predominantly by telephon e interviews, to collect demographic characteristics and to determine the hunting, fishing, and non-consumptive recreation activitie s o f 

### household members during 1980. The second part was a detailed questionnaire administere d (typically in person) to selected individuals who indicated they had hunted o r fished in 1880, collecting 

### information o n activities and expenditures. Of the 30,300 fishermen and hunters and 6,000 non-consumptive users interviewed nationwide, 760 pursued som e or all o f these activities i n 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 an d estuarine species, wit h striped baas the only recorded species relevant to Maryland. One hundre d eighty-four individuals indicate d they fishe d fo r 

### striped baa s in 1960. The survey waa designed to provide estimates of recreation activities and expenditures at the state level, and states were divided int o large subregions for purpose . o f identifying trip destinations. Maryland was divided into four such regions, thre e 

### of which border the Chesapeake and were the location of stripe d bass fishing. Broadly defined, the fou r 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 th e sample
, 

### 16 reporte d fishing in Delaware, 46 indicated they fished fo r striped baaa in the Northern Chesapeake, 59 fishe d i n 

### the Southeastern Chesapeake region, and 86 in the Southwestern Chesapeake (Table 6.2). The data aet includes days fished fo r 

## ● trtpe d bass and other species, rather than number of trips by specie., the latter bein g the preferable measure for trave l cost models. The survey did, however, include the total number o f trips to each region. Aggregating over all areas to get total trips and all specie s to ge t total days about 4.1 million trips and fished o f 1.17 days/trip. Thus, the two 

### on e another. fished, it was determined that anglers took 

### about 4.8 million days, yielding an average measures may not be bad approximation s of

92Table 6.2 

### Sample Distribution Numbe r o f 

### Fishermen, Days of Stripe d Bass Fishing in 1880, an d Catch Rate, B y Regions
Numbe r Mean Striped Mean Catch Rate Region Who Visitad Baas Days Fish/Day

Sussex D B 16 6.8 4.4 Norther n Chesapeake a 46 9.6 4.9 Southeaster n Chesapeake b 59 11.3 3.3 Southwestern Chesapeake 88 8.8 2.8 ‘Baltimore City and Bait imore, Carrol l , Ceci l , Harford, Kent , and Quee n Anne’s counties. b Carol ine , Dorcester, Somerset , Talbot, Wicomico, and Worcester coun t ies. c

Anne Arundel, Calvert, Charles, Howard, Montgomery, Prince George’s, and St. Mary’ s counties.



### While detailed information was collected o n costs of travel, lodging, food, fees, 

### and other expenses incurred during recreation trip., these costs were not are a specific; instea d total expenditures over all saltwater fishing trips to all areas (regardless of species sought) wer e collected for eao h cost category. The variabl e cost o f 

### trips to a single area could be determined only by proratin g total variabl e costs according to distanc e travelled. The method ueed i n this analysis waa to 

### determine the total miles travelled by the individual for all saltwater fishing trip. in 1980, aa th e ● 

### m of products of round trip miles travelled 

### to (the usual fishing location in) each area and the 

### numbe r o f 

### trips taken t o each area, The fraction of total variable fishing expenses prorated for eaoh trip t o each site waa the round tri p miles travelled to the site divided by total miles travelled. The money coat of a trip to eac h site wa a this fractio n times the reported total variabl e costs for saltwater fishing . Espressed as a formula,

wher e MC i j is the money coa t o f 

### a trip by individual i to are a j, M i j is the roun d tri p miles 

### travelled by individual i to are a j, 01 j is the number of trips ‘ individual i takes to are a j, VC 1 is individual i’. reported saltwater fishing variabl e costs, and there are n areaa. The coat of time spent i n recreation demand. Th e ● urvey data were no t 

### ideal

93 is also an important determinant for determining this coa t because of noinformatio n 

### was collected on th e time spent i n 

### travel or at th e site on each trip. However , miles travele d is a reasonably good proxy for time spent in travel. Th e 

### procedure used here waa t o assume an averag e rate o f speed durin g travel o f 40 miles per hour an d that the annua l household income divided by the number o f 

### hours in th e average full-time work year (2,040) was a suitabl e approximatio n 

### for the wag e rate. Then, the value o f time 

### travelled was 

### determine d as the produc t of the amoun t o f 

### time spent in travel and 40 percent of the wage rate. Expresse d as a formula, 

# [1 Y* =Mtj-m



### where TC i j is the time coat for individual i travelling to household o f person i, and M i I is, again, round-trip miles. area j, y i is the Of course, thi s is 

### a rather arbitrary formulation for time cost 

### based on a series o f 

### restrictive assumptions, but preferable ways of treating th e value o f tim e were no t possible given the available data . The full price of a tri p is then calculate d as th e sum of the time an d money prices for each individual TP 1 

### = M C 1 

### + TC i 

### . In the survey, respondents were asked t o estimate their averag e catch rate per day for 

### selected species. Unfortunately, ther e was a la g o f 

### u p to a year or more between the time th e fishin g 

### tri p was taken and the time the questionnair e was answered . Ther e is evidenc e (e.g. Deuel, Hiett an d Worrall) that fishermen do not accurately remember numbers o f fis h caught or their sizes well beyond a period of a fe w months . A comparison of th e USFWS data and dat a collected by the State of Marylan d suggests that th e USFWS data might contain an upward bias i n reported catch rates. The sampl e and population averag e catch rates were both somewhat over three striped bass pe r day, whic h is considerably higher than the State of Maryland data which suggest s a catch rate for the came perio d of on e 

### stripe d bass per day. When the 

### sample catc h rates wer e extrapolate d to estimate total 1980 catch, the estimat e was an orde r o f 

### magnitude or more larger than the published estimates noted in the introduction, although some o f this difference may be attributable to difference. in estimates baaed on tota l trips versus tota l days. The fact that sampl e catch rates do not predict aggregat e catch wel l does not invalidate their ua e as quality indicators, however. As indicators of the qualit y 

### factors whic h signal individuals' fishin g decisions, sampl e catch rates may perform quite well.
Th e survey data contained a categorical variable measure of household income. A second measur e was also calculated: total budget for fishing and hunting recreation, the sum o f all fishin g and hunting-relate d expenditures in 1980. If th e individua l has a weakl y separable utilit y 

### functio n and determines 

### first the tota l amount of income to allocat e to hunting an d fising recreation,



### 94the fishin g and hunting budge t to a more relevan t income constraint than overal l household income . The fishing/huntin g budget measures, however, is subject to potential errors Of measurement, both from 

### faulty recall by 

### respondents and fro m year-to-year fluctuations du e to purchase of major durabl e goods.



### The Basic Model For a variet y o f 

### reasons, the model we estimat e fo r recreational fishin g is different from the recreational beach uee and boatin g model . I n th e beach use 

### and boating estimation s , trip data existed for a number of quality-differentiate d sites. In the fishing data , trips ar e available only by region. There are onl y fou r 

### of thes e regions, an d each is large so there can 

### easily b e as muc h variation within any regio n M 

### there in among the regions. Further, 164 of the 184 stripe d bass anglers in the sampl e visited only one region. Rathe r than 

### estimating four demand curves, we have estimated a single equatio n 

### where the dependent variable is th e sum o f th e trips to all sites.
The handlin g 

### of th e 

### qualit y variables differs al- 

### Fo r recreational boating, w. uee d a varyin g Paramete r model because the quality variable, scientifi c measures of water quality, varie d across sites but not across individuals. The quality variable in recreationa l fishting, catch rate, varies across individuals. Consequently, w e need no t use a varying parameter model. Instead w e 

### uee the observation on the reported catch in aren a where the individua l took his trips. The dat a set includes many individuals who did not fish for striped 

### bass . Fo r these individual , costs and catc h rates were inferred.


### Th e fishin g mode l estimated waa

(6.1)



### where x i is the number of days taken by th e i th individual, TC i 

### is the individual’. full coat (in dollars pe r trip) of stripe d 

### bass fishing , CR 1 is the catch rate (fish pe r day), IB 1 an d OB i ar e (0,1) variables denoting availability of a n 

### inboard or outboard boat for fishing, respectively; and BD 1 

### is the individual’. fishing/hunting budge t 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. Ther e is probably extensive substitution amon g site s 

### within each region that cannot be captured at all given the level o f 

### 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 Participan t 

### who visited more than one site were calculated as the mean of price. and catch rate . a t each region visited, weighted by the day . fished. Only slightl y 

### more tha n one-quarte r o f th e 

### respondents who either hunted or fished in Maryland reported having fishe d 

### fo r stripe d bass . This level of



### 95

### non-participation implies a serious censored variables problem. There are severa l ways of handling this problem i n recreational deman d models. We explor e these approaches in Chapter 4 o f 

### Volume I of this report. For the
curren t task o f estimating fisherie s 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 vecto r 

### of explanatory variable.. Th e Tobit mode l imposes come rather extreme restrictions on individual behavior that more general sample selection models avoid. But fo r 

### preliminary results, we accept these restrictions for the sake o f simplicit y. Determining the relevant price and catch rate fo r 

### non-participants was problematic. For these individuals, i t was not known which of th e four 

### price-qualit y combinations were moat relevant to their decisio n to go/not go stripe d bass fishing. In the application we used the minimum price to access a stripe d bass “site” an d its corresponding catch rate.
Welfar e measures are calculated, in principle, the “ same wa y as for the varying parameter. model. Tha t is, th e benefits of a n increase in catc h rates are given by the change in consumer’ a 

## ● mplu a which, for the linear model above , is

(6.3) ,cs=?M!z!LK@El -2;1 -2;,

where 81 is th e own-pric e 

### coefficient, and x is the individual’s trip level.

Empirica l 

### Results The model i n equations (6.1) and (6.2 ) was estimated using the maximum likelihood method o f LIMDBP. Table 6. 3 gives the results which will be used for preliminary benefit estimation, along with the sampl e mean s o f the variables . 

### The result s in Table 6.3 ar e fo r 

### a model in whic h actual catch rates reported were used for participant, and a predicted catch rate was used fo r non-participants. We also estimated a model in which predicted catch rates were used fo r every individual In the latter estimation, the coefficient estimates remained basically unchanged, but the standard erro r 

### on the catch rat e coefficien t 

### increased d 

### resulting i n 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 motorboa t seems to induce mor e striped bass tripa tha n having an outboar d motorboat. The own-pric e elasticity for Participan t is abou t minus one, while the catch rata elasticity fo r 

### participant is about .10,

96

### Tabl e 6.3 Tobit Estimation of the Demand for Stripe d Bass Fishing

Explanatory Coefficient Mean of Variable Estimat e 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 Moto r (OB) 6.66 3.47 .31 Budge t (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 

### consume r surplus are provided. Method A employs predicte d trips plus changes in prediction s whereas Method B uses actual trip s plus changes in predictions.

It is rather eaa y to expand sampl e results to the population, since the Fish an d 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 estimate s of 

### aggregate surplus. The first column is valu e o f access t o striped baas fishin g as

198 0 prices . The actual and predicted the actual being more than three time.

97 i t was perceived estimates differ larger than the

### Table 6.4 Aggregat e Consumers' Surplus for Stripe d Bass Fishing: Effec t o f Changin g 

### Catc h Rates, 1960 (Population o f 

### Maryland Hunters and Fishermen)

Aggregate Consumer Surplus Change with Change in Catch Rate Surplus for Access 20 % Decrease 10% Increase 20 % 

### Increase

. ..*..

. . 

### Thousands o f Dollars . . . . . . . . . Predicted (Method A) 14,652 -572 314 1,501
Actual (Method B) 54,196 -422 231 4s1

The second, third and fourt h 

### columns in Table 6.4 give the net impact of a 20 percent reduction , 1 0 percen t increase and 20 percent increase, respectively, in th e 

### striped baa. catch rate compared with the level perceived in 1960. Here the actual and predicted results are closer, especiall y fo r the 10 percen t changes.


### Th e numbers that are most 

### interesting for environmental policy on the Chesapeake are foun d in the third and fourth columns. These figures are rough estimate. of the dollar amount people who currently fish or hunt in Maryland migh t gain annually fro m improving striped baas fishing. There are a number of complicating factors which cannot be integrated into 

### our preliminary calculation s of 

### benefi t estimates. First, consider how long it would take for environmental policy t o produce a 

## ● ubdantid, sustainable increase i n catch rate. Reduction in effluents for one year will have only a small effect. To improve ambient water quality enough to bring about better stripe d bass reproduction and survival could take man y years. The secon d 

### question relate s to the role of expectations regarding catch. Aaide from th e likely bias and high noise in the catch rate estimate, what respondents repor t is the o x post realisation o f catch rates, while their decision s regardin g whether, when, and how frequently to go ar e baaed on expectation about 

### the catc h rate, ex ante. Consequently, while recalled ex post catch rate is the best quality variable we could obtain for striped baas

96fishing, 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 i f 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 th e activities which would benefit from the enhancement o f 

### Bay water quality. Chapter s 2 and 3 described ways in which people think about th e Bay and benefi t 

### from better 

### water quality. Chapters 4 throug h 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 derivin g benefits, sometimes w e 1000 eigh t o f 

### the informational content of th e models behind th e benefit estimates, 

### the estimated demand functions themselves. Chapters 2 through 6 contai n 

### substantial new information about the structur e 

### of demand fo r recreationa l 

### activitie s associated with the Chesapeake Bay. I n nearly ever y 

### instance where sufficient data were available, recreators responded to travel and time costs i n a manner consistent with our theoretica l model. They were also observed to be responsive to even the crudest of water quality measures. Additionally, demographi c 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 pictur e o f 

### 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 th e Baltimore/Washington SMSA’s (BWSMSA). This survey revealed that a full 43 percent of th e BWSMSA population used th e 

### Bay o r 

### intended to use it for recreation in 19S4. Geographical distribution of user s showed Anne Arundel County reaidenta (69 percent) moa t likely to be Bay users 

### and District of Columbia residents (21 percent) to be least likely (ace Table 3.1). The moat commo n 

### recreational activities were fishing, swimming and boating, with about a third of the Bay users participatin g i n 

### all 

### three activities. Of these activities, swimming was enjoye d by more people than either of the other two, with 77 percent of users participating. In the re maining chapters, eac h of these activities was looked at in greater detail using specific surveys of subsamples of the population. 

### In Chaptar 4, wa provide tw o types 

### of demand models fo r western shore beach uae activity. Each draw . 

### on an on-site sample o f 

### beach users at western shore beaches in the summer of 19S4. The varying parameter model is a modification of traditiona l demand models where the demand s 

### 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 mode l explains the choice among cites directly, as a function of site characteristics, but does no t handle the total number of trips well. Each type o f mode l gives a good descriptio n of one aspec t o f the recreational decision.

100

### From the results i n Chapter 4 it is char that both money and time access costs are extremel y 

### important in determining demand for trips to any given beach, as are 

### the costs of accessing alternative beaches. Whether or not an individua l owns a boat or recreational vehicle also affects deman d for a 

### subset of beaches, those which have facilities for these capital 

### goods. Demand 

### functions fo r trips to a site tend to pivot inward, becoming more elastic, with declining wate r quality. 

### The results of the nested multinomina l logit or discrete choice model of beac h use suggested acme similar and acme additional characteristics of demand fo r this activity. Once again , 

### money and tim e costs of access were important this time in explaining the choice amon g sites. Additionally, the availability of boating and recreational vehicle facilities increased the likelihoo d o f a boat or recreational vehicle owner to choose a site. An interestin g 

### hypothesi s 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 tha n for state beaches. Individual with larger parties or families were more likely to attend stat e beaches where a variet y of activitie s 

### were available. Th e 

### longer a n individual had attended western shor e beaches, the more likely he was to use local rather than state beaches. Chapte r 5 

### provides a rather extensiv e profile of 

### boaters and boat owners derived from a survey of boaters sponsored by Maryland Sea Grant and Marylan d Coastal Zone Management and from the BWSMSA telephone survey. The boater surve y subsample includes registered boat owners in Maryland. The profil e 

### includes a n analysis of characteristics which distinguish boat owners from others and looks at these distinguishing characteristics b y geographica l area. Average household income, for example, is higher for boat owners than non-owners, but this difference is onl y 

## ● tatiatically significant in Princ e 

### Georges, Anne Arundel and Calvert counties. 

### Considering the boats themselves, a different profile characterizes those which ar e 

### kept in the water al l season (in marinas, moored, etc. ) than characterizes 

### boats 

### which are trailered. As would be expected, trailered boats are significantly smaller an d less valuable, they are more likely to be runabouts o r 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 fo r fishin g 

### a t leas t t Occasionally. About three-quarters of the non-trailered boats were used for swimming at least t Occasionally. 

### Table 5.4 summarizes the boa t owners survey by county of residence, revealing mor e 

### about the geographical distribution of Bay users. Residents of Baltimore and Ann e 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.
Th e last o f th e descriptive information suggests the importance of water 

### qualit y to boaters. Water quality was considered either moderately o r very 

### important in the selection of a boating area by 75 percent of th e trailered boat owners and by 76 percen t o f th e non-trailered boat owners,

101A varying parameters model similar to the one used i n Chapter 4 revealed that trailered boat owners’ deman d 

### fo r trips from launc h sites was 

### affecte d by access costs to the launch site an d costs o f accessing alternativ e sites. In 

### general, the demand for trips to any given site was Positively affecte d by the value of th e boat; i.e., individual s with mor e valuable boats took mor e 

### trips. The demand function fo r 

### any give n site 

### tended to pivot inward an d become more elastic as wat e r 

### quality declined.
Owners o f boats kept in th e water do not choose launc h sites when they take a trip, and consequently w e have n o way of knowing where they boat. As a result we canno t model thei r decisions in responnse to varyin g water 

### quality. For these individuals? simple demand functions wer e estimated. Factors which significantl y affecte d 

### their demand fo r boating 

### trip. included the coat of a trip (negatively) and the valu e o f 

### the boat (positively). Additionally i t was determined that sailboat owners tend to tak e fewe r 

### trips and their deman d fo r 

### trips is more pric e inelastic . c

. 

### Finally, boat owners who fish while boating tend to demand more trips and their demand tend. to be mor e pric e elastic.



### In Chapter 6 information about 

## ● portfishing on the Bay is presented. Estimate s o f 

### sportfishing activity vary by data sourc e and range 

### from 539,000 to 900,00 0 anglers in 19S0 and from 2.7 million to 4.1 million trips for that came year. The two prominen t sources o f 

### information on sportfishing are the U. S. Fish and Wildlife Hunting and Fishing Survey and the U. S. National Marine Fisheries Survey.
Ou r analysis in this chapter concentrated o n 

## ● tripod bass fishing since this was the only species important to Chesapeake recreational fishing for whic h 

## ● ufflciently detailed data existed. One source (U. S. Fish and Wildlife) reports that in 1960, 239,00 0 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 o f striped 

### bass fisherme n 

### an d other Chesapeake Ba y 

### fishermen. In the analytical section of Chapter 6, deman d fo r 

### 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 expecte d direction, with owners of 

### inboard motorboat likel y 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 , mor e information about th e 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 compare d to the costs of restoration; they cannot even be added across 

### activities. A da y of 

### fishing is different from a da y 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 pa y for clean water by people who do not use the Bay. If we limit ourselves to descriptiv e measures such as user days, we ignore the returns to people



### 102who value cleaner water but do not uae it. Consequently, we have take n 

### a first step toward the logical, albeit venturesome, task of estimating the aggregate benefit s 

### of improving the Bay’s water quality.

Caveats The aggregatio n 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 vie w o f these potential errors b y recallin g the link s 

### between environmental policies designed to reduce effluent pollution and the benefits of environmental improvements. Policie s 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, implyin g benefits. 

### Further, when non-users perceive improvements in the ambient water quality, they too will be bette r off. Ther e is potential fo r errors i n 

### th e measurement of each link in this process.
Th e analysis of the previous chapters has concentrated on the connection between ambient qualit y 

### and economic benefits. I t rests, however, on the relationship between environmental policy, effluents, and ambient quality. The considerable debate regarding the connection betwee n effluents and ambient quality suggests the potential for honest differences of opinion on the nature of the ecologica l links. Similar uncertainty over the behavioral and perception links exists. While a complete catalog of th e sources o f 

### potential error would take an entire chapter, we describe broadly what we think the majo r 

### difficulties are. If the problems inherent in explaining the link betwee n policy and ambient quality are ignored, the foremost uncertaint y is between ambient quality and behavior. Recall briefly ho w 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 nitroge n readings in 1977 and trips in 19S4. Ther e is clearly substantial roo m fo r 

### 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. I t is not unreasonable to expect such a relationship to hold in principle. Chapter 2 describes ways in which individuals form perceptions o f wate r 

### quality. Some of the deductive and media-baaed mean s by which individuals form quality perceptions may be directly related to effluent discharges. Others, such as stimulants of sensory perceptions, may be highly correlate d with, or even caused by, nitrogen and phosphorus levels. Previous studie s 

### which hav e attempted to link behavior to individual ambient water quality indicator. (e.g. Binckley and Hanemann) have detected a correspondence. Chapter 2 describes acm e 

### evidence whic h supports this , hypothesize d link derive d 

### from our telephone survey of the BWSMSA and the field surve y 

### o f western shore beaches. Through the telephone a significant relationship waa detected between a household perception of the water quality in the Bay and ita likelihoo d to quit 

### using 

### the Bay. Additionally, a significant 

### relationship appeared between objective measures o f 

### th e Bay's wate r quality over time and the proportion of households who atopped using the Bay for recreation because they 

### perceived th e Bay’s water quality to be unacceptable. Finally, the use r (field ) surve y 

### showe d a positiv e 

### correlation between

103meaaures of fecal coliform at each of nine beaches and the proportion of households that found each beach unacceptable. 

### A further difficult y i s the seve n year s which separate the nitrogen/phosphorus readings and the recreational behavior, since 1977 was th e latest year for which complete information was available. 

### While this is clearly a source of potentia l error, there are a few reasons why it might not b e 

### as 

### bad as it seems. For one thing, th e relative levels 

### across different regions o f the Bay may have 

### remained approximatel y constant even if absolute levels have changed. Additionally, i t 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 o f 

### different quality, our behavioral models depend more on the relative levels of ambient water quality rather than o n absolute levels; and if relative levels have remained fairly constant, our behaviora l models are likely to be quite good. Extracting benefi t 

### measures fro m 

### these models, however, must be done with caution since th e 

### absolute levels of nitrogen and phosphorus readings used may not be trustworthy y. For recreational fishing the proble m is in some way a a little simpler. Here w e 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 growt h in the density of fish stocks. There is furthe r 

### uncertainty in the connection betwee n fish stocks and catch rates. These are largely, although not completely, problems of biology and are not addressed here, but nonetheless remai n 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 . wer e 

### taken a good measure of 

### quality? Fishermen may value higher catch rates but their demand (behavior) for 

### tripa this year may be baaed on catc h 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 enjoymen t of trips t o catch fish. Fo r example , catching on e five-pound striped baa a may be batter than catching two two-pound 

### stripers.
In addition to the severe difficulties i n inferring the relationships between ambient quality, there are two other significant sources o f error in computing aggregate benefits. First, ther e 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 surve y is a good example of non-sampling erro r for 

### trips This survey waa a mail survey , so in a sense 

### the respondents are volunteers . Th e return rate was 70 percent. We have no way of knowing whether those who competed their questionnaire . wer e representativ e o f the boating population as a whole or if ther e 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 fisherme n 

### to those who fish for striped bass and the beach users to those wh o use public-accees western shore beaches. In the boating and fishing analysis we have excluded non-Maryland households. I n the contingent valuation and beach uae analysis, onl y 2 0 percen t o f 

### Virginia’s population wae include d and abou t 

### 8 0 percen t of 

### Maryland’s households . In every instance, a major portion o f users is excluded so any estimates derived will be lower bounda.


### Another source of error in aggregating benefits across activities is aggregatio n bias . This comes in two forms: simple doublecounting and conceptual aggregatio n bias. Doublecounting occurs because a ● ubatantial numbe r o f 

### boater s also fish, and many fishermen have boats. The conceptual aggregatio n bia a 

### occurs because of the jointness of choice among sites for a give n activity and among activities. For example, the choice of visiting Sandy Point versus Point Lookout may depend i n part 

### on water quality. Enhancing water quality at both sites may only increase attendance at one sites making the addition o f benefits across sites incorrect. A discussion of this problem 

### is offered i n 

### 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, an d swimming . There are many 

### other recreational and commercial uses of the Bay whose valu e is enhanced by cleaner water. For example, our analysis o f fishing rover s 

### only striped bass; fishing for species besides stripe d bass (e.g. crabbing) is widespread and not covered by our analysis. And ou r analysis o f 

### the effect of changes in wate r 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 swimmin g because we could obtain data of adequat e quality only for these activities 

### Estimates With these 

### difficulties firmly in mind, we are prepared to hazard some judgment s o n 

### the magnitude of the aggregate benefits of improving the Bay’s wate r 

### quality. Cur approac h is to present low, middle and high benefits for the beac h use (Chapter 4), boating (Chapter 5), and fishing (Chapter 6) and qualitativel y compare those benefits with the total benefits derived from Chapter 3. Comparing the ranges of these independent 

### sources of benefits will hel p us to form a judgment, but nothin g more , 

### of the magnitude of aggregat e benefits. Chapters 4 through 6 giv e benefit 

### estimates 

### for activities conditioned on the computational method and th e proportionate change in ambient qualit y 

### and catc h rate. 

### We adopt the convention of analyzing a 20 percent reduction in nitrogen an d phosphorus for boating and beach use and a 20 percent increase in the catch rata for striped bass fishing. These changes should be interprete d 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 phosphoru s as an “all else equal” effect. The change in nitrogen and phosphoru s 

### is 

### a prox y 

### for changes in moat ambient

105

### determinant ts of water quality so that the implicit assumption is that a range of ambien t factors may 

### be improving. Further, to counteract the problem of aggregating across sites for a given activity, w e select as a pessimistic estimate the lowest estimate of the benefit s of 

### improving the quality by 20 percent at the one moat important site.
Table 7.1 summarize s som e 

### of the estimates of aggregat e benefita for our 

### groups o f 

### boaters, sportfishermen and beach users, translated into 1987 dollars. The variation fro m pessimistic to optimistic is provided by two sources: variation induced by the method of calculating benefits (i.e., using actua l trips 

### versus predicted trips) and variation caused by choosing one site rather than th e 

### sum over all 

### sites. Recal l that because each site's benefits are calculate d assuming other sites' quality remains unchanged aggregating thes e 

### measures ove r sites will produce an upwardly biased aggregate benefit. Th e pessimistic estimates for beach use and for boating are the lower of the tw o estimates of the benefits for a 20 percent improvement in water quality from Sand y Poin t fo r 

### beach uae and Ann e Arundel County for boating. One sit e was chosen as a lower bound because wtth only one site all (upward) aggregatio n bias is eliminated. The average estimates fo r 

### beach use and boating are the lower of the two calculation methods for 

### sums across all sites. The optimistic estimates are the highe r o f 

### the two calculation methods for the sums across all sites. For striped bass fishing, the pessimistic estimate is the lowe r of th e tw o 

### methods of calculation. The sites have already been aggregate d for the fishin g case, and as we show in Chapter 5 of the accompanyin g volume, the natur e o f 

### 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 Chesapeak e Bay’s Water Quality in 1967 dollars Benefit Estimate Activity Pessimistic “Average” (Mimistic ($ Thousand) Publi c 

### Western Shore Beach Use a 16, 853 

### 34,658 44,960 Boating with Trailered Boat b 4,717 8,129 Stripe d Bass Sportf ishingc 1,366 2,071
● 

### From Table 4.6 . Pessimistic estimate is the Method B value for Sandy Point, the average is the sum o f 

### Method B values over all ten sites, and the optimisti c is the sum of Method A values over all sites. b From Table 5.13. All per boater estimates expanded to 60,000 boaters “ trailering boats. Pessimistic 

### estimate is th e 

### low value (Method A) fo r 

### Anne Arundel County, the average estimate in the sum of low values (Method A) across all counties and the optimistic value is the su m 

### of high values (Method B ) across all counties. c From Table 6.4. Pessimistic value is the value using Method B, the “average” valu e is the average of the pessimistic and optimistic value, the optimistic valu e is the value using 

### Method A.

106

### The aggregate meaaurea of willingness to pay for 

### water quality 

### improvement s are revealing for several reasons. First , 

### regardless of which benefi t measure we use (pessimistic, average, or optimistic), the returns to beach use are the greatest. This ia primaril y 

### 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-fishin g group. A second interesting implication o f the results, although not obvious from looking at Tabl e 7.1, is the importance of regional variation in water quality. If w e wer e 

### 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 progra m 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 compariso n o f th e 

### behaviorally based measures of benefits presented in Table 7.1 with benefi t 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 on e activity and involve onl y ● 

### 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 th e 

### population include s those i n 

### the BWSMSA who found water quality unacceptable for swimming or related uses.

Table 7.2 Aggregate s 

### Benefits from Water Quality ImprovementsContingent Valuation 

### in 198 4 dollars Willingness t o Pay for Improved Water Quality b 

### Group Pessimistic 

### c Average c Optimistic c ( $ 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 b Wi ll ingness to acceept tax increase to raise Chesapeake Bay Water quality from a level unacceptable fo r swimming end/or other related activities to a level acceptable fo r swimming. c The average will ingness to pay 

### plus or minus one standard error in est imate.

Se e Table 3.8.

107The numbers in Tables 701 and 7.2 give likely magnitudes for the annual benefit s 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 th e 

### 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 o f 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 tru e 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 

### deman d for and valu e o f 

### improvements i n wate r 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

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### Michael Hanemann and Ivar Strand. 1985. “An Economic Assessment of Marine Recreational Fishing in Southern California.” Report submitted to Nationa l 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. ” Marylan d 

### 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 Ad justments." Journa l 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.” J ournal of the American Statistical Association. 74:365:185-193.

120

### Appendix 

### A
The Random Digit Dialing Survey Telephone Procedures

For the Random Digit Dialin g 

### Survey, three instruments (copies included in the Appendix) were developed: a two-page screenin g 

### form, a n 1 l-page, 

### 28-item questionnaire, and a Record o f 

### Calls sheet.
1. Screening Form The screening for m was intende d 

### to determine th e 

### eligibility of the location 

### served by a randomly generated telephone number. The number was printed on a label affixe d to th e to p 

### of th e screening form. Pa y phones and phones use d only b y businesses wer e not considered eligible, since people answering ouc h telephones would be eligible at their residence phone.. In addition, if th e household served by the phone wa a not located within the counties/citie s 

### making up the selected SMSA’s the n that phone (residence) was not eligible. Once a n eligible phone (residence) waa identified, a membe r of th e 

### household who waa 1 8 years of age or older waa required. I f all residents were under 18, the screening waa completed wit h ● 

### membe r of the household who waa 14 or older.
The screenin g form wa a 

### composed of fiv e sections : a n Identification sections consisting of an area code, telephone number, and five-digi t case identification number, all printed on the aforementione d label; ● 

### brief introduction to be read by the interviewe r 

### which explained 

### the study; a screenin g section which waa used t o eliminate pa y phones, businesses without living accommodations. and residences not located in certain specific SMSA's; a 

### screening status section to record the screening eligibility o f the location; a questionnaire status section to record whether or no t a 

### questionnaire completed with an eligible person.
2. 

### Questionnaire Th e Rando m Digit Dialin g 

### Survey Questionnair e was intended to determine th e following:
Uae or intent t o 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 th e 

### respondent or other members of his/her family do not go in the wate r during visits to the western shore beaches; Changes in swimmin g participation in the Chesapeake brought about 

### by change. in the water quality; The respondent’s perception of th e water quality in the The value respondents place on the Bay and how they 

### improvement should be made and financed. Chesapeake; visualize that

121As i n 

### the User Intercept Survey, a series of demographic questions which wil l 

### enabl e 

### analysts to establish profiles of beac h users and nonuser were included in the 

### questionnaire.

The Random Digit Dialin g 

### Surve y Questionnaire wa a also divide d into sections. Th e first, Identification, had space 

### provided for recording the case ID number from th e screening 

### forms the telephone interviewer’ s initials, and the date the interview was 

### completed. Th e 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 informin g th e 

### responden t o f th e 

### voluntary nature o f his/her participation in the study and assurance s of the 

### confidentiality of the dat a collected . The thir d section
, 

### Recreational Uae of the Chesapeake Bay, sough t 

### specific responses 

### which would: (1) enable analysts to determin e if and how the beaches were used and (2) wha t the overal l 

### perception of the water quality waa. Thie waa followed by a fourth and fina l section consisting of some 1 8 demographic questions.

Data Collectio n Methods Two field interviewers were trained in Baltimore for the data collection of the Ueer Intercept Survey on Ma y 25, 1884 . A 

### Field Interviewe r Manual was developed (whic h is available upo n request) and include d quemtion-by-question 

### specification, probing techniques , confidentiality procedures, refusal conversion strategies, and other measure ● 

### necessary t o assure the collection o f standardized, quality data during the course of the fiel d survey process. Alao covered in the manual were: background information, assignment information, sampling procedures and administrative procedures.
Th e fina l 

### day o f 

### fiel d wor k on the 

### uee r survey was August 16, 1984 . The confirmation portion of the user surve y was completed o n 

### September 1. The followin g represents the respons e rates fo r 

### the field work:

Table A.1
Response Rates fo r 

### Beach User Survey

Sample 

### S uccessfull y 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 th e folowing 

### reasons: Ineligibl e 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 othe r 

### piec e of information through which contact could be mad e to conduc t 

### a confirmation/intention 

### interview. Approximately 240 (71 percent) o f 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 tota l of 1 1 

### telephone interviewer. were hired wit h three
o

f 

### thes e spending the majorit y o f 

### their tim e making confirmation/intention calls to participants in th e Use r Intercep t Survey.

As i n 

### the User Intercept Survey t 

### 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 Manua l (available upo n request) include d question-byquestion specification, probing techniques, confidentiality porcedures, refusal conversio n 

## ● trategiea, and othe r measures 

### necessary to 

### assure the collection
o

f 

### 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 fina l totals for 

### screenin g and questionnair e status codes.

123

### Table A.2 Questionnaire Completions Per Strata

Stratu m 

### Cases Cases Quest. Quest

. Tota l Quest . 

### Number Avail. Assigned Complet e 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 1,01 4 1,01 4 

### 9 6 4 100 112 - 1 2

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/Screene r 

### Completed

. . . . . . . . . . . . Nota Working 

### Telephone Number

. . . . . . . . . . . . . . . . Pay Telephone . . . . . . . . . . . . . . . . . . . . . . . . Business Telephone . . . . . . . . . . . . . . . . . . . . . . 

### N o Answer After Repeated Calls . . . . . . . . . . . . . . . . Telephone Not Located in Bait./Wash . SMSA . . . . . . . . . . No Eligible Respondent Available After Repeated Calls

. . . . Refused to Answer Screen ing Questions

. . . . . . . . . . . . Language Barrier O.... . . . . . . . . . . . . . . . . . . Other . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Questionnair e 

### 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

124Appendix B
Telephone Survey instrumen t



### 125t

A. IDENTIFICATION TsuPllGNs NUMBER (_)-
- - CASE ID NUMBER I I I

B. INTRODUCTION I m callia
- frsa the Rescmch conduc t ● 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. T O typo of place , X nee d 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 (R ANG UP AN D DI AL NUPfSSR A G A IN) 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 fo r 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 ● ilabl e 

### t o talk to a? AllsuR: (CALL SACKIFZ csssAsY To CO?IPLSTS SCRSESING) Io tkis rosidoace located or B- Otkor 

### place? Horylaad Virgini a 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 (GOT O 7)
VIRGINIA (Arlio@on, Fairfsx, Loudoa, ● od Prisce Uillias. Also iocludo Alexmdri~ cicY, Fairfw city sad ~~lla Church cit y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ~ (@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 thi s 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 Qm IONMIRS 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 Cell o . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 & ‘1 Date

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 non 

### users 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 ques 

### tions 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 a inthewater foranyreason? . . . . . . . . . . . . 1 (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 YEs BEACH 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.)

a Yes . . . ;. . . . . . 2

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 . . . . . . 

# ● z l 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 Surve y 

### and Sampling

This section is devoted to a description of the samplin g 

### procedures used in a survey of Chesapeake Beac h Use conducted in 1984. Unlike the data used i n analyses of boating and fishin g in Chapters 5 and 6, th e dat a use d 

### in this chapter were collecte d during a n earlier budget period o f 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 o f 

### th e survey instruments can b e found in Appendix C.

From Ma y 26, 198 4 to August 19, 1984 , Research Triangle Institut e (RTI) interviewed individual on the western shore beaches i n 

### Maryland

. The study 

### population consisted o f all resident s 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 recreatio n in 1984. More recreationa l users of the following 12



### 1 0 2. 3. 4. 5. 6. 7. 8. 9. 

### 10. 11. 12. sandy Point Point Lookout For t 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 a t the beach to lear n of their recreational patterns and perception of water quality a t these beaches. These individual were randomly selected fro m 

### sample beaches and days. The sampling design can be described as a two-stag e stratified sample in whic h a probabilit y sample o f beaches and day s was systematic sampl e of persons was interviewed at each combination). 

### selected, and a random sample 

### site (day-beach



### i The discussion of th e sampl e is compose d o f 

### selected excerpts fro m Devore, McDonald , Myers and Williams , Ch esapea ke Ba y Beac h Use Survey, Research Triangl e Institute, 1984.

139

### A sample of at 

### least 300 completed interviews was intende d so that relative sampling errors would be approximately 11 percent o r less for estimating proportions of .30 or larger. Thi s assumes that a design effect of 

### approximately 1.5 might result because of clustering (a beach-day unit constitutes a cluster of individuals) an d unequal . 

### weighting . A 

### self-weighting sample was sought but difficult to obtai n because of errors i n size measures (projected number o f 

### users for a beach-day unit).
The number o f sample primar y sampling unit s 

### (PSU) waa baaed o n the requirement to obtain a t least 300 completed interview . such that each PS U would involve approximately one-half day o f interviewing for a two-person team. T o 

### match this design, eac h PS U was randoml y designated a . a morning or afternoon interview 

### period, beginning at 

### 1000 and 1300 

### hours, respectively

. 

### Hence, a n averag e o f 

### eight o r nine completed interview. waa expected for each of the 36 PSU’s (see Tabl e 4.1 for sample allocation).

Stratification an d Sample Selection Primary 

### sampling unit . (PSU’s) consisted of beach-day units (1,20 4 PSU’s

= 66 days x 14 locations). The 14 locations consisted of 11 beaches plus three beac h locations at Sandy Point — partititioned into three segments because of its relativel y large size an d usage . Beaches were stratified into north (Sand y Point an d beaches north) and couth (th e prio r listing of beaches indicates the stratification). The beache s wor e further stratifie d by size (expected weekly usage) . Large is define d as greater tha n 7,00 0 visitors per week i n 

### north beaches and greater than 3,000 in th e 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 

### Populatio n Counts and PS U Allocation by Stratum s 

### Anticipate d Number of Beach Number o f PSU's (beach-day Visitors May 26 to units ) Allocated t o Bach August 19, 196 4 (thousands) Stratus Sa t Su n Weekday Total Sa t Sun Weekda y Total North/large 7 7 110 106 293 3 5 5 13 North/smal l 676 4 94 243 3 a 4 2 

### 4 

### 13 south/large 37 43 44 124 2 2 2 2 

### 6 South/small 20 24 17 61 2 2 

### 2 2 6 Totals 201 261 259 721 10 13 13 36

1 

### Population counts are baaed on cit e interviews prior to th e survey, an d PSU

allocatio n is proportiona l to number o f visitors with th e constrain t that greater or equal to two per cell are selected. 2 

### One fewe r resulted in these cells 

### because o f 

### rando m subsampling needed in

the latter part of the surve y period. (A slightly larger number of interviews than expected were obtained early in the survey.)

140

### Th e selection of PSU’s within strat a involved equal probability y for days and probabilit y proportional 

### to size (expected numbe r o f 

### visitors ) fo r beaches. Within selected PSU’s, individual were selected on site with equal probabilities. Approximatel y 

### eight or nine interviews were needed from each PSU

. The procedure 

### used t o satisfy thi s ob jective waa for th e interviewer s 

### to estimate the number of beach 

### users just prior to startin g time for 

### interviewing. B y 

### comparing thi s 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 patter n for 

### the entire site, a valid systematic sample of users 

### was obtained.


### Sampling Weights Whenever observation unit s (i n 

### this caae the individua l users) enter the sample with different probabilities, weights must be used with the observation to obtain unbiased estimates for th e 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 , an d final-stage selectio n o f 

### individual can be reasonably estimated (even though a systematic sample o f 

### 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 th e 

### j th 

### individual is calculated a a follows:

wher e



### P i



### d

n

s,

P i 

### j


### f i 

### j and f; j 

## 1 

# ‘s L the "selection probability" (expected number 

### of hits) d 

# ‘ + 

# ‘ for PSU (i);
th e number of daya during the survey period for the particular type of day bein g sampled (d = 13 for Saturdays, for example);
numbe r of PSU’s 

### being selecte d fo r the stratum (12 strata);
siz e measure fo r PSU (i) = the expecte d number of visitors for that type of da y (S + = Zsf); 

### sampling rate of users withi n sample PSU (i); and

factors t o adjust for number o f houra use r spent at the beach 

### on th e sample day an d number of trips to th e study beaches during th e summer, respectively.

141

### Note that f ij an d f i j are baaed in part on intentions. I n fact, f; j is particularly uncertai n 

### if the intervie w 

### is 

### being conducted early in the season. T O 

### verif y th e accuracy o f 

### these intentions both t o obtain selection multiplicity and to estimate 

### related statistics, intention-based questions were verified b y telephone at the end of the season fo r 

### interviews taken early in the survey.

Screening 

### and Confirmation A 

### screening form wa a designe d to identif y as eligible respondents only those people living in certain counties of Virginia or Maryland, or Washington, Do C. I n addition, the screenin g process ruled ou t as ineligible an y person wh o was under 1 4 years 

### of age ; however, if the selected sample individual was over 14 bu t less than 18 

### years of 

### age and i n th e company of someone he/she lived with who 

### was ove r 1 8 years o f age , 

### th e interviewer deferred to th e 

### older individual.
Th e Use r 

### Intercept Survey Questionnaire was designed to record and collect th e following:

* Frequenc y 

### of visits made to beaches o n the western shore o f th e 

### Chesapeake

● 

### Activities that the respondent (and his/her family) participated in when visitin g beaches

* 

### Activities not participated in and th e reason why they were 

### n o t

. 

### Coat related to a typical trip t o each beach that the respondent had visite d since January 1, 1984

● The respondent’s perceptio n of 

### th e 

### quality of the beach and the beach facilitie s ● 

### 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 th e use of the beach were to rise.
In addition , a 

### series o f 

### demographi c questions to enabl e analysts to establis h profiles of beac h users wer e also included in the questionnaire.

A Confirmation/Intention Contact sheet (used in conjunction wtth a Record of Calls sheet ) was developed not onl y to confirm 

### the numbe r o f 

### visits to 

### sampl e beaches reported in the Intercept Survey Questionnaire, bu t also to ascertain the number o f visits th e respondents intende d 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 contac t the m 

### by 

### telephone.

142

### Dat a Collection Procedures The dat a collection schedule originally called for a total o f 36 visits to the 12 sample beaches, with an expected yiel d of 300 o r 

### more completed interviews. Thi s schedule was later revised and th e subsequent number of tota l visits wae reduced. Tabl e 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 mornin g 

### at 10 o’clock, and PM indicates interviewing beginning at 1 o’clock in the afternoon):

Beach Beech AM P M Total er Name Vis i ts Vimit s Visits 1 Sand y 

### Point 5 6 11 2 Point Lookout 1 1 2 3 For t Smallwoo d 1 0 1 4 Miam i 

### o 3 3 6 Elm s 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

Fo r the Use r Intercep t Survey, eac h fiel d interviewer waa given a Uatin# of the names, addresses, and I D numbers for each PSU (beach) in the assignment, alon g 

### with area maps 

### with the beaches marked. I n 

### addition, each interviewer wae given a sketch of each beach. Other materials included were the table. to determine sampling rates and listing forma fo r 

### counting and selectin g sample individuals. The interviewers always worked as a team, splitting up only to interview eligibl e respondents.


### The fiel d 

### interviewers were asked to review thei r material s and determine the moat efficient rout e o f 

### travel to reach each beach. Upon arrival at a beach, they ha d first to check that they had correctly identified and located th e precis e boundaries of the area. Once this waa verified, the interviewer s estimated the number of sample individual on the beach , spending no more than 30 minutes in doing ao. When th e estimate was determined , they looked up that number in the table of sampling intervals. Marking th e estimate and sampling interval at the top of the listing form
, 

### they next consulted thei r list of random numbers t o 

### determin e th e number o f 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 prope r 

### interval, and circled th e last interval number as th e next 

### selecte d person. This activity continued unti l 

### they had gone through the entir e list interviewing the selected individuals.

143

### Tabl e C.2 Chesapeake Bay Beach Use Study Data Collection Schedule

Beach Beach Sample Beach Beach Sample Date Name 

### Number Time Date Name Numbe r 

### 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 Ba y 

### 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 Sand y Pt. (NofS) 

### 100 0 1

sand y Pt

. (SofS) 

### sand y Pt. 

### (SofS) Rod & 

### Reel 1300 1300 1000

Sand y 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 Au g 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1000 * Pt. Lookout Rock y Pt. 

### North Beach Sandy Pt

. (E) Rocky Pt. 1000 1000 1000 1300 Bay Ridge Ft

. Smal 

### lwood 1300 1000* Rocky Pt. 

### 1000

1300* 1000 

### Kurtz Pt. Lookou t Breezy Pt. sandy Pt . (SofS) 1300
1000 Sandy Pt. 

### (E) 1300 * July 1 2 3 4 5 6 7 

### 100 0 sand y Pt. (SofS) 

### Sandy Pt. (E) 

### 130 0 1 18 19 Sandy Pt

. (NofS) 1 

### 1000*

%riginally scheduled but subsequently eliminated.Appendix D
User Survey instrument



### 145-- —--- .-Au k 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

. Researcher s ● 

### t the University of Maryland are currently studying citizens’ us e of 

### th e Chesapeake Bay and they are looking for certain types of people to ansuer ● 

### questionnaire. I would like t o ● 

### sk you 4 questions we are looking for. First. . . III. ELIGIBILITY: A. Do you currently live in t o determin e 

### if yo u ● 

### 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 LISTE D ● 

### . . . . . ● 

### . . . . . . . . . . . ● 

### . . . . . . . . . . . m
c

. 

### How old ● 

### re you? (12 ) 

### Less than 14years of age. . . . . . . . . . . . . . . . . . m Over 14, but less than 18 years of age . . . . . 2 u 18 years ofage or older. . . . . . . . . . . . . . . . . . . El D. Did you cone her e . ..with other people including someon e you live with who is (13) c1 1 8 year s of ● ge o r 

### older? . . . . . . 1

. ..other people but none you live with who is ~ears of age or older? . . . . . . . . . . . . . . . m

. ..by your self? . . . . . . . . . . . . . . . . . . m (Go to C)

(G o 

### to C) (Go toF)

(Go to F) (Go to D) (Go to G)

(Go to 

### E)

(Go to G) (G o to G) 

### E. I would like to talk to the perso n yo u live with who as 18 years of age or older and is here with you today. Point tha t 

### 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. BEFOR E YO U 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~ ~ -’”’~
TI~ LEAVING BEACH: * IV. RECORD OF ELIGIBILITY (Screening Status) (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 1CHESAPEAKE B AY 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


### A S 

### 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, parti 

### cularly ● 

### 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
1 0 

### 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 . . . . . . . . .(Z13. [SAHPLE SITE NAME] 

### is situated on the Western Shore of the Chesapeake Bay. 1 1983, have you (and members of your family who ““’e ‘~LLOWING) live with you

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 BLANK000, 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?

. . . . 

# P

.m

. . . . . . . . ..m. (~2.4. ”””1J-.4.” 

# ““”””””””~,

hooch ● ctivitioomabathiac, picnicking , shollins?
. . . . . . . . . . m< . . . . . . . ..m. (~ ’). ”””” ~)”” ““””””””g,

beach ● ctivitiosplaygroaad?
. . . . .

boociw?

. . . . . .

wstor8M*?

. . . . .

Other Activitioo? (Specify) . . . . 

# P m......,...m.Q2).”..” 

# ~-24) -”-g J

. . . . 

# P m..,....’..m”(~’)..... 

# #-2#)””

. . ..” Q)” Q)

. . . . 

# P P m.......”..”.(s~j....” ~-J41.”..””.... 3s-s6 ) 

# P m..........m.~’)..... 

# ,-J$l-”-. ”Q)

. . . . 

# P m..........”.(ft)..”.” ~A4,”......*.” 

# P 4S4U )

. . . . P m..........m.~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 facilitie s 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. . . . . . . . m ImF) ““TG%?) 

# P .JT-l #3-64)”””””””” (Ss-ds)

. . . . . . . . . . P . . . . . . 66-63)” “ “ “ II 1 “77Z7T) 

### 1~~ 7 . 2-$0 = B SIT U CUE 011

w P7 -)”””””””””” 4-)

!T -6)”””””””””” P - 9-10)

### 6. H OW 

### 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 visite d 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

m a . . . . . . . . . . . . 0748) . . . . . . . . . . .(1



# m

. . . . . m

m a . . . . . . . . . . . . . (22-23 ] . . . . . . . . . . .(24 CD

. . . . . Q). (mu) . . . . . . . . . . 

# m C!3 . (27-28 ) . . . . . . . . . . . (2

. . . . . ~.(32)#. o....... m .(ss-s3)

. . . . . . . . . . m!
. Q

. . . . . m a . . . . . . . . . . . 

# m ml

. 674) . . . . . . . ...”

.

. . . . . m

lTJ. (4z)

. . . . . . . . . . . (42-43 ) . . . . . . . . . . .U4 m l

. . . . 

# m .~ . (46) . . . . . . . . ..- .tiza)

. . . . . . ..o. m
.

. .. 0 .~. (sz)

. . . . . . ..oi m l .’(28-ss) . . . . . . . . . . 

# m
. (

. . . . . ~. (m

. . . . . . . . . . 

# m .(2z4t)

. . . . . . . . . . m
.

. . . . 

# m l .~. (cl) . . . . . . . . . . .f#A#s)

. . . . . . . . . . 

# m l . (s

?Oiot kokout St. ?mk

. . ..~. (ue)

. . . . . . . . . ..[-rl. D $7-88 ) . . . . . . . . . . .U9

( COLS

. 71-80 = WSITE/CX

UB’ssoack.

. . . . . . . . @

. (J) . . . . . . . . . . 

# m m . ( 24) . . . . . . . . . . .(
Borgootouo Sooth . . . . . . .m. (u)

. . ..- . . . . ..m. m ( 7-4 ) . . . . . . . . . . .(

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

. . . . . 

# m . . m. (

~ . (26) . . . . . . . . . . . (2748 )........ _

—.” 

# m

.~. (31)

. . . . . . . ..o . (32-33 ) . . . . . . . . . . .(Z m9. 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 .m. up ) . . . . . . . . . . . . .UI-42) BoyRidmBoMk

. . . . . . . . . . ..m . (43) . . . . . . . . . . . . . In . (44-4s )

Eorriagtoohrbor

. . . . . . . . . . .m. (mu)
. : . . . . . . . . . . . m l . (4?-48)

xuru Plumrosooch....

. . . . ..~. (as) . . . . . . . . . . . . . 

# m . (s041)

WIlorrick

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

# m

..~. ($z)

. . . . . . . . . . . . . .(s=4)

BroosyPointBo8ch.

. . . . . . . . . 

# m .m. (as)

. . . . . . . . . . . . . . .(34-s7) 

### chos8poako Botch . . . . . . . . . . 

# ..m. (i)

. . . . . . . . . . . 

# ..m. (S&so)

morthBeack

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

# m

..~. (~1) . . . . . . . . . . . . . .(62-43)

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

. . . . . . . . . . 

# m

..~. (Z)

. . . . . . . .. 

### S... .(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



# w w u Total E

R-t Tin 

# P 18-19

P 4S-46

P 8-19:

P 4s48 1

P 8-1s 1

P 4S-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 

# W 

# w, la

COLS. 73-s0 = Sml CAss 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 o f
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 A Rml “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 )“

P 2

. Ill

P M) “

P 22) “

P 2

. 24)

R S1 “

P M) “

P 41) “

P a) “

P ‘ 

### Sz) “

‘w )“

‘w )“

P ‘ 66) “

. 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

. . m w

. . ~ (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)

. ~ (2 0

. ~ (2 s

. ~ (3 0

. ~ (3s

. m (4 0

. ~ (4 s

. ~ (s0

. ~ (s s

. ~(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 

### OF THIS S-.

——— ADD THE NUMBER ENTERED FOR BOTliimS- ~ ~ NERE. + 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.

. . . . . . . . . ● . . ● ● . . . . $30.0 0 5. . . . . . . . . .
● .*

. . ● ● . . $25.0 0 6 . . . . . . . . . . . . . . . . . .. $20.00 7.
● . . . . . . . . . . . . . . . . . $15.0 0 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 ● xtr a 

### 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

m Don’t fiOW

. . . . . . . (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.....

.

a 

### f. . . . . . . . . . 06 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 

### member s 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 

### wil l 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 GO TO 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 

### TELEPHON E CONFIRMATION SCRIPT AND INTRODUCTION

I am calling to spea k with ( INSERT NAHE ). 

### IF NOT THERE, FIND OUT WHEN HE/SHE WIL L 

### BE AVAILABLE. WHEN AVAILABLE SAY: Muy name is (INSERT NAME ) ● nd I 'm callin g 

### for the University of Maryland. O n (INSER T DAY AND DATE you were interviewe d ● t (INSERT THE NAME 

### OF 

### THE BEACH WHERE INTERVIE W 

### TOOK PLACE) b y ● lady working for the University o n ● survey about th e 

### Chesapeak e Bay. Do yo u 

### remember that interview? (IF YES PROCEED. IF NO, TRY TO REFRESH THE RESPONDENT’S MEMORY BY TELLING HIM OR HER THE TYPE O F QUESTIONS ASKED.

. 

### I F STILL NO, TERMINATE THE CALL).
I’m callin g today t o confirm som e o f the information you reported the day 

### you were interviewed. I want to know which beaches you have visited or plan to visit on the Wester n Shore of th e Chesapeake Bay. I will read through ● list o f beache s ● n d ● s k ● bout each one individually. First,
GO TO QUESTION 34.A.