HomeMy WebLinkAboutPeconic Estuary System Recreational & Resource Economic Values Feb 1999Economic Analysis, Inc.
FINAL REPORT
Recreational and Resource Economic Values
for the Peconic Estuary System
Prepared by:
James J. Opaluch, Thomas Grigalunas,
Jerry Diamantides, Marisa Mazzotta, and Robert Johnston
ECONOMIC ANALYSIS, INC.
P.O. Box 3462
Peace Dale, Rhode Island 02883
With a contribution by
Applied Science Associates
Narragansett, RI
Prepared for:
PeconicEstuaryProgram
Suffolk County DepartmentofHealth Services
County Center
Riverhead, N.¥.11901
February, 1999
P.O. Box 3462
Peacedale, RI 02883 (401) 789-0050
DISCLAIMER
Although the information in this documem has been funded wholly or in part by the United States
Environmental Protection Agency under assistance agreement number CE992002 to Suffolk County,
it may not necessarily reflect the views of the Agency, and no official endorsement should be
inferred.
Acknowledgments
This study could not have been carded out without the generous assistance of a great many people
who gave of their time throughout our project. Special thanks are due to Vito Minei and Walt
Dawydiack of the Suffolk County Health Depa~hnent, Kevin McDonald and Gayle Marriner-Smith
of the Citizen's Advisory Council for the Peconic Estuary Program, and Cynthia Decker of the New
York State Department of Environmental Conservation, all of whom were ail especially helpful, and
provided a great deal of useful information.
We would aiso like to thank the many people who took time fxom their busy schedules to participate
in focus groups, interviews, and pre-tests as part of our survey-development project. The many focus
groups, interviews, and pre-tests were criticai for the successful development of the recreation and
resource surveys used in our study; and we greatly appreciate the help of ail those who participated.
The two surveys summarized in this report could not have been administered without the hard work
of the members of the survey teams: Wendy Mitchell, Mike McNair, Paul Lamb, Maureen Devitt,
Helen Besedina and Jeff Kline. Special thanks are extended to Mike McNair whose research
assistance, persistence, organizational and communications skills were tremendously helpful. We
aiso acknowledge the survey respondents--over two thousand residents, second homeowners, and
visitors to the East End--who gave their time and opinions.
Thanks also are due to staff of the Tax Assessor's Office of the Town of Southold who not only
made available to us property transactions data for the town for 1996, but aiso provided important
and useful insights into the locai property market that helped guide our property vaiue case study.
We aiso acknowledge the time spent with us by realtors in Southold who gave us the benefit of their
insights into local real estate issues. Collectively, the expert comments from the Tax Assessors
Office and local realtors greatly contributed to our anaiysis of property values and how they are
affected by neighboring amenities.
Two other individuals who also made important contributions to this report should be recognized.
Mary Hutchinson made an important contribution to the property value study by helping us
incorporate a Geographic Information System into this analysis. Clarice Coleman also provided
valuable support in coding some of the data, and she helped edit the finai draft.
To all of those mentioned above, and to the many others (too numerous to mention by name) who
helped along the way--our heartfelt thanks! Of course, the authors alone bear responsibility for any
errors.
ii
Table of Contents
Disclaimer.
Acknowledgments .............................................................. ii
Table of Contents
· ....'''''""''"'...-...-.........-........................ iii
List of Tables.
List of Figures ................................................................ viii
EXECUTIVE SUMMARY ..................................................... 1
IA.
IB.
PURPOSE AND SCOPE 1
ESTUARINE-RELATED USES, RESOURCES AND
ECONOMIC VALUES ................................................. 2
I.B.1. Introduction and Overview ....................................... 2
SUMMARY OF SELECTED RESULTS ................................ 3
I.C.1. Property Value Study (Chapter III) .................................. 2
I.C.2. Recreational Uses and Economic Value (Chapter IV) ................... 3
I.C.3. Wetlands Productivity Values (Chapter IV) ........................... 5
I.C.4. Resource Preferences and Economic Values (Chapter VI) ................. 5
REFERENCES ....................................................... 6
II. BACKGROUND AND INTRODUCTION ..................................... 7
II.A.
II.B.
II.C.
II.D.
BACKGROUND ................................................. .-... 7
NATURE AND IMPORTANCE OF NO-MARKET RESOURCE VALUES ..... 8
II.B.1 .Overview of Studies and Basic Concepts ............................. 8
PURPOSE AND SCOPE
REFERENCES
III.ENVIRONMENTAL AMENITIES AND PROPERTY VALUES:
A CASE STUDY OF THE TOWN OF SOUTHOLD 13
III.A.
III.B.
III.C.
III.D.
INTRODUCTION .................................................... 13
RELATIONSH/P BETWEEN ENVIRONMENTAL AMENITIES AND
PROPERTY VALUES ..................................... ~: .......... 14
APPLICATION OF PROPERTY VALUE MODEL TO SOUTHOLD .......... 15
III.C.1. Explanation of The Property Values Method ......................... 15
STATISTICAL ANALYSIS OF SOUTHOLD LAND VALUES ............... 17
iii
III.E.
III.F.
III.G.
III.H.
III.I.
ANALYSIS OF SOUTHOLD LAND VALUES: RESULTS .................. 17
SIMPLE ILLUSTRATION OF PROPERTY VALUE IMPACTS:
VALUE OF OPEN SPACE ............................................ 18
LIMITATIONS IN PROPERTY VALUE ANALYSIS ....................... 20
SUMMARY ........................................................ 21
REFERENCES ...................................................... 21
APPENDIX A.
APPENDIX B.
APPENDIX C.
Variables in the Hedonic Analysis ................................. 24
The Statistical Model ........................................... 29
OLS Model Results ............................................. 30
IV.OUTDOOR RECREATIONAL USES AND USE VALUES IN THE PES .......... 31
IV .A.
IV.B.
IV.C.
IV.D.
IV.E.
IV.F.
IV.G.
IV.H.
IV.I.
IV.J.
IV.K.
INTRODUCTION .................................................... 31
IDENTIFICATION OF RECREATIONAL USES AND LOCATIONS .......... 32
SURVEY DEVELOPMENT AND IMPLEMENTATION .................... 33
DESCRIPTION OF THE RECREATIONAL USE SURVEY
QUESTIONNAIRE ................................................... 34
DESCRIPTIVE RESULTS: DEMOGRAPHICS ............................ 35
DESCRIPTIVE RESULTS: KEY RECREATION ACTIVITY PROFILE ........ 40
DESCRIPTIVE RESULTS: PARTICIPATION AND TRIP ESTIMATES ....... 45
DESCRIPTIVE RESULTS: SELECTED EXPENDITURE ESTIMATES ........ 49
DESCRIPTIVE RESULTS: REFERENDA AND BROWN TIDE RESULTS ..... 53
ESTIMATES OF ECONOMIC VALUE OF RECREATION .................. 56
IV.J.1. Introduction ................................................... 56
IV.J.2. Methodology .................................................. 57
ESTIMATES OF ECONOMIC VALUE: RESULTS ........................ 59
IV.K. 1. Value (Consumer Surplus) Per Trip ........................................ 59
IV.K.2. Total Annual Economic Value for All Trips ......................... 59
IV.K.3. Asset Value o£PES for Recreation Activities ........................ 60
IV.K.4. Change in Swimming Trips and Benefits Due
to Hypothetical Water Quality Changes ......................... 60
IV.K.4. Change in Fishing Trips and Benefits Due
to Hypothetical 10% Change in Catch Rate ...................... 63
IV.L. REFERENCES ...................................................... 64
Appendix A. Supplementary Tables ........................................... 65
Appendix B. Technical Appendix: Swimming Trips and Value
Estimation .................................................... 69
Appendix C. Comparison of Recreation Activity Estimates:
Phase I and Phase II Reports 84
iv
V. WETLAND PRODUCTIVITY VALUES IN THE PES .......................... 86
INTRODUCTION .................................................... 86
METHODOLOGY AND DATA ........................................ 87
V.B.1. Introduction ................................................... 87
V.B.2. Food Web Estimates ............................................ 88
V.B.3. Habitat Estimates ............................................... 88
RESULTS .......................................................... 89
V.C. 1. Introduction ................................................... 89
V.C.2. Results ....................................................... 89
SUMMARY AND CONCLUSIONS ........ ............................. 90
REFERENCES ...................................................... 91
VI. NATURAL RESOURCE PRIORITIES AND VALUES ......................... 92
VI.A.
VI.B.
VI.C.
VI.D.
VI.E.
VI.F.
Appendix A
Appendix B
INTRODUCTION .................................................... 92
THE NATURAL RESOURCE SURVEY ................................. 92
VI.B.1. Survey Development and Implementation ........................ 92
VI.B. 1.a. Initial Meetings, Interviews, and Information Gathering ...... ~93
VI.B. 1.b. Informal Interviews With the Public ...................... 93
VI.B.I.c. Focus Groups and Preliminary Survey .................... 93
VI.B.I.d Survey Pretests and Implementation ...................... 96
VI.B.2. Questionnaire Design ........................................ 97
VI.B.2.a. Selection of Survey Format ............................. 98
VI.B.2.b. Selection of Natural Resources and Level~0
VI.B.2.i. Natural Resources Included in the Survey ................. 99
VI.B.2.ii. Levels of Nataral Resources in the Survey ................ 100
VI.B.2.iii. Description of Survey ................................ 103
DESCRIPTIVE RESULTS ............................................ 104
VI.CA Who Responded to the Survey? .................................. 104
VI.C.2 Participation in Recreational Activities ............................ 108
VI.C.3 Support for Management Actions ................................. 108
VI.C.4 Opinions About Brown Tide ' 111
VI.C.5 Membership in Organizations .................................... 112
ESTIMATES OF ECONOMIC VALUE ................................. 113
VI.D.1 Responses to Contingent Choice Questions ......................... 113
VI.D.2 Results and Discussion ......................................... 114
VI.D.3 Discounted Present Values ...................................... 116
CONCLUSIONS AND SUMMARY .................................... 117
REFERENCES ....................................... 118
Obtaining the Survey ........................................... 120
Technical Appendix ........................................... 120
V
List of Tables
Table II. 1.
Table B. 1
Table IV. 1
Table IV.2
Table IV.3
Table IV.4
Table IV.5
Table IV.6
Table IV.7
Table IV.8
Table IV.9
Table IV. 10
Table IV. 11
Table IV. 12
Table IV. 13
Table IV. 14.
Table IV.15.
Table IV. 16
Table IV.17
Table IV.18
Table IV.18
Table IV.19
Table S. 1
Table S.2
Table S.3
Table S.4
Table S.5
Table S.6
Table S.7
Table S.8
Table S.9
Summary of EAI Non-Market Valuation Studies
for PES ............................................. 12
Mean Values of Model Variables ............................. 26
Residency Stares of Survey Respondents ........................ 36
Population Demographics vs. Survey Respondent Demographics ........ 39
Participation in Key Recreation Activities - By Residency Stares ........ 41
Water body Selection by Activity - (N = 1267) .................... 42
Town Selection by Activity - (n= 1267) ........................ 43
PES Key Recreational Activity Outings - (n= 1267) ................ 44
PES Key Recreational Activity Outings by Residency Status - (n= 1217) .. 45
PES Key Recreational Activity Locations by Residency Status
-(n= 1217) .......................................... 47
Average Annual Outings per Participant ........................ 51
Estsimated Annual Key Recreation Activity Outings ................ 48
Comparison to Participation Estimates Phase I Versus Phase II ......... 49
Survey Farm stand, Winery, and Rental Lodging Expenditures (1995 dollars) 51
Farm stand, Winery, and Rental Lodging Expenditures .............. 52
Managemem Action Referenda Results ......................... 54
Awareness of Brown Tide ................................. 55
Activities Affected By Brown Tide ........................... 56
Total Annual Value of Recreational Activities at the PES (Baseline) ...... 60
Benefits to Swimmers of 10 % Improvement in Each Water Quality Indicator
in Each PES Water Body ................................. 62
Benefits to Swimmers of 20 % Improvement in Each Water Quality Indicator
in Each PES Water Body ................................. 63
Benefits of Improving Recreational Catch by 10 Percem .............. 64
Supplementary Tables
East End Resident Respondem Distribution by Town ................ 65
Second Home Owner Primary Residence ....................... 65
Second Home Owner Distribution by Town ...................... 66
Primary Residency of Visitors to the East End .................... 66
Accommodations for Overnight Visitors ........................ 67
Overall Participation in Key Recreation Activities - All Respondents
(N= 1354) ........................................... 67
Participation in PES Water Based Recreation - All Respondents
(N= 1354) ........................................... 68
Participation in Land Based Recreation - Ail Respondents (n= 1354) ..... 68
Estimated Trips for Key Recreation Activities for Sample ......... ~ ....69
Table A1
Table A2
Table A3
Table A4
Table A5
Table A6
Count
Table A7
PES Sampling Station Locations ............................. 76
Average Input Value for the Water Quality Model (Ordered Logit) ...... 77
Results of the Water Quality Model (Ordered Logit) ................ 78
Baseline Probabilities for Nine Sites ........................... 79
Baseline Probabilities for PES Water Bodies ..................... 79
Probabilities at Nine Sites for 10% Improvements in Brown Tide Cell
Probabilities at Nine Sites for 10% Improvements in Total Kjeldahl
Nitrogen Readings ........................................... 80
Table A8 Probabilities at Nine Sites for 10% Improvements in Total Coliform
Readings ................................................. 81
Table A9 Probabilities at Nine Sites for 10% Improvements in Secchi Disk Depth ... 81
Table Al0 Probabilities at PES Water Bodies for a 10% Improvement in Brown
Table A11
Table A12
Table Al3
Table Al4
Table IV.B.1
Table V. 1
Table VI.2
Table VI.3
Table VI.4
Table VI.5
Table VI.6
Table VI.7
Table VI.8
Table VI.9
Table VI. 10
Table VI. 11
Table VI. 12
Table VI. !3
Table VI. 14
Table VI.15
Table B-1
Tide Cell Counts ....................................... 82
Probabilities at PES Water Bodies for a 10% Improvement in Total
Kjeldahl Nitrogen Readings ................................ 82
Probabilities at PES Water Bodies for a 10% Improvement in Total
Coliform Readings ...................................... 83
Probabilities at PES Water Bodies for a 10% Improvement in
Secchi Disk Depth ...................................... 83
Results of the Travel Cost Model (Poisson Regression) .............. 84
Marginal Values of PES Wetlands ............................ 90
Focus Groups and Pretests ................................. 94
Concerns of Preliminary Survey Respondents, Montauk,
March 19, 1995 ........................................ 95
Survey Locations and Number Collected ........................ 98
Past, Presem and Projected Natural Resoume Levels ............... 102
Levels of Natural Resources and Cost in the Survey ............... 103
Respondent Demographics, Full Sample ....................... 105
Population Demographics vs. Survey Respondent Demographics ....... 107
Numbers of Respondents by Town ........................... 108
Participation in Recreational Activities ........................ 109
Support for Actions .................................... 110
Responses to Brown TideQuestions .......................... 112
Membership in Organizations .............................. 113
Number of Choice Questions Answered ....................... 114
Estimation Results
..................................... 115
Comparison of Discounted Present Values ...................... 117
Comparison of Model Results .............................. 122
vii
List of Figures
Figure II.1. Depiction of the Peconic (Long Island) Estuary System
Figure II.2. Economic Studies by EAI for the PES Program
Figure 11.3. Depiction of EAI's Phase II Non-Market Value Economic Studies
Figure III. 1. Town of Southold Used for Property Value Case Study
GIS Maps Used to Define Model Variables: Examples
viii
Recreational and Resource Economic Values
for the Peconic Estuary System
EXECUTIVE SUMMARY
I.A. PURPOSE AND SCOPE
The environmental and natural resources ("natural assets") of the Peconic Estuary System--the bay
waters, beaches, wetlands, ecosystems, habitats, and parks and watershed lands--provide many
services to the public. Outdoor recreation, scenic views, and the productivity of wetlands that
contribute to stocks of fish, birds, and other species used for commercial and recreational purposes
are but a few examples of thehe services.
While the quality of PES coastal amenities is widely recognized, little information exists about the
uses and users of PES natural resources. Even less is known about the value that the public holds
for the natural asset services of the PES--that is, what they are "worth" to the public. This is
because recreation, scenic views, and ecosystem productivity are not directly valued in markets.
Lacking information on recreational and resources values, comparisons cannot be made of the
benefits and costs of prospective actions to preserve or restore PES natural assets. This report is
designed to help fill this major gap.
In this report, we present the results of four non-market valuation studies carried out by Economic
Analysis Inc. ("EM") to estimate the uses and economic value that the public holds for the natural
assets of the PES. We provide estimates of (1) outdoor recreational uses and of the non-market
economic values of key recreational activities, and (2) other resource values provided by the natural
assets of the PES. The economic valuation studies described herein were done to contribute to
benefit-cost analyses of proposed management actions.
This document is Phase II of a series of economic studies being done by EAI for the Peconic Estuary
Program. A Phase I report (Grigalunas and Diamantides, 1996) (1) provided an assessment of the
"economic impacts" of the PES, in terms of business revenues, employment, and wages of
esntarine-related sectors, and (2) summarized available information on recreational uses. Subsequent
work by EAI will include benefit-cost analyses of resource management actions and sustainable
financing options for the selected, preferred actions.
I.B. ESTUARINE-RELATED USES, RESOURCES AND ECONOMIC VALUES
I.B.1. Introduction and Overview
No single method can capture the value of the variety of services provided by the natural assets of
the PES. Recognizing the many uses of PES natural resources, we designed and implemented a suite
of four non-market valuation studies in order to provide estimates of the value of particular services:
(1)
A Property Value study examines the contribution of environmental amenities to the market
price of property. Using the Town of Southold as a case study, the Property Value study was
designed to measure values of amenities to residents living in the immediate vicinity.
(2)
A Travel Cost study uses original survey results to estimate outdoor recreational uses in the
PES and the economic value that users have for four, key PES outdoor recreation activities:
swimming, boating, fishing, and bird and wildlife viewing. This study also examines the
impact that (A) water quality has on the number of trips and the value of swimming and (B)
the effect of the catch rate on recreational fishing, important recreational uses of the' estuary
and activities much affected by water quality and resource abundance.
(3)
A Wetlands Productivity Value study, carried out in collaboration with Applied Science
Associates, gives estimates of the economic value of eelgrass, intertidal salt marsh, and
sand/mud bottoms, based on the value of the fish, shellfish and bird species that these
ecosystems help "produce". The primary focus is on the nursery and habitat services of the
wetland ecosystems in the production of commercial fisheries.
(4)
The Resource Value study uses original survey results to estimate the relative preferences
that residents and second homeowners have for preserving and restoring key PES natural and
environmental resources: Open space, farmland, unpolluted shellfish grounds, eelgrass beds,
and intertidal salt marsh. This study also provides a perspective on the economic value the
public has for these resources, as indicated in their stated willingness to pay for programs to
preserve and restore them.
Key results for each of these studies are outlined below. A detailed discussion of each method, its
purpose, the data used, assumptions and limitations, and results is given in the chapters that follow.
A note on the style adopted for this document. We believe that the results of the economic analyses
presented in this report provide a wealth of data to decision makers and to the public. To make the
report accessible to a wide audience, we deliberately adopt a non-technical style. For those
interested, Appendixes present the technical details of the methodologies used. ·
2
I.C. SUMMARY OF SELECTED RESULTS
I.C. 1. Property Value Study (Chapter III).
Data for property sales in the Town of Southold were used to estimate the contribution of specific
environmental attributes to the market value of nearby property. Using a property value model, we
found that proximity to open space and other environmental attributes had a significant, positive
impact on nearby property values.
For example, a parcel of land located next to open space has, on average, a 12.83% higher per-acre
value than a similar parcel located elsewhere. To illustrate the how this result might be used, we
estimate that a hypothetical contribution of a parcel of approximately 10 acres of open space would
increase adjoining property values by $410,907.
The model results also show that density of development and other attributes affect property values.
For example, 2 or 4 acre zoning (i.e., R-80 or R-120) has, on average, a 16.65% higher per-acre
value than a similar parcel located elsewhere (i.e., in a 1/2-acre zone). Conversely, property located
on a main road (highway 25 or48), or property adjoining a farm, has a lower value, after taking into
account other property attributes.
I.C.2. Recreational Uses and Economic Value (Chapter IV).
A recreational survey adminstered to residents, second homeowners, and visitors allowed us to
estimate (1) participation in outdoor recreation, and (2) the economic value the public has for four
key PES recreational activites. We estimate that in 1995:
127,762 people took some 3.3 million swimming, boating, fishing, or shell fishing, outings
156,184 people engaged in about 5.2 million beach use, bird watching, wildlife viewing, or
hunting trips
Swimming and beach use were the most popular activities, followed by bird and wildlife viewing,
boating, and fishing. Shell fishing and hunting had the fewest estimated number of trips.
Measurement problems prevented us from including other common activities, such as
hiking/walking and bicycling.
Among the PES Bays, Great Peconic Bay is the most popular, accounting for 28% of all recreational
trips to the PES. Flanders Bay is the least frequently used, with 8% of PES trips. Great Peconic Bay
is the most used for swimming (30%), fishing (29%), and boating (25%). For shell fishing,
Gardiners Bay is the most popular PES location, with 33% of all PES shell fishing trips.
Outdoor recreation in the PES is enormously valuable to users. Using a Travel Cost model, we
estimate the economic value per person, per trip, for four key recreation activities: Swimming,
boating~, fishing, and for viewing of birds and wildlife. The estimated valuesper trip range from
$49.83 for viewing birds and wildlife to $8.59 for swimming (in 1995 dollars). These are estimates
of what participants would be willing to pay, per trip, above and beyond the amount that they
actually pay to participate.
Adding across all trips, in 1995 Viewing of Birds and Wildlife ($49.3 million) has the highest total
annual value, followed by Recreational Fishing ($22.4 million), Boating ($18.1 million), and
Swimming ($12.1 million). The corresponding asset values of the PES (over 25 years at 7 percent)
for these key recreational activities range fxom $318 million for Bird Watching and Wildlife Viewing
to $141 million for Swimming. The PES has an asset value of $276 million for Recreational Fishing
and $210 million for Boating.
Quality is important to PES recreationists. Swimming was found to depend upon perceptions of
water quality, and swimmers perceptions, in mm, were related to objective (i.e., SCDH field
sampling) measures of water quality for ni~xogen, water clarity, Brown Tide, and coliform bacteria.
To illustrate how the results might be used in a Benefit-Cost analysis, we simulated hypothetical,
uniform improvements in water quality. For example, a 10 percent uniform improvementjn water
quality in each Bay increases the estimated number of annual swimming trips by 151 thousand and
adds yearly benefits of $1.3 million. Most of the benefits ($754 thousand) are due to improvements
in water clarity (as measured by Secchi depth). Swimming benefits increase further with a
hypothetical, 20 percent uniform water quality improvement, although the added benefits from the
second 10 percent improvement is slightly less than that due to the first 10 percent improvement.
The present value of the $1.3 million increase in benefits due to the 10 percent hypothetical water
quality improvement is $15.1 million, using a 7 percent discount rate and a 25-year time horizon.
Thus, if the present value of the costs of the policy (or set of policies) to improve water quality did
not exceed $15.1 million over the same period, it would be a good investment of scarce resources.
Recreational fishing also was found to depend upon the quality of the experience--in particular, the
catch rate. A hypothetical 10 percent increase in catch rates raises the economic value per trip by
$0.80, the number of trips by 11,249, and total annual fishing benefits by $472,359. The present
value of this increase in catch rates--the increase in the asset value of the PES in providing this
service-- is $5.5 million, using the 7 percent rate and time horizon of 25 years used for all cases.
On another issue, to address a data gap identified in EAI's Phase I study, Phase II survey
respondents were asked how much they spent at road side farmstands and at vineyards and for rental
accomodations. In total, in 1995, the public spent some $19.4 million at East End roadside
farmstands and $5.4 million at vineyards. Using the same assumption as in the Phase I study that
45 percent of these activities are PES-related (Grigalunas and Diamantides, 1996), the annual PES-
related expenditures for these two activities are $8.7 and $2.4 million, respectively.
~Boating for the primary purpose of fishing is valued under recreational fishing.
4
I.C.3. Wetlands Productivity Values (Chapter V)
Eelgrass, saltmarsh and intertidal mud bottoms provide food web and habitat services for many
species of fish, shell fish, waterfowl and birds, and thus directly or indirectly benefit people who
consume, hunt, or view these species. A simple food web model was developed that used available
biological data, commercial fish and shell fish prices, and viewing values for birds to provide a
perspective on the economic value of the productivity of these habitat types.
The results suggest an asset valueper acre for existing wetlands of approximately $12.4 thousand
for eelgrass, $4.3 thousand for salt marsh, and $786 for mud flats, using a discount rate of 7 percent
and 25 year period for the services valued. Restored wetlands are worth somewhat less since it may
take several years to restore fully their natural functions as a food source or habitat.
I.C.4. Resource Preferences and Economic Values (Chapter VI)
Early discussions revealed that the public has a strong attachment to environmental and amenity
resources of the PES, even if they do not use these resources directly. As outlined above, the
Property Value study captures the value of amenities (or disamenities) to nearby properties, the
Recreation study estimates use value for key outdoor recreational activities, and the Wetlands
Productivity study yields estimates of the value of wetland ecosystems in the "production" of fish,
shell fish, and birds. However, none of these studies reflects the value residents and second
homeowners hold for the general ambience of the PES--the "sense of place" it provides, a phrase
used often by participants in EAI's focus groups.
To try to capture these elusive but important non-market resource values, a survey developed and
administered to 968 residents and second homeowners was used to learn which amenities are most
important to people in the study area, and the amount that they would pay to preserve or restore
them. Our purpose was (1) to account for the preferences many residents have for resources, even
if they do not use these resources directly, and (2) to measure these preferences in dollars, if possible,
to compare with other study results.
Most respondents to the resource survey (97 percent) supported at least one hypothetical action to
protect resources, and indicated they would financially support such actions. The relative priorities
of respondents, in order, were for farmland, eelgrass, wetlands, shellfish, and undeveloped land.
We also estimated the monetary value that the survey respons&s imply for the resources concerned.
The estimated per acre dollar values were about $14.0 thousand for undeveloped land, $30 thousand
for unpolluted shellfish grounds, $56.7 thousand for saltmarsh, $70 thousand for eelgrass and $74.5
thousand for farmland, using a 25-year time horizon and 7 percent discount rate; However, we
believe that the resource priorities or relative values of resources are more reliable than are the dollar
estimates of values and recommend that relative values, rather than dollar values, be used in the
process of selecting management actions.
I.D. REFERENCES
Freeman, A.M.III, 1993. The Measurement of Environment and Resource Values: Theory and
Methods. Washington, D.C.: Resources for the Future.
Grigalunas, T.A. and J. Diamantides, 1996. The Peconic Estuary System: Perspective on Uses,
Sectors, and Economic Impacts. Peace Dale, RIi Economic Analysis, Inc.
Kopp, R. and V.K. Smith, 1993. Valuing Natural Assets: The Economics of Natural Resource
Damage Assessment. Washington, D.C.: Resource for the Future.
Tietenberg, Tom, 1995. Environmental andNatural Resource Economics (4th ed.) New York: John
Wiley & Sons
6
II. BACKGROUND AND INTRODUCTION
II.A. BACKGROUND
Located within Suffolk County at the East End of Long Island, the PES is comprised of the Peconic-
Flanders Bays system, Gardiners Bay and part of Block Island Sound, and the adjoining watershed
lands (Figure IL1). Included within the PES are five towns: East Hampton, Southampton,
Riverhead, Southold, and Shelter Island, as well as a small part of a sixth town, Brookhaven. In
total, the five towns comprise about 38 pement of the land area and 8 percent of the year-round
population of Suffolk County (SCDHS, 1992; Long Island Business News, 1994).
Important characteristics of the PES include: (1) the generally high quality of its coastal estuarine
environment, (2) its economy, which is strongly influenced by seasonal, estuarine-related activities,
particularly tourism and recreation, and (3) the population, which is highly seasonal and for year-
round residents is comprised of smaller, more elderly, and lower-income households than for the
County or Long Island as a whole (Long Island Business News, 1994; Grigalunas and Diamantides,
1996).
Water quality in the PES generally is very good, and the study area contains many beaches, parks,
open space, and habitat for birds and other wildlife, including some endangered species. These and
other environmental and natural resources of the PES can be viewed as natural assets. A significant
feature of assets--natural or otherwise--is that they can provide a stream of valuable services
("interest") over time, if maintained.
Some of the natural assets of the PES, however, are not being maintained, and others are at risk.
Localized water quality problems exist due to runoff, failing septic systems, and inadequate sewage
treatment. Substantial areas of open space and farms have been lost due to development; and
pollution has closed thousands of acres of shellfish grounds used for commercial and recreational
purposes. Further, large areas of ecologically productive eelgrass and saltmarsh ecosystems have
been lost due to development, Brown Tide, and water pollution. Fin fish and shell fish landings have
declined substantially over time, in part due to these problems. The combination of all of these
adverse developments threaten the quality of life of residents of, and visitors to, the PES.
Management programs being developed by the Peconic Estuary Program would preserve or restore
key PES environmental or natural resources. None of these programs is free, however, and some
could be very costly. Deciding whether, where, how much, and when to invest in preserving or
restoring PES natural resources requires consideration of many factors. One of these is the economic
benefits and cost of such actions.
CT
Orient
Plum ls.
Atlantic Ocean
Long Island -. Block Island
Sound
'- Sound
;
' Riverhead -
.-" Peconic
Bay Montauk
·. Peconic
-. , East Point
, ' Southampton ' Hampton
Brook- i ".. ;
haven ,, ' I Legend:
I ...... Study Area
I ....... Town Bounda~
~ (map not drawn to scale)
Figure II. 1. Depiction of the Peconic (Long Island) Estuary System
II.B. NATURE AND IMPORTANCE OF NON-MARKET RESOURCE VALUES
II.B. 1. Overview of Studies and Basic Concepts
Economic benefits provided by natural asset services show up in at least two ways:
(1)
As market benefits to the owners, operators and employees of over 1,000 businesses that
engage in or support recreation and tourism, commercial fishing and seafood activities, and
other estuarine-related economic operations (Grigalunas and Diamantides, 1996).
(2)
As non-market benefits to the thousands of recreationists, visitors, property owners, renters,
and others who use or otherwise enjoy the quality of the PES's natural amenities.
EAI's Phase I study summarized available information for the first of these two categories by
quantifying "economic impacts" of esmarine-dependent economic sectors (Figure 11.2). Estimates
were made of employment, wages, sales, and number of establishments for 29 economic sectors
identified as esmarine-related, in whole or in part. These sectors include commercial fishing,
marinas, eating and drinking establishments, hotels/motels, and other estuarine-related activities.
The Phase I results show that estuarine-related economic activity is a major component of the PES
economy and an important part of the livelihood of over 10,000 residents who own, operate, or are
employed in more than 1,000 marine and tourism-related businesses (Grigalunas and Diamantides,
1996).
Market data alone, however, give only a very limited view of the overall benefit or value of the
services provided by PES estuary-related resources. This is because much of what people enjoy in
the PES--for example, natural amenities, like open space, attractive views, good beaches and high
levels of water quality--are not bought and sold in markets. Benefits cannot be counted in economic
impact studies if they are not bought and sold on markets; and many benefits that do show up on
markets are indirect and hence not linked to the natural asset that provides the service (e.g., fish or
shell fish "produced" by PES wetlands and later harvested by fishermen).
This Phase II report focuses on non-market benefits provided by amenities in the Peconic Estuary
System ("PES"). We adopt the view that the natural resources and environmental amenities of the
PES can be looked at as natural assets (Freeman, 1993; Kopp and Smith, 1993; Tietenberg, 1995)
that provide a flow of benefits over time, if maintained.
Several standard economic concepts are used throughout this report. Two of these are Consumer
Surplus and Present Value. To avoid later confusion, these concepts should be explained at the
outset. '
Consumer Surplus. People buy goods or engage in activities like recreation when the benefit they
receive is at least as great as the cost to them. For example, if I can buy a discount airplane ticket
Phase I
Economic
Impacts
- _- _- _- _-; ~; _- _- _- _- _-; _¥ _- _- _-; _- _-~
Phase II
Recreational IResource
Su .rvey ISurvey
Benefit I_.Wetlands
I Property I . .
ValueI Analysis ['Productivity
--. · I _ IProject I_. J Cost
mnanc, ng I'-IAn~,ys,s I- I Assessment
,., .... :~ Figure 11.2. Economic Studies by EAI for the PES Program
for $200, I would do so only if it was worth at least that amount to me. If I would pay up to $500
for the flight, then I will buy the ticket and receive a net banefit--an unpaid-for benefit--of $300.
Similarly, on a nice summer day I might be willing to pay as much as $15 to use a be~ch for the day,
but if nsing it costs me only $5, then I receive an unpaid-for benefit orS10 for that trip to the beach2.
In the above examples, the most that I am willing to pay is my own assessment of the economic
value of the airplane ticket or the day at the beach--what each is worth to me. The net benefit to me
in each case is the difference between the most I would pay, less what I actually pay. This 'hmpaid-
for benefit" realized in each example captures the notion of "Consumer's Surplus". Consumer
Surplus--the benefit people receive above and beyond the cost to them--provides the basis for the
measurement of economic benefits used in this document.
Of course, the "trick" is to use sound methods to discover the Consumer Surplus the public receives
for the services provided by the natural assets of the PES. Fortunately, standard methods are
available to do just that. These methods, and how they were used to value the services of PES
natural assets, are summarized in the next section and explained in detail in succeeding chapters.
Discounting and Present Value. A second important concept involves discounting. Many benefits
or costs occur over time; disctunting is the process used to convert future benefits and costs to an
equivalent, lump sum value today. This discounting process is used, for example, to convert a
retirement of lottery annuity (an equal cash amount each year) to a lump sum equivalent today. The
equivalent, lump sum value today arrived at by discounting is called the "present value".
To estimate the present value of a slxeam of dollar flows over years 1 through "T" we discount each
annual flow using the formula:
Present Value = V~/(I+ r)~ + ....+ V~/(l+r)T
where V is a dollar value and r is the discount rate-the interest rate used to convert future flows into
a value today. For example, if the total Consumer Surplus from swimming is $1,000 in year 1 and
$1,000 in year 2, and the discount rate is 10 pement, then the present value of these annual amounts
is $1,735 (= $909 +$826) today.
The present value calculated with the above formula depends upon three factors: (1) the size of the
annual moneta~ values, V~ (2) when they occur, and O) the discount rate, r, used. We estimate the
annual monetary values, Vt for non-market services in the chapters that follow, employ a time
horizon, T, of 25 years, and use a discount rate of 7 percent, a standard rate recommended for use
in many resource management projects3.
~Of course in some cases the price is more than we are willing to pay, and we will not buy the item because it is "too
expensive" or "not worth it".
Sin later work the effect on results of uncertainty in choice the discount rote and other variables will bc,,examined.
II.C. PURPOSE AND SCOPE
This report describes the four non-market valuation studies comprising Phase II of a series of studies
being carded out by EAI for the Peconic Estuary Program. The Phase II studies provide estimates
of recreational uses and the economic values that the public holds recreation and for a variety of
other services provided by key PES environmental and natural resources.
The Phase II studies reported on here are a major component of a larger program to assess the
benefits and costs of proposed management actions as part of the Peconic Estuary Program (Figure
11.3). Phase I was designed to estimate market effects provided by the PES. As discussed above,
Phase I measures "economic impacts" of the PES, including employment, sales, wages, and the
number of establishmants associated with 29 economic sectors identified as estuarine-related. Phase
II provides estimates of non-market benefits. Our studies were carded out for the purpose of
eventually contributing to benefit-cost analyses of proposed management actions by the PES
Management Committee in Phase III of the project.
Phase III analyses will (1) provide cost analyses for specific management actions designed to protect
or restore PES amenities, (2) combine this cost information and the benefit information from Phases
I and II to provide benefit-cost analyses of potential management actions, and (3) compar~ivarious
financing options for the proposed management actions.
We designed a suite of non-market valuation studies in order to be able to estimate the value of key
services provided by amenities in the PES. Multiple non-market valuation studies are needed
because of the many different types of services provided by PES environmental and natural
resources. For example, recreational participants directly use Bay wate/s and beaches; hence, for
these activities we want to measure participants' recreational use values. In other cases, PES
resources may provide amenity or aesthetic benefits to residents, which will be captured in property
values, another type of use value. Additionally, some may also enjoy just knowing that PES
environmental amenities, such as open space and salt marsh, are being preserved or restored, whether
or not they directly use these resources. For these resources, other values, not involving direct use,
may also be important and should be included. In sum, our use of a variety of methods was
designed to allow us to gain a better understanding of the non-market values provided by different
resource services, information that will prove useful for later benefit-cost studies.
The key Phase II studies are the property value study, the recreation survey, the wetlands
productivity analysis, and the resource survey. The property value study was designed to estimate
benefits that PES amenities provide to individuals who own adjacent properties. The recreational
survey allows us to estimate recreational use and use values for a variety of outdoor activities. This
survey was designed fill data gaps identified in Phase I report, and to estimate recreational uses and
use values to contribute to assessment of management actions in Phase III. The wetlands
productivity analysis uses available data to estimate the productivity of specific habitat types, and
to place a dollar measure on the ecosystem services provided. The productivity study includes only
food web and habitat services and focuses on the "production" of commercial species, and ~? a lesser
Figfire
Phase I ~'nase
Econom,c
Impacts
Property
Value
,---------.------------T---------.---._-__.-...-.._-_.7...-___-_..-...-_ 5 ..-.__-_ 5.7 _~ 5 ~ 5 .~ _r .~ 5 .................... Phase III
· . . Project Cost
F~nanc~ng
I [ ~na~ys~s IAssessment J
11.3, Depiction of EAI's Phase II Non-Market Value Economic Studies
extent, on non-commercial values (viewing and hunting values). Finally, the resource survey is used
to identify public priorities and values for specific environmental amenities. The resource survey
used a contingent choice model to learn which amenities are most important to the public and the
amount that respondents indicate that they would pay to preserve or restore them. Summary
information for each study is give in Table II.l; each study is described in detail in the chapters and
appendices that follow.
II.D. REFERENCES
Grigalunas, T.A. and J. Diamantides, 1996. The Peconic Estuary System: Perspective on Uses,
Sectors, and Economic Impacts. Peace Dale, RI: Economic Analysis, Inc.
Long Island Business News, 1994. Long Island Almanac 1994. Ronkonkoma, NY: Long Island
Business News.
Suffolk County Department of Health Services, 1992. Brown Tide Comprehensive Assessment and
Management Program. 3 volumes. Suffolk County Dept. of Health Services (November).
Table IL1. Summary of EAI's Phase II Non-Market Valuation Studies for PES
Study Purpose Data and Method(s)
Recreational Uses and Use Values 1. Estimate Resident, Second Home Convenience sampling/
Owners & Visitor Recreational Uses: Intercept Survey
n Activities Self administered
· k 8 Key Activities 1,354 completed surveys
· ' 8 Locations
5 PES Bays
Atlantic Ocean
LI & BI Sounds
~k $ Towns
2. Estimate Use Value for: Travel Cost Method
a Swimming Demand for swimming
· Boating and recreational fishing
n Fishing depends on quality
· Bird & Wildlife Viewing
Resource Values Estimate Residents' and Second Convenience sampling/
Homeowners' Preferences and Intercept Survey
Economic Value for preserving and Self administered
restoring PES: 968 completed surveys
· Open space
· Farmland preservation Contingent Choice
· Unpolluted shellfish beds Method
a Wetlands
· Eelgrass beds
Wetlands Productivity Values Estimate the Economic Value for Based on Productivity
Nursery and Habitat Productivity approach and results in
functions for: available literature.
· Saitmarsh
· Eelgrass
· Intertidal mud bottom
Property Value Estimate the effect on Property Value in All property sales in
Town of Southhold of attributes, Town for 1996
including: 374 sales transactions
· Site Geographical
~ Lot & home size Information System
,k Garage
~ Special features Property Value
· Neighborhood ("Hedonic') Model
~r Main road
· Environmental
· k' Wetlands
~ Open Space
· k Farmlands
,k Zoning Size
III. ENVIRONMENTAL AMENITIES AND PROPERTY VALUES:
A CASE STUDY OF THE TOWN OF SOUTHOLD
III.A. INTRODUCTION
Property values in coastal communities depend upon many factors. These include not only the size
of the property and the characteristics of the home on the property, but also on environmental
amenities, such as open space and proximity to the shoreline. For example, we would expect a
home located near the PES waterfront to be more valuable than another home, identical in all
respects, but located a greater distance from the shore. If we could find two such homes, we could
simply compare the prices at which they sell and, by that, calculate the value of being near the shore.
Of course, rarely are two homes identical in all respects except for one attribute. Instead, as anyone
who has bought a home knows, the price that a property commands on real estate markets reflects
a great many factors. These include: lot size, size of the home and its location, the characteristics
of the surrounding neighborhood, and a wide range of other environmental factors. Nevertheless,
if all of these important facto.rs can be taken into account, it is possible to isolate the value of
individual factors, much as a real estate broker or tax assessor does when appraising a property.
This Chapter exploits these simple insights. We analyze data from many residential housing
transactions using a standard property value model, as we explain in detail below. The model
allows us to estimate the value--as captured in market prices--that people attach to various
environmental attributes, such as proximity to open space and farmland. If efforts to estimate the
value of environmental attributes are successful, the results can be used to assess the potential
benefits of possible management actions that would at'feet any of the factors studied. That is, we
could estimate the potential benefits from a proposed management action, as measured through
changes in market prices for property.
We use the Town of Southold, located on the North Fork of the PES, as a case study (Figure III.1).
Southold is an interesting example, for several reasons. First, it has a wide range of environmental
amenity levels and neighborhood conditions. For example, Southold has large areas of farraland and
open space, although its population density (0.67 persons per acre) is highest among all five PES
towns. In addition, Southold has a long and varied coastline, both on the Peconic Estuary and on
Long Island Sound, and is characterized by a variety of development densities and types. About a
quarter (26%) of the town is currently in agricultural use, compared with 30% in residential use, 12%
is preserved as open space, and 18% is classified as vacant. Less than 3% is classified as commercial
or industrial (Suffolk County Department of Planning 1997a).
Southold's population increased 8.9% between 1980 and 1990, yet the number of housing units
increased at nearly twice this level (16.6%). Thus, the growth of housing and the associated loss of
undeveloped lands has far out paced population growth (Suffolk County Department of Planning
1997b). Given the recent rapid pace of development, the protection of open space, un,developed
Orient
Plum Is.
Long Island Block Island
Sound Sound
Gardiners .!~
~ Peconic
Great Say Montauk
; Peconic ; East Point
Brook- i Southampton "., Hampton
haven i i
Riverhead
Atlantic Ocean
Figure III. 1. Town of Southold Used for Property Value Case Study
land, and other environmental amenities will play an important role in determining future quality of
life in the Town and, perhaps, in the PES area.
The impacts of environmental amenities on Southold property values, and the impacts resulting from
possible PES management actions, will depend upon the unique characteristics of the town (and
perhaps the quality of surrounding waters and land areas). To assess these impacts, EAI conducted
a property value analysis specific to the town of Southold. We apply economic methods using the
property value (or "hedonic" method) to a database comprised of all Southold real estate transactions
in 1996 and GIS parcel coverage data for the town. Briefly, the analysis estimates correlations
between property values and levels of valued environmental attributes, including open space.
The policy relevance of the results lies in the assumption that established relationships between
environmental amenities and property values estimated using the property value model will continue
to hold in the future, as future events (including policies) lead to changes in the level of these
amenities. Through the detailed study of existing property values, we seek the best possible
statistical estimate of the environmental impacts on local property values, given the available data.
IH.B. RELATIONSHIP BETWEEN ENVIRONMENTAL AMENITIES AND PROPERTY
VALUES
Environmental amenities provide valued services to residents of local communities. For example,
open space contributes, in a physical sense, to the character of local communities, while providing
a wide variety of services, including scenic views, outdoor recreation, insulation from noise and
other aspects of the urban landscape, and protection from erosion, flooding, and other physical
hazards (Johnston 1997a). These amenities are valued by local homeowners, making communities
with a high level of valued environmental amenities (i.e., environmental quality) more attractive than
similar communities without such amenities. As a result, home buyers are willing to pay more for
land or housing with higher levels of environmental quality. Tbe property value method (also called
the hedonic model) can be used to estimate the impact of environmental amenities on the values of
local property, thereby estimating the value of these amenities to local residents, as evidenced by
their actual willingness to pay higher prices for properties with higher levels of desired
environmental characteristics.
A well-documented example of the relationship among environmental amenities and property values
involves open space. Many studies show that nearby open space increases property values, reflecting
home buyers' values for the services and character offered by open space (e.g., Freeman, 1993).
In a recent, coordinated property value/geographic information system study conducted in
Middletown, Rhode Island, Johnston (1997) shows that positive property values impacts of open
space can range from less than 1% to greater than 13%, depending on the size of nearby contiguous
open space parcels, the total amount of open space in a region, and the distance of property from
open space parcels. Larger property value impacts are associated with larger contiguous parcels,
larger acreage of open space, and a closer proximity between valued parcels and op. en space
14 '
(Johnston 1997a). Similar results hold elsewhere in the New England region. A study in
Womhester, MA, for example, found that homes located less than 20 feet from a community park
sell for over $2,000 more than similar houses located more than 2000 feet from the park (More, et
al., 1982). In an analysis of cluster zoning in Amherst, Massachusetts, Lacy (1990) estimates that
cluster-zoned housing with permanently protected open space appreciates at a faster rate that similar
housing in conventional subdivisions. As of 1989, this difference had created an average $17,100
difference between the average selling price in the two types of developments.
Comparable results cOnCerning the value of open space also have been found in other coastal areas.
For example, a study of land values adjacent to Chesapeake Bay shows a positive relationship
between the pementage of preserved open space in a 250 acm area surrounding a parcel of land, and
the per-acm selling price of that land. This study also shows that agricultural pasture land and forest
land increases the per acre value of nearby land, other things being the same (Bockstael 1996).
Similar results are shown by Oeoghegan et al. (1995). Parsons (1992) estimates that land use
restrictions in the Chesapeake Bay watershed cause a 14-27% increase in housing prices within
1,000 feet of the bay, and a 4-11% increase up to a 3 mile distance. Garrod and Willis (1992)
conclude that housing prices are positively related to the existence of nearby bmadleaf forests.
Cormll et al. (1978) find that, on average, residential homing prices in Boulder, Colorado decrease
by $4.20 for each additional foot of distance from a central green way, up to a distance of 3200 feet.
Of course, the impacts of environmental amenities on Southold property values depend on the
unique characteristics of the town, and may be greater or less than those cited above. To assess these
impacts, we conducted a property value analysis specific to the town of Southold. We apply
economic methods 0aedonie methods) to a database comprised of GIS parcel coverage data and real
estate sales dam. In simple terms, the property value model estimates correlations between property
values and levels of valued environmental araibutes, including open space. The steps involved and
data used are described next.
III.C. APPLICATION OF PROPERTY VALUE MODEL TO SOUTHOLD
This study applies the property value model to real estate parcels sold in Southold during 1996. The
analysis estimates how various site, neighborhood and environmental characteristics affect the value
of individual properties. Property value analysis is a common means of estimating the effect of
environmental changes on land values, as described in detail by Freeman (1993) and Garrod and
Willis (1992), among many others.
III.C. 1. Explanation of The Property Value Method
People buying homes in effect purchase many site, neighborhood, and environmental characteristics.
For example, the characteristics of a residential property include: the size of the house, the number
of baths, the square footage of the structure, the zoning classification of the land, wbethex the land
is served by public sewers and water, the existence of wetlands on the property, and the proximity
of the property to amenities such as open space, coastlines, and farms. These and other
characteristics defme the property, and make it more or less attractive to potential buyers. Other
things being the same, home buyers are willing to pay more for properties that they find have
desirable characteristics, and less for properties with undesirable characteristics. These likes and
dislikes across many prospective buyers and sellers are reflected in market prices. The Property
Value Method attempts to estimate the portion of a property's value related to each relevant
characteristic, by that providing an estimate of the (implicit)value of each characteristic. The model
works by statistically comparing values ora large number of properties with differing levels of the
identified characteristics (e.g. lot sizes, number of rooms, proximity to roads, farms, open space, etc.)
(Freeman, 1993; Johnston, 1997a).
The Property Value technique is based on the assumption that a relationship exists between the
market value ora property, and the characteristics of the property. The Property Value method uses
a statistical technique called "multiple regression" to assess the impact of each characteristic on the
market value of the property. The technique simultaneously compares a large number of properties
with different prices and different levels of each characteristic. The method establishes which
characteristics are associated vdth higher values, which are associated with lower values, and which
have no significant impact on values. The model also estimates the dollar magnitude of these
impacts--that is, it estimates how large an impact is likely to be caused by a specific level of a
specific characteristic. Using this technique, the impact of different environmental amenities on
nearby property values can be estimated,n The technical details of the property value model (or
hedonic technique) are presented in Appendix A.
Actual 1996 sales prices are used in the analysis of Southold property values. The data for the
analysis is drawn from two sources:
(1)
Town of Southold property record cards for all properties sold during 1996;
and
(2)
The computerized geographic information system (GIS) maintained by the
Suffolk County Planning Department.
4 The accuracy of hedonic analysis depends on a well-functioning real estate market, in which consumers
have accurate information regarding all home characteristics. The technique also assumes that a large number
of different housing types are available for purchase, so that consumers can choose the "package" of housing
characteristics that is most to their liking, and can "mix and match" different types of characteristics. Finally,"
the technique assumes the availability of appropriate data, concerning all characteristics that influence property
values. Bias can result from the application of bedonic techniques to ill-functioning real estate markets, in
which few different types of housing options are available, and/or in which consumers do not have accurate
information regarding housing characteristics (Johnston 1997a).
Although sales data were available for 401 parcels, complete (31S coverage data exists for only 374
of these parcels. Thus, the Southold data set used in the analysis has full information on 374 parcels.
III.D. STATISTICAL ANALYSIS OF SOUTHOLD LAND VALUES
Model variables are the characteristics that influence buyers' willingness to purchase a land parcel,
or pay higher prices for that parcel. These include proximity to amenities such as open space and
coastlines; the size of the parcel; the size and other characteristics of the structures on the parceli
applicable use restrictions such as zoning codes; and the existence of features such as wetlands on
the parcel. These variables may be grouped into three general categories: parcel characteristics,
neighborhood characteristics, and environmental characteristics. To distinguish the effects of
parcel size on land values, we consider the effect of the characteristics onper-acre value. This is
calculated by dividing selling price by the number of acres in the pamel. Note that these per-acre
land values include the value of all structures built on the land. For a full description of the 23
variables included in the £mal. analysis, see Appendix A.
The statistical analysis uses the ordinary least squares (OLS) multiple regression approach. This
approach estimates the directidn, magnitude, and statistical significance of eorrelatious between each
of a set of independent variables (the characteristics of the property), and a single dependent variable
(per-acre property value).
To establish the nature of the specific functional relationship between these variables and assessed
land values, the study relies on the findings of prior research. In particular, the regression analysis
applies a "Ixanscendental" or "translog functional form'? The transcendental form is chosen for its
ability to capture realistic relationships between parcel characteristics and land values (Chieoine
1981). Such functional forms are also preferred for their statistical properties in cases where data
concerning certain relevant variables may not exist in the data set (Garrod and Willis 1992). For a
technical description of the statistical model and its properties, see Appendix B.
III.E. ANALYSIS OF SOUTHOLD LAND VALUES: RESULTS
The hedonic analysis provides information on the effect of 20 characteristics on per-acre land values
in Southold. Final model results are illustrated in Appendix C. Overall measures of model fit
indicate high levels of correlation between land characteristics and property values. An R2 value of
0.8352 indicates that over 83% of the variation in per-acre value is "explained" by the model, or is
correlated with model variables. An F-statistic of 75.945 indicates only a .01% chance that the set
of correlations reported by the model could be observed through random chance alone. The signs
~ Wichelns and Kline (1996), Chicoine (1981), Shonkwiler and Reynolds (1986), and Gm'rod and Willis
(1992).
associated with model variables are consistent with prior expectations and with the findings of earlier
hedonic studies (e.g., Bockstael 1996; Johnston 1997a; Des Rosiers and Theriault 1992; Garrod
andWillis 1992).Of those variables with demonstrated impacts on property values, seven have
potential implications for environmental policy. These include the following:
openspace: This variable identified parcels adjacent to open space land, as
defined by the GIS database..4 parcel of land located next to open space has,
on average, 12.83% higher per-acre value than a similar parcel located
elsewhere. Parcels adjacent to open space are defined as those with a border
within 25 f~. (7.62 meters) of an open space parcel. As defined by the GIS for
Suffolk County, open space includes: parks; nature and wildlife preserves;
recreational fields; unbuildable swamps or wetlands; large tracts of land
associated with schools, cemeteries, or other institutions; and selected large
parcels of preserved farmland.
farmdistance: This variable represents the distance between a specific parcel
and the nearest farmland (farmland on which agricultural crops or nursery
products are grown), in meters. For every meter of additional distance between
a parcel and the nearest farmland, average per-acre property value increases
by 0.0017% (i.e., the magnitude of the impact is quite small).
onfarm: This variable identifies parcels contiguous to farmland..4 parcel of
land located next to farmland has, on average, a 13.32% lower per-acre value
than a similar parcel located elsewhere.
onroad: This variable identifies parcels within 20 meters of a major road
(def'med as Rts. 25 and 48)..4 parcel of land located next to a major road has,
on average, a 16.16% lower per-acre value than a similar parcel located
elsewhere.
III.F.
largezone: This variable identifies parcels located in districts zoned R-80 or R-
120 (two- or three-acre zoning). ,4 parcel of land located within a district
zoned R-80 or R-120 has, on average, 16. 65% higher per-acre value than a
similar parcel located elsewhere.
wetland: This variable indicates the percentage of a parcel classified as a
freshwater wetland. For every one percentage point increase in the percent of
a parcel classified as wetland,, average per-acre property value increases by
0.27%. (However, this variable is only significant at the 14% level.)
SIMPLE ILLUSTRATION OF PROPERTY VALUE IMPACTS:
VALUE OF OPEN SPACE
Given the definition of the open space variable, the impact of open space on neighboring property
values will differ depending on the characteristics of the surrounding property. However, to
illustrate the basic implications of the model results for policy, the following section simulates the
predicted property value impact of the hypothetical loss of 10 acres of open space, in a highly
simplified scenario. Given specific locations for the open space land under consideration, and the
characteristics of the surrounding parcels, similar results could be generated for actual areas in
Southold.
According to the model results, properties adjacent to open space land (or within 25 ft.) are an
average of 12.83% more valuable than those not adjacent to open space. Within the Southold data
sample, the average parcel sales price was $213,514, with an average parcel size of 2941.95 square
meters (0.72 acre). Given this average value, a loss of 12.83% (related to the loss of our
hypothetical 10 acres of adjacent open space) implies that average property value for the adjacent
parcels would decrease by $27,394, all else held constant.
To estimate a hypothetical total impact in a simple case, assume that the lost open space was ten acres
in a square shape, surrounded by parcels of average 0.72 acre size (the average in our sample), also
square in shape. Assuming a square shape for the open space parcel, it would have a perimeter of 805.2
meters that would be bordered by other parcels. Given an average per-parcel area of 2941.95 square
meters (also assumed square), the length per-side of these bordering parcels would be approximately
54 meters. Given these measurements, approximately 15 parcels would fit adjacent to the 10 acre open
space parcel, assuming each had a full 54 meter side bordering the open space (805.2 + 54 = 14.9).
Accordingly, for this simple example, we assume that 15 average parcels would be affected by the loss,
or development, of our square ten acres of open space.
As stated previously, development of the open space parcel would result in a loss of 12.83% of average
value per lot or $213,514, according to the calculations made above. Thus, the average parcel would
lose $27,394 in total value. Multiplied by the 15 parcels adjacent to the newly developed parcel, the
total property value loss is equal to $27,394x 15=$410,907. This equates to an average loss of $41,090
in total property value per-acre of open space lost. Looked at another way, the estimated benefits of
preserving the ten acres of open space thus would be $410,907, as this is the increase in nearby property
value associated with this open space parcel.
For this illustration we do not compare costs with benefits since we do not have information on the
costs of acquiring specific undeveloped property for open space. However, in the illustration, if the
10 acres of undeveloped property for open space could be acquired for less than $410,907 or $41,097
per acre, then the benefits would be greater than costs. Note that even if the property to be acquired for
open space costs more than $410,907, benefits still may exceed costs. This is because not all of the
public benefits of open space are captured in our hedonic analysis. For example,.our results in this
section do not capture general amenity benefits enjoyed by all local residents, regardless of the location
of their homes (see Chapter VI for discussion of this issue).6
We note that the impacts illustrated above should not be regarded as exact results expected in any single
instance, and are included for illustration purposes only. A full benefit-cost analysis would require
much more detailed analysis of the proposed project and its impacts, including impacts on surrounding
property values. However, the illustrated results do indicate the general types of benefits and costs that
may be expected, on average, over the entire community. Although the results require various
assumptions as noted above, they are robust with respect to the quantity of open space preserved.
III.G. LIMITATIONS IN PROPERTY VALUE ANALYSIS
As noted, the model provides a good fit for sales data for the town, and the results of the Southold
property value analysis correspond to prior expectations and with the results of similar analyses.
Nevertheless several potential limitations should be noted. The most important of these limitations
is that the results of the statistical analysis are sensitive to characteristics of the data set. The
combined property card-GIS database may exclude information on certain variables that may affect
property values. Although numerous environmental variables may influence property purchase
decisions, data limitations allowed us to include only a sub-set of these variables in the model. For
example, the data set includes water quality monitoring data for the Peconic Estuary, conducted at
numerous sites. However, no parcels in the 1996 sales database are located within 100 meters of the
Peconic Estuary. Thus, for those parcels in the current database, water quality is not expected to
have an impact. However, were data to exist for parcels close to the estuary, one would expect that
water quality might have an impact on property values. Other variables for which data was
unavailable included fresh water (including groundwater) quality; beach erosion (likely only
important for beaclffront properties); and proximity to creeks, all of which would be expected to
affect property values. If in fact important variables are excluded from the model, statistical results
may show upward or downward bias. Omission of important variables can either increase or
decrease the estimated effects of environmental amenities on property values, based on the extent
to which these variables are correlated with environmental variables. (If these omitted variables are
uncorrelated with the environmental policy variables of interest, then their absence should not
influence reported results.)
For all of the above reasons, the estimated Property Value model results should not be interpreted
as "exact". Rather, they provide the best possible estimate, given available data, of the impacts of
existing environmental attributes on current per-acre land values.
A second limitation is that increased property values, related to improved environmental quality,
may not be favored by all residents. For example, residents may resist open space .preservation on
61n this regard, we note that recent support for an open space initiative in Suffolk County suggests that the electorate
has a strong preference for open space as a public good. ~
the grounds that it tends to increase property values, thereby increasing the costs of housing for
young home buyers. Others may reject increases in property taxes that accompany higher property
values. Still others may object that higher property values tend to "squeeze out" low income
residents. Real estate agents may oppose additional open space on the basis that fewer housing units
are available, despite the act that average property values in the area affected will be higher with the
additional open space. Such arguments should be acknowledged when considering environmental
policy changes using the results of property value methods.
Finally, we note that many people beyond the immediate area may derive benefits from open space
but their benefits are not included in numbers given in the above illustration. For example, residents
of the tom who live outside of the grid used in the illustration may value open space elsewhere in
town (see Chapter V).
III.H. SUMMARY
Environmental amenities increase property values of nearby parcels, reflecting the valuable services
offered by these amenities. An analysis of Southold property values supports this contention, and
corresponds with the results of prior studies. In Southold, existing open space increases the values
of adjacent properties by an estimated 12.83%. In addition, average per-acre property values are
higher in parcels located further from farms and major roads. Finally, higher property values appear
to be associated with the existence of wetlands on the property, and with large lot (2- and 3-acre)
zoning. These results indicate that environmental policies can have significant impacts on property
values, reflecting the influence of local environmental amenities on local quality of life.
IIl.I REFERENCES
Bockstael, N.E. 1996. "Modeling Economics and Ecology: The Importance of a Spatial
erspect~ve, Arnertcan Journal of ,4grtcultural Economics 78:1168-1180.
Chicoine, D. L., 1981. Farmland Values at the Urban Fringe: An Analysis of Sales Prices." Land
Economics 57(3): 353-362.
Correll, M., J. Lillydahl and L. Singell, 1978. "The Effects of G-reenbelts on Property Values: Some
Findings on the Political Economy of Open Space." Land Economics 54: 207-217.
Des Rosiers, F. and M. Theriault. 1992. "Integrating Geographic Information Systems to Hedonic
Price Modeling: An Application to the Quebec Region. Property Tax Journal 11 (1): 29-57.
Edwards, Steven and Glenn Anderson, 1984. "Land Use Conflicts in the Coastal Zone: An
Approach for the Analysis of the Opportunity Costs of Protecting Coastal Resources."
Journal of the Northeastern Agricultural and Resource Economics Association (April).
Freeman, M. A., 1979. The Benefits of Environmental Improvement: Theory and Practice.
Baltimore: Johns Hopkins University Press.
Freeman, M.A. 1993. The Measurement of Environmental and Natural Resource Values.
Washington, DC: Resoureas for the Future.
Garrod, G. and K. Willis, 1992. "The Environmental Economic Impact of a Woodland: A Two-
Stage Hedonic Price Model of the Amenity Value of Forestry in Britain." Applied
Economics 24: 715-728.
Geoghegan, J., N. Bockstael, and D. Lipton. 1996. The Economics of Land Use Change in the
Patuxent Watershed prepared for the Land Use and Management in' Maryland Seminar,
1996. College Park, MD: Center for Agricultural and Natural Resource Policy.
Geoghegan, J., N. Bockstael, and L. Wainger. 1995. "Spatial Landscape Indices in a Hedonic
Framework: An Ecological Economics Analysis Using GIS." presented at the annual
meetings of the Association of Environmental and Resource Economics, Indianapolis, IN.
Hammer, T.R., R.E. Coughlin and E.T. Horn IV. 1974. Research Report: The Effect of a Large:
Park on Real Estate Values. Journal of the American Institute of Planners, July.
Johnston, R.J. 1997a Aquidneck Island and Open Space: An Economic Perspective: Technical
Manual Narragansett, RI: The Aquidneck Island Partnership and the Coastal Resources
Center, University of Rhode Island.
Johnston, R.J. 1997b. Aquidneck lsland and Open Space: An Economic Perspective. Narragansett,
RI: The Aquidneck Island Partnership and the Coastal Resources Center, University of
Rhode Island.
Kask, S.B. and S.A. Maani, 1992. "Uncertainty, Information, and Hedonic Pricing." Land
Economics 68(2): 170-184.
Kimmel, M.M., 1985. Parks and Property Values: An Empirical Study in Dayton and Columbus,
Ohio. Thesis. Oxford, OH: Miami Univ., Inst. of Environmental Sciences.
Lacy, J. 1990. An Examination of Market Appreciation for Clustered Housing with Permanently
Protected Open Space. Center for Rural Massachusetts Monograph Series. Amherst, MA:
University of Massachusetts.
Lardaro, L., 1993. Applied Econometrics. New York: Harper Collins College Publishers.
More, T. A., Thomas Stevens and P. Geoffrey Allen, 1992. "The Economics of Urban Parks."
Parks and Recreation. ~:
National Park Service, 1995. The Economic Impacts of Protection Rivers, Trails, and Greenway
Corridors. Washington, D.C.: Rivers, Trails, and Conservation Assistance Program.
Nelson, A. C., 1985. "A Unifying View of Greenbelt Influences on Regional Land Values and
Implications for Regional Planning Policy." Growth and Change 16(2): 43-48.
Parsons, G.R., 1992. "The Effect of Coastal Land Use Restrictions on Housing Prices: A Repeat
SaleAnalysis." dour. of Environmental Economics and Management 22: 25-37.
Rosen, S., 1974. Hedonic Prices and Implicit Markets: Product Differentiation in Pure
Competition." Journal of Political Economy 82(1): 34-55.
SAS Institute Inc. 1996. S~tS User's Guide: Statistics. Cary: NC: SAS Institute Inc.
Shonkwiler, J.S. and J.E Reynolds, 1986."A Note on the Use of Hedomc' Models ~n' the Analys~s'
of Land Prices atthe Urban Fringe." £andEconomics 62(1).
Wichelns, D. and J. Kline, 1993. "The Impact of Parcel Characteristics on the Cost of Development
Rights to Farmland". ~lgric. and Resource Economics Review: 150-158.
23
APPENDIX A. Variables in the Hedonic Analysis
All model variables were generated from one of two sources: 1] Official Southold Property Record
Cards for all parcels sold during 1996; 2] GIS coverages maintained Suffolk County
Planning Department, updated 1995.
Dependent Variable:
LVALACRE: The natural log of per-acre sales price, in dollars. Based on Southold property
rccurd cards for properties sold during 1996.
Independent Variables:
LNACRES: the natural log of acres in each parcel. Estimated using GIS coverages.
WATFRONT: dummy variable: value of 1 assigned if property is located on the waterfront
(Long Island Sound or Peconic); value of value of 0 assigned if property is
not located on a waterfront. Value assigned based on waterfront
classification of property record cards. ~
BULKHEAD: dummy variable: value of 1 assigned if property has a bulkhead on the
waterfront, value of 0 assigned if no bulkhead. Value assigned based on
property record cards.
LARGEZONE: dummy variable: value of 1 assigned for parcels located in districts zoned R-
80 or R-120; value of zero assigned to all other zoning classifications.
YEAR: year the house was built, based on property records, minus 1950.
YEARSQ: YEAR * YEAR (year squared).
GARSQFT: the number of square feet in the garage, based on property records. If no
garage exists, value of 0 is assigned.
PATIO: dummy variable: value of 1 assigned if the house has a patio or deck; value
of 0 assigned if there is no patio or deck, based on property records.
BATHS: the number of bathrooms, based on property records.
NOHEAT: dummy variable: value of 1 assigned if the house has no-heat, value of 0
assigned if house has heat, based on property records.
24 - )-
GIS Maps Used to Del'me Model Variables: Examples
Many of the variables in the analysis were derived from GIS parcel coverages of Southold.
The following GIS map illustrates the type of analysis applied. In this case, the map illustrates the
process used to determine the existence of agricultural land within 1 O0 meters of each parcel in the
sales database.
,a{tricultuml I_and
The following GIS map covers the same area of Southold, yet illustrates the distribution of wetlands,
used to assess the percentage of each sales parcel covered by freshwater wetlands.
Wetlands
APPENDIX B. The Statistical Model
To establish the functional (mathematical) relationship between model variables and assessed land
values, the analysis on the findings of prior research. In particular, the analysis follows Wichelns
and Kline (1996), Chicoine (198 I), Shonkwiler and Reynolds (1986), and Garrod and Willis (1992)
applying a transcendental or translog functional form to the property price - characteristics equation
Vi =
where V~ is the 1996 selling price of the ith parcel, in dollars per-acre, Xi~ is parcel size in acres,
and X~j are measures of the j = 2...n other characteristics that affect land value. The [5's are
parameters to be estimated, and represent the effects of each of the characteristics on per-acre land
price. Estimated parameters are used to calculate the effects of different open space characteristics
on the value of land in Southold. The transcendental form is chosen for its ability to capture
realistic relationships between parcel characteristics and land values (Chicoine 1981). For
example, the transcendental form permits a positive or negative marginal relationship between
value and parcel size (acres), requires value to be zero when parcel size is zero, can detect
proportional value-size relationships, and allows for increasing or decreasing returns to scale for
land parcel characteristics (Chicoine 1981; Wichelns and Kline 1993). Moreover, functional forms
such as the semi-log or transcendental are preferred (to the more flexible functional forms) when
there is potential omitted variable bias (Garrod and Willis 1992).
The empirical version of the translog model is as follows:
ln(V) = lnl30 + [11 In(X1) + [52X2 + ...+ ~nXn + ~:
where the variables are as defmed above, and In(.) represents the natural log. This is the form used
for statistical analysis in the Southold property value study.
The estimated Wans-log model appears to fit the data well. Overall model statistics and individual
variable significance levels indicate good model fit, and trials with alternate functional forms (e.g.,
linear, semi-log) indicate that these alternate forms result in lower significance levels and
explanatory power. A White test for heteroskedasticity (White 1980) fails to reject the null
hypothesis of homoskedasticity at the 5% level. Although multicollinearity is ubiquitous in hedonic
models, tests for multicollinearity indicate that it does not have severe impacts on model results in
the present application. As the data is cross-sectional and data order has been randomized, tests for
autocorrelation are unnecessary.
APPENDIX C. OLS Model Results
Dependent Variable: LVALACRE
Sum of
Source DF Squares
Mean
Square
Model 20 152.03723 7.60186
E~or 355 30.00860 0.08453
C Total 375 182.04583
F Value
Prob>F
89.930
Root MSE 0.29074 R-square 0.8352
Dep Mean 12.83621 Adj R-sq 0.8259
C.V. 2.26502
Parameter Estimates
Parameter Standard T for HO:
Variable Estimate Error Parameter=0 Prob > ITl
1NTERCEP 11.409748 0.08174613 139.575 0.0001
LNACRE -0.905956 0.02606823 -34.753 0.0001
WATFRONT 0.333735 0.04497479 7.420 0.0001
BULKHEAD 0.200245 0.06780928 2.953 0.0034
YEARSQ 0.000302 0.00011945 2.528 0.0119
YEAR -0.012017 0.00484814 -2.479 0.0136
GARSQFT 0.000165 0.00006797 2.428 0.0157
PATIO 0.103269 0.03166099 3.262 0.0012
BATHS 0.109587 0.02678655 4.091 0.0001
NOHEAT -0.099710 0.04189498 -2.380 0.0178
FIRE 0.117428 0.03516537 3.339 0.0009
SQFT 0.000264 0.00004262 6.193 0.0001
SPECIAL 0.123980 0.05349404 2.318 0.0210
SOUNDI00 0.294474 0.07076478 4.161 0.0001
PECONI00 0.671739 0.30810728 2.180 0.0299
ONKOAD -0.176308 0.06582347 -2.678 0.0077
FARMDISTANCE 0.000017320 0.00000374 4.625 0.0001
ONFARM -0.142932 0.07661228 -1.866 0.0629
WETLAND 0.002729 0.00181653 1.502 0.1339
LARG£ZONE 0.154045 0.06636055 2.321 0.0208
OPENSPACE 0.120691 0.07154488 1.687 0.0925
30 ~
IV. OUTDOOR RECREATIONAL USES AND USE VALUES IN THE PES
IV.A. INTRODUCTION
This chapter presents results of EAl's study of outdoor recreational uses and use values 'for the
Peconic Estuary System ("PES"). The information presented includes: (1) a review of participation
and location choice for key recreational uses, and (2) estimates of the economic value of those uses.
Also presented is a summary of expenditures at PES farm stands, wineries, and rental housing, data
that was identified as a gap in EAI's Phase I study and is of interest to decision makers.
The main tool for providing information for this analysis is a recreational use survey carded out in
August, 1995. The recreational survey was designed to:
identify key outdoor recreational activities and locations in the PES
estimate the level of participation in key recreational activities
identify key characteristics and preferences of users
estimate the'value of recreational uses of the PES
fill data gaps identified in the Phase I study.
Key recreational uses and locations. More than 15 recreational uses of the PES were identified
through focus groups and discussions with local citizens and officials. One key component of the
recreational use survey is to assess where these recreational uses take place throughout the PES.
Participation in key recreational uses. EAI's Phase I study (Diamantides and Cu'igalunas, 1996)
estimated participation in only a few key recreational uses, using "off-the-shelf" information.
Phase I also identified many data gaps concerning participation rates and values for outdoor
recreation in the PES. The Phase II recreational survey is designed to fill these data gaps.
Characteristics and preferences of users. Prior to the recreational use survey, only anecdotal
information existed concerning recreational users of the PES. Information, such as residency,
accommodations, recreational preferences, etc., are provided by EAl's Phase II recreational survey.
Recreational use values. Data from the Phase II recreational survey is used to estimate the economic
value of key recreational activities in the PES and, when possible, to estimate the change in
recreational value resulting from changes in water quality.
Phase I economic impact data gaps. Key data gaps identified in the Phase I study include the
economic impact of specialized sectors of the local economy. These sectors include: summer
rentals, winery tours and sales, and farm stand sales. Each of these sectors was identified by local
citizens and officials as playing an important, but unquantified role, in the local economy.
Economic data for these sectors--sales-- is presented in this report.
IV.B. IDENTIFICATION OF RECREATIONAL USES AND LOCATIONS
Outdoor recreation is a major activity in the PES. Based on recreational uses identified during the
survey development process (see Sec. C, Survey Development), the survey questionnaire included
16 major natural resource-based recreational uses. Of these, eight key recreational activities were
identified:
· Fishing · Beach Use
· Boating · Bird Watching
· Swimming · Wildlife Viewing
· Shell fishing · Hunting
Data were collected for these eight key recreational uses, including annual participation during the
past year. Detailed information also was obtained on these activities for the respondent's most
recent recreational outing to the East End.
Annual participation data on key recreational activities include the number of times the respondent
had done each activity at specific East End loeatious during that year (1995). The survey instrurnent
identified eight water bodie~ as potential locations for fishing, boating, swimming, and Shell
fishing. The eight locations identified by water body are:
· Flanders Bay · Gardiners Bay
· Great Peconic Bay · Block Island Sound
· LiMe Peconic Bay · Long Island Sound
· Shelter Island Sound · Atlantic Ocean.
Locations for the remaining key recreational activities (beach use, bird watching, Wildlife viewing,
and hunting) were categorized by the town in which the activity took place. Five East End towns
were identified by the survey instrument. The five recreation locations identified by town are:
· East Hampton · Southampton.
· Riverhead · Southold.
· Shelter Island
Single-day data on key recreational activities include infomaation on activities the respondent
participated in during their most recent recreation day at the East End. This question was followed
by occasion-specific, write-in questions concerning the: (1) location of the activity, (2) perceived
water quality, (3) number of people in the recreation party, (4) travel time, and (5) time on site.
Other activity-specific data collected included fishing catch, shellfish harvest, choice of. marina or
ramp, perception of beach facility quality, and number of birds or type of wildlife sighted?.
?Hunting was not included in the occasion-specific questioning, due to the time of year and anticipated low
participation. ~
32 '
IV.C. SURVEY DEVELOPMENT AND IMPLEMENTATION
.The development and implementation of the recreational use survey parallels the development and
nmplementation of the resource survey. Meetings, interviews, and focus groups were instrumental
in the development of both surveys. Similarly, implementation of the two surveys occured at the
same time and places using the same survey staff. Only significant aspects of the development of
the recreational survey that differ from the resource survey will be discussed here. For a full
discussion of the resource survey development and implementation including similarities with the
recreational survey (see Chapter VI, Resource Value Survey).
A major challenge in the development of the recreational use survey was the creation of a question
format. A single instrument was selected to cover all the major recreational uses and locations of
the PES. The single-insmanent format allowed collection of data for multiple current and past uses
and was a much easier, and less costly, method for acquiring data, an important issue given realistic
limits on budgets for this project.
Questionnaire development began with meetings with the Management Committee, the Citizen's
advisory group, and "expert ififormants" from major stakeholder groups, such as marina operators
and Baymen. Based on issues raised by the three groups, questionnaire development then proceeded
with informal interviews with the public, focus groups, and pretests in order to determine the most
relevant questions, wording of questions, and survey presentation.
Informal interviews with the public were a critical component of questionnaire development. From
these interviews we gained insight into who the users are and their perspective on recreation in the
PES. For example, during these interviews, it became apparent that many recreational users did not
think in terms of the PES as an estuary system. Instead individuals more easily identified the East
End or individual bays within the PES as geographic entities. The questionnaire, therefore, asks
about recreational uses at locations at the East End with which individuals could easily identify.
Focus groups and pretests were used to hone the questionnaire down to an efficient presentation that
would allow us to collect all the required data. For example, one issue we used focus groups to
refine was the questionnaire's approach to eliciting water quality perceptions. Originally we had
hoped to use the questionnaire to ask very specific water quality questions, such as clarity, smell,
debris, etc., so that we might be able to estimate the contribution of these different attributes to an
individual's overall perception of water quality. However, during focus groups we found that
recreational users can have very complex and inconsistent ways of assessing water quality, which
include numerous additional attributes such as beach condition, signs, and plant abundance.
Eliciting information on all of these attributes became too cumbersome, and a more simplified
approach was eventually used. The simplified approach asks the respondent to rank water quality
at the site on a four point scale from "excellent" to "poor". As is described below, this approach
worked satisfactorily in that we found a statistical association between respondents' subjective
estimates of water quality and objective measures of water quality based on field sampling
undertaken by the County.
A key objective in the survey development phase was to keep the questioning simple and consistent
across uses. Many versions of the recreational use survey were pretested, to assess the ease with
which respondents could understand and answer the questions. The survey pretests were
instrumental in reducing the size of the questionnaire. One of our concerns was that if the
questionnaire was too long, only the first few questions would be answered, or respondents would
only answer selected questions. Another important concern was that the survey be clear and
relatively brief in order to allow us to use an intercept-survey, self-administration approach, to
economize on scarce research funds. The final version of the recreational use survey questionnaire
is available from the authors.
The recreational use survey and the resource use survey were implemented simultaneously during
the week of August 22-29, 1995, in pre-selected, public places in each of the five East End towns.
Interviewers solicited potential respondents by simply approaching them and asking if they would
be willing to spend 10-15 minutes filling out a survey to be used to help develop a plan to protect
and manage the bays. Survey locations included beaches, shopping areas, libraries, Post Offices,
and miscellaneous public places throughout the study area.
Other natural resource-based recreational activities were identified in the survey development
process. These activities include: walking/hiking, biking, sightseeing, jet skiing, jogging,
windsurfmg, art work, and photography. Questions on these activities were retained in the
questionnaire for comprehensiveness. However, these activities are considered less significant than
the key activities identified above, and data collected on these "non-key" activities are limited to
a single question on participation.
IV.D. DESCRIPTION OF THE RECREATIONAL USE SURVEY QUESTIONNAIRE
The survey booklet cover provides a map of the East End to orient respondents. The first set of
questions asks whether the respondent is an East End resident, owns a second home in the East End,
or is a visitor. Visitors are also asked the mode of transportation they used to travel to the East End
and the type of accommodations used, if any.
Subsequent questions address the issue of Brown Tide ("BT'). BT refers to the discoloration of the
waters in and around the PES due to an algae that sporadically grows in enormous numbers. Recent
incidents of BT may have contributed to a significant decline in ecologically productive eelgrass
beds and caused major disruptions in the formerly valuable scallop industry. BT also was a
significant concern to recreationists, as described in some detail later.
Throughout the survey development process, the issue of BT repeatedly surfaced. Questions
concerning the BT were placed in the beginning of the questionnaire in part to diffuse this issue
early on, allowing respondents to focus on the questions that follow. We asked respondents whether
they were aware of BT and which, if any, of their recreational activities were affected by BT.
The next set of questious asked respondents to indicate their annual participation in key recreational
activities by filling out two brief charts. One chart identified recreation locations by water body. The
other chart identified recreation locations by town. Respondents indicated the number of times that
they did each activity in each location during the past year (1995).
The annual participation questions were followed by single-day recreation activity questions.
Respondents were asked to identify their most recent recreation day at the east End. They then were
asked to identify all the recreational activities participated during that day. Shell fishing, swimming
and beach use, boat use, fishing, bird watching, and wildlife viewing were selected as key activities.
For each of these recreational pursuits, a separate page of questions asked for activity-specific
information. Activity-specific questions included: location, travel distance, time on site, number
of people in the party, catch or harvest, and water quality and site quality ratings.
After the participation questions, respondents were asked about selected expenditures made at the
East End during the past twelve months. As noted, these questions were designed to fill data gaps
in the Phase I study for farm' stands, wineries, and housing rentals. Residents and second home
owners were also asked a referendum-type question about their willingness to support selected
water quality initiatives (see section on water quality initiative referendum). Finally, a. set of
questions was asked in order to obtain socio-demographie data, such as residency, household status,
income, age, education, and employment status.
IV.E. DESCRIPTIVE RESULTS: DEMOGRAPHICS
A total of 1,354 respondents provided usable survey queStionnaires, although not every respondent
answered all questions. Selected, major demographic results of the survey are summarized as
follows:
Residents and second homeowners from all East End towns were represented in the sample
A majority (59 %) of respondents were visitors to the East End
Most visitors (76%) came from either Long Island or New York City
Most visitors (60 %) stayed at the East End overnight
The most common overnight accommodation was with friends or relatives (39%)
Compared to 1990 Census data, the sample has a slightly higher share of women and people
in the middle age groups; is better educated; and is wealthier than the resident population.
Residency Status. Of the 1,267 respondents who provided this information, over half of
those responding (59%) were visitors from outside the East End (Table IV.l). Of the remainder,
28% indicated that their primary residence was at one of the five East End towns, and 13% owned
a second home at the East End, but had a primary residence somewhere else. Some Brookhaven
residents considered themselves East End residents but were not counted as such in this study.
Residents and Second Home Owners - Place of Residency. Residents and second home
owners from each of the five East End towns were represented in the survey sample. Southampton
(30%) and Southold (28%) had the largest percentages of resident respondents. The remainder was
closely distributed among Shelter Island (16%), East Hampton (14%), and Riverhead (12%). The
relatively high proportion of Shelter Island resident respondents is not expected to have a significant
effect on survey results.
Table IV. 1 - Residency Status of Survey Respondents
Residency Number of Respondents Percentage of ali
Respondents
East End Residents 358 28%
Second Home Owners 159 13%
Visitors 750 59%
Total 1267 100%
For second home owners, New York City was the dominant primary place of residence (45
% of second home owners surveyed), followed by Nassau (19 %) and Suffolk counties (14 %).
Other locations, largely distributed across Westchester and Rockland counties in New York and
northeastern New Jersey, were the primary residences of the remaining 22%.
Visitors -Place of Residency. Visitors to the East End predominately came from Long Island
and New York City. 40% of visitors came from Suffolk county, 20% came from New York City,
and 16% from Nassau county. Only 8% came from other locations in New York State and New
Jersey, and just 5% came from Connecticut and Massachusetts. Other locations, such as Florida,
California, and several foreign countries accounted for the primary residences of the remaining
11%.
Visitor Classification and Accommodations. Most visitors in our sample (60%) were
overnight visitors. Day trippers (those who returned to a home outside the East End at the end of
the day) accounted for 39% of visitors surveyed.
Of the 700 overnight visitors who provided accommodation information, the most Common (39%)
type of accommodation was with friends or relatives. Rental (24%) and hotel or motel (2~2%)
accommodations were closely matched. Surprisingly, 15% of overnight visitors to the East End
reported that they stayed at accommodations other than friends or relatives, hotels or motels, or
rental properties. Presumably these other accommodations are predominately boats and camp sites.
Sample characteristics: comparison with resident census data. Gender, age, education, and
income characteristics of survey respondents can be compared to the characteristics of the resident
population as identified by the most recent (1990) census. Only comparisons to the resident
population are possible, since characteristics of the second homeowner and visitor populations are
unknown.
For all residency and visitor categories, the share of female respondents was only slightly more than
in the resident population. The age distribution of the sample reflects the survey implementation
practice of not including individuals less than 18 years of age.
Our survey sample is much more educated than the resident population. For example, census data
indicates that 49% of the resident population had some level of post-secondaxy education. The same
level of education was achieved by 80% of the resident sample, 95% of second homeowners, 91%
of overnight visitors, and 82% of day trippers.
The survey sample also has higher household incomes than the resident population. According to
the census, 34% of the resident population has a household income of $50,000 or more. However,
nearly half (48%) the residents in the survey sample have household incomes of $50,000 or more,
as do 80% of second homeowners, 64% of overnight visitors, and 59% of day trippers.
Sample characteristics: comparison among resident and visitor categories. The distribution
among female and male respondents slightly favored females in all categories. Day trippers have
the most respondents under 24 years of age (10%), and overnight visitors have the most respondents
in the 25-44 age group (60%). Second homeowners have the largest percentage of 45-74 year olds
(62%). Second homeowners are also the most highly educated (95% post-secondary schooling) and
have the highest incomes (80% above $50,000 household income).
Sample characteristics: East End Town of Residence. Residents and second home owners
from Shelter Island and Southold were-over represented in the survey sample, based on
comparisons to year-round and seasonal population statistics. Residents were under represented in
Riverhead and Southampton. Second home owners were under represented slightly in East
Hampton and also in Southampton. This disproportionate distribution of the sample most likely has
little, if any, effect on survey results since the type and frequency of recreational activity is not
strongly related to the East End town the respondent lives in.
37
Table IV.2 - Population Demographics vs. Survey Respondent Demographics
Second Overnight
1990 Resident Homewner Visitor
Population Sample Sample Sample
Day
tripper
Sample
Gender
Female 51.83% 53.57% 54.67% 53.63% 55.34%
Male 48.17% 46.43% 45.33% 46.37% 44.66%
Age
up to 20 23.89% 3.83% 3.36% 1.49% 2.76%
21-24 4.53% 1.91% 1.34% 4.95% 7.09%
25-34 16.23% 14.48% 8.72% 25.50% 22.83%
35-44 12.84% 21.86% 19.46% 34.65% 24.02%
45-54 11.01% 18.85% 30.87% 21.29% 20.87%
55-64 11.04% 13.39% 18.79% 8.42% 12.20%
65-74 11.17% 21.31% 12.08% 2.97% 7.87%
75-84 7.25% 3.83% 4.70% 0.50% 1.97%
85 and up 2.04% 0.55% 0.67% 0.25% 0.39%
38
Table IV.2 - Population Demographics vs. Survey Respondent Demographics, cont.
Second Overnight
1990 Resident Homewner Visitor Day tripper
Population Sample Sample Sample Sample
Education
<High 7.44% 1.27% 0.00% 0.28% 0.46%
School
Some HS 11.55% 2.55% 0.77% 0.84% 2.78%
HS Grad. 31.77% 16.56% 4.62% 7.58% 14.35%
Some Coll. 18.35% 21.66% 13.85% 17.42% 26.39%
Assoc. Deg. 6.66% 12.74% 4.62% 7.30% 12.04%
Bachelor's 13.51% 21.97% 23.85% 29.78% 19.44%
Deg.
Advanced 10.72% 23.25% 52.31% 36.80% 24.54%
Deg.
Income
<$15,000 19.17% 9.42% 1.57% 5.16% 3.70%
$15,000 - 14.60% 12.01% 3.15% 4.01% 8.80%
$24,999
$25,000 - 14.40% 13.31% 5.51% 10.32% 8.33%
$34,999
$35,000 - 17.52% 17.53% 10.24% 16.62% 20.37%
$49,999
$50,000 - 18.90% 23.38% 13.39% 19.48% 26.85%
$74,999
$75,000 - 7.04% 11.36% 14.96% 11.75% 17.59%
$99,999
$100,000 -
$149,999 4.93% 6.82% 18.11% 17.19% 9.26%
$150,000
and up 3.41% 6.17% 33.07% 15.47% 5.09%
IV.F. DESCRIPTIVE RESULTS: KEY RECREATION ACTIVITY PROFILE
Respondents were asked the number of times that they performed each of the following
outdoor recreational activities at various East End locations (Bays and towns) this year (1995):
Activities:
Locations:
Fishing, Boating, Swimming, Shell fishing
Flanders Bay, Great Peconic Bay, Little Peconic Bay, Shelter Island Sound,
Gardiners Bay, Block Island Sound, Long Island Sound, and Atlantic Ocean
Activities:
Locations:
Beach Use, Bird Watching, Wildlife Viewing, and Hunting
East Hampton, Riverbead, Shelter Island, Southampton, and Southold.
The water bodies of the Peconic Estuary System are identified as Flanders Bay, Great Peconic Bay,
Little Peconic Bay, Shelter Island Sound, and Gardiners Bay. Substitute water bodies included in
the survey were Long Island Sound, Block Island Sound, and the Atlantic Ocean.
Swimming and beach use were treated as separate activities in the questionnaire in order to
identify swimming locations by body of water and identify the number of beach goers by town.
Since each of the five towns has multiple water body options for swimming, it was necessary to ask
for location by both water body and town.
Figures for swimming and beach use should not be summed. This is because the activities are not
mutually exclusive, and it is expected that most respondents gave a positive answer to both
categories even though they may be referring to a single outing.
Major characteristics of respondents' annual recreational activity during the year are:
Most (83%) engaged in at least one key recreational activity, half in more than one activity.
Most residents (95 %) participated in key recreational activities
Swimming was the most popular activity
The PES was the most popular swimming location (42% of all swimming trips)
Boating, fishing, and Shell fishing were predominantly done in the PES.
Sample Participation Overall. Most respondents (83%) engaged in at least one outdoor
recreational activity at the East End and during the past year averaged 38 recreational experiences
per respondent. In total, respondents engaged in more than 48,400 outdoor recreational experiences
during the past year. Of this number, more than 26,000 were water-based outdoor recreational
experiences, and the majority of these (15,506) were in the PES. Land-based outdoor recreational
experiences the five East End towns amounted to 22,300.
Sample Participation By Activity. Among all respondents, swimming and beach use were
the most popular activities. Swimming was done on more than 15,000 occasions by respondents.
The most popular area for swimming was the PES, which accounted for 42% of all swimming trips.
40 ~
More than 12,000 beach visits at the five East End towns were reported.
Shell fishing was the least frequent water- based activity (1,304 trips). More than half (59%) of
these Shell fishing lrips took place in the PES. Hunting was the least popular land-based activity
(255 occasions). Note that bird watching and wildlife viewing responses given in Table IV.3 were
adjusted to include only trips for viewing, i.e., activity less than one mile from home was not
included.
Table IV.3 - Participation in Key Recreation Activities - By Residency Status
Residents SHO Overnight
(n=358) (n--159) (n=427)
Fishing 88 (25%) 41 (26%) 62 (15%)
Boating 132 (37%) 63 (40%) 88 (21%)
Swimming 186 (52%) 80 (50%) 153 (36%)
Shell fishing 49 (14%) 19 (12%) 14 (3%)
Beach use 282 (79%) 103 (65%) 235 (55%)
Bird
watching 36 (10%) 13 (8%) 23 (5%)
Wildlife
viewing 73 (20%) 39 (17%) 45 (11%)
Hunting 10 (3%) 3 (2%) 6 (1%)
Any key
activity* 342 (96%) 132 (83%) 287 (67%)
Multiple key
activities 262 (73%) 103 (65%) 182 (43%)
Day Total
trippers (n--1217)
(n=273)
13 (5%) 204 (17%)
39 (14%) 322 (26%)
28 (10%) 447 (37%)
6 (2%) 88 (7%)
74 (27%) 694 (57%)
32 (12%) 104 (9%)
40 (15%) 185 (15%)
2 (1%) 21 (2%)
117 (43%) 878 (72%)
65 (24%) 612 (50%)
*May include bird watching and wildlife viewing trips less than one mile from home.
Participation Rate Overall By Residency Status. East End residents are more likely to
participate in outdoor recreation than any other group. Fully 95% of the residents surveyed engaged
in at least one, key water-based activity in the PES or one key land-based activity in one of the East
End towns. By comparison, 83% of second homeowners and 67% of overnight visitors also
participated in outdoor recreation. Day trippers have the lowest rate of participation (43%).
A direct comparison between the number of people engaging in land-based and water-based
activities cannot be made. This is due to the inability to tell the difference between beacl~.use in the
PES and beach use on the Atlantic Ocean or Long Island Sound shoreline at the East End. However,
the majority of residents (73%) and .second home owners (65%)--and 50% of all survey
respondents-- engaged in more than one key recreational activity during the past year.
Participation Rate in Key Recreational Activities-By Residency Status. Proportionately
more residents (70%) and second homeowners (65%) swim, boat, fish, and shellfish than overnight
visitors (45%) and day trippers (22%). Among these activities, the highest participation rotes across
all residency status categories are for swimming (37%) followed by boating (26%) and fishing
(17%). Shell fishing is more popular with residents (14%) and second home owners (12%) as
compared with the overall sample (only 7 %).
Residents and second homeowners are more likely to go to the beach, watch birds, view wildlife,
and hunt in the East End than other groups. Proportionately more residents (85%) and second
homeowners (70%) engage in these activities than ovemight visitors (59%) and day trippers (37%).
Among these activities, the highest participation rates across all groups are for beach use (57%)
followed by wildlife viewing (21%) and bird watching (19%). Only 2% of survey respondents hunt.
Location of Key Recreational Activities. Respondents used the PES more times (15,506
occasions) for all key outdoor'recreation activities--swimming, boating, fishing, and Shell fishing--
than any other water body at the East End (Table IV.4).
Table IV.4 - Water Body Selection by Activity - (n=1267)
Fishing Shell Boating Swimming Totals
fishing
Block Island 320 (6%) 71 (5%) 467 (5%) 380 (2%) 1238 (5%)
Sd.
Long Island Sd. 1422 266 (20%) 1760 2801 (18%) 6249 (24%)
(25%) (20%)
Atlantic Ocean 1125 201 (15%) 1056 5685 (37%) 7116 (27%)
(20%) (12%)
PES 2732 766 (59%) 5470 6538 (42%) 15506 (60%)
(49%) (62%)
Totals 5599 1304 8753 15404 26060
(100%) (100%) (100%) (100%) (100%)
* Percentages may not sum precisely due to rounding
No single town is the dominant site for all categories of recreation by respondents. For example,
beach use occurs most often at Southampton (3334 occasions) and East Hampton (3139). Shelter
Island was the most used for bird watching (567) and hunting (92), and East Hampton had the most
(1123) wildlife viewing experiences. On the other hand, Riverhead was the least used town for
beach use (1075), bird watching (68), and wildlife viewing (220). Southampton had the least
number of hunting occasions (26).
Table IV.5 - Town Selection by Activity - (n=1267)
Bird Wildlife Total
Beach Use Watching Viewing Hunting Trips
East 3139 (26%) 300 (20%) 1123 (29%) 50'(20%) 4612 (25%)
Hampton
Riverhead 1075 (9%) 68 (5%) 220 (6%) 56 (22%)
Shelter Island 2461 (20%) 567 (37%) 1030 (27%) 92 (36%)
Southampton 3334 (27%) 386 (25%) 690 (18%) 26 (10%)
Southold 2239 (18%) 215 (14%) 764 (20%) 31 (12%)
Totals 12248 1536 3827 255
(100%) (100%) (100%) (100%)
*Percentages may not sum precisely due to rounding
1419 (7%)
4150 (25%)
4436 (24%)
3249 (18%)
17866 (100)
Location ofPES Water-BasedActivities. Great Peconic Bay is the most popular water body
in the PES for recreational activity, with 28% of recreational trips in the PES, while Flanders Bay
is the least frequently used, with 8%. Great Peconic Bay is also the most popular location in the
PES for swimming (30%), fishing (29%), and boating (25%). Gardiners Bay is the most popular
PES location for Shell fishing, accounting for 33% of all PES Shell fishing trips.
Table IV. 6 - PES Key Recreational Activity Outings - (n=1267)
Fishing Shell Boating Swimming Totals
fishing
Flanders
BaY 288 (11%) 45 (6%) 562 (10%) 366 (6%) 1261 (8%)
Gt. Peconic
Bay 793 (29%) 199 (26%) 1362 (25%) 1991 (30%) 4345 (28%)
Ltl.
Peconic 486 (18%) 109 (14%) i021 (19%) 1298 (20%)
Bay
2914 (19%)
Shelter Isl.
Sound 511 (19%) 158 (21%) 1321 (24%) 1410 (22%) 3400 (22%)
Gardiners
Bay 654 (24%) 255 (33%) 1204 (22%) 1473 (23%) 3586 (23%)
Totals 2732 766 5470 6538 15506
(100%) (100%) (100%) (100%) (100%)
* Percentages may not sum precisely due to rounding
PES Key Recreational Activities by Residency Status. Swimming is the most popular water-
based activity in the PES for all residency groups and overall accounts for 42% of all water-based
recreation trips (Table IV.7). Boating is nearly as popular (36% of all PES trips).
Overnight visitors fish more (22% of their outdoor recreation activity) than any other group. Day
trippers allocate a larger percentage of their PES trips to Shell fishing (16°/3) than to fishing (9%),
although Shell fishing overall is a modest activity (5% of PES all trips).
Table IV.7 - PES Key Recreational Activity Outings by Residency Status - (n=1217)
Residents SHO Overnight Day tripper Total
Fishing 1114 (17%) 401 (11%) 947 (22%) 30 (9%) 2492 (17%)
Boating 2354 (37%) 1275 (36%) 1450 (34%) 110 (34%) 5189 (36%)
Swimming 2520 (39%) 1676 (48%) 1839 (43%) 132 (41%) 6167 (42%)
Shell fishing 396 (6%) 154 (4%) 92 (2%) 52 (16%) 694 (5%)
Totals 6384 3506 4328 324 14542
(100%) (100%) (100%) (100%) (100%)
Percentages may not sum precisely due to rounding ~
44 ~
Locations ofPES~lctivities - By Residency Status. For residents, Shelter Island Sound (27%)
is the most popular location in the PES, followed closely by Great Peconic Bay (25%) (Table V.8).
Gardiners Bay (31%) is the most frequented destination for second home owners, with Great
Peconic Bay (30%) again nearly as popular. Over night visitors and day trippers prefer Great
Peconic Bay (30% and 34%, respectively) over all other locations.
Flanders Bay is by far the least Often visited location for residents (7%), second home owners 0%),
and overnight visitors (10%). The least visited location by day tr/ppers is Gardiners Bay (13%),
presumably because it is the furthest of the bays from their homes. Flanders Bay is more popular
with day trippers (17%) than with any other group.
Table IV. 8 - PES Key Recreational Activity Locations by Residency Status (n=1217) .
Flanders
Bay
Gt. Peconic
Bay
Ltl.
Peconic
Bay
Shelter Isl.
Sound
Oardiners
Bay
Total
Residents
451 (7%)
1589 (25%)
1260 (20%)
SHO Overnight Day tripper Total
114 (3%) 446 (10%) 54 (17%) 1065 (7%)
1048 (30%) 1304 (30%) 110 (34%) 4051 (28%)
740 (21%) 736 (17%) 60 (19%) 2796 (19%)
1752 (27%) 525 (15%) 1031 (24%) 58 (18%) 3366 (23%)
1332 (21%) 1079 (31%) 811 (19%) 42 (13%) 3264 (22%)
6384 3506 4328 324 14542
(100%) (100%) (100%) (100%) (100%)
* Percentages may not sum precisely due to rounding
IV.G. DESCRIPTIVE RESULTS: PARTICIPATION AND TRIP ESTIMATES
Introduction The total number of trips (or "outings") for outdoor recreation is the product
of three factors: (1) The potential number of participants, (2) the proportion who actually
participate, and (3) the number of times those who participate engage in an recreation activity.
Thus, we have:
Trips = No. of Potential Participants x Percent Participating x No. of Times ~.articipate
For example, assume that there are 2 million potential participants (i.e., residents, second home
owners, over night visitors, and day trippers) for swimming, that 20 % of these actually swim, and
that of these participants, each swims an average of 10 times per year. In this case, the number of
swimming trips or outings is 4 million:
= 2 million x 20 % x 10
= 400,000 x 10 = 4 million trips
The data and assumptions used to estimate trips for each PES recreation activity is explained below.
Estimation of Total Number of Trips. The population of potential participants include ail East End
residents, second homeowners, overnight visitors, and day trippers. To estimate this population,
we used 1990 census data from the Long Island Regional Planning Board ("LIRPB"). The LIRPB
analysis provides estimates of the year-round resident population; the seasonal population, including
second homeowners and guests; year-round resident guests; and hotel, motel, and campsite
capacities. The number of participants in each recreational activity was estimated separately for
each of these residency category. The resident population over age 15 is estimated by the LIRPB
to be 84,871.
Data are not available for the age breakdown of the seasonal population. Lacking authoritative data,
we assume that the seasonal population has the same percentage over the age of 15 (80%) as the
resident population. This implies an estimated seasonal population over age 15 of 135,620.
The seasonal population is split into two groups, based on EAI's recreational use survey results:
Second Home Owners (27%) and Overnight Visitors (73%). Our survey results indicate that the
two components of the seasonal population have different participation rates and therefore should
be considered separately, when possible. Note that many overnight visitors stay with second
homeowners and residents as renters or guests.
Population data is not available for day trippers; therefore this group is excluded from our
participation estimates. The absence of this information will slightly understate outdoor recreation
participation.
Information concerning the percent of potential participants that participates in one or more
recreational activity was presented earlier. The trips per participant are given in Table IV.9.
The major results of estimated annual participation in PES outdoor recreation (excluding day
trippers ) for 1995 can be summarized as follows:
SDay tripper are excluded because we have no reliable way of estimating the total number in this group. This
omission slightly understates total participation in PES recreation (see text). ;~
127,762 people participate in either swimming, boating, fishing, or Shell fishing in the PES,
taking 3.3 million outings.
156,184 people participated in either beach use, bird watching, wildlife viewing, or hunting
in the East End towns, engaging in 5.2 million trips.
Table IV.9 - Avera[e Annual Outings per Participant by Activit~ and Residence*
Residents SHO Overnight Day trippers
Fishing 12.66 9.78 15.27 2.31
Boating 17.83 20.24 16.48 2.82
Swimming 13.55 20.95 12.02 4.71
Shell fishing 8.08 8.11 6.57 8.67
Beach Use 22.50 25.85 9.20 6.11
Bird Watching 37.55 23.49 8.83 3.28
Wildlife Viewing 34.36 25.39 9.99 3.80
Hunting 13.20 16.33 7.00 5.50
*Includes activities at the five East End towns and PES water bodies only
The total number ofaips by each group, for each activity, is estimated by multiplying the estimated
number of trips per participant (Table IV.9) for each group (i.e., residents, second homeowners,
etc.) by the estimated total number of participants by group. The results are shown in Table IV.10.
Overall, there are 5.15 million beach use, bird watching, wildlife viewing, and hunting outings
taken in East End towns and 3.35 million swimming fishing, boating, and Shell fishing outings
taken in the PES in 1995 (Table IV.10). Most outdoor recreational activity is accounted for by
residents, who engaged in 3.3 million land-based and 1.5 million water-based occasions locally.
Second homeowners took 1.1 million land-based trips and more than 800 thousand water-based
trips. Overnight visitors engaged in more than 780 thousand land-based, and 1 million water-based,
trips.
Total trip estimates slightly understate actual recreation trips because day tripper txip estimates are
not included due to the lack of population data for this group, as noted earlier. According to EAI's
recreational use survey, day trippers account for 3% of land-based and 2% of water-based
recreational trips taken by the sample. ~
47 '
Comparison with Phase I Total Outdoor Recreational Trip Estimates: Introduction. The Phase I
study (Grigalunas and Diamantides, 1996) gave estimates of total recreation trips to the area for
selected activities based on available "off the shelf" data and were comprised of a variety of
sources. No original data was collected during Phase I. In Phase II original data was assembled
and it is important to note and to explain the major differences between the two sets of results
(Table IV.11).
Table IV.10 - Estimated Annual Key Recreation Activity Outings*
Residents SHO Overnight Totals
Fishing 268,617 93,110 226,766 588,493
Boating 559,902 296,451 342,630 1,198,983
Swimming 598,001 383,563 428,406 1,409,970
Shell fishing 96,006 35,636 19,513 151,155
Beach Use 1,508,582 615,257 500,955 2,624,794
Bird Watching 219,008 32,139 41,699 292,846
Wildlife 174,861 39,218 40,392 254,471
Viewing
Hunting 33,609 11,959 6,930 52,498
Totals 3,458,586 1,507,333 1,607,291 6,573,210
*Includes activities at the five East End towns and PES water bodies only
In general, we found major that the Phase I estimates of recreational activity were understated--in
some cases, greatly understated-- as compared to the Phase II results. The major reason for the large
differences in estimates between the Phase I and II reports can be explained as follows. Our survey-
based estimates fred much high participation rotes and trips per participant than the Phase I report,
which used a state-wide average for New York. For example, in Phase II for bird watching and
wildlife viewing we found a high participation rate (19% for bird watching and 21% for wildlife
viewing) for respondents and a large number of trips per participant (37.55 bird watching trips and
34.36 wildlife viewing trips for residents). The Phase I survey, in contrast, was based on NFWS
statewide estimates for New York. These statewide participation rates (9.4%) and number or,ps
per participant (8.3) are very much lower than those found in the PES survey sample.
This major difference between the Phase I and Phase II results is not surprising (at least in
retrospect). Residents and seasonal visitors to the PES are attracted to the area precisely because
they are interested in estuary-related outdoor recreation. We thus would expect this group to be
much more interested in outdoor recreation than the general population of the state. Similar large
differences between the Phase I and I1 results were found for other activities (see Table IV. 11 and
also see Appendix for details) and the same explanation seems relevant.
Table IV. 11 - Comparison of Participation Estimates Phase I Versus Phase II
Beach Use
Non-
Consumptive
Hunting
Fishing
Boating
Swimming
PES - Phase II PES - Phase I*
2,624,794 NA**
547,317 91,713
~52,498 77,300
588,493 113,589
1,198,983 NA
1,409,970 714,600
Grigalunas and Diamantides (1996).
** NA = Not available
IV.H. DESCRIPTIVE RESULTS: SELECTED EXPENDITURE ESTIMATES
Introduction. During the early survey development phase of our work, members of the
Citizens Advisory Committee and officials at the Suffolk County Dept. of Health Services
expressed interest in our collecting information on sectors of the PES economy for which data are
difficult to fred using standard economic sources. Specific sectors of the economy targeted for
special consideration were Farm Stands, Wineries, and Rental Lodging.
The survey questionnaire asked respondents to estimate the amount that their household
spent at Farm stands and Wineries at the East End over the past year. Visitors also were asked
whether they rented a house, condo, apartment, etc., at the East End over the past twelve months.
Respondents who answered "yes" to this question were asked the length of the rental agreement and
the amount they spent. Responses to these questions were given in 1995 dollarsl summarized in
Table IV. 12 and explained briefly below.
Population of Potential Participants. Expenditure data on Farm stands, Wineries, and
Rental Lodging collected in this survey are household (not per person) expenditures. The number
of East End resident households (44,241) and seasonal households (29,183) are based on 1995 Long
Island Lighting Company estimates.
The number of overnight visitor households cannot be adequately estimated with available
data. LIRPB seasonal population estimates account for occupancy of some rental lodgings, such
as hotels, motels, and campsites. However, this component of the population estimate does not
a.cc.ount for turnover, i.e., the same rental lodgings will be used by an unknown number of overnight
wsxtors over the course of a season. Estimating the number of overnight visitor households is
further complicated by the large percentage of overnight visitors (39%) who indicated that they
stayed with friends or relatives, which implies that some amount of their expenditures may be
included in resident and seasonal household estimates. For these reasons, expenditures of overnight
visitors are not estimated in this analysis. Still, the average annual expenditure for rental
accommodations per household from our survey --$5,400--make it evident that seasonal rentals are
a major source of income for PES rental property owners and owners of hotels, motels, and inns.
Estimated Total Participation and Expenditures. Table IV.13: Estimated Farm Stand and
Winery Expenditures presents estimates (in 1995 dollars) of participation and expenditures in these
sectors for resident and seastnal households. Table IV.13 also presents estimates of PES-related
expenditures in these sectors. PES-related expenditures were estimated using the same seasonal
adjustment (45 %) used in the Phase I Report to estimate the PES-related impacts of tourism and
recreation sectors. Total rental lodging expenditures could not be estimated due to incomplete
information on the number of participants.
Table IV.12 - Survey Farm Stand, Winery, and Rental Lodgin~ Expenditures (1995 dollars)~
Farm stand Winery Rentals
Residents
Participation 316 (88%)
Total Expenditures $86,400
Average Expenditures $273
Second Home
Owners
Participation 131 (82%)
Total Expenditures $48,122
Average Expenditures $367
Overnight Visitors
Participation 225 (53%)
Total Expenditures $41,228
Average Expenditures $183
Day Trippers
Participation 153 (56%)
Total Expenditures $12,755
Average Expenditures $83
Totals
Participation
Total Expenditures
Average Expenditures
* Figures in parentheses
114 (32%)
$20,871
$183
67 (42%)
$15,350
$229
114(27%)
$18,360
$161
103 (26%)
$556,000
$5,398
94 (34%)
$9,121
$97
825 (68%) 389(32%)
$188,505 $63,702
$228 $164
indicme the percent who made such expenditures
103 (9%)
$556,000
$5,398
Table IV.13 - Estimated PES Farm Stand and Winery Expenditures
Residents
Estimated Participating
Households
Estimated Total Expenditures
Estimated PES-Related
Expenditures
Second Home Ownera
Estimated Participating
Households
Estimated Total Expenditures
Estimated PES-Related
Expenditures
Totals
Estimated Participating
Households
Estimated Total Expenditures
Estimated PES-Related
Expenditures
Farm stand Winery
38,932 14,157
$10,628,436 $2,590,731
$4,782,796 $1,165,829
23,929 12,256
$8,781,943 $2,806,624
$3,951,874 $1,262,981
62,861 26,413
$19,410,379 $5,397,355
$8,734,671 $2,428810
An interesting finding is that PES-related Farm stand expenditures are greater than earlier
estimates for several other food-related sectors presented in the Phase I Report. For example, PES-
related farm stand expenditures are greater than expenditures (in 1995 dollars) in Meat and Fish
Markets ($6.5 million), Fruit and Vegetable Markets ($3.8 million), Retail Bakeries ($2.9 million),
and Drinking Establishments ($6.0 million) (Grigalunas and Diamantides, 1996). ~
IV.I. DESCRIPTIVE RESULTS: REFERENDA AND BROWN TIDE RESULTS
IV.I. 1. Hypothetical Referenda
Introduction. One section of the recreational use survey posed referendum-type policy
questions to East End residents and second home owners. These questions were added to "test the
waters" on selected policy issues and were not designed as a basis for resource valuation, which
would have required considerably more effort and data than was possible for this project.
Referenda Questions. The referenda questions focus on four policy actions:
(1) reseeding scallops following Brown Tide,
(2) planting hard clams,
(3) upgrading the Riverhead sewage treatment plant, and
(4) reducing road runoff.
Respondents were presented with one of these programs and were asked whether they support the
program, if they had to pay a given annual cost. Annual costs were varied for each 0fthe proposed
policy actions as described in Table IV.14.
Referenda Results. Respondents overwhelmingly supported the policy actions and indicated
a willingness to pay the annual costs. Support ranged from a high of 100% for reseeding scallops
following Brown Tide at an annual cost of $10, to a low of 75% for upgrading the Riverhead
sewage treatment plant at an annual cost of $100. This high level of support for PES management
programs was consistent across residents and second home owners. Overall, 87% of respondents
indicated they support the policy actions at the given costs (Table IV.14).
IV.I.2. Descriptive Results: Effects of Brown Tide
Respondents were asked whether they had heard of Brown Tide ("BT"). Those who had heard of
BT were asked about their level of concern and about any activities that may have been affected by
BT. Overall, most respondents (83%) had heard of BT, and of those, most (71%) were also very
concerned (Table IV. 15).
BT had the biggest effect on swimming. Fifty-eight percent of those who had heard of BT indicated
that it had affected their swimming. Recreational fishing (34 percent) and shell fishing (33 percent)
also were affected by BT.
Residents and second home owners had the most knowledge of and the highest concern for BT.
Overnight visitors had the least knowledge and indicated that their activities were affected the least.
Table IV.14. Management Action Referenda Results
Proposal; Annual Cost
Resident SHO Total Total
s Y/N s %Yes
Y/N YfN
Reseed Scallops Following Brown Tide; $5 21/2 6/0 27/2 93%
Reseed Scallops Following Brown Tide; $10 14/0 10/0 24/0 100%
Reseed Scallops Following Brown Tide; $25 17/2 5/1 22/3 88%
Plant Hard Clams; $5 21/4 12/0 33/4 89%
Plant Hard Clams; $10 18/3 5/0 23/3 88%
Plant Hard Clams; $25 14/2 2/2 16/4 80%
Upgrade Riverhead Sewage Treatment Plant;S5 16/2 6/0 22/2 92%
Upgrade Riverhead Sewage Treatment Plant;S25 18/3 9/1 27/4 87%
Upgrade Riverhead Sewage Treatmem Plant;S50 15/4 6/0 21/4 84%
Upgrade Riverhead Sewage Treatment 73/28 30/6 103/34 75%
Plant;$100
Reduce Road Run Off; $10 19/1 11/0 30/1 97%
Reduce Road Run Off; $25 22/1 15/1 37/2 95%
Reduce Road Run Off; $50 22/1 6/0 28/1 97%
Totals 290/53 123/11 413/64 87%
54
Table 1V.15
Heard of Brown
Have NOT Heard of
Brown Tide
Awareness of Brown Tide
Second Overnight Day
Residents Home Owners Visitors Trippers Total
370 159 279 202 1010
97.11% 91.91% 67.72% 78.29% 82.52~
11 14 133 56 214
2.89% 8.09% 32.28% 21.71% 17.48~
Very Concerned 295 113 174
about Brown Tide* 80.16% 71.52% 62.37%
A Little Concerned 70 44 100
about Brown Tide* 19.02% 27.85% 35.84%
NOT Concerned 3 1 5
about Brown Tide* 0.82% 0.63% 1.79%
* Includes only resoondents who have heard of l~r~wr~ Tide
131 713
65.17% 70.87%
64 278
31.84% 27.63%
6 15
2.99% 1.49%
55
Table IV.16. Activities Affected By Brown Tide
Swimming
Fishing
Shell
fishing
Boating
Other
Second
Home Overnght Day
Resident Owners Visitors Trippers Total
s
224 98 151 118 591
60.54% 61.64% 54.12% 58.42% 58.51%
148 49 82 69 348
40.00% 30.82% 29.39% 34.16% 34.46%
168 52 62 58 340
45.41% 32.70% 22.22% 28.71% 33.66%
72 30 43 37 182
19.46% 18.87% 15.41% 18.32% 18.02%
32 13 20 10 75
8.65% 8.18% 7.17% 4.95% 7.43%
* Includes only, respondents who have heard of Brown Tide
IV.J. ESTIMATES OF THE ECONOMIC VALUE OF RECREATION
IV.J. 1. Introduction
Our intention was to use data from the recreational survey to assess the use value for many water-
based and shore-based recreational activities. The survey collected activity-specific data on fishing,
Shell fishing, boating, and swimming in the PES and surrounding waters. Information also was
collected on beach use, birdwatehing, wildlife viewing, and hunting in the five PES towns.
Unfortunately, the total number of surveys was limited to 1,354 completed responses due to
budgetary reasons. The number of completed responses for each activity was the limiting factor in
assessing which activities could be valued. Values were estimated for pr/mary activities of interest:
swimming, fishing, boating, and for birdwatching and wildlife viewing (the last two of which were
combined into one activity). However, the number of responses was insufficient to estimate the
value of Shell fishing and hunting. Beach use was not valued separately from swimming.
Swimming is the most popular activity, and swimming and recreational fishing are the activities
most directly affected by water quality and hence by management actions aimed at enhancing water
quality (e.g., Table IV.15) For this reason, swimming is a focus of our efforts to esi!mates the
benefits of management actions in terms of changes in swimming values. We recognize that
participation in, and the economic value of, all activities depends upon the quality of the experience.
For example, substantial research on recreational fishing, including the results of this study, shows
the importance of the quality of the experience--the catch rate--on the number of trips and their
economic value (e.g., Freeman, 1995; McConnell and Strand, 1994). However, establishing the
necessary cause-and-effect scientific links between policies to preserve or restore habitat, for
example, with changes in salt water recreational catch rates appears to be beyond the state of the
art9. Nevertheless, we can simulate how benefits to recreation fishing would change if the catch rate
increased by, say, 10 percent.
Individual recreation trip value estimates and annual aggregate value estimates are given for three
cases. First, we present the existing ("baseline") conditions for swimming, fishing, boating, and for
birdwatching and wildlife viewing. For the baseline, we show consumer surplus per trip, total
estimated number of trips in 1995, and total benefits for the year for the four key recreation
activities studied.
Second, we show how economic value for swimming changes for illustrative, hypothetical policies
affecting water quality. One such policy is assumed to increase water quality by 10 percent in each
of the five PES bays. We also simulate a 20 percent uniform improvement in water quality for
comparative purposes. Finally, a 10 percent improvement in the recreational fishing catch rate is
simulated.
IV.J.2. Methodology
Value estimates for each activity are derived using the Travel Cost method of valuation (see, for
example, Freeman, 1993). The rationale behind this methodology is that, although there is no
market price that reveals the value of a recreational activity, an implicit price can be observed
through the cost of traveling to the activity's location. Included in this implicit price is the cost of
transportation, such as gas, wear and tear on the automobile, etc., and the value of the individual's
travel time. In theory, an individual's opportunity cost is valued as his or her next best use of their
time. However, a fraction of the individual's wage is generally used, in practice, to represent income
foregone in favor of recreation.
In the travel cost model, the number of trips a person takes for recreation at a specific site depends
on the costs of getting to that site, the comparative costs of getting to substitute sites, and the quality
of the recreational experience at the sites. In other words, the cheaper it is to get to a site, the
cheaper travel to that site is relative to other sites, and the better the quality of the recreational
experience at that site, the more times the individual will visit that site, all else equal. Thus,
observed recreation choices reveal tradeoffs between cost of participation, as measured by travel
~'his contrasts with policy actions to replant shellfish or to stock fish, the latter of which is often done in flesh water
to enhance recreational fishing. In these cases, it may be easier to link a policy to a change in catch rate and hence
to benefits. .~.
cost, and participation rates. In addition, participants might reveal values for site quality by
participating more at sites of higher quality, and less at sites of lower quality, all else being the
same.
Carrying out the travel cost analysis requires data on participation rates, cost of travel to sites, and
site quality. Data on participation rates is collected in the recreational survey. Data on travel costs
to the target and substitute sites, including the opportunity cost of travel time, are collected from
the survey and augmented, as necessary, with distance information from road maps. Data on water
quality comes from two sources. First, we use field measurements of various water quality
parameters from the SCDHS PES water sampling program, including nitrogen, coliform counts,
water clarity (Secchi disk measurements) and Brown Tide cell counts. These are objective
measures of quality. Second, we use data from the recreation survey which asked respondents their
subjective evaluation of water quality (excellent, good, fair or poor). These evaluations of course
are subjective.
For swimming, our analysis combines the subjective assessments by individuals and the objective
field measurements of water quality. Ultimately, we are interested in whether we can provide
sequential linkages between:
(1) management actions that effect water quality parameters, '~
(2) the resultant perceptions of water quality, and
(3) participation in specific recreation activities.
If we can establish such a linkage we can evaluate water quality policy alternatives.t°
To attempt to do so, we relate participation to subjective ratings of water quality, as indicated in the
recreational survey, and in mm, we relate these subjective water quality ratings to field
measurements of water quality. These are: water clarity, nitrogen concentration, coliform counts,
and Brown Tide cell counts for each location and time period. This allows us to estimate the
recreational benefits associated with water quality improvements resulting from management
options.
The annual benefits of a policy improving water quality has two parts. One is that individuals will
enjoy the swimming experience more; the other is that they will make more trips. This is the basis
of our approach for estimating the economic benefits of water quality improvements in the PES for
swimming.
For example, consider an assessment of proposed improvements in the Riverhead sewage treatment
plant. Upgrading the sewage treatment plant will result in improvements in water clarity, reductions
in coliform counts, etc., with the largest effect in Flanders Bay and progressively small changes in
~°We note that a water quality model developed by Te~'a Tech of Fairfax VA for the PES Program will estimate
water quality changes in each of the bays due to proposed program policies. ~,
bays to the east. Our model allows us to predict how these changes in water quality parameters
effect the subjective assessment of water quality by recreational users, and the resultant changes in
participation rates and in recreational values at various sites. Thus, we can identify benefits to
recreational swimming that result fxom management options that improve water quality.
In order to implement this methodology, we will coordinate our assessments with ongoing water
quality modeling efforts in the PES. Thus, we anticipate that water quality modeling will be used
to project changes in quality parameters from prospective management policies. Our recreational
modeling will then be used to estimate recreational benefits resulting from these changes in quality.
Similarly for recreational fishing, quality of the experience depends upon catch rates, which in turn
depend upon fish populations. Our recreational fishing model includes catch rates as a predictor
of participation rates and recreational values. Thus, ffpredicfions can be made concerning the effect
of management actions on fish populations, we could use our recreational model to estimate
resultant benefits to recreational anglers. However, it may not be feasible for natural scientists to
predict changes if salt water fish populations that would likely result from management actions.
Nevertheless, the oppommity exists for estimating recreational fishing benefits from management
actions, if resultant changes in populations can be estimated. Even in cases where precise
predictions are not possible, it may be possible to provide possible ranges of population changes,
which will enable us to provide order-of-magnitude estimates of possible benefits.
The economic benefits from improving (or maintaining) water quality could be long-mn benefits,
if the policy is sustained. To recognize the long-nm benefits, we sum the annual benefits over
time, discounting them as appropriate to get an equivalent value today--the asset value of the
resource in providing services supporting that activity.
IV.K. ESTIMATES OF ECONOMIC VALUE: RESULTS
IV.K. 1. Value (Consumer Surplus) Per Trip
The methodology described above yields a consumer surplus estimate per person, per trip ranging
from $8.59 for swimming to $49.83 for non-residential bird watching and wildlife viewing (Table
IV. 17). Fishing and boating values per lxip fall within that range at $40.25 and $19.23, respectively.
These estimates of consumer surplus are the average value individuals receive per trip over and
above the cost of their recreational trip. Looked at another way, it is the unpaid for benefit that
individuals receive, on average, from a recreational trip. The relative sizes of these values are
consistent with the literature (e.g. Walsh et al., 1988; Freeman, 1995).
IV.K.2. Total Annual Economic Value for All Trips
Total annual benefits from each of the four recreational activities studied are given in Table IV.17.
The total benefits are estimated by multiplying the average consumer surplus for an actiyity by the
estimated total number of trips to engage in that activity during the year (1995). Viewing of Birds
and Wildlife is the most valued of the activities studied ($27.3 million). Of the water-based
activities, recreational fishing is the most highly valued ($23.7 million).
We emphasize that total values rarely are useful for policy analyses since most policies involve
small changes in an activity or its quality and are not "all or nothing" choices. Nevertheless, we
recognize that information on total values, such as that given in Table IV.17 helps put the scale of
these activities in some perspective.
Table IV.17 Total Annual Value of Recreational Activities at the PES (Baseline Water
Swimming Boating*
Total Trips/Year 1,409,970 937,387
Consumer Surplus
Per Trip $8.59 $19.23
Total Annual
Consumer Surplus $12, i 13,216 $18,025,952
* Excludes boating trips taken primarily for fishing
Recreatonal
Fishing Wildlife
Viewing
5g$,493 547,317
$40.25 $49.83
$23,685,985 $27,272,806
IV.K.3. Asset Value of PES for Key Recreation Activities
Using the results given above, the asset value of the PES for providing each of the recreational
activities ("services") can be estimated. We use a discount rate of 7 percent and time horizon of
25 years and assume that the estimated values remain the same over the 25 year period. Using these
assumptions, and our annual estimates from the above table, the asset values range from $318
million for Bird Watching and Wildlife Viewing to$141 million for Swimming. The PES has an
asset value of $276 million for Recreational Fishing and $210 million for Boating.
IV.K.4. Change in Swinuning Trips and Benefits Due to Hypothetical Water Quality Changes
To illustrate how the economic benefits estimated in this report can be used to contribute to estuary
management policy, consider a hypothetical policy or set of policies that improves all water quality
measures by lOpercent in all five PES Bays. That is, the policy reduces field measurements of
Nitrogen, Total Coli, Brown Tide cell counts by 10 percent and increases Secchi depth by 10
percent throughout the PES. Note that since water quality measures differ among individual bays
(and indeed within Bays), the absolute water quality changes due to a 10 percent improvement will
60 ~
also differ among and within PES Bays. Only benefits are considered; the costs of achieving the
water quality improvements are not taken into account in this reportn.
Note that EAI's research on recreational activity, we found that only swimming was affected by the
five water quality estimates available, within the range of data available~2 for these variables. We
also found that recreational fishing was affected by the catch rate and by Brown Tide cell counts.
The hypothetical water quality policy illustrated here is restricted to the effects on swimming in
the PES; a hypothetical policy affecting recreational fishing is described below.
The hypothetical uniform 10 % improvement for all quality measures for all Bays increases the
number of yearly swimming trips in the PES as a whole by about 151 thousand and has annual
benefits of $1.3 million dollars (Table IV.18). This is an increase ofabont 11 percent in use and
in benefits. If maintained for 25 years, this improvement has apresent value of $15.1 million, using
a discount rate of 7 percent. This number represents the change in the asset value of the PES for
swimming due to the quality improvement, all else remaining the same. Thus, a policy (or set of
policies) that created and maintained such an improvement water quality would be a good
investment of scarce resources if the present value of these costs was less than $15.1 million.
Most of the incremental benefit to swimmers ($752 thousand of the $1.3 million annual benefit)
is due to increased water clarity (Secchi depth). The lowest benefit is due to the 10 percent
improvement in Coli counts ($81 thousand per year). Among the five PES Bays, the 10 percent
uniform water quality improvement has the highest incremental benefit for those who swim in
Shelter Island Sound (an annual increase in swimming benefits of $312 thousand).
n Subsequent research by EAI will address benefit-cost analysis of proposed water quality action~.
~Recall, the recreational model for swimming used objective data fi.om SCDH water quality sampling. No data are
available outside the range of these field measurements. Obviously, a major deterioration of water quality would
have enormous adverse impacts on recreation and could even drive activity at some locations to zero if serious
enough (e.g., beach or fishing closures). ~:
Table IV.18 Benefits to Swimmers of 10% Improvements in Each Water Quality Indicator
at Each PES Water Body
Total Kjeldahi Total Brown Tide Secchi Water
PES Water Nitrogen Colifrm Cell Counts Disk Body
Body Depth Total
Flanders Bay $65,278 $71,310 $14,424 $125,753 $276,766
7,598 8,300 1,679 14,637 32,215
Great Peconic $24,801 $3,522 $48,095 $126,362 $202,779
Bay 2,887 410 5,598 14, 708 23,603
Little Peeonic $20,584 $1,140 $70,207 $139,929 $231,860
Bay 2,396 133 8,172 '16288 26988
Shelter Is. Sd. $22,598 $4,553 $109,790 $175,093 $312,033
2, 630 530 12, 779 20, 381 36 320
Gardiners Bay $14,138 $129 $76,863 $185,286
1,646 15 8, 947 21,567
PES Total $147,399 $80,653 $319,378 $752,423
17,156 9,387 37,175 87,581
Italics indicate number of trips
$276,416
32,174 ~l
1
$1,299,854I
151,2991
A hypothetical uniform 20 % improvement in water quality increases the number of trips by 169
thousand and annual swimming benefits by $2.6 million (Table IV.19 Again most of the increase
in benefits ($1.46 million) is attributable to greater water clarity. However, benefits due to reduced
Brown Tide cell count (about $652 thousand) also are important.
In policy analyses, it is important to examine the incremental benefits of policies in order to know
what the public gets from stricter policies and the higher costs that they entail. The annual,
incremental swimming benefits of going fi-om an initial 10 percent improvement in water quality
($1.299 million) are somewhat larger than the added benefits ($1.264 million)of the second 10
percent water quality improvement (i.e. going from 10 percent to 20 improvement). The issue of
incremental benefits from alternative management actions that affect different PES bays differently
will be addressed more comprehensively in Phase III economic studies.
Table IV.19. Benefits of 20% Improvements in Each Water Quality Indicator at Each PES
Water Body
PES Water
Body
Flanders Bay
Total Kjeldahi Total Brown Tide
Nitrogen Colifrm Cell Counts
$130,030 $141,908 $28,888
15,135 16,518 3,362
Great Peconic $48,869 $6,200 $95,937
Bay 5,688 722 11,1678
Little Peconic $41,319 $2,510 $139,892
Bay 4,809 292 16,283
Shelter Is. Sd. $44,207 $7,907 $235,633
5,145 920 27,427
Gardiners Bay $28,555 $5567 $151,556
3, 324 648 17, 641
PES Total $292,979 $164,653 $651,905
34,102 19,100 75,881
Italics indicate number of trips
Secchi Water
Disk Body
Depth Total
$253,807 $554,633
29,543 64,558
$249,204 $400,201
29,152 46, 729
$279,957 $463,678
32,262 53,647
$315,939 $603,685
36, 775 70, 268
$356,181 $541,859
41,459 63,071
$1,455,088 $2,564,065
169,191 298,273
IV.K.5. Change in Fishing Trips and Benefits Due to Hypothetical 10 Percent Change in Catch Rate
We also simulate the effects of an assumed policy that leads to a 10 pement increase in the
recreational fishing catch rate. The 10 percent increase in catch rates raises the benefit (Consumer
Surplus) per trip by $0.80, increases the number of recreation trips by 11,249, and boosts total
annual benefits by $472,359. The presem value of this increase in catch rates--the increase in the
asset value of the PES in providing this service-- is $5.5 million, using the 7 percent discount rate
and time horizon of 25 years used for all cases.
Table IV.20. Benefits of Improving Expected Recreational Catch Rate by 10 Percent
10 Percent Increase Incremental
Baseline in Expected Catch Benefits
Rate
Number of Fishing
Trips 588,493 599,742 11,249
Consnmer
Surplus/Trip $40.25 $41.05 $0.80
Total Consumer
Surplus $23,685,985 $24,158,344 $472,359
We re-emphasize that the results presented here are for benefits only; the cost of water: quality
improvements, or of improvements that might increase recreational fishing catch rates, have not
been considered. Thus, we do not know the net benefits of potential management actions. Later
Benefit-Cost analyses to be done by EAI of proposed program actions will address these issues.
IV.L. REFERENCES
Freeman. A. Myrick III, 1993. The Measurement of Environmental and Resource Values: Theory
andMethocls. Washington, D.C.: Resources for the Future.
Grigalunas, Thomas A. and Jerry Diamintidas, 1996. "The Peconic Estuary System: Perspective
on Uses, Sectors and Economic Impacts". Peacedale, RI: Economic Analysis, Inc.
McConnell, Kenneth and Ivar Strand, 1996. "Marine Recreational Sportfishing". College Park:
University of Maryland.
Walsh, Richard G., Donn M. Johnson, and John R. McKean, 1988. "Review of Outdoor Recreation
Economic Demand Studies with Nonmarket Benefit Estimates, 1968-1988". Fort Collins,
CO: Water Resources Center, Colorado State University.
Supplementary Tables and Appendices
Supplementary Tables
Table S. 1
East End Resident Respondent Distribution by Town
Number of Percentage of All Percentage of Year-
Town Respondents Respondents round Population-a
East Hampton 50 14% 15%
Riverhead 42 12% 22%
Shelter Island 57 16% 2%
Southampton 107 30% 43%
Southold 102 28% 19%
Totals 358 100% 100%*
*adjusted to account for rounding
a- Source: Long Island Regional Planning Board
Table S.2
Second Home Owner Primary Residence
Percentage of All SHO
Area Number of Respondents Respondents
New York City 72 45%
Nassau County 30 19%
Suffolk County 22 14%
Other Areas ~ 35 22%
Totals 159 100%
tlncludes Westchester and Rockland Counties in New York and parts of Northeastern New Jersey.
Table S.3
Second Home Owner Distribution by Town
Percentage of
Number of Percentage of all Seasonal Home
Respondents Respondents Population-a
East Hampton 29 22% 29%
Riverhead 6 5% 5%
Shelter Island 17 13% 3%
Southampton 39 29% 49%
Southold 42 32% 14%
Totals 133 100%* 100%*
*adjusted for rounding
a- Source: Long Island Regi?nal Planning Board
Table S.4
Primary Residency of Visitors to the East End
Percentage of All
Area Number of Respondents Respondents
New York City 146 19%
Nassau County 118 16%
Suffolk County 300 40%
Other New York State and
New Jersey 63 8%
Connecticut and
Massachusetts 39 5%
Other 84 11%
Totals 750 100%*
*adjusted for rounding
Table S.5
Accommodations for Overnight Visitors
Percentage of All
Accommodation Number of Respondents Overnight Visitor
Respondents
Hotel or Motel 95 22%
Rental I01 24%
Friends or Relatives 168 39%
Other 63 15%
Totals 427 100%*
Adjusted for rounding
Table S.6
Overall Participation' in Key Recreation Activities - All Respondents (n=1354)
Number of Participants Percentage of all
Respondents
Any Activity* 944 70%
Multiple Activities 648 48%
Water Based Activities 820 61%
Land Based Activities 644 48%
Any of the eight activities listed in the survey questionnaire. Water Based Activities: Swimming,
Boating, Fishing, Shell fishing; Land Based Activities: Beach Use, Bird Watching, Wildlife
Viewing, Hunting.
a Includes water-based recreation at PES, BIS, LIS, and Atlantic Ocean.
Table S.7
Participation in PES Water Based Recreation - All Respondents (n--1354)
Percentage of Average
Number of Total Number of Experiences
Activity Participants Participation Experiences Per
Participant
Fishing 224 35% 2732 12
Boating 338 52% 5470 16
Swimming 470 73% 6538 14
Shell fishing 92 14% 766 8
Totals 644 100% 15506 24 *
* Includes more than one activity.
Table S.8
Participation in Land Based Recreation - All Respondents (n=1354) ?
Percentage of Average
Number of Total Number of Experiences
Activity Participants Participation Experiences Per
Participant
Beach Use 736 90% 12248 17
Bird Watching 243 30% 5371 22
Wildlife 275 34% 5628 20
Viewing
Hunting 23 3% 255 11
Totals 820 100% 23502 29 *
* Includes more than one activity.
S.9 - Estimated Trips for Key Recreation Activities for Sample*
Residents SHO Overnight Totals
Fishing 21,218 9,520 14,850 45,588
Boating 31,402 14,647 20,791 66,840
Swimming 44,133 18,309 35,641 98,083
Shell fishing 11,882 4,394 2,970 19,246
Beach Use 67,048 23,801 54,452 145,301
Bird Watching 8,555 3,032 5,346 16,933
Wildlife Viewing 17,313 6,225 10,098 33,636
Hunting 2,546 732 990 4,268
*Includes activities at the five East End towns and PES water bodies only
Appendix B - Technical Appendix: Swimming Trips and Value Estimation
Overview
A "count data" model is used to estimate the demand for recreational activities in the PES. Count
data models are appropriate for estimating recreational demand because the number of recreational
trips taken by an individual must take on an integer value. The Poisson distribution is used to
estimate the number of trips to each site in the PES and to estimate the economic value (consumer
surplus) of those trips.
For swimming, the number of trips to each site is modeled as a function of the travel cost to the site,
costs of travel to substitute sites, the number of boating occasions at the same site, and a subjective
measure of water quality at the site. The measure of water quality is the probability that water
quality at the site will be ranked either good or excellent by survey respondents who recreate at that
site.
This model can be used to estimate the effect of management actions to improve water quality on
the number of recreational trips to each site, and to the value of a recreational trip to a site. In order
to do so, we need to link management actions to changes in water quality, and to link objective field
measures of water quality to subjective perceptions of water quality by recreational users.
Predictions of changes in water quality due to management actions is done as part of ongoing water
quality modeling work, carried out as part of the Peconic Estuary Program (Tetra Tech, 1998). The
relationship between objective water quality measures and subjective pemeived water quality is
estimated by linking data collected in the recreational survey with field measure of water quality.
Specifically, for those respondents who indicate that they went swimming on their most recent
recreation day, the survey asked where they went swimming and how highly they rated water
quality (excellent, good, fair or poor) at that site. We also obtained data on measures of key water
quality variables, including brown tide cell counts, total coliform bacteria, total Kjeldahl nitrogen,
and Secchi disk depth readings from sampling locations distributed across the PES. The data was
grouped into ten sites - north fork and south fork sites for Flanders, Great Peconic, Little Peconic,
Shelter Island Sound and Gardners bay.
An ordered LOGIT model was used to estimate the probability that an individual would rate water
quality high (good or excellent), fair or poor as a function of the objective field measures of water
quality, discussed above. We then include these predicted probabilities into the travel cost
estimation, so that the number of trips to a site is related to the cost of traveling to the site, the cost
of traveling to key substitute sites, and the predicted probabilities of the subjective rankings of
water quality. These steps are described in detail below.
Water Quality Model ;~
The water quality model links individuals' perception of water quality - expressed in a ranking of
poor, fair, good, and excellent - to physical measures of water quality collected at sampling stations
by the SCDHS. An ordered logit model is used to estimate the probabilities for each rank based on
physical measures of water quality and on the effects of Brown Tide on the individual's swimming
that year. The purpose of the water quality model is to provide input into the travel cost model i.e.,
the probability that the water quality of a site would be ranked good or excellent.
Data for the water quality model consists of site readings during August 1995 for the following
water quality indicators:
2.
3.
4.
Brown Tide, Aureococcus anophagefferens, in cells per milliliter.
Total Kjeldahl nitrogen in milligrams per liter.
Total coliform bacteria in MPN per 100 milliliters.
Secchi disk depth measured in feet.
These water quality indicators were selected for their relevance to potential management actions
and for their modeling compatibility. Also included in the water quality model data set are
responses to the Recreational Use Survey Questionnaire administered during August 1995. Three
questions fxom the questionnaire are included. One question asks individuals ~f thetr sw~mn)~ mg was
affected by Brown Tide. Another question asks respondents to rank the water quality - poor, fair,
good, excellent - at their last swimming outing in the PES. The third question asks respondents to
identify the swimming location of their last swimming outing in the PES.
70 -~
The data is grouped into ten sites: Flanders bay north and south, Great Peconic bay noH_h and south,
Little Peeonlc bay north and south, Shelter Island sound north and south, and Gardiners bay north
and south. Swimming locations were allocated to the ten sites according to locations indicated by
the survey respondent. Only observations that indicate an identifiable swimming location for trips
taken during August 1995 were included in the data set. Water quality sampling stations were
allocated to the ten sites according to SCDHS sampling station maps, as described in Table Al. The
number of samples recorded from each sampling station varies. An average of the data recorded
at each station during August 1995 (the period during which our survey was administered) was
taken. Sampling stations with a central position, as opposed to north or south, were included in the
averages of both north and south sites.
Data for Brown Tide, total Kjeldahl nitrogen, and tOtal coliform bacteria were used to construct
indices of water quality relative to threshold values for each parameter to make the measures more
comparable.
The average Brown Tide cell count for each of the ten sites was divided by 250,000cells/mi - the
visibility threshold for Brown Tide. The resulting value was then multiplied by 1 if the respondent
indicated that their swimming, was affected by Brown Tide, or by 0 if not. In this way, the Brown
Tide cell count is modeled to affect the water quality ranking of only those respondents who
indicated they had knowledge of and were affected by Brown Tide.
The average total Kjeldahl nitrogen reading for each of the ten sites was divided by .5mg/1, the
nitrogen guideline established by the SCDHS. The average total coliform bacteria reading for each
of the ten sites was divided by 400MPN/100 ml, a threshold for bathing water quality. The total
Kjeldahl nitrogen index and the total coliform bacteria index were added together to form a joint
nitrogen-bacteria index. This joint index was used in the ordered logit model instead of the
individual indices because high eolinearity in field measurements of nitrogen and coliform bacteria
precluded separate estimation of the effects of these two water quality variables. All indices were
multiplied by 100 to preclude the use of fractions. Average input values for each site are presented
in table A2.
The modeling procedure uses an ordered logit model that predicts water quality rank based on the
Brown Tide Index, Nitrogen-Bacteria Index, and Secchi Disk depth readings. The ordered logit
model is based on the logistical distribution,
Prob(Y= 1)= l+e~'~'
where ~ represents parameters to be estimated and x represents water quality fiel_d measures (e.g.,
water clarity) that contribute to the subjective water quality rankings. Given that the rankings are
discrete and finite (excellent, good, fair and poor), it is reasonable to talk about the probability that
any ranking would be chosen based on the levels of the water quality indicators encountered by an
individual. Since the individual is faced with a predetermined ranking order - poor, t~air, good,
71 :
excellent - the choice made reflects the rank order that is most similar to the individual's own
pemeption. In other words, the individual may feel that water quality at their swimming location
that day was somewhere between good and excellent, but becanse of the ranking order presented
by the questionnaire, the individual will choose either good or excellent as the best choice. This
selection process is modeled as a multinomiai logit model where
y* = [~'x + e.
Y* is a continuous measure of site quality, not constrained by the discrete rankirig, [3 is the vector
of coefficients estimated by the ordered logit model, x is the vector of field measures of water
quality, and E is a vector of unknown factors that might affect the individual's rank order choice
(e.g., the individual's mood or the prevailing weather conditions). The unknown factors are
assumed to be logistically distributed across individuals with a mean of zero and a variance of one.
Although y* is unobserved, the rank order selections indicated in the survey questionnaire are
observed. These selections can be modeled as
y: 0 (poor)
y = 1 (fair)
y = 2 (good)
y = 3 (excellent)
· if Y* < 0,
if0<y* ~ [tl,
if[ti <y* < [t2,
if g2 ~ y*.
where the [ti are threshold values that determine when water quality ranking would change for a
"representative" individual. The ordered logit model estimates the [t threshold parameters along
with the vector of [5 coefficients.
The results of the ordered logit model are presented in Table A3. The estimated coefficients indicate
that a reduction in Brown Tide cell counts or a reduction in the nitrogen-bacteria index would result
in higher water quality rankings. Similarly, an increase in Secchi disk depth (increased water
clarity) would raise the subjective water quality ranking by survey respondents.
The probabilities that water quality would be ranked poor, fair, good, or excellent are calculated
from the ordered logit model results. These probabilities are:
Prob (y = 0; poor) = A (-fl'x),
Vrob(y= 1; fair) =A(gl-fl'x)- A (-fl'x),
Prob (y = 2; good) = A 012 -fl'x) - A (~tl -fl'x),
Prob (y=3; excellent) = 1 - A (}~2 -fl'x),
where A indicates the logistic cumulative distribution function.
2~
72
The rank order probabilities are given for nine sites only, as no survey respondents reported
swimming at Flanders Bay south. These probabilities are generated by substituting the site-specific
water quality indicator values for the mean values of the entire sample. An average of each
probability weighted by the number of observations for each site is calculated for the five PES
water bodies. For example, the probability that water quality at Gardiners bay will be ranked good
is based on the weighted average of the probability for good at Gardiners bay north and Gardiners
bay south. The probabilities for the nine sites are presented in table A4. The probabilities for the
five PES water bodies are presented in table A5.
The effects of management actions on the rank order probability at any or all of the nine sites can
be simulated by changing the water quality measures for the sample station readings. Changes in
these values will in turn change the value of the water quality indices and/or Secchi Disk readings
used as input into the ordered logit model. These simulated values for objective water quality field
measures are used to provide at a new set of probabilities for subjective ratings of water quality for
the sites, which in mm affect predicted participation rates for swimming in each of the PES water
bodies.
Tables A6-A9 contain estimat~ed water quality ranking probabilities i.e., the probability that water
quality will be ranked poor, fair, good, or excellent, for each of nine PES sites at a 10%
improvement in an objective water quality parameter. The objective water quality parameters used
in the 10% improvement model are Brown Tide cell counts, Total Kjeldahl Nitrogen readings, Total
Coliform readings, and Secchi Disk depth.
For example, under baseline water quality levels in 1995, Great Peconic Bay (Table A5) is
predicted to obtain a "poor" rating for water quality by 8% of sample respondents, a "fair" rating
by 37% of sample respondents, a "good" rating by 45% of sample respondents and an "excellent"
rating by 10% of sample respondents. These probabilities are the weighted averages of the ranking
probabilities for Great Peconic Bay North and Great Peconic Bay South (Table A4). If, continuing
our example, Brown Tide Cell counts were to improve (decrease) by 10% at sampling stations in
Great Peconlc Bay North and Great Peconlc Bay South, the model predicts that Great Peconic Bay
overall (Table Al0) would obtain a "poor" rating by 7% of sumple respondents, a "fair" rating by
36% of sample respondents, a "good" rating by 46% of sample respondents and an "excellent"
rating by 10% of sample respondents.
Travel Cost Model
The travel cost model predicts the annual number of swimming trips to each of the five water bodies
in the PES based on the cost of travel to the swimming location, the cost of travel to substitute
swimming locations, the number of times the individual went to the same location that year, and
the probability that the water quality at that location would be ranked good or excellent. The
predicted number of trips and the estimated coefficient on the travel cost variable are used to
calculate the value of swimming trips to each PES water body.
73
The travel cost model is also used to estimate the increased number of trips and increased value per
trip that results due to management actions that increase water quality. This is done in three steps.
First, the changes in water quality resulting from specific management actions (e.g. upgrading the
Riverhead sewage treatment plant) are to be simulated using the water quality modeling results
(Tetra Tech, 1998). Next, changes in subjective probabilities are predicted using the methods
discussed above. Finally, predicted perceptions of water quality by users are substituted into the
Travel cost model, to calculated the estimated number of trips and value per trip under the new level
of water quality.
Data for the travel cost model comes from the Recreational Use Survey and from the results of the
water quality model. The survey questionnaire asks respondents to indicate the number of times
they went swimming this year at a variety of water bodies on the East End. These water bodies
include Flanders Bay, Great Peconic Bay, Little Peconic Bay, Shelter Island Sound, Gardiners Bay,
Long Island Sound, Block Island Sound, and the Atlantic Ocean.
The price, or marginal cost, of swimming in the PES is not identified in any existing market. Since
there is no readily available market data on the price of swimming in the PES, a proxy for price is
used. In the travel cost modelt the cost of getting to and from a swimming location is considered
the implicit price of swimming at that location. Travel costs to swimming locations are based on
distance traveled, travel time, and the household income according to the formula:
Travel Cost = Round Trip Distance * $0.32 + {Round Trip Distance/40mph * 40% hourly wage}.
The round lrip distance traveled is double the one-way highway distance between the respondent's
point of origin and each swimming location. This distance was determined on a case by case basis
using a road atlas and assessing the most direct travel route. Verification of distance traveled was
made possible when respondents answered the question concerning distance traveled on their most
recent swimming occasion. Travel time was estimated by dividing the round trip distance by 40
miles per hour. This speed was used as a reasonable compromise between highway speeds of 55
mph and more and local speeds between 25 and 40 mph. The opportunity cost of travel time was
estimated at 40% of the wage rate, a common practice in benefit estimation. For respondents who
did not indicate household income, income was estimated using a simple OLS regression that
modeled income as a function of a constant value and education.
In the case of substitute swimming locations, the same travel cost formulation used for the PES
locations was applied to the substitute swimming locations. The average of travel costs to Long
Island Sound, Block Island Sound, and the Atlantic Ocean, for each respondent, was used as the
cost of travel to substitute sites and input into the travel cost model. Data on the number of trips to
the PES swimming location comes directly from the survey questionnaire. The water-quality
variable used in the travel cost model is the sum of the probabilities that the PES swimming
location would be ranked good and excellent. These probabilities are generated by the water quality
model.
74 '~
Table A2. Average Input Value for the Water Quality Model (Ordered Logit)
Brown Tide Nitrogen - Bacteria Secchi Disk
Site Index Index Depth (Feet)
Flanders Bay North 5.57 818.09 3.2
Great Peconic Bay North 18.33 169.85 3.13
Great Peconic Bay South 14.81 117.75 3.5
Little Peconic Bay North 28.04 126.39 3.5
Little Peconic Bay South 16.21 126.76 3.5
Shelter Is. Sd. North 39.51 136.44 4.35
Shelter Is. Sd. South 47.5 256.26 4.37
Gardiners Bay North 32.41 94.21 5.75
Gardiners Bay South 33.37 124.7 5.08
Table A3. Results of the Water Quality Model (Ordered Logit)
Observations 199
Log Likelihood -225.26
Res. L. Likelihood -239.00
Variable Coefficient Std.
Err.
Constant 2.0679 1.1136
Brown Tide Index -0.02297 0.00499
Nitro. - Bact. Index -0.00147 0.00089
Secchi Disk 0.34515 0.23232
g 1 2.2812 0.25915
~ 2 4.6968 0.3522
Chi-
Squared
D. Frdm.
Sig. Level
T Stat.
1.857
-4.605
-1.652
1.486
27.50
3
.000005
Sig. Level
0.06332
0.00000
0.09860
0.13737
Mean of X
33.83
173.4
4.397
78
Table A4. Baseline Probabilities for Nine Sites
Site Prob. Poor Prob. Fair Prob. Good Prob.
Excellent
Flanders Bay North 0.14 0.47 0.34 0.05
Great Peconic Bay North 0.08 0.37 0.45 0.10
Great Peconic Bay South 0.06 0.32 0.49 0.13
Little Peconic Bay North 0.08 0.38 0.45 0.10
Little Peconic Bay South 0.06 0.33 0.49 0.12
Shelter Is. Sd. North 0.08 0.38 0.45 0.10
Shelter Is. Sd. South 0.11 0.43 0.39 0.07
Gardiners Bay North 0.04 0.25 0.53 0.18
Gardiners Bay South 0.05 0.30 0.50 0.14
*Probabilities may not sum to one due to rounding
Table AS. Baseline Probabilities for PES Water Bodies
PES Water Body Prob. Prob. Prob. Prob.
Poor Fair Good Excellent
Flanders Bay 0.14 0.47 0.34 0.05
Great Peconic Bay 0.08 0.37 0.45 0.10
Little Peconic Bay 0.08 0.37 0.45 0.10
Shelter Is. Sd. 0.08 0.39 0.44 0.09
Gardiners Bay 0.05 0.28 0.52 0.16
79
Table A6. Probabilities at Nine Sites for 10% Improvements in Brown Tide Cell Count
Site
Prob.
Poor
Flanders Bay North 0.14
Great Peconic Bay North 0.07
Great Peconic Bay South 0.06
Little Peconic Bay North 0.08
Little Peconic Bay South 0.06
Shelter Is. Sd. North 0.07
Shelter Is. Sd. South 0.10
Gardiners Bay North 0.04
Gardiners Bay South 0.05
Prob. Fair Prob. Good Prob.
Excellent
0.47 0.34 0.05
0.37 0.46 0.10
0.32 0.50 0.13
0.37 0.46 0.10
0.32 0.49 0.13
0.36 0.46 0.10
0.42 0.41 0.08
0.24 0.53 0.19
0.29 0.51 0.15
Table A7.
Probabilities at Nine Sites for 10% Improvements in Total Kjeldahl Nitrogen
Readings
Site Prob. Prob. Fair Prob. Good Prob.
Poor Excellent
Flanders Bay North 0.13 0.46 0.35 0.06
Great Peconic Bay North 0.08 0.37 0.45 0.10
Grea~ Peconic Bay South 0.06 0.32 0.49 0.13
Little Peconic Bay North 0.08 0.38 0.45 0.10
Little Peconic Bay South 0.06 0.33 0.49 0.12
Shelter Is. Sd. North 0.08 0.37 0.45 0.10
Shelter Is. Sd. South 0.11 0.43 0.39 0.07
Gardiners Bay North 0.04 0.25 0.53 0.18
Gardiners Bay South 0.05 0.30 0.51 0.14
80
Table A8. Probabilities at Nine Sites for 10% Improvements in Total Coliform
Readings
Site Prob. Prob. Fair Prob. Good Prob.
Poor Excellent
Flanders Bay North 0.13 0.46 0.35 0.06
Great Peconic Bay North 0.08 0.37 0.45 0.10
Great Peconic Bay South 0.06 0.32 0.49 0.13
Little Peconic Bay North 0.08 0.38 0.45 0.10
Little Peconic Bay South 0.06 0.33 0.49 0.12
Shelter Is. Sd. North 0.08 0.38 0.45 0.10
Shelter Is. Sd. South 0. I 1 0.43 0.39 0.07
Gardiners Bay North 0.04 0.25 0.53 0.18
Gardiners Bay South 0.05 0.30 0.50 0.14
Table A9. Probabilities at Nine Sites for 10% Improvements in Secchi Disk Depth
Site Prob. Prob. Fair Prob. Good Prob.
Poor Excellent
Flanders Bay North 0.12 0.46 0.36 0.06
Great Peconic Bay North 0.07 0.35 0.47 0.11
Great Peeonic Bay South 0.05 0.30 0.51 0.14
Little Peconic Bay North 0.07 0.36 0.46 0.11
Little Peconic Bay South 0.06 0.31 0.50 0.13
Shelter Is. Sd. North 0.07 0.35 0.47 0.11
Shelter Is. Sd. South 0.09 0.41 0.41 0.08
Gardiners Bay North 0.03 0.22 0.54 ' 0.21
Gardiners Bay South 0.05 0.27 0.52 0.16
81
Table Al0. Probabilities at PES Water Bodies for a 10% Improvement in Brown Tide
Cell Counts
PES Water Body Prob. Prob. Prob. Prob.
Poor Fair Good Excellent
Flanders Bay 0.14 0.47 0.34 0.05
Great Peconic Bay 0.07 0.36 0.46 0.10
Little Peconic Bay 0.07 0.36 0.46 0.10
Shelter Is. Sd. 0.08 0.37 0.45 0.10
Gardiners Bay 0.04 0.26 0.52 0.17
Table All. Probabilities at PES Water Bodies for a 10% Improvement in Total
Kjeldahl Nitrogen Readings
PES Water Body Prob. Prob. Prob. Prob.
Poor Fair Good Excellent
Flanders Bay 0.13 0.46 0.35 0.06
Great Peconic Bay 0.07 0.37 0.46 0.10
Little Peconic Bay 0.08 0.37 0.45 0.10
Shelter Is. Sd. 0.08 0.38 0.44 0.09
Gardiners Bay 0.05 0.27 0.52 0.16
82
Table Al2. Probabilities at PES Water Bodies for a 10% Improvement in Total
Coliform Readings
PES Water Body Prob. Prob. Prob. Prob.
Poor Fair Good Excellent
Flanders Bay 0.13 0.46 0.35 0.06
Great Peconic Bay 0.08 0.37 0.46 0.10
Little Peconi¢ Bay 0.08 0.37 0.45 0.10
Shelter Is. Sd. 0.08 0.39 0.44 0.09
Gardiners Bay 0.05 0.28 0.52 0.16
Table Al3. Probabilities at PES Water Bodies for a 10% Improvement in Seechi Disk
Depth
PES Water Body P(Poor) P(Fair) P(Good) P(Excellent)
Flanders Bay 0.12 0.46 0.36 0.06
Great Peconie Bay 0.07 0.35 0.47 0.11
Little Peconic Bay 0.07 0.35 0.47 0.11
Shelter Is. Sd. 0.07 0.36 0.46 0.11
Gardiners Bay 0.04 0.25 0.53 0.19
83
Table Al4. Results of the Travel Cost Model (Poisson Regression)
Observations 995
Log Likelihood
Res. L. Likelihood
Variable
-3707.225
.4630.572
Coefficient
Constant -0.76984
Travel Cost -0.028321
Boating 0.0781
Travel Cost to
Substitute Locations 0.00978
Water Quality Rank 2.688
Std.
Err.
0.16607
0.00210
0.00157
0.00119
0.28209
Chi-
Squared
D. Frdm.
Sig. Level
T Stat.
-4.636
-13.494
49.757
5.282
9.529
1846.693
4
.000000
Sig. Level
0.00000
0.00000
0.00000
0.00000
0.00000
Scale Factor 1.5002
Marginal Effect for
Water Quality Rank 4.0325
Mean of X
23.29
0.9246
31.74
0.5404
Appendix C. Comparison of Recreation Activity Estimates: Phase I and Phase II Reports
Comparison with Phase 1 Total Outdoor Recreational Trip Estimates: Beach Use. Beach
use, in this report, refers to any beach in the five East End towns, including Long Island Sound and
Atlantic Ocean beaches, as well use of PES beaches. The Phase I report did not estimate beach use
on Long Island Sound or the Atlantic Ocean and therefore is not presented for comparison.
Comparison with Phase I Total Outdoor Recreational Trip Estimates: Non-Consumptive
Wildlife Use. Bird watching and wildlife viewing estimates in this Phase. II report are combined to
create a "non-consumptive wildlife use" estimate of 547 thousand trips (Table V.11). This estimate
greatly exceeds the PES Phase I estimate of non-consumptive wildlife use (92 thousand trips).
The major reason for the large differences in estimates between the Phase I and II reports is
explained as follows. Our survey-based estimate finds a high participation rate (19% for bird
watching and 21% for wildlife viewing) for respondents and a large number of trips per participant
84
(37.55 bird watching trips and 34.36 wildlife viewing trips for residents). The Phase I survey, in
contrast, was based on NFWS statewide estimates for New York. These statewide participation
rates (9.4%) and number of trips per participant (8.3) are very much lower than those found in the
PES sample. This major difference is not surprising since residents and seasonal visitors to the PES
would be expected to be more interested in outdoor recreation than the general population of the
state.
Comparison with Phase I Total Outdoor Recreational Trip Estimates: Recreational Fishing.
Not surprisingly the recreational use survey data yields a much larger estimate (588 thousand trips)
of recreational fishing trips in the PES than the Phase I study (114 thousand). The Phase I estimate
is based on the NFWS survey covering all of New York State. In the NFWS study, the average
estimates of participation rate (9%) and trips per participant (8 for residents, 4.3 for visitors) is
much lower than found in the PES survey. This estimated participation rate is 17% and ~ps per
participant range from 15.27 for overnight visitors to 9.78 for second homeowners. Again, we
would expect PES residents and visitors to be more avid recreational fishers than the New York
population at large.
Comparison with Phase I Total Outdoor Recreational Trip Estimates: Swimming. The
Phase I swimming estimate (715 thousand) is simply the sum of all available beach attendance data
for the PES. The Phase II survey-based estimate (1.4 million) includes participation not only at
official beaches but also at the many unadministrated beaches throughout the PES, and in addition
includes the Atlantic Ocean and Long Island Sound, as well as the PES.
Comparison with Phase ! Total Outdoor Recreational Trip Estimates: Hunting. The discrepancy
between the Phase I (77 thousand trips) and Phase II (52 thousand trips) hunting estimates also is
based on differences in participation rate and number of hunting days per participant, as estimated
by the two surveys. The NFWS estimates that 11% of the New York State population over 16
hunts, and that participants average 18.6 hunting trips per year. On the other hand, the PES survey
indicates that only 2% of residents and visitors to the PES hunt in the PES. These participants also
hunt less than the state average, ranging from 16.33 trips per second homeowner participant to 5.5
trips per participating day tripper. The Phase I hunting estimate was based only on residents and
did not attempt to account for participation by seasonal residents and visitors since such data are
unavailable.
V. WETLAND PRODUCTIVITY VALUES IN THE PES
V.A. INTRODUCTION
Eelgrass, saltmarsh, and intertidal mud bottom ("wetlands") provide many services to the public.
These services include contributing to the production of commercial and recreational harvests of
fin fish and shell fish, and of birds and other wildlife used for viewing and for hunting. Other
services include protection of shoreline property from storm damage and erosion.
Wetland services may occur on site or off site and may or may not be valued in markets. For
example, some wetland services, such as scallop harvests, occur on site, while others are realized
offsite, for example, when fin fish or birds "produced" by an eelgrass bed of intertidal salt marsh
are harvested or viewed many miles away. Some wetland services are valued in the market place
(e.g., commemially harvested fish or shellfish), while others are not (e.g., bird species used for
viewing and waterfowl that are hunted).
Understanding the economic value of the various natural services provided by ecosystems can
provide useful information for policy analyses about preservation and restoration decisions., For
this purpose, the most useful information is the value of a small change in wetlands, that i~, the
marginal value of wetlands. Marginal values rather than the total value of all wetlands are
important because most policies address relatively small changes in wetlands--not whether or not
to preserve all wetlands.
This chapter adapts~3 estimates by French and Schuttenberg (1998) of the marginal value of PES
eel grass beds, salt marshes, and intertidal mud flats. Two types of wetland productivity
(biological) gains are considered:
(1)
The increase in food produced by the habitat which is utilized by higher trophic
levels (such as fish and shellfish) in the PES, and
(2)
The increase in production of higher trophic levels brought about by the increased
availability of habitat.
The biological gains from restoring or protecting increments of each wetland type (eelgrass,
saltmarsh, and intertidal mud flats) are assigned an economic value. This value is based on the (1)
commercial value of the fin fish and shell fish, (2) the viewing value of birds, and (3) the hunting
UTwo major changes are noted. French and Schuttenberg valued PES we[lands over a 100-year period and used a 3
% discount rate. We use a 7 % discount rate and a 25-year time period for consistency with results given elsewhere
in this report. The higher discount rate and shorter time horizon means that our results for estimated economic
values will be lower than in French and Schuttanberg.
86
value of waterfowl ultimately "produced" by wetlands. Other direct and indirect services and
values wetlands may provide are not considered
Two assumptions are critical to the analysis that follows. One is that food and habitat are
biologically limiting factors for the species considered; that is, fish, shell fish and birds depend upon
the availability of wetlands, so that small changes in wetlands will causes changes in the
populations of these species. If wetlands are not limiting for these species, then an increment of
wetlands would have no productivity value for the species concerned, although it may have other
values (e.g., shoreline erosion protection, esthetics, or existence value)
The second critical assumption concerns effort and its cost. Fishing, viewing or hunting require the
use of labor, capital and other inputs used for harvesting, viewing or hunting; the net gain from
these activities is the benefit (e.g., value of fish landings) minus the costs of the effort required.
However, very small changes in the abundance offish, shellfish or birds due to a small change in
wetland areas will lead to only a very slight increase in harvests per unit of effort. Slight increases
in catch per unit effort will have a negligible effect on the level of effort itself. The changes in the
availability of each wetland category due to preservation or restoration actions are presumed to be
small enough so that fishing, hunting, or viewing effort remains the same. This assumption implies
that we do not need to net out the cost of any change in effort due to small changes in abundance
of fish, or birds arising from marginal changes in wetlands. Under these assumptions, the value of
additional harvests, viewing or hunting is the gross value.
V.B. METHODOLOGY AND DATA
V.B. 1. Introduction
Several studies estimate the economic values provided by natural ecosystems~4. For example,
Lyune et al. (1981) estimated the relationship between mangrove area, fishing effort, and landings
of a single species, blue crab in Florida. They estimated a marginal value for blue crab of several
dollars. Bell (1989) also used a time series of data on mangrove area, effort, and catch to evaluate
the marginal productivity of Florida mangroves in the production of principal Florida commercial
and recreational fish. He estimated a marginal value of several thousand dollars per acre of
mangrove over all commercial and recreational species. Kalm and Kemp (1985) estimated the
incremental value of subtidal vegetation in contributing to striped bass populations in the
Chesapeake Bay and their subsequent harvest by recreational fishermen. Costanza and Father
(1986) assessed the per acre services of Louisiana wetlands for recreation and as a buffer for storm
protection, in addition to contributing to the production of commercial fisheries. Finally,
Pompiuatepong (1997) used the results in the Natural Resource Damage Assessment Model
(French, et al, 1996) to estimate the asset value of coastal wetland services in the Northeast.
summary of the productivity approach and examples for coastal areas is given in Grigalanas and Cougar (1995).
87 '
The approach used in this chapter derives estimates of wetland productivity using data specific to
the PES. The virtue of this approach is that it captures the underlying biological structure and
productivity of PES wetlands, and it uses biological information and economic values specific to
the PES. The method--in effect a "simulation"-- side steps some of the data and other problems
faced in statistical studies, such as that by Lynne et al., by Bell, and by Costanza and Farber. On
the other hand, the method used does not allow for statistical tests of significance and hence heavily
relies upon professional judgement.
The methodology used is described next. First, we explain how the food web estimates were made.
Then, the methodology and data used for habitat values is presented. A detailed statement of the
methodology, data and assumptions is given in French and Scuttenberg (1998).
V.B.2. Food Web Estimates
Saltmarsh and eelgrass beds benefit the entire food web by primary (plant) production. Similarly,
the net gain in lower trophic level, animal production is passed up the food web. Ultimately this
production via the food web results in the production of species of economic value to people.
To estimate the economic value of these food-web effects, several pieces of inform~ation are
needed. First, it is necessary to quantify the amount of food produced by a habitat. T9 do this,
primary (plant) and bottom (amphipods, worms, etc., in and on the sediments) production rotes were
estimated for PES wetland categories, using results in the literature. Then, the fraction of the
additional production passed up the food web was estimated. Next, this additional production is
translated into commercial fm fish and shell fish production and landings using average
relationships estimated across many estuaries by Nixon (1982). Finally, the estimated fish and shell
fish landings are valued using species-specific fishery values for PES landings.
Thus, lower trophic levels are ~anslated into equivalent upper trophic level fish and shellfish
production harvested and valued by people. Specifically, fishery production is estimated to be 0.16
percent of primary production and 4 % percent of bottom (benthic macrofaunal) production. Details
of these calculations are given in French and Schuttenberg (1998).
V.B.3. Habitat Estimates
Habitat values are estimated for species (bay scallops, blue crab, softshell clams, and birds) having
human use values. Habitat values are based on (1) the expected yield offish or shellfish dependent
upon the habitat, and (2) the abundances of wildlife (birds) that utilize the habitat. Fish and
shellfish values are commercial values; wildlife values are for hunting (waterfowl) and viewing
(waders).
Bay scallops depend upon eelgrass as nursery habitat for juveniles. The grass provides a refuge
from predators for juvenile scallops. It is assumed that eelgrass is a limiting factor for scallops, so
that the entire PES scallop fishery depends upon eelgrass beds. Blue crab use saltmarsh and
88
eelgrass habitats preferentially. Again, it is assumed that the entire blue crab fishery depends upon
the saltmarsh and eelgrass beds of the estuary. Softshell clams prefer intertidal mud flats and sand
flats. Softshell clams are assumed to all have been produced in intertidal mudflats and shoals in the
PES.
Abundance of birds depends upon habitat type. An average abundance per unit area of habitat is
assumed, based on the results for the coastal area including the PES, as given in the Natural
Resource Damage Assessment Model (Version 2.4, April, 1996) developed by the authors for the
US Department of the Interior (French, et al, 1996a,b,c). Birds that are specifically benefitted by
salunarsh, eelgrass or mud flats were selected and included in the present analysis. These species
are waders (herons, egrets, and ibis), shorebirds, brant and black ducks. Waders use all three of the
habitat types, while shorebirds use marsh and mud flats. Brants specifically feed on eelgrass. Black
ducks are know to require structured habitat, marsh and eelgrass.
The value of bird species usage of the habitat is based on the benefits human receive from viewing
or hunting (waterfowl) birds. The values are marginal values, that is, the additional value people
obtain by seeing additional birds per day. The values per animal per year are proportional to the
number of viewing trips and the rareness of the species in the local area (see French et al.
(1996a, b,c) for a full description of the developmem of these values.
V.C. RESULTS
V.C. 1. Introduction
Results are provided for the 0) Marginal value of existing wetlands, and (2) the marginal value for
restored wetlands. Restored wetlands have a lower value than existing wetlands since it may take
years for an existing wetland to become fully functional.
V.C.2. Results
Table V.1 shows the marginal value for existing and restored wetlands. The value for existing
wetlands would be used for policy issues dealing with preservation decisions; results for restoration
are critical for assessing policies to restore wetlands.
Two values are calculated: An annual value and an asset value. The annual value is the sum of the
food web values and the habitat values. Asset values were calculated by discounting the annual
value over a 25 period using a discount rate of 7 percent, the same time frame and discount rate
used elsewhere in this reportt~. Eelgrass has a 20 % higher marginal value per acre than that for
saltmarsh. Intertidal mud flats have the lowest marginal value.
~Use of lower discount rate and longer time horizon would lead to a larger value. Use of the 7 % discount rate
reflects the opportunity cost of resourees used in restoration/preservation. That is, resources used in these activities
to some extent will be drawn away from private and public investments.
Estimated marginal values for restoring wetlands are much lower than the value of existing
wetlands of the same category. This is because it takes several years before a restored wetland
becomes fully functional. Thus, in the table it is noted that eelgrass, saltmarsh, and intertidal mud
flats take 15, 10, and 3 years, respectively, to become fully functional.
The marginal asset values of PES wetlands appear to be substantial, especially in light of the fact
that other services wetlands may provide, such as protection from erosion and storms, aesthetics
and existence value, are not considered.
Table V.1. Marginal Values of PES Wetlands
Existing Habitats Created Habitats
Estimated
Years to Number of
Annual Become Acres in
Wetland Value Asset Value Fully Asset Value PES
Type per Acre ' per Acre b Funetinal ' per Acre b (millions)
Eelgrass $ 1,065 $12,412 10 $9,996 6.04
Saltmarsh $338 $4,291 15 $3454 13.51
Intertidal 14.05
Mud Flat $67 $786 3 $626
French and Schuttenberg (1998) estimate habitat values using a discount rate of 3 % and time horizon of 25
years. Our use of 7 % and 25 years reduces the estimated wetland values by almost one half.
Using a discount rate of 7 percent and a time horizon of 25 years. Assumes linear recovery to full (99%)
restoration over the period estimated by French and Schuttenberg (1998). French and Schuttenberg used a
sigmoid function to approximate the time path or recovery.
V.D. SUMMARY AND CONCLUSIONS
This chapter presents estimates of the marginal economic value of the productivity services
provided eelgrass, salt marsh, and intertidal mud flats in the PES. The productivity services
covered are food web and habitat services. Economic values estimated are for commercial fishing
value for crab, scallop, and clams; viewing values for birds; and hunting values for waterfowl.
Other possible values provided by PES wetland services, such as erosion and storm protection,
aesthetics, and existence value are not considered.
V.E. REFERENCES
CostanTa. Robert and Steven Farber, 1986 "The Economic Value of Coastal Louisiana Wetlands".
Baton Rouge: Lousiana State University.
Bell, Frederick W., 1989. Application of Wetland valuation Theory to Commercial and
Recreational Fisheries in Florida. Sea Grant Program. Tallahassee: Florida State Univeristy
(June).
French, et al., 1996. Natural Resource Damage Assessment Model for Coastal and marine
Environments. Version 2.
French, Deborah and Heidi Shuttenberg, 1998. Estimated Food Web and Habitat Values for
Habitats in the Peconic Estuary System. Submitted to Economic Analysis Inc. (January 23).
Gfigalunas, Thomas and Richard Congar, 1995. Environmental Economics for Integrated Coastal
Area Management. Nairobi: United Nations Environmental Programme.
Kahn,
James and W.M. Kemp, 1985. "Economic Losses Associated With the Degredation of An
Ecosystem: The Case of Submerged Aquatic Vegetation in Chesapeake Bay", Journal of
Environmental Economics and Management
Lyune, G., P. Couroy, and F. Prohaska, 1981. "Economic Valuation of Marsh Areas for Marine
Production Processes", Journal of Environmental Economics and Management 8(2): 175-
186.
Nixon, ScottW., 982. NutnentDynanucs, Primary pmduction and Fisheries Yields ofLagoons".
Narragansett: Graduate School of Oceanography, University of Rhode Island.
Pompinatapong, K., 1997. "Valuing Coastal Wetlands: Insigths from the Type A Natural Resource
Damage Assesment model". Unpublished M.S. Major Paper. Kingston: Department of
Environmental and Natural Resource Economics, University of Rhode Island.
VI. NATURAL RESOURCE PRIORITIES AND VALUES
VI. G. INTRODUCTION
This section describes a study of natural resource values for the Peconic Estuary System (PES). In
August 1995, we surveyed 968 year-round and seasonal residents of the area surrounding the
Peconic Estuary. Respondents to the survey were asked about their priorities and values for
protecting and restoring important natural resources of the PES. The results include:
(1)
(2)
:.(3)
an analysis of values and priorities for a set of important natural resources of the
area--farmland, undeveloped land, wetlands, safe shellfishing areas, and
eelgrass;
estimates of economic benefits that would result from protecting or restoring
these resources; and
public opinions related to the Estuary.
The survey was designed to complement scientific and technical studies, using data available at the
time, to provide information that will be useful in the final policy analysis. This information will be
used in a cost-benefit analysis of policy alternatives (Phase III of the Comprehensive Economic
Valuation Study), that will help policy makers prioritize alternative management actions.
The remainder of this section is organized as follows: Part B describes the survey development and
implementation, and the questionnaire design; Part C gives descriptive results of the survey; Part D
presents the estimates of economic values; and Part E is a summary and conclusions.
VI. B. THE NATURAL RESOURCE SURVEY
VI.B. 1. Survey Development and Implementation
We developed the survey over a six-month period, from February to August 1995, in an extensive
process that included individual interviews, focus groups, and pretests. The primary goal of the
survey was to learn about the public's preferences, priorities and values for natural resources of the
Peconic Estuary that might be affected by preservation and restoration actions?
t~ Other important goals were to create a survey that would minimize some of the problems often associated with
valuation surveys; would be easily understandable to members of the public; could be answered in a reasonable
amount of time; and could be administered in a variety of public places.
92
VI.B. 1.a. Initial Meetings, Interviews, and Information Gathering
We began the survey development process in February, 1995 by meeting with members of the
Management Committee. Shortly afterwards, we met with representatives of different interests,
including the head of the Local Government Committee; the Chairman of the Citizen's Advisory
Group, who is also president of a local environmental group; a representative of the Nature
Conservancy; a biologist fxom the NY State Department of Environmental Conservation; a marina
owner who was head of the local Marine Trades Association; a commercial fisherman who
represented the Long Island Inshore Trawiermen's Association; and a bank president. These
meetings, and a meeting with the Citizen's Advisory Group, helped us to learn about the area and
about the concerns of various interest groups.
VI.B.1 .b. Informal Interviews With the Public
Next, we conducted a set of informal interviews with members of the public, where we asked about
their uses of and concerns about the Estuary. A total of sixteen randomly selected people were
interviewed in Montauk, Springs, Sag Harbor, Shelter Island and Greenport. They included business
owners, store clerks, a police .officer, visitors, and residents.
These interviews provided information on some of the most important natural resources and related
concerns, and how people think and talk about the bays. Most people interviewed were very
concemed about water quality, declines in fish populations over the years, and impacts on business
if water quality continues to decline. We also learned that many people were not familiar with the
word "estuary;" and most people in Montauk did not consider themselves to be located on the
Peconic. Consequently, in the final survey, the Peconic Estuary System was referred to as the
Peconic Bays System, and was defined by a map at the beginning of the survey.
VI.B. 1.c. Focus Groups and Preliminary Survey
The next step was a series of focus groups, and a short preliminary survey. Table VI. 1 lists the dates
of the focus groups and pretests, their locations, and the number of participants. In the first three
focus groups, we asked the participants general questions about how they define the study area; their
familiarity with the Peconic Estuary Program; the characteristics of the area that are most and least
important to them; their uses of the local waters; the attributes they look for in choosing recreational
sites; their pemeptions of water quality; and their concerns about the natural environment and natural
resources of the Estuary.
A short preliminary survey was carried out in Montauk on March 19, before the St. Patrick's Day
parade, an event that draws a large number of visitors and residents. This survey included both
closed- and open-ended questions, including questions about:
(1)
(2)
participation in recreational activities around the water;
concerns related to the Estuary; and
93
(3) positive and negative aspects of the East End in general.
Table VIA - Focus Groups and Pretests
Number of
Date Location Participants
March 18 Montauk Focus Group 1 6
March 19 Montauk Preliminary Survey 35
March 22 Jamesport Focus Group 2 10
March 31 Riverhead Focus Group 3 7
April 19 Shelter Island Focus Group 4 3
April 20 Southampton Focus Group 5 9
April 29 East Hampton Focus Group 6 6
May 2 Springs Focus Group 7 7
May 11 Rhode Island Focus Group 8 5
May 18 Springs Pretest 1 10
June 21 Rhode Island Pretest 2 5
June 27 Rhode Island Pretest 3 5
July9 Jamesport Pretest 4 13
July 9 Mattituck Pretest 5 17
The thirty-five usable responses gave some preliminary information about important attributes of
the area and its recreational sites; and about people's greatest concerns for the Estuary. Respondents
were asked to rank a list of concerns on a scale of 1 to 10, where 1 indicates the highest level of
concern. Table V.2 lists these concerns, in order of importance to respondents. The most important
were declining stocks of shellfish and finfish, water quality, trash on beaches, and areas closed to
shellfishing. The least important were crowding in boating harbors, public access to the water and
availability of wildlife for viewing.
In the next several focus groups, we asked participants general questions about their concerns, their
pemeptions of water quality, and actions they would like to see. We also tested preliminary survey
questions and question formats, which were revised between groups. Several of these questions were
primarily intended to stimulate discussion. For example, one question asked about participation in
recreational activities, and was used to generate a discussion of these activities and related concerns.
Other questions asked participants which of a list of human impacts on the Bays were most
important to them; which ora list of environmental problems they believed were most serious; and
which potential actions should be given highest priority.
The questions asking participants to prioritize impacts, concerns, and actions were difficult for
people to answer, for two important reasons. First, many people in the focus groups believed that
almost everything was important, and therefore resisted ranking their priorities. Instead, they wanted
to rate everything as most important. Second, many participants stated that they did not know
enough to rank the causes of environmental problems and the effects of potential actions; but
believed that priorities should be decided by experts. Nonetheless, these questions provided a go'od
focus for the discussion of people's concerns.
Table VI.2 - Concerns of Preliminary Survey Respondents,
Montauk, March 19, 1995
Concern Mean Rank'
Declining stocks of shellfish 3.09
Declining stocks of fintish 3.34
Water quality 3.43
Trash on beaches 3.59
Areas closed to shellfishing 3.66
Overcrowding of recreational sites 4.23
Declining open space 4.57
Crowding in boating harbors 4.74
Too little public access to the water 4.88
Need to improve public access points 4.90
Less wildlife available for viewing 5.21
a - Ranked from I to 10, with I denoting the most important.
Other questions in these focus groups asked about priorities related to natural resources, and tested
possible question formats for the survey, including standard contingent valuation questions and
contingent choice questions. Contingent valuation asks people directly to state their willingness to
pay to preserve or improve a natural resource. Contingent choice asks people to make tradeoffs
between alternative actions with results specified in terms of the natural resources that would be
protected or restored and the cost. ~? Contingent choice questions are similar to marketing surveys
~ Several additional questions were also tested in these focus groups. One question asked respondents to indicate
their three most important reasons for living on the East End, from a list of fifteen reasons given by participants in
earlier focus groups. This question was later dropped because it did not provide enough information to justify the
space and time it took in the survey. A set of environmental attitude questions was also tested, with the intention of
correlating respondents' attitudes with their values for natural resources. The attitude questions generated quite a bit
of controversy, as they were designed to elicit strong opinions, and were dropped from the final survey for several
reasons. First, they tended to draw respondents' focus from the more important questions in the survey; second,
they took too long to answer; and finally, it was feared that the conU'oversial nature of the questions might result in a
95 '-'
often done by businesses to allow them to understand tradeoffs that customers are willing to make
among product characteristics under diffrent programs and costs.
As discussed in more detail below, the contingent choice method appeared to be the most promising
format, and thus we developed this method further in the next focus groups. As the contingent choice
questions were refined through the focus group process, the survey became less complicated and less
wordy, with fewer ataibutes included in each comparison; and graphics were added to the questions.
VI.B. 1 .d. Survey Pretests and Implementation
In May and June, we conducted several pretests of preliminary survey instmments.~8 This series of
pretests helped to refine the question wording and layout of the survey, resulting in large
improvements in respondents' ability to easily make the comparisons. In July, a fifth draft of the
survey was pretested on Long Island, with a group of people at the First Parish Church in Jamesport,
and with randomly selected people on the beach in Mattituck. In these pretests, people were able to
easily comprehend and answer the survey questions. In early August, after meeting with members
of the Management Committee, we made final modifications to the survey, and conducted final
pretesting. An example of the final survey is included in Appendix A~9.
We implemented the survey during the week of Aug. 22-29, 1995 in a variety of pre-selected public
locations around the East End, using convenience intercept sampling. We selected this method over
a mall survey with probability sampling primarily because budget limitations made it necessary to
administer both the natural resource survey and the recreation survey together, and because names
and addresses of visitors were not available. Thus, we designed the surveys to be administered in
public places where visitors to the area could be intercepted. We selected a wide variety of
locations, in order to intercept a representative sample in terms of demographic characteristics and
location of residence. The survey locations, and the number of surveys collected at each, are listed
in Table 3. A total of 968 resource surveys were collected from year-round and seasonal residents
of the East End.
In implementing the survey, interviewers were instructed to approach people and say:
Hi, my name is ., and I'm working for Suffolk County. We're doing a
survey of the public to help develop a plan to protect and manage the bays. Would
you be willing to fill out a survey, which will take about 5-10 minutes?
negative reaction to the entire survey. .r.
t8 Several of these pretests were conducted in Rhode Island, in order to save time and money. Because the objectNe
at this point was to reirme the format, layout and wording, rather than the content, it was not necessary to condu,~'all
tests on the East End.
~s Note that the example shown in the Appendix is only one of 12 different survey booklets, each of which contains
five different contingent choice questions.
96 ~
The interviewer then asked those who agreed to fill out the survey if they were visitors, year-round
residents, or seasonal residents of the East End. Only year-round and seasonal residents were asked
to fill out the resource survey. All visitors, and every third resident, were given the recreational use
survey. When the survey was handed to the person, they were told that there are no right or wrong
answers, and that all answers would be confidential. Interviewers were instructed to give brief and
neutral answers to any questions.
VI.B.2. Questionnaire Design
VI.B.2. a. Selection of Survey Format
As mentioned above, we considered two methods for asking the public about their values for
protecting and restoring natural resources--contingent valuation and contingent choice. We selected
the contingent choice format for several reasons. At the time the survey was developed and
implemented, the Management Committee had not yet determined specific actions that might be
taken to protect and restore natural resources of the Estuary, and their results. Therefore, the survey
needed to ask the public about their preferences for important natural resources that would most
likely ~ affected by restoration and preservation programs. The contingent choice format allows for
comparisons and valuation of many different combinations of improvements in resources that might
be obtained by management actions. This will allow for assessment of programs that would affect
any combination of the natural resources evaluated in the survey, either by ranking alternative
programs or valuing benefits of specific programs.
Contingent choice may also minimize some of the problems associated with contingent valuation.
For example, people often have trouble putting dollar values on specific natural resources.
Contingent choice does not require that values be expressed in monetary terms, but elicits choices
among alternative outcomes of actions. Therefore, responses are focused on tradeoffs among
resources, and respondents are not asked to directly express monetmy values, although these can be
inferred from the analysis. Focus group participants found this type of question to be easier to
answer.
An additional issue, which may be a factor in both contingent valuation and contingent choice
surveys is the expression of"symbolic" values. This might be related to expressions of approval or
disapproval of the actions to be taken to protect natural resources, or expressions of the importance
of improving or protecting the environment in general, versus values for specific levels of natural
resources. For example, a survey might ask respondents to state how much they would be willing
to pay to protect a particular species of birds from oil spills. A person answering the survey may not
care about those specific birds, but may state a positive willingness to pay because they think that
oil spills should be prevented. Thus, their response may not reveal the value of the birds, per se.
97 --';
Beaches:
Shopping
Areas:
Libraries and
Post Offices:
Miscellaneous:
Table VI.3 - Survey Locations and Number Collected
Location
Orient State Beach, Southold
Southold Town Beach
Cedar Point County Beach, Southold
New Suffolk Beach, Southold
East Creek Marina Beach, Southold
Alberts Landing Town Beach, East Hampton
Fresh Pood Town Beach, East Hampton
Maidstone Beach, East Hampton
Indian Wells Town Beach, East Hampton
Hither Hills Beach, East Hampton
Muntauk Point State Park, East Hampton
Wades and Cascade Beaches, Shelter Island
Greenport Comer
Greenport IGA
Genovese Shopping Center, Southold
King Kullen Shopping Center, Matfituck, Southold
K-Mart Shopping Center, Riverhead
Downtown Southampton
1GA Southampton
King Kullen and Caldor Shopping Centers, Bridgehampton
IGA Shopping Center, Shelter Island
Southold Library
Cutchogue Library, Southold
Riverhead Free Library
East Hampton Post Office
Sag Harbor Post Office
Shelter Island Heights Post Office
Shelter Island Center Post Office
New London-Orient Point Ferry
Goose Creek, Southold
County Center Cafeteria, Riverhead
Okeanos Aquarium~ Riverhead
Department of Motor Vehicles, Riverhead
Pindar Wine~, Southold
John Drew Theatre, East Hampton
Location unidentified
Number
Collected
3
10
3
4
2
16
9
7
16
4
4
4
7O
11
72
33
16
107
27
4
I0
16
87
37
34
60
19
3
23
71
39
19
8
5
98
Because the survey used in this study presents only outcomes of actions, respondents are not given
the oppommity to focus on the means of achieving results. Thus, responses are less likely to be
expressions of approval or disapproval for actions, and more likely to focus on the specific natural
resources. However, people may still express symbolic values for protecting the environment,
beyond their values for the natural resource improvements. This is more easily detected and
corrected for in contingent choice than contingent valuation, as discussed below.
Finally, it has been pointed out that "survey methods are better at estimating relative demand than
absolute demand," even for market goods, and "absolute willingness to pay is hard to pin down"
(National Oceanic and Atmospheric Administration 1993, p. 4609). Thus, the relative values or
priorities for natural resources elicited by a contingent choice survey may be valid, even if the
absolute dollar values of specific resource changes are not. For a policy analysis that focuses on
prioritizing actions, information on relative values and priorities for resources is more important than
the estimation of total dollar values. Hence, contingent choice, rather than contingent valuation, may
be more effective in obtaining the information needed to compare alternative policies.
Based on the results of focus groups and past research (Mazzotta and Opaluch 1995), each
comparison was designed to include only three attributes: Two of the five natural resources included
in the survey, and the cost. Tl~is simplifies the choice, so that it is more likely that choices will be
based on considering and balancing all of the attributes.
In the final survey, respondents were asked to select from a set of three hypothetical choices: no new
action or one of two restoration/protection programs. Each option was described by different levels
of resulting natural resources, and the annual cost to each household. In early focus groups, only the
two programs were presented, but focus groups participants indicated that a "no new action" option
should be added, for two reasons. First, it allowed people to express a preference for no action if they
did not support either program. Second, it provided a baseline from which to judge the benefits of
each of the programs.
VI.B.2.b. Selection of Natural Resources and Levels
VI.B.2.i. Natural Resources Included in the Survey
Based on concerns expressed by participants in focus groups and natural resources identified as
impon'atnt by the Technical Advisory Committee, we selected five natural resources to be included
in the survey:
· farmland
· undeveloped land
· wetlands
· safe shellfishing areas
· eclgrass
99
We included farmland and undeveloped land because most people in preliminary interviews and
focus groups expressed concern about the rate of development in the area and resulting loss of
farmland and open space. Both farmland and open space are important components of the quality
of life, or "sense of place," (Sagoff 1992; Kellert 1995) for many residents of the East End, who
enjoy the rural quality of the area and shopping at numerous local farm stands. The amount of
development also affects environmental quality of the Estuary. Thus, it is important to consider uses
of the surrounding land as well as resources more directly associated with the Estuary.
Many people were aware of the importance of wetlands to water quality and as wildlife habitat,
and expressed euncem for declines in the quantity of wetlands in the area. People also expressed
concern for reopening closed shellfishing areas. This is due to the historical significance of
shellfishing to the local economy, and its importance as a recreational activity, combined with the
declines in shellfish caused by' brown tide2° and the recent large increases in areas closed to
shellfishing.
Finally, we included eelgrass for two reasons. First, much of the Estuary's eelgrass was destroyed
by the brown tide, and one proposed action is restoring eelgrass areas. Second, eelgrass serves as a
proxy for fish and shellfish populations. Many participants in the preliminary interviews and focus
groups expressed great concern over declines in fish over the years. However, the technic~l
consultants could not easily determine potential changes in these populations resulting fro~
proposed actions. Therefore, we hoped that by including eelgrass, which serves as fish and shellfish
habitat, the survey would capture some of these concerns.
VI.B.2. ii. Levels of Natural Resources in the Survey
The objective of the survey was to determine respondents' values for improvements in natural
resources above a baseline level. We defined the baseline as the level that would exist in the year
2020, if no action is taken to preserve or restore the resource. We determined the baseline in
consultation with the Technical Advisory Committee, based on historical declines and the judgment
of experts, for each resource.
~o Brown Tide is an algal bloom of the species .4ureococcus anophagefferens, which first occurred in 1985-1988, and
has subsequently recurred for shorter periods. The major brown tide episodes severely affected scallops, eelgrass, and
other shellfish in the estuary (see Suffolk County Department of Health Services 1992). ~
100 _).
First, we determined the level of each resource in 1981 and the current level.2t Next, we projected
the levels in 2020 based on information and judgments provided by members of the Technical
Advisory Committee regarding the anticipated change in the resource if no new actions were to be
taken. In cases where no technical projections were available, we based the projection on
extrapolation of past u'ends.
In the survey, respondents were presented with two pages of background information, which
described the level of each resource in 1981, the current level, and projections of levels in 2020 if
no new actions are taken. These levels are shown in Table 4, and the survey pages are shown in
Appendix A. Respondents were told that "trends indicate approximate conditions in 2020," in order
to make it clear that these are not precise, scientificaily-based projections.
When the survey was created, the Technical Advisory Committee had not completed their scientific
studies of how potential actions might affect specific natural resources. To get around this lack of
information, we chose the levels of each natural resource to be included in the survey in order to
bracket a reasonable range of changes under various policy actions, including the "no new action"
option. The Technical Advisory Committee assisted us in determining the largest realistic and
feasible change for each reso ,urce. Although, in some cases, anticipated results of restoration and
preservation programs are likely to be smaller than those presented in the survey, this approach is
conservative because it brackets the full range of feasible resource changes. This allows for
interpolation when valuing resource changes, rather than extrapolating results beyond the range
included in the survey.
In the contingent choice questions, each resource was included at three different levels: The
projected level for 2020 (the "no new action," or baseline, scenario), and two levels associated with
hypothetical programs that would preserve or restore the resource. In order to make the hypothetical
context clear, survey respondents were told:
"The following programs are hypothetical. We are trying to learn which resources are most
important to you and how much you would pay to protect them."
The levels for each resource and for cost are shown in Table IV.5.
If no action is taken, farmland is projected to decrease by twenty-five percent. The results of
different hypothetical preservation programs were projected to reduce the loss of farmland to fifteen
n We compiled this information fi.om the recent brown tide study (Suffolk County Deparmaent of Health Services
1992); a 1981 land use report (Long Island Regional Planning Board 1981 ); and information provided by the New
York State Department of Environmantal Conservation and other members of the Technical Advisory Committee.
The acres of farmland and undeveloped land in the Long Island Regional Planning Board's land use report were
presented for the entire East End. Therefore, to calculate the number of acres within the Peconic Estuary Program
study area, we assumed that the fraction of farmland and undeveloped land within the study area is proportional to
the amount of total land on the East End that is within the study area. ~:
101 ~
percent or maintain it at the current level. Undeveloped land was also projected to decrease by
twenty-five percent if no new action is taken. Because it did not seem realistic to preserve
undeveloped land at the current level, hypothetical preservation programs were projected to reduce
the loss of undeveloped land to ten percent or five percent.
Table VI.4 - Past, Present and Projected Natural Resource Levels
Projected
Natural Resource 1981 Level 1995 Level 2020 Level
Farmland~
Undeveloped Land"
Wetlandsb
Safe Shellfishing Areasb
Eelgrassb
13,500 acres
74,000 acres
18,000 acres
28,000 acres
10,000 acres
12,000 acres
66,000 acres
16,000 acres
26,000 acres
9,000 acres
9,000 acres
50,000 acres
12,000 acres
25,000 acres
8,000 acres
- Calculated based on Long Island Regional Planning Board 1981, and Suffolk County
Department of Health Services 1992.
b - Calculated based on Suffolk County Department of Health Services 1992 and information
provided by NY State DEC.
Wetlands were projected to degrade by twenty-five percent if no new action is taken; or they might
be preserved at the current level or restored to ten percent above the current level. The baseline for
safe shellfishing areas is a twenty-five percent decrease, and hypothetical programs might maintain
the current level or increase safe areas by ten percent. The baseline for eelgrass is a ten percent
decrease, and it might be preserved at the current level or increased by twenty-five percent.
The costs of the hypothetical programs were designed to learn how much people would pay for the
resource changes presented above, and to bracket a range of costs that seemed feasible to members
of the Management Committee. However, they are not intended to reflect actual costs of some
particular set of programs. The no new action option was always presented at no additional cost to
each household. The other levels of annual household cost included were $50, $100, $200, $300, and
$500.
Table VI.5 - Levels of Natural Resources and Cost in the Survey
Level with Moderate Level with High
No Action, Preservation or Preservation or
Baseline Level Restoration Restoration
(% Change) (% Change) (% Change)
Farmland 9,000 acres 10,000 acres 12,000 acres
(-25%) (-15%) (current)
Undeveloped Land 50,000 acres 59,000 acres 63,000 acres
(-25%) (-10%) (-5%)
Wetlands 12,000 acres 16,000 acres 17,500 acres
(-25%) (current) (+10%)
Safe Shellfishing 25,000 acres 26,000 acres 29,000 acres
Areas (-5%) (current) (+10%)
Eelgrass 8,000 acres 9,000 acres 11,000 acres
(-10%) (current) (+25%)
Cost Levels $0 $50 $100 $200 $300 $500
In order to statistically estimate values for each resource, we chose a statistical design that required
sixty different comparisons? However, we wanted to make sure that the survey could be answered
in a reasonable amount of time (10-15 minutes). Thus, we included five comparisons in each of
twelve different survey booklets.
VI.B.2. iii. Description of The Survey Questionnaire
The first page of the full survey booklet described the goal of the survey and showed a map of the
relevant area. The first three questions asked about participation in recreational activities in the area;
support for different possible actions; and opinions about brown tide.
Next the background information for the contingem choice questions were presented, followed by
a page of instructions and the five comparison questions. The survey ended with a set of
demographic questions about the respondent, in order to obtain information that could be used to
adjust the results to reflect the values of a representative member of the public.
n The combinations of a~ibutes and levels were selected using a method based on Addelman's fractional factorial
design, which produces orthogonal arrays of atUibutes (Addelman 1962a, 1962b; Addelman and Kempthome,
1961). This allows for statistical independence among the attributes so that the model estimation will be statistically
efficient. ~:
103 -*';
VI. C. DESCRIPTIVE RESULTS
VI. C.1. Who Responded to the Survey?
Table VI.6 repons demographic information for all respondents, and Table VI. 7 compares the
sample demographics to actual population demographics for gender, age, education, and income.
Table VI. 8 gives the number of respondents by town.
The major characteristics of our sample can be summed up as follows:
· Most are year-round residents (73%).
· Most are homeowners (85%).
· A majority live closer to the Peconic Estuary than to Long Island Sound or the Atlantic
Ocean (58%).
· 15% live on the waterfront and a majority live ½ mile or less from the water (51%).
· A majority have lived in the area for more than 10 years (63%).
· The average household size is just under 3 members, and 46% have no children under
age 18 in their household.
· Most are employed (67%), and 21% are retired. ~
· Compared to 1990 population figures, the sample contains a slightly higher percent of
women and people in the middle age groups, and the sample population is better
educated and more wealthy than the general population.~
n Note that census data are only available for year-round residents, so these results are for year-round residents in
the sample. ;;
Table VI.6
Question
9:
Type of
residence
10:
Own or
rent
12:
Closest
water body
12:
Distance
from any
water body
12:
Dist. from
Peeonic Bays
13:
Length of
residence
14:
Gender
15:
Size of
household
16:
Number
of children
- Respondent Demographics, Full Sample
Percent
Category Number of Sample
Primary Home 673 73.1%
Second Home 248 26.9%
Did Not Answer 47 4.86%
Own 773 84.7%
Rent 140 15.3%
Did Not Answer 55 5.7%
Peconic Bays 564 58.3%
Long Island Sound 216 22.3%
Atlantic Ocean 219 22.6%
Waterfront 143 14.8%
~ 1/2 Mile From Water 347 35.8%
>1/2 Mile From Water 382 39.5%
· Did Not Answer 96 9.9%
Waterfront 116 12.0%
~ 1/2 Mile From Water 234 24.2%
>1/2 Mile From Water 618 63.8%
0-10 Years 297 30.7%
11-20 Years 245 25.3%
21-30 Years 176 18.2%
>30 Years 185 19.1%
Did Not Answer 65 6.7%
Female 540 57.8%
Male 394 42.2%
Did Not Answer 34 3.5%
1-2 Household Members 481 49.7%
3-6 Household Members 430 44.4%
>6 Household Members 24 2.5%
Did Not Answer 33 3.4%
Average hh size 2.94
No Children 440 45.5%
1-2 Children 254 26.2%
>2 Children 66 6.8%
Did Not Answer 208 21.5%
105
Question
17:
Age
18:
Education
19:
Employment
20:
Income
Table VI.6 (Continued)
Percent
Category Number of Sample
up to 20 23 2.5%
21-24 36 3.9%
25-34 121 13.1%
35-44 207 22.5%
45 -54 194 21%
55-64 162 17.6%
65-74 144 15.6%
75-84 33 3.6%
85 and older 2 .2%
Did Not Answer 46 4.8%
Less Than High School 5 .5%
Some High School 16 1.7%
High School Graduate 126 13.6%
Some College 167 18.1%
Associate's Degree 90 9.7%
Bachelor's Degree 236 25.5%
Advanced Degree 284 30.7%
Did Not Answer 44 4.6%
Employed Full Time 497 53.9%
Employed Part Time 123 13~3%
Full Time Homemaker 66 7.2%
Full Time student 28 3%
Retired 197 21.4%
Unemployed 11 1.2%
Did Not Answer 46 4.8%
<$15,000 43 5.1%
$15,000-$24,999 77 9.1%
$25,000-$34,999 102 12.1%
$35,000-$49,999 111 13.2%
$50,000-$74,999 201 23.8%
$75,000-$99,999 113 13.4%
$100,000-$149,999 86 10.2%
$150,000 or more 111 13.2%
Did Not Answer 124 12.8%
106
Table ¥.7 o
Gender
Age
Education
(over
age24)
Income
Population Demographics vs. Survey Respondent Demographics
Round Seasonal
1990 Resident Resident
Population" Sampleu Sampleb
Female 51.83% 60.85% 51.36~
Male 48.17% 39.15% · 48.64~
up to 20 23.89% 3.17% 0.45%
21-24 4.53% 4.76% 3.62%
25-44 29.07% 40.39% 26.24%
45-54 11.01% 19.05% 24.89%
55-64 t 1.04% 14.99% 22.62%
65-74 11.17% 14.11% 19.00%
75-84 7.25% 3.17% 3.17%
85 up 2.04% 0.35% 0.00%
< High School 7.44% 0.00% 0.00%
Some H.S. 11.55% 1.53% 0.94%
H.S. Graduate 31.77% 15.33% 2.83%
Ed. categories 1-3 50.76% 16.86% 3.77%
Some College (Ed. cat. 4) 18.35% 21.26% 8.49%
Assoc. Degree 6.66% 11.30% 7.08%
Bachelor's Deg. 13.51% 23.18% 31.60%
Advanced Deg. 10.72% 27.39% 49.06%
Ed. categories 5-7 30.90% 61.88% 87.74~
< $15,000 19.17% 5.45% 1.81%
$15,000-$24,999 14.60% 11.60% 1.81%
Inc. categories l&2 33.77% 17.05% 3.62%
$25,000-$34,999 14.40% 14.94% 4.98%
$35,000-$49,999 17.52% 15.99% 5.88%
Inc. categories 3&4 31.93% 30.93% 10.86%
$50,000-$74,999 18.90% 27.94% 14.48%
$75,000-$99,999 7.04% 11.07% 18.55%
$100,000-$149,999 4.93% 7.38% 18.10%
Inc. categories 5-7 30.86% 46.40% '51.13%
$150,000 and up (Inc. cat. 8) 3.41% 5.62% 34.39%
a - The 1990 population figures w~ fi~r year.round residents only; sources of data are Long Island Lighting Company 1995
and Suffolk County Department of Planning 1991, based on 1990 census data and projections. No demographic data are
available for seasonal residents.
b - Sample percents refer to percentages oflhos~ who answered thc question.
Table VL8 - Numbers of Respondents by Town
Town Number of Percent
Respondents of
Sample
Brookhaven 19 1.9%
Riverhead ! 10 11.4%
Southold 247 25.5%
Shelter Island 136 14.1%
East Hampton 182 18.8%
Sag Harbor 66 6.8%
Southampton 139 14.4%
Other 25 2.6%
Did not Answer 44 4.5%
VI.C. 2. Participation in Recreational Activities
The responses to Question 1, which asked about participation in recreational activities, are shown
in Table VI.9. Almost all of the respondents (97%) participate in at least one of the listed activities,
and 81% participate in at least one activity in the Peconic Estuary. Swimming is the most popular
activity, with 86 percent parfcipating. Swimming is followed by walking and hiking (71%), boating
(54%), fishing (53%), other beach use (34%), shellfishing (32%), artwork (26%) and other activities
(11%).
More people participate in activities on or around the Peconic Estuary than any other water body.
This result is consistent with results of a telephone survey of residents conducted in the fall of 1993
by the Center for Community Research of Suffolk Community College (the SCC survey), which also
found that the Peeonic Estuary System was used more frequently than any other water body (Suffolk
Community College 1994).
VI. C.3. Support for Management Actions
Table VI.10 summarizes the responses to Question 2, which asked about support for specific
management actions. We included this question in order to allow respondents to express their
opinions about proposed actions, and to give them an idea of the types of actions that might be taken
to obtain the natural resource changes described in the contingent choice question_s.
108
Table VI.9 - Participation in Recreational Activities
Atlantic All
Activity Peconic Bays Ocean L.I. Sound Other Locations
Fishing 391 (40.4%) 167 (17.3%) 216 (22.3%) 74 (7.6%) 516
(53.3%)
Shellfishing 261 (27.0%) 18 (1.9%) 55 (5.7%) 39 (4.0%) 311
(32.1%)
Walking/
Hiking 518 (53.5%) 339 (35.0%) 306 (31.6%) 69 (7.1%) 684
(70.7%)
Swimming 610 (63.0%) 468 (48.3%) 362 (37.4%) 79 (8.2%) 833
(86.1%)
Other Beach
Usc 201 (20.8%) 169 (17.5%) 131 (13.5%) 44 (4.5%) 324
(33.5%)
Boating 432 (44.6%) 144 (14.9%) 224 (23.1%) 67 (6.9%) 520
(53.7%)
Axtwork 204(21.1%) 120(12.4%) 129(13.3%) 40(4.1%) 252
(26.0%)
Other 68 (7.0%) 46 (4.8%) 51 (5.3%) 24 (2.5%) 103
(1o.6%)
Activities 779 (80.5%) 586 (60.5%) 534 (55.2%) 205 (21.2%) 936
(96.7%)
As expected, all of the actions are supported by a majority of respondents. The actions with the
.greates! level of support are building more pumpout stations so that boat discharge can be prohibited;
maprovmg sewage treatment plants; public education; and more research on water quality issues. The
actions with the least support are required pumpouts of septic systems, and restrictions on the use
of lawn chemicals.
The Suffolk Community College survey also asked about support for actions. Although their
questions were more general and not perfectly comparable to those presented here, their results are
similar. For example, 83 percent of their sample support stronger building regulations, which is
comparable to the 76.3 percent of our sample who support zoning regulations, the 78.7 percent who
support waterfront restrictions, and the 76.7 percent who support restrictions on vegetation. The
SCC survey found that 71 percent of respondents were willing to change fertilizers, which is
consistent with the 68.2 percent of this sample who support restrictions on fertilizers and lawn
Table VI.10 - Support for Actions
Strongly Strongly No Average
~ction Support Support Neutral Oppose Oppose Opinion Rank
Prohibit sewage discharge
~'om boats. (requires building
aaore pumpout stations) 79.0% 10.5% 1.0% 1.2% 1.4% 6.7% 1
Improving sewage treatment
~lants. 78.0% 11.8% 1.5% 0.3% 0.6% 7.7% 2
Public education to teach
)eople how to reduce their 70.0% 17.4% 4.6% 0.7% 1.0% 6.2% 3
impacts on the environment.
More research on water
quality issues. 64.9% 19.4% 6.9% 0.7% 1.1% 6.9% 4
Better enforcement of
existing regulations. 66.3% 17.0% 3.2% 0.4% 0.9% 12.1% 5
Restrictions on waterfront
~roperXy, including requiring
auffer zones (areas
antouched) between
Jevelopment and the water, 61.9% 16.8% 8.5% 2.5% 2.5% 7.9% 6
drywells for roof runoff, etc.
Zoning to limit future
development. 62.3% 14.0% 11.1% 2.4% 2.2% 8.1% 7
Limiting removal of
vegetation on newly 58.5% 18.2% 9.9% 2.8% 2.6% 8.1% 8
developed land.
Requiring repair or upgrade
of septic systems when 51.4% 21.8% 12.4% 2.5% 2.6% 9.3% 9
property is sold or improved.
Controlling stormwater
runoffwithdiversion, catch 47.6% 26.1% 12.9% 1.7% 0.9% 10.7% 10
basins, etc.
Restricting use of fertilizer
and lawn chemicals for 43.4% 24.8% 16.7% 5.4% 2.2% 7.5% 11
residential property.
Requiring pumpouts of
existing septic systems every 32.7% 21.3% 20.9% 6.7% 4.2% 14.2% 12
i4 years.
chemicals. While 75 percent oftbe SCC sample stated that they were willing to improve their own
septic system, 73.2 percent of our sample support required upgrades when property is sold or
improved. However, only 54 percent support required pumpouts every four years.
It was hypothesized that certain groups of people would be mom likely to support or oppose specific
actions. For example, boaters, who would be most inconvenienced by discharge prohibitions, were
expected to show less support for this action. However, 90.4 percent of boaters, versus 88.2 percent
of non-boaters support no-discharge regulations ( although, based on a test of equality of the means,
the difference in the mean strength of support for discharge prohibitions between boaters and non-
boaters is not statistically significant). Thus, boaters appear to be willing to undergo some
inconvenience in exchange for cleaner water. This is consistent with the SCC survey finding that
63 percent of boaters are willing to change their boating habits to improve water quality.
Based on similar reasoning, waterfi.ont residents were expected to be less likely to support
restrictions on waterfront property. There is some support for this hypothesis, with 76.2 percent of
waterfront residents, 79.8 percent of residents who live up to ½ mile fi.om the water, and 82.5
percent of people who live more than ½ mile from the water supporting restrictions on waterfront
property. However, there was no statistically significant difference in the mean strength of support
across these groups.
Finally, the strength of support for all actions was compared for members of environmental groups
versus those who are not members? Although the average level of support was slightly greater for
members of environmental groups, the means were not statistically significantly different.
From these results, it can be concluded that the majority of respondents are in favor of
environmental actions, with slightly lower support for some actions that are personally
inconvenient. However, as expected, the results appear to be primarily symbolic of a general desire
for action. That is, people seem to support most actions. Thus, these results do not provide adequate
information with which to make policy decisions.
VI.C.4. Opinions About Brown Tide
Question 3 asked respondents whether they have heard of brown tide, and if they have, about their
level of concern, whether their activities have been affected, and whether they support funding for
further study. The results are reported in Table V.1.
Most people (90%) had heard of brown tide, and 75 percent said that at least one activity was
affected by the brown tide. The most commonly affected activity was swimming, with 59 percent
24 A total score for all actions was calculated by summing the level of support for all actions, where 1 indicates
sffong support and $ indicates strong opposition. The mean score for members of environmental groups was 19.51,
and that of non-members was 25. I, with a lower score indicating greater strength of support. ;:
111 --~
of those who had heard of brown tide affected, followed by shellfishing (40%) and fishing (37%).
A small number of people said that boating or other activities were affected. The most commonly
mentioned "other" activity affected was eating seafood. The majority (97%) of those who had heard
of brown tide are concerned (21%) or very concerned (76%) about it. Most of those who had heard
of brown tide (86%) also support more funding to study brown tide and possible remedies for it.
Table V.11 - Responses to Brown Tide Questions
Heard of Heard of and Affected
Brown Tide by Brown Tide
872 (90.1%) 725 (74.9%)
Q. 3B - How concerned are you about brown tide?
No Opinion 22 (2.5%) 8 (1.1%)
Not Concerned 5 (0.6%) 1 (0.1%)
A Little Concerned 183 (21.0%) 120 (16.6%)
Very Concerned 662 (75.9%) 596 (82.2%)
'~. 3D - Do you support or oppose more funding to study
the causes of brown tide and how to end it?
No Opinion 26 (3.0%) 14 (1.9%)
Opposed 17 (1.9%) 13 (1.8%)
Neutral 79 (9.1%) 61 (8.4%)
Support 750 (86.0%) 637 (87.9%)
Q. 3C - Activities Affected by Brown Tide
Swimming 512 (58.7%)
Boating 121 (13.9%)
Fishing 319 (36.6%)
Shellfishing 349 (40.0%)
Other 89 (10.2%)
VI.C.5. Membership in Organizations
Question 21 asked respondents whether they are members of different types of organizations. The
choices listed are sports, community service, environmental, political, business-related, farm or
agricultural, religious, and civic organizations. The purpose of this question was to'Supplement the
standard demographic questions by providing information about respondents' interests and level
of. commitment to different causes. Table VI.12 shows the statistics for membership in
organizations.
Sixty-five percent of respondents belong to at least one organization, and 31 percent belong to an
environmental organization. This is comparable to the results of the SCC survey, which found that
57 percent of respondems had donated to environmental organizations, and 21 percent had been
active in an environmental group.
Table VI.12 - Membership in Organizations
Organization Number (%)
Sports 171 (17.7%)
Community Service 182 (18.8%)
Environmental 300 (31.0%)
Political 146 (15.1%)
Business 94 (9.7%)
Agricultural 24 (2.5%)
Religious 275 (28.4%)
Civic 152 (15.7%)
Other 91 (9.4%)
Any Organization 628 (64.9%)
VI.D. ESTIMATES OF ECONOMIC VALUE
VI.D.I. Responses to Contingent Choice Questions
As discussed above, there were 60 different choice questions, with five included in each booklet,
to create twelve different booklets. There were between 73 and 85 responses to each of the twelve
booklets. The demographic groups--year-round vs. seasonal residence, gender, age, education,
employment, and income--were fairly evenly distributed across booklets.
Table VI.13 shows how many of the contingent choice questions were answered by respondents.
Of the 968 people who completed the survey, 897 (92.7%) answered at least one of the five
contingent choice questions in their survey booklet, and 790 (81.6%) answered all of the choice
questions. Of the 4,840 total choice questions, 4,307 (89%) were answered. Seventy-one
respondents (7.3% of the sample) did not answer any of the choice questions.
Table VI.13 - Number of Choice Questions Answered
Number Number of Percent of
Answered Respondents Total
Sample
0 71 7.3%
1 8 .8%
2 8 .8%
3 31 3.2%
4 60 6.2%
5 790 81.6%
Older respondents, and those with lower education and income levels, were slightly less likely to
answer all of the choice questions. Of those who answered at least one choice question, almost all
(91.6%) chose an action, rather than no action, for more than half of their answers. Only 27 people
(3.0%) chose no action for all of the choice questions answered. These results demonstrate the
strong environmental concern expressed by survey respondents, but also may suggest some
symbolic bias, if respondents voted to "take action," irrespective of the associated levels of resource
protection and cost. Below we discuss some methods that we employed to identify and control for
this possible bias.
Of those who chose either Program A or Program B, more people chose Program A than Program
B? This may indicate a tendency to select the option placed in the center, or may simply indicate
that the options presented as Program A were preferred more often. This is discussed further in the
Technical Appendix.
VI.D.2. Results and Discussion
The statistical models used to estimate the results are described in detail in the Technical Appendix.
These methods calculate the relative weights, or values, for an additional acre of each natural
resource, and for an additional dollar of cost to each household. These weights are measured by the
estimated coefficients for each resource and cost. From these coefficients, relative values for the
2s Of respondents who answered more than one question, 434 (48%) chose Program A for more than half of the
questions they answered, and 76 (8.5%) chose Program A for all of the questions they answered. This compares to
287 (32%), who chnsc Program B for more than half of thc questions they answered, and 15 (2%) who chose
Program B for all of the questions they answered. ;~
different resources, and dollar values for protecting an additional acre of each resource, can be
calculated?
The results for two different models are reported in Table VI. 14.27 The model results indicate that
the order of priorities for protection or restoration of resources is as follows: farmland, eelgrass,
wetlands, shellfish, and undeveloped land.
Table VI.14 - Estimation Results
Value
]Acre/hh] 95% Confidence Avg. Value
Coefficient year Interval' /Acre/Year~
Model 1:6
Farmland 0.000511 $0.136 $0.122 $0.150 $ 9,979
Undeveloped Land 0.000107 $0.028 $0.025 $0.032 $ 2,080
Wetlands 0.000336 $0.089 $0.079 $0.100 $ 6,560
Shellfish Areas 0.000233 $0.062 $0.053 $0.071 $ 4,555
Eelgrass' 0.000419 $0.111 $0.098 $0.125 $8,186
Cost -0.003765
Model 2:d
Program B -.1586
Farmland .000300 $0.087 $0.073 $0.101 $6,398
Undeveloped Land .000056 $0.016 $0.013 $0.019 $1,203
Wetlands .000228 $0.066 $0.056 $0.077 $ 4,863
Shellfish Areas .000128 $0.037 $0.031 $0.044 $ 2,724
Eelgrass .000281 $0.082 $0.069 $0.094 $6,003
Cost -.003441
a - The 95% confidence interval indicates the range within which the "tree" value is likely to fall, with a 95%
probability.
b - Calculated based on 73,423 households.
c - Conditional Logit model.
d - Nested Logit model.
These calculations are described in detail the Technical Appendix.
Model I is the standard conditional logit model, and Model 2 is the nested logit model.
115
As discussed above, symbolic values are often an issue for both contingent valuation and contingent
choice methods. The estimation results show that this is an issue in this survey, and that respondents
to the survey are more likely to choose to take action, independent of the specific results of the
action. Thus, these respondents may be expressing a symbolic willingness to pay to take action, as
opposed to revealing values for the specific natural resources of concern. The higher probability of
choosing to take action is not surprising, given the level of concern among residents of the area for
the environment of the Estuary.
However, Model 2 provides a correction for such values, by separating the probability of taking
action vs. no action, from the probability of selecting either Program A or Program B. Thus, the
estimated dollar values for Model 2 are approximately half to two-thirds as large as those estimated
from Model 1. The estimated dollar values range from around $2.1 thousand per acre per year for
undeveloped land, to around $10 thousand for farmland for Model 1; and around $1.2 thousand to
$6.4 thousand for Model 2. The values from Model 2 might be interpreted as the portion of
respondents' willingness to pay to take action which can be atU-ibuted to the described changes in
natural resource levels. This is smaller than the estimated value in Model 1, which includes a
"symbolic" effect.
Although the estimated dollar '~alues differ, both models result in the same ordering of priorities and
relative values for the natural resources. These results indicate that priorities and relative values are
robust with respect to different model specifications, and are independent of symbolic effects, but
that the estimated dollar values vary somewhat, although they are close in magnitude. Therefore, it
may be concluded that the model is relatively robust to different specifications, and that the
proportion of value that is "symbolic" is not great.
3. Discounted Present Values
Discounting is a means of aggregating dollar values over time, and is based on the idea that natural
resources are assets that provide a flow of services over time. Thus, "[t]he economic value of a
resource-environment system as an asset can be defined as the snm.ofthe discounted present values
of the flows of all of the services. The benefit of any public policy that increases the flow of one type
of service is the increase in the present value of that service" (Freeman 1993, p. 5).
The discounted present values for two discount rates are presented in Table IV. 15. The 7 percent rate
is used to approximate a "typical" interest rote for public planning. For example, 7.625 percent was
the official water resources planning discount rate for fiscal year 1996 (U.S. Department of the
Interior 1995).28 The 3 percent rate is included for comparison, because it is often argued that the
social discount rote should be lower. The 25 year time horizon was chosen to match the time period
This is the rate required by the government for Federal water and related land resources planning, and is based on
th~ average yield during the preceding fiscal year on interest-bearing marketable securities of the United States with
5 years or more remaining to maturity (U.S. Department of the Interior 1995).
presented in the survey. The 100 year time horizon was used to identify values associated with a
longer time horizon.
Table IV. IS - Comparison of Discounted Present Values
PV/Acre PV/Acre PV/Acre PV/Acre
r=.03, t=100* r=.03, t=25' r'=.07, t=100* r=.07, t=25'
Model 1:
Farmland $315,314 $173,759 $142,387 $116,286
Undeveloped $65,728 $36,221 $29,681 $24,240
Land
Wetlands - $207,286 $114,228 $93,605 $76,446
Shellfish Areas $143,945 $79,323 $65,001 $53,086
Eelgrass $258,675 $142,547 $116,811 $95,398
Model 2:
Farmland $~02,175 $111,412 $91,297 $74,562
Undeveloped $38,028 $20,956 $17,172 $14,024
Land
Wetlands $153,659 $84,676 $69,388 $56,669
Shellfish Areas $86,070 $47,430 $38,867 $31,742
Eelgrass $189,703 $104,539 $85,665 $69,962
* r is the discount rate; t is the time horizon in years.
CONCLUSIONS AND SUMMARY
This section described the development, implementation, and results of a survey of public values for
important natural resources of the Peconie Estuary System. We developed the survey through an
extensive six-month process of interviews, focus groups, and pretests. We selected the contingent
choice method to elicit values for five important resources: farmland, undeveloped land, wetlands,
safe shellfishing areas, and eelgrass. The survey was implemented in August, 1995, to 968 year-
round and seasonal residents of the East End of Long Island.
In summary, residents of the East End are very concerned about protecting of the area's natural
resources, and are willing to pay a significant amount to do so. Our statistical results indicate that
respondents are so concerned about protecting the East End's environment that they would be
willing to pay to take action, independent of specific results of the action. We corrected for this
"symbolic" value in our estimation models, and present the results of both the standard and
"corrected" model.
Overall priorities for the five natural resources included in this survey are for farmland, followed by
wetlands, eelgrass, shellfish areas, and undeveloped land. Discounted present values for the most
conservative model range from around $14 thousand to preserve an acre of undeveloped land, to
around $75 thousand to preserve an acre of farmland. The results of this survey will be combined
with those of several other studies to evaluate benefits of a set of proposed management actions for
the Estuary.
REFERENCES
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Addelman, Sidney. 1962b. "Symmetrical and Asymmetrical Fractional Factorial Plans,"
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Freeman, A. Myrick III. 1993. The Measurement of Environmental and Resource Values: Theory
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Software, Inc.
Hanemann, W. Michael. 1982. "Applied Welfare Analysis with Qualitative Response Models,"
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Hanemann, W. Michael. 1984. "Welfare Evaluations in Contingent Valuation Experiments with
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Kellert, Stephen. 1995. "Environmental Values, Coastal Context, and a Sense of Place," in L.
Anathea Brooks and Stacy VanDeveer, edS., Saving the Seas: Science, Values, and
International Governance. New Haven: Yale University Press.
Kling, Catherine L., and Joseph A. Herriges. 1995. "An Empirical Investigation of the Consistency
of Nested Logit Models with Utility Maximization," American Journal of Agricultural
Economics 77 No. 4 (November): 875-884.
Kling, Catherine L., and Cynthia J. Thomson. 1996. "The Implications of Model Specification for
Welfare Estimation in Nested Logit Models," American Journal of Agricultural Economics 78
No. 1 (February): 103-114.
Long Island Lighting Company. 1995. Population Survey 1995: Current Population Estimates fo?
Nassau and Suffolk Counties. Hicksville, NY. ~
Long Island Regional Planning Board. 1981. Land Use: Quantification and Analysis of Land Us~
for Nassau and Suffolk County.
Maddala, G.S. 1983. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge:
Cambridge University Press.
Mazzotta, Marisa J. and James J. Opaluch. 1995. "Decision Making When Choices are Complex:
A Test of Heiner's Hypothesis," Land Economics 71, No. 4 (November): 500-515.
McFadden, Daniel. 1981. "Econometric Models of Probabilistie Choice," in C. F. Manski and D.
McFadden, eds., Structural Analysis of Discrete Data with Econometric Applications.
Cambridge: MIT Press.
National Oceanic and Amaospheric Administration. 1993. Natural Resource Damage Assessments
Under the Oil Pollution Act of 1990. 58 Federal Register 4601 (January 15).
Neave, H. R., and P. L. Worthington. 1988. Distribution-Free Tests. London: Unwin Hyman.
Sagoff, Mark. 1992. "Settling America or The Concept of Place in Environmental Ethics," Journal
ofEnergy, Natural Resources and Environmental Law 12No. 2: 349-418.
Suffolk Commtmity College. 1994. Peconic Estuary Public Opinion Survey. Center for Community
Research, Riverhead, NY.
Suffolk County Department of Health Services. 1992. Brown Tide Comprehensive Assessment and
Management Program.
Suffolk County Department of Health Services. 1994. Peconic Estuary Program Action Plan.
Suffolk County Department of Planning. 1991. Estimated Peak Seasonal Population - 1990.
Unpublished spre~adsheet.
Suffolk County DcparLrnent of Planning. 1991. Estimated Peak Seasonal Population - 1990.
Unpublished spreadsheet.
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60 Federal Register 66323 (December 21)
119 -~
Appendix A - Example Survey *
· Readers who would like to obtain a copy of the survey should contact the
authors.
Appendix B - Technical Appendix
Based on the random utility model, relative values and priorities for the natural resources were
estimated using the standard conditional logit method (Greene 1993; Maddala 1983), where
exp(ffzj + a'zjwi)
= exp(p'z + 'z wi) '
(1)
P~j is the probability that individual i will select option j; a~ is a vector of attributes of the choice'(e.g.,
the levels of natural resources and the cost), which may also vary across individuals; wi is a vector
of characteristics of the individual; and 15 and ~z are vectors of parameters of the model, estimated
using maximum likelihood techniques. Box-Cox tests were carried out to select an appropriate
fanctional form and, based on these tests, the linear form was chosen for all subsequent estimation.
The second alternative specification is the nested logit model, which allows for correlations between
the two action alternatives. The nested logit model was structured so that respondents are assumed
to first choose whether to take action or not, and then to choose a specific program conditional on
taking action. In the nested logit model, the probability that an individual chooses alternative k is
P~= P(klj)P(j).
(2)
The choice probability for each of the three lowest level alternatives is conditional on the choice to
take action or not. In this case, if the person chooses not to take action, then the probability of
selecting the "N~a New Action" alternative is 1, since it is the sole altemative on that branch of the
nested model. If they choose to take action, the probability of selecting "Program A" or "Program
B" is
P(klj)-
exp(Vku) exp(ffx~u) exp(ffx~li).
5'.exp(%)- Y.
exp(ffx,u) exp(Js)
(3)
120 _.-~
where k is one of the two alternatives, Program A or Program B; j is the choice to take action; n is
the number of alternatives in choice j, which would be 2; and Jj is the inclusive value for choice j,
which represents the expected maximum utility from the choice of an alternative that sub,branch.
This is defined as
J1 = log(~'~, exp(fl'x.lj)).
nlj
The probability of choosing to take action or not is
(4)
exp(odYj + fiji.)
P(J) = ~exp(a'Y,, + rmOrm)'
(5)
where m is the number of branches, which in this case is two.
The parameter J is the inclusive value coefficient, which is related to the correlation between
alternatives within a branch. A value of x between 0 and 1 indicates that there is greater
substitutability within, rather than across, groups of alternatives. In terms of this study, this would
indicate that Program A and Program B are closer substitutes than either program and the "No New
Action" alternative. If ~ is equal to 1, then all alternatives are equally substitutable, and the model
becomes identical to the standard conditional logit model (McFadden 1981; Kling and Herfiges
1995; Kling and Thomson 1996).
The results of the conditional logit model were compared to two alternative specifications. These
results are presented in Table B-1. The first is the conditional logit model with two alternative-
specific constants, one for the choice of an action versus no action, and one for Program B. Thus,
the coefficient on the first constant term reflects factors other than the levels of attributes that affect
the choice of an action versus the choice of no action24 For example, respondents may be expressing
a symbolic willingness to pay to take action, as opposed to revealing values for the specific natural
resources of concern. The coefficient on the second constant term reflects any difference in
preference for Program B versus Program A that is unrelated to the levels of attributes of A and B,
such as an order effect.
The results of both of these models indicate that there may be effects on choices unrelated to the
described attributes. In the alternative-specific constant model, both constant tenn~ are statistically
significant, indicating that there is an effect on choices unrelated to the quantities of the individual
attributes, but is instead related to the choices themselves. The positive and significant coefficient
~4 Note that, alternatively, this could also suggest use of the incorrect functional form, although the Bo~'-Cox test
shows that the linear form is the most appropriate of the commonly_reed functional forms. ~
121
for "Action" indicates that people are more likely to choose an action rather than "No New Action,"
independent of the action's specific effects on natural resources. Similarly, the negative and
significant coefficient for "Program B" indicates that, even if Program A and Program B produced
the same results in terms of preservation of natural resources, respondents are more likely to choose
Program A. The coefficient on Action indicates that there is an 87 percent probability that the
average respondent would select action over no action, if that action cost nothing and provided zero
resource protection. Similarly, conditional on taking action, there is a 55 percent probability that
the representative respondent would choose Program A over Program B if their costs and levels of
resource protection were identical.
Table B-1 - Comparison of Model Results
Value 95% Confidence Avg. Value
Coefficient /Acre Interval /Acre/Year'
Conditional Logit Model:
Farmland 0.000511 $0.136 $0.122 $0.150 $9,979
Undeveloped Land 0.000107 $0.028 $0.025 $0.032 $ 2,080
Wetlands 0.000336 $0.089 $0.079 $0.100 $ 6,560
Shellt'rsh Area~ 0.000233 $0.062 $0.053 $0.071 $ 4,555:
Eelgrass 0.000419 $0.111 $0.098 $0.125 $ 8,186
Cost -0.003765
p~l-(L ([[I)/L (0)) .138
Model with Alternative-
Specific Constants:
Action 1.2866
Program B -0.1799
Farmland .000300 $0.094 $0.078 $0.109 $ 6,872
Undeveloped Land .000057 $0.018 $0.014 $0.022 $1,304
Wetlands .000179 $0.056 $0.045 $0.066 $ 4,090
Shellfish Areas .000108 $0.034 $0.023 $0.044 $ 2,467
Eelgrass .000214 $0.067 $0.052 $0.081 $ 4,909
Cost -.003207
Nested Logit Model:
Program B -.1586
Farmland .000300 $0.087 $0.073 $0.101 $6,398
Undeveloped Land .000056 $0.016 $0.013 $0.019 $1,203
Wetlands .000228 $0.066 $0.056 $0.077 $ 4,863
Shellfish Areas .000128 $0.037 $0.031 $0.044 $ 2,724;':
Eelgrass .000281 $0.082 $0.069 $0.094 $6,003
Cost -.003441
x 3397
p' .309
* - Calculated based on 73,423 households.
122
These constant terms for taking action and for differences in probability of selecting A and B may
be interpreted as representing a qualitative or symbolic dimension of respondents' preferences, while
the coefficients on the natural resources represent the quantitative dimension that can be attributed
to the stated levels of resource protection. Thus, if respondents exhibit a tendency to a resource
protection action, rather than "No New Action," beyond that which can be associated with the stated
levels of resource protection and the cost, they may be expressing a symbolic willingness to pay to
take action to protect the environment of the East End. Similarly, the coefficient on the constant
term for Program B indicates that there is some qualitative reason that people choose Program A,
and measures the degree of preference for A over B which cannot be explained by the described
attributes of the two programs. Note that this constant term is statistically significant, but
quantitatively small.
The higher probability of choosing to take action is not surprising, given the level of concem among
residents of the area for the environment of the Estuary. However, the preference of one program
over another beyond the described effects is not expected, and could occur for a variety of reasons.
For example, the effect could be related to the ordering of the two programs, their placement on the
page, or could possibly indicate that respondents infer some preference from the labels (e.g., an A
is better than a B).
The constant for the choice of action versus no action accounts for qualitative aspects of the decision
to choose an action rather than no new action. However, it is estimated as a constant term and thus
assumes a fixed effect, where the constant represents a mean "bias" towards action versus no action,
beyond that which can be explained by the described levels of resource protection and cost. An
aitemative approach to modeling is to use a random effects model, where the random components
of preferences for the two action programs are correlated. This implies that an action/no action bias
might exist, but that the bias is randomly distributed across choices. For example, some
individuals might exhibit a bias towards taking action, while others might exhibit a bias against
taking action.
The random effects model can be implemented using the nested logit approach, which captures the
correlation of the random components of utility associated with the two action alternatives. Tests
of the inclusive value parameter in the nested logit model indicate that there is greater substitutability
between Program A and Program B than between taking action or not, and that there is significant
correlation between the Program options. The constant term for Program B is similar in magnitude
to that estimated in the previous model.
123
Economic values for the conditional logit model were estimated based on Hanemann (1984), and
are measured by the cost, C, that would make a person indifferent between the choice selected and
the baseline, no action, which has zero cost. Thus, for the conditional logit model,
U(Rv, Mi) = U(Rik, Mi - Ck) for all k v j,
(6)
where j represents the "No New Action" alternative, or the baseline levels of the resources, so that
Cj--0; k is the option selected; and Ck is the maximum willingness to pay for option k. For the linear
approximation of the utility function presented above, this can be solved for Ck as follows:
+/(mi): +/(mi - q) ano = - -
(7)
Thus, for the conditional logit model, the dollar value to the average respondent for a unit change
in each of the natural resources is calculated as the ratio of the coefficient on the resource, [3, to the
coefficient on cost, ¥.
The calculation of dollar values for the nested logit model must account for the nested structure and
the inclusive value parameter, z. The formula for the compensating variation associated with a
change in one of the attributes of the choice is (Kling and Thomson 1996; Hanemann 1982):
(8)
where V is the utility function, the superscripts on V indicate whether the attributes are set at the new
level or the old level, and ¥ is the coefficient on cost.
The estimated dollar values and relative values, calculated as ratios between the coefficients on each
pair of resources, were compared for each model using Friedman's test for more than two related
samples (Neave and Worthington, 1988). Based on this test, the hypothesis of equality of the
estimated dollar values for the three models is rejected. However, a comparison of the estimated
dollar values for the nested logit model and the alternative-specific constants model using the
Wilcoxon signed rank test does not reject the hypothesis of equality of values for these two models.
The Friedman test does not reject the hypothesis that the relative values for natural resources are
equal for all three models. Additionally, the ordinal priorities for all three models are the same, with
farmland most important, followed by eelgrass, wetlands, shellfishing areas and undeveloped land.
These results indicate that priorities and relative values are robust with respect to different model
specifications, and are independent of symbolic effects, but that the estimated dollar v~lues vary
somewhat between the base model and the two alternative specifications. However, the estimated
dollar values for the three models'are close in magnitude. Therefore, it may be conCludtd that the
124
model is relatively robust to different specifications, and that the proportion of value that is
"symbolic" is not great.
These results indicate, however, that there may be a statistically significant s~nbolic component to
choices, which is comprised of an effect that is unrelated to the described levels of resource
protection provided by the hypothetical programs and the associated cost. The similarity of results
from the nested logit and conditional logit with constants models, and the fact ~at the results are not
statistically different, indicate that these biases are likely overwhelmingly in one direction---towards
taking action rather than no action. Thus, both of these models appear to account for a "symbolic"
aspect of values, and to separate that from estimated values for specific natural resource
improvements. Note, however, the Nested Logit model provides considerable improvement in fit,
as measured by the p2 statistic.
125