### Economic

Analysis, Inc.

FINAL REPORT 

# Recreational and Resource Economic Values

for the Peconic Estuary System

Prepared by: James J. Opalueh, 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 ScienceAssociates Narragansett,

RI Preparedfor: Peconic Estuary Program Suffolk County Department of Health Services County Center Riverhead, N.Y. 11901
February, 1999

P.O. Box 3462 Peacedale, RI 02883 (401) 789-0050DISCLAIMER

Although the information in this document has been fundedwhollyor in part by the United States Environmental

Protection Agency

under assistance agreementnumber 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 generousassistance 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 CountyHealth Department, Kevin McDonald and Gayle Marriner-Smith of the Citizen’s Advisory Councilfor the PeconicEstuary Program,and CynthiaDecker of the New York State Department

of Environmental

Conservation,all of whom wereall especially helpful, and provided a great deal of useful information. We would also like to thank the many peoplewho

took time fromtheir busyschedules to participate in focus groups, interviews, andpre-tests as part of our survey-development project. Themany

focus groups, interviews, and pre-tests were critical for the successful development of the recreation and resourcesurveysusedin our study; and wegreatly appreciatethe help of all those who participated. Thetwo surveys summarized in this report could not have been administeredwithout 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 whoseresearch assistance, persistence, organizational and communications skills weretremendously helpful. We also acknowledge

the survey respondents--over two thousand residents, second homeowners, and visitors to the East End--who gave their time and opinions. Thanksalso are due to staff of the TaxAssessor’s Office of the Town of Southold who

not only made available to us property transactions data for the townfor 1996, but also provided important and useful insights into the local property market that helped guide our property value case study. We also acknowledge

the time spent with us by realtors in Southold who gaveus the benefit of their insights into local real estate issues. Collectively, the expert comments fromthe Tax Assessors Office and local realtors greatly contributed to our analysis of property values and how they are affected by neighboringamenities. Two other individuals who

also made important contributions to this report should be recognized. MaryHutchinson 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 final draft. To all of those mentioned above, and to the many

others (too numerous

to mentionby name)who helped along the way-our

heartfelt thanks! Ofcourse, the authors alone bear responsibility for any errors.

iiTable of Contents

Disclaimer

...................................................................... i Acknowledgments

.............................................................. ii Table of Contents

............................................................. iii List of Tables

................................................................. vi List of Figures

................................................................ viii EXECUTIVE

SUMMARY

..................................................... 1 IA. IB.

I.C.

I.D. PURPOSE AND SCOPE " "  ̄ ° " " " " " " " " " * " "  ̄ " " " " "  ̄  ̄  ̄  ̄  ̄  ̄ *  ̄  ̄  ̄  ̄  ̄  ̄  ̄ *  ̄  ̄ ° *  ̄  ̄  ̄ *  ̄  ̄ 1

ESTUARINE-RELATED USES, RESOURCESAND ECONOMIC

VALUES

................................................. 2 I.B.1. Introduction and Overview

....................................... 2 SUMMARY

OF SELECTEDRESULTS

................................ 3 I.C. 1. Property Value Study (Chapter III)

.................................. 2 I.C.2. Recreational Uses and EconomicValue (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

ANDINTRODUCTION

..................................... 7 BACKGROUND

...................................................... 7 NATURE AND IMPORTANCE OF NO-MARKETRESOURCE VALUES

..... 8 II.B. 1.Overview of Studies and Basic Concepts

............................. 8 PURPOSE

ANDSCOPE

.............................................. 10 REFERENCES  ̄
....................... .......................... .... 11 III.ENVIRONMENTAL AMENITIES AND PROPERTY VALUES: A CASE STUDYOF THE TOWN OF SOUTHOLD

...................... 13 III.A. III.B. III.C. III.D. INTRODUCTION

.................................................... 13 RELATIONSHIP BETWEENENVIRONMENTAL AMENITIES AND PROPERTY

VALUES

................................................ 14 APPLICATION OF PROPERTYVALUEMODEL TO SOUTHOLD

.......... 15 III.C. 1. Explanation of The Property Values Method

......................... 15 STATISTICAL ANALYSISOF SOUTHOLD LAND VALUES

............... 17

°°° 111III.O.

III.H. III.I. ANALYSISOF SOUTHOLD LANDVALUES: RESULTS

.................. 17 SIMPLE ILLUSTRATION OF PROPERTYVALUEIMPACTS: VALUE OF OPENSPACE

............................................ 18 LIMITATIONSIN 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 ANDUSE VALUESIN 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 ANDLOCATIONS

.......... 32 SURVEYDEVELOPMENT ANDIMPLEMENTATION

.................... 33 DESCRIPTION OF THE RECREATIONAL USE SURVEY QUESTIONNAIRE

................................................... 34 DESCRIPTIVE RESULTS: DEMOGRAPHICS

............................ 35 DESCRIPTIVE RESULTS: KEY RECREATION ACTIVITY PROFILE

........ 40 DESCRIPTIVE RESULTS: PARTICIPATION ANDTRIP ESTIMATES

....... 45 DESCRIPTIVE RESULTS: SELECTEDEXPENDITURE ESTIMATES

........ 49 DESCRIPTIVE RESULTS: REFERENDA ANDBROWN

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 of PES for Recreation Activities

.... .................... 60 IV.K.4. Changein 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 AppendixB. Technical Appendix: Swimming Trips and Value Estimation

.................................................... 69 Appendix C. Comparison of Recreation Activity Estimates: Phase I and Phase II Reports

..................................... 84

ivV. WETLAND PRODUCTIVITY

VALUES IN THE PES .......................... 86

g.c. INTRODUCTION

.................................................... 86 METHODOLOGY

ANDDATA

........................................ 87 V.B. 1. Introduction

................................................... 87 V.B.2. Food WebEstimates

............................................ 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 ANDVALUES ......................... 92

VI.C.

VI.D.

VI.E. VI.F. Appendix Appendix INTRODUCTION

.................................................... 92 THE NATURAL

RESOURCE

SURVEY

................................. 92 VI.B. 1. Survey Developmentand 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.1.c. Focus Groups and Preliminary Survey

..................... 93 VI.B. 1 .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 Resourcesand Level/0 VI.B.2.i. Natural Resources Included in the Survey

................. 99 VI.B.2.ii. Levels of Natural Resources in the Survey

................ 100 VI.B.2.iii. Description of Survey

................................ 103 DESCRIPTIVE

RESULTS

............................................ 104 VI.C. 1 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 Membershipin 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 A Obtaining the Survey

........................................... 120 B Technical Appendix

........................................... 120

VList of Tables

Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table B.1 IV.1 IV.2 IV.3 IV.4 IV.5 IV.6 IV.7 IV.8 IV.9 IV.10 IV. 11 IV. 12 IV. 13 IV. 14. IV. 15. IV.16 IV.17 IV.18 IV. 18 IV.19 Summary

of EAI Non-MarketValuation Studies for PES

............................................. 12 Mean Values of Model Variables

............................. 26 Residency Status of Survey Respondents

........................ 36 Population Demographics vs. Survey Respondent Demographics

........ 39 Participation in KeyRecreation Activities - ByResidency Status

........ 41 Water body Selection by Activity - (N = 1267)

.................... 42 Town Selection by Activity - (n= 1267)

........................ 43 PES Key Recreational Activity Outings - (n= 1267)

................ 44 PESKeyRecreational Activity Outings by Residency Status - (n= 1217)

.. PESKeyRecreational 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 Farmstand, Winery, and Rental LodgingExpenditures (1995 dollars) Farm stand, Winery, and Rental Lodging Expenditures

.............. 52 Management Action Referenda Results

......................... 54 Awareness of Brown Tide

................................. 55 Activities Affected By Brown Tide

........................... 56 Total AnnualValue 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 Percent

.............. 64

Supplementary Tables Table Table Table Table Table Table Table Table Table S.1 S.2 S.3 S.4 S.5 S.6 S.7 S.8 S.9 East End Resident Respondent 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 KeyRecreation Activities - All Respondents (N= 1354)

............................................ 67 Participation in PESWaterBased Recreation - All Respondents (N= 1354)

........................................... 68 Participation in Land Based Recreation - All Respondents (n= 1354)

..... 68 Estimated Trips for Key Recreation Activities for Sample

............. 69 viTable

Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table A1 A2 A3 A4 A5 A6 Count 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

..... ° ° ° ° "  ̄  ̄ *  ̄  ̄ °

....... ° ° ° °  ̄
.... °  ̄ *  ̄  ̄  ̄ ° ° °  ̄
....  ̄  ̄  ̄  ̄ * 80 Probabilities at Nine Sites for 10% Improvements

in Total Kjeldahl Nitrogen Readings

........................................... 80 A8 Probabilities at Nine Sites for 10% Improvements

in Total Coliform Readings

................................................. 81 A9 Probabilities at Nine Sites for 10% Improvements

in Secchi Disk Depth

... 81 A10 Probabilities at PESWater Bodies for a 10% Improvement

in Brown Tide Cell Counts

....................................... 82 All Probabilities at PESWater Bodies for a 10% Improvement

in Total Kjeldahl Nitrogen Readings

................................ 82 A12 Probabilities at PESWater Bodies for a 10% Improvement

in Total Coliform Readings

...................................... 83 A13 Probabilities at PES Water Bodies for a 10%Improvement in Secchi Disk Depth

...................................... 83 A14 Results of the Travel Cost Model (Poisson Regression)

.............. 84 IV.B. 1 Marginal Values of PES Wetlands

............................ 90 V. 1 Focus Groups and Pretests

................................. 94 VI.2 Concerns of Preliminary Survey Respondents, Montauk, March 19, 1995

........................................ 95 VI.3 Survey Locations and Number Collected

........................ 98 VI.4 Past, Present and Projected Natural Resource Levels

............... 102 VI.5 Levels of Natural Resources and Cost in the Survey

............... 103 VI.6 Respondent Demographics, Full Sample

....................... 105 VI.7 Population Demographics vs. Survey Respondent Demographics

....... 107 VI.8 Numbers of Respondents by Town

........................... 108 VI.9 Participation in Recreational Activities

........................ 109 VI.10 Support for Actions

.................................... 110 VI. 11 Responses to Brown TideQuestions

.......................... 112 VI. 12 Membership in Organizations

.............................. 113 VI. 13 Number of Choice Questions Answered

....................... 114 VI. 14 Estimation Results

..................................... 115 VI. 15 Comparison of Discounted Present Values

...................... 117 B-1 Comparison of Model Results

.............................. 122 viiList of Figures

Figure II. 1. Depictionof the Peconic (Long Island) Estuary System Figure II.2. Economic

Studies by EAIfor the PES Program Figure II.3. Depiction of EAI’s Phase II Non-Market Value Economic

Studies Figure III. 1. Town

of SoutholdUsed

for Property Value CaseStudy GIS Maps Used to Define ModelVariables: Examples

viii

## Recreational and Resource Economic Values for the Peconic Estuary System

EXECUTIVE SUMMARY

I.A. PURPOSE

AND SCOPE Theenvironmental

and natural resources ("natural assets") of the PeconicEstuary System--the bay waters, beaches, wetlands, ecosystems, habitats, and parks and watershed lands--provide many services to the public. Outdoorrecreation, scenic views, and the productivity of wetlands that contribute to stocks of fish, birds, andother species usedfor commercial and recreational purposes are but a few examples

of these services. While the quality of PES

coastal amenitiesis widelyrecognized,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, whatthey are "worth" to the public. This is becauserecreation, scenic views, and ecosystem productivity are not directly valued in markets. Lacking information on recreational and resources values, comparisons cannot be madeof the benefits and costs of prospectiveactions to preserve or restore PES natural assets. This report is designed to help fill this major

gap. In this report, wepresent the results of four non-market valuation studies carried out by Economic AnalysisInc. 

### CEAI")

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 resourcevalues provided by the natural assets of the PES. The economic valuation studies described herein were done to contribute to benefit-cost analyses of proposedmanagement actions. This document

is PhaseII of a series of economic studies being done

by EAI for the PeconicEstuary Program.A PhaseI report (Grigalunas and Diamantides, 1996) (1) provided an assessment of "economic impacts" of the PES, in terms of business revenues, employment, and wages of estuarine-related sectors, and(2) summarized available information

on recreational uses. Subsequent workby EAIwill include benefit-cost analyses of resource management actions and sustainable financingoptions for the selected, preferred actions.I.B. ESTUARINE-RELATED USES, RESOURCES

AND ECONOMIC

VALUES I.B. 1. Introduction and Overview Nosingle method

can capture the value of the variety of services providedby 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 estimatesof the value of particular services: 

### (1) A PropertyValue study examines the contribution of environmental amenities to the market price of property. Using

the Town of Southoldas a case study, the PropertyValuestudy was designedto measure

values of amenitiesto residents living in the immediate vicinity. 

### (2) A Travel Cost study uses original surveyresults to estimate outdoor recreational uses in the PES and the economic

value that users havefor four, keyPES outdoorrecreation activities: swimming,

boating, fishing, and bird and wildlife viewing. This study also examines the impactthat (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, carded 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 primaryfocus is on the nursery and habitat services of the wetland ecosystems

in the production of commercial fisheries. (4) TheResource

Valuestudy uses original surveyresults to estimate the relative preferences that residents and secondhomeowners have for preserving and restoring key PES natural and environmental

resources: Open

space, farmland, unpollutedshellfish grounds,eelgrass beds, and intertidal salt marsh.This study also providesa perspective on the economic value the public has for these resources, as indicatedin their stated willingness to payfor programs

to preserve and restore them. Key results for each of these studies are outlined below.Adetailed discussion of eachmethod, its purpose,the data used, assumptions andlimitations, andresults is given in the chaptersthat follow. A note on the style adopted for this document.

We believe that the results of the economic analyses presentedin this report provide a wealth of data to decision makers and to the public. Tomake 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.I.C. SUMMARY OF SELECTED RESULTS I.C. 1. Property

Value Study(ChapterIII). Data for property sales in the Town of Southold

wereused to estimate the contribution of specific environmental

attributes to the marketvalue of nearby property. Usinga property value model,we foundthat proximityto openspace and other environmental attributes had a significant, positive impact on nearby property values. For example,

a parcel of land located next to openspace has, on average, a 12.83 % higher per-acre value than a similar parcel located elsewhere.Toillustrate the how this result mightbe used, we estimate that a hypothetical contribution of a parcel of approximately 10 acres of openspace would increase adjoining property values by $410,907. The model results also show

that density of development andother 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 valuethan a similar parcel located elsewhere (i.e., in a 1/2-acre zone). Conversely, propertylocated on a main road (highway

25 or 48), or property adjoininga farm, has a lower value, after taking into accountother 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 key PES recreational activites. We estimate that in 1995: 127,762 peopletook some

3.3 million swimming,

boating, fishing, or shell fishing, outings 156,184 people engaged

in about 5.2 million beachuse, bird watching,wildlife viewing, or huntingtrips 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 numberof trips. Measurement problems prevented us from including other common activities, such as hiking/walking and bicycling. Among the PES Bays, Great PeconicBayis the mostpopular, accountingfor 28% of all recreational trips to the PES.FlandersBay 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, GardinersBay

is the mostpopular PES

location, with 33%

of all PES

shell fishing trips. Outdoorrecreation 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,

3boating

l, fishing, and for viewing of birds and wildlife. Theestimated values per trip range from $49.83for viewing

birds and wildlife to $8.59 for swimming (in 1995 dollars). These

are estimates of what participants wouldbe willing to pay, per trip, above and beyondthe amount that they actually payto 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), Swimming ($12.1 million). Thecorresponding asset values of the PES

(over 25 years at 7 percent) for these key recreational activities range from$318 million for Bird Watching and Wildlife Viewing to $141million for Swimming. ThePES has an asset value of $276million for Recreational Fishing and $210million for Boating. Quality is important to PES recreationists. Swimming was found to depend uponperceptions of water quality, and swimmers perceptions, in turn, were related to objective (i.e., SCDH field sampling) measures of water quality for nitrogen, water clarity; Brown Tide, and coliformbacteria. Toillustrate how

the results mightbe used in a Benefit-Costanalysis, wesimulated hypothetical, uniform improvements

in water quality. For example, a 10 percent uniform improvement in water quality in each Bayincreases the estimated number of annual swimming

trips by 151thousandand adds yearly benefits of $1.3 million. Most of the benefits ($754thousand)are due to improvements in water clarity (as measuredby Secchi depth). Swimming benefits increase further with hypothetical, 20 percent uniformwater quality improvement, although the added benefits from the second 10 percent improvement is slightly less than that due to the first 10 percent improvement. Thepresent 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 waterquality did not exceed$15.1 million over the same period, it would

be a goodinvestmentof scarce resources. Recreationalfishing also was foundto depend

upon the quality of the experience--inparticular, 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.Thepresent value of this increase in catch rates--the increase in the asset value of the PES in providingthis service-- is $5.5 million, usingthe 7 percentrate and time horizon of 25 years usedfor all cases. Onanother issue, to address a data gap identified in EAI’s Phase I study, Phase II survey respondents

wereasked how

much they spent at road side farmstands and at vineyardsand 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. Usingthe sameassumption as in the PhaseI study that 45 percent of these activities are PES-related(Grigalunasand Diamantides, 1996), the annualPESrelated expenditures

for these twoactivities are $8.7 and$2.4 million, respectively.
~Boating for the primary

purposeof fishing is valuedunder recreational fishing. 4I.C.3. Wetlands Productivity Values (Chapter V) Eelgrass, saltmarsh and intertidal mud bottoms provide food weband habitat services for many species of fish, shell fish, waterfowl andbirds, and thus directly or indirectly benefit peoplewho consume, hunt, or view these species. A simple food webmodel 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. Theresults suggest an asset value per acre for existing wetlandsof approximately $12.4 thousand for eelgrass, $4.3 thousand for salt marsh,and $786

for mud flats, using a discountrate of 7 percent and 25 year period for the services valued. Restoredwetlandsare worth somewhat less since it may take several years to restore fully their natural functionsas a foodsourceor habitat. I.C.4. Resource Preferences and Economic Values (Chapter VI) Early discussions revealed that the public has a strong attachment to environmentaland amenity resources of the PES, even if they do not use these resources directly. As outlined above, the Property Valuestudy 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’sfocus groups. To try to capture these elusive but important non-market resource values, a survey developedand administered to 968 residents and second homeowners was used to learn whichamenities are most important to people in the study area, and the amount that they would

pay to preserve or restore them. Our purposewas(1) to account for the preferences many residents have for resources, even if they donot usethese 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) supportedat least one hypothetical action protect resources, and indicated they would financially support such actions. The relative priorities of respondents, in order, werefor farmland,eelgrass, wetlands, shellfish, and undeveloped land. We also estimated the monetary value that the surveyrespons@s implyfor the resources concerned. Theestimated per acre dollar values wereabout $14.0 thousand for undeveloped

land, $30thousand for unpollutedshellfish grounds, $56.7thousand for saltmarsh, $70thousand for eelgrass and$74.5 thousand for farmland, using a 25-year time horizon and 7 percent discount rate. However, we believethat the resource

priorities or relative valuesof 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.
5I.D. REFERENCES

Freeman, A.M.III, 1993. The Measurement of Environment and Resource Values: Theory and Methods.Washington,

D.C.: Resourcesfor the Future. Grigalunas, T.A. and J. Diamantides, 1996. The Peconic Estuary System: Perspective on Uses, Sectors, and Economic

Impacts. Peace Dale, R.I: Economic Analysis, Inc. Kopp, R. and V.K. Smith, 1993. Valuing Natural Assets: The Economicsof Natural Resource Damage Assessment. Washington,D.C.: Resource for the Future. Tietenberg, Tom,1995. EnvironmentalandNaturalResource Economics (4th ed.) New

York: John Wiley & Sons

6II. BACKGROUND AND INTRODUCTION

II.A. BACKGROUND Located within Suffolk County

at the East Endof Long

Island, the PES

is comprised

of the PeconicFlanders Bays system, Gardiners Bayand part of BlockIsland Sound, and the adjoining watershed lands (Figure II.1). 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 townscomprise about 38 percent of the land area and 8 percent of the year-round population of Suffolk County(SCDHS, 1992; LongIsland 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 yearround residents is comprised of smaller, moreelderly, and lower-income householdsthan for the County or Long Island as a whole(LongIsland Business News,1994; Grigalunas and Diamantides, 1996). Water quality in the PES

generally is very good, and the study area contains many beaches, parks, openspace, 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. Asignificant feature of assets--natural or otherwise--is that they can provide a stream of valuable services ("interest") overtime, 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 thousandsof acres of shellfish groundsused for commercial and recreational purposes. Further, large areas of ecologically productive eelgrass and saltmarsh ecosystems have beenlost dueto development, Brown Tide, andwater pollution. Fin fish and shell fish landings have declined substantially over time, in part due to these problems.The combination of all of these adversedevelopments

threaten the quality of life of residents of, andvisitors to, the PES. Management

programsbeing developedby the Peconic Estuary Program wouldpreserve or restore key PES environmentalor natural resources. None of these programs

is free, however,and some could be very costly. Decidingwhether, where, how much, and when

to invest in preserving or restoring PES

natural resourcesrequires considerationof many factors. One

of these is the economic benefits andcost of such actions.

7# I / I

r

# b

0

o
= 0

O
= 0 o~.=~

o ,~==I



## =
o~=...Ill.B. NATURE AND IMPORTANCE OF NON-MARKET RESOURCE VALUES

II.B. 1. Overview

of Studies and Basic Concepts Economic benefits providedby natural asset services show up in at least twoways: (1) Asmarket benefits to the owners, operators and employees of over 1,000 businesses that engage in or support recreation and tourism, commercial fishing and seafoodactivities, and other estuarine-related economic operations (Grigalunas and Diamantides, 1996). (2) As

non-market

benefits to the thousands

of recreationists, visitors, propertyowners, 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 estuarine-dependent economic sectors (Figure II.2). Estimates were madeof employment, wages, sales, and number of establishments for 29 economicsectors identified as estuarine-related, in wholeor in part. These sectors include commercial fishing, marinas, eating and drinking establishments,hotels/motels, andother estuarine-related activities. ThePhaseI results show

that estuarine-related economic activity is a majorcomponent of the PES economy and an important part of the livelihood of over 10,000residents who own,operate, or are employed in morethan 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 becausemuch of what people enjoy in the PES--forexample,natural amenities, like openspace, attractive views, goodbeaches and high levels of water quality--are not boughtand sold in markets.Benefits cannotbe 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 andhencenot linkedto the natural asset that provides the service (e.g., fish or shell fish "produced"

by PES wetlands and later harvested by fishermen). This PhaseII report focuses on non-market benefits provided by amenities in the PeconicEstuary 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 providea 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 goodsor engagein activities like recreation when the benefit they receive is at least as great as the cost to them.For example, if I can buya discount airplane ticket

## W

m~| Im

..... ~m



### -- C

|



### ..._ C

nm 

### C~ C~ c-- 

## LL

L-- 

# ~o

0 0for $200, I would do so only if it wasworthat least that amount o me. If I would

pay up to $500 for the flight, then I will buythe ticket and receive a net benefit-an unpaid-forbenefit--of $300. Similarly, on a nice summer dayI mightbe willing to pay as much as $15to use a beachfor the day, but if usingit costs me

only$5, then I receive an unpaid-for benefit of $10for that trip to the beach 2.
In the above examples, the most that I amwilling to pay is myown assessment of the economic value of the airplane ticket or the day at the beach--what eachis worth

to me. The

net benefit to me in each case is the difference between the most I would pay, less whatI actually pay. This "unpaidfor benefit" realized in each examplecaptures the notion of "Consumer’s Surplus". Consumer Surplus--the benefit peoplereceive aboveand beyond the cost to them--provides the basis for the measurement

of economic

benefits used in this document. Ofcourse, the "trick" is to use soundmethods 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 sunlmarized in the next section andexplainedin detail in succeeding chapters. Discounting and Present Value. A second important concept involves discounting. Many benefits or costs occur over time; discountingis the processused to convertfuture benefits andcosts to an equivalent, lumpsumvalue today. This discounting process is used, for example, to convert a retirement of lottery annuity (an equal cash amount eachyear) 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 stream of dollar flows over years 1 through "T" wediscount each annual flow using the formula:

Present Value = VI/(I+ r) 1 +
.... + VT/(I+r) x where Vis a dollar value andr is the discount rate--the interest rate usedto convert future flowsinto a value today. For example,if the total Consumer Surplus from swimming

is $1,000in year 1 and $1,000 in year 2, andthe discountrate is 10 percent, then the present value of these annual amounts is $1,735 (= $909 +$826)today. Thepresent value calculated with the above formuladepends

uponthree factors: (1) the size of the annualmonetary

values, Vt, (2) when

they occur, and (3) the discount rate, r, used. We estimate annual monetary

values, V t for non-marketservices 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

projects 3.

2Of course ill some

cases the price is more

than weare willing to pay, and wewill not buy the item because it is "too expensive"

or "not worthit". 3In later work

the effect onresults of uncertainty in choice the discount rate and other variables will be examined. 9II.C. PURPOSEAND 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 PhaseII 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 proposedmanagement actions as part of the PeconicEstuary Program (Figure II.3). Phase I was designed to estimate marketeffects provided by the PES. Asdiscussed above, Phase I measures "economic impacts" of the PES, including employment, sales, wages, and the number of establishmentsassociated with 29 economic sectors identified as estuarine-related. Phase II provides estimates of non-market benefits. Our studies were carried 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. PhaseIII analyses will (1) providecost analyses for specific management actions designed

to protect or restore PES

amenities, (2) combine

this cost informationandthe benefit information fromPhases I and II to provide benefit-cost analyses of potential management actions, and (3) compare

various financing options for the proposedmanagement 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-marketvaluation studies are needed because of the many

different types of services provided by PES environmental and natural resources. For example,recreational participants directly use Baywaters and beaches; hence, for these activities wewant to measureparticipants’ recreational use values. In other cases, PES resources may

provideamenity

or aesthetic benefits to residents, which will be capturedin property values, another type of use value. Additionally, somemayalso enjoy just knowing that PES environmental

amenities, such as openspace and salt marsh,are being preservedor restored, whether or not they directly use these resources. For these resources, other values, not involvingdirect use, mayalso be important and should be included. In sum, our use of a variety of methods was designedto allow us to gain a better understanding of the non-market

values providedby different resourceservices, 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. Theproperty value study wasdesignedto estimate benefits that PES

amenities provide to individuals who own adjacent properties. The recreational surveyallowsus to estimaterecreational use anduse values for a variety of outdooractivities. This surveywas

designed fill data gapsidentified in Phase I report, andto estimaterecreational 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. Theproductivity study includes only foodweb andhabitat services andfocuses on the "production" of commercial

species, andto a lesser
10

# \. \



# ~_>,

L.L.J ~ 

## Q.. 

# 8~



# T

im 

### 0 c c 

## 13_ L.L.. °~

0

I 

### 0 

# Z

0 0 °v--( °~,,-,(extent, on non-commercial values (viewingand huntingvalues). Finally, the resource surveyis used to identify public priorities and values for specific environmental amenities. Theresource survey used a contingent choice model to learn whichamenities are mostimportant to the public and the amountthat respondents indicate that they would pay to preserve or restore them. Summary information

for each studyis give in Table II. 1; eachstudyis described in detail in the chaptersand 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, R.I: Economic Analysis, Inc. Long Island Business News, 1994. Long Island Almanac1994. Ronkonkoma, NY: Long Island Business News. Suffolk CountyDepartment of Health Services, 1992. Brown Tide Comprehensive

Assessment and Management

Program.3 volumes. Suffolk County Dept. of Health Services (November).

11Table lI.1. Summaryof EAI’s Phase II Non-Market Valuation Studies for PES
Study Purpose Data and Method(s) Recreational Uses and Use Values 1. Estimate Resident, SecondHome Convenience sampling/ Owners & Visitor Recreational Uses: Intercept Survey

 ̄ Activities Self administered ~r 8 Key Activities 1,354 completedsurveys Sr 8 Locations 5 PESBays Atlantic Ocean LI & BI Sounds * 5 Towns 2. Estimate Use Value for: Travel Cost Method  ̄ Swimming Demand for swimming  ̄ Boating and recreational fishing

 ̄ 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

 ̄ Farmlandpreservation Contingent Choice  ̄ Unpolluted shellfish beds Method  ̄ Wetlands

 ̄ Eelgrass beds Wetlands Productivity Values Estimate the Economic Value for Based on Productivity Nursery and Habitat Productivity approach and results in functionsfor: available literature.  ̄ Saltmarsh  ̄ Eelgrass

 ̄ Intertidal mud

bottom Property Value Estimate the effect on Property Valuein All property sales in Town of Southhold of attributes, Town for 1996 including:

 ̄ 374 sales transactions Site Geographical ~r Lot & homesize Information System Sr Garage Special features Property Value

 ̄ Neighborhood ("Hedonic") Model Sr Mainroad

 ̄ Environmental Sr Wetlands ~" Open Space Sr Farmlands

~- Zoning Size

12III. ENVIRONMENTAL AMENITIES AND PROPERTY VALUES: A CASE STUDY OF THE TOWN OF SOUTHOLD

III.A. INTRODUCTION Property values in coastal communities depend uponmany

factors. Theseinclude 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, wewouldexpect a home located near the PES

waterfront to be morevaluable than another home, identical in all respects, but located a greater distance fromthe shore. If we could find two such homes, wecould simply compare the prices at which

they sell and, by that, calculate the value of beingnear the shore. Ofcourse, rarely are twohomes identical in all respects exceptfor one attribute. Instead, as anyone who has boughta home

knows,the price that a property commands on real estate marketsreflects a great many

factors. These

include: lot size, size of the home andits location, the characteristics of the surroundingneighborhood, and a widerange of other environmental factors. Nevertheless, if all of these importantfactors can be taken into account, it is possible to isolate the value of individual factors, much

as a real estate broker or tax assessor doeswhen 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 openspace 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

affect any of the factors studied. That is, we could estimate the potential benefits from a proposed management action, as measuredthrough changes in market prices for property. We use the Town

of Southold,located on the NorthFork 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 farmlandand openspace, 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 PeconicEstuary 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 openspace, and 18%

is classified as vacant. Lessthan 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 undeveloped

lands has far out paced population growth (Suffolk CountyDepartment of Planning 1997b). Giventhe recent rapid pace of development,the protection of open space, undeveloped
13

### O

r~



### O
0 r~ O

Oland, and other environmental amenitieswill play an importantrole in determining future quality of life in the Town

and, perhaps, in the PES

area. Theimpactsof environmental amenities on Southold

property values, and the impactsresulting from possible PES

management

actions, will dependuponthe unique characteristics of the town (and perhaps the quality of surrounding waters and land areas). Toassess these impacts, EAI conducted a property value analysis specific to the townof Southold. We apply economic

methods using the property value (or "hedonic" method) to a databasecomprised

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 openspace. Thepolicy 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 impactsonlocal property values, given the available data.

III.B. RELATIONSHIP BETWEEN ENVIRONMENTAL

AMENITIES AND PROPERTY VALUES Environmental

amenities provide valued services to residents of local communities. For example, openspace contributes, in a physical sense, to the character of local communities, while providing a widevariety of services, including scenic views, outdoor recreation, insulation fromnoise 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, makingcommunities with a high level of valuedenvironmental amenities(i.e., environmental quality) more

attractive than similar communities

without such amenities. Asa result, home buyers are willing to pay morefor land or housing

with higher levels of environmental quality. Theproperty value method (also called the hedonicmodel)can be used to estimate the impact of environmental amenities on the values of local property, thereby estimating the value of these amenitiesto 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 environmentalamenities and property values involves openspace. Many studies show

that nearbyopenspace increases property values, reflecting home buyers’ values for the services and character offered by openspace (e.g., Freeman,1993). In a recent, coordinated property value/geographic information system study conducted in Middletown,

Rhode Island, Johnston (1997) showsthat positive property values impacts of open space can range fromless than 1% to greater than 13%,

depending

on the size of nearbycontiguous openspace parcels, the total amount of openspace in a region, and the distance of property from openspace parcels. Larger property value impacts are associated with larger contiguousparcels, larger acreage of open space, and a closer proximity betweenvalued parcels and open space 14(Johnston 1997a). Similar results hold elsewhere in the New England region. A study Worchester, MA,

for example, found that houses located less than 20 feet from a community park sell for over $2,000more than similar houseslocated more than 2000feet fromthe park (More, et al., 1982). In an analysis of cluster zoningin Amherst, Massachusetts,

Lacy (1990) estimates that cluster-zonedhousing

with permanently

protected openspace appreciates at a faster rate that similar housingin conventional subdivisions. Asof 1989, this difference had created an average $17,100 difference between

the average selling price in the twotypes of developments. Comparable

results concerning

the value of openspace also have beenfoundin other coastal areas. For example, a study of land values adjacent to Chesapeake Bayshowsa positive relationship between the percentageof preservedopenspace in a 250 acre area surrounding a parcel of land, and the per-acre selling price of that land. This study also shows that agricultural pasture land andforest land increases the per acre value of nearby land, other things being the same (Bockstael 1996). Similar results are shown by Geoghegan

et al. (1995). Parsons (1992) estimates that land restrictions in the Chesapeake Baywatershed 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 housingprices are positively related to the existence of nearbybroadleaf forests. Correll et al. (1978)fred that, on average, residential housing prices in Boulder,Colorado decrease by $4.20for each additional foot of distance froma central greenway,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, andmay be greater or less than those cited above.Toassess these impacts, we conducted a property value analysis specific to the town of Southold. We apply economic methods (hedonic methods)

to a database comprised

of GIS parcel coveragedata and real estate sales data. In simpleterms, the property value model estimates correlations between property values and levels of valued environmental attributes, including openspace. Thesteps involvedand data used are describednext.

III.C. APPLICATION OF PROPERTY VALUE MODELTO 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 environmentalchanges 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 Peoplebuying

homes in effect purchasemany

site, neighborhood,

and environmental

characteristics. For example,

the characteristics of a residential propertyinclude: the size of the house,the number of baths, the squarefootageof the structure, the zoning classification of the land, whether the land
15is served by public sewersand water, the existence of wetlandson the property, and the proximity

of the property to amenities such as open space, coastlines, and farms. These and other characteristics define the property, and make it more or less attractive to potential buyers. Other things being the same, home buyers are willing to pay morefor properties that they find have desirable characteristics, and less for properties with undesirablecharacteristics. These likes and dislikes across many

prospective buyers and sellers are reflected in marketprices. The Property Value Method

attempts to estimate the portion of a property’s value related to each relevant characteristic, bythat providing an estimateof the (implicit) value of each characteristic. The model works by statistically comparing values of a 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 betweenthe market value of a property, and the characteristics of the property. The Property Value

method uses a statistical technique

called "multipleregression" to assess the impact

of each characteristic onthe marketvalue of the property. Thetechnique simultaneously compares a large number

of properties with different prices and different levels of each characteristic. The method establishes which characteristics are associated with higher values, which are associated with lowervalues, andwhich have no significant impact on values. The model also estimates the dollar magnitudeof these impacts--that is, it estimates how large an impactis likely to be causedby a specific level of a specific characteristic. Usingthis technique, the impact of different environmental amenities on nearby property values can be estimated. 4 The technical details of the property value model (or hedonic technique) are presented in Appendix A. Actual 1996sales prices are used in the analysis of Southoldproperty 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 hedortic analysis dependson a well-functioning real estate market, in which consumers have accurate information regarding all home characteristics. Thetechnique also assumes that a large number of different housingtypes are available for purchase, so that consumers can choose the "package"of housing characteristics that is mostto their liking, andcan "mixand match" different types of characteristics. Finally, the technique assumes

the availability of appropriate data, concerning all characteristics that influence property values. Bias can result fromthe application of hedonictechniquesto ill-functioning real estate markets,in whichfew different types of housing options are available, and/or in whichconsumers do not have accurate information regarding housingcharacteristics (Johnston 1997a).
16Although sales data wereavailable for 401parcels, complete GIS coveragedata exists for only 374 of these parcels. Thus,the Southold data set usedin the analysis has full information on 374parcels.

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. Theseinclude proximityto amenities such as openspace and coastlines; the size of the parcel; the size andother characteristics of the structures on the parcel; applicable use restrictions such as zoningcodes; and the existence of features such as wetlands on the parcel. Thesevariables may be grouped

into three general categories: parcel characteristics, neighborhood

characteristics, and environmentalcharacteristics. To distinguish the effects of parcel size on land values, weconsider the effect of the characteristics onper-acrevalue. This is calculated by dividing selling price by the number of acres in the parcel. 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 final analysis, see Appendix A. The statistical analysis uses the ordinary least squares (OLS) multiple regression approach.This approach estimatesthe direction, magnitude, andstatistical significance of correlations between each of a set of independent

variables (the characteristics of the property), anda single dependent variable (per-acre property value). Toestablish the nature of the specific functional relationship between these variables and assessed land values, the studyrelies onthe findings of prior research. In particular, the regressionanalysis applies a "transcendental" or "translog functional form".5 Thetranscendental form is chosen for its ability to capture realistic relationships between parcel characteristics and land values (Chicoine 1981). Such

functional formsare 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 technical description of the statistical model andits properties, see Appendix B.

III.E. ANALYSIS OF SOUTHOLD LAND VALUES: RESULTS The hedonic analysis provides

information

onthe effect of 20 characteristics on per-acre land values in Southold. Final modelresults are illustrated in Appendix C. Overall measuresof modelfit indicate high levels of correlation between land characteristics andproperty values. An R 2 value of 0.8352indicates 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 75.945indicates only a .01% chance that the set of correlations reported by the modelcould be observedthrough random chancealone. The signs

5 Wichelnsand Kline (1996), Chicoine (1981), Shonkwiler and Reynolds(1986), and Garrod and (1992).
17associated with model

variables are consistent withprior expectations andwith 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. Theseinclude the following:
openspace:

This variable identified parcels adjacent to open space land, as defined by the GIS

database. A parcel of land located next to openspacehas, on average, 12.83%higher per-acre value than a similar parcel located elsewhere. Parcels adjacent to openspace are defined as those with a border within 25 ft. (7.62 meters) of an openspace parcel. Asdefined 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; andselected large parcels of preserved farmland. farmdistance: This variable represents the distance between a specific parcel and the nearest farmland (farmland on whichagricultural crops or nursery products are grown),in meters. Forevery meterof additional distance between a parcel and the nearest farmland,average per-acre property value increases by 0.0017%

(i.e., the magnitude

of the impactis quite small). onfarm:This variable identifies parcels contiguous to farmland. A parcel of land located next to farmland has, on average, a 13.32% lowerper-acre value than a similar parcel located elsewhere. onroad: This variable identifies parcels within 20 meters of a major road (defined as Rts. 25 and 48). Aparcel of land located next to a major roadhas, on average, a 16.16%lower per-acre value than a similar parcel located elsewhere. largezone:

This variable identifies parcels located in districts zoned R-80 or R120 (two- or three-acre zoning). A parcel of land located within a district zoned R-80 or R-120has, 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. Forevery onepercentage point increase in the percent of a parcel classified as wetlands, average per-acreproperty value increases by 0.27%. (However, this variable is only significant at the 14% level.)

III.F. SIMPLE ILLUSTRATION OF PROPERTYVALUE IMPACTS: VALUE OF OPEN SPACE
18Given

the definition of the openspace variable, the impact of openspace on neighboringproperty values will differ depending on the characteristics of the surrounding property. However, to illustrate the basic implicationsof the model results for policy, the following section simulatesthe predicted property value impact of the hypothetical loss of 10 acres of open space, in a highly simplified scenario. Givenspecific locations for the openspace land under consideration, and the characteristics of the surrounding parcels, similar results could be generatedfor actual areas in Southold. According

to the model

results, properties adjacent to openspace land (or within 25 ft.) are average of 12.83%

morevaluable than those not adjacent to open space. Within the Southolddata sample,the averageparcel sales price was$213,514,with an averageparcel size of 2941.95 square meters (0.72 acre). Given this average value, a loss of 12.83% (related to the loss of hypothetical 10 acres of adjacent openspace) implies that average property value for the adjacent parcels would

decrease by $27,394, all else held constant. Toestimate a hypothetical total impact in a simplecase, assume

that the lost openspace was ten acres in a square shape, surrounded by parcels of average 0.72 acre size (the averagein our sample), also square in shape. Assuming a square shapefor the openspace parcel, it would have a perimeter of 805.2 meters that would

be bordered by other parcels. Givenan average per-parcel area of 2941.95square 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,

weassume that 15 average

parcels would

be affected by the loss, or development,

of our square ten acres of openspace. Asstated previously, development of the openspace 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 averageparcel 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 averageloss of $41,090 in total property value per-acre of openspace lost. Looked at another way,the estimated benefits of preservingthe ten acres of openspace thus would be $410,907,

as this is the increase in nearby property value associated with this openspace parcel. For this illustration we do not compare costs with benefits since wedo not have information on the costs of acquiring specific undeveloped property for openspace. However, in the illustration, if the 10 acres of undeveloped

property for openspace could be acquired for less than $410,907 or $41,097 per acre, then the benefits would be greater than costs. Note that evenif the propertyto be acquired for openspace costs more

than $410,907,benefits still may exceed costs. This is becausenot all of the public benefits of openspace are captured in our hedonicanalysis. For example, our results in this

19section do not capturegeneralamenity 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

shouldnot be regarded

as exact results expected

in any single instance, and are included for illustration purposes only. Afull benefit-cost analysis would require much more detailed analysis of the proposed project and its impacts, including impactson surrounding propertyvalues. However,

the illustrated results doindicate the generaltypes of benefits andcosts that maybe 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 openspace preserved. III.G. LIMITATIONS IN PROPERTYVALUEANALYSIS Asnoted, the model

providesa 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. Neverthelessseveral potential limitations shouldbe noted. Themost importantof these limitations is that the results of the statistical analysis are sensitive to characteristics of the data set. The combined property card-GISdatabase 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 PeconicEstuary, conducted at numerous ites. However,

no parcels in the 1996

sales databaseare located within 100meters of the PeconicEstuary. Thus, for those parcels in the current database, water quality is not expectedto have an impact. However,

weredata to exist for parcels close to the estuary, onewould 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 beachfront properties); and proximity to creeks, all of which would be expectedto affect propertyvalues. If in fact important variables are excluded

from the model,

statistical results may showupward 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 omittedvariables are uncorrelated with the environmental policy variables of interest, then their absence should not influence reportedresults.) For all of the abovereasons, the estimatedProperty Valuemodel results should not be interpreted as "exact". Rather, they provide the best possible estimate, given available data, of the impactsof existing environmental

attributes on current per-acre land values. Asecondlimitation is that increased property values, related to improved environmental

quality, may not be favored by all residents. For example, residents may

resist openspace preservation on

6In this regard, wenote that recent support for an openspaceinitiative in Suffolk County suggeststhat the electorate has a strong preference for openspace as a public good.20the grounds that it tends to increase property values, thereby increasing the costs of housingfor younghome

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 openspace on the basis that fewerhousing units are available, despite the act that average property valuesin the area affected will be higherwith the additional open space. Such arguments should be acknowledged when considering environmental policy changesusing the results of property value methods. Finally, wenote that many people beyond

the immediate

area may derive benefits from open space but their benefits are not included in numbers

givenin the above

illustration. For example, residents of the townwho

live outside of the grid used in the illustration may value openspace elsewhere in town (see ChapterV). III.H. SUMMARY Environmental

amenitiesincrease property values of nearbyparcels, reflecting the valuableservices offered by these amenities. An analysis of Southold

property values supports this contention, and corresponds

with the results of prior studies. In Southold,existing openspace 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 farmsand majorroads. 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 havesignificant impactson property
~. values, reflecting the influenceof local environmental amenitiesonlocal quality of life.

III.I REFERENCES Bockstael, N.E. 1996. "Modeling Economics and Ecology: The Importance of a Spatial Perspective," American

Journal of Agricultural Economics 78:1168-1180. Chicoine, D. L., 1981. Farmland Values at the Urban

Fringe: AnAnalysis of Sales Prices." Land Economics57(3): 353-362. Correll, M., J. Lillydahl and L. Singell, 1978. "TheEffects of Greenbelts on Property Values:Some Findings on the Political Economy of OpenSpace." LandEconomics 54: 207-217. Des Rosiers, F. and M. Theriault. 1992. "Integrating Geographic Information Systemsto Hedonic Price Modeling:

AnApplication 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 NortheasternAgricultural andResourceEconomics Association (April).

21Freeman, 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:Resources for the Future. Garrod, G. and K. Willis, 1992. "The Environmental Economic Impact of a Woodland:

A TwoStage 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 ResourcePolicy. Geoghegan,

J., N. Bockstael, and L. Wainger. 1995. "Spatial LandscapeIndices in a Hedonic Framework: An Ecological Economics Analysis Using GIS." presented at the annual meetings of the Association of Environmental and ResourceEconomics,

Indianapolis, IN. Hammer, T.R., R.E. Coughlin and E.T. Hom IV. 1974. Research Report: The Effect of a Large Park on Real Estate Values. Journalof the American Institute of Planners, July. Johnston, R.J. 1997a Aquidneck Island and OpenSpace: An Economic Perspective: Technical Manual Narragansett, R,I: 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 EnvironmentalSciences. Lacy, J. 1990. An Examinationof MarketAppreciation for Clustered Housingwith Permanently Protected OpenSpace. 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 Economicsof Urban Parks." Parks and Recreation.
22National Park Service, 1995. The Economic Impacts of Protection Rivers, Trails, and Greenway Corridors. Washington,D.C.: Rivers, Trails, and ConservationAssistance Program. Nelson, A, C., 1985. "A Unifying View of Greenbelt Influences on Regional Land Values and Implications for Regional Planning Policy." Growth and Change16(2): 43-48. Parsons, G.R., 1992. "The Effect of Coastal Land Use Restrictions on Housing Prices: A Repeat Sale Analysis." Jour. of Environmental Economicsand 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. SAS User’s Guide: Statistics. Cary: NC:SAS Institute Inc. Shonkwiler, J.S. and J.E Reynolds, 1986. "A Note on the Use of Hedonic Models in the Analysis of LandPrices at the UrbanFringe." LandEconomics 62(1). Wichelns,

D. and J. Kline, 1993. "TheImpactof Parcel Characteristics on the Cost of Development Rights to Farmland". Agric. and Resource Economics Review: 150-158.

23APPENDIX A. Variables in the Hedonic Analysis All model

variables were generated fromone of two sources: 1] Official SoutholdProperty Record Cards for all parcels sold during 1996; 2] GIScoverages maintained Suffolk County Planning Department, updated 1995. Dependent

Variable: LVALACRE: Thenatural log of per-acre sales price, in dollars. Based on Southold

property record cards for properties sold during 1996. IndependentVariables: 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 not located on a waterfront. Value assigned based on waterfront classification of propertyrecord 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 zonedR80 or R-120;

value of zero assignedto all other zoningclassifications. YEAR: year the housewasbuilt, based on property records, minus1950. YEARSQ: YEAR

* YEAR (year squared). GARSQFT: the number of square feet in the garage, based on property records. If no garageexists, value of 0 is assigned. PATIO: dummy

variable: value of 1 assigned if the househas a patio or deck; value of 0 assigned

if there is no patio or deck, basedon propertyrecords. 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 househas heat, basedon property records.

24FIRE: dummy variable: value of 1 assigned if the house has a fireplace or woodbuming

stove; value of 0 assigned if there is no fireplace or woodburning

stove, based on property records. SQFT: square footage of the house, basedon property records. SPECIAL: dummy

variable: value of 1 assigned if the house has a "special" feature (tennis court, swimming

pool, etc.); value of 0 assigned if no special features are present, basedon property records. DSOUNDT: Thelinear distance to Long Island Sound,

truncated at 800meters (i.e., the maximum possible value is 800). Based on GIScoverages. PECON 100: dummy

variable: value of 1 assignedif the parcel is within 100meters of the Peconic Estuary;value of zero assigned if the parcel is further than 100linear meters from the estuary. Basedon GIScoverages. SOUND100: dummy

variable: value of 1 assigned if the parcel is within 100 meters of Long Island Sound;

value of zero assigned if the parcel is further than 100 linear meters from the Sound. Basedon GIS coverages. ONROAD: dummy

variable: value of 1 assigned if the parcel is within 20 meters of a mainroad (Rts. 25 and48); value of 0 assignedif the parcel is further than 20 meters from a main road. Based on GIS coverages.
FARMDISTANCE:

the linear distance betweenthe parcel and the nearest farmland (farmland), in meters. Based on GIScoverages. ONFARM: dummy

variable: value of 1 assignedif the parcel is contiguous (distance=0) to farmland; value of 0 assignedif the parcel is further than 0 metersfrom farmland. Based on GIS coverages. WETLAND: indicates the percentageof the property coveredby freshwater wetlands. OPENSPACE: dummy

variable: value of 1 assigned if the parcel is adjacent to openspace (whereadjacent is defined as a parcel whose border is within 25 feet of an openspace parcel); value of 0 assignedif the parcel is further than 25 feet from open space. Based on GIS coverages. TheSoutholdGIS

coverage classifies the followingland uses as openspace: fish, game,

and wildlife preserves; public golf courses; private golf courses and country clubs; improved beaches; camps

and camping

facilities; parks; nature trails and bike paths; cemeteries; private huntingand fishing clubs;

25state owned forest land; reforestedlandand otherconservation

land; public parks; otherwildor conservation lands; and

taxablestate owned

conservation easements. Farms and undeveloped

private property,

in general, are not considered open space. Mean values of variables includedinthe analysis, for the 374 observations in the database,are illustrated below. Table B.1 Mean

Values of Model Variables
Variable MeanValue
LVALACRE 12.8362 LNACRE -0.7521 WATFRONT 0.2340 BULKHEAD 0.0718 PORCH 0.5266 YEAR 16.7500 GARSQFT 265.1861 PATIO 0.4734 BATHS 1.6303 NOHEAT 0.1197 FIRE 0.5372 SQFT 1270.3200 SPECIAL 0.1064 PECON100 0.0053 SOUND 100 0.0848 ONROAD 0.0585 ONFARM 0.0452 DFARM 2395.1400 OPENSPACE 0.050398 WETLAND 2.4548 LARGEZONE 0.1011

26GIS Maps Used to Define ModelVariables: Examples Many of the variables in the analysis were derivedfromGIS

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 existenceof agricultural land within1 O0

meters of each parcelin the sales database.

Long Island Sold /

ii " i

27Thefollowing GIS map covers the same

area of Southold,

yet illustrates the distribution of wetlands, used to assess the percentageof each sales parcel coveredby freshwater wetlands.

28APPENDIX B. The Statistical Model Toestablish the functional (mathematical) relationship between

model variables and assessed land values, the analysis onthe findings of prior research. In particular, the analysis followsWichelns and Kline (1996), Chicoine(1981), Shonkwiler and Reynolds

(1986), and Garrod

and Willis applying a transcendental

or translog functional form to the propertyprice - characteristics equation
Vi = 130Xu~lexp[~]13jXij]

j=2...n where V i is the 1996

selling price of the ith parcel, in dollars per-acre, Xit is parcel size in acres, and Xij are measures

of the j = 2...n other characteristics that affect land value. The 13’s are parameters

to be estimated, andrepresent the effects of eachof the characteristics on per-acre land price. Estimated

parameters

are usedto calculate the effects of different openspacecharacteristics on the value of land in Southold. The transcendental form is chosenfor its ability to capture realistic relationships betweenparcel characteristics and land values (Chicoine 1981). For example,the transcendental formpermits a positive or negative marginal relationship between value and parcel size (acres), requires value to be zero when parcel size is zero, can detect proportionalvalue-size relationships, andallows for increasing or decreasing returns to scale for land parcel characteristics (Chicoine 1981; Wichelns and Kline 1993). Moreover, functional forms such as the semi-logor transcendental are preferred (to the more flexible functional forms)when there is potential omitted variable bias (Garrodand Willis 1992). Theempirical version of the translog model is as follows:
In(V) = ln130 +131ln(X1) + 2 +... +[3nX n +8 where the variables are as definedabove,andIn(.) represents the natural log. This is the form used for statistical analysis in the Southold propertyvalue study. Theestimatedtrans-log model appearsto fit the data well. Overallmodel statistics and individual variable significancelevels indicate good model fit, andtrials with alternate functional forms (e.g., linear, semi-log) indicate that these alternate formsresult in lower significance levels and explanatory power. A Whitetest for heteroskedasticity (White 1980) fails to reject the null hypothesisof homoskedasticity at the 5% level. Although

multicoUinearity

is ubiquitousin hedonic models, tests for multicollinearity indicate that it doesnot havesevere impactson model results in the present application. Asthe data is cross-sectional anddata order has beenrandomized, tests for autocorrelation are unnecessary.

29APPENDIX C. OLSModel Results
Dependent Variable: LVALACRE

Sum of Source DF Squares Mean Square

Model 20 152.03723 Error 355 30.00860 C Total 375 182.04583 7.60186 0.08453

Root MSE 0.29074 R-square Dep Mean 12.83621 Adj R-sq C.V. 2.26502

Variable
INTERCEP LNACRE WATFRONT BULKHEAD YEARSQ YEAR GARSQFT PATIO BATHS NOHEAT FIRE SOFT SPECIAL SOUND100 PECON100 ONROAD FARMDISTANCE ONFARM WETLAND LARGEZONE OPENSPACE Parameter Estimates

Parameter Standard Estimate Error 11.409748 0.08174613 -0.905956 0.02606823 0.333735 0.04497479 0.200245 0.06780928 0.000302 0.00011945 -0.012017 0.00484814 0.000165 0.00006797 0.103269 0.03166099 0.109587 0.02678655 -0.099710 0.04189498 0.117428 0.03516537 0.000264 0.00004262 0.123980 0.05349404 0.294474 0.07076478 0.671739 0.30810728 -0.176308 0.06582347 0.000017320 0.00000374 -0.142932 0.07661228 0.002729 0.00181653 0.154045 0.06636055 0.120691 0.07154488 F Value Prob>F 89.930

0.8352 0.8259

T for H0: Parameter=0 139.575 -34.753 7.420 2.953 2.528 -2.479 2.428 3.262 4.091 -2.380 3.339 6.193 2.318 4.161 2.180 -2.678 4.625 -1.866 1.502 2.321 1.687 Prob> ITI

0.0001 0.0001 0.0001 0.0034 0.0119 0.0136 0.0157 0.0012 0.0001 0.0178 0.0009 0.0001 0.0210 0.0001 0.0299 0.0077 0.0001 0.0629 0.1339 0.0208 0.0925
30IV. OUTDOOR RECREATIONAL USES AND USE VALUES IN THE PES

IV.A. INTRODUCTION This chapter presents results of EAI’sstudy of outdoor recreational uses and use values for the PeconicEstuary System

("PES"). Theinformationpresentedincludes: (1) a reviewof participation and location choicefor keyrecreational uses, and(2) estimatesof the economic value of those uses. Also presentedis a summary of expendituresat PES

farmstands, wineries, and rental housing, data that wasidentified as a gap in EAI’s Phase I study andis of interest to decisionmakers. The main tool for providing

information

for this analysis is a recreational use surveycarried out in August, 1995. The recreational survey was designed to: identify key outdoor

recreational activities andlocations in the PES estimatethe level of participation in keyrecreational activities identify key characteristics andpreferencesof users estimate the value of recreational uses of the PES fill data gapsidentified in the Phase I study. Key recreational uses and locations. More than 15 recreational uses of the PES were identified through focus groupsand discussions with local citizens and officials. One key component

of the recreational use surveyis to assess where these recreational uses take place throughout the PES. Participation in key recreational uses. EAI’sPhase I study (Diamantides and Grigalunas, 1996) estimated participation in only a fewkey 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 PhaseII recreational surveyis designed to fill these data gaps. Characteristics andpreferences of users. Prior to the recreational use survey, only anecdotal information existed concerningrecreational users of the PES. Information, such as residency, accommodations,

recreational preferences, etc., are provided by EAI’s PhaseII recreational survey. Recreational

use values. Datafrom

the PhaseII recreational surveyis usedto estimate the economic value of key recreational activities in the PES and, when

possible, to estimate the changein recreational value resulting fromchanges in water quality. PhaseI economic

impact data gaps. Keydata gaps identified in the Phase I study include the economicimpact of specialized sectors of the local economy. These sectors include: summer rentals, winery

tours andsales, and farmstand sales. Each of these sectors wasidentified by local citizens and officials as playing an important, but unquantified role, in the local economy. Economic data for these sectors--sales-- is presentedin this report.

31IV.B. IDENTIFICATION OF RECREATIONAL USES AND LOCATIONS

Outdoor recreation is a majoractivity in the PES.Based on recreational uses identified during the survey development

process (see Sec. C, SurveyDevelopment), the survey questionnaire included 16 majornatural resource-based recreational uses. Ofthese, eight key recreational activities were identified:

 ̄ Fishing  ̄ Beach Use

 ̄ Boating  ̄ Bird Watching

 ̄ Swimming  ̄ Wildlife Viewing

 ̄ Shell fishing  ̄ Hunting Datawere collected for these eight key recreational uses, includingannualparticipation duringthe past year. Detailed information also wasobtained on these activities for the respondent’s most recent recreational outing to the East End. Annual participation data on keyrecreational activities include the number of times the respondent had done each activity at specific East End locations during that year (1995). The surveyinstrument identified eight water bodies as potential locations for fishing, boating, swimming, and Shell fishing. Theeight locations identified by water bodyare:

 ̄ Flanders Bay  ̄ Gardiners Bay

 ̄ Great Peconic Bay  ̄ Block Island Sound

 ̄ Little PeconicBay  ̄ Long Island Sound

 ̄ Shelter Island Sound  ̄ Atlantic Ocean. Locations for the remaining

keyrecreational activities (beachuse, bird watching,wildlife viewing, and hunting) werecategorized by the townin which the activity took place. Five East Endtowns wereidentified by the surveyinstrument. Thefive recreation locations identified by town are:

 ̄ East Hampton  ̄ Southampton.

 ̄ Riverhead  ̄ Southold.

 ̄ Shelter Island Single-day data on key recreational activities include information on activities the respondent participated in during their mostrecent recreation day at the East End.This question wasfollowed by occasion-specific, write-in questionsconcerning the: (1) location of the activity, (2) perceived water quality, (3) number of peoplein the recreation party, (4) travel time, and(5) time on Otheractivity-specific data collected included fishing catch, shellfish harvest, choiceof marina or ramp,perceptionof beachfacility quality, and number of birds or type of wildlife sighted 7.

7Hunting wasnot included in the occasion-specific questioning, due to the time of year and anticipated low participation. 32IV.C. SURVEY DEVELOPMENT AND IMPLEMENTATION

Thedevelopment

and implementation

of the recreational use survey parallels the development and implementation

of the resource survey. Meetings,interviews, and focus groups wereinstrumental in the development

of both surveys. Similarly, implementation of the two surveys occuredat the same time and places using the same survey staff. Onlysignificant aspects of the development of the recreational survey that differ fromthe resource survey will be discussed here. For a full discussion of the resource survey development and implementation

including similarities with the recreational survey (see ChapterVI, Resource ValueSurvey). Amajorchallengein the development of the recreational use surveywasthe creation of a question format. Asingle instrumentwasselected to cover all the majorrecreational uses and locations of the PES. Thesingle-instnunent format allowed collection of data for multiple current andpast uses andwas a much easier, andless costly, method for acquiringdata, an important issue givenrealistic limits onbudgets

for this project. Questionnaire development began with meetings with the Management Committee,the Citizen’s advisory group, and "expert informants" from major stakeholder groups, such as marinaoperators and Baymen.

Basedon 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 surveypresentation. Informalinterviews with the public werea critical component of questionnaire development. From these interviewswegainedinsight into who the users are andtheir perspectiveon 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 Endor individual bays within the PES as geographic

entities. Thequestionnaire, therefore, asks about recreational uses at locations at the East End with which

individuals could easily identify. Focus groups and pretests were

usedto hone

the questionnaire

down to an efficient presentation that would allow us to collect all the required data. For example,one issue weused focus groups to refine wasthe questionnaire’s approach to eliciting water quality perceptions. Originally wehad hoped to use the questionnaireto ask very specific waterquality questions, such as clarity, smell, debris, etc., so that we

might be able to estimatethe contribution of these different attributes to an individual’s overall perception of water quality. However, during focus groups wefound 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 moresimplified approachwaseventually used. Thesimplified approach asks the respondentto rank water quality at the site on a four point scale from "excellent" to "poor". Asis describedbelow,this approach worked satisfactorily in that wefounda statistical association between respondents’subjective estimates of water quality and objective measures of water quality based on field sampling

33undertaken by the County.

A key objective in the survey development phasewasto 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. Oneof our concerns was that if the questionnaire was too long, only the first few questions would be answered,or respondentswould 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. Thefinal version of the recreational use surveyquestionnaire is available from

the authors. Therecreational use survey and the resource use survey were implemented simultaneouslyduring the week of August 22-29, 1995,in pre-selected, public places in each of the five East End towns. Interviewers solicited potential respondentsby simply approaching themand asking if they would be willing to spend 10-15minutes filling out a surveyto be usedto help developa plan to protect and manage

the bays. Survey

locations included beaches, shoppingareas, libraries, Post Offices, and miscellaneouspublic places throughoutthe 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, windsurfing, art work, and photography. Questions on these activities were retained in the questionnairefor comprehensiveness. However, these activities are considered less significant than the keyactivities identified above,anddata collected on these "non-key" activities are limited to a single questionon participation.

IV.D. DESCRIPTION OF THE RECREATIONAL USE SURVEY QUESTIONNAIRE Thesurvey booklet cover provides a map of the East Endto orient respondents. Thefirst 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 transportationthey usedto 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 aroundthe PES due to an algae that sporadically grows in enormous

numbers. Recent incidents of BT

may havecontributed to a significant decline in ecologically productiveeelgrass beds and caused major disruptions in the formerly valuable scallop industry. BTalso was a significant concem

to recreationists, as describedin some detail later. Throughoutthe survey developmentprocess, the issue of BTrepeatedly surfaced. Questions concerning

the BT were placed in the beginning

of the questionnaire in part to diffuse this issue

34early on, allowingrespondents to focus on the questions that follow. We askedrespondents

whether they wereaware

of BT and which,if any, of their recreational activities wereaffected by BT. The next set of questionsaskedrespondents to indicate their annualparticipation in keyrecreational activities byfilling out twobrief charts. One chart identified recreationlocations bywater body.The other chart identified recreation locations by town. Respondents indicated the number

of times that they did each activity in eachlocation duringthe past year (1995). The annual participation questions werefollowed by single-day recreation activity questions. Respondents

wereaskedto identify their most recent recreation dayat the east End. They then were asked to identify all the recreational activities participated during that day. Shell fishing, swimming andbeach use, boat use, fishing, bird watching, andwildlife viewing

were selected as keyactivities. 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 peoplein the party, catch or harvest, and waterquality andsite quality ratings. After the participation questions, respondents wereasked about selected expendituresmade at the East End during the past twelvemonths. As noted, these questions weredesignedto fill data gaps in the Phase I study for farm stands, wineries, and housing rentals. Residents and second home ownerswere also asked a referendum-typequestion about their willingness to support selected water quality initiatives (see section on water quality initiative referendum). Finally, a. set

.... questions

was askedin order to obtain socio-demographic data, suchas residency,household status, income, age, education, and employment status.

IV.E. DESCRIPTIVE RESULTS: DEMOGRAPHICS A total of 1,354respondents providedusable surveyquestionnaires, althoughnot every respondent answeredall questions. Selected, major demographic results of the survey are summarized as follows: Residents and secondhomeowners fromall East Endtownswere represented in the sample A majority (59 %) of respondents werevisitors to the East End Most visitors (76%) came from either Long

Island or New

YorkCity Most visitors (60 %) stayed at the East Endovernight The most common

overnight accommodation was with friends or relatives (39%) Compared to 1990 Census

data, the samplehas a slightly higher share of women and people in the middle

age groups;is better educated;andis wealthier than the resident population. Residency Status. Of the 1,267 respondents who provided this information, over half of those responding(59%)

werevisitors fromoutside the East End(Table IV. 1). Of the remainder, 28% indicated that their primaryresidence was at one of the five East End towns, and 13%

owned

35a second home at the East End, but had a primary residence somewhere else. Some

Brookhaven residents consideredthemselves East End residents but werenot countedas such in this study. Residents and Second Home Owners- Place of Residency. Residents and second home ownersfromeach of the five East Endtownswere represented in the survey sample. Southampton (30%) and Southold(28%)

had the largest percentages of resident respondents. Theremainder closely distributed among Shelter Island (16%), East Hampton (14%), and Riverhead(12%). relatively highproportion of ShelterIsland 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 all Respondents East End Residents 358 28% Second HomeOwners 159 13% Visitors 750 59% Total 1267 100%

For second home

owners, New

YorkCity 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 Westchesterand Rockland counties in New

Yorkand northeastern New

Jersey, were the primaryresidences of the remaining 22%. Visitors -Place of Residency.Visitors to the East End predominately

came fromLong Island and New York City. 40%

of visitors camefrom Suffolk county, 20% camefrom New

York City, and 16% from Nassau county. Only 8%camefrom other locations in New York State and New Jersey, and just 5%

came from Connecticut and Massachusetts. Other locations, such as Florida, Califomia, and several foreign countries accountedfor the primaryresidences of the remaining 11%. Visitor Classification and Accommodations. Most visitors in our sample (60%)were ovemight visitors. Daytrippers (those who returned to a home

outside the East Endat 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 (22%) accommodations

were closely matched. Surprisingly, 15% of overnight visitors to the East End

36reported 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 censusdata. Gender,age, education, and income characteristics of surveyrespondents can be compared

to the characteristics of the resident population as identified by the most recent (1990) census. Only comparisonsto the resident populationare possible, since characteristics of the second homeowner and visitor populationsare unknown. For all residency

and visitor categories, the share of female respondents

was only slightly more

than in the resident population. Theage distribution of the samplereflects the survey implementation practice of not includingindividuals less than 18 years of age. Our survey sampleis much more educated than the resident population. For example,census data indicates that 49%

of the resident populationhadsome level of post-secondary

education. The

same level of education was achieved by 80% of the resident sample, 95% of second homeowners,

91% of overnightvisitors, and 82% of day trippers. The survey samplealso has higher household incomes than the resident population. According to the census, 34%

of the resident population has a household incomeof $50,000 or more. However, nearly half(48%)the residents in the survey samplehave householdincomes of $50,000or more, as do 80%

of second homeowners,

64% of overnight visitors, and 59% of day trippers. Sample characteristics: comparison among resident andvisitor categories. The distribution among female and malerespondentsslightly favored females in all categories. Day trippers have the most respondents

under24 years of age (10%),and overnightvisitors havethe mostrespondents in the 25-44 age group (60%). Second homeowners

have the largest percentage of 45-74year olds (62%). Second

homeowners

are also the most highly educated (95% post-secondary schooling) have the highest incomes (80% above $50,000 household income). Samplecharacteristics: 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 populationstatistics. Residents were under represented in Riverhead and Southampton. Second homeowners were under represented slightly in East Hampton and also in Southampton.

This disproportionate distribution of the sample mostlikely has little, if any, effect on surveyresults since the type andfrequency of recreational activity is not strongly related to the East End town the respondent

lives in.

37Table IV.2 - Population Demographics vs. Survey Respondent Demographics
Second Overnight 1990 Resident Homewner Visitor Day Population Sample Sample Sample 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%

38Table 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% HSGrad. 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%

39IV.F. DESCRIPTIVE RESULTS: KEY RECREATIONACTIVITY PROFILE Respondents

were asked the number

of times that they performed each of the following outdoor recreational activities at various East End locations (Baysand towns) this year (1995): Activities: Locations: Fishing, Boating, Swimming, Shell fishing Flanders Bay, Great PeconicBay, Little PeconicBay, Shelter Island Sound, Gardiners Bay, BlockIsland Sound, Long Island Sound, and Atlantic Ocean Activities: Locations: BeachUse, Bird Watching, Wildlife Viewing, and Hunting

East Hampton,

Riverhead, Shelter Island, Southampton, and Southold. Thewater bodies of the PeconicEstuary System are identified as Flanders Bay, Great PeconicBay, Little PeconicBay, Shelter Island Sound, and GardinersBay. Substitute water bodies included in the survey were Long

Island Sound,Block Island Sound,and the Atlantic Ocean. Swimming andbeachuse 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 wasnecessaryto ask for location by both water body and town. Figures for swimming

and beachuse should not be summed. This is becausethe activities are not mutually exclusive, and it is expected that most respondents gave a positive answer to both categories eventhoughthey may be referring to a single outing. Major characteristics of respondents’annualrecreational activity during the year are: Most (83%) engaged in at least one keyrecreational activity, half in more than oneactivity. Most residents (95 %)participated in key recreational activities Swimming was the most popular activity The PESwas the most popular swimming location (42%

of all swimming

trips) Boating, fishing, and Shell fishing werepredominantly done in the PES. SampleParticipation Overall. Mostrespondents (83%) engagedin 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 48,400outdoor

recreational experiences during the past year. Of this number,morethan 26,000 were water-basedoutdoor recreational experiences, and the majority of these (15,506) werein the PES.Land-based outdoorrecreational experiences the five East Endtowns amounted to 22,300. SampleParticipation By Activity. Among all respondents, swimming and beach use were the most popular activities. Swimming was done on morethan 15,000 occasions by respondents. The mostpopular area for swimming was the PES, whichaccountedfor 42% of all swimming

trips.

40More

than 12,000beachvisits at the five East End townswere reported. Shell fishing wasthe least frequent water- basedactivity (1,304 trips). More than half (59%) these Shell fishing trips took place in the PES. Hunting wasthe least popularland-basedactivity (255 occasions). Notethat bird watchingand 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 wasnot included. Table IV.3 - Participation in Key RecreationActivities - 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%) Beachuse 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 watchingand wildlife viewing trips less than one mile from home. Participation Rate Overall By Residency Status. East Endresidents are morelikely to participate in outdoor

recreation than anyother group. Fully 95% of the residents surveyed engaged in at least one, keywater-based activity in the PES

or one keyland-based

activity in oneof the East End towns. By comparison, 83%of second homeowners and 67%of overnight visitors also participated in outdoorrecreation. Day trippers have the lowest rate of participation (43%). A direct comparison between the numberof people engaging in land-based and water-based activities cannot

be made. Thisis dueto the inability to tell the differencebetween beach use in the 41PES

and beachuse 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 KeyRecreational Activities-By Residency Status. Proportionately more residents (70%)

and secondhomeowners

(65%) swim,boat, fish, and shellfish than overnight visitors (45%)

andday trippers (22%).

Among these activities, the highest participation rates 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%) compared with the overall sample(only 7 %). Residents and secondhomeowners are morelikely to go to the beach, watch birds, viewwildlife, and hunt in the East Endthan other groups. Proportionately moreresidents (85%)and second homeowners

(70%) engagein these activities than ovemight visitors (59%)

and day trippers (37%). Among these activities, the highest participation rates across all groupsare for beachuse (57%) followed by wildlife viewing(21%) and bird watching(19%). Only2% of survey respondentshunt. Location of KeyRecreational Activities. Respondents used the PES

more times (15,506 occasions)

for all keyoutdoor

recreation activities--swimming, boating, fishing, andShell fishing-- than any other water bodyat 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 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

( 100% ) (100%) ( 100% * Percentages may

not sumprecisely due to rounding 15404 26060 (100%) (100%)

No single townis 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
42Island was the mostused for bird watching (567) and hunting (92), and East Hampton had the (1123) wildlife viewing experiences. Onthe other hand, Riverhead was the least used townfor beach use (1075), bird watching (68), and wildlife viewing (220). Southampton had the 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%) 1419 (7%) Shelter Island 2461 (20%) 567 (37%) 1030 (27%) 92 (36%) 4150 (25%) Southampton 3334 (27%) 386 (25%) 690 (18%) 26 (10%) 4436 (24%) Southold 2239 (18%) 215 (14%) 764 (20%) 31 (12%) 3249 (18%) Totals 12248 1536 3827 255 17866(100)

(100%) (100%) (100%) (100%) i *Percentages may

not sumprecisely due to rounding

Location ofPESWater-BasedActivities. Great Peconic Bayis the most popular water body in the PES

for recreational activity, with 28% of recreational trips in the PES, while FlandersBay is the least frequently used, with 8%.Great PeconicBay is also the mostpopular location in the PES for swimming

(30%), fishing (29%), and boating (25%). Gardiners Bayis the most popular PES location for Shell fishing, accounting for 33% of all PES

Shell fishing trips.

43Table IV. 6 - PES KeyRecreational Activity Outings - (n=1267)

Flanders Bay Gt. Peconic Bay Ltl. Peconic Bay Shelter Isl. Sound Gardiners Bay Totals Fishing Shell Boating Swimming Totals fishing
288 (11%) 45 (6%) 562 (10%) 366 (6%) 1261
793 (29%) 199 (26%) 1362 (25%) 1991 (30%) 4345
486 (18%) 109 (14%) 1021 (19%) 1298 (20%) 2914

511 (19%) 158 (21%) 1321 (24%) 1410 (22%) 3400
654 (24%) 255 (33%) 1204 (22%) 1473 (23%) 3586 (23%) 2732 766 5470 6538 15506 (100%) (100%) (100%) (100%) (100%) * Percentages may

not sumprecisely due to rounding PES KeyRecreationalActivities by Residency Status. Swimming

is the mostpopular waterbasedactivity in the PES for all residency groupsand overall accountsfor 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 outdoorrecreation activity) than any other group. Day trippers allocate a larger percentage of their PES

trips to Shell fishing (16%) than to fishing (9%), althoughShell fishing overall is a modest activity (5%

of PES all trips). Table IV.7 - PES

KeyRecreational Activity Outings by ResidencyStatus - (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 sumprecisely due to rounding
44Locations of PES Activities - ByResidency Status. For residents, Shelter Island Sound (27%) is the mostpopular location in the PES,followedclosely by Great PeconicBay(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. Overnight visitors and day trippers prefer Great PeconicBay(30%

and 34%,respectively) over all other locations. FlandersBay

is by far the least often visited location for residents (7%), second home owners (3%), and overnight visitors (10%).The least visited location by day trippers is Gardiners Bay(13%), presumably

becauseit is the furthest of the bays fromtheir homes. Flanders Bay

is more popular with day trippers (17%)

than with any other group. Table IV. 8 - PES

KeyRecreational Activity Locations by Residency Status (n=1217)
Residents SHO Overnight Day tripper Total Flanders Bay 451 (7%) 114 (3%) 446 (10%) 54(17°,/o) 1065 (7%) Gt. Peconic Bay 1589 (25%) 1048 (30%) 1304 (30%) 110 (34%) 4051 (28%) Ltl. Peconic 1260 (20%) 740 (21%) 736 (17%) 60 (19%) 2796 (19%) Bay Shelter Isl. Sound 1752 (27%) 525 (15%) 1031 (24%) 58 (18%) 3366 (23%) Gardiners Bay 1332 (21%) 1079 (31%) 811 (19%) 42 (13%) 3264 (22%) Total 6384 3506 4328 324 14542

(100%) (100%) (100%) (100%) (100%) * Percentages may

not sumprecisely due to rounding

IV.G. DESCRIPTIVE RESULTS: PARTICIPATIONAND TRIP ESTIMATES IntroductionThetotal 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 engagein an recreation activity. Thus, we have: Trips = No. of Potential Participants x Percent Participating x No. of Times Participate
45For example, assume that there are 2 million potential participants (i.e., residents, secondhome owners, over night visitors, and day trippers) for swimming, that 20 % of these actually swim,

and that of these participants, each swims an averageof 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 Thedata andassumptions

usedto estimatetrips for eachPES recreation activity is explained below. Estimationof Total Number of Trips. Thepopulation

of potential participants include all East End residents, second

homeowners,

overnight visitors, and day trippers. To estimate this population, we used 1990 census data from the LongIsland Regional Planning Board ("LIRPB"). The LIRPB analysis provides

estimates of the year-round resident population;the seasonalpopulation,including second homeowners

and guests; year-round resident guests; and hotel, motel, and campsite capacities. Thenumber

of participants in each recreational activity wasestimated separately for each of these residency category. The resident population over age 15 is estimated by the LIRPB to be 84,871. Dataare not available for the age breakdown of the seasonalpopulation. Lacking authoritative data, weassume

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. Theseasonal population is split into two groups, basedon EAI’srecreational use survey results: SecondHome

Owners (27%) and Overnight Visitors (73%). Our survey results indicate that two components

of the seasonal populationhave 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. Theabsence of this information

will slightly understate outdoor recreation participation. Information concerningthe percent of potential participants that participates in one or more recreational activity waspresentedearlier. Thetrips perparticipant are givenin TableIV.9. The major results of estimated annual participation in PES outdoor recreation (excluding day trippers 8) for 1995 can be summarized as follows: ~:
8Day tripper are excludedbecausewehave no reliable way of estimating the total number in this group. This omission slightly understates total participation in PES recreation (see text). 46127,762 peopleparticipate in either swimming, boating, fishing, or Shell fishing in the PES, taking 3.3 million outings. 156,184 peopleparticipated in either beach use, bird watching,

wildlife viewing,or hunting in the East End

towns, engaging

in 5.2 million trips.

Table IV.9 - Average

Annual Outings per Participant b~’ Activit~ and Residence* Residents SHO Overnight Daytrippers 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 Endtowns and PES water bodies only

The total number

of trips by each group,for each activity, is estimatedby multiplying the estimated number of trips per participant (Table IV.9) for each group (i.e., residents, second homeowners, etc.) by the estimatedtotal number of participants by group.Theresults are shown in TableIV. 10. Overall, there are 5.15 million beachuse, 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 residents, who

engaged in 3.3 million land-based and 1.5 million water-basedoccasions locally. Secondhomeowners

took 1.1 million land-based trips and more than 800 thousand water-based trips. Overnight

visitors engaged

in more than 780thousand

land-based, and 1 million water-based, trips. Total trip estimatesslightly understateactual recreationtrips because daytripper trip estimates are not includeddueto the lack of population data for this group, as notedearlier. 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.
47Comparison with PhaseI Total Outdoor Recreational Trip Estimates: Introduction. The Phase I study (Grigalunasand Diamantides, 1996)gave estimates of total recreation trips to the area for selected activities based on available "off the shelf’ data and werecomprisedof a variety of sources. Nooriginal data was collected during PhaseI. In Phase II original data wasassembled and it is important to note and to explain the major differences between the two sets of results (TableIV. 11).

Table IV.10 - Estimated AnnualKeyRecreation 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 townsand PES

water bodies only

In general, wefoundmajorthat the PhaseI estimates of recreational activity were understated--in some cases, greatly understated--as compared to the PhaseII results. The major reasonfor the large differences in estimates between the PhaseI andII reports can be explained as follows. Our

surveybasedestimatesfind much

high participation rates andtrips per participant than the Phase I report, whichused a state-wide average for New York. For example, in Phase II for bird watching and wildlife viewingwefound a high participation rate (19% for bird watching

and 21% for wildlife viewing) for respondents

anda large number

of trips per participant (37.55 bird watching trips and 34.36 wildlife viewingtrips for residents). ThePhaseI survey, in contrast, wasbased 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 foundin the PES survey sample.

48This major difference betweenthe PhaseI and Phase II results is not surprising (at least in

retrospect). Residentsand 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 groupto be much more interested in outdoor

recreation than the general populationof the state. Similar large differences between

the Phase I andII results were

foundfor 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 PhaseI VersusPhase II

Beach UseNonConsumptive 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 developmentphase of our work, members of the Citizens Advisory Committeeand officials at the Suffolk CountyDept. of Health Services expressed interest in our collecting informationon sectors of the PES economy for which

data are difficult to find 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 Farmstands and Wineriesat the East Endover the past year. Visitors also were asked whether they rented a house, condo, apartment, etc., at the East Endover the past twelvemonths. Respondents

who answered "yes" to this question wereaskedthe length of the rental agreement and the amount

they spent. Responses

to these questions were given in 1995 dollars, summarized in Table IV. 12 and explained briefly below. Population of Potential Participants. Expenditure data on Farmstands, Wineries, and
49Rental 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 Island Lighting Company estimates. Thenumber

of overnight visitor householdscannotbe 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 accountfor turnover, i.e., the same rental lodgingswill be used by an unknown number of overnight visitors over the course of a season. Estimatingthe number of overnight visitor householdsis further complicated

by the large percentage of overnight visitors (39%) who indicated that they stayed with friends or relatives, whichimplies that someamount of their expenditures may be includedin 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

fromour 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: EstimatedFarmStand and Winery Expenditures

presents estimates (in 1995 dollars) of participation andexpenditures in these sectors for resident and seasonal households.Table IV. 13 also presents estimates of PES-related expenditures in these sectors. PES-relatedexpenditures wereestimated using the sameseasonal adjustment(45 %)used in the PhaseI Report to estimate the PES-relatedimpacts of tourism and recreation sectors. Total rental lodging expenditures could not be estimated due to incomplete information on the number of participants.

50Table IV.12 - SurveyFarm Stand, Winery,and Rental Lod[in~Expenditures(1995 dollars Farm stand Winery Rentals Residents Participation 316 (88%) 114 (32%) Total Expenditures $86,400 $20,871 Average Expenditures $273 $183 Second Home Owners Participation 131 (82%) 67 (42%) Total Expenditures $48,122 $15,350 Average Expenditures $367 $229 Overnight

Visitors Participation 225 (53%) 114 (27%) Total Expenditures $41,228 $18,360 Average Expenditures $183 $161 Day Trippers

Participation 153 (56%) 94 (34%) Total Expenditures $12,755 $9,121 Average Expenditures $83 $97 Totals Participation Total Expenditures Average Expenditures 825 (68%) 389(32%) $188,505 $63,702 $228 $164 * Figuresin parenthesesindicatethe percent whomadesuch expenditures 103

(26°,/0) $556,000 $5,398

103 (9%) $556,0O0 $5,398

51Table IV.13 - Estimated PESFarm Stand and WineryExpenditures

Residents EstimatedParticipating

Households Estimated Total Expenditures Estimated PES-Related

Expenditures Second HomeOwners EstimatedParticipating

Households Estimated Total Expenditures Estimated PES-Related

Expenditures Totals EstimatedParticipating

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 expendituresare greater than earlier estimates for several other food-related sectors presentedin the PhaseI Report. For example,PESrelated farm stand expendituresare 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).

52IV.I. DESCRIPTIVE RESULTS: REFERENDA AND BROWN

TIDE RESULTS IV.I. 1. HypotheticalReferenda Introduction. Onesection of the recreational use survey posed referendum-typepolicy questions to East End

residents and secondhome owners. Thesequestions were addedto "test the waters" on selected policy issues and werenot designedas a basis for resource valuation, which would haverequired considerablymore effort and data than was possible for this project. Referenda

Questions. Thereferenda questions focus on four policy actions: (1) reseeding scallops following Brown Tide, (2) planting hard clams, (3) upgradingthe Riverheadsewage treatment plant, and (4) reducingroad runoff. Respondents

werepresented with one of these programsand wereasked whether they support the program, if they had to pay a given annualcost. Annual costs werevaried for each of the proposed policy actions as describedin Table IV. 14. Referenda Results. Respondents

overwhelmingly

supportedthe policy actions and indicated a willingness to pay the annual costs. Supportranged froma 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 supportthe policy actions at the given costs (Table IV. 14). IV.I.2. Descriptive Results: Effects of Brown Tide Respondentswere asked whether they had heard of Brown Tide ("BT"). Those whohad heard BT wereaskedabout their level of concern and about any activities that may have beenaffected by BY.Overall, most respondents (83%)had heard of BT, and of those, most (71%)were also 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) andshell 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 hadthe least knowledge andindicatedthat their activities wereaffected the least.

53Table IV.14. Management Action Referenda Results
Proposal; Annual Cost Resident S Y/N Reseed Scallops Following Brown Tide; $5 21/2 Reseed Scallops Following Brown Tide; $10 14/0 Reseed Scallops Following Brown Tide; $25 17/2 Plant Hard Clams; $5 21/4 Plant Hard Clams; $10 18/3 Plant Hard Clams; $25 14/2 UpgradeRiverhead Sewage Treatment Plant;S5 16/2 UpgradeRiverhead Sewage Treatment Plant;S25 18/3 UpgradeRiverhead SewageTreatment Plant;S50 15/4 Upgrade Riverhead SewageTreatment 73/28 Plant;$100 Reduce Road Run Off; $10 19/1 Reduce Road Run Off; $25 22/1 Reduce Road Run Off; $50 22/1 Totals 290/53 SIlO Y/N
6/0 10/0 5/1 12/0 5/0 2/2 6/0 9/1 6/O 30/6
11/0 15/1 6/O 123/11 Total

S Y/N 27/2 24/0 22/3 33/4 23/3 16/4 22/2 27/4 21/4
103/34
30/1 37/2 28/1
413/64 Total %Yes
93% 100% 88% 89% 88% 80% 92% 87% 84% 75%
97% 95% 97% 87%

54Table IV.15 Awareness of BrownTide Second Overnight Residents Home Owners Visitors Heard of Brown 370 159 279 97.11% 91.91% 67.72% Have NOT

Heard of 11 14 133 Brown Tide 2.89% 8.09% 32.28%
Very Concerned 295 about Brown

Tide* 80.16% Day Trippers 202 78.29% 56 21.71%

A Little Concerned 70 about BrownTide* 19.02% 113 174 131 71.52% 62.37% 65.17%

44 1 O0 64 27.85% 35.84% 31.84%
NOTConcerned 3 1 5 about BrownTide* 0.82% 0.63% 1.79% * Includes only respondents whohave heard of Brown Tide 6

2.99% Total

1010 82.52% 214 17.48%

713 70.87%

278 27.63%
15 1.49%

55Table IV.16.

Swimming Fishing
Shell fishing Boating
Other Activities Affected B~, Brown Tide 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 onl]¢ respondents who have heard of Brown

Tide

IV.J. ESTIMATES OF THE ECONOMIC VALUE OF RECREATION IV.J. 1. Introduction Ourintention wasto use data fromthe recreational surveyto assess the use value for manywaterbasedandshore-based

recreational activities. The surveycollected activity-specific data on fishing, Shell fishing, boating, and swimming in the PES

and surrounding waters. Information also was collected on beachuse, birdwatching,wildlife viewing, and hunting in the five PES towns. Unfortunately, the total number of surveys was limited to 1,354 completed responses due to budgetaryreasons. Thenumber of completed

responsesfor each activity wasthe limiting factor in assessing which

activities could be valued. Values were estimatedfor primary

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 mostpopular activity, and swimming and recreational fishing are the activities mostdirectly affected by water quality and henceby management actions aimedat enhancing water quality (e.g., Table IV.15) For this reason, swimming is a focus of our efforts to estimates the
56benefits of management actions in terms of changes in swimming values. We

recognize that participation in, andthe economic value of, all activities depends upon the quality of the experience. For example,

substantial research on recreational fishing, includingthe 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 art 9. Nevertheless,

wecan simulatehow

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, wepresent the existing ("baseline") conditionsfor swimming, fishing, boating, andfor birdwatchingand 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 suchpolicy 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, wearandtear on the automobile, etc., and the value of the individual’s travel time. In theory, an individual’s oppommity cost is valuedas his or her next best use of their time. However,

a fraction of the individual’s wage is generallyused, in practice, to representincome foregone in favor of recreation.
In the travel cost model,

the number

of trips a persontakes for recreation at a specific site depends onthe costs of gettingto 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, andthe better the quality of the recreational experienceat 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

9This contrasts withpolicy actions to replant shellfish or to stock fish, the latter of which is often done

in fresh water to enhance

recreational fishing. In these cases, it may be easier to link a policy to a change in catch rate andhence to benefits. 57cost, and participation rates. In addition, participants mightreveal values for site quality by

participating more

at sites of higherquality, and less at sites of lower quality, all else beingthe san’le. Carrying out the travel cost analysis requiresdata onparticipation rates, cost of travel to sites, and site quality. Data

on participation rates is collectedin the recreational survey.Dataontravel costs to the target andsubstitute sites, includingthe opportunity cost of travel time, are collected from the survey and augmented, as necessary, with distance information fromroad maps.Data on water quality comesfrom two sources. First, we use field measurements of various water quality parameters from the SCDHS PESwater sampling program, including nitrogen, coliform counts, water clarity (Secchi disk measurements)and Brown Tide cell counts. These are objective measures of quality. Second,

weuse data fromthe 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 assessmentsby 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 perceptionsof waterquality, and (3) participation

in specific recreation activities. If wecan establish such a linkage wecan evaluatewater quality policy alternatives. ~°
Toattempt to do so, we

relate participation to subjectiveratings of water quality, as indicatedin the recreational survey, and in turn, werelate these subjective water quality ratings to field measurements

of water quality. Theseare: water clarity, nitrogen concentration, coliformcounts, 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 annualbenefits of a policy improving water quality has twoparts. One is that individuals will enjoy the swimming

experiencemore;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 assessmentof proposedimprovements in the Riverheadsewage

treatment plant. Upgrading

the sewage

treatmentplant will result in improvements in water clarity, reductions in coliform

counts, etc., with the largest effect in FlandersBay andprogressivelysmall changes in

~°We note that a water quality model developedby Tetra Techof Fairfax, VA for the PES

Program will estimate  ̄ water quality changes

in each of the bays due to proposed program policies. 58bays to the east. Ourmodelallows us to predict how these changesin 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, wecan identify benefits to recreational swimming

that result frommanagement options that improvewater quality.
In order to implement

this methodology,

wewill coordinate our assessments with ongoingwater quality modeling

efforts in the PES.Thus,we anticipate that water quality modeling will be used to project changesin quality parameters fromprospective management policies. Our recreational modeling will then be usedto estimaterecreational benefits resulting from these changes

in quality. Similarly for recreational fishing, quality of the experience depends upon catch rates, which

in turn depend uponfish populations. Our recreational fishing model includes catch rates as a predictor of participation rates andrecreational values. Thus,if predictions can be made concerning

the effect of management

actions on fish populations, we could use our recreational modelto estimate resultant benefits to recreational anglers. However, it may not be feasible for natural scientists to predict changesif salt water fish populations that would likely result frommanagement actions. Nevertheless,the opportunity exists for estimating recreational fishing benefits frommanagement 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 populationchanges, which will enable us to provide order-of-magnitude estimates of possible benefits. Theeconomic

benefits from improving

(or maintaining) water quality could be long-run benefits, if the policy is sustained. To recognize the long-run benefits, wesumthe annual benefits over time, discounting themas appropriate to get an equivalent value today--the asset value of the resourcein providingservices supporting that activity.

IV.K. ESTIMATES OF ECONOMICVALUE: RESULTS IV.K. 1. Value (Consumer Surplus) Per Trip Themethodology

described aboveyields a consumer surplus estimate per person, per trip ranging from$8.59 for swimming

to $49.83for non-residential bird watchingand wildlife viewing(Table IV. 17). Fishingandboatingvaluesper trip fall withinthat rangeat $40.25 and$19.23,respectively. Theseestimates of consumer surplus are the averagevalue individuals receive per trip over and above the cost of their recreational trip. Looked at anotherway,it is the unpaid for benefit that individuals receive, on average, froma recreational trip. Therelative 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

eachof the four recreational activities studied are givenin Table IV. 17. The total benefits are estimatedby multiplying the average

consumer surplus for an activity by the

59estimatedtotal number of trips to engage

in that activity duringthe 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 mostpolicies involve small changes

in an activity or its quality and are not "all or nothing"choices. Nevertheless,we recognize that information

ontotal values, suchas that given in TableIV. 17 helps put the scale of these activities in some

perspective.

Table IV.17 Total AnnualValue of Recreational Activities at the PES(Baseline Water

Quali~) Recreatonal Bird Watching&

Swimming Boating* Fishing Wildlife Viewing Total Trips/Year ConsumerSurplus Per Trip Total Annual ConsumerSurplus 1,409,970 937,387 588,493 547,317 $8.59 $19.23 $40.25 $49.83 $12,i13,216 $18,025,952 $23,685,985 $27,272,806 * Excludes

boating trips taken primarily for fishing

IV.K.3. Asset Value of PES for KeyRecreation Activities Usingthe 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 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 PEShas an asset value of $276million for Recreational Fishing and $210million for Boating. IV.K.4. Changein Swimming Trips and Benefits Dueto Hypothetical Water Quality Changes To illustrate how

the economic

benefits estimatedin this report can be usedto contribute to estuary management

policy, consider a hypotheticalpolicy or set of policies that improves all water quality measuresby 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 throughoutthe PES.Notethat since water quality measures differ among

individual bays (and indeedwithin Bays), the absolute water quality changes due to a 10 percent improvement will

60Table IV.18 Benefits to Swimmers of 10% Improvements

in EachWaterQuality Indicator at Each PESWater Body
Total Kjeldahl 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 Peconic $20,584 $1,140 $70,207 $139,929 $231,860 Bay 2,396 133 8,172 16,288 26,988 ShelterIs. Sd. $22,598 $4,553 $109,790 $175,093 $312,033

2,630 530 12, 779 20,381 36,320 Gardincrs Bay $14,138 $129 $76,863 $185,286 $276,416 1,646 15 8,947 21,567 32,174 PES Total $147,399 $80,653 $319,378 $752,423 $1,299,854

17,156 9,387 37,175 87,581 151,299 Italics indicate number

of trips A hypothetical uniform 20 %improvement in water quality increases the number of trips by 169 thousandand annual swimming benefits by $2.6 million (Table IV. 19 Againmostof the increase in benefits ($1.46million) is attributable to greater waterclarity. However, benefits dueto reduced Brown Tide cell count (about $652thousand) also are important.
In policy analyses, it is important to examine

the incremental

benefits of policies in order to know whatthe public gets fromstricter policies and the higher costs that they entail. The annual, incremental swimming

benefits of going from an initial 10 percent improvement in water quality ($1.299 million) are somewhat larger than the added benefits ($1.264 million)of the second percent water quality improvement (i.e. going from 10 percent to 20 improvement). The issue incremental

benefits fromaltemative management actions that affect different PES bays differently will be addressed more

comprehensively

in Phase III economic

studies.

62Table IV.19. Benefits of 20% Improvements

in EachWater

Quality Indicator at EachPES Water Body
Total Kjeldahl Total Brown Tide Secchi Water PES Water Nitrogen Colifrm Cell Counts Disk Body Body Depth Total Flanders Bay $130,030 $141,908 $28,888 $253,807 $554,633

15,135 16,518 3,362 29,543 64,558 Great Peconic $48,869 $6,200 $95,937 $249,204 $400,201 Bay 5,688 722 11,1678 29,152 46,729 Little Peeonic $41,319 $2,510 $139,892 $279,957 $463,678 Bay 4,809 292 16,283 32,262 53,647 ShelterIs. Sd. $44,207 $7,907 $235,633 $315,939 $603,685

5,145 920 27,427 36, 775 70,268 Gardiners Bay $28,555 $5567 $151,556 $356,181 $541,859 3,324 648 17,641 41,459 63,071 PES Total $292,979 $164,653 $651,905 $1,455,088 $2,564,065

34,102 19,100 75,881 169,191 298,273 Italics indicate number

oft’rips

IV.K.5. Change

in Fishing Trips and Benefits Due to Hypothetical 10 Percent Change in CatchRate We also simulate the effects of an assumed policy that leads to a 10 percent increase in the recreational fishing catch rate. The 10percent 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 annualbenefits by $472,359. Thepresent value of this increase in catch rates--the increase in the asset value of the PES

in providing

this service-- is $5.5million, usingthe 7 percent discountrate andtime horizonof 25 years usedfor all cases.

63Table IV.20. Benefits of Improving ExpectedRecreational 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 Consumer 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 mightincrease 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 doneby EAI of proposedprogram

actions will address these issues.

IV.L. REFERENCES Freeman.A. MyrickIII, 1993. The Measurement of Environmentaland Resource Values: Theory and Methods.Washington,D.C.: Resources for the Future. Grigalunas, Thomas

A. and Jerry Diamintides, 1996. "The Peconic Estuary System: Perspective on Uses, Sectors and Economic Impacts". Peacedale, RI: Economic Analysis, Inc. McConnell,

Kenneth and Ivar Strand, 1996. "MarineRecreational Sportfishing". College Park: University of Maryland. Walsh, Richard G., Dorm M. Johnson, and John R. McKean, 1988. "Review

of OutdoorRecreation Economic Demand Studies with Nonmarket

Benefit Estimates, 1968-1988". Fort Collins, CO:WaterResources Center, ColoradoState University.

64Table

S. 1 SupplementaryTables and Appendices Supplementary Tables

East EndResident RespondentDistribution by Town

Number of Percentage of All Percentage of YearTown 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 OwnerPrimary 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 1 35 22% Totals 159 100% 1Includes Westchester and Rockland Counties in New

Yorkand parts of Northeastem New Jerse’i.

65Table 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: LongIsland Regional Planning Board Table S.4 Primary Residencyof Visitors to the East End Percentage

of All Area Number of Respondents Respondents NewYork 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

66Table S.5 Accommodations for Overnight Visitors Percentage of All Accommodation Number of Respondents OvernightVisitor Respondents Hotel or Motel 95 22% Rental 101 24% Friends or Relatives 168 39% Other 63 15% Totals 427 100%* * Adjustedfor rounding

" Table S.6 Overall Participationa in Key RecreationActivities - All Respondents (n=1354) Number of Participants Percentage of all Respondents AnyActivity* 944 70% MultipleActivities 648 48% Water Based Activities 820 61% Land BasedActivities 644 48% *Any of the eight activities listed in the surveyquestionnaire. Water Based Activities: Swimming, Boating, Fishing, Shell fishing; Land Based Activities: BeachUse, Bird Watching, Wildlife Viewing, Hunting. Includes water-basedrecreation at PES,BIS, LIS, and Atlantic Ocean.

67Table 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.

68S.9 - EstimatedTrips for KeyRecreationActivities 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 Endtowns and PES water bodies only AppendixB - 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 becausethe number

of recreational trips taken by an individual musttake 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 eachsite is modeled as a function of the travel cost to the site, costs of travel to substitute sites, the number of boatingoccasions

at the same

site, anda subjective measure of water quality at the site. Themeasure 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 recreationaltrips to eachsite, and to the valueof a recreational trip to a site. In order to do so, weneedto link management actions to changes

in waterquality, and to link objective field measures of water quality to subjective perceptionsof water quality by recreational users.

69Predictions of changes in water quality due to management actions is doneas part of ongoing water quality modeling

work, carded out as part oft_he PeconicEstuary Program (Tetra Tech, 1998). The relationship between

objective water quality measures and subjective perceived water quality is estimatedby linking data collected in the recreational surveywith field measure of water quality. Specifically, for those respondents who indicate that they went swimming on their mostrecent recreation day, the survey asked where they went swimming and howhighly they rated water quality (excellent, good,fair or poor) at that site. We also obtaineddata on measures of keywater quality variables, including brown tide cell counts, total coliform bacteria, total Kjeldahl

nitrogen, and Secchi disk depth readings fromsamplinglocations distributed across the PES.Thedata was grouped into ten sites - north fork andsouth fork sites for Flanders,Great Peconic,Little Peconic, Shelter Island Sound

and Gardnersbay. 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 objectivefield 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 travelingto the site, the cost of traveling to keysubstitute sites, and the predicted probabilities of the subjective rankingsof water quality. These

steps are describedin detail below. Water Quality Model Thewater quality model

links individuals’ perception of water quality - expressed in a rankingof poor, fair, good,andexcellent - to physicalmeasures of waterquality collected at sampling stations by the SCDHS.

An ordered logit model

is used to estimate the probabilities for each rank basedon physical measures

of water quality and on the effects of Brown Tide on the individual’s swimming that year. The

purpose of the waterquality model is to provide

input into the travel cost model i.e., the probability that the water quality of a site would be ranked goodor excellent. Data for the water quality model consists of site readings during August1995for the following water quality indicators:

. 2. 3. 4. Brown

Tide, Aureococcus

anophagefferens,

in cells per milliliter. Total Kjeldahlnitrogen in milligrams per liter. Total coliform bacteria in MPN per 100milliliters. Secchi disk depth measured in feet. These water quality indicators wereselected for their relevance to potential management actions and for their modelingcompatibility. Also included in the water quality modeldata set are responses to the Recreational Use SurveyQuestionnaire administered during August1995. Three questions fromthe questionnaireare included. One question asks individuals if their swlmrnmg 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.

Thethird question asks respondents to identify the swimming

location of their last swimming outing in the PES.
70Thedata is grouped into ten sites: Flandersbaynorth andsouth, Great Peconic baynorth andsouth, Little Peconicbay north and south, Shelter Island sound north and south, and Gardiners bay north and south. Swimming

locations wereallocated to the ten sites according to locations indicated by the surveyrespondent.Onlyobservationsthat indicate an identifiable swimming location for trips taken during August 1995were included in the data set. Waterquality sampling stations were allocated to the ten sites according to SCDHS sampling station maps,as describedin TableA1. The number of samplesrecorded from each samplingstation varies. An average of the data recorded at each station during August 1995 (the period during whichour survey was administered) was taken. Sampling

stations with a central position, as opposed to north or south, were

includedin the averagesof both north and south sites. Data for Brown

Tide, total Kjeldahl nitrogen, and total coliform bacteria wereused to construct indices of water quality relative to threshold values for each parameter to make the measures

more comparable. TheaverageBrown

Tide cell count for each of the ten sites wasdivided by 250,000cells/ml - the visibility threshold for Brown Tide. The resulting value was

then multiplied by 1 if the respondent indicated that their swimming wasaffected 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. Theaveragetotal Kjeldahl nitrogen reading for each of the ten sites was divided by .5mg/1, the nitrogen guideline established by the SCDHS. Theaveragetotal coliform bacteria reading for each of the ten sites was divided by 400MPN/100 ml, a threshold for bathing water quality. Thetotal Kjeldahl nitrogen index and the total coliform bacteria index wereaddedtogether to forma joint nitrogen-bacteria index. This joint index was used in the ordered logit modelinstead of the individual indices becausehigh colinearity in field measurements of nitrogen and coliformbacteria precluded separate estimation of the effects of these twowaterquality variables. All indices were multipliedby 100to precludethe use of fractions. Average input values for each site are presented in table A2. Themodeling

procedureuses 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 basedon the logistical distribution,
Prob(Y = 1)- 1 +e

a’~ " where [3 represents parameters

to be estimated andx represents water quality field measures (e.g., water clarity) that contribute to the subjective waterquality rankings. Given that the rankingsare 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, fair, good,
71excellent - the choice made reflects the rank order that is mostsimilar to the individual’s own perception. In other words,the individual may feel that water quality at their swimming location that day wassomewhere

betweengoodand excellent, but because of the ranking order presented by the questionnaire, the individual will choose either goodor excellent as the best choice. This selection process is modeled as a multinomiallogit model where y* = ~’x + ~. Y* is a continuous

measure of site quality, not constrained bythe discrete ranking, [3 is the vector of coefficients estimated by the ordered logit model,x is the vector of field measures of water quality, and ~ 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 anda 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 g l < y*_<

~t2, if bt2_<y*. wherethe ~ti are threshold values that determinewhen water quality ranking wouldchangefor a "representative" individual. The ordered logit model estimates the g threshold parametersalong with the vector of [3 coefficients. The results of the orderedlogit model are presentedin TableA3.The estimatedcoefficients indicate that a reductionin Brown

Tide cell countsor a reduction in the nitrogen-bacteriaindexwould result in higher water quality rankings. Similarly, an increase in Secchi disk depth (increased water clarity) would

raise the subjective water quality rankingby surveyrespondents. The probabilities that water quality would be rankedpoor, fair, good, or excellent are calculated from the orderedlogit model

results. These

probabilities are: Prob (y = 0; poor) = A (-[3’x), Prob (y = 1; fair) =A(btl -[3’x ) - A (-[3’x), Prob (y = 2; good) = A (p2 -[3’x) - A (p.1 -[3’x), Prob (y=3; excellent) = 1 - 

### ( t2 -13 ’x),
where Aindicates the logistic cumulative distribution function.

72The rank order probabilities are given for nine sites only, as no survey respondents reported

swimming at FlandersBay

south. These

probabilities are generated by substituting the site-specific water quality indicator values for the mean values of the entire sample. Anaverage of each probability weightedby 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 rankedgood is basedon the weighted

averageof the probability for good at Gardiners

bay north and Gardiners baysouth. Theprobabilities for the nine sites are presentedin table A4.Theprobabilities 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 samplestation readings. Changes in these values will in turn change the value of the water quality indices and/or SecchiDiskreadings used as input into the orderedlogit model. These simulated values for objective waterquality field measures are usedto provide

at a new set of probabilities for subjective ratings of waterquality for the sites, which

in turn affect predicted participation rates for swimming in each of the PES

water bodies. TablesA6-A9

contain estimatedwater quality ranking probabilities i.e., the probability that water quality will be ranked poor, fair, good, or excellent, for each of nine PESsites at a 10% improvement

in an objective water quality parameter. Theobjective water quality parametersused in the 10%

improvement

model are Brown Tide cell counts, Total Kjeldahl Nitrogenreadings, Total Coliformreadings, and Secchi Disk depth. For example, under baseline water quality levels in 1995, Great Peconic Bay (Table A5) predicted to obtain a "poor"rating for water quality by 8% of sample respondents,a "fair" rating by 37% of samplerespondents, a "good"rating by 45% of samplerespondents and an "excellent" rating by 10%

of samplerespondents.Theseprobabilities are the weighted averages of the ranking probabilities for Great PeconlcBayNorthand Great PeconicBaySouth (Table A4). If, continuing our example,Brown

Tide Cell counts were to improve(decrease) by 10% at samplingstations Great PeconicBay

North and Great PeconicBaySouth, the modelpredicts that Great PeconicBay overall (Table A10)

would obtain a "poor" rating by 7% of samplerespondents, a "fair" rating 36% of sample respondents, a "good" rating by 46% of samplerespondents and an "excellent" rating by 10%

of samplerespondents. Travel Cost Model Thetravel cost model

predicts the annualnumber of swimming

trips to each of the five water bodies in the PES

basedon 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.

73The

travel cost model

is also usedto estimate the increasednumber of trips andincreased valueper trip that results dueto management actions that increase water quality. Thisis done in three steps. First, the changes

in water quality resulting fromspecific management actions (e.g. upgrading

the Riverheadsewage

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 Travelcost model,

to calculated the estimatednumber of trips andvalueper trip underthe new level of waterquality. Data for the travel cost model comes fromthe Recreational UseSurvey and fromthe 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 FlandersBay, Great PeconicBay,Little PeconicBay,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 proxyfor price is used. In the travel cost model,the cost of getting to and froma 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 accordingto the formula: Travel Cost = Round

Trip Distance * $0.32 + {Round Trip Distance/40mph

* 40% hourly wage}. Theroundtrip distance traveled is double the one-way highway distance between

the respondent’s point of origin and each swimming location. This distance wasdetermined on a case by case basis using a road atlas andassessing the most direct travel route. Verification of distance traveled was made possible when

respondents answered

the question concerningdistance traveled on their most recent swimming

occasion. Travel time was estimated by dividing the round trip distance by 40 miles per hour. Thisspeed was used as a reasonable compromise betweenhighwayspeeds of 55 mph and moreand 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 incomeas 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 wasapplied to the substitute swimming locations. The average of travel costs to Long Island Sound,BlockIsland Sound,and the Atlantic Ocean, for each respondent, wasused as the cost of travel to substitute sites andinput into the travel cost model. Data on the number

of trips to the PESswimming

location comesdirectly from the survey questionnaire. The water: quality variable used in the travel cost modelis the sumof the probabilities that the PES swimming location would

be ranked goodandexcellent. These probabilities are generatedby the water quality model.

74Overall, 199respondents provided sufficient informationto be included in the travel cost model. Given that there are five PES

swimming locations, 995 observations wereused in the travel cost modelestimation. Thetravel cost modeling

procedureis basedon a Poissonregression model.ThePoissonregression model is often used when

the dependent

variable is discrete and includes zero as a viable choice. In this case, many

individuals took no trips to one or moreof the PES swimming locations. This type of data is referred to as count data, meaning that the data indicates the number of times some phenomenon

occurs, such as swimming trips to Flanders Bay. The Poisson regression modelis based on the assumptionthat each observation is drawnfrom a Poissondistribution. The probability that the actual number of trips taken is equivalent to the estimated number

of trips is formulated

as: Prob (Yi = Y3 = eXi ~’i yi / Yi I Where yj = 0,1,2,3,

.... ; In ~i = [3’xi; [3 is the vector of estimated coefficients; and

x is the vector of independent

variables. The results of the Poisson

estimation are consistent with prior expectations, as presentedin Table A14. The estimatedcoefficients indicate that the number of trips to a site decreases as the cost of travel to the site increases;increaseswhen the cost of travel to a substitute site increases;increases the more times the individual participates in swimming at the same

location; andincreases as water quality at the site increases. Thevalue of swimming

trips to PES

locations is estimatedfromthe results of the travel cost model accordingto the formula: Consumer Surplus = -(Predicted number of trips)/(coefficient on travel costs). Consumer surplus is the net value of swimming at a PES location above and beyond the costs of participating. Consumer

surplus is affected by the same variables that influence the number of trips to a swimming

location. Therefore, the value of a swimming trip varies among

the five PES swimming locations due to differences in water quality. Thevalue of a swimming

trip to a PES

location will be affected by management actions to improve water quality. The

results of the travel cost model can be usedto estimate the changes in consumer surplus due to water quality improvements.The recreational swimming benefits of improving waterquality are estimatedby applyingnew probabilities of water quality ratings to the results of the travel cost model.This can be calculated using the following formula:

75

## NewCS = {T + AT * A P} 

# -(c)
Where T represents the number

of trips taken prior to the quality change,AT represents the change in the number

of trips that occurs due to a 1% change in the probability of obtaining a "good" or "excellent" water quality rating, Ap represents the change

in the probability of obtaining a "good" or "excellent" rating that results from the management

action, andC represents the coefficient on the travel cost variable. Increases in the number

of swimming

days to PES

locations and increases in consumer surplus due to a hypothetical 10%

improvement

in water quality measures are presented in Table A15. Aggregate benefits are based on the estimated baseline number of swimming

trips to the PES.This number of trips - 1,409,970 - was estimated by extrapolating the number of trips respondents identified in the Recreational UseSurvey to the populationof residents, secondhome owners,and overnight visitors. Thepopulation of overnight visitors wasestimatedbased on the proportion of overnight visitors in the survey sample (see section on Participation). The aggregate value swimming in the PES

under baseline conditions is presented in table A16. Table A1. PES Sampling Station Locations

Location SamplingStation Flanders Bay North Flanders Bay South Great Peconic Bay North Great Peconic BaySouth Little Peconic BayNorth Little Peconic BaySouth Shelter Island Sound

North

Shelter Island Sound

South Gardiners Bay North Gardiners Bay South 170 (Central), 220, 240 170 (Central) 101,130 (Central) 130 (Central) 102, 103,105, 113 (Central) 113 (Central) 106,107,108,109,111,112,114, 115,119,122 118,121,126,127,131 116 132,133,134

76Table 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 BaySouth 14.81 117.75 3.5 Little Peconic BayNorth 28.04 126.39 3.5 Little PeconicBaySouth 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

77Table A3. Results of the Water Quality Model (Ordered Logit)

Observations 199 Log Likelihood Res. L. Likelihood Variable -225.26

-239.00 Coefficient Std.

Err. ChiSquared D. Frdm. Sig. Level T Star. 27.50

3 .000005 Sig. Level Meanof X
Constant BrownTide Index Nitro. - Bact. Index Secchi Disk


### p2 2.0679 -0.02297 -0.00147 0.34515 2.2812 4.6968 1.1136

0.00499 0.00089 0.23232 0.25915 0.3522 1.857

-4.605 -1.652 1.486 0.06332

0.00000 0.09860 0.13737 33.83

173.4 4.397

78Table 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 BayNorth 0.08 0.38 0.45 0.10 Little Peconic BaySouth 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 sumto one due to rounding

Table A5. 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 PeconicBay 0.08 0.37 0.45 0.10 ShelterIs. Sd. 0.08 0.39 0.44 0.09 Gardiners Bay 0.05 0.28 0.52 0.16

79Table A6. Probabilities at Nine Sites for 10% Improvements

in Brown

Tide Cell Count
Site Prob. Prob. Fair Prob.Good Prob.

Poor Excellent Flanders Bay North Great Peconic Bay North Great Peconic Bay South Little Peconic BayNorth Little Peconic BaySouth Shelter Is. Sd. North Shelter Is. Sd. South Gardiners Bay North Gardiners Bay South 0.14 0.47 0.34 0.05 0.07 0.37 0.46 0.10 0.06 0.32 0.50 0.13 0.08 0.37 0.46 0.10 0.06 0.32 0.49 0.13 0.07 0.36 0.46 0.10 0.10 0.42 0.41 0.08 0.04 0.24 0.53 0.19 0.05 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 BayNorth 0.08 0.37 0.45 0.10 Great Peconic Bay South 0.06 0.32 0.49 0.13 Little Peconic BayNorth 0.08 0.38 0.45 0.10 Little Peconic BaySouth 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

80Table 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 BayNorth 0.08 0.38 0.45 0.10 Little Peconic BaySouth 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

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 BayNorth 0.07 0.35 0.47 0.11 Great Peconic Bay South 0.05 0.30 0.51 0.14 Little Peconic BayNorth 0.07 0.36 0.46 0.11 Little Peconic BaySouth 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

81Table A10. Probabilities at PESWater 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 PeconicBay 0.07 0.36 0.46 0.10 ShelterIs. 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°/0 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 PeconicBay 0.08 0.37 0.45 0.10 ShelterIs. Sd. 0.08 0.38 0.44 0.09 Gardiners Bay 0.05 0.27 0.52 0.16

82Table A12. Probabilities at PESWater 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 PeconicBay 0.08 0.37 0.45 0.10 ShelterIs. Sd. 0.08 0.39 0.44 0.09 Gardiners Bay 0.05 0.28 0.52 0.16

Table A13. Probabilities at PESWater Bodies for a 10%

Improvement

in Secchi Disk Depth
PES Water Body P(Poor) P(FaiO P(Good) P(Excellent) Flanders Bay 0.12 0.46 0.36 0.06 Great Peconic Bay 0.07 0.35 0.47 0.11 Little PeconicBay 0.07 0.35 0.47 0.11 ShelterIs. Sd. 0.07 0.36 0.46 0.11 Gardiners Bay 0.04 0.25 0.53 0.19

83Table A14. Results of the Travel Cost Model(Poisson Regression)

Observations 995 Log Likelihood Res. L. Likelihood Variable -3707.225

-4630.572 Coefficient Std.

Err. Chi- 1846.693 Squared D. Frdm. 4 Sig. Level .000000 T Stat. Sig. Level Mean of X
Constant -0.76984 0.16607 -4.636 0.00000 Travel Cost -0.028321 0.00210 -13.494 0.00000 23.29 Boating 0.0781 0.00157 49.757 0.00000 0.9246 Travel Cost to Substitute Locations 0.00978 0.00119 5.282 0.00000 31.74 Water Quality Rank 2.688 0.28209 9.529 0.00000 0.5404 Scale Factor Marginal Effect for Water Quality Rank 1.5002
4.0325

Appendix

C. Comparison

of Recreation Activity Estimates: Phase I and Phase II Reports Comparison

with Phase I Total Outdoor Recreational Trip Estimates: BeachUse. Beach use, in this report, refers to anybeachin the five East End towns,includingLong

Island Sound

and Atlantic Ocean

beaches, as well use of PES beaches. ThePhaseI report did not estimate beachuse 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 andwildlife viewing

estimates in this PhaseII report are combined to create a "non-consumptive wildlife use" estimate of 547thousand trips (TableV. 11). This estimate greatly exceedsthe PES

PhaseI estimate of non-consumptive wildlife use (92 thousandtrips). The major reason for the large differences in estimates betweenthe Phase I and II reports is explained as follows. Our survey-basedestimate finds a high participation rate (19% for bird watching and 21% for wildlife viewing)for respondentsand a large number of trips per participant
84(37.55 bird watching trips and 34.36wildlife viewing trips for residents). ThePhaseI survey, 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 lowerthan those foundin PES sample.This majordifference is not surprising since residents andseasonal visitors to the PES would be expectedto be more

interested in outdoor recreation than the general population of the state. Comparison

with Phase

I Total Outdoor

Recreational Trip Estimates: RecreationalFishing. Notsurprisingly the recreational use surveydata yields a much larger estimate (588thousand trips) of recreational fishing trips in the PES than the Phase

I study (114thousand).The PhaseI estimate is based on the NFWS

survey covering all of New York State. In the NFWS study, the average estimatesof participation rate (9%) andtrips per participant (8 for residents, 4.3 for visitors) much lower than foundin the PES survey. This estimated participation rate is 17% and trips 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 moreavid recreational fishers than the New York population

at large. Comparison

with Phase I Total Outdoor Recreational Trip Estimates: Swimming.The PhaseI swimming

estimate (715 thousand)

is simply the sum

of all available beach attendancedata for the PES.ThePhaseII survey-based estimate (1.4 million) includes participation not only official beachesbut also at the many unadministrated

beachesthroughout

the PES,and in addition includes the Atlantic Ocean and Long Island Sound,as well as the PES.
Comparison

with PhaseI Total Outdoor Recreational Trip Estimates: Hunting. The discrepancy between the PhaseI (77 thousand

trips) and PhaseII (52 thousand trips) huntingestimates also basedon differences in participation rate and number of huntingdaysper 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 huntingtrips per year. On the other hand, the PES

survey indicates that only 2%

of residents andvisitors to the PES hunt in the PES.

These participants also hunt less than the state average, ranging from16.33 trips per second homeowner participant to 5.5 trips per participating day tripper. ThePhaseI hunting estimate wasbased only on residents and did not attemptto account for participation by seasonal residents andvisitors since such data are unavailable.

85V. WETLAND PRODUCTIVITY VALUES IN THE PES

V.A. INTRODUCTION Eelgrass, saltmarsh, and intertidal mud bottom("wetlands") provide many services to the public. Theseservices include contributing to the productionof 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 fromstorm 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 viewedmany miles away. Some

wetland services are valued in the market place (e.g., commercially

harvestedfish or shellfish), while others are not (e.g., bird species used viewingand waterfowl

that are hunted). Understandingthe 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 informationis the value of a small change in wetlands, that is, the marginal value of wetlands. Marginal values rather than the total value of all wetlands are important becausemostpolicies address relatively small changesin wetlands--notwhetheror not to preserveall wetlands. This chapter adapts 13 estimates by Frenchand 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) Theincrease in food produced by the habitat which

is utilized by higher trophic levels (such as fish andshellfish) in the PES, and 

### (2) Theincrease in productionof higher trophic levels broughtabout 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 assignedan economic value. This value is basedon the (1) commercial

value of the fin fish andshell fish, (2) the viewing value of birds, and (3) the hunting

13Two major changes are noted. French andSchuttenberg

valued PES we[lands overa 100-year

period and used a 3 % discount rate. We usea 7 % discount rate anda 25-year

time period for consistency

with resultsgiven

elsewhere in this report.The

higher discount rate and shorter time horizon means that ourresults

for estimated

economic values will belower

thanin French

and Schuttenberg.

86value 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. Oneis that food and habitat are biologicallylimitingfactors for the species considered; that is, fish, shell fish andbirds depend upon the availability of wetlands, so that small changes in wetlands will causes changes in the populations of these species. If wetlandsare not limiting for these species, then an incrementof wetlandswould

have no productivity value for the species concerned, althoughit may have other values (e.g., shoreline erosionprotection, esthetics, or existencevalue) The second critical assumption

concerns effort andits cost. Fishing, viewing or huntingrequire 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 changein wetland areas will lead to onlya very slight increasein harvestsper unit of effort. Slight increases in catchper unit effort will have a negligible effect onthe level of effort itself. The changes in the availability of eachwetland categorydue to preservationor restoration actions are presumed to be small enough

so that fishing, hunting, or viewing effort remainsthe same.This assumption implies that wedo not needto net out the cost of any change in effort due to small changes in abundance of fish, or birds arising frommarginal changes in wetlands. Under

these assumptions,

the value of additional harvests, viewing or huntingis the gross value.

V.B. METHODOLOGY AND DATA V.B. 1. Introduction Several studies estimate the economic values provided by natural ecosystems 14. For example, Lynne et al. (1981) estimatedthe 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, andcatch to evaluate the marginalproductivity 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 commercialand recreational species. Kahnand Kemp (1985) estimated the incremental value of subtidal vegetation in contributing to striped bass populations in the Chesapeake

Bayand their subsequent harvest by recreational fishermen. Costanza and Farber (1986) assessedthe per acre services of Louisiana wetlands for recreation andas a buffer for storm protection, in addition to contributing to the production of commercial fisheries. Finally, Pornpinatepong (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.

14A summary of the productivity

approach and examples for coastal

areasis given

in Grigalunas

and Congar (1995). 87Theapproach usedin this chapter derives estimates of wetland productivity using data specific to the PES.Thevirtue of this approach is that it captures the underlyingbiological structure and productivity of PES

wetlands, and it uses biological informationand economic values specific to the PES. Themethod--ineffect a "simulation"-- side steps someof 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 useddoesnot allowfor statistical tests of significanceand hence heavily relies uponprofessional judgement. Themethodology

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. Adetailed statement of the methodology,

data and assumptions

is given in French and Scuttenberg (1998). V.B.2. Food Web

Estimates Saltmarshand eelgrass beds benefit the entire food web by primary(plant) production. Similarly, the net gain in lowertrophic level, animal productionis passedup the foodweb.Ultimatelythis productionvia the food web results in the productionof species of economic value to people. To estimate the economic value of these food-webeffects, several pieces of information are needed.First, it is necessary to quantify the amount of food produced

by a habitat. To do this, primary (plant) and bottom

(amphipods,

worms, etc., in and on the sediments)productionrates 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 productionis translated into commercialfin fish and shell fish production and landings using average relationships estimatedacross many estuaries by Nixon

(1982). Finally, the estimatedfish and shell fish landings are valuedusing species-specific fishery values for PES landings. Thus, lower trophic levels are translated into equivalent uppertrophic level fish and shellfish production

harvested and valuedby people. Specifically, fishery production is estimatedto be 0.16 percent of primaryproduction and 4 %percent of bottom (benthic macrofaunal)

production. Details of these calculations are given in Frenchand Sehuttenberg(1998). V.B.3. Habitat Estimates Habitat values are estimatedfor species (bay scallops, blue crab, soflshell clams, andbirds) having human use values. Habitat values are based on (1) the expectedyield offish or shellfish dependent uponthe habitat, and (2) the abundances of wildlife (birds) that utilize the habitat. Fish shellfish values are commercial values; wildlife values are for hunting (waterfowl) and viewing (waders). Bayscallops depend

uponeelgrass as nursery habitat for juveniles. Thegrass provides a refuge from predatorsfor 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

88eelgrass habitats preferentially. Again, it is assumed

that the entire blue crab fishery depends upon the saltmarsh andeelgrass beds of the estuary. Softshell clamsprefer intertidal mud flats andsand flats. Softshell clams

are assumed

to all havebeen

produced in intertidal mudflats

and shoalsin the PES. Abundance

of birds depends

uponhabitat 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 ResourceDamage

Assessment

Model (Version 2.4, April, 1996) developedby the authors for the US Department

of the Interior (French, et al, 1996a,b,c). Birds that are specifically benefitted saltmarsh, eelgrass or mud flats wereselected and includedin the present analysis. Thesespecies are waders

(herons, egrets, and ibis), shorebirds,brant andblackducks.Waders use all three of the habitat types, while shorebirdsuse marsh and mud flats. Brantsspecifically feed on eelgrass. Black ducksare know

to require structured habitat, marshand eelgrass. Thevalue of bird species usageof the habitat is basedon the benefits human receive fromviewing or hunting(waterfowl)

birds. Thevalues are marginal values, that is, the additional value people obtain by seeing additional birds per day. Thevalues per animalper 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 development of these values. :V.C. RESULTS V.C. 1. Introduction Results are provided

for the (1) Marginal

value of existing wetlands,and (2) the marginal value restored wetlands. Restoredwetlandshave a lowervalue than existing wetlands since it may take years for an existing wetlandto become fully functional. V.C.2. Results Table V. 1 shows

the marginal value for existing and restored wetlands. Thevalue for existing wetlands would be usedfor policy issues dealingwith preservationdecisions; results for restoration are critical for assessingpolicies to restore wetlands. Two values are calculated: Anannualvalue and an asset value. Theannual value is the sum of the food web values and the habitat values. Asset values werecalculated 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 report~5. Eelgrass has a 20 %higher marginalvalue per acre than that for saltmarsh. Intertidal mud flats havethe lowest marginal value.

~SUse of lower discount rate and longer time horizon would lead to a larger value. Useof the 7 % discount rate reflects the opportunity

cost of resourcesusedin restoration/preservation. Thatis, resources usedin these activities to some extent will be drawnaway from private and public investments.

89Estimated marginal values for restoring wetlands are much lower than the value of existing wetlands of the same

category. This is becauseit takes several years before a restored wetland becomes fully functional. Thus,in the table it is notedthat eelgrass, saltmarsh, and intertidal mud flats take 15, 10, and 3 years, respectively, to become fully functional. Themarginal

asset values of PES

wetlands appearto be substantial, especially in light of the fact that other services wetlandsmay provide, such as protection fromerosion and storms, aesthetics and existence value, are not considered. Table V.1. MarginalValues of PESWetlands

Existing Habitats Created Habitats Estimated Years to Number of Annual Become Acres in Wetland Value Asset Value Fully Asset Value PES Type per Acre a per Acre b Functinal a 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 Frenchand Schuttenberg(1998) estimate habitat values using a discount rate of 3 %and time horizon of years. Ouruse of 7 %and 25 years reduces the estimated wetlandvalues 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 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. Theproductivity services covered are food web

and habitat services. Economic values estimated are for commercial fishing value for crab, scallop, and clams; viewingvalues 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.

90V.E. REFERENCES

Costanza, 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 GrantProgram. Tallahassee: Florida State Univeristy (June). French, et al., 1996. Natural Resource Damage Assessment Modelfor Coastal and marine Environments. Version 2. French, Deborah and Heidi Shuttenberg, 1998. Estimated Food Web and Habitat Values for Habitats in the Peconic

EstuarySystem. Submitted to Economic

AnalysisInc. (January 23). Grigalunas, 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 ChesapeakeBay", Journal of Environmental Economics and Management
 ̄ Lyrme, G., P. Conroy, and F. Prohaska, 1981. "Economic Valuation of Marsh

Areas for Marine Production Processes", Journal of EnvironmentalEconomics and Management

8(2): 175- 186. Nixon, Scott W., 1982. "Nutrient Dynamics, Primary production and Fisheries Yields of Lagoons". Narragansett: Graduate School of Oceanography, University of Rhode

Island. Pompinatapong,

K., 1997. "ValuingCoastal Wetlands: Insigths from the TypeANatural Resource Damage Assesment model". Unpublished M.S. Major Paper. Kingston: Department of Environmentaland Natural ResourceEconomics, University of Rhode

Island.

91VI. NATURAL RESOURCE PRIORITIES AND VALUES

vI. G. INTRODUCTION This section describes a study of natural resource values for the PeconicEstuary System (PES). 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 importantnatural resources of the PES.Theresults include: (1) 

### (2) (3) an analysis of valuesandpriorities for a set of important natural resourcesof the area--farmland, undevelopedland, wetlands, safe shellfishing areas, and eelgrass; estimates of economic

benefits that would

result fromprotecting or restoring these resources; and public opinions

related to the Estuary. Thesurveywas

designedto complement

scientific and technical studies, using data available at the time, to provide

information

that will be useful in the fmalpolicy analysis. This information will be used in a cost-benefit analysis of policy alternatives (Phase III of the Comprehensive Economic ValuationStudy), that will help policy makers prioritize alternative management actions. Theremainder

of this section is organizedas follows: Part B describes the survey development and implementation,

andthe questionnairedesign; 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 developedthe 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 surveywas

to learn aboutthe public’s preferences,priorities andvalues for natural resourcesof the Peconic Estuary that mightbe affected by preservation and restoration actions.t6

16 Other important goals wereto create a survey that would minimize some of the problemsoften associated with valuation surveys; would be easily understandableto members of the public; could be answered in a reasonable amount of time; and could be administered in a variety of public places.

92VI.B. 1.a. Initial Meetings, Interviews, and InformationGathering
We began the survey developmentprocess in February, 1995 by meeting with members of the Management

Committee.

Shortly afterwards, wemet with representatives of different interests, including the head of the Local Government Committee;

the Chairman

of the Citizen’s Advisory Group, whois also president of a local environmental group; a representative of the Nature Conservancy;

a biologist from 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 LongIsland Inshore Trawlermen’sAssociation; and a bank president. These meetings, and a meetingwith the Citizen’s Advisory Group,helped us to learn about the area and about the concernsof various interest groups. VI.B. 1.b. InformalInterviews Withthe 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 interviewedin Montauk,

Springs, SagHarbor, Shelter Island and Greenport. They included business owners, store clerks, a police officer, visitors, andresidents. These interviews providedinformation on some of the mostimportant natural resources and related concerns, and howpeople think and talk about the bays. Most people interviewed were very concerned about water quality, declines in fish populations over the years, and impactson business if water quality continuesto decline. We also learned that many

peoplewerenot 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 PeconicBays System, and was defined by a map at the beginning of the survey. VI.B. 1 .c. FocusGroups and Preliminary Survey The next step was

a series of focus groups,anda short preliminary survey.Table

VI. 1 lists the dates of the focus groups

and pretests, their locations, andthe number of participants. In the first three focus groups, we

askedthe participants general questionsabout how they define the study area; their familiarity with the Peconic EstuaryProgram;

the characteristics of the area that are mostandleast important to them; their uses of the local waters;the attributes they lookfor in choosing recreational sites; their perceptions

of waterquality; andtheir concerns aboutthe natural environment andnatural 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 drawsa 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; concernsrelated to the Estuary; and 93(3) positive and negative aspects of the East End in general.
Table VI.1 - Focus Groups and Pretests

Numberof 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 RhodeIsland 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

Thethirty-five usable responses gave some preliminary information about important attributes of the area and its recreational sites; andabout people’sgreatest concerns for the Estuary. Respondents wereaskedto rank a list of concernson a scale of 1 to 10, where1 indicates the highest level of concern. TableV.2 lists these concerns, in order of importance to respondents. Themost

important weredeclining stocks of shellfish and finfish, water quality, trash on beaches, andareas closed to shellfishing. Theleast importantwerecrowding in boating harbors, public access to the water and availability of wildlife for viewing. In the next several focus groups, weaskedparticipants general questionsabout their concems, their perceptionsof water quality, and actions they would like to see. We

also tested preliminarysurvey questions and question formats, which wererevised between

groups. Several of these questions were primarily intended to stimulate discussion. For example, one question asked about participation in recreational activities, and was usedto generate

a discussion

of these activities andrelated concerns. Other questions asked participants which of a list of human impacts on the Bays were most important to them; whichof a list of environmental problems they believed weremostserious; and which potential actions shouldbe given highest priority.

94Thequestions asking participants to prioritize impacts, concems, and actions weredifficult for peopleto answer,for two important reasons. First, many people in the focus groups believed that almosteverything

was important,and therefore resisted rankingtheir 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 environmentalproblemsand the effects of potential actions, but believed that priorities shouldbe decided by experts. Nonetheless,

these questions provided

a good focus for the discussion of people’s concerns.

Table VI.2 - Concernsof Preliminary Survey Respondents,

Montauk, March19, 1995 Concern Mean Rank a Decliningstocks of shellfish Decliningstocks of finfish Waterquality Trash on beaches Areasclosed to shellfishing Overcrowding

of recreational sites Declining open space Crowding in boating harbors Too little public access to the water Need to improvepublic access points Less wildlife available for viewing 3.09 3.34 3.43 3.59 3.66 4.23 4.57 4.74 4.88 4.90 5.21 a - Ranked

from 1 to 10, with 1 denoting the most important.
Otherquestionsin these focus groups askedabout priorities related to natural resources, and tested possible question formats for the survey, including standard contingent valuation questions and contingentchoicequestions. Contingent valuation asks people directly to state their willingness to pay to preserve or improvea natural resource. Contingentchoice 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. 17 Contingent choice questions are similar to marketing surveys

17 Several additional questions werealso tested in these focus groups. One question askedrespondentsto indicate their three mostimportant reasons for living on the East End,froma list of fifteen reasons givenby participants in earlier focus groups. This question waslater dropped becauseit did not provide enough informationto justify the space andtime 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. Theattitude questions generated quite a bit of controversy, as they weredesignedto elicit strong opinions, and weredropped fromthe fmal survey for several reasons. First, they tended to drawrespondents’ focus fromthe more important questions in the survey; second, they took too long to answer;and fmally, it was feared that the controversial nature of the questions might result in a 95often doneby businesses to allow themto understandtradeoffs 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 mostpromising format, andthus wedeveloped this method

further in the next focus groups. Asthe contingentchoice questions were

refined throughthe focus groupprocess, the surveybecame less complicated

and less wordy, with fewerattributes includedin each comparison; and graphics wereadded

to the questions. VI.B.1.d. SurveyPretests and Implementation
In May and June, we conducted

several pretests of preliminary surveyinstruments. ~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 surveywaspretested on Long Island, with a groupof people at the First Parish Church in Jamesport, and with randomly

selected people on the beachin Mattituck. In these pretests, peoplewereable to easily comprehend

and answerthe 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 surveyis included in Appendix A 19.
We implemented

the survey during the week of Aug.22-29, 1995in a variety of pre-selected public locations around

the East End, using convenience intercept sampling.We

selected this method

over a mail surveywith probability sampling primarily becausebudget limitations made it necessary to administer both the natural resource survey and the recreation survey together, and becausenames and addresses of visitors werenot available. Thu s, wedesigned the surveys to be administeredin 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. Thesurveylocations, and the number of surveyscollected at each, are listed in Table 3. Atotal of 968 resource surveys werecollected fromyear-roundand seasonal residents of the East End.
In implementing

the survey, interviewers wereinstructed to approachpeople and say: Hi, my nameis ., 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 yoube willing to fill out a survey, which will take about 5-10 minutes?

negative reaction to theentiresurvey. ~8Several of these pretests were conducted in Rhode Island, in order to save time and money. Because the objective at this point

was to ref’me theformat, layout and wording, rather than the content,

it was not necessary to conduct: all tests ontheEast

End. 19Note that the example

shown in the Appendix

is onlyone

of 12different

survey booklets, each of which contains five different

contingent choice questions. 96Theinterviewer then askedthose who agreedto fill out the surveyif they were visitors, year-round residents, or seasonal residents of the East End. Only year-round

and seasonal residents wereasked to fill out the resource

survey.All visitors, andeverythird resident, weregiventhe recreational use survey. When

the survey washanded

to the person, they weretold that there are no right or wrong answers, and that all answers would be confidential. Interviewerswereinstructed to give brief and neutral answersto any questions. VI.B.2. Questionnaire Design VI.B.2. a. Selection of SurveyFormat As mentioned above, we considered two methods for asking the public about their values for protecting andrestoring natural resources---contingent valuation and contingentchoice. 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 takento protect andrestore natural resourcesof the Estuary, andtheir results. Therefore, the survey neededto ask the public about their preferences for important natural resources that would most likely be affected by restoration and preservation programs. Thecontingentchoice formatallows for comparisons

and valuation of many

different combinations

of improvements

in resources that might be obtained by management actions. This will allow for assessmentof programs that wouldaffect 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 minimizesome

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 outcomesof actions. Therefore, responses are focused on tradeoffs among resources, and respondents are not askedto directly expressmonetary values, althoughthese can be inferred from the analysis. Focus groupparticipants foundthis type of question to be easier to answer. Anadditional issue, whichmay be a factor in both contingent valuation and contingent choice surveys is the expressionof "symbolic" values. This mightbe related to expressionsof 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 wouldbe willing to payto protect a particular species of birds fromoil spills. A personanswering the surveymay

not care about those specific birds, but may state a positive willingness to paybecausethey think that oil spills shouldbe prevented. Thus,their response

may not reveal the value of the birds, per se.

97Table V1.3 - Survey Locations and Number Collected

Location Number Collected Beaches: Orient State Beach, Southold

Southold Town

Beach Cedar Point CountyBeach, Southold New Suffolk Beach, Southold East Creek Marina Beach, Southold Alberts Landing Town

Beach, East Hampton Fresh Pond Town

Beach, East Hampton Maidstone Beach, East Hampton Indian Wells Town

Beach, East Hampton Hither Hills Beach, East Hampton Montauk Point State Park, East Hampton Wades and CascadeBeaches, Shelter Island 3 10 3 4 2 16 9 7 16 4 4 4 Shopping Areas: Greenport Comer

Greenport IGA GenoveseShoppingCenter, Southold King Kullen Shopping

Center, Mattituck, Southold K-Mart Shopping Center, Riverhead DowntownSouthampton IGA Southampton King Kullen and Caldor ShoppingCenters, Bridgehampton IGA Shopping Center, Shelter Island 70 44 71 11 72 33 16 107 27 Libraries and Post Offices: Southold Library

Cutchogue

Library, Southold Riverhead Free Library East Hampton

Post Office Sag HarborPost Office Shelter Island HeightsPost Office Shelter Island Center Post Office 4 10 16 87 37 34 60 Miscellaneous: New

London-Orient Point Ferry Goose Creek, Southold CountyCenter Cafeteria, Riverhead Okeanos Aquarium, Riverhead Departmentof Motor Vehicles, Riverhead Pindar Winery, Southold John Drew

Theatre, East Hampton Location unidentified 19 3 23 71 39 19 8 5



### 98Because the surveyused in this study presents only outcomes of actions, respondentsare not given the opportunityto focus on the means of achieving results. Thus,responses are less likely to be expressionsof approvalor disapproval for actions, andmore

likely to focus on the specific natural resources. However,

people maystill express symbolic values for protecting the environment, beyondtheir values for the natural resource improvements.This is moreeasily detected and corrected for in contingent choice than contingent valuation, as discussed below. Finally, it has been pointed out that "surveymethods are better at estimating relative demand than absolute demand,"

even for market goods, and "absolute willingness to pay is hard to pin down" (National Oceanicand Atmospheric Administration 1993, p. 4609). Thus, the relative values 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 onrelative values andpriorities for resources is more important han the estimationof total dollar values. Hence, contingent

choice, rather than contingent valuation, may be more effective in obtaining the information neededto compare altemative policies. Based on the results of focus groups and past research (Mazzotta and Opaluch 1995), each comparison

wasdesigned

to include only three attributes: Two of the five natural resourcesincluded in the survey, andthe cost. This simplifies the choice, so that it is more likely that choiceswill be basedon consideringandbalancingall of the attributes.
In the final survey, respondents were askedto select froma set of three hypothetical choices: no new action or one of tworestoration/protection programs. Each option wasdescribedby different levels of resulting natural resources, andthe annualcost to eachhousehold. In early focus groups, only the twoprograms

werepresented, but focus groups participants indicated that a "no new action" option should be added,for two

reasons. First, it allowed people to expressa preference

for no action if they did not support either program. Second, it provideda baseline from which to judge the benefits of each of the programs. VI.B.2.b. Selection of Natural Resourcesand Levels VI.B.2.i. Natural Resources Included in the Survey Basedon concems

expressed by participants in focus groups and natural resources identified as important by the Technical Advisory Committee,

weselected five natural resources to be included in the survey:
 ̄ farmland  ̄ undeveloped land  ̄ wetlands  ̄ safe shellfishing areas  ̄ eelgrass

99We

included farmland and undevelopedland 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. Bothfarmland 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 amountof 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 wereaware of the importanceof wetlands to water quality and as wildlife habitat, and expressedconcem

for declines in the quantity of wetlands in the area. Peoplealso expressed concem for reopening closed shellfishing areas. This is due to the historical significance of shell fishing to the local economy, and its importance

as a recreational activity, combined with the declines in shellfish caused by brown tide 2° and the recent large increases in areas closed to shell fishing. Finally, weincluded eelgrass for tworeasons. First, much of the Estuary’s eelgrass wasdestroyed bythe brown

tide, andone proposed

action is restoring eelgrass areas. Second, eelgrass serves as a proxyfor fish andshellfish populations. Many participants in the preliminaryinterviews and focus groups expressed great concern over declines in fish over the years. However, the technical consultants could not easily determine potential changes in these populations resulting from proposed actions. Therefore,wehoped that by including eelgrass, which serves as fish and shellfish habitat, the survey wouldcapture some of these concems.

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 determinedthe baseline in consultation with the TechnicalAdvisory Committee,

based on historical declines and the judgment of experts, for eachresource.

20 Brown Tide is an algal bloom

of the species Aureococcus anophagefferens,whichfirst occurred in 1985-1988, and has subsequently

recurred for shorter periods. Themajorbrown tide episodes severely affected scallops, eelgrass, and other shellfish in the estuary (see Suffolk County Department

of Health Services 1992). 100First, we determined the level of each resource in 1981and the current tevel. 2t Next, weprojected the levels in 2020 based on information and judgmentsprovided by members of the Technical Advisory Committee

regarding the anticipated changein the resource if no newactions were to be taken. In cases where no technical projections were available, we based the projection on extrapolationof past trends.
In the survey, respondents were presented with two pages of backgroundinformation, which describedthe level of each resourcein 1981,the current level, and projections of levels in 2020 if no newactions are taken. These levels are shown in Table 4, and the survey pages are shown in Appendix A. Respondents

weretold that "trends indicate approximate conditions in 2020," in order to make it clear that these are not precise, scientifically-based projections. When the survey was created, the TechnicalAdvisory Committee had not completed

their scientific studies of how

potential actions mightaffect specific natural resources. Toget around this lack of information, wechose the levels of each natural resource to be includedin the surveyin order to bracket a reasonable range of changes under various policy actions, including the "no new action" option. The Technical AdvisoryCommittee assisted us in determining the largest realistic and feasible changefor eachresource. Although, in some cases, anticipated results of restoration and preservation programs

are likely to be smaller than those presentedin 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 includedin 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 twolevels associated with hypothetical programs

that would

preserve or restore the resource. In order to make the hypothetical context clear, surveyrespondents weretold: "The following programs

are hypothetical. We

are trying to learn which resources are most important to you and how much you wouldpay to protect them." Thelevels for eachresource and for cost are shown in TableIV.5. If no action is taken, farmlandis projected to decrease by twenty-five percent. The results of different hypotheticalpreservation programs were projected to reducethe loss of farmland to fifteen

21 We compiledthis information fromthe recent brown tide study (Suffolk County Department

of Health Services 1992); a 1981 land use report (LongIsland Regional Planning Board 1981); and information provided by the YorkState Departmentof EnvironmentalConservation and other members of the Technical Advisory Committee. Theacres 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 PeconicEstuary Program study area, weassumed

that the fraction of farmlandand undeveloped land within the study area is proportional to the amount

of total land on the East Endthat is within the study area. 101percent or maintain it at the current level. Undeveloped land wasalso projected to decrease by twenty-five percent if no newaction is taken. Becauseit did not seemrealistic to preserve undeveloped

land at the current level, hypothetical preservation programs wereprojected to reduce the loss of undeveloped

land to ten percent or five percent.
Table VIA

- Past, Present and Projected Natural ResourceLevels

Natural Resource Projected 1981 Level 1995 Level 2020 Level Farmland a Undeveloped Land a Wetlands b Safe Shellfishing Areas b Eelgrass

b 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 a - Calculated based on Long Island Regional Planning Board 1981, and Suffolk County Department

of Health Services 1992. b - Calculated based on Suffolk County Departmentof Health Services 1992 and information provided by NY

State DEC.

Wetlands wereprojected to degradeby twenty-five percent if no new action is taken; or they might be preserved

at the current level or restored to ten percentabove the current level. The

baseline for safe shellfishing areas is a twenty-fivepercent decrease, andhypothetical programs mightmaintain the current level or increase safe areas by ten percent. Thebaseline for eelgrass is a ten percent decrease, and it mightbe preservedat the current level or increased by twenty-fivepercent. The costs of the hypothetical programs weredesigned 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. Theno new

action option wasalways

presented at no additional cost to each household.

Theother levels of annualhousehold cost included were$50, $100, $200, $300, and $50O.

102Table Vl.5 - Levels of Natural Resources and Cost in the Survey
NoAction, Baseline Level (% Change) Level with Moderate

Preservationor Restoration (% Change) Level with High

Preservationor Restoration (% 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 %) (eta’rent) (+ 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 estimatevaluesfor eachresource, we chosea statistical design that required sixty different comparisons. 22 However,

wewanted to make sure that the survey could be answered in a reasonable amount

of time (10-15 minutes). Thus, weincluded five comparisons in each twelve different surveybooklets.

VI.B.2. iii. Description of The Survey Questionnaire Thefirst page of the full surveybooklet described the goal of the survey and showed a map of the relevantarea. The

first three questions

asked about participation in recreationalactivities in the area; support for different possible actions; andopinions about brown tide. Nextthe background

information for the contingent 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 representativemember of the public.

22The combinations

of attributes

and levelswere

selected using a method based onAddelman’s

fractional factorial design,which

produces orthogonal arraysof attributes (Addelman 1962a, 1962b; Addelman dKempthome, 1961). This allows for statistical independence

among theattributes

so thatthe model

estimation will bestatistically efficient. 103VI. C. DESCRIPTIVE RESULTS

VI. C. 1. Who

Responded

to the Survey? Table VI.6 reports 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 majorcharacteristics of our samplecan be summed up as follows:

 ̄ Most are year-round residents (73%).

 ̄ Most are homeowners (85%).

 ̄ Amajority 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 1⁄2mile or less fromthe 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.

 ̄ Mostare employed

(67%), and 21%

are retired.

 ̄ Compared

to 1990population figures, the samplecontains a slightly higher percent of women and people in the middle age groups, and the sample population is better educated and morewealthy than the general population] 3

23 Notethat censusdata are only available for year-round residents, so these results are for year-round residents in the sample. 104Table VI.6 - Respondent Demographics,Full Sample Question Percent

Category Number of Sample 9: Type of residence Primary Home 673 73.1%

Second Home 248 26.9% Did Not Answer 47 4.86% 10: Ownor rent Own 773 84.7%

Rent 140 15.3% Did Not Answer 55 5.7% 12: Closest water body Peconic Bays 564 58.3%

Long Island Sound 216 22.3% Atlantic Ocean 219 22.6% 12: Distance from any water body Waterfront

_< 1/2 Mile From

Water >1/2 Mile FromWater Did Not Answer 143 14.8% 347 35.8% 382 39.5% 96 9.9% 12: Dist. from Peconic Bays Waterfront

_< 1/2 Mile From

Water > 1/2 Mile From

Water 116 12.0%

234 24.2% 618 63.8% 13: Length of residence 0-10 Years

11-20 Years 21-30 Years >30 Years Did Not Answer 297 30.7% 245 25.3% 176 18.2% 185 19.1% 65 6.7% [4: Gender Female

Male Did Not Answer 540 57.8% 394 42.2% 34 3.5%
Size of household 1-2 Household Members

3-6 Household Members >6 Household Members Did Not Answer Averagehh size 481 49.7% 430 44.4% 24 2.5% 33 3.4% 2.94 16: Number of children No Children

1-2 Children >2 Children Did Not Answer 440 45.5% 254 26.2% 66 6.8% 208 21.5%

105Table VI.6 (Continued) Percent Question Category Numberof Sample 17: Age 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% 18: Education Less Than High School

Some High School High School Graduate SomeCollege Associate’s Degree Bachelor’s Degree Advanced Degree Did Not Answer 5 .5% 16 1.7% 126 13.6% 167 18.1% 90 9.7% 236 25.5% 284 30.7% 44 4.6% 19: Employment EmployedFull Time 497 53.9%

Employed

Part Time 123 13.3% Full Time Homemaker 66 7.2% Full TimeStudent 28 3% Retired 197 21.4% Unemployed 11 1.2% Did Not Answer 46 4.8% 20: Income <$15,000

$15,000-$24,999 $25,000-$34,999 $35,000-$49,999 $50,000-$74,999 $75,000-$99,999 $100,000-$149,999 $150,000 or more DidNotAnswer 43 5.1% 77 9.1% 102 12.1% 111 13.2% 201 23.8% 113 13.4% 86 10.2% 111 13.2% 124 12.8%

106Table V.7 - Population Demographics vs. Survey Respondent Demographics Year Round Seasonal 1990 Resident Resident Population" Sample

b Sample

b Gender Female 51.83% 60.85% 51.36% Male 48.17% 39.15% 48.64% Age 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 11.04% 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% Education (over age 24) < 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% Income < $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% $150,000and up (Inc. cat. 8) 3.41% 5.62% 34.39%
a - The 1990 population figures are foryear-round

residents only; sources ofdata are Long Island Lighting Company 1995 and Suffolk County Department ofPlanning 1991, based on1990 census dataand projections.

No demographic dataare available forseasonal residents. b - Sample prcents refer to percentages

ofthose who answered the question.

107Table VI.8 - Numbers of Respondents by Town Town Numberof Percent

Respondents of

Sample Brookhaven 19 1.9% Riverhead 110 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 Theresponsesto Question1, whichaskedabout participation in recreational activities, are shown in Table VI.9. Almost

all of the respondents

(97%) participate in at least oneof the listed activities, and 81% participate in at least one activity in the PeconicEstuary. Swimming is the mostpopular activity, with 86 percent participating. Swimming is followed

by walking

and hiking (71%),boating (54%),fishing (53%),other beachuse (34%),shellfishing (32%),artwork(26%) andother (11%). More people participate in activities on or around the PeconicEstuary than any other water body. Thisresult is consistent withresults of a telephone survey of residents conducted

in the fall of 1993 by the Center for Community Researchof Suffolk Community College (the SCC

survey), whichalso foundthat the PeconicEstuary System wasused more

fi:equently 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 opinionsabout proposed

actions, andto give them

an idea of the types of actions that might be taken to obtain the natural resource changes describedin the contingent choice questions.

108Table 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%) 74 516 (53.3%) Shellfishing 261 (27.0%) 18 (1.9%) 55 (5.7%) 39 311 (32.1%) Walking/ Hiking 518 (53.5%) 339 (35.0%) 306 (31.6%) 69 684 (70.7%) Swimming 610 (63.0%) 468 (48.3%) 362 (37.4%) 79 833 (86.1%) Other Beach Use 201 (20.8%) 169 (17.5%) 131 (13.5%) 44 324 (33.5%) Boating 432 (44.6%) 144 (14.9%) 224 (23.1%) 67 520 (53.7%) Artwork 204 (21.1%) 120 (12.4%) 129 (13.3%) 40 252 (26.0%) Other 68 (7.0%) 46 (4.8%) 51 (5.3%) 24 103 (10.6%) All Activities 779 (80.5%) 586 (60.5%) 534 (55.2%) 205 (21.2%) 936 (96.7%) Asexpected, all of the actions are supported by a majority of respondents. Theactions with the greatest level of supportare buildingmore pumpout stations so that boat dischargecan be prohibited; improving 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 weremore

general and not perfectly comparable to those presentedhere, their results are similar. For example,83 percent of their samplesupport stronger building regulations, which is comparable

to the 76.3 percent of our sample who support zoningregulations, 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 changefertilizers, whichis consistent with the 68.2 percent of this sample who supportrestrictions on fertilizers andlawn

109Table VI.10 - Support for Actions Strongly Strongly No Average Action Support Suppo~ Neutral Oppose Oppose Opinion Rank Prohibit sewage

discharge from boats. (requiresbuilding more 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 people how to reduce their 70.0% 17.4% 4.6% 0.7% 1.0% impacts on the environment. 6.2% 3 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 property,including

requiring bufferzones

(areas left untouched)

between development

and the water, 61.9% 16.8% 8.5% 2.5% 2.5% 7.9% 6 drywells for roofrunoff,etc. Zoning to limit future development. 62.3% 14.0% 11.1% 2.4% 2.2% 8.1% 7 Limitingremoval

of vegetation on newly 58.5% 18.2% 9.9% 2.8% 2.6% developed land. 8.1% 8 Requiring repair or upgrade of septic systems

when 51.4% 21.8% 12.4% 2.5% 2.6% property is sold or improved. 9.3% 9 Controlling

stormwater runoff withdiversion,

catch 47.6% 26.1% 12.9% 1.7% 0.9% 10.7% basins, etc. 10 Restricting use of fertilizer andlawn chemicals for 43.4% 24.8% 16.7% 5.4% 2.2% residential

property. 7.5% 11 Requiring pumpouts of existing septic systems every 32.7% 21.3% 20.9% 6.7% 4.2% 14.2% 12 4 years.

110chemicals. While 75 percent of the 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 groupsof peoplewould be more likely to support or oppose specific actions. For example,boaters, who would be mostinconvenienced

by discharge prohibitions, were expected to show less supportfor this action. However, 90.4 percent of boaters, versus 88.2 percent of non-boaters

supportno-discharge

regulations ( although,basedon a test of equality of the means, the difference in the mean strength of support for dischargeprohibitions between boaters and nonboaters is not statistically significant). Thus, boaters appear to be willing to undergosome 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, waterfront residents were expected to be less likely to support restrictions on waterfrontproperty. Thereis some supportfor this hypothesis,with 76.2 percent of waterfront residents, 79.8 percent of residents who live up to 1⁄2mile from the water, and 82.5 percent of people who

live more than 1⁄2mile fromthe 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 wascompared for members

of environmental

groups versus those who

are not members.

24 Although

the averagelevel of support wasslightly greater for members of environmental

groups, the means

werenot statistically significantly different. Fromthese 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 appearto be primarily symbolic of a general desire for action. Thatis, peopleseem to supportmostactions. Thus,these results do not provideadequate information with which

to make policy decisions. VI.C.4. Opinions About Brown Tide Question3 asked respondents whether they have heard of brown tide, and if they have, about their level of concern,whether their activities havebeen affected, and whether they supportfunding

for further study. Theresults are reportedin TableV. 1. Mostpeople (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

z4 Atotal score for all actions wascalculated by summing the level of support for all actions, where 1 indicates strong support and 5 indicates strong opposition. Themean score for members

of environmentalgroups was 19.51, and that of non-members

was25.1, with a lower score indicating greater strength of support. 111of those who had heard of brown

tide affected, followedby shellfishing (40%) and fishing (37%). A small number

of people said that boating or other activities were affected. The mostcommonly mentioned

"other" activity affected waseating seafood. Themajority (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 remediesfor it.

Table V.11 - Responses to Brown Tide Questions Heard of Heard

of and Affected Brown Tide by BrownTide 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%) Q. 3D - Do you support or oppose more funding to study the causes of brown

tide and howto 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 Question21 asked respondents whetherthey 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 wasto 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 membershipin organizations.
112Sixty-five percent of respondents belongto at least one organization, and 31 percent belongto an environmental

organization. This is comparable to the results of the SCC survey, which

foundthat 57 percent of respondents had donated to environmentalorganizations, 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 ECONOMICVALUE VI.D. 1. Responsesto Contingent Choice Questions
Asdiscussed above, there were60 different choice questions, with five included in each booklet, to create twelve different booklets. Therewerebetween 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 showshowmany of the contingent choice questions were answeredby 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.

113Table 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%

Olderrespondents, and those with lowereducation and income levels, wereslightly less likely to answer all of the choice questions. Of those who answered at least one choice question, almost all (91.6%) chosean action, rather than no action, for more than half of their answers.Only27 people (3.0%) chose no action for all of the choice questions answered.These results demonstratethe strong environmental concern expressed by survey respondents, but also maysuggest some symbolic bias, if respondents

votedto "take action," irrespective of the associated levels of resource protection and cost. Below wediscuss some

methods that weemployed

to identify and control for this possiblebias. Of those who

chose either Program

A or Program

B, morepeople chose Program A than Program B.25This may

indicate a tendency

to select the option placedin the center, or may simplyindicate that the options presented as Program A were preferred more

often. This is discussedfurther in the Technical Appendix.

VI.D.2. Results and Discussion The statistical models

usedto estimatethe 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 eachhousehold. These weightsare measured

by the estimatedcoefficients for eachresource and cost. From these coefficients, relative values for the

zs Of respondents whoanswered morethan one question, 434 (48%)chose Program Afor morethan half of the questions they answered, and 76 (8.5%) chose Program A for all of the questions

theyanswered.

This compares 287 (32%), whochose Program B for morethan half of the questions they answered, and 15 (2%) whochose Program B for all of the questions they answered. 114different resources, and dollar values for protecting an additional acre of each resource, can be

calculated. 26
Theresults for two different models are rePorted in Table VI. 14. 27 Themodelresults indicate that the order of priorities for protection or restoration of resources is as follows: farmland, eelgrass, wetlands, shellfish, and undevelopedland.

Table VI.14 - Estimation Results

Value /Acre/hh/ 95% Confidence Avg. Value Coefficient year IntervaP /Acre/Year b Model 1: c 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 Model2: 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 whichthe "true" value is likely to fall, with a 95% probability. b - Calculated based on 73,423 households. c - Conditional Logit model. d - Nested Logit model.

26 These calculations are described in detail the Technical Appendix. 2z Model1 is the standard conditional logit model, and Model 2 is the nested logit model.
115Asdiscussed above, symbolic values are often an issue for both contingent valuation and contingent choicemethods.

The estimationresults show

that this is an issue in this survey, andthat respondents to the survey are more

likely to chooseto take action, independent of the specific results of the action. Thus,these respondents may be expressinga 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 concem 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, fromthe 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. Theestimated dollar values range from around$2.1 thousandper acre per year for undeveloped

land, to around $10thousand for farmlandfor Model1; and around $1.2 thousandto $6.4 thousand for Model2. The values from Model2 might be interpreted as the portion of respondents’

willingness to pay to take action which can be attributed to the described changes in natural resource levels. This is smaller than the estimated value in Model1, which includes a "symbolic"

effect. Although the estimateddollar values differ, both models result in the same

orderingof priorities and relative valuesfor the natural resources.These results indicate that priorities andrelative valuesare robust with respect to different model specifications, and are independent of symbolic

effects, but that the estimated dollar values vary somewhat, althoughthey are close in magnitude. Therefore, it may be concluded

that the model

is relatively robust to different specifications, and that the proportionof valuethat is "symbolic" is not great. 3. Discounted Present Values Discounting

is a means

of aggregating

dollar values over time, andis basedon the idea that natural resources are assets that provide a flow of services over time. Thus, "[t]he economic value of a resource-environment

systemas an asset can be defined as the snm of the discountedpresent values of the flows

of all of the services. The benefit of anypublicpolicythat increasesthe flowof onetype of service is the increase in the present value of that service" (Freeman 1993,p. 5). Thediscounted

present values for twodiscountrates are presented in Table IV. 15. The

7 percent rate is used to approximate

a "typical" interest rate for public planning.For example, 7.625percent was the official water resources planning discount rate for fiscal year 1996(U.S. Department of the Interior 1995).

28The3 percent rate is includedfor comparison, because it is often arguedthat the social discount rate shouldbe lower. The25 year time horizon waschosen to match the time period

28 This is the rate required by the government for Federal water and related land resources planning, and is based on the averageyield during the preceding fiscal year on interest-bearing marketable securities of the UnitedStates with 15 years or more

remainingto maturity (U.S. Department of the Interior 1995).presented in the survey. The 100year time horizon wasused to identify values associated with a

longer time horizon.

Table IV.15 - 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 $202,175 $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 .... i

* r is the discount rate; t is the timehorizon in years.

VII.E. CONCLUSIONS AND SUMMARY This section describedthe development, implementation,

and results of a survey of public values for important natural resources of the Peconic Estuary System. We developedthe survey through an extensive six-month

process of interviews, focus groups, and pretests. We selected the contingent choice method

to elicit values for five importantresources: farmland, undeveloped land, wetlands, safe shellfishing areas, and eelgrass. The survey wasimplemented in August, 1995, to 968 yearround and seasonal residents of the East Endof Long Island.
In summary,

residents of the East Endare very concemed about protecting of the area’s natural resources, andare willing to paya significant amount o do so. Our

statistical results indicate that respondents are so concerned about protecting the East End’s environmentthat they wouldbe willing to pay to take action, independent of specific results of the action. We corrected for this

117"symbolic" value in our estimation models, and present the results of both the standard and

"corrected" model. Overallpriorities for the five natural resourcesincluded in this surveyare for farmland, followed by wetlands, eelgrass, shellfish areas, and undeveloped land. Discounted

present values for the most conservative modelrange from around $14 thousand to preserve an acre of undeveloped land, to around$75thousandto preserve an acre of farmland. Theresults 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 Addelman, Sidney. 1962a. "Orthogonal Main-Effect Plans for Asymmetrical Factorial Experiments," Technometrics4 No. 1 (February): 21-46. Addelman, Sidney. 1962b. "Symmetrical and Asymmetrical Fractional Factorial Plans," Technometrics4 No. 1 (February): 47-57. Addelman, Sidney, and O. Kempthorne. 1961. Orthogonal Main Effect Plans. United States Airforce, Office of AerospaceResearch, Aeronautical ResearchLaboratory, ARL Technical Report 79. Freeman,A. MyrickIII. 1993. The Measurement of Environmental and Resource Values: Theory and Methods. Washington,D.C.: Resources for the Future. Greene, William H. 1993. EconometricAnalysis. New York: Macmillan. Greene, William H. 1995. LIMDEP Version 7.0 User’s Manual. Bellport, NY: Econometric Software,Inc. Hanemann,

W. Michael. 1982. "Applied Welfare Analysis with Qualitative Response Models," California Agricultural Experiment Station, October. Hanemann,

W. Michael. 1984. "Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses,"American Journal of Agricultural Economics 66 (August): 332-341. 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 EmpiricalInvestigation of the Consistency of Nested Logit Models with Utility Maximization," AmericanJournal of Agricultural Economics77 No. 4 (November): 875-884. Kling, Catherine L., and CynthiaJ. Thomson. 1996. "TheImplications 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 for Nassau and Suffolk Counties. Hicksville, NY. Long Island Regional Planning Board. 1981. Land Use: Quantification andAnalysis of Landuse for Nassauand Suffolk County.

118Maddala, 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," LandEconomics 71, No. 4 (November):

500-515. McFadden,

Daniel. 1981. "Econometric Models of Probabilistic Choice," in C. F. Manski and D. McFadden,

eds., Structural Analysis of Discrete Data with Econometric Applications. Cambridge: MITPress. National Oceanic and AtmosphericAdministration. 1993. Natural ResourceDamage 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 TheConceptof Place in Environmental Ethics," Journal of Energy, Natural Resources and EnvironmentalLaw 12 No. 2: 349-418. Suffolk Community

College. 1994. Peconic Estuary Public Opinion Survey. Center for Community Research, Riverhead, NY. Suffolk CountyDepartment of Health Services. 1992. Brown Tide Comprehensive

Assessment and Management Program. Suffolk County Department of Health Services. 1994. Peconic Estuary Program Action Plan. Suffolk CountyDepartment of Planning. 1991. Estimated PeakSeasonal Population - 1990. Unpublishedspreadsheet. Suffolk County Department of Planning. 1991. Estimated Peak Seasonal Population - 1990.

~ Unpublished spreadsheet. U.S. Department

of the Interior. 1995. Change in DiscountRatefor WaterResources Planning. 60 Federal Register 66323(December 21)

119Appendix A - Example Survey *

* Readers who would like to obtain a copy of the survey should contact the authors.

Appendix B - Technical Appendix Basedon 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(fl’zy + a"zjwi)

= ~ cxp(fl’Zy + a’zywi)" (1) P~jis the probability

that individual

i will select option

j; ~ is a vectorof attributes of the choice (e.g., the levels of natural resourcesandthe cost), which may also vary across individuals; wi is a vector of characteristics of the individual; and I 3 and a are vectors of parameters of the model,estimated using maximum

likelihood techniques. Box-Cox tests were carried out to select an appropriate functional formand, basedon these tests, the linear form was chosen for all subsequent

estimation. Thesecond

altemative specification is the nested logit model, which allows for correlations between the two action alternatives. Thenested logit model wasstructured so that respondentsare 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
Pks = P( k[j)P(j). 

### (2)
The choice probability for each of the three lowestlevel altemativesis conditional on the choiceto take action or not. In this case, if the person choosesnot to take action, then the probability of selecting the "No

New Action" alternative is 1, since it is the sole alternative onthat branch of the nested model.If they choose to take action, the probability of selecting "Program A"or "Program B" is exp(Vklj) exp(fl’xkij.) exp(fl’xkl:)

P(klj)= ~ exp(V,,ij)= ~ exp(fl,x.lj)= exp(Jj)

nlj nlj 

### (3)

120where

k is oneof the twoalternatives, Program Aor Program

B; j is the choice to take action; n is the number

of altemativesin choicej, which would be 2; andJj 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 

# exp(yx.tj)). (4) Theprobability of choosing to take action or not is
P(j) = 

### exp(a’Y+ r,.Jm)’

m 

### (5) where mis the number

of branches, which

in this case is two. TheparameterJ is the inclusive value coefficient, which is related to the correlation between alternatives within a branch. A value of z between 0 and 1 indicates that there is greater substitutability within, rather than across, groups of alternatives. In termsof this study, this would indicate that Program

Aand Program

B are closer substitutes than either program and the "No

New Action" alternative. If z is equalto 1, then all alternatives are equally substitutable, andthe model becomes identical to the standard conditional logit model(McFadden 1981; Kling and Herriges 1995; Kling and Thomson 1996). The results of the conditional logit model werecompared

to two altemative specifications. These results are presented in TableB-1. Thefirst is the conditional logit model with two alternativespecific constants, one for the choice of an action versus no action, and one for Program B. Thus, the coefficientonthe first constantterm reflects factors other than the levels of attributes that affect the choiceof an action versus the choiceof no action. 14 For example, respondents

may be expressing a symbolic

willingnessto 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 Aand B, suchas an order effect. Theresults of both of these models indicate that there may

be effects on choices unrelated to the described attributes. In the alternative-specific constantmodel, bothconstanttermsare statistically significant, indicatingthat there is an effect on choicesunrelated to the quantities of the individual attributes, but is instead related to the choicesthemselves. The positive andsignificant coefficient
,4 Note that, alternatively,

this could alsosuggest

use of the incorrect

functional form, although the Box Cox test shows that the linear form

is the most

appropriate of the commonly

used functional forms. 121for "Action" indicates that people are more likely to choosean action rather than "No New Action," independentof the action’s specific effects on natural resources. Similarly, the negative and significant coefficient for "Program B" indicates that, even if Program Aand Program

Bproduced the same results in terms of preservationof natural resources, respondents are more likely to choose Program A. Thecoefficient on Action indicates that there is an 87 percent probability that the average respondent

would select action over no action, if that action cost nothingand provided zero resource protection. Similarly, conditional on taking action, there is a 55 percent probability that the representative respondentwould chooseProgram

A over Program

B if their costs and levels of resource protection wereidentical.
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,O8O 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 pZ=l.(L (13)/L (0)) .138 Modelwith AlternativeSpecific 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 ShellfishAreas .000108 $0.034 $0.023 $0.044 $ 2,467 Eelgrass .000214 $0.067 $0.052 $0.081 $ 4,909 Cost -.003207 p 5 .171 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 $6,0O3 Cost -.003441

.3397 p 2 .309 * - Calculated

based on 73,423 households. 122Theseconstant terms for taking action and for differences in probability of selecting A and Bmay

be interpreted as representinga qualitative or symbolic dimension of respondents’

preferences, while the coefficients on the natural resourcesrepresent 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 expressinga symbolic

willingness to payto take action to protect the environment of the East End.Similarly, the coefficient on the constant term for Program

Bindicates that there is some qualitative reason that people choose Program A, and measuresthe degree of preference for A over B whichcannot be explained by the described attributes of the two programs.Note that this constant term is statistically significant, but quantitatively small. Thehigher 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 anotherbeyond

the described effects is not expected, andcould occur for a variety of reasons. For example,

the effect could be related to the ordering of the twoprograms, their placement

on the ipage, or could possibly indicate that respondents infer some

preferencefromthe labels (e.g., an is better than a B). The constantfor the choiceof action versusno action accounts for qualitative aspects of the decision to choosean 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 alternative approachto modeling is to use a random

effects model,wherethe random components of preferencesfor the twoaction 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 mightexhibit a bias towardstaking action, while others mightexhibit a bias against taking action. Therandom

effects model

can be implemented

using the nested logit approach, whichcaptures the correlation of the random components

of utility associated with the two action alternatives. Tests of the inclusivevalueparameter in the nestedlogit model

indicate that there is greater substitutability between Program Aand Program

Bthan between

taking action or not, and that there is significant correlation between

the Program

options. Theconstant term for Program B is similar in magnitude to that estimatedin the previousmodel.

123Economic values for the conditional logit modelwere 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(Ro.,Mi)= U(Rik, Mi-Ck) forallk¢ j, (6) where j represents the "No

New Action"altemative, or the baseline levels of the resources, so that Cj=O; k is the option selected; andCkis the maximum willingnessto payfor option k. For the linear approximation

of the utility function presented above, this can be solved for Ckas follows:

(7) Thus,for the condkional

logit model,the dollar value to the averagerespondent for a unit change in eachof the natural resources is calculated

as the ratio of the coefficientonthe resource, 13, to the coefficient on cost, y. The calculation of dollar values for the nested logit model mustaccount

for the nested structure and the inclusive value parameter, ~. The formulafor the compensating variation associated with a changein one of the attributes of the choice is (Kling and Thomson 1996; Hanemann

1982):



### (8) where Vis the utility function, the superscriptsonVindicate whether the attributes are set at the new level or the old level, andy is the coefficient on cost. The estimateddollar valuesand relative values, calculated as ratios between the coefficients on each pair of resources, werecompared for each modelusing Friedman’s test for morethan two related samples (Neave and Worthington, 1988). Basedon 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 modeland the alternative-specific constants modelusing the Wilcoxon signedrank test doesnot reject the hypothesisof equality of values for these twomodels. TheFriedman

test does not reject the hypothesisthat the relative values for natural resources are equalfor all three models. Additionally,

the ordinal priorities for all three models are the same,

with farmland mostimportant, followedby 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 values vary somewhat between the base modeland the two alternative specifications. However, the estimated dollar values for the three models are close in magnitude.

Therefore, it may

be concluded

that the
124model

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 symbolic component to choices, which

is comprisedof 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 andconditional logit withconstantsmodels, andthe fact that the results are not statistically different, indicate that these biases are likely overwhelmingly in onedirection--towards taking action rather than no action. Thus, both of these models appearto accountfor a "symbolic" aspect of values, and to separate that from estimated values for specific natural resource improvements.

Note, however,the Nested Logit modelprovides considerable improvement in fit, as measured

by the p2 statistic.

125