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FINAL REPORT 

# Recreational and Resource Economic Values for the Peconic Estuary System

Final Report on Phase 

### I1 of the Estuarine Use and EconomicValue Assessment of the Peconic Estuary System

Prepared by: James 

### J. Opaluch, Thomas A. Grigalunas, Jerry Diamantides, Marisa Mazzotta and Robert Johnston ECONOMIC ANALYSIS, INC. 

### P.O. 

### Box 3462 Peace Dale, Rhode Island 

### 02883 With a contributionby Applied Science Associates Narragansett, 

### RI Prepared for: Peconic Estuary Program Suffolk County Department of Health Services County Center Riverhead, NY 

### 1 

### 1901

PO Box 3462 

### Peace Dale, Rhode Island 02883 (401 )789-0050

## Estuarine Use and Economic Value 

## of the Peconic Estuary System Phase 11: A 

## Perspective on Nonmarket Values.
This is the final report of the Phase 

### I1 entitled “Estuarine Use and Economic Value of the Peconic Estuary System”. The goal 

### ofthe study isto provide economic information to support management decisions for the Peconic estuaryunder the National Estuaries Program. Phase 

### I of the study used availabledata to summarize the level ofkey estuarine-related uses and to estimate market values associated with the Peconic Estuary system. Major estuarine uses are: 

### (1) commercial fishing, 

### (2) sportfishing, 

### (4)beach use, 

### (5)non-consumptive wildlife-associateduse, 

### (6)hunting and 

### (7) other uses. Market values, or “economic impacts”, are measures of economic activity supported by the estuary, and include number of establishments, employment, wages and total revenues. Phase 

### I also included a study ofthe potential for, and constraints on, aquaculture in the Peconic estuary. Thegoal ofPhase

### I1 wasto collectprimary data to quantifynon-market uses and non-market values of the Peconic estuary. Phase 

### I1 was comprised of 

### (1) a recreational survey, 

### (2) a property value study, (3) a wetlands productivity study, and (4) an amenityvaluation survey. Therecreation study collecteddata onthe levelparticipation invariousrecreational activities. Therecreational studyalso estimated the economic value the public has for key activities and determined how the value 

### of recreation activities changes with key indicators ofthe quality ofthe experience (e.g., water quality for swimming, or changes catch rates for fishing). The property value study identifies how amenities like farmland and open space affect the value of nearby properties, providing a measure 

### of amenity (or disamenity) services to nearby residents. The wetlands productivity analysis estimatesbiological “production” 

### of important fish and bird speciesby wetlandsbased on habitat and nursery functions. The amenity valuation survey identifies priorities and values for key amenities contained in the Peconic estuary study area, including open space, eel grass, unpolluted shellfish areas, farmland and wetlands. The goal 

### of Phase 

### I11 is to draw together these various studies in order to prioritize specific management actions for the Peconic estuary. Phase 

### 111, scheduled for completion in Winter 1999, will link together science oriented studies, such as water quality modeling, with’the economic studies described above to carry out cost-benefit analysis of specific policy actions.

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

EXECUTIVE 

### SUMMARY

I.A. PURPOSE 

### AND SCOPE The environmental andnaturalresources (“natural assets”) ofthe Peconic 

### Estuary System--thebay waters, beaches, wetlands, ecosystems, habitats, and parks and watershed lands--provide many services to the public. Outdoor recreation, scenic views, and the productivity of wetlands that contribute to stocks of fish, birds, and other speciesused for commercial andrecreational purposes are but a few examples of these services. While the quality 

### ofPES coastal amenities is widely recognized, little information exists about the uses and users 

### of PES natural resources. Even less is known about the value that the public holds for the natural asset services of the PES--that is, what they are “worth” to the public. This is because recreation, scenic views, and ecosystem productivity are not directly valued in markets. Lacking information on recreational and resources values, comparisons cannot be made of the benefits and costs of prospective actions to preserve or restore PES natural assets. This report is designed to help fill this major gap. In this report, we present the results of four non-market valuation studies carried out by Economic Analysis Inc. (“EAI”) 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 valuesof key recreational activities, and 

### (2) otherresource valuesprovidedby the natural assets of the PES. The economic valuation studies described herein were done to contribute to benefit-cost analyses of proposed management actions. This document is Phase 

### I1 of a series 

### of economic studies being done by 

### EA1 for the Peconic Estuary Program. 

### A Phase

### I report (Grigalunasand Diamantides, 

### 1996) 

### (1)provided an assessment 

### of the “economic impacts” of the PES, in terms of business revenues, employment, and wages of estuarine-relatedsectors,and 

### (2) summarized availableinformationonrecreationaluses. Subsequent work by 

### EA1 will include benefit-cost analyses of resource management actions and sustainable financing options for the selected, preferred actions.

1



### i

### I.B. ESTUARINE-RELATEDUSES, RESOURCES AND ECONOMIC VALUES 

### T.B. 1.Introduction and Overview 

### No single method can capture the value of the variety of services provided by the natural assetsof thePES. Recognizing themany uses ofPES naturalresources, wedesigned andimplemented a suite of fournon-market valuation studiesin ordertoprovideestimatesofthevalue

### ofparticular services: 

### (1) A 

### Property Value study examines the contribution of environmental amenities to the marketprice ofproperty. Using the Town 

### of Southold as a case study, the Property Value study was designed to measure values of amenities to residents living in the immediate vicinity. 

### (2) A 

### Travel 

### Cost study uses original survey results to estimate outdoor recreational uses in the PES and the economic value that users have for four, key PES outdoor recreation activities: swimming, boating, fishing, and bird and wildlife viewing. This study also examines the impact that 

### (A) water quality has on the number of trips and the value of swimming and 

### (B) the effect of the catch rate on recreational fishing, important recreational uses ofthe estuary and activities much affectedby water quality and resource abundance. (3) A 

### WetlandsProductivity Value study, carried out in collaboration with Applied Science Associates, gives estimates 

### of the economic value of eelgrass, intertidal salt marsh, and sandimud bottoms, based on the value of the fish, shellfish and bird species that these ecosystems help “produce”. The primary focus is on the nursery and habitat services of the wetland ecosystems in the production of commercial fisheries. (4) The

### Resource Value study uses original survey results to estimate the relativepreferences that residents and second homeowners have for preserving and restoring key PES natural and environmentalresources:Open space,farmland,unpolluted shellfish grounds, eelgrass beds, and intertidal salt marsh. This study also provides a perspective 

### on the economic value the public has for these resources, as indicated in their stated willingness to pay for programs to preserve and restore them. Key results for each of these studies are outlined below. A detailed discussion of each method, its purpose, the data used, assumptions and limitations, and results is given inthe chapters that follow. A note onthe style adopted for this document. We believe that theresults ofthe economic analyses presented in this report provide a wealth of data to decision makers and to the public. To make the report accessible to a wide audience, we deliberately adopt a non-technical style. For those interested, Appendixes present the technical details of the methodologies used.

2

### I.C. SUMMARY 

### OF SELECTED RESULTS I.C. 

### 1.Property Value Study (Chapter 111). Data for property sales in the Town of Southold were used to estimate the contribution of specific environmental attributes to the market value ofnearby property. Using a property valuemodel, we found that proximity to open space and other environmental attributes had a significant, positive impact on nearby property values. For example, a parcel ofland located next to open spacehas, on average, a 12.83%higher per-acre value than a similar parcel located elsewhere. 

### To illustrate the how this result might be used, we estimate that a 

### hypothetical contribution ofa parcel 

### of approximately 10acres ofopen spacewould increase adjoining property values by $410,907. Themodel results also showthat density 

### ofdevelopment andother attributes affectproperty values. For example, 2 or 4 acre zoning (Le., 

### R-80 or R-120) has, on average, a 16.65% higher per-acre valuethan a similarparcel locatedelsewhere (Le.,in a 1/2-acrezone). Conversely,property located on a main road (highway 25 or48), orproperty adjoining a farm,has a lowervalue, aftertakinginto account other property attributes. I.C.2. Recreational Uses and Economic Value (Chapter IV). A recreational survey adminstered to residents, second homeowners, and visitors allowed us to estimate (1)participation in outdoor recreation, and (2) the economic value the public has for four key PES recreational activites. We estimate that in 1995: 127,762people took some 

### 3.3million swimming, boating, fishing, or shellfishing,outings 156,184people engaged in about 5.2 million beach use, bird watching, wildlife viewing, or hunting trips Swimming and beach use were the most popular activities, followedby bird and wildlife viewing, boating, and fishing. Shell fishing and hunting had the fewest estimated number 

### of trips. Measurement problems prevented us from including other common activities, such as hikinglwalking and bicycling. Among the 

### PES Bays, Great Peconic Bay isthe most popular, accounting for

### 28% ofallrecreational trips to the PES. Flanders Bay is the least frequently used, with 

### 8% ofPES trips. GreatPeconic

### Bay is the most used for swimming 

### (30%), fishing (29%), and boating (25%). For shell fishing, Gardiners Bay is the most popular PES location, with 33%.

### of all PES shell fishing trips. Outdoor recreation in the PES is enormously valuable to users. Using a Travel Cost model, we estimate the economic value per person, per trip, for four key recreation activities: Swimming,

3boating', fishing, and for viewing of birds and wildlife. The estimatedvalues

### per trip range fiom $49.83for viewingbirds andwildlifeto $8.59 for swimming (in 1995 dollars). These are estimates of what participants would be willing to pay, per trip, 

### above and beyond the amount that they actuallypay to participate. Adding across alltrips, in 1995 Viewing ofBirds andWildlife ($49.3 million) has the highesttotal 

### annual value, followed by Recreational Fishing ($22.4 million), Boating ($1 8.1 million), and Swimming($12.1 million). The corresponding

### asset values ofthe PES (over25 years at 

### 7percent) for these key recreational activities range from $318 million for Bird Watching and Wildlife Viewing to $141 million for Swimming. The 

### PES has an asset value of $276 million for RecreationalFishing and $2 10million for Boating. Quality is important to PES recreationists. Swimming was found to depend upon perceptions of water quality, and swimmers perceptions, in turn, were related to objective (Le., SCDH field sampling)measures ofwater qualityfornitrogen,water clarity, Brown Tide, and coliformbacteria. To illustratehow the results might be used in a Benefit-Cost analysis, we simulatedhypothetical, uniform improvementsin water quality. For example, a 10percent uniform improvement in water quality in each Bay increases the estimatednumber of annual swimming trips by 15 1 thousand and addsyearlybenefits of $1.3million. Most ofthe benefits ($754 thousand) are dueto improvements in water clarity (as measured by Secchi depth). Swimming benefits increase further with a hypothetical,20 percent uniform water quality improvement, although the added benefits from the second 

### 10 percent improvement is slightly less than that due to the first 10percent improvement. The present value of the $1.3 million increase in benefits due to the 10percent hypothetical water quality improvement is $15.1 million, using a 7 percent discount rate and a 25-year time horizon. Thas, ifthe present value of the costs of the policy (or set ofpolicies)to improve water quality did not exceed $15.1 million over the same period, it would be a 

### good investment of scarceresources. Recreationalfishing also was found to depend upon the qualityofthe experience--inparticular,the catchrate. A hypothetical 10percent increase in catchrates raises the economic value per trip by $0.80, the number of trips by 11,249,and total annual fishingbenefits by $472,359. The present value of this increase in catch rates--the increase in the asset value of the PES in providing this service-- 

### is $5.5 million,using the 

### 7 percent rate and time horizon of 

### 25 years used for all cases. On another issue, to address a data gap identified in EAI's Phase I study, Phase 

### I1 survey respondents were askedhow much they spent atroad sidefarmstandsand atvineyardsand forrental accomodations. In total, in 1995, the public spent some $19.4 million at East End roadside farmstands and $5.4 million at vineyards. Using the same assumption as in the Phase I study that 45 percent 

### ofthese activitiesarePES-related (Grigalunas andDiamantides, 1996),the annual

### PESrelated expendituresfor these two activities are $8.7 and $2.4 million, respectively.

'Boating for the primary purpose of fishing is valued under recreational fishing. 4I.C.3. Wetlands ProductivityValues (ChapterV) Eelgrass, saltmarsh and intertidal mud bottoms provide food web and habitat services for many species of fish, shell fish, waterfowl and birds, and thus directly or indirectlybenefit people who consume,hunt, or view these species. 

### A simplefoodweb model was developed thatused 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 ofthese habitat types. The results suggest an asset valueper awe for existing wetlands of approximately$12.4 thousand for eelgrass,

### $4.3thousand for saltmarsh, and 

### $786formud flats,using a discountrate 

### of7percent and 25 yearperiod forthe servicesvalued. Restoredwetlandsareworth somewhat less sinceit may take severalyears to restore fully their natural functions as a food source or habitat. 

### I.C.4. Resource Preferences and EconomicValues (Chapter 

### VI) Early discussions revealed that the public has a strong attachment to environmental and amenity resources of the PES, even if they do not use these resources directly. 

### As outlined above, the Property Value study captures the value 

### of amenities (or disamenities)to nearby properties, the Recreation study estimates use value for key outdoor recreational activities, and the Wetlands Productivity study yields estimates

### of the value of wetland ecosystems in the “production” of fish, shell fish, and birds. However, none of these studies reflects the value residents and second homeowners hold for the general ambience of the PES--the “sense ofplace” it provides, a phrase used often by participantsin 

### EAI’s focus groups. To try to capture these elusive but important non-market resource values, a survey developed and administered to 

### 968 residents and secondhomeowners was used to learn which amenities are most important to people in the study area, and the amount that they would pay to preserve or restore them. Our purpose was (1) to account for the preferences many residents have for resources, even if they do not use these resources directly, and 

### (2) to measure these preferences in dollars, if possible, to compare with other study results. Most respondentsto the resource survey 

### (97 percent) supported at least one hypotheticalaction to protect resources, andindicatedthey would financiallysupport such actions. The relativepriorities of respondents, in order, were for farmland, eelgrass, wetlands, shellfish,and undeveloped land. We also estimated the monetary value that the surveyresponses imply for the resources concerned. The estimated per acre dollar values were about $13 thousand for undeveloped land, $30 thousand for unpolluted shellfish grounds, 

### $54 thousand for saltmarsh, 

### $66 thousand for eelgrass and 

### $70 thousand for farmland, using a 25-year time horizon and 

### 7 percent discount rate. However, we believethat theresourceprioritiesorrelativevalues ofresourcesaremorereliablethan arethe dollar estimates of values and recommend that relative values, rather than dollar values, be used in the process of selectingmanagement actions.

5

### I.D. REFERENCES Freeman, A.M.111, 1993. 

### The Measurement 

### of Environment and Resource Values: Theory and Methods. Washington, D.C.: Resources for the Future. Grigalunas, T.A. and 

### J. Diamantides, 1996. 

### The Peconic Estuary System: Perspective 

### on 

### Uses, Sectors, and EconomicImpacts. Peace Dale, 

### RI: Economic Analysis, Inc. 

### Kopp, R. and 

### V.K. Smith, 1993. 

### Valuing Natural Assets: The Economics 

### ofNatural Resource DamageAssessment. Washington, D.C.: Resource for the Future. Tietenberg,Tom, 1995. 

### EnvironmentalandNaturalResourceEconomics(4thed.)New 

### York:John Wiley & Sons

611. 

### BACKGROUND AND INTRODUCTION

1I.A. BACKGROUND Located within SuffolkCounty at the East End ofLongIsland,the PES is comprised 

### ofthe PeconicFlanders Bays system,GardinersBay andpart ofBlock Island Sound,and the adjoiningwatershed lands (Figure 11.1). 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 towns comprise about 

### 38 percent of the land area and 

### 8 percent of the year-round population of Suffolk County 

### (SCDHS, 1992;Long Island BusinessNews, 1994). Important characteristicsof the PES include: (1) the generallyhigh quality of its coastal estuarine environment, (2)its economy,which is stronglyinfluencedby seasonal,estuarine-relatedactivities, particularlytourism and recreation, and (3)the population, which is highly seasonal and for yearround residents is comprised of smaller, more elderly, and lower-income households than for the County orLongIslandas a whole (Long IslandBusinessNews, 1994;GrigalunasandDiamantides, 1996). Water quality in the PES generally is very good, and the study area containsmany beaches,parks, open space,and habitat for birds and otherwildlife, including some endangered species. These and other environmentalandnaturalresources ofthe PES can be viewed as natural assets. 

### A significant feature 

### of assets--natural or otherwise--is that they can provide a stream of valuable services (“interest”) over time, if maintained. Some of the natural assets of the PES, however, are not being maintained, and others are at risk. Localized water qualityproblemsexist due to runoff, failing septic systems, and inadequate sewage treatment. Substantial areas 

### of open space and farms have been lost due to development; and pollutionhas closed thousands of acres 

### of shellfish groundsused for commercial and recreational purposes. Further, large areas of ecologically productive eelgrass and saltmarsh ecosystems have been lost due to development,Brown Tide, and water pollution. Fin fish and shell fish landings have declinedsubstantiallyovertime, in part dueto theseproblems. The combination of all ofthese adverse developmentsthreaten the quality 

### of life of residents of, and visitors to, the PES. Managementprogramsbeing developedby the Peconic EstuaryProgram wouldpreserve orrestore key PES environmental or natural resources. None of these programs is free, however, and some could be very costly. Deciding whether, where, how much, and when to invest in preserving or restoring PES natural resources requires consideration of many factors. One 

### of these is the economicbenefits and cost of such actions.



### 7

### 1I.B. NATURE AND IMPORTANCE 

### OF NON-MARKET RESOURCE VALUES 

### 1I.B.1. Overview of Studies and Basic Concepts Economic benefits provided by natural asset services show up in at least 

### two ways: (1) 

### As market beneJits to the owners, operators and employees of over 

### 1,000businesses that engageinor supportrecreation andtourism, commercial fishing and seafood activities, and other estuarine-related economic operations (Grigalunas and Diamantides, 1996). (2) As 

### non-market benefits to the thousands of recreationists, visitors, property owners, renters, and others who use or otherwise enjoy the quality ofthe 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 11.2). Estimates were made of employment, wages, sales, and number of establishments for 

### 29 economic sectors identified as estuarine-related, in whole or in part. These sectors include commercial fishing, marinas, eating and drinking establishments, hotels/motels, and other estuarine-related activities.

The Phase 

### I results show that estuarine-related economic activity is a major component ofthe PES economy and an important part of the livelihood of over 10,000residents who own, operate,or are employed in more than 1,000marine and tourism-relatedbusinesses (Grigalunas and Diamantides, 

### 1996). Market data alone, however, give only a very limited view of the overall benefit or value of the servicesprovided by PES estuary-relatedresources. This is because much 

### of what people enjoy in the PES--for example, natural amenities, like open space, attractive views, good beaches and high levels ofwater quality--are not bought and soldinmarkets. Benefits cannotbe countedin economic impact studies if they are not bought and sold on markets; and many benefits that do show up on markets are indirect and hence not linked to the natural asset that provides the service (e.g., fish or shell fish “produced” by PES wetlands and later harvested by fishermen). This Phase 

### I1 report focuses on non-market benefits provided by amenities in the Peconic Estuary System (“PES”). We adopt the view that the natural resources and environmental amenities ofthe PES can be looked at as natural assets (Freeman, 1993;Kopp and Smith, 1993;Tietenberg, 1995) that provide a flow of benefits over time, if maintained. Several standard economic concepts are used throughout this report. Two 

### of these are 

### Consumer Surplzis and 

### Present Value. 

### To avoid later confusion, these concepts should be explained at the outset.

8

### ConsumerSurplus. Peoplebuy goods or engage in activities like recreation when the benefit they receive 

### is atleast as great asthe cost to them. For example, if 

### I can buy a discount airplaneticket for 

### $200,

### I would do so only if it was worth at least that amount to me. If 

### I would pay up to 

### $500 for the flight, then 

### I will buy the ticket and receive a net benefit--an unpaid-for benefit--of 

### $300. Similarly, 

### on a summer day 

### I might be willing to pay as much as $1

### 5 to use a beach, but ifusing it costs me only 

### $5,then 

### I receive an unpaid-for benefit of $10 for that trip to the beach2. In the above examples, the most that 

### I am willing to pay is my assessment of the economic value of the airplane ticket or the day at the beach--what each is 

### worth to me. The net benefit to me in each caseisthe differencebetweenthemost

### Iwouldpay, 

### lesswhat 

### I actuallypay. This “unpaid-for benefit” realized in each example captures the notion of “Consumer’s Surplus”. Consumer Surplus--the benefit people receive from a good or service above and beyond the cost to them-- provides the basis for the measurement of economic benefits used in this document. Ofcourse, the “trick” 

### is to use sound methods to discoverthe Consumer Surplusthepublic receives for the services provided by the natural assets of the PES. Fortunately, standard methods are available to dojust that. These methods, and how they were used to value the services of 

### PES natural assets, are summarized in the next section and explained in detail in succeeding chapters. 

### Discounting and 

### Present Value. A second important concept involves discounting. Many benefits or costs occur over time, and discounting is the process used to convert future benefits or costs to an equivalent, lump sum value today. This discounting process is used, for example, to convert a retirement or lottery annuity (an equal cash amount each year) to a lump sum equivalenttoday. The equivalent, lump sum value today arrived at by discounting is called the “present value”. Toestimatethepresent value of a stream of dollar flows overyears 1 through 

### “T”, wediscount each annual flow using the formula: Present Value =V,/(l+r)’ 

### + ....+V,/(l+r)T where V is a dollar value and r isthe discountrate--the interestrate used to convert futureflowsinto a value today. For example, if the total Consumer Surplus from swimming is $1,000 in year 

### 1 and $1,000in year 

### 2, and the discount rate is 10percent, then thepresent value ofthese annual amounts is $1,735 (= 

### $909 +$826) today. The present value calculatedusing the above formula depends upon three factors: (1) the size ofthe annual monetary values, 

### V,, (2) when they occur, and (3) the discount rate, r, used. We estimate the annual monetary values, V, for non-market services in the chapters that follow, employ a time

‘Of course if the price is more than we are willing to pay we will not buy the item because it is “too expensive” or “not worth it”. 

### 9horizon, 

### T, of 

### 25 years, and use a discount rate of 

### 7 percent, a standard rate recommended for use in many resource management projects3. 

### 1I.C. 

### PURPOSE AND 

### SCOPE Thisreport describesthefournon-market valuation studiescomprisingPhaseI1 ofa seriesofstudies being carried out by EA1 for the Pec.onic Estuary Program. The Phase 

### 11 studies provide estimates of recreational uses and the economic values that the public holds recreation and for a variety of other servicesprovided by key PES environmental and natural resources. The Phase 

### I1 studies reported on here are a major component of a larger program to assess the benefits and costs ofproposed management actions aspart ofthe Peconic Estuary Program (Figure 11.3). Phase I was designed to estimate market effects provided by the PES. As discussed above, Phase 

### I measures “economic impacts” of the 

### PES, including employment, sales, wages, and the number ofestablishments associatedwith 

### 29 economic sectorsidentifiedasestuarine-related. Phase 

### I1 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 I11 of the project. PhaseI11 analyseswill 

### (1)provide costanalysesfor specificmanagement actionsdesignedtoprotect orrestorePES amenities, 

### (2) combine this cost information and thebenefit information from Phases I and 

### I1 to provide benefit-cost analyses ofpotential management actions, and (3) compare various financing options for the proposed management actions. We designeda suite ofnon-market valuation studiesin order to be able to estimate the value ofkey services provided by amenities in the 

### PES. Multiple non-market valuation studies are needed because of the many different types 

### of services provided by PES environmental and natural resources. For example, recreational participants directly use Bay waters and beaches; hence, for these activities we want to measure participants’ recreational use 

### values. In other cases, PES resources may provide amenity or aestheticbenefits to residents, which will be captured inproperty values, another type of use value. Additionally, some may also enjoy just knowing that PES environmental amenities, such as open space and salt marsh, are being preserved or restored, whether or not they directly use these resources. For these resources, 

### othervalues, not involving direct use, may also be important and shouldbe included. In sum, our use of a variety of methods was designed to allow us to gain a better understanding of the non-market values provided by different resource services, information that will prove useful for later benefit-cost studies. The key Phase I1 studies are the property value study, the recreation survey, the wetlands productivity analysis, and the resource survey. The property value study was designed to estimate benefits that 

### PES amenitiesprovide to individuals who own adjacent properties. The recreational survey allowsusto estimaterecreational use and use values for a variety of outdoor activities. This

’7 % is the representative value used in this report. In later efforts, the effects of different rates will be examined. 10surveywas designedfill data gaps identifiedin Phase 

### Ireport, and to estimaterecreational uses and use values to contribute to assessment of management actions in Phase 

### 111. The wetlands productivity analysisuses available data 

### to estimatethe productivity of specifichabitat types, and toplace a dollarmeasure on the ecosystemservicesprovided. Theproductivity studyincludes only foodweb andhabitat servicesandfocusesonthe “production” ofcommercialspecies, andtoa lesser extent, onnon-commercialvalues(viewingandhuntingvalues). Finally,theresource surveyisused to identifypublic priorities and values for specificenvironmentalamenities. The resource survey used a contingent choice model to learn which amenities are most importantto the public and the amount that respondents indicate that they would pay to preserve or restore them. Summary informationfor each study is give in Table 11.1 ; each study is described in detailin the chaptersand appendicesthat follow.



### 1I.D. 

### REFERENCES Grigalunas, T.A. and 

### J. Diamantides, 

### 1996. The Peconic Estuary System: Perspective 

### on Uses, Sectors, and Economic Impacts. Peace Dale, 

### RI:Economic Analysis, Inc. Long Island Business News, 

### 1994.Long Island Almanac 

### 1994. Ronkonkoma, 

### NY: Long Island BusinessNews. Suffolk County Department ofHealth Services, 

### 1992.Brown Tide Comprehensive Assessmentand ManagementProgram. 3 volumes. SuffolkCountyDept. 

### ofHealth Services(November).

11j, 

### Table 11.1. Summary 

### of EAI’s Phase I1 Non-Market Valuation Studies for PES Purpose 1. Estimate Resident, Second Home 3wners & Visitor Recreational Uses:

0 Activities 

# * 8 Key Activities 

# * 8 Locations 5 PES Bays Atlantic Ocean LI & BI Sounds 

# * 5 Towns 2. Estimate Use Value for:

0 Swimming 0 Boating

Fishing 0 Bird & Wildlife Viewing Estimate Residents’ and Second Homeowners’ Preferences and Economic Value for preserving and restoring PES:

0 Openspace 0 Farmland preservation 0 Unpolluted shellfish beds 0 Wetlands

Eelgrass beds

~~ Estimate the Economic Value for Nursery and Habitat Productivity functions for:

0 Saltmarsh

Eelgrass 0 Intertidal mud bottom Estimate the effect on Property Value in Town of Southhold of attributes, including:

e Site j, Lot & home size It Garage 

# * Special features Neighborhood 

# * Main road 0 

### Environmental Wetlands 

# * Openspace 

# * Farmlands 

# * Zoning Size 

### 111. ENVIRONMENTAL AMENITIES AND PROPER?
12 

### Data and Method(s) Convenience samplingl htercept Survey Self administered 1,354 completed surveys

Travel Cost Method Demand for swimming and recreational fishing depends on quality

Convenience sampling Intercept Survey Self administered 968 completed surveys Contingent Choice Method

Based on Productivity approach and results in available literature.

All property sales in Town for 1996 374 sales transactions Geographical Information System Property Value (“Hedonic”) Model



### VALUES:

f 

# I

### A CASE STUDY 

### OF THE TOWN 

### OF SOUTHOLD

1II.A. INTRODUCTION Propertyvalues in coastal communities depend upon many factors. These include not onlythe size of the property and the characteristics of the home on the property, but also on environmental amenities, such as open space and proximity to the shoreline. For example, we would expect a home located near the PES waterfront to be more valuable than another home, 

### identical in all respects, but located a greater distance from the shore. Ifwe could find 

### two such homes, we could simply compare theprices atwhichthey selland,by that, calculatethe value ofbeingnearthe shore. Ofcourse, rarely are two homes identical in allrespects except for one attribute. Instead, as anyone who has bought a home knows, the price that a property commands on real estate markets reflects a great many factors. These include: lot size, size of the home and its location, the characteristics of the surrounding neighborhood, and a wide range of other environmental factors. Nevertheless, if all of these important factors can be taken into account, it is possible to isolate the value of individual factors, much as a real estate broker or tax assessor does when appraising a property. This Chapter exploits these simple insights. We analyze data from many residential housing transactions using a standard property value model, as we explain in detail below. The model allows us to estimate the value--as captured in market prices--that people attach to various environmental attributes, such as proximity to open space and farmland. If efforts to estimate the value of environmental attributes are successful, the results can be used to assess the potential benefits of possible management actions that would affect any of the factors studied. That is, we could estimate the potential benefits from a proposed management action, as measured through changes in marketprices for property. We use the Town of Southold, located on the North Fork of the PES, 

### as a case study (Figure 111.1). Southold is an interesting example, for severalreasons. First, ithas a widerange of environmental amenity levels and neighborhood conditions. Forexample, Southoldhaslargeareas offarmlandand open space, although its population density 

### (0.67 persons per acre) ishighest among all five PES towns. In addition, Southold has a long and varied coastline, both on the Peconic Estuary and on Long Island Sound, and is characterized by a variety of development densities and types. About a quarter (26%) of the town is currently in agricultural use, compared with 

### 30% in residential use, 12% is preserved as open space, and 18% is classified as vacant. Less than 3% is classified as commercial or industrial (Suffolk County Department of Planning 1997a). Southold’s population increased 8.9% between 1980 and 1990, yet the number of housing units increased at nearly twice this level 

### (1 6.6%). Thus, the growth ofhousing and the associated loss of undeveloped lands has far out paced population growth (Suffolk County Department of Planning 1997b). Given the recent rapid pace of development, the protection of open space, undeveloped land, and other environmental amenities will play an important role in determining future quality of life in the Town and, perhaps, in the PES area.
13



### 9 iTheimpactsofenvironmental amenitieson Southoldproperty values, andtheimpactsresulting 

### from possible 

### PES management actions, will depend upon the unique characteristics 

### of the town (and perhaps the qualityof surroundingwaters and land areas). 

### To assess these impacts, 

### EA1 conducted a property value analysis specific to the town of Southold. We apply economic methods using the propertyvalue (or“hedonic”method) to 

### a databasecomprised ofall Southoldrealestatetransactions in 

### 1996 and 

### GIS parcel coverage data for the town. Briefly, the analysis estimates correlations between property values and levels 

### of valued environmental attributes, including open space. The policy relevance of the results lies in the assumption that established relationships between environmentalamenities andpropertyvaluesestimatedusingthepropertyvaluemodelwill 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 ofthe environmental impacts on local property values, giventhe available data.



### 1II.B. RELATIONSHIPBETWEENENVIRONMENTALAMENITIES

### ANDPROPERTY VALUES EnvironmentaLamenitiesprovide valued services to residents of local communities. For example, open space contributes, in a physical sense, to the character 

### of local communities, while providing a wide variety of services, including scenic views, outdoor recreation, insulation from noise and other aspects 

### of the urban landscape, and protection from erosion, flooding, and other physical hazards (Johnston 1997a). These amenities are valuedby localhomeow ners, making communities with a high level of valued environmental amenities (Le., environmental quality) more attractive than similar communities without such amenities. 

### As aresult, home buyers arewillingtopay more for land or housing with higher levels of environmental quality. The property value method (also called the hedonic model) can be used to estimate the impact of environmental amenities on the values of local property, thereby estimating the value of these amenities to local residents, as evidencedby their actualwillingnesstopay higherprices forpropertieswith higher levels ofdesired environmental characteristics. Awell-documented example oftherelationshipamong environmentalamenitiesandpropertyvalues involves open space. Many studies show that nearby open space increases property values, reflecting home buyers’ values for the services and character offeredby open space (e.g.,Freeman, 1993). In a recent, coordinated property value/geographic information system study conducted in Middletown, Rhode Island, Johnston (1997) shows that positive property values impacts of open spacecanrange fromless than 1% to greater than 13%,depending on the size ofnearby contiguous open space parcels, the total amount 

### of open space in a region, and the distance of property from open space parcels. Larger property value impacts are associated with larger contiguousparcels, larger acreage of open space, and a closer proximity between valued parcels and open space (Johnston 1997a). Similar results hold elsewhere in the New England region. A study in Worchester, MA, for example, found that houses located less than 20 feet from a community park sell for over $2,000more than similarhouses located more than 2000 feet from the park (More, et
14al., 1982). 

### In an analysis 

### of clusterzoning in Amherst,Massachusetts, Lacy (1990) estimates that cluster-zonedhousingwithpermanentlyprotectedopen spaceappreciatesat a fasterratethat similar housing in conventional subdivisions. 

### As of 1989,this differencehad created an average $17,100 differencebetween the average sellingprice in the 

### two types 

### of developments. Comparableresults concerningthe value of open space alsohavebeen found in other coastal areas. For example, a study of land values adjacent to Chesapeake Bay shows a positive relationship betweenthepercentageofpreservedopen spacein a 250 acre area surroundinga parcel ofland, and theper-acre sellingprice ofthat land. This study also showsthat agriculturalpastureland 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 use restrictions in the Chesapeake Bay watershed cause a 14-27% increase in housing prices within 1,000 feet of the bay, and a 4-1 

### 1% increase up to a 3 mile distance. Garrod and Willis (1992) conclude that housing prices are positively related to the existence of nearby broadleaf forests. Correll et al. 

### (1978) findthat, on average,residentialhousing prices in Boulder,Coloradodecrease by $4.20 for each additionalfoot ofdistancefrom a central green way, up to a distanceof 3200 feet.

Of course, the impacts of environmental amenities on Southold property values depend on the unique characteristics of the town, and may be greater or less than those cited above. To assess these impacts,we conducted a property value analysis specific to the town of Southold. We apply economicmethods (hedonicmethods)to a databasecomprisedof 

### GISparcel coverage data andreal estate sales data. In simpleterms, theproperty valuemodel estimates correlationsbetweenproperty values and levels 

### ofvalued environmentalattributes,including open space. The stepsinvolved and data used are described next.

PI1.C. 

### APPLICATION OF 

### PROPERTY VALUE MODEL TO SOUTHOLD This study appliesthepropertyvaluemodel to real estateparcels soldin Southoldduring 1996. The analysisestimateshow various site,neighborhood andenvironmentalcharacteristicsaffectthevalue of individual properties. Property value analysis is a cornmon means of estimating the effect of environmental changes on land values, as described in detail by Freeman (1993) and Garrod and Willis (1 

### 992), among many others. 

### 1II.C.1. Explanation of The Property Value Method Peoplebuyinghomes in effectpurchasemany site,neighborhood, and environmentalcharacteristics. For example, the characteristicsof a residentialproperty include: the size of the house, the number 

### ofbaths, the square footage of the structure,the zoning classification of the land, whether the land is served by public sewers and water, the existenceof wetlands on 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 attractiveto potential buyers. Other things being the same, home buyers are willing to pay more for properties that they find have
15



## I edesirable characteristics, and less for properties with undesirable characteristics. These likes and

. dislikes across many prospectivebuyers and sellers are reflected in market prices. The Property Value Method attempts to estimate the portion of a property’s value related to each relevant characteristic,by thatprovidingan estimateofthe (implicit)value ofeachcharacteristic. Themodel works by statisticallycomparing values of a largenumber ofpropertieswith differinglevels ofthe identified characteristics (e.g. lot sizes, number of rooms, proximity to roads, farms, open space, etc.) (Freeman, 1993;Johnston, 1997a). The Property Value technique is based 

### on the assumption that a relationship exists between the market value of aproperty,andthe characteristicsoftheproperty. ThePropertyValuemethod uses a statisticaltechniquecalled “multipleregression”to assessthe impact ofeach characteristicon the market value oftheproperty. The technique simultaneously compares a largenumber ofproperties with different prices and different levels of each characteristic. The method establishes which characteristicsare associatedwith higher values,which are associatedwith lowervalues, andwhich have no significant impact on values. The model also estimates the dollar magnitude of these impacts--that is, it estimates how 

### large an impact is likely to be caused by a specific level of a specific characteristic. Using this technique, the impact of different environmental amenities on nearby property values can be e~timated.~ The technical details of the property value model (or hedonic technique) are presented in Appendix A. Actual 1996 sales prices are used in the analysis of Southold property values. The data for the analysis is drawn from two sources: (1) Town of Southold property record cards for all properties sold during 

### 1996; and 

### (2) The computerized geographic information system 

### (GIS) maintained by the Suffolk County Planning Department. Although sales data were availablefor 401 parcels, complete 

### GIS coveragedata exists for only 

### 374 oftheseparcels.Thus,the Southolddata setused inthe analysishas fullinformationon 

### 374parcels.



### 1II.D. STATISTICAL ANALYSIS 

### OF SOUTHOLD LAND VALUES

The accuracy of hedonic analysis depends on a well-functioning real estate market, in which consumers have accurate information regarding all home characteristics. The technique also assumes that a large number of different housing types are available for purchase, so that consumers can choose the “package” of housing characteristics that is most to their liking, and can “mix and match” different types of characteristics. Finally, the technique assumes the availability of appropriate data, concerning all characteristics that influence property values. Bias can result from the application of hedonic techniques to ill-functioning real estate markets, in which few different types of housing options are available, andor in which consumers do not have accurate information regarding housing characteristics (Johnston 1997a).

16Model variables are the characteristics that influence buyers’ willingness topurchase a land parcel, or pay higher prices for that parcel. These includeproximity to amenities such as open space and coastlines; the size of the parcel; the size and other characteristics of the structures on the parcel; applicableuse restrictions such as zoning codes; and the existence

### of features such as wetlands on the parcel. These variables may be grouped into three general categories:

### parcel characteristics, neighborhood characteristics, and 

### environmental characteristics. To distinguish the effects 

### of parcel size on land values, we consider the effect of the characteristics 

### onper-acre value. This 

### is calculatedby 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 approachestimatesthe direction,magnitude,and statisticalsignificanceofcorrelationsbetween each ofa setofindependentvariables (thecharacteristicsoftheproperty), and a singledependentvariable (per-acre property value). 

### To establish the nature ofthe specific functional relationship between these variables and assessed land values, the study relies on the findings ofprior research. Inparticular, the regression analysis applies a “transcendental” or “translog functional Thetranscendentalform is chosen for its ability to capture realistic relationships between parcel characteristics and land values (Chicoine 198

### 1). Such functional forms are also preferred for their statistical properties in cases where data concerning certain relevant variables may not exist in the data set (Garrod and Willis 1992). For a technical description of the statistical model and its properties, see Appendix 

### B.

1II.E.ANALYSIS OF 

### SOUTHOLD LAND VALUES: 

### RESULTS Thehedonic analysisprovides information onthe effect of20 characteristics onper-acre landvalues in Southold. Final model results are illustrated in Appendix 

### C. Overall measures of model fit indicate high levels of correlation between land characteristics and property values. An R2value of 0.8352 indicates that over 83% ofthe variation in per-acre value is “explained” by the model, or is correlated with model variables. An F-statistic 

### of 75.945 indicates only a .01% chance that the set

### ofcorrelations reportedby themodel couldbe observedthroughrandomchancealone. The signs associated with model variables are consistent with prior expectations and with the findings of earlier hedonic studies (e.g., Bockstael 1996; Johnston 1997a; Des Rosiers and Theriault 1992; Garrod andWillis 1992).0fthose variables with demonstrated impacts on property values, seven have potential implications for environmental policy. These include the following:


### openspace: This variable identified parcels adjacent to open space land, as definedby the 

### GIs database. 

### Aparcel ofland located next to open space has, 

### on average, 12.83% higher per-acre value than a similar parcel located

Wichelns and Kline (1996), Chicoine (1981), Shonkwiler and Reynolds (1986), and Garrod and Willis (1992).

17

### elsewhere. Parcels adjacent to open space are defined as those with a border within 

### 25 

### ft. (7.62 meters) of an open spaceparcel. 

### As definedby the 

### GIS for Suffolk County, open space includes: parks; nature and wildlife preserves; recreational fields; unbuildable swamps or wetlands; large tracts 

### of land associated with schools, cemeteries, or other institutions; and selected large parcels of preserved farmland. 

### farmdistance: This variable represents the distance between a specific parcel

and the nearest farmland (farmland on which agricultural crops or nursery products are grown), inmeters. 

### For everymeterof

### additional distance between aparcel and the nearest farmland, averageper-acreproperty value increases by 0.001 7% (i.e., the magnitude of the impact is quite small). 

### onfarm: This variable identifiesparcels contiguousto farmland. 

### Aparcel of land locatednext to farmland has, on 

### average, a 13.32% 

### lower per-acre value than a similarparcel 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 road has, on 

### average, a 16.16% lowerper-acre value than a similar parcel located elsewhere. largezone: This variable identifiesparcels located in districts zoned 

### R-80 or R-120 (two- or three-acre zoning). 

### Aparcel of land located within a district zoned R-80 or R-120 has, on 

### average, 16.65% 

### higher per-acre value than a similarparcel located elsewhere. wetland: This variable indicates the percentage of a parcel classified as a freshwaterwetland. 

### For every onepercentagepoint increase in thepercent of 

### aparcel classiJed as wetlands, averageper-acreproperty value increases by 0.27%. (However,this variable is only significant at the 14%level.)



### 1II.F. SIMPLE ILLUSTRATION OF 

### PROPERTY VALUE IMPACTS: VALUE OF 

### OPEN SPACE Given the definition of the open spacevariable,the impact of open space on neighboring property values will differ depending on the characteristics of the surrounding property. However, to illustratethe basic implicationsof the model results �orpolicy, the following section simulatesthe predicted property value impact 

### of the hypothetical 

### loss of 10 acres 

### of open space, in a highly simplified scenario. Given specificlocations for the open space land under consideration, and the characteristics 

### of the surrounding parcels, similar results could be generated for actual areas in Southold.

18 

## tAccording to the model results, properties adjacent to open space land (or within 25 ft.) are an average of 12.83% more valuable than those not adjacentto open space. Within the Southolddata sample,the averageparcel salesprice was $213,514,with an averageparcel sizeof 2941.95 square meters (0.72 acre). Given this average value, a loss of 12.83% (related to the loss 

### of our hypothetical 10 acres 

### of adjacent open space) impliesthat averageproperty value for the adjacent parcels would decreaseby $27,394, all else held constant. 

### To estimate a hypotheticaltotal impactin a simple case, assumethat the lost open spacewas ten acres in a square shape, surroundedby parcels of average 0.72 acre size (the average in our sample), also squarein shape. Assuming a squareshapeforthe open spaceparcel,itwouldhave 

### aperimeter of 805.2 meters that would be bordered by otherparcels. Given an averageper-parcel area of 2941.95 square meters (also assumed square), the length per-side of theseborderingparcels would be approximately 54meters. Giventhesemeasurements, approximately 15parcelswould fit adjacentto the 10acreopen space parcel, assuming each had a full 54 meter side bordering the open space (805.2 f 54 = 14.9). Accordingly,forthis simpleexample,we assumethat 15averageparcelswouldbe affectedby the loss, or development,of our square ten acres of open space. As statedpreviously,development ofthe open spaceparcel would resultin a loss 

### of 12.83% ofaverage value per lot or $213,514, accordingto the calculations made above. Thus, the average parcel would lose $27,394 in total value. Multiplied by the 15parcels adjacentto the newly developed parcel, the totalproperty value lossis equalto $27,394~15=$410,907. This equatesto an average loss of $41,090 in total property value per-acre 

### of open space lost. Looked at another way, the estimated 

### benefits of preservingthe ten acres

### ofopen spacethus wouldbe $410,907,asthisisthe increaseinnearby property value associatedwith this open spaceparcel. For this illustrationwe do not compare costs with benefits since we do not have information on the costs of acquiring specificundeveloped property for open space. However, in the illustration,if the 10 acres of undevelopedproperty for open space could be acquired for less than $410,907 or $41,097 per acre, then the benefits would be greater than costs. Note that even if the property to be acquired for open spacecostsmore than $410,907,benefits stillmay exceed costs. This isbecause not all ofthe public benefits of open space are captured in our hedonic analysis. For example, our results in this section

### donot capturegeneralamenitybenefitsenjoyedby alllocalresidents,regardless

### ofthe location of their homes (see Chapter

### VI for discussion 

### of this issue).6 We notethat the impactsillustratedabove shouldnot be regarded asexactresults expectedin any single instance, and are included for illustrationpurposes only. 

### A full benefit-cost analysis would require much more detailed analysisoftheproposed project and itsimpacts,including impacts on surrounding property values. However, the illustratedresults do indicate the generaltypes ofbenefitsandcoststhat may be expected, on average, over the entire community. Although the results require various assumptions as noted above, they are robust with respect to the quantity of open space preserved.

61nthis regard, we note that recent support for an open space initiative in Suffolk County suggests that the electorate has a strong preference for open space as a public good. 19

### 1II.G. LIMITATIONS IN 

### PROPERTY VALUE ANALYSIS As noted, the model provides a good fit for sales data for thetown, and the results ofthe Southold property value analysis correspond to prior expectations and with the results of similar analyses. Nevertheless severalpotential limitations shouldbe noted. The most important ofthese limitations is that the results of the statistical analysis are sensitive to characteristics of the data set. The combined propertycard-GIS database may exclude informationoncertainvariablesthatmay affect property values. Although numerous environmental variables may influence property purchase decisions, data limitations allowedus to include only a sub-set 

### ofthese variables 

### in the model. For example, the data set includes water quality monitoring data for the PeconicEstuary, conducted at numerous sites. However, 

### no parcels in the 

### 1996 sales database are located within 

### 100meters of the Peconic Estuary. Thus, for those parcels in the current database, water quality is not expected to have an impact. However, were data to exist for parcels close to the estuary, one would expect that water quality might have an impact on property values. Other variables for which data was unavailable included fresh water (including groundwater) quality; beach erosion (likely only important for beachfront properties); and proximity to creeks, all 

### of which would be expected to affectproperty values. Ifinfact importantvariables are excluded from the model, statistical results may show upward or downward bias. Omission of important variables can either increase or decrease the estimated effects of environmental amenities on property values, based on the extent to which these variables are correlatedwith environmentalvariables. (Ifthese omittedvariables are uncorrelated with the environmental policy variables of interest, then their absence should not influence reported results.) For all of the above reasons, the estimated Property Value model results should not be interpreted as "exact". Rather, they provide the best possible estimate, given available data, 

### ofthe impacts 

### of e.xisting environmental attributes 

### on current per-acre land values. A second limitation is that increased property values, related to improved environmental quality, may not be favored by all residents. For example, residents may resist open spacepreservation on the grounds that it tends to increase property values, thereby increasing the costs of housing for young home buyers. Othersmay reject increases in property taxes that accompany higher property values. Still others may object that higher property values tend to ''squeeze out" low income residents. Real estate agents may oppose additional open space on the basis that fewer housing units are available, despite the act that average property values in the area affected will be higher with the additional open space. Such arguments should be acknowledged when considering environmental policy changes using the results of property value methods. Finally, we note that many people beyond the immediate area may derive benefits from open space but theirbenefits arenot included innumbers giveninthe aboveillustration. For example,residents 

### of the town who live outside 

### ofthe grid used in the illustration may value open space elsewhere in town (see Chapter

### V). 1II.H. SUMMARY



### 20Environmental amenitiesincreaseproperty valuesofnearbyparcels,reflectingthevaluable services offered by these amenities. An analysis of Southold property values supports this contention, and corresponds with the results ofprior studies. In Southold, existing open spaceincreases 

### the values of adjacent properties by an estimated 12.83%. In addition, average per-acre property values are higher 

### inparcels located furtherfromfarmsandmajor roads. Finally,higherproperty values appear to be associated with the existence of wetlands on the property, and with large lot (2- and 3-acre) zoning. Theseresults indicate that environmentalpolicies can have significantimpacts onproperty values, reflecting the influence of local environmental amenities on local quality 

### of life.



### 1II.IREFERENCES Bockstael, 

### N.E. 1996. “Modeling Economics and Ecology: The Importance of a Spatial Perspective,”

### American Journal 

### of Agricultural Economics 78: 

### 1 168-1 

### 180. Chicoine, D. L., 1981. Farmland Values at the Urban Fringe: An Analysis of SalesPrices.” 

### Land Economics 57(3): 353-362. Correll, M., 

### J. Lillydahl and 

### L. Singell, 1978. “The Effects of Greenbelts on Property Values: Some Findings on the Political Economy of Open Space.” 

### Land Economics 54: 207-217. Des Rosiers, F. and M. Theriault. 1992. “IntegratingGeographic Information Systems to Hedonic Price Modeling: An Applicationto the Quebec Region. 

### Property TaxJournal 1l(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 

### theNortheastern Agricultural and Resource Economics Association (April). Freeman, 

### M. 

### A., 1979. 

### The Benefits of 

### Environmental Improvement: Theovy 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. CollegePark, 

### MD: Center for Agricultural and Natural Resource Policy.

21Geoghegan, J., 

### N. Bockstael, and 

### L. Wainger. 1995. “Spatial Landscape Indices in a Hedonic Framework: An Ecological Economics Analysis Using 

### GIS.” presented at the annual meetings of the Association 

### of Environmental and Resource Economics, Indianapolis, 

### IN. Hammer, T.R., R.E. Coughlin and E.T. Horn IV. 1974. Research Report: The Effect of a Large Park on Real Estate Values. 

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### 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 inDayton and Columbus, Ohio. Thesis. Oxford, OH: Miami Univ., Inst. of Environmental Sciences. Lacy, J. 1990.

### An Examination ofMarket Appreciationfor 

### Clustered Housing with Permanently Protected Open Space. Center for RuralMassachusetts Monograph Series. Amherst, MA: University of Massachusetts. Lardaro, L. 

### , 1993. 

### Applied Econometrics. New York: Harper Collins College Publishers. More, T. A., Thomas Stevens and 

### P. Geoffrey Allen, 1992. “The Economics of Urban Parks.” 

### Parks and Recreation. National Park Service, 1995. 

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### Protection Rivers, Trails, and Greenway Corridors. Washington, D.C.: Rivers, Trails, and Conservation Assistance Program. Nelson, A. 

### C., 1985. “A Unifying View of Greenbelt Influences on Regional Land Values and Implications for Regional Planning Policy.” 

### Growthand Change 16(2): 43-48. Parsons, G.R., 1992. “The Effect of Coastal Land Use Restrictions on Housing Prices: A Repeat Sale Analysis.” 

### Jour. of

### EnvironmentalEconomics and Management 22: 25-37. Rosen, S., 1974. Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition.” 

### Journal ofPolitical Economy 82( 1): 34-55. SAS Institute Inc. 1996. SAS 

### User’s Guide: Statistics. Cary: NC: SAS Institute Inc.

22Shonkwiler,

### J.S. and 

### J.E Reynolds, 1986. “A Note on the Use 

### of Hedonic Models in the Analysis 

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### ofParcelCharacteristicsonthe Cost 

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### Agric. and ResourceEconomicsReview: 150-158.

23

### APPENDIX A. Variables in the Hedonic Analysis Allmodel variables were generated from one of

### two sources: 

### I] Official SoutholdProperty Record Cards for all parcels sold during 1996; 

### 21 GIS coverages maintained Suffolk County Planning Department, updated 1995. 

### DependentVariable: LVALACRE: The natural log of per-acre salesprice, in dollars. Based on Southold property record cards for properties sold during 1996. 

### Independent Variables: LNACRES: the natural log of acres in eachparcel. Estimated using 

### GIS coverages. WATFRONT: dummy variable: value of 1assigned ifproperty is located onthe waterfront (Long Island Sound or Peconic); value of value of 

### 0 assigned if property is not located on a waterfront. Value assigned based on waterfront classification of property record cards. BULKHEAD: dummy variable: value of 

### 1 assigned if property has a bulkhead on the waterfront, value of 0 assigned if no bulkhead. Value assigned based on property record cards. LARGEZONE: dummy variable: value of 

### 1assigned forparcels locatedin districtszoned 

### R80 or R-120; value of zero assigned to all other zoning classifications. YEAR: year the house was built, based on property records, minus 1950. YEARSQ: YEAR 

## * YEAR (year squared). GARSQFT: the number of square feet in the garage, based on property records. If no garage exists, value 

### of 

### 0 is assigned. PATIO: dummy variable: value of 

### 1 assigned ifthe house has a patio or deck; value of 0 assigned if there is no patio or deck, based on property records. BATHS: the number of bathrooms, based on property records. NOHEAT: dummy variable: value of 

### 1 assigned if the house has no heat, value of 0 assigned if house has heat, based on property records.

24FIRE: dummy variable: value of 

### 1 assigned if the house has a fireplace or woodburning stove; value 

### of 0 assigned 

### if there 

### is no fireplace or woodburning stove, based on property records. SQFT: square footage of the house, based onproperty records. SPECIAL: dummy variable: value of 

### 1 assigned if the house has a “special” feature (tennis court, swimming pool, etc.); value 

### of

### 0 assigned ifno specialfeatures are present, based on property records. DSOUNDT: The linear distance to Long Island Sound, truncated at 

### 800 meters (i.e., the maximum possible value is 800). Based on GIS coverages. PECONl

### 00: dummy variable: value of 

### 1assigned iftheparcel is within 

### 100meters ofthe PeconicEstuary; value ofzeroassigned iftheparcel isfurtherthan 100linear meters from the estuary. Based on GIS coverages. SOUNDl00: dummy variable: value of 1 assigned if the parcel is within 100meters 

### of Long Island Sound; value of zero assigned if the parcel is further than 

### 100 linear meters from the Sound. Based on GIS coverages. ONROAD: dummy variable: value 

### of 1 assigned if the parcel 

### is within 

### 20 meters of a main road (Rts. 

### 25 and 48); value of 0 assigned if the parcel is further than 20 meters from a main road. Based on GIS coverages.
FARMDISTANCE: the linear distance between the parcel and the nearest farmland (farmland), in meters. Based on GIS coverages. ONFARM: dummy variable: value of 1 assigned iftheparcel is contiguous (distance=O) to farmland; value of 

### 0 assigned if the parcel is further than 

### 0 meters from farmland. Based on GIS coverages. WETLAND: indicates the percentage of the property covered by freshwater wetlands. OPENSPACE: dummy variable: value of 1 assigned if the parcel 

### is adjacent to open space (where adjacent is defined as a parcel whose border is within 

### 25 feet of an open space parcel); value of 0 assigned if the parcel is further than 

### 25 feet from open space. Based on GIS coverages. The Southold GIScoverage classifies the following landuses as open space: 

### 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 hunting and fishing clubs; state owned forest land; reforested land and other conservation land; public


### 25parks;otherwildorconservationlands; andtaxable stateownedconservation easements. Farms andundevelopedprivateproperty, in general, arenotconsidered open space. Mean values of variables included in the analysis, for the 

### 374 observations in the database, are illustratedbelow. 

### Table 

### B.l Mean Values of Model Variables
Variable

LVALACRE LNACRE WATFRONT BULKHEAD PORCH YEAR GARSQFT PATIO BATHS NOHEAT FIRE SQFT SPECIAL PECON 100 SOUND100 ONROAD ONFARM DFARM OPENSPACE WETLAND LARGEZONE 

### Mean Value

12.8362 -0.7521 0.2340 0.07 18 0.5266 16.7500 265.1861

0.4734 1.6303 0.1197 0.5372 1270.3200 0.1064 0.0053 0.0848 0.0585 0.0452 2395.1400

0.050398

2.4548 0.1011

26

### GIS Maps Used to Define Model Variables: Examples Many 

### of the variables in the analysis were derived from 

### GIS parcel coveragesof Southold. The following 

### GIS map illustratesthe type 

### of analysis applied. 

### In this case,the map illustratesthe processused to determine the existence

### of agriculturalland within 

### 100meters

### ofeachparcel in the sales database.

27The following

### GISmap coversthe same areaof Southold,yet illustratesthe distributionofwetlands, used to assess the percentage 

### of each sales parcel coveredby freshwaterwetlands.

28



# t

### APPENDIX 

### B. The Statistical Model 

### To establish thefunctional (mathematical)relationship between model variables and assessed 

### land values, the analysis on the findings ofprior research. In particular, the analysis followsWichelns andKline(1996),Chicoine(l981), ShonkwilerandReynolds (1986), andGarrodandWillis (1992) applying a transcendental or translog functional form to the property price 

### - characteristics equation Vi = PoXilP'exp[CPjXij] J=2.,.n where Viis the 1996 sellingprice of the 

### ith parcel, in dollars per-acre, 

### Xi,isparcel size in acres, and 

### X, are measures of thej = 2...n other characteristics that affect land value. The 

### p's are parameters tobe estimated, and represent the effectsofeachofthe characteristics 

### on per-acre land price. Estimated parameters are used to calculate the effects of different open space characteristics onthe value of land in Southold. The transcendental form is chosen for its ability to capture realistic relationships between parcel characteristics and land values (Chicoine 198

### 1). For example,thetranscendentalformpermits apositive ornegative marginalrelationshipbetween value and parcel size (acres), requires value to be zero when parcel size is zero, can detect proportional value-size relationships, and allows for increasing or decreasing returns to scale for land parcel characteristics (Chicoine 1981; Wichelns and Kline 1993). Moreover, functional forms such asthe semi-log or transcendental are preferred (tothemore flexible functionalforms) when there is potential omitted variable bias (Garrod and Willis 1992). The empirical version of the translog model is as follows:

where thevariables are as defined above, and In(.)representsthe natural log. This isthe form used for statistical analysis in the Southold property value study. The estimated trans-log model appears to fit the data well. Overall model statistics and individual variable significancelevels indicate good model fit, and trials with alternate functional forms (e.g., linear, semi-log) indicate that these alternate forms result in lower significance levels and explanatory power. 

### A White test for heteroskedasticity (White 1980) fails to reject the null hypothesisofhomoskedasticityatthe

### 5% level.Although multicollinearityisubiquitousinhedonic models, tests for multicollinearity indicate that it does not have severe impacts 

### on model results in the present application. As the data 

### is cross-sectional and data order has been randomized, tests for autocorrelation are unnecessary.

29

### APPENDIX C. 

### OLS Model Results
Dependent Variable: LVALACRE

Source

Model Error C Total Root 

### MSE Dep Mean C.V.

Variable
INTERCEP LNACRE WATFRONT BULKHEAD YEARSQ YEAR GARSQFT PATIO BATHS NOHEAT FIRE SQFT SPECIAL SOUND100 PECON 100 ONROAD 

## sum of 

### DF Squares

20 152.03723 355 30.00860 375 182.04583 0.29074 12.83621 2.26502 Mean Square 

### F Value Prob>F 7.60186 89.930 0.08453
R-square 0.8352 Adj R-sq 0.8259

Parameter Estimates Parameter Estimate 11.409748 -0.905956 0.333735 0.200245 0.000302 -0.012017 0.000165 0.103269 0.109587 -0.099710 Standard 

### T for 

### HO: Error Parameter=O Prob > 

### IT( 0.08174613 0.02606823 0.04497479 0.06780928 0.0001 1945 0.00484814 0.00006797 0.03 166099 0.02678655 0.04189498 139.575 0.0001 -34.753 

### 0.0001

7.420 0.0001 2.953 0.0034 2.528 0.01 19 -2.479 0.0136 2.428 0.0157 3.262 0.0012 4.091 0.0001 -2.380 0.0178 0.117428 0.03516537 3.339 0.0009 0.000264 0.00004262 6.193 0.0001 0.123980 0.05349404 2.318 0.0210 0.294474 0.07076478 4.161 0.0001 0.671739 0.30810728 2.180 0.0299 -0.176308 0.06582347 -2.678 0.0077 

### 0.000017320 0.00000374 4.625 0.0001 -0.142932 0.07661228 -1.866 0.0629 0.002729 0.00181653 1.502 0.1339 0.154045 0.06636055 2.321 0.0208 0.120691 

### 0.07 154488 1.687 0.0925 FARMDISTANCE ONFARM WETLAND LARGEZONE OPENSPACE

30

### IV. OUTDOOR RECREATIONAL USES AND 

### USE VALUES 

### IN THE PES 1V.A. INTRODUCTION This chapter presents results of EAI’s study of outdoor recreational uses 

### and use values 

### for the Peconic Estuary System (“PES”). The information presented includes: 

### (1) a review 

### of participation andlocation choice forkeyrecreationaluses, and 

### (2) estimates ofthe economic value 

### ofthoseuses. Also presented is a summary ofexpenditures atPES farm stands,wineries, andrental housing, data that was identified as a gap in EAI’s Phase I study and is of interest to decision makers. The main tool for providing information for this analysis is a recreational use survey carried out in August, 1995. The recreational survey was designed to: identify key outdoor recreational activities and locationsin the PES estimate the level of participation in key recreational activities identify key characteristics and preferences of users estimate the value 

### of recreational uses 

### of the PES fill data gaps identified in the Phase I study. 

### Key recreational uses and locations. More than 

### 15 recreational uses of the PES were identified through focus groups and discussions with local citizens and officials. Onekey component 

### of the recreational use survey is to assess where these recreational uses take place throughout the PES. 

### Participation in key recreational uses. EAI’s Phase 

### I study (Diamantides and Grigalunas, 

### 1996) estimated participation in only a few key recreational uses, using “off-the-shelf’ information. Phase 

### I also identified many data gaps concerning participation rates and values for outdoor recreation in the 

### PES. The Phase I1 recreational survey is designed to fill these data gaps. 

### Characteristics andpreferences of 

### users. Prior to the recreational use survey, only anecdotal information existed concerning recreational users of the PES. Information, such as residency, accommodations, recreationalpreferences, etc. 

### ,areprovidedby

### EAI’ sPhaseI1recreational survey. 

### Recreational use values. Data from the Phase I1 recreational survey is used to estimate the economic value ofkeyrecreational activities in the PES and, when possible,to estimatethe change in recreational value resulting from changes in water quality. 

### Phase 

### I economic impact data gaps. Key data gaps identified in the Phase I study include the economic impact of specialized sectors of the local economy. These sectors include: summer rentals, winery tours and sales, and farm stand sales. Each 

### ofthese sectors was identified by local citizens and officials as playing an important, but unquantified role, in the local economy. Economic data for these sectors--sales-- 

### is presented in this report.

31

### 1V.B. IDENTIFICATION 

### OF RECREATIONAL 

### USES AND LOCATIONS Outdoor recreation is a major activity inthe PES.Based on recreational uses identified during the survey development process (see Sec.

### C, Survey Development),the survey questionnaire included 

### 16major natural resource-based recreational uses. Ofthese, eight key recreational activities were identified: Fishing Beach Use Boating Bird Watching Swimming Wildlife Viewing Shell fishing Hunting Data were collected forthese eightkey recreational uses, including annualparticipation duringthe past year. Detailed information also was obtained on these activities for the respondent’s most recent recreational outing to the East End. Annual participation data onkey recreational activities includethe number oftimes therespondent had doneeach activity at specificEastEnd locations duringthatyear 

### (1 

### 995).The surveyinstrument identified eight water bodies as potential locations for fishing, boating, swimming, and Shell fishing. The eight locations identified by water body are: Flanders Bay Great Peconic Bay Little Peconic Bay Shelter Island Sound Gardiners Bay Block Island Sound Long Island.Sound Atlantic Ocean. Locations fortheremainingkey recreational activities (beachuse, bird watching, wildlife viewing, and hunting) were categorized by the town in which the activity took place. Five East End towns were identified by the survey instrument. The five recreation locations identified by town are: East Hampton Riverhead Shelter Island Southampton. SouthoId.
Single-day data on key recreational activities include information on activities the respondent participated in during their 

### most recentrecreation day at the East End.This question was followed by occasion-specific, write-in questions concerning the: (1) location ofthe activity, 

### (2) perceived water quality, 

### (3) number of people in the recreation party, 

### (4) travel time, and 

### (5) time on site. Other activity-specific data collected included fishing catch, shellfish harvest, choice ofmarina or ramp, perception 

### of beach facility quality, and number of birds or type 

### of wildlife sighted7.

7Huntingwas not included in the occasion-specific questioning, due to the time of year and anticipated low participation. 32

### 1V.C. SURVEY DEVELOPMENTAND IMPLEMENTATION The development and implementation oftherecreational use surveyparallels the development and implementation oftheresource survey.Meetings,interviews, andfocus groups were instrumental in the development ofboth surveys. Similarly, implementation of the two surveysoccured at the sametime and places using the same survey staff. Only significant aspects of the development 

### of the recreational survey that differ fiom the resource survey will be discussed here. For 

### a full discussion

### oftheresource survey developmentandimplementation including similaritieswith the recreational survey (see Chapter 

### VI, Resource Value Survey). 

### A major challengeinthe development 

### ofthe recreational use surveywas the creation of a question format. 

### A singleinstrument was selectedto cover all the major recreational uses and locations

### of the PES. The single-instrumentformat allowedcollection ofdata for multiple current andpast uses andwas amuch easier, andless costly,method for acquiringdata, animportantissuegivenrealistic limits on budgets for this project. Questionnairedevelopment began with meetings with the Management Committee, the Citizen’s advisory group, and “expert informants” frommajor stakeholder groups, such asmarina operators andBaymen. Based onissuesraisedby thethree groups,questionnairedevelopment thenproceeded with informal interviews with thepublic, focus groups, andpretests in order to determinethemost relevant questions, wording of questions, and survey presentation. Informal interviewswith thepublic were a critical component ofquestionnairedevelopment.From these interviews we gainedinsight into who the users are andtheir perspectiveonrecreation inthe PES. For example, duringthese interviews,itbecame apparentthat many recreational users didnot think in terms of the PES as an estuary system. Instead individuals more easily identified the East End or individual bays within the PES as geographic entities. The questionnaire, therefore, asks about recreational uses at locations at the East End with which individuals could easily identify. Focus groups and pretests were used to hone the questionnaire down to an efficient presentation that would allow us to collect all the required data. For example, one issue we used focus groups torefine was the questionnaire’s approachto elicitingwater qualityperceptions. Originallywe had hoped to use the questionnaire to ask very specific water quality questions, such as clarity, smell, debris, etc., so that we might be able to estimate the contribution ofthese different attributes to an individual’s overall perception of water quality. However, during focus groups we found that recreational users can have very complex and inconsistent ways of assessing water quality, which include numerous additional attributes such as beach condition, signs, and plant abundance. Eliciting information on all of these attributes became too cumbersome, and a more simplified approach was eventually used. The simplified approach asks the respondent to rank water quality at the site on a four point scale from “excellent” to “p00r’~. 

### As is described below, this approach worked satisfactorily in that we found a statistical association between respondents’ subjective estimates of water quality and objective measures of water quality based on field sampling undertaken by the County.

33A key objective inthe surveydevelopment phasewas tokeep thequestioning simpleand consistent across uses. Many versions of the recreational use survey were pretested, to assess the ease with which respondents could understand and answer the questions. The survey pretests were instrumental in reducing the size of the questionnaire. One of our concerns was that if the questionnairewas too long, onlythe first few questions would be answered, orrespondents would only answer selected questions. Another important concern was that the survey be clear and relatively brief in order to allow 

### us to use an intercept-survey, self-administration approach, to economizeon scarceresearch funds. The finalversion oftherecreational use survey questionnaire is available from the authors. Therecreational use survey and theresource use surveywere implemented simultaneously during the week ofAugust 22-29,1995, in pre-selected, public places in each ofthe five East End towns. Interviewers solicitedpotential respondents by simply approaching them and askingifthey would be willing to spend 10-1

### 5 minutes filling out a survey to be used to help develop a plan to protect and manage the bays. Survey locations included beaches, shopping areas, libraries, Post Offices, and miscellaneous public places throughout the study area. Other natural resource-based recreational activities were identified in the survey development process. These activities include: walkinghiking, biking, sightseeing, jet skiing, jogging, windsurfing, art work, and photography. Questions on these activities were retained in the questionnaire forcomprehensiveness.However,these activities areconsideredless significantthan the key activities identified above, and data collected on these “non-key” activities are limited to a single question on participation.



### 1V.D. DESCRIPTION 

### OF THE RECREATIONAL USE SURVEY QUESTIONNAIRE The survey booklet cover provides a map of the East End to orient respondents. The first set of questionsaskswhether therespondent is anEast End resident, ownsa secondhome inthe East End, or is a visitor. Visitors are also asked the mode oftransportationthey used to travel to the East End and the type of accommodations used, if any. Subsequent questions address the issue of Brown Tide (“BT”). BT refers to the discoloration of the waters in and around the PES due to an algae that sporadically grows in enormous numbers. Recent incidents of BT may have contributed to a significant decline in ecologically productive eelgrassbeds and caused major disruptions in the formerlyvaluable scallop industry.BT also was a significant concern to recreationists, as described in some detail later. Throughout the survey development process, the issue of BT repeatedly surfaced. Questions concerning the 

### BT were placed in the beginning of the questionnaire in part to diffuse this issue earlyon, allowingrespondentsto focusonthe questionsthatfollow.We askedrespondentswhether they were aware of BT and which, if any, of their recreational activities were affected by BT. Thenext set ofquestionsaskedrespondentsto indicatetheir annualparticipation inkey recreational
34activitiesby filling out 

### two brief charts. One chart identifiedrecreation locationsby 

### water 

### body. The otherchartidentifiedrecreation locationsby 

### town. Respondentsindicatedthenumber oftimes that they did each activity in each location during the past year 

### (1 995). The annual participation questions were followed by single-day recreation activity questions. Respondentswere askedto identifytheir

### most recentrecreation day atthe eastEnd.Theythenwere askedtoidentifyalltherecreationalactivitiesparticipatedduringthatday. Shellfishing, swimming andbeachuse,boat use, fishing,birdwatching,andwildlifeviewingwere selectedaskey activities. For each of these recreationalpursuits, a separate page of questions asked for activity-specific information. Activity-specific questions included: location,travel distance,time on site,number of people in the party, catch or harvest, and water quality and site quality ratings. After the participationquestions,respondents were asked about selected expenditures made at the East End duringthe past twelve months. 

### As noted, these questionswere designed to fill data gaps in the Phase 

### I study for farm stands, wineries, and housing rentals. Residents and second home owners were also asked a referendum-type question about their willingness to support selected water quality initiatives (see section on water quality initiative referendum). Finally, a. set of questionswas askedin orderto obtainsocio-demographicdata,suchasresidency,household status, income, age, education, and employment status.



### 1V.E. DESCRIPTIVE RESULTS: 

### DEMOGRAPHICS A total of 1,354respondentsprovided usable surveyquestionnaires,althoughnot everyrespondent answered all questions. Selected, major demographic results of the survey are summarized as follows: Residents and secondhomeowners from all East End towns wererepresented in the sample A majority (59 %) of respondents were visitors to the East End Most visitors (76%) came 

### from either Long Island or New York City 

### Most visitors (60 %) stayed at the East End overnight The most common overnight accommodation was with friends or relatives (39%) Comparedto 1990Censusdata,the samplehas a slightlyhigher shareofwomen andpeople in the middle age groups; is better educated;and is wealthier than the resident population. 

### Residency Status. Of the 1,267respondents who provided this information, over half of those responding (59%) were visitors from outside the East End (Table 

### IV. 1). Of the remainder, 

### 28% indicated that their primary residencewas at one ofthe five East End towns, and 13%owned a second home at the East End, but had a primary residence somewhere else. Some Brookhaven residents considered themselves East End residentsbut were not counted as such in this study. 

### Residents and Second 

### Home Ownevs
- 

### Place ofResidency. Residents and second home owners from each ofthe five East End towns were representedin the survey sample. Southampton (30%) and Southold(28%)had the largestpercentages

### ofresidentrespondents.The remainderwas
35closely distributed among ShelterIsland (16%), East Hampton (14%), andRiverhead (12%). The relatively high proportion of Shelter Island resident respondents 

### is not expected to have a significant effect on survey results. 

### Table IV.l- Residency Status 

### of Survey Respondents Residency Number of 

### Respondents Percentage of all

East End Residents Second Home Owners Visitors

Total 

### Respondents 358 28% 159 13% 750 59% 1267 100%

For secondhome owners,New York Citywas the dominant primaryplace ofresidence (45 % of second home owners surveyed), followed by Nassau (19 %) and Suffolk counties (14 %). Other locations, largely distributed across Westchester and Rockland counties in New York and northeastern New Jersey, were the primary residences 

### of the remaining 22%. 

### Visitors-Placeofliesidency.Visitorsto theEastEnd predominately came from Long Island and New York City. 40% 

### of visitors came from Suffolk county, 20% came fromNew York City, and 16% from Nassau county. Only 

### 8% came from other locations in New York State and New Jersey, andjust 5% came from Connecticut and Massachusetts. Other locations, such as Florida, California, and several foreign countries accounted for the primary residences of the remaining 11%. 

### Visitor Classijication and 

### Accommodations. Most visitors in our sample 

### (60%) were overnight visitors. Day trippers (those who returned to a home outside the East End at the end of the day) accounted for 39% of visitors surveyed. Ofthe 700 overnight visitors who provided accommodation information,themost 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 reported that they stayed at accommodations other than friends or relatives, hotels or motels, or rentalproperties.Presumably these other accommodations arepredominatelyboats and camp sites. 

### Samplecharacteristics: comparison with resident census data. Gender, age,education,and income characteristics of surveyrespondents canbe compared to the characteristicsoftheresident population as identified by the most recent (1990) census. Only comparisons to the resident population are possible, since characteristics ofthe secondhomeowner and visitorpopulations are unknown.
36 

# ?For all residency and visitor categories,the share of female respondents was only slightly more than in the resident population. The age distribution of the sample reflects the survey implementation practice of not including individualsless than 18years of age. Our survey sample

### is much more educatedthan the residentpopulation. For example,census data indicatesthat49% oftheresidentpopulationhad somelevel

### ofpost-secondary education.The same level of educationwas achievedby 

### 80% ofthe resident sample, 95% 

### of second homeowners, 9 

### 1% of overnight visitors, and 82% 

### of day trippers. The survey sample also has higher household incomes than the residentpopulation.According to the census, 34% 

### ofthe residentpopulation has a household income of $50,000 or more. However, nearlyhalf (48%) the residents in the survey samplehave household incomes of 

### $50,000 or more, as do 80% of second homeowners, 64% of overnightvisitors, and 59% of day trippers. 

### Samplecharacteristics: comparisonamongresidentand visitorcategories.Thedistribution among female and male respondents slightly favored females in all categories.Day trippers have the most respondents under 24 years of age 

### (IO%), and overnight visitors have the most respondentsin the 25-44 age group (60%). Secondhomeowners have the largestpercentage of45­ 74 year olds (62%). Second homeowners are also the most highly educated (95% post-secondary schooling) and have the highest incomes (80% above 

### $50,000 household income). 

### Sample characteristics: East End Town ofResidence. Residents and secondhome owners from Shelter Island and Southold were-over represented in the survey sample, based on comparisonsto year-round and seasonalpopulation statistics.Residentswereunderrepresentedin Riverhead and Southampton. Second home owners were under represented slightly in East Hampton and alsoin Southampton.This disproportionatedistributionofthe samplemost likelyhas little, if any, effect on survey results since the type and frequency of recreational activity is not stronglyrelated to the East End town the respondent lives in.

37

### Table 

### IV.2 

## - Population Demographics vs. SurveyRespondent Demographics

Gender Female

Male 

### Age up to 20

2 1-24 25-34 35-44 45-54 55-64 65-74 75-84 85 and UD 

### 1990 Population

51.83% 48.17%

23.89% 4.53% 16.23% 12.84% 11.01% 

### 1 1.04% 11.17% 7.25% 2.04% 

### Resident Sample

53.57% 46.43%

3.83% 1.91% 14.48% 21.86% 18.85% 13.39% 21.31% 3.83% 0.55% 

### Second Homewner

Sample

54.67% 45.33%

3.36% 1.34% 8.72% 19.46% 30.87% 18.79% 12.08% 4.70% 0.67% 

### Overnight Visitor Sample

53.63% 46.37%

1.49% 4.95% 25.50% 34.65% 2 1.29% 8.42% 2.97% 0.50% 0.25% 

### Day tripper Sample

55.34% 44.66%

2.76% 7.09% 22.83% 24.02% 20.87% 12.20% 7.87% 1.97% 0.39%

38

### Table 

### IV.2 

## - Population Demographicsvs. SurveyRespondentDemographics, cont.

Education <High School Some 

### HS HS Grad. Some Coll. Assoc. Deg. Bachelor's Deg. Advanced Deg. 

### 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 and UD 1990 

### Population

7.44%
11.55% 3 1.77% 18.35% 6.66% 13.51%
10.72%

19.17% 14.60%
14.40%
17.52%
18.90%
7.04%

4.93%
3.41% 

### Resident Sample

1.27%
2.55% 16.56% 2 1.66% 12.74% 21.97%
23.25%

9.42% 12.01%
13.31%



### 17.53'Yo
23.38%



### 1 1.36%

6.82%
6.17% 

### Second Homewner Sample

0.00%

0.77% 4.62% 13.85% 4.62% 23.85%
52.31%

1.57% 3.15%
5.51%
10.24%
13.39%
14.96%

18.11%
33.07% 

### Overnight Visitor Sample

0.28%
0.84% 7.58% 17.42% 7.30% 29.7

### 8%

36.80%

5.16% 4.01%
10.32%
16.62%
19.48%
11.75%



### I 7.19%
15.47% 

### Day tripper Sample

0.46%

2.78% 14.35% 26.39% 12.04% 19.44%
24.54%

3.70% 

### 8.80%
8.33%

20.37%
26.85%
17.59%

9.26%
5.09%

39

### 1V.F. DESCRIPTIVE RESULTS: 

### KEY RECREATION ACTIVITY PROFILE Respondents were asked the number of times that they performed each of the following outdoor recreational activities at various East End locations (Bays and towns) this year 

### (1

### 995): Activities: Fishing, Boating, Swimming, Shell fishing 

### Locations: FlandersBay, GreatPeconicBay, Little Peconic Bay, ShelterIsland Sound, GardinersBay,BlockIsland Sound,LongIsland Sound, andAtlantic Ocean 

### Activities: Locations: Beach Use, Bird Watching, Wildlife Viewing, and Hunting East Hampton, Riverhead, Shelter Island, Southampton, and Southold. Thewaterbodies ofthePeconic Estuary Systemareidentified asFlanders Bay, Great PeconicBay, Little Peconic Bay, Shelter Island Sound, and Gardiners Bay. Substitute water bodies included in the survey were Long Island Sound,Block Island Sound, and the Atlantic Ocean. Swimming and beach use were treated as separate activities in the questionnaire in order to identify swimming locationsby body 

### ofwater and identify the number ofbeach goersby town. Since each ofthe fivetowns has multiplewaterbody options for swimming, itwasnecessaryto ask for locationby both water body and town. Figures for swimming and beach use should not be summed. This isbecause the activities are not mutually exclusive, and it is expected that most respondents gave a positive answer to both categories even though they may be referring to a single outing. Major characteristics of respondents’ annual recreational activity during the year are: Most (83%) engaged in at least onekey recreational activity,halfinmore than one activity. Most residents (95 %) participated in key recreational activities Swimming was the most popular activity The PES was the most popular swimming location (42% of all swimming trips) Boating, fishing, and Shell fishing were predominantly done in the PES. 

### Sample Participation Overall. Most respondents (83%) engaged in at least one outdoor recreational activity at the East End and duringthe past year averaged 38 recreational experiences perrespondent. Intotal,respondentsengaged inmore than

### 48,400 outdoorrecreational experiences during the past year. Of this number, more than 26,000 were 

### water-based outdoor recreational experiences, and the majority ofthese (15,506) were in the PES. Land-based outdoor recreational experiences the five East End towns amounted to 22,300. 

### Sample ParticipationBy

### Activity. Among all respondents, swimming and beach use were the most popular activities. Swimming was done on more than 15,000 occasions by respondents. Themostpopular area for swimming was the PES, which accountedfor42% of all swimming trips. More than 12,000beach visits at the five East End towns were reported.
40Shell fishing was the least frequent water- based activity (1,304 trips). More than half 

### (59%) of these Shell fishingtrips took place in the PES. Hunting was the least popular land-based activity (255 occasions). Note thatbird watching and wildlife viewing responses given in Table IV.3 were adjusted to include only trips for viewing, i.e., activity less than one mile 

### from home was not included. 

### Table IV.3 

### - Participation in Key Recreation Activities

## - By Residency Status

Fishing Boating Swimming Shell fishing

Beach use Bird watching Wildlife viewing Hunting Any key activity* Multiple key activities 

### Residents (n=358) 

### SHO (n=159) Overnight (n=427) Day trippers Total (n=1217) (n=273) 88 (25%) 41 (26%) 62 (15%) 13 

### (5%) 204 (17%) 132 (37%) 63 (40%) 

### 88 (21%) 39 (14%) 322 (26%) 186 (52%) 

### 80 (50%) 153 (36%) 28 (10%) 447 (37%) 49 (14%) 19 (12%) 14 (3%) 6 (2%) 88 

### (7%) 282 (79%) 103 (65%) 235 (55%) 74 (27%) 694 (57%)

36 (10%) 13 

### (8%) 23 (5%) 32 (12%) 104 (9%)

73 (20%) 39 (17%) 45 

### (11%) 40 

### (15%) 185 (15%)

10 (3%) 3 (2%) 6 (1%) 2 (1%) 21 (2%)

342 (969'0) 132 (83%) 287 (67%) I17 (43%) 

### 878 (72%)

262 (73%) 103 (65%) 182 (43%) 65 (24%) 612 (50%) *May include bird watching and wildlife viewing trips less than one mile from home. 

### Participation Rate Overall By Residency Status. East End residents are more likely to participate in outdoor recreation than any other group. Fully 959'0 of the residents surveyed engaged in at least one, key water-based activity in the PES or one key land-based activity in one 

### oftheEast End towns. By comparison, 83% of secondhomeowners and 67% ofovernight visitors also participated in outdoor recreation. Day trippers have the lowest rate of participation (43%). A direct comparison between the number of people engaging in land-based and water-based activities cannotbemade. This is dueto the inability to tell the differencebetweenbeach use inthe PES and beach use on the Atlantic Ocean or Long Island Sound shoreline at the East End.
41However,the majority of residents (73%) and secondhome owners (65%)--and 50% of all survey respondents-- engaged in more than one key recreational activity during the past year. 

### Participation Rate in Key Recreational Activities-By Residency Status. Proportionately moreresidents(70%)and secondhomeowners (65%) swim,boat, fish,and shellfishthan overnight visitors (45%) and day 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%). Shellfishingismorepopularwith residents (14%) and secondhome owners 

### (12%) as compared with the overall sample (only 

### 7 %). Residents and second homeowners are more likelyto go to the beach, watch birds, view wildlife, and hunt in the East End than other groups. Proportionately more residents (85%) and second homeowners (70%) engageinthese activitiesthan overnightvisitors(59%) anddaytrippers (37%). Among these activities,the highest participationrates across all groups are for beach use (57%) followedby wildlifeviewing (2 1%0) andbirdwatching(19%). Only2% 

### of surveyrespondentshunt. 

### Location ofKey Recreational Activities. Respondents used the PES more times (15,506 occasions)forall key outdoorrecreationactivities--swimming, boating,fishing,and Shellfishing-- than any other water body at the East End (Table IV.4).



### Table IV.4 -Water 

### Body Selection by Activity 

## - (n=1267)

Block Island Sd. Long Island Sd.

Atlantic Ocean

PES

Totals 

### Fishing Shell Boating fishing 320 (6%) 

### 71 (5%) 467 (5%)

1422 266 (20%) 1760 (25%) (20%)

1125 201 (15%) 1056, (20%) (1 2%) 2732 766 (59%) 5470 (49%) (62%) 5599 1304 8753 

### (100%) (1

### 00%) ( 100%) 

### Swimming Totals

380 

### (2%) 1238 (5%)

2801 (18%) 6249 (24%)

5685 (37%) 71 16 (27%)

6538 (42%) 15506 (60%)

15404 26060 (100%) (1

### OO%> 

## * Percentagesmay not sum precisely due to rounding 

### No singletown is the dominant site for all categories

### of recreation by respondents. 

### For example, beach 

### use occursmost often at Southampton (3334 occasions)and East Hampton (3139). Shelter Islandwas the most used forbird watching (567)and hunting (92), and East Hamptonhad the most
42(1 123) wildlife viewing experiences. On the other hand, Riverhead was the least used town for beach use (1075), bird watching (68), and wildlife viewing (220). Southampton had the least number 

### of hunting occasions (26).

East Hampton Riverhead ShelterIsland Southampton Southold Totals 

### Table 

### IV.5 

## - Town Selection by 

### Activity 

## - (n=1267)
Bird Wildlife Total Beach Use Watching Viewing Hunting Trips 3 139 (26%) 300 (20%) 1123 (29%) 50 (20%) 4612 (25%)

1075 

### (9%) 

### 68 (5%) 220 (6%) 56 (22%) 1419 (7%) 2461 (20%) 567 (37%) 1030 (27%) 92 (36%) 4150 (25%) 3334 (27%) 386 (25%) 690 (18%) 26 (10%) 4436 (24%) 2239 (18%) 215 (14%) 764 (20%) 31 (12%) 3249 (18%) 12248 1536 3827 255 17866 (100) (100%) 

### (1 00%) (100%) (1

### 00%) *Percentagesmay not sum precisely due to rounding



### Location 

### ofPES Water-BasedActivities. Great PeconicBay isthe most popular water body in the PES for recreational activity, with 28% of recreational trips in the PES, while Flanders Bay 

### is the least frequently used, with 

### 8%. Great Peconic Bay 

### is also the most popular location in the PES for swimming (30%), fishing (29%), and boating 

### (25%). Gardiners Bay is the most popular 

### PES location for Shell fishing, accounting for 33% of all PES Shell fishing trips.

43

### Table 

### IV. 6 

## - PES Key Recreational Activity Outings 

## - (n=1267)

I 

### Fishing Shell Boating Swimming Totals fishing Flanders Bay 288 (11%) 45 (6%) 562 (10%) 366 (6%) 1261 

### (8%) Gt. Peconic Bay 793 (29%) 199 (26%) 1362 (25%) 1991 (30%) 4345 (28%)

~ Ltl.

Peconic 486 (18%) 109 (14%) 1021 (19%) 1298 (20%) 2914 (19%) Bay Shelter Isl. Sound 511 (19%) 158 (21%) 1321 (24%) 1410 (22%) 3400 (22%) Gardiners Bay 654 (24%) 255 (33%) 1204 (22%) 1473 (23%) 3586 (23%) Totals 

# I 2732 ( 100%) 766 (1

### 00%) 5470 (1

### 00%) 6538 (1

### 00%) 15506 (1

### 00%) 

### * Percentages may not sum precisely due to rounding 

### PESKeyRecreationalActivities by

### Residency Status. Swimmingisthemostpopular waterbased activity in the PES for all residency groups and overall accounts for 42% of all water-based recreation trips (Table IV.7). Boating is nearly as popular (36% of all PES trips). Overnight visitors fish more (22% 

### of their outdoor recreation activity) than any other group. Day trippers allocate a larger percentage oftheir PES trips to Shell fishing (1 6%) than to fishing 

### (9%), although Shell fishing overall 

### is a modest activity (5% 

### of 

### PES all trips). 

### Table IV.7 

## - PES Key Recreational Activity Outings by Residency Status

## - (n=1217)
Residents SHO Overnight Day tripper Total Fishing 1114 (17%) 401 

### (11%) 947 (22%) 30 (9%) 2492 (17%) Boating 2354 (37%) 1275 (36%) 1450 (34%) 110 (34%) 5 189 (36%) Swimming 2520 (39%) 1676 (48%) 1839 (43%) 132 (41%) 6167 (42%) Shell fishing ,396 (6%) 154 (4%) 92 (2%) 52 (16%) 694 (5%) Totals 6384 3506 4328 324 14542 (100%) ( 100%) ( 100%) ( 

### 100%) (100%) 

### * Percentages may not sum precisely due to rounding

44

### Locations of 

### PES Activities
- 

### By Residency Status. For residents, Shelter Island Sound (27%) is the most popular location in the PES, followed closely by Great Peconic Bay (25%) (Table

### V.8). GardinersBay (3 

### 1

### YO) isthe

### mostfrequenteddestinationfor secondhome owners,with GreatPeconic Bay (30%) againnearly aspopular. Overnight visitors and daytrippersprefer Great Peconic Bay (30% and 34%, respectively) over all other locations. FlandersBay isby farthe least oftenvisitedlocation forresidents 

### (7%), secondhome owners 

### (3%), and overnight visitors (10%). The least visited location by day trippers 

### is GardinersBay (13%), presumably because it is the furthestof the bays from their homes. Flanders Bay is more popular with day trippers (17%) than with any other group. 

### Table IV. 8 

### - PES Key Recreational Activity Locations by Residency Status (n=1217)
Residents Flanders Bay 451 (7%) Gt. Peconic Bay 1589 (25%) Ltl. Peconic 1260 (20%) Bay Shelter

### Isl. Sound 1752 (27%) Gardiners Bay 1332 (21%) 

### Total 6384 (100%) 

### SHO

114 (3%)



### 1048 (30%)



### 740 (21%)

525 (15%)

1079 

### (3 1

### Yo) 3506 (100%) 

### Overnight Day tripper Total

446 (10%) 54 (1

### 7%) 1065 (7%)

1304 (30%) 110 (34%) 4051 (28%)

736 (17%) 60 

### (1 9%) 2796 (19%)

1031 (24%) 58 (18%) 3366 (23%)

811 (19%) 42 (13%) 3264 (22%) 4328 324 14542 

### (100%) (100%) (100%) 

## * Percentagesmay not sum precisely due to rounding
[V.G. 

### DESCRIPTIVE RESULTS: PARTICIPATION AND TRIP ESTIMATES Introduction The total number of trips (or “outings”) for outdoorrecreation is the product 

### of three factors: 

### (1) The 

### potential number of participants, (2) the 

### proportion who actually participate, and (3) the 

### number of times those who participate engage in an recreation activity. Thus, we have:



### Trips = No. of 

### Potential Participants 

### x Percent Participating 

### x No. of 

### Times Participate

45For example, assume that there are 

### 2 million potential participants (i.e., residents, second home owners, over night visitors, and day trippers) for swimming, that 

### 20 %ofthese actually swim, and that of these participants, each swims an average of 

### 10times per year. In this case, the number of swimmingtrips or outings is 

### 4 million:

=

### 2 million x 

### 20 % 

### x 10

=

### 400,000 

### x 10 =

### 4 million trips Thedata and assumptionsusedto estimatetripsforeachPESrecreation activity isexplainedbelow. 

### Estimation ofTotalNumber ofTrips. The 

### population ofpotentialparticipants include allEast End residents, second homeowners, overnight visitors, and day trippers. 

### To estimate this population, weused 1990census data from the Long Island Regional Planning Board (“LIRPB”). The LIRPB analysis provides estimates of the year-round resident population; the seasonal population, including second homeowners and guests; year-round resident guests; and hotel, motel, and campsite capacities. The number of participants in each recreational activity was estimated separately for each of these residency category. The resident population over age 

### 15 is estimated by the 

### LIRF’B to be 84,87 1. Data are not available for the age breakdown of the seasonal population. Lacking authoritative data, we assume that the 

### seasonal population has the same percentage over the age of 

### 15 (80%) as the 

### resident population. This implies an estimated seasonal population over age 15 of 135,620. The seasonal population is split into two groups, based on 

### EAI’s recreational use survey results: Second Home Owners (27%) and Overnight Visitors (73%). Our survey results indicate that the 

### two components ofthe seasonalpopulation have different participation rates and therefore should be considered separately, when possible. Note that many overnight visitors stay with second homeowners and residents as renters or guests. Population data is not available for day trippers; therefore this group is excluded from our participation estimates. The absence ofthisinformationwill slightlyunderstate outdoorrecreation participation. Information concerning the 

### percent of potential participants that participates in one or more recreational activity was presented earlier. The 

### tripsper participant are given in Table IV.9. The major results of estimated annual participation in PES outdoor recreation (excluding day tripped) for 1995can be summarized as follows:

*Daytripper are excluded because we have no reliable way of estimating the total number in this group. This omission slightly understates total participation in PES recreation (see text). 46127,762peopleparticipate in either swimming, boating, fishing, or Shellfishinginthe

### PES, taking 3.3 million outings. 156,184peopleparticipated in eitherbeach use, bird watching, wildlife viewing, orhunting in the East End towns, engaging in 5.2 million trips.



### Table IV.9 

## - Average Annual Outings per Participant by Activity and Residence* Residents SHO Overnight Day trippers Fishing Boating Swimming Shell fishing

Beach Use Bird Watching Wildlife Viewing Hunting 12.66 9.78 15.27 2.3 1 17.83 20.24 16.48 2.82 13.55 20.95 12.02 4.71 8.08 8.1 1 6.57 8.67 22.50 25.85 9.20 6.1 1 37.55 23.49 8.83 3.28 34.36 25.39 9.99 3.80 13.20 16.33 7.00 5.50 *Includes activities at the five East End towns and PES water bodies only

Thetotalnumber 

### oftrips by each group, for each activity, is estimatedby multiplyingthe estimated number of trips per participant (Table IV.9) for each group (Le., residents, second homeowners, etc.) by the estimatedtotal number ofparticipantsby group. Theresults are shown in Table 

### IV. 10. Overall, there are 5.15 million beach use, bird watching, wildlife viewing, and hunting outings taken in East End towns and 3.35 million swimming fishing, boating, and Shell fishing outings taken in the 

### PES in 1995 (Table IV.10). Most outdoor recreational activity is accounted for by residents, who engaged in 3.3 million land-based and 1.5 million water-based occasions locally. Second homeowners took 1.1 million land-based trips and more than 800 thousand water-based trips. Overnightvisitors engaged inmore than 780thousand land-based, and 1 millionwater-based, trips. Totaltrip estimates slightlyunderstate actualrecreation trips because daytripper trip estimates are not included due to the lack ofpopulation data forthis group, as noted earlier. According to EAI’s recreational use survey, day trippers account for 3% of land-based and 2% of water-based recreational trips taken by the sample.

### Comparison with Phase 

### I Total OutdoorRecreational TripEstimates: Introduction. The Phase 

### I study (Grigalunas and Diamantides, 1996)gave estimates of 

### total recreation trips to the area for selected activities based on available 

### “off the shelf’ data and were comprised of a variety of sources. 

### No original data was collected during Phase I. In Phase I1 original data was assembled and it is important to note and to explain the major differences between the 

### two sets of results (Table IV. 1 1).



### Table IV.10 

## - Estimated Annual 

### Key Recreation Activity Outings”

Fishing Boating Swimming Shell fishing Beach Use Bird Watching Wildlife Viewing Hunting

Totals 

### Residents 

### SHO Overnight Totals 268,6 17 93,110 226,766 588,493 559,902 296,45 1 342,630 1

### ,198,983 598,001 383,563 428,406 1,409,970 96,006 35,636 19,513 15 1,155 1,508,582 6 15,257 500,955 2,624,794 2 19,008 32,139 41,699 292,846 174,86

### 1 39,218 40,392 254,47 1
33,609 11,959 6,930 52,498 3,458,586 1,507,333 1,607,291 6,573,2 10 *Includesactivities at the five East End towns and PES water bodies only

In general, we found major that the Phase 

### I estimates ofrecreational activity wereunderstated--in some cases, greatly understated-- as compared to the Phase I1 results. The major reason for the large differences in estimatesbetween the Phase 

### I and 

### I1 reports canbe explained as follows. Our survey-based estimates find much high participation rates and trips per participant than the Phase 

### Ireport, which used a state-wide average forNew York. For example,inPhaseI1 forbird watching and wildlife viewing we found a high participation rate (19% for bird watching and 21% for wildlife viewing) for respondents and a large number oftrips per participant (37.55bird watching trips and 34.36 wildlife viewing trips for residents). The Phase 

### I survey, in contrast, was based on NFWS statewide estimates forNew York. These statewideparticipation rates (9.4%) and number of trips per participant (8.3) are very much lower than those found in the PES survey sample. This major difference between the Phase 

### I and Phase 

### I1 results 

### is not surprising (at least in retrospect). Residents and seasonal visitors to the PES are attracted to the area precisely because they are interested in estuary-related outdoor recreation. We thus would expect this group to be 48much more interested in outdoorrecreation than the general population ofthe state. Similar large differencesbetween the Phase 

### I and 

### I1 results were found for other activities (see Table 

### IV. 11and also see Appendix for details) and the same explanation seems relevant.



### Table IV.ll- Comparison

### of Participation Estimates Phase 

### I Versus Phase 

### I1
PES 

# - PhaseI1 Beach Use 2,624,794 Non- 547,3 17 Consumptive Hunting 52,498 Fishing 588,493 Boating 1,198,983 Swimming 1,409,970 

## * Grigalunas and Diamantides (1996). 

## ** NA =Not available 

### PES 

## - Phase 

### I* 

### NA*

## * 91,713
77,300 113,589

NA 

### 7 14,600



### 1V.H. DESCRIPTIVE RESULTS: SELECTED EXPENDITUREESTIMATES Introduction. During the early survey development phase 

### of our work, members of the Citizens Advisory Committee and officials at the Suffolk County Dept. of Health Services expressed interest in our collecting information on sectors ofthe PES economy for which data are difficult to find using standard economic sources. Specific sectors of the economy targeted for special considerationwere Farm Stands, Wineries, and Rental Lodging. The survey questionnaire asked respondents to estimate the amount that their household spent at Farm stands and Wineries at the East End over the past year. Visitors also were asked whether they rented a house, condo, apartment, etc., at the East End over the past twelve months. Respondents who answered “yes” to this question were asked the length 

### of the rental agreement and the amount they spent. Responses to these questions were given in 1995 dollars, summarized in Table 

### IV. 12 and explained briefly below. 

### Population of 

### Potential Participants. Expenditure data en Farm stands, Wineries, and Rental Lodging collected in this survey are household (not per person) expenditures. The number of East End resident households (44,241) and seasonal households (29,183) are based on 1995 Long Island Lighting Company estimates.

49The number ofovernightvisitorhouseholdscannotbe adequately estimatedwith available data. LIRPB seasonalpopulation estimates account for occupancy of somerental lodgings, such as hotels, motels, and campsites. However, this component of the population estimate does not account for turnover, i.e., the same rental lodgings will be used by an unknown number of overnight visitors over the course of a season. Estimating the number of overnight visitor households is further complicated by the large percentage of overnight visitors 

### (39%) who indicated that they stayed with friends 

### or relatives, which implies that some amount of their expenditures may be included in resident and seasonal household estimates. For these reasons, expenditures of overnight visitors are not estimated in this analysis. Still, the average annual expenditure forrental accommodations per household from our survey --$5,40O--makeit evident that seasonalrentals are a major source of income for PES rental property owners and owners of hotels, motels, and inns. 

### Estimated Total Participation and Expenditures. Table IV. 

### 13: Estimated Farm Stand and WineryExpenditurespresentsestimates (in 

### 1995dollars)ofparticipationandexpendituresinthese sectorsfor resident and seasonal households. Table IV. 

### 13 also presents estimates of PES-related expendituresin these sectors. PES-related expenditureswere estimated using the same seasonal adjustment (45 

### YO) used in the Phase I Report to estimatethe PES-related impacts of tourism and recreation sectors. Total rental lodging expenditures could not be estimated due to incomplete information on the number of participants.

50

### rable IV.12 -Survey Farm Stand, Winery, and Rental Lodging Expenditures (1995dollars)"

Residents Participation Total Expenditures Average Expenditures 

### Second Home Owners Participation Total Expenditures Average Expenditures 

### OvernightVisitors Participation Total Expenditures Average Expenditures 

### Day Trippers Participation Total Expenditures Average Expenditures 

### Totals Participation Total Expenditures Average ExDenditures " 

### Farm stand Winery

316 

### (88%) 114 (32%)

$86,400 

### $20,871

$273 $183

131 (82%) 67 (42%)

$48,122 $15,350

$367 $229

225 (53%) 114 (27%)

$4 1,228 $18,360

$183 $161

153 (56% 94 (34%)

$12,755 $9,121

$83 $97

825 (68%) 389 (32%) $188,505 $63,702 $228 $164 $5,398 

## * Figures in parentheses indicate the percent who made such expenditures

### Table IV.13 

### - Estimated PES Farm Stand and Winery Expenditures
Farm stand Winery Residents Estimated Participating Households 38,932 14,157 Estimated Total Expenditures $10,628,436 $2,590,731 Estimated PES-Related Expenditures $4,782,796 $1,165,829 Second 

### Home Owners Estimated Participating Households 23,929 12,256 Estimated Total Expenditures $8,78 1,943 $2,806,624 Estimated PES-Related Expenditures $3,95 1,874 $1,262,98 1 

### Totals Estimated Participating Households 62,861 26,413 Estimated Total Expenditures $19,410,379 $5,397,355 Estimated PES-Related Exoenditures 1 ~- $8,734,67 1 $2,4288 10

An interesting finding is that PES-related Farm stand expenditures are greater than earlier estimates for several other food-related sectorspresented in thePhase

### I Report. For example,PESrelated farm stand expenditures are greater than expenditures (in 1995 dollars) in Meat and Fish Markets ($6.5million),Fruit and Vegetable Markets ($3.8million),Retail Bakeries ($2.9million), and Drinking Establishments ($6.0 million) (Grigalunas and Diamantides, 1996).

52

### IV.1. DESCRIPTIVE RESULTS: REFERENDAAND 

### BROWN TIDE RESULTS N.I.l.Hypothetical Referenda 

### Introduction. One section of the recreational use survey posed referendum-type policy questions to East End residents and second home owners. These questions were addedto “test the waters” on selected policy issues and were not designed as a basis for resource valuation, which would have required considerably more effort and data than was possible for this project. 

### Referenda Questions. The referenda questions focus on four policy actions: 

### (1) reseeding scallops following Brown Tide, 

### (2) planting hard clams, (3) upgrading the Riverhead sewagetreatment plant, and (4) reducing road runoff. Respondents were presented with one 

### of these programs andwere askedwhether they supportthe program, ifthey had to pay a given annual cost. Annual costswere varied for each ofthe proposed policy actions as described in Table IV. 

### 14. ReferendaResults. Respondents overwhelmingly supportedthepolicy actionsand indicated a willingness to pay the annual costs. Support ranged from a high of 

### 100%for reseeding scallops following Brown Tide at an annual cost of $10, to a low of 75% for upgrading the Riverhead sewage treatment plant at an annual cost of 

### $100. Thishigh level of support for 

### PES management programs was consistent across residents and second home owners. Overall, 87% ofrespondents indicated they support the policy actions at the given costs (Table 

### IV.14). IV.I.2. Descriptive Results: Effects of Brown Tide Respondents were asked whether they had heard of Brown Tide (“BT”). Those who had heard of BT were asked about their level 

### of concern and about any activities that may have been affected by BT. Overall,most respondents (83%) had heard ofBT, and ofthose, most (71%) were alsovery concerned (Table IV. 15). BT had thebiggest effect on swimming. Fifty-eightpercent ofthose who hadheard ofBTindicated that ithad affectedtheir swimming. Recreational fishing(34percent) and shellfishing(33percent) also were affectedby BT. Residents and second home owners had the most knowledge of and the highest concern for BT. Overnightvisitorshad the leastknowledge and indicatedthat their activitieswere affectedthe least.

53

### Table IV.14. 

### ManagementAction Referenda Results

Proposal; Annual Cost

Reseed Scallops Following Brown Tide; 

### $5 Reseed Scallops Following Brown Tide; $10 Reseed Scallops Following Brown Tide; $25 Plant Hard Clams; $5 Plant Hard Clams; $10 Plant Hard Clams; $25 Upgrade Riverhead Sewage Treatment Plant;$5 Upgrade Riverhead Sewage Treatment Plant;$25 Upgrade Riverhead Sewage Treatment Plant;$50 Upgrade Riverhead Sewage Treatment Plant;$lOO Reduce Road Run Off; $10 Reduce Road Run Off; $25 Reduce Road Run 

### Off; $50 Totals

54 

### Resident SHO S 

### Y/N Y/N 2112 610 14/0 

### I010 1712 511 2114 1210 1813 510 1412 212 1612 610 1813 911 1514 610 73/28 3016

1911 1110 2211 1511 2211 

### 610 

### Total Total S 

### %Yes Y/N 2712 93% 2410 100% 2213 

### 88% 3314 89% 2313 88% 1614 80% 2212 92% 2714 87% 2114 84% 103134 75%

3011 97% 3712 95% 2811 97% 290153 123111 413/64 87%

### Table IV.15 Awareness 

### of Brown TideSecond Overnight 

### Day Residents Home Owners Visitors Trippers Total Heard ofBrown

Have NOT Heard 

### of Brown Tide
Very Concerned about Brown Tide"

A 

### Little Concerned about Brown Tide"
NOT Concerned about Brown Tide" 370 159 279 202 1010 97.1 1% 91.91% 67.72% 78.29% 82.52% 11 14 133 56 214 2.89% 8.09% 32.28% 21.71% 17.48%

295 113 174 131 713 80.16% 7 1.52% 62.37% 65.17% 70.87%

70 44 100 64 27 

### 8 19.02% 27.85% 35.84% 31.84% 27.63%
3 1 5 6 

### 15 0.82% 0.63% 1.79% 2.99% 1.49%

55

### Table IV.16. Activities Affected By Brown Tide
L 

### Second Home Overnght Day Resident Owners Visitors Trippers Total 

### s Skmming 224 98 151 118 59 1 60.54% 61.64% 54.12% 58.42% 58.51% 

### Fishing 148 49 82 69 348 40.00% 30.82% 29.39% 34.16% 34.46% 

### Shell 168 52 62 58 340 

### fishing 45.41% 32.70% 22.22% 28.71% 33.66% 

### Boating 72 30 43 37 182 19.46% 

### 18.87% 15.41% 18.32% 

### 18.02% 

### Other 32 13 20 10 75 8.65% 8.18% 7.17% 4.95% 7.43% Includes only respondents who have heard of Brown Tide



### 1V.J. ESTIMATES 

### OF THE ECONOMIC VALUE 

### OF RECREATION 1V.J. 1. Introduction Our intention was touse data from therecreational survey to assess the use value for many waterbasedand shore-basedrecreationalactivities. The surveycollectedactivity-specificdataonfishing, Shell fishing, boating, and swimming in the 

### PES and surrounding waters. Information also was collected on beach use, 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 budgetary reasons. The number of completed responses for each activity was the limiting factor in assessing which activities could be valued. Values were estimated for 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 most popular activity, and swimming and recreational fishing are the activities most directly affectedby water quality andhenceby management actions aimed atenhancingwater

.56quality (e.g., Table IV.15) For this reason, swimming is a focus of 

### our efforts to estimates the benefits of management actions in terms of changes in swimming values. We recognize that participation in, and the economic value of, all activities depends upon the quality of the experience. For example, substantialresearch onrecreational fishing, including theresults ofthis study, showsthe importance ofthe quality ofthe experience--thecatchrate--on thenumber oftrips and their economic value (e.g., Freeman, 1995; McConnell and Strand, 1994). However, establishing thenecessary cause-and-effect scientific linksbetween policies topreserve orrestore habitat, for example, with changes in salt water recreational catchrates appears to be beyond the state of the art’. Nevertheless, we can simulate how benefits to recreation fishing would change if the catch rate increased by, say, 10percent. Individualrecreation trip value estimatesand annual aggregate value estimates are given for three cases.First,wepresent the existing (“baseline”) conditionsfor swimming, fishing,boating, andfor birdwatching and wildlife viewing. For the baseline, we show consumer surplus per trip, total estimated number of trips in 1995, and total benefits for the year for the four key recreation activities studied. Second,we show how economic value for

### swimming changes forillustrative,hypothetical policies affecting water quality. One suchpolicy is assumed to increasewater qualityby 10percent in each of the five 

### PES bays. We also simulate a 20 percent uniform improvement in water quality for comparative purposes. Finally, a 10percent improvement in the recreational fishing catch rate is simulated. IV.J.2. Methodology Value estimates for each activity are derived using the Travel Cost method of valuation (see, for example, Freeman, 1993). The rationale behind this methodology is that, although there is no market price that reveals the value of a recreational activity, an implicit price can be observed through the cost of traveling to the activity’s location. Included in this implicit price is the cost of transportation, such as gas, wear and tear on the automobile, etc., and the value of the individual’s travel time. In theory, an individual’s opportunity cost is valued ashis or her next best use of their time. However, a fraction of the individual’s wage is generally used, in practice, to represent income foregone in favor of recreation. Inthe travel cost model, the number oftrips a person takes for recreation at a specific site depends on the costs of getting to that site, the comparative costs of getting to substitute sites, and the quality of the recreational experience at the sites. In other words, the cheaper it is to get to a site, the cheaper travel to that site is relative to other sites, and the better the quality ofthe recreational experience at that site, the more times the individual will visit that site, all else equal. Thus, observed recreation choices reveal tradeoffs between cost of participation, as measured by travel

’This contrasts with policy 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 and hence to benefits. 57cost, and participation rates. In addition, participants might reveal values for site quality by participating more at sites of higher quality, and less at sites of lower quality, all else being the same. Carrying out the travel cost analysisrequires data on participationrates, cost oftravel to sites,and site quality. Data onparticipationrates is collectedin the recreationalsurvey. Data ontravel costs to the target and substitute sites, includingthe opportunitycost 

### of travel time, are collected 

### from the surveyand augmented, asnecessary,with distanceinformation

### fromroad maps. Data onwater quality comes from 

### two sources. First, we use field measurements 

### of various water quality parameters from the SCDHS 

### PES water sampling program, includingnitrogen, coliform counts, water clarity (Secchi disk measurements) and Brown Tide cell counts. These are objective measures ofquality. Second,weuse data fromtherecreation surveywhich askedrespondentstheir subjectiveevaluationof water quality (excellent,good, fair orpoor). These evaluations of course are subjective. For swimming, our analysis combines the subjectiveassessmentsby individualsand the objective field measurements of water quality. Ultimately, we are interested in whether we can provide sequential linkagesbetween: 

### (1) management 

### actions that effect water quality parameters, (2) the resultant 

### perceptions of water quality, and 

### (3)participation in specificrecreation activities. If we can establish such a linkagewe can evaluatewater qualitypolicy alternatives." To attempt to do so, we relate participation to subjectiveratings of water quality, as indicated in the recreational survey, and in turn, we relate these subjective water quality ratings to field measurements 

### ofwater quality. These are: water clarity,nitrogen concentration, coliform counts, and Brown Tide cell counts for each location and time period. This allows us to estimate the recreational benefits associated with water quality improvements resulting from management options. The annualbenefits of apolicy improvingwater qualityhas two parts. One is that individualswill enjoythe swimming experiencemore; the other isthat they will make more trips. This 

### is thebasis ofour approach for estimatingthe economicbenefits ofwater qualityimprovements in the 

### PES for swimming. For example,consideran assessmentofproposed improvementsintheRiverhead sewagetreatment plant. Upgrading the sewage treatment plant will result in improvements in water clarity, reductionsin coliform counts, etc., with the largesteffectin FlandersBay andprogressively small changes in bays to the east. Our model allows us to predict how these changes in water quality

"We note that a water quality model developed by Tetra Tech of Fairfax, VA for the PES Program will estimate water quality changes in each of the bays due to proposed program policies. 58parameterseffectthe subjectiveassessment ofwater qualitybyrecreationalusers, andtheresultant changes in participation rates and in recreational values at various sites. Thus, we can identify benefitstorecreationalswimmingthatresult frommanagement optionsthat improvewater quality. In order to implement this methodology,we will coordinate our assessmentswith ongoingwater qualitymodeling effortsin the 

### PES. Thus,we anticipatethat water qualitymodeling will be used to project changesin qualityparameters

### fromprospectivemanagement policies. Ourrecreational modelingwillthenbeusedtoestimaterecreationalbenefitsresulting

### fromthesechangesin quality. Similarlyforrecreationalfishing,quality ofthe experiencedependsupon catchrates,which in

### turn depend upon fish populations. Our recreationalfishing model includes catch rates as a predictor of participation rates and recreational values. Thus, if predictions can be made concerning the effect of management actionson fishpopulations,we coulduse ourrecreational model to estimate resultantbenefits to recreational anglers. However, it may not be feasiblefor natural scientiststo predict changes if salt water fish populations that would likely result from management actions. Nevertheless,the opportunityexistsfor estimatingrecreationalfishingbenefits frommanagement actions, if resultant changes in populations can be estimated. Even in cases where precise predictions are not possible, it may be possible to providepossible ranges ofpopulation changes, which will enableus to provide order-of-magnitudeestimates

### of possiblebenefits. The economicbenefits from improving (or maintaining) water quality could be long-runbenefits, if the policy is sustained. To recognize the long-runbenefits, we sum the annualbenefits over time, discounting them as appropriate to get an equivalent value today--the asset value of the resource in providing services supportingthat activity.



### 1~7.~. ESTIMATES 

### OF ECONOMIC VALUE: 

### RESULTS IV.L.1. Value (Consumer Surplus)Per Trip The methodology describedaboveyields a consumer surplusestimateper person,per trip ranging from $8.59 for swimmingto $49.83 for non-residentialbird watching andwildlifeviewing (Table IV.17). Fishing andboating valuesper trip fallwithinthatrange at $40.25 and 

### $19.23,respectively. These estimates of consumer surplus are the average value individualsreceive per trip over and above the cost of their recreational trip. Looked at another way, it is the 

### unpaid for benefit that individuals receive, on average, from a recreational trip. The relative sizes of these values are consistent with the literature (e.g. Walsh et al., 1988;Freeman, 1995). IV.L.2. Total Annual Economic Value for All Trips Total annualbenefitsfrom each ofthe four recreationalactivitiesstudied are givenin Table

### IV.17. The total benefits are estimatedby multiplying the average consumersurplusfor an activityby the estimated total number oftrips to engagein that activityduringthe year (1 995). Viewing ofBirds and Wildlife is the most valued of the activities studied ($27.3 million). Of the water-based
59activities, recreational fishing is the most highly valued ($23.7 million). We emphasizethat total values rarely are useful for policy analyses since most policies involve 

### smaZZ changes in an activity or its quality and are not “all or nothing” choices. Nevertheless,we recognizethat informationon total values, such as that given in Table IV.17 helps put the scale of these activitiesin some perspective.



### Table IV.17 Total Annual Value of 

### Recreational Activities at the PES (Baseline Water

.~
~ Recreational Bird Watching& Swimming Boating” Fishing Wildlife Viewing Total Tripsmear 1,409,97 937,387 588,493 547,3 17 Consumer Surplus Per Trip $8.59 $19.23 $40.25 $49.83 Total Annual

I , Consumer Surdus $12.113.2 6 $18,025,952 $23,685,985 $27,272,806 

## * Excludes boating trips taken primarily for fishing

IV.L.3. Asset Value of PES for Key Recreation Activities Using the results given above, the asset value of the 

### PES for providing each of the recreational activities (“services”) can be estimated. We use a discount rate of 7 percent and time horizon of 25 years and assume that the estimated values remain the same over the 25 year period. Using these assumptions,and our annualestimatesfromthe above table,the asset valuesrange from $3 18 million for Bird Watching and Wildlife Viewing to$141 million for Swimming. The 

### PES has an asset value of $276 million for Recreational Fishing and $21

### 0 million for Boating. IV.L.4. Change in Swimming Trips and Benefits Due to HypotheticalWater Quality Changes 

### To illustratehow the economicbenefits estimatedin thisreportcanbe used to contributeto estuary management policy, consider a hypothetical policy or set of policies that improves all water quality measures by 

### IO percent in all five 

### PES Bays. That is, the policy reduces field measurements ofNitrogen, Total Coli, Brown Tide cell countsby 10percent and increases Secchi depth by 10 percent throughout the 

### PES. Note that since water quality measures differ among individualbays (and indeed within Bays), the 

### absolute water quality changes due to a 

### 10percent

60improvementwill also differ among and within 

### PES Bays. Onlybenefits are considered;the costs of achieving the water quality improvements are not taken into account in this report". Note that 

### EAI's research on recreational activity, we found that only swimming was affected by the five water quality estimates available,within the range of data available" for these variables. We also found that recreational fishing was affected by the catch rate and by Brown Tide cell counts. The hypothetical water quality policy illustrated here is restricted to the effects on swimming in the PES; a hypothetical policy affectingrecreationalfishing is describedbelow. The hypothetical uniform 10 % improvement for all quality measures for all Bays increases the number of yearly swimming trips in the 

### PES as a whole by about 151 thousand and has 

### annual benefits of $1.3 million dollars (Table IV. 18). This is an increase 

### of about 1 1percent in use and inbenefits. Ifmaintained for25 years,thisimprovementhas apresent valueof $15.1million,using a discount rate of 

### 7 percent. This number represents the change in the asset value of the PES for swimming due to the quality improvement, all else remaining the same. Thus, a policy (or set of policies) that created and maintained such an improvement water quality would be a good investment of scarceresources if the present value of these costs was less than $15.1 million. Most of the incrementalbenefit to swimmers ($752 thousand of the $1.3 million annual benefit) is due to increased water clarity (Secchi depth). The lowest benefit is due to the 10 percent improvement in Coli counts ($81 thousand per year). Among the five 

### PES Bays, the 10percent uniform water quality improvement has the highest incremental benefit for those who swim in Shelter Island Sound (an annual increase in swimming benefits of $312 thousand).

"Subsequent research by EA1 will address benefit-cost analysis ofproposed water quality actions. "Recall, the recreational model for swimming used objective data from SCDH water quality sampling. No data are available outside the range of these field measurements. Obviously, a major deterioration of water quality would have enormous adverse impacts on recreation and could even drive activity at some locations to zero if serious enough (e.g., beach or fishing closures). 61

### FableIV.18 Benefitsto Swimmers

### of 10%Improvements in Each WaterQuality Indicator at Each PES Water 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 I33 8,172 16,288 26,988 

### Shelter 

### Is. Sd. $22,598 $4,553 $109,790 $175,093 $3 12,033 

### 2,630 530 12,779 20,381 36,320 

### Gardiners Bay $14,138 $129 $76,863 $185,286 $276,416 

### 1,646 15 

### 8,947 21,567 32,I74 

### 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 thousand and annual swimming benefits by $2.6 million (Table 

### IV. 19 Again most ofthe increase inbenefits ($1.46million) is attributableto greater water clarity.However, benefits due to reduced Brown Tide cell count (about $652 thousand) also are important. Inpolicy analyses, it is important to examine the incrementalbenefits ofpolicies in.orderto know what the public gets from stricter 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 mil1ion)of the second 10 percent water quality improvement (i.e. going from 10percent to 20 improvement). The issue of incrementalbenefitshomalternativemanagement actionsthat affectdifferent

### PESbaysdifferently will be addressed more comprehensively in Phase 

### 111 economic studies.

62

### TableIV.19. Benefits 

### of20% Improvements in Each Water Quality Indicator atEach 

### PES Water Body
Total Kjeldahl 

### PES Water Nitrogen Body Flanders Bay $130,030 

### 15,135 GreatPeconic $48

### ,869 

### Bay 5,688 Little Peconic $41,319 

### Bay 4,809 Shelter Is. Sd. $44,207 

### 5,I45 Gardiners Bay $28,555 

### 3,324 PES 

### Total $292,979 

### 34,102 ltalics indicate number 

### oftrips 

### Total Brown Tide Secchi Water Colifrm Cell Counts Disk Body Depth Total $141,908 $28,888 $253,807 $554,633



### 16,518 3,362 29,543 64,558 $6,200 $95,937 $249,204 $400,201



### 722 

### I

### I, I678 29,I52 46,729 $2,5 10 $139,892 $279,957 $463,678



### 292 16,283 32,262 53,64 7 $7,907 $235,633 $315,939 $603,685



### 920 27,427 36,775 70,268 $5567 $151,556 $356,181 $541,859



### 648 1 

### 7,641 41,459 63,O71 $164,653 $65 1,905 $1,455,088 $2,564,065



### 19,100 75,881 I 69,I91 298,2 73

IV.L.5. Change in Fishing Trips and Benefits Due to Hypothetical 10 Percent Change in Catch Rate We also simulate the effects of an assumed policy that leads to a 10 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 annual benefits by $472,359. The present value ofthis increase in catch rates--the increase in the asset value of the 

### PES in providing this service-- is $5.5 million, using the 7 percent discount rate and time horizon of 25 years used for all cases.

63

### Number ofFishing Trips Consumer SurplusRrip Total Consumer Surplus 1 

### 10 Percent Increase Incremental Baseline in Expected Catch Benefits Rate
588,493 599,742 

### 1 1,249
$40.25 $4 1.05 

### $0.80 $23,685,985 $24,158,344 $472,359

We re-emphasize that the results presented here are for benefits only; the 

### cost of water quality improvements, or 

### of improvements that might increase recreational fishing catch rates, have not been considered. Thus, we 

### do not 

### know the 

### net benefits of potential management actions. Later Benefit-Cost analyses to be done by EA1 of proposed program actions will address these issues.



### 1V.M. REFERENCES Freeman. 

### A.Myrick 

### 111, 1993. 

### TheMeasurement of

### Environmental and 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. “Marine Recreational Sportfishing”. College Park: University of Maryland. Walsh, Richard G.,Donn 

### M. Johnson, and John R. McKean, 1988.“Review ofOutdoorRecreation Economic Demand Studies with Nonmarket Benefit Estimates, 1968-1988”. Fort Collins, CO: Water Resources Center, Colorado State University.

64

### Supplementary Tables and Appendices Supplementary Tables



### Town East Hampton Riverhead Shelter Island Southampton Southold

Totals

Table S.2 Number of Percentage of All Percentage of YearRespondents Respondents round Population-a 50 14% 

### 15% 42 12% 22% 57 16% 2% 

### 107 

### 30% 43% 102 28% 19% 358 100% 1

### OO%*

Second Home Owner Primary Residence I I 

# I I Percentage 

### of All 

### SHO 

### Area New York City Nassau County Suffolk County

Other Areas Totals Number of Respondents 72 30 22 35 159

65 Respondents 45% 19% 14% 22% 100% 

# 11Table S.3



### Southampton Southold

Totals

rable S.4 

### Primal

Area New York City Nassau County

~ 

### Suffolk County Other New York State and New Jersey Connecticut and

Massachusetts Other 39 29% 49% 

### 42 32% 14% 133 

### 100%" loo%*



### Number of Respondents 146 118 300

39 84 

### Percentage 

### of All Respondents 19% 16% 40%



### 5% 11% 

### Totals 

# I 750 

# I 100%" *adjusted for rounding

66

### Accommodationsfor OvernightVisitors

Accommodation Number 

### ofRespondents

Hotel or Motel 95 I 

### Rental 101 

### Friends or Relatives 168

I 

### Other 63 

### Totals 

# I 427 

# I * Adjusted �orrounding

Table S.6 

### Percentage ofAll Overnight Visitor Respondents 22% 24% 

### 39% 

### 15% 100%"

Overall Participation"in Key Recreation Activities

## - All Respondents (n=1354) 1 

# I Any Activity* Multiple Activities Water Based Activities Land Based Activities I 

### Number of 

### Participants Percentage of 

### all 

# I Respondents 

# I/

944 70% 

### 648 48%

~~ ~­ 

### 820 61% 644 48%I

15506 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% Totals 644 100% 

# 1

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 

### yo 255 11 Totals 820 

### 100% 23502 29 

## *

68S.9 

### - Estimated Trips for Key Recreation Activities for Sample*


### Residents 

### SHO Overnight Fishing 21,218 9,520 14,850 Boating 3 1,402 14,647 20,791 Swimming 44,133 18,309 35,641 Shell fishing 1 1,882 4,394 2,970

Beach Use 67,048 23,801 54,452 Bird Watching 8,555 3,032 5,346 Wildlife Viewing 17,313 6,225 10,098 Hunting 2,546 732 990 

### Totals 45,588 66,840 98,083 19,246 145,301 16,933 33,636 4,268 *Includes activities at the five East End towns and 

### PES water bodies only 

### Appendix B 

### - Technical Appendix: Swimming Trips and Value Estimation Overview 

### A “count data” model is used to estimate the demand for recreational activities in the PES. Count data models are appropriate for estimating recreational demandbecause thenumber ofrecreational trips taken by an individual must take on an integer value. The Poisson distribution is used to estimate the number of trips to each site in the PES and to estimate the economic value (consumer surplus) of those trips. For swimming, the number 

### oftrips to each site ismodeled as a function ofthe travel costto the site, costs oftravel to substitute sites, the number ofboating occasions at the same site, and a subjective measure of water quality at the site. The measure of water quality is the probability that water quality at the site willbe ranked either good or excellent by survey respondents who recreate atthat site. This model can be used to estimate the effect ofmanagement actions to improve water quality on the number ofrecreational trips to each site, and to the value of a recreational trip to a site. In order to do so, we need to linkmanagement actionsto changes in water quality, and to link objective field measures of water quality to subjective perceptions of water quality by recreational users. Predictions ofchanges in water quality dueto management actions is done as part ofongoingwater quality modeling work, carried out aspart ofthe Peconic Estuary Program (Tetra Tech, 1998). The relationship between objective water quality measures and subjective perceived water quality is

69 

# Iestimated by linking data collected in the recreational survey with field measure of water quality. Specifically, for those respondents who indicate that they went swimming on their most recent recreation day, the survey asked where they went swimming and how highly they rated water quality (excellent, good, fair or poor) atthat site. We also obtained data 

### on measures 

### ofkey water qualityvariables, including brown tide cell counts, total coliformbacteria, total Kjeldahl nitrogen, and Secchi disk depth readings hom sampling locations distributed across the PES. The data was grouped into ten sites 

### - north fork and south fork sites for Flanders, Great Peconic, Little Peconic, Shelter Island Sound and Gardners bay. An ordered LOGIT model was used to estimate the probability that an individual would rate water quality high (good or excellent), fair orpoor as a function ofthe objective field measures ofwater quality, discussed above. We then include these 

### predicted probabilities into the travel cost estimation, sothat the number oftrips to a site is related to the cost oftraveling to the site, the cost 

### of traveling to key substitute sites, and the predicted probabilities of the subjective rankings of water quality. These steps are described in detail below. 

### Water QualityModel The water quality model links individuals’ perception of water quality 

### - expressed in a ranking of poor, fair, good, and excellent 

### - to physical measures ofwater quality collected at sampling stations by the SCDHS. An ordered logit model isused to estimate the probabilities for each rank based on physical measures ofwater quality and onthe effects ofBrown Tide onthe individual’s swimming that year. The purpose ofthe water quality model is toprovide input into the travel cost model Le., the probability that the water quality of a site would be ranked good or excellent. Data for the water quality model consists of site readings during August 1995 for the following writer quality indicators: 

### 1. Brown Tide, Auereococcus

### anophageffeerens,in cells per milliliter. 2. Total Kjeldahl nitrogen in milligrams per liter. 3. Total coliform bacteria in 

### MPN per 

### 100milliliters. 

### 4. Secchi disk depth measured in feet. These water quality indicators were selected for their relevance to potential management actions and for their modeling compatibility. Also included in the water quality model data set are responses to the Recreational Use Survey Questionnaire administered during August 1995.Three questions fromthe questionnaire areincluded. One question asks individuals iftheir swimmingwas affected by Brown Tide. Another question asks respondents to rank the water quality 

### - poor, fair, good, excellent
- at their last swimming outing in the 

### PES. The third question asks respondents to identi6 the swimming location 

### of their last swimming outing in the 

### PES. The data is grouped intoten sites: Flanders bay north and south, Great Peconic bay north and south, Little Peconic bay north and south, Shelter Island sound north and south, and Gardiners bay north and south. Swimming locations were allocated to the ten sites according to locations indicated by

70the survey respondent. Only observationsthat indicate an identifiable swimming location for trips taken during August 1995 were included in the data set. Water quality sampling stations were allocatedto the ten sites accordingto SCDHSsampling stationmaps, asdescribed inTableA1


### .The number of samples recorded from each sampling station varies. An average of the data recorded at each station during August 1995 (the period during which 

### our survey was administered) was taken. Sampling stationswith a central position, as opposedtonorth or south, were included in the averages of both north and south sites. Data for Brown Tide, total Kjeldahl nitrogen, and total coliform bacteria were used to construct indices ofwater quality relative to threshold values for each parameter to make the measures more comparable. The average Brown Tide cell count for each of the ten sites was divided by 250,000cells/ml- the visibility threshold for Brown Tide. The resulting value was then multiplied by 1ifthe respondent indicated that their swimming was affected by Brown Tide, or by 

### 0 if not. In this way, the Brown Tide cell count is modeled to affect the water quality ranking of only those respondents who indicated they had knowledge of and were affected by Brown Tide. The average total Kjeldahl nitrogen reading for each of the ten sites was divided by .5mg/l, the nitrogen guidelineestablishedby the SCDHS. The averagetotal coliformbacteriareading for each of the ten sites was divided by 400MPN/100 ml, a threshold for bathing water quality. The total Kjeldahl nitrogen index and the total coliform bacteria index were added together to form ajoint nitrogen-bacteria index. This joint index was used in the ordered logit model instead of the individualindicesbecause high colinearityin fieldmeasurements ofnitrogen and coliformbacteria precluded separate estimation of the effects of these two water quality variables. All indices were multiplied by 100to preclude the use offractions. Average input values for each site arepresented in table A2. The modeling procedure uses an ordered logit model that predicts water qualityrank based on the Brown Tide Index, Nitrogen-Bacteria Index, and Secchi Disk depth readings. The ordered logit model is based on the logistical distribution,

G Prob(Y = 1) = - 

### I t 

## 2” ’ where 

### j3 represents parameters to be estimated and x represents water quality field measures (e.g., water clarity)that contribute to the subjective water quality rankings. Given that the rankings are discrete and finite (excellent, good, fair and poor), it isreasonable to talk about the probabilitythat 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, excellent
- the choice made reflects the rank order that is most similar to the individual’s 

### own perception. In other words, the individual may feel that water quality at their swimming location that day was somewhere between good and excellent, but because of the ranking order presented

71by the questionnaire, the individual will choose either good or excellent as the best choice. This selection process is modeled as a multinomial logit model where y* = p’x 

## + E. 

### Y* is a continuousmeasure of site quality, not constrainedby the discrete ranking, 

## p is the vector of coefficients estimated by the ordered logit model, x is the vector 

### of field measures of water quality, and E is a vector of unknown factors that might affect the individual’srank order choice (e.g., the individual’s mood or the prevailing weather conditions). The unknown factors are assumedtobe logistically distributed across individualswith a mean 

### of zero and a variance 

### of one. Although y* is unobserved, the rank order selections indicated in the survey questionnaire are observed. These selections can be modeled as
y = 

### 0 (poor) ify* 0, y = 1 (fair) ifO<y* pl, y = 2 (good) ifp1 <y* p2, y=3 (excellent) if p2 y*. where the pi are threshold values that determine when water quality ranking would change for a “representative” individual. The ordered logit model estimates the 

### p threshold parameters along with the vector of 

## p coefficients. The results of the ordered logit model are presented in Table A3. The estimated coefficients indicate that a reduction in Brown Tide cell counts or a reduction in the nitrogen-bacteria index wouldresult inhigherwater qualityrankings. Similarly, an increasein Secchidisk depth (increased water clarity) would raise the subjective water quality ranking by survey respondents. The probabilities that water quality would be ranked poor, fair, good, or excellent are calculated from the ordered logit model results. These probabilities are: Prob (y = 0; poor) =

### A 

# (-P’X), Prob (y = 

### 1; fair) 

### =A(p1 

### -P’x) 

### - A 

# (-P’x), Prob (y = 

### 2; good) 

### =A (p2 -P’x) 

### - A (pl 

# -P’x), Prob (y=3; excellent) = 

### 1

- 

### A (p2 

### -P’x),
where 

### A indicates the logistic cumulative distribution function. The rank order probabilities are given for nine sites only, as no survey respondents reported swimming atFlanders Bay south. Theseprobabilities are generated by substitutingthe site-specific water quality indicator values for the mean values of the entire sample. An average of each probability weighted by the number of observations for each site is calculated for the five PES water bodies. For example, the probability that water quality at Gardiners bay will be ranked good

72isbased on the weighted average ofthe probability for good at Gardiners bay north and Gardiners bay south. The probabilities for the nine sites are presented in table 

### A4. The probabilities for the five PES water bodies are presented in table 

### A5. The effects of managementactions on the rank order probability at any or all of the nine sites can be simulated by changing the water quality measures for the sample stationreadings. Changesin these values will in 

### turn change the value of the water quality indices and/or Secchi

### Disk readings used as input into the ordered logit model. These simulated values for objectivewater quality field measures are used to provide at a new set ofprobabilities for subjectiveratings ofwater quality for the sites, which in hun affect predicted participation rates for swimming in each ofthe PES water bodies. Tables A6-A9 containestimated water quality rankingprobabilities i.e., the probability that water quality will be ranked poor, fair, good, or excellent, for each of nine PES sites at a 10% improvement in an objectivewater quality parameter. The objectivewater qualityparameters used in the 10% improvementmodel are Brown Tide cell counts, Total Kjeldahl Nitrogen readings, Total Coliform readings, and Secchi Disk depth. For example, under baseline water quality levels in 1995, Great Peconic Bay (Table A5) is predicted to obtain a “poor” rating for water quality by 

### 8% of sample respondents, a “fair” rating by 37% of sample respondents, a “good” rating by 45% 

### of sample respondents and an “excellent” ratingby 10% 

### of samplerespondents. These probabilities are the weighted averages oftheranking probabilities for Great Peconic Bay North and Great Peconic Bay South (Table 

### A4). If, continuing our example, Brown Tide Cell counts were to improve (decrease) by 10% at sampling stations in GreatPeconic Bay North and Great Peconic Bay South,the model predicts that Great Peconic Bay overall (Table A10) would obtain a “poor” rating by 

### 7% of sample respondents, a “fair” rating by 

### 36% of sample respondents, a “good” rating by 46% of sample respondents and an “excellent” rating by 10% 

### of sample respondents. 

### Travel Cost

### Model The travel cost model predicts the annual number of swimming trips to each of the five water bodies in the PES based on the cost of travel to the swimming location, the cost of travel to substitute swimming locations, the number of times the individual went to the same location that year, and the probability that the water quality at that location would be ranked good or excellent. The predicted number of trips and the estimated coefficient on the travel cost variable are used to calculate the value of swimming trips to each 

### PES water body. The travel cost model 

### is alsoused to estimatethe increased number oftrips and increased valueper trip that results due to management actionsthat increase water quality. This is done in three steps. First, the changes in water quality resulting from specific management actions (e.g. upgrading the Riverhead sewage treatment plant) are to be simulated using the water quality modeling results (Tetra Tech, 1998). Next, changes in subjective probabilities are predicted using the methods discussed above. Finally, predicted perceptions ofwater quality by users are substituted into the

73 6Travel cost model, to calculated the estimated number of trips and value per trip under the new level of water quality. Data for the travel cost model comes from the Recreational Use Survey and from the results ofthe 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 includeFlanders Bay, GreatPeconicBay, Little Peconic Bay, ShelterIsland Sound, Gardiners Bay, Long Island Sound, Block Island Sound, and the Atlantic Ocean. Theprice, ormarginal cost, of swimmingin the PES is not identified in any existing market. Since there is no readily available market data on the price of swimming in the PES, a proxy for price 

### is used. In the travel cost model, the cost of getting to and from a swimming location is considered the implicit price of swimming at that location. Travel costs to swimming locations are based on distance traveled, travel time, and the household income according to the formula: Travel Cost =Round Trip Distance 

## * $0.32 

## + (Round Trip Distancei40mph 

## * 40% hourly wage}. The round trip distance traveled is doublethe one-way highway distance between the respondent’s point 

### of origin and each swimming location. This distance was determined on a case by case basis using a road atlas and assessing the most direct travel route. Verification of distance traveled was made possible when respondents answered the question concerning distancetraveled ontheir most recent swimming occasion. Travel time was estimated by dividing the round trip distance by 40 miles per hour. This speed was used as a reasonable compromise between highway speeds of 55 mph and more and local speeds between 

### 25 and 40 mph. The opportunity cost of travel time was estimated at 40% 

### ofthe wage rate, a common practice in benefit estimation. For respondents who did not indicate household income, income was estimated using a simple 

### OLS regression that modeled income as a function 

### of a constant value and education. In the case 

### of substitute swimming locations, the same travel cost formulation used for the PES locations was applied to the substitute swimming locations. The average of travel costs to Long Island Sound, Block Island Sound, and the Atlantic Ocean, for each respondent, was used as the cost oftravel to substitute sites and input into the travel cost model. Data on the number 

### oftrips to the PES swimming location comes directly from the survey questionnaire. The water quality variable used in the travel cost model is the sum of the probabilities that the PES swimming location wouldberanked good and excellent. Theseprobabilities are generated by the water quality model. Overall, 199respondents provided sufficient information to be included in the travel cost model. Given that there are five PES swimming locations, 

### 995 observations were used in the travel cost model estimation. Thetravel costmodeling procedure isbased on aPoisson regression model. ThePoisson regression 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 more 

### of the PES swimming locations. This


### 74type of data is referred to as count data, meaning that the data indicates the number 

### oftimes some phenomenon occurs, such as swimming trips to Flanders Bay. The Poisson regression model 

### is based on the assumption that each observation 

### is drawn from a Poisson distribution. The probability that the actual number of trips taken is equivalent 

### to the estimated number of trips is formulated as: Prob 

### (Yi=yi) = 

### eli

### Aiyi/

### yi !
Where

### yi= 

### 0,1,2,3, 

### ....; In 

### Ai = 

### p’xi; 

## p is the vector 

### of estimated coefficients; and 

### x is the vector 

### of independent variables. The results ofthe Poisson estimation are consistent with prior expectations, as presented in Table A14. The estimated coefficients indicate that the number 

### of trips to a site decreases as the cost of travel to the site increases; increases when the cost oftravel to a substitute site increases; increases the more times the individual participates in swimming atthe same location; and increases aswater quality at the site increases. The value of swimming 

### trips to PES locations isestimated fromthe results ofthetravel costmodel according to 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 samevariables that influence the number oftrips to a swimming location. Therefore, the value of a swimming trip varies among the five PES swimming locations due to differences in water quality. The value 

### ofa swimmingtrip to a PES location will be affectedby management actions to improve water quality. The results ofthe travel cost model can be used to estimate the changes in consumer surplus due to water quality improvements. The recreational swimming benefits of improving water quality are estimated by applying new probabilities 

### ofwater quality ratings to the results of the travel cost model. This can be calculated using the following formula:

New CS = (T 

### + AT* AP} 

### -(C)

Where Trepresents the number oftrips 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, and 

### C represents the coefficient on the travel cost variable.Increases in the number 

### of swimming days to PESlocations and increasesin consumer surplusdue to a hypothetical 

### 10%improvement in water quality measures are presented in Table A15. Aggregate benefits arebased onthe estimatedbaselinenumber 

### of swimming tripstothe PES. This number 

### of trips 

### - 1,409,970 

### - was estimated by extrapolating the number 

### of trips respondents identified in the RecreationalUse Surveyto the population 

### ofresidents, second home owners, and overnight visitors. The population 

### of overnight visitors was estimated based on the proportion 

### of overnight visitors in the survey sample (see section on Participation). The aggregate value 

### of swimmingin the PES under baseline conditions 

### is presented in table 

### A16. Table Al. PES 

### Sampling Station Locations

~~ ~ ~~~~~ 

### Location Flanders Bay North Flanders Bay South Great Peconic Bay North Great Peconic Bay South Little Peconic Bay North Little Peconic Bay South Shelter Island Sound North

Shelter Island Sound South Gardiners Bay North Gardiners Bay South 

### Sampling Station 170 (Central), 220,240 170 (Central) 101, 130 (Central) 130 (Central) 102, 103, 105, 113 (Central) 1 13 (Central) 106,107,108,109,111,112,114, 115J 19,122 1 18,121,126,127,13 1 116 132,133,134

76

### Table A2. Average Input Value for the Water Quality Model (OrderedLogit)

Site 

# I

Flanders Bay North Great Peconic Bay North Great Peconic Bay South Little Peconic Bay North Little Peconic Bay South Shelter Is. Sd. North Shelter 

### Is. Sd. South Gardiners Bay North Gardiners Bav South 

### Brown Tide Nitrogen 

## - Bacteria Secchi Disk Index Index Depth (Feet)

5.57 818.09 3.2 18.33 169.85 3.13 14.81 117.75 3.5 28.04 126.39 3.5 16.2

### 1 126.76 3.5 39.5

### I 136.44 4.35 47.5 256.26 4.37 32.41 94.2 

### 1 5.75 33.37 124.7 5.08

77--- ---

--- 

### --- 'TableA3. 

### Results of the Water Quality Model (Ordered Logit)

Observations Log Likelihood Res. L. Likelihood Variable Constant Brown Tide Index Nitro.
- Bact. Index Secchi Disk 

### PI 

### r.12 ~~ ~~~ 199 Chi- 27.50 Squared -225.26 

### D. Frdm. 3 -239.00 Sig. Level .000005 Coefficient Std. 

### Err. 

### T Stat. Sig. Level Mean 

### of 

### X 2.0679 1.1136 1.857 0.06332 -0.02297 0.00499 -4.605 

### 0.00000 33.83 -0.00147 0.00089 -1.652 0.09860 173.4 0.34515 0.23232 1.486 0.13737 4.397 2.2812 0.259 15 4.6968 0.3522



# F 78

### Table 

### A4. Baseline Probabilities for Nine Sites
Site Prob. Poor Prob. Fair Prob.Good Prob.

Flanders Bay North Great Peconic Bay North Great Peconic Bay South Little Peconic Bay North Little Peconic Bay South Shelter 

### Is. Sd. North Shelter 

### Is. Sd. South 

### Excellent 0.14 0.47 0.34 

### 0.05 0.08 0.37 0.45 

### 0.10 0.06 

### 0.32 0.49 0.13 

### 0.08 0.38 0.45 0.10 

### 0.06 0.33 0.49 0.12 0.08 0.38 0.45 0.10 0.1 1 0.43 0.39 0.07 Gardiners Bay North 0.04 0.25 Gardiners Bay South 

# I 0.05 0.30 0.53 0.50 0.18 0.14 *Probabilitiesmay not 

### sum to one due to rounding



### Table A5. 

### Baseline Probabilities for PES Water Bodies

PES Water Body

Flanders Bay Great Peconic Bay Little Peconic Bay Shelter 

### Is. Sd. Gardiners Bay 

### Prob. Poor Prob. Fair Prob. Good Prob. Excellent 

# 1

0.14 0.47 0.34 0.05 

### 0.08 0.37 0.45 0.10 

### 0.08 0.37 0.45 

### 0.10 0.08 0.39 0.44 0.09 

### 0.05 0.28 0.52 0.16

79

### rable A6. 

### Probabilities at Nine Sites for 

### 10% Improvements in Brown Tide Cell Count
Site

Flanders Bay North Great Peconic Bay North Great Peconic Bay 

### South Little Peconic Bay North Little Peconic Bay South Shelter 

### Is. Sd. North Shelter 

### Is. Sd. South Gardiners Bay North Gardiners Bav South 

### Prob. Prob. Fair Prob.Good Prob. Poor Excellent 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 0.5 

### 1 0.15



### Table A7. 

### Probabilities at Nine Sites for 

### 10%Improvements in Total Kjeldahl Nitrogen Readings
Site

Flanders Bay North Great Peconic Bay North Great Peconic Bay South Little Peconic Bay 

### North Little Peconic Bay South Shelter 

### Is. Sd.North Shelter 

### Is. Sd. South Gardiners Bay North Gardiners Bay South ~ ~~ ~­ 

### Prob. Prob. Fair Prob.Good Prob. Poor Excellent 0.13 0.46 0.35 0.06 0.08 0.37 0.45 0.10 0.06 0.32 0.49 0.13 

### 0.08 0.38 0.45 0.10 0.06 0.33 0.49 0.12 

### 0.08 0.37 0.45 0.10 0.1 1 0.43 0.39 0.07 0.04 0.25 0.53 0.18 0.05 0.30 0.51 0.14

80

### Table 

### AS. Probabilities at Nine Sites for 

### 10% Improvements in Total Coliform Readings
Site

Flanders Bay North Great Peconic Bay North Great Peconic Bay South Little Peconic Bay North Little Peconic Bay South Shelter 

### Is. Sd. North Shelter 

### Is. Sd. South Gardiners Bay North Gardiners Bay South 

### Prob. Prob. Fair Prob.Good Prob. Poor Excellent 

### 0.13 0.46 0.35 

### 0.06 0.08 0.37 

### 0.45 0.10 0.06 0.32 

### 0.49 

### 0.13 0.08 0.38 0.45 

### 0.10 0.06 0.33 0.49 0.12 

### 0.08 0.38 0.45 

### 0.10 0.1 1 0.43 0.39 0.07 

### 0.04 0.25 

### 0.53 0.18 0.05 0.30 0.50 0.14



### Table A9. 

### Probabilities at Nine Sites for 10% 

### Improvements in 

### Secchi Disk Depth
Site Prob. Prob. Fair Prob.Good Prob. Poor Excellent

~ ~~~ Flanders Bay North 0.12 0.46 0.36 0.06 Great Peconic Bay North 

### 0.07 0.35 0.47 0.1 1 Great Peconic Bay South 0.05 0.30 0.51 

### 0.14 Little Peconic Bay North 

### 0.07 0.36 0.46 0.11 Little Peconic Bay South 0.06 0.3 

### 1 0.50 

### 0.13 Shelter 

### Is. Sd. North 

### 0.07 0.35 0.47 

### 0.1 1 Shelter 

### Is. Sd. South 0.09 0.41 0.4 1 0.08 Gardiners Bay North 

### 0.03 

### 0.22 0.54 

### 0.2 1 Gardiners Bay South 0.05 0.27 0.52 0.16

### Table 

### A10. Probabilities at PES Water Bodies for 

### a 10% Improvement in Brown Tide Cell Counts
PES Water Body

Flanders Bay Great Peconic Bay Little Peconic Bay Shelter 

### Is. Sd. Gardiners Bay



### PES Water Body

Flanders Bay Great Peconic Bay Little Peconic Bay Shelter 

### Is. Sd. Gardiners Bay 

### Prob. Poor Prob. Fair Prob. Good Prob. Excellent 

# I

0.14 0.47 0.34 0.05 0.07 0.36 0.46 

### 0.10 0.07 0.36 

### 0.46 0.10 0.08 0.37 0.45 0.10 0.04 0.26 0.52 0.17



### Prob. Prob. Prob. Prob. Poor Fair Good Excellent 0.13 0.46 0.35 0.06 0.07 0.37 

### 0.46 0.10 0.08 0.37 0.45 

### 0.10 0.08 0.38 0.44 

### 0.09 0.05 0.27 0.52 0.16

### TableA12. 

### Probabilities at PES Water Bodies for a 10% Improvement in Total Coliform Readings
PES Water Body

Flanders Bay Great Peconic Bay Little Peconic Bay Shelter 

### Is. Sd. Gardiners Bay * 

### Prob. Prob. Prob. Prob. Poor Fair Good Excellent 0.13 0.46 

### 0.35 0.06 

### 0.08 

### 0.37 0.46 0.10 

### 0.08 0.37 0.45 0.10 

### 0.08 0.39 0.44 0.09 0.05 0.28 0.52 0.16



### Table A13. 

### Probabilities at PES Water Bodies for a 10% 

### Improvement in Secchi Disk Depth
PES Water Body Flanders Bay Great Peconic Bay Little Peconic Bay Shelter 

### Is. Sd. Gardiners Bay P(Poor) P(Fair) P(

### Good) P(Excellent) 0.12 0.46 0.36 0.06 

### 0.07 0.35 0.47 0.1 1 

### 0.07 0.35 0.47 0.11 

### 0.07 0.36 0.46 0.1 1 0.04 0.25 0.53 0.19

83

I

### rable A14. Results ofthe Travel 

### Cost Model (Poisson Regression)
Observations 

### Log Likelihood Res. L. Likelihood Variable

Constant Travel Cost Boating Travel Cost to Substitute Locations Water Quality Rank Scale Factor Marginal Effect for Water Quality Rank 995 Chi- 1846.693 Squared -3707.225 

### D. Frdm. 

### 4 -4630.572 Sig. Level .OOOOOO Coefficient Std. T Stat. 

### Sig. Level Mean of 

### X Err.

~ ~ ~ -0.76984 0.16607 -4.636 

### 0.00000 -0.028321 0.00210 - 13.494 

### 0.00000 23.29

0.0781 0.00 157 49.757 

### 0.00000 0.9246

0.00978 0.001 19 5.282 

### 0.00000 3 1.74

2.688 0.28209 9.529 

### 0.00000 0.5404 1.5002

4.0325



### Appendix C. Comparisonof 

### Recreation Activity Estimates: Phase 

### I and Phase I1 Reports Comparison with Phase 

### I Total OutdoorRecreational TripEstimates: Beach Use.Beach use, in this report, refers to anybeach in the five East End towns, including LongIsland Sound and Atlantic Ocean beaches, as well use ofPESbeaches. The Phase 

### I report did not estimatebeach use on Long Island Sound 

### or the Atlantic Ocean and therefore is not presented for comparison. 

### Comparison with Phase 

### I Total OutdoorRecreational TripEstimates: Non-Consumptive Wildlife Use.Bird watching and wildlife viewing estimates in this Phase 

### I1report are combined to create a “non-consumptive wildlife use” estimateof 547thousand trips (Table

### V. 1 1). This estimate greatly exceeds the PES Phase 

### I estimate of non-consumptive wildlife use (92 thousand trips). The major reason for the large differences in estimates between the Phase 

### I and 

### I1 reports 

### is explained as follows. Our survey-based estimate finds a high participation rate (19% for bird watching and 2 1%forwildlife viewing) for respondents and a largenumber oftrips per participant (37.55 bird watching trips and 34.36 wildlife viewing trips for residents). The Phase I survey, incontrast, was based on 

### NFWS statewide estimates for New York. These statewide participation rates 

### (9.4%) and number oftrips per participant (8.3) are very much lower than those found in the PES sample. Thismajor differenceisnot surprising sinceresidents and seasonalvisitorsto the PES wouldbe expected to be more interested in outdoor recreation than the general population of the state. 

### ComparisonwithPhase

### I Total Outdoor Recreational TripEstimates:RecreationalFishing. Not surprisinglytherecreationaluse sweydata yields a much larger estimate (588thousand trips) ofrecreational fishing trips in the PESthan the Phase I study (1 

### 14thousand). The Phase I estimate is based on the 

### NFWS survey covering all of New York State. 

### In the NFWS study, the average estimates 

### of participation rate (9%) and trips per participant (8 for residents, 4.3 for visitors) is much lower than found in the PES survey. This estimated participation rate is 17%and 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 more avid recreational fishers than the New York population at large. 

### Comparison with Phase 

### I Total OutdoorRecreational Trip Estimates: Swimming. The Phase I swimming estimate (7 15thousand) is simplythe sum ofall availablebeach attendancedata for the PES. The Phase I1 survey-based estimate 

### (1.4 million) includes participation not only at officialbeachesbut also at the many unadministrated beaches throughout the PES, and in addition includes the Atlantic Ocean and Long Island Sound, as well as the PES.


### Comparisonwith Phase 

### I Total OutdoorRecreational Trip Estimates: Hunting. The discrepancy between the Phase 1(77thousand trips) and Phase 

### I1 (52 thousand trips) hunting estimates also 

### is based on differences in participation rate and number ofhunting days per participant, as estimated by the two surveys. The NFWS estimates that 11% 

### of the New York State population over 16 hunts, and that participants average 18.6hunting trips per year. 

### Onthe otherhand, the PES survey indicates that only 

### 2% ofresidents and visitors to the PES hunt in the PES. These participants also hunt less than the state average, ranging from 16.33trips per second homeowner participant to 5.5 trips per participating day tripper. The Phase I hunting estimate was based only on residents and did not attempt to account for participation by seasonal residents and visitors since such data are unavailable.

### V. WETLAND PRODUCTIVITY VALUES IN 

### THE PES

V.A. 

### INTRODUCTION Eelgrass, saltmarsh, and intertidal mud bottom (“wetlands”) provide many services to the public. These services include contributing to the production of commercial and recreational harvests of fin fish and shell fish, and of birds and other wildlife used for viewing and for hunting. Other services include protection of shoreline property from storm damage and erosion. Wetland services may occur on site or off site and may or may not be valued in markets. For example, somewetland services, such as scallopharvests, occur on site,while others are realized offsite, for example, when fin fish or birds “produced”by an eelgrass bed of intertidal salt marsh are harvested or viewed many miles away. Somewetland services are valued in the market place (e.g., commercially harvested fish or shellfish), whileothers are not (e.g., bird species used for viewing and waterfowl that are hunted). Understanding the economic value of the various natural services provided by ecosystems can provide useful information for policy analyses about preservation and restoration decisions. For this purpose, the most useful information 

### is the value of a small change in wetlands, that 

### is, the 

### marginal value of wetlands. Marginal values rather than the total value of all wetlands are important because most policies addressrelatively small changes in wetlands--not whether or not to preserve all wetlands. This chapter adapts13estimatesby French and Schuttenberg 

### (1998) of the marginal value of 

### PES eel grass beds, salt marshes, and intertidal mud flats. 

### Two types of wetland productivity (biological) gains are considered: (1) The increase in 

### food produced by the habitat which is utilized by higher trophic levels (such as fish and shellfish) in the PES, and 

### (2) The increase in production of higher trophic levels brought about by the increased availability of 

### habitat. The biological gains from restoring or protecting increments of each wetland type (eelgrass, saltmarsh, and intertidal mud flats) are assigned an economic value. This value is based on the 

### (1) commercial value of the fin fish and shell fish, 

### (2) the viewing value of birds, and (3)the hunting value 

### of waterfowl ultimately “produced” by wetlands. 

### Other direct and indirect sewices and values wetlands mayprovide are not considered.

“Two major changes are noted. French and Schuttenberg valued PES wetlands over a 100-year period and used a 3 % discount rate. We use a 7 % discount rate and a 25-year time period for consistency with results given elsewhere in this report. The higher discount rate and shorter tim horizon means that our results for estimated economic values will be lower than in French and Schuttenberg. 86Two assumptions are critical to the analysis that follows. One is that food and habitat are biologically limiting factors for the species considered; that is, fish,,shell fish and birds depend upon the availability of wetlands, 

### so that small changes in wetlands will causes changes in the populations of these species. If wetlands are not limiting for these species, then an increment 

### of wetlands would have no productivity value for the species concerned, although it may have other values (e.g., shoreline erosion protection, esthetics, or existence value) The second critical assumption concerns effort and its cost. Fishing, viewing or hunting require the use of labor, capital and other inputs used for harvesting,viewing or hunting; the net gain 

### from these activities is the benefit (e.g., value of fish landings) minus the costs of the effort required. However,very small changes in the abundance of fish, shellfish or birds due to a small change in wetland areas will lead to only a very slight increase in harvestsper unit of effort. Slightincreases in ctch per unit effort will have a negligible effect on the level of effort itself. The changes in the availability of each wetland category due topreservation orrestoration actions are presumed to be small enough sothat fishing,hunting, or viewing effortremains the same. This assumption implies that we do not need to net out the cost of any change in effort due to small changes in abundance of fish, orbirds arising frommarginal changes in wetlands.Under these assumptions,the value of additional harvests, viewing or hunting is the gross value.

V.B. 

### METHODOLOGY AND 

### DATA V.B. 1. Introduction Several studies estimate the economic values provided by natural 

### ecosystem^'^. For example, Lynne et al.(198 1)estimated the relationship between mangrove area, fishing effort, and landings 

### of a single species, blue crab in Florida. They estimated a marginal value for blue crab of several dollars. Bell (1989) also used a time series of data on mangrove area, effort, and catch to evaluate the marginal productivity of Florida mangroves in the production ofprincipal Florida commercial and recreational fish. He estimated a marginal value of several thousand dollars per acre of mangrove over all commercial and recreational species. Kahn and Kemp (1985) estimated the incremental value of subtidal vegetation in contributing to striped bass populations in the Chesapeake Bay and their subsequent harvest by recreational fishermen. Costanza and Farber (1 986) assessed the per acre services of Lousiana wetlands for recreation and as a buffer for storm protection, in addition to contributing to the production of commercial fisheries. Finally, Pornpinatepong (1 997) 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. The approach used in this chapter derives estimates 

### ofwetland productivity using data specific to the 

### PES. The virtue 

### of this approach is that it captures the underlying biological structure and

14A summary of the productivity approach and examples for coastal areas is given in Grigalunas and Congar (1995). 87

:­ 

### .."..,I 

# 3productivity of

### PES wetlands, and it uses biological information and economic values specificto the 

### PES. The method--in effect a “simulation”-- side steps some of the data and other problems faced 

### in statistical studies, such as that by Lynne et al., by Bell, and by Costanza and Farber. 

### On the otherhand,themethod used doesnot allowfor statisticaltests of significanceandhence heavily relies upon professionaljudgement. Themethodology used is describednext. First,we explainhow the food web estimates were made. Then, the methodology and data used for habitat values 

### is presented. 

### A detailed statement of the methodology, data and assumptions is given in French and Scuttenberg (1

### 998). V.B.2. Food Web Estimates Saltmarsh and eelgrass beds benefit the entire food web by primary (plant)production. Similarly, the net gain in lower trophic level, animal production is passed up the food web. Ultimately this production via the food web results in the production of species of economic value to people. To estimate the economic value of these food-web effects, several pieces of 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) production rates were estimated for 

### PES wetland categories,using results in the literature. Then, the fraction ofthe additional production passed up the food web was estimated. Next, this additional production is translated into commercial fin fish and shell fish production and landings using average relationshipsestimated across many estuariesby Nixon 

### (1982). Finally,the estimated fish and shell fish landings are valued using species-specific fishery values for 

### PES landings. Thus, lower trophic levels are translated into equivalent upper trophic level fish and shellfish productionharvested andvalued by people. Specifically, fisheryproduction 

### is estimatedto be 0.16 percent of primary production and 

### 4 % percent of bottom (benthic macrofaunal) production. Details of these calculations are given in French and Schuttenberg (1998). 

### V.B.3. Habitat Estimates Habitat values are estimated for species (bay scallops,blue crab, softshellclams, and birds) having humanuse values. Habitatvalues arebased on 

### (1) the expectedyield offish or shellfish dependent upon the habitat, and (2) the abundances of wildlife (birds) that utilize the habitat. Fish and shellfish values are commercial values; wildlife values are for hunting (waterfowl) and viewing (waders). Bay scallops depend upon eelgrass as nursery habitat forjuveniles. The grass provides a refuge frompredators forjuvenile 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 eelgrasshabitats preferentially. Again, it is assumed that the entireblue crab fishery dependsupon the saltmarsh and eelgrass beds ofthe estuary. Softshellclamsprefer intertidalmud flats and sand

88flats. Softshell clams are assumed to all have been produced in intertidal mudflats and shoals in the 

### PES. Abundance ofbirds depends upon habitat type. An average abundance per unit area of habitat is assumed, based on the results for the coastal area including the 

### PES, as given in the Natural Resource Damage Assessment Model (Version 

### 2.4, April, 1996)developed by the authors for the 

### US Department ofthe Interior (French, et al, 1996a,b,c). Birds that are specificallybenefittedby saltmarsh, eelgrass or mud flats were selected and included in the present analysis. These species arewaders (herons, egrets, and ibis), shorebirds,brant andblack ducks. Waders use allthree ofthe habitat types, while shorebirdsuse marsh andmud flats. Brants specificallyfeed on eelgrass. Black ducks are know to require structured habitat, marsh and eelgrass. The value ofbird speciesusage ofthe habitat isbased on the benefits humanreceive from viewing or hunting (waterfowl)birds. The values are marginal values, that is, the additional value people obtain by seeing additional birds per day. The values per animal per year are proportional to the number of viewing trips and the rareness of the species in the local area (see French et al. (1996a,b,c) for a full description of the development of these values. 

### V.C. RESULTS V.C. 

### 1. Introduction Results areprovided for the 

### (1) Marginal value ofexistingwetlands, and (2)the marginal value for restored wetlands. Restored wetlands have a lower value than existingwetlands since it may take years for an existing wetland to become fully functional. V.C.2. Results Table V.l shows the marginal value for existing and restored wetlands. The value for existing wetlands would be used for policy issues dealing with preservation decisions; results for restoration are critical for assessing policies to restore wetlands. Two values are calculated:An annual value and an asset value. The annual value is the sum ofthe food web values and the habitat values. Asset values were calculated by discounting the annual value over a 25 period using a discount rate of 

### 7 percent, the same time frame and discount rate used elsewhere in this report15. Eelgrass has a 20 % higher marginal value per acre than that for saltmarsh. Intertidal mud flats have the lowest marginal value. Estimated marginal values for restoring wetlands are much lower than the value of existing wetlands of the same category. This 

### is because it takes several years before a restored wetland

15Use of lower discount rate and longer time horizon would lead to a larger value. Use of the 7 % discount rate reflects the opportunity cost of resources used in restoratiodpreservation. That is, resources used in these activities to some extent will be drawn away from private and public investments. 89
*-becomesfully functional. Thus, in the table it is noted that eelgrass, saltmarsh,and intertidalmud flats take 15, 

### 10, and 

### 3 years, respectively, to become fully functional. The marginal asset values of 

### PES wetlands appear to be substantial, especially in light of the fact that other services wetlands may provide, such as protection from erosion and storms, aesthetics and existence value, are not considered.



### Existing Habitats Created Habitats

Years to Annual Become Wetland Value Asset Value Fully Asset Value

Type per Acre a 

### per Acre Functinal a 

### per Acre Eelgrass $1,065 

### $12,412 10 $9,996 

### Saltmarsh $338 $4,29 

### 1 

### 15 $3454 

### Intertidal Mud Flat 

### $67 $786 3 

### $626

a



### V.D. SUMMARY

### AND CONCLUSIONS Estimated Number of

Acres in

PES (millions) 

### 6.04 13.51 

### 14.05

This chapter presents estimates of the marginal economic value of the productivity services provided eelgrass, salt marsh, and intertidal mud flats in the 

### PES. The productivity services covered are food web and habitat services. Economic values estimated are for commercial fishing value for crab, scallop, and clams; viewing values for birds; and hunting values for waterfowl. Other possible values provided by 

### PES wetland services, such as erosion and storm protection, aesthetics, and existence value are not considered.

90

### V.E. REFERENCES Costanza,Robert and StevenFarber, 1986“The EconomicValue ofCoastalLouisianaWetlands”. Baton Rouge: Lousiana State University. Bell, Frederick W., 1989. 

### Application of 

### Wetland valuation Theory to Commercial and RecreationalFisheries inFlorida. SeaGrantProgram.Tallahassee:Florida StateUniveristy (June). French, et al., 1996. 

### Natural Resource Damage Assessment Model for 

### Coastal and marine Environments. Version2. French, Deborah and Heidi Shuttenberg, 1998. 

### Estimated Food Web and Habitat Valuesfor 

### Habitats in thePeconicEstuarySystem. Submittedto EconomicAnalysisInc. (January

### 23). Grigalunas,Thomas and Richard Congar, 1995.

### EnvivonmentalEconomics 

### forIntegrated Coastal Area Management. Nairobi: United Nations EnvironmentalProgramme. Kahn, James and W.M. Kemp, 1985.”Economic Losses Associated With the Degredation of An Ecosystem: The Case of SubmergedAquatic Vegetation in Chesapeake Bay”,

### Journal of 

### Environmental Economics and Management Lynne, 

### G., P. Conroy, and F. Prohaska, 198 1. “Economic Valuation of Marsh Areas for Marine Production Processes”, Journal 

### of Environmental Economics and Management 8(2): 175­ 186. Nixon, ScottW., 1982. “NutrientDynamics,PrimaryproductionandFisheriesYields ofLagoons”. Narragansett: Graduate School of Oceanography,University of Rhode Island. Pompinatapong, K., 1997.“ValuingCoastalWetlands:Insigthsfromthe Type

### ANatural Resource Damage Assesment model”. Unpublished 

### M.S. Major Paper. Kingston: Department of Environmental and Natural Resource Economics, University of Rhode Island.

91

### VI. NATURAL, RESOURCE PRIORITIES AND VALUES

VI. G. INTRODUCTION This section describes a study of natural resource values for the Peconic Estuary System 

### (PES).In August 1995, we surveyed 968 year-round and seasonal residents of the area surrounding the Peconic Estuary. Respondents to the survey were asked about their priorities and values for protecting and restoring important natural resources of the 

### PES. The results include: (1) an analysis of values and priorities for a set 

### of important natural resources of the area-farmland, undeveloped land, wetlands, safe shellfishing areas, and eelgrass; 

### (2) estimates 

### of economic benefits that would result from protecting or restoring these resources; and (3) public opinions related to the Estuary. The survey was designed to complement scientific and technical studies,using data available at the time, to provide information that will be useful in the finalpolicy analysis. This information will be used in a cost-benefit analysis of policy alternatives (Phase I11 

### of the ComprehensiveEconomic Valuation Study), that will help policy makers prioritize alternative management actions. The remainder of this section is organized as follows: Part 

### B describes the survey development and implementation, and the questionnaire design; Part 

### C gives descriptive results of the survey; Part 

### D presents the estimates of economicvalues; and Part 

### E is a summary and conclusions. 

### VI. B. 

### THE NATURAL RESOURCE SURVEY 

### V1.B. 1. Survey Development and Implementation We developed the survey over a six-month period, from February to August 1995, in an extensive process that included individual interviews, focus groups, and pretests. The primary goal of the survey was to learn about the public’s preferences,priorities and values for natural resources ofthe Peconic Estuary that might be affected by preservation and restoration actions.I6

l6 Other important goals were to create a survey that would minimize some of the problems often associated with valuation surveys; would be easily understandable to members of the public; could be answered in a reasonable amount of time; and could be administered in a variety ofpublic places. 92V1.B. 

### 1.a. Initial Meetings, Interviews, and Information Gathering We began the survey development process in February, 

### 1995 by meeting with members of the Management Committee. Shortly afterwards, we met with representatives of different interests, including the head of the Local GovernmentCommittee; the Chairman of the Citizen’s Advisory Group, who is also president of a local environmental group; a representative of the Nature Conservancy; a biologist fromthe

### NY State Department of Environmental Conservation; a marina owner who was head of the local Marine Trades Association; a commercial fisherman who represented the Long Island Inshore Trawlermen’s Association; and a bank president. These meetings, and a meeting with the Citizen’s Advisory Group, helped us to learn about the area and about the concerns of various interest groups. VI.B.l .b. Informal Interviews With the Public Next, we conducted a set of informal interviewswith members ofthe public, wherewe asked about their uses of and concerns about the Estuary. 

### A total of sixteen randomly selected people were interviewed in Montauk, Springs, SagHarbor, ShelterIslandand Greenport.They includedbusiness owners, store clerks, a police officer, visitors, and residents. These interviewsprovided information on some ofthe most important natural resources and related concerns, and how people think and talk about the bays. Most people interviewed were very concerned about water quality, declines in fish populations over the years, and impacts on business if water quality continues to decline. We also learned that many people were not familiar with the word “estuary;” and most people in Montauk did not consider themselves to be located on the Peconic. Consequently, in the final survey, the Peconic Estuary System was referred to as the Peconic Bays System, and was defined by a map at the beginning 

### of the survey. V1.B. 

### 1.c. Focus Groups and Preliminary Survey The next step was a series of focus groups, and a shortpreliminary survey. Table VI. 

### 1lists the dates of the focus groups and pretests, their locations, and the number of participants. In the first three focus groups, we asked theparticipants general questions about how they definethe study area; their familiarity with the Peconic Estuary Program; the characteristicsof the area that are most and least importantto them; their uses ofthe localwaters; the attributesthey look for in choosing recreational sites;theirperceptions ofwaterquality;andtheir concerns aboutthenatural 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 draws a large number of visitors and residents. This survey included both closed- and open-ended questions, including questions about: (1) participation in recreational activities around the water; (2) concerns related to the Estuary; and (3) positive and negative aspects of the East End in general.
93

### Table VI.l- 

### Focus Groups and Pretests Number 

### of Participants Date Location March 

### 18 Montauk Focus Group 

### 1 

### 6 March 19 Montauk Preliminary Survey 

### 35 March 22 Jamesport Focus Group 

### 2 10 March 

### 3 1 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

May2 Springs Focus Group 

### 7 7 May 11 Rhode Island Focus Group 8 

### 5 May 

### 18 Springs Pretest 1 10 June 21 Rhode Island Pretest 2 

### 5 June 27 Rhode Island Pretest 

### 3 

### 5

July 9 Jamesport Pretest 

### 4 13 July 

### 9 Mattituck Pretest 

### 5 17

The thirty-five usable responses gave some preliminary information about important attributes of the area and its recreational sites; and aboutpeople’s greatest concerns forthe Estuary. Respondents were asked to rank a list of concerns on a scale of 1 to 

### 10, where 1 indicates the highest level of concern. Table 

### V.2 lists these concerns, in order ofimportance to respondents. The most important were declining stocks of shellfish and finfish, water quality, trash on beaches, and areas closed to shellfishing. The least important were crowding in boating harbors, public access to the water and availability of wildlife for viewing. In the next several focus groups, we asked participants general questions about their concerns, their perceptions of water quality, and actions they would like to see. We also tested preliminary survey questions and question formats, which were revised between groups. Several ofthese questions were primarily intended to stimulate discussion. For example, one question asked about participation in recreational activities, andwasused to generate a discussion ofthese activities andrelated concerns. Other questions asked participants which 

### of a list of human impacts on the Bays were most important to them; which of a list of environmental problems they believed were most serious; and which potential actions should be given highest priority. The questions asking participants to prioritize impacts, concems, and actions were difficult for people to answer, for two important reasons. First, many people in the focus groups believed that almost everything was important, andtherefore resisted rankingtheirpriorities. Instead, they wanted 94to rate 

### everything as most important. Second, many participants stated that they did not know enough to rank the causes of environmental problems and the effects of potential actions, but believed that priorities shouldbe decidedby experts. Nonetheless, these questions provided a good focus for the discussion of people's concerns.



### Table VI.2 

## - Concerns

### of Preliminary Survey Respondents, Montauk, March 

### 19,1995 

# I Concern Mean Rank"

Declining stocks of shellfish Declining stocks of finfish Water quality Trash on beaches Areas closed to shellfishing Overcrowdingof recreational sites Declining open space Crowding in boating harbors Too little public access to the water Need to improve public 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 1to 10,with 1denoting the most important.

Other questions in these focus groups asked about priorities related to natural resources, and tested possible question formats for the survey, including standard contingent valuation questions and contingent choice questions. Contingent valuation asks people directly to state their willingness to pay to preserve or improve a natural resource. Contingent choice asks people to make tradeoffs between alternative actions with results specified in terns of the natural resources that would be protected or restored and the cost. l7 Contingent choice questions are similarto marketing surveys often done by businesses to allow them to understand tradeoffs that customers are willing to make amongproduct characteristics under diffrent programs and costs.

Several additional questions were also tested in these focus groups. One question asked respondents to indicate their three most important reasons for living on the East End, from a list of fifteen reasons given by participants in earlier focus groups. This question was later dropped because it did not provide enough information tojustify the space and time it took in the survey. A set of environmental attitude questions was also tested, with the intention of correlating respondents' attitudes with their values for natural resources. The attitude questions generated quite a bit of controversy, as they were designed to elicit strong opinions, and were dropped from the final survey for several reasons. First, they tended to draw respondents' focus from the more important questions in the survey; second, they took too long to answer; and finally, it was feared that the controversial nature of the questions might result in a negative reaction to the entire survey. 95As discussedin more detailbelow,the contingent choicemethod appeared tobe the mostpromising format, and thus we developed this method further in the next focus groups. As the contingent choice questionswererefined through the focus groupprocess, the surveybecame less complicated and less wordy, with fewer attributes included in each comparison; and graphics were added to the questions. V1.B. 

### 1.d. Survey Pretests and Implementation In May and June, we conducted severalpretests 

### ofpreliminary survey instruments.18This series 

### of pretests helped to refine the question wording and layout of the survey, resulting in large improvements in respondents’ ability to easily make the comparisons. In July, a fifth draft of the survey waspretested onLong Island,with a group 

### ofpeople at the First Parish Church in Jamesport, and with randomly selectedpeople on the beach in Mattituck. In these pretests, people were able to easily comprehend and answer the survey questions. In early August, after meeting with members 

### of the Management Committee, we made final modifications to the survey, and conducted final pretesting. 

### An example of the final survey is included in Appendix A19. We implemented the survey during the week ofAug. 22-29,1995 in a variety ofpre-selected public locations aroundthe East End,using convenience intercept sampling. We selected this method over a mail survey with probability sampling primarily because budget limitations made it necessary to administerboth the natural resource survey and the recreation survey together, and because names and addresses 

### of visitors were not available. Thus, we designed the surveys to be administered in public places where visitors to the area could be intercepted. We selected a wide variety of locations, in order to intercept a representative sample in terms of demographic characteristics and location of residence. The survey locations, and the number of surveys collected at each, are listed in Table 3. 

### A total of 968 resource surveys were collected from year-round and seasonal residents of the East End. In implementing the survey, interviewers were instructed to approach people and say: Hi, my name is 

### , and I’m working for Suffolk County. We’re doing a survey of the public to help develop a plan to protect and manage the bays. Would you be willing to fill out a survey, which will take about 

### 5-10 minutes? The interviewer then asked those who agreed to fill out the survey ifthey were visitors, year-round residents, or seasonalresidents ofthe East End. Only year-round and seasonalresidents were asked to fill out the resource survey. All visitors, and every third resident, were given the recreationaluse



## ’*Several of these pretests were conducted in Rhode Island, in order to save time and money. Because the objective at this point was to refine the format, layout and wording, rather than the content, it was not necessary to conduct all tests on the East End. 

## ’’ Note that the example shown in the Appendix is only one of 12different survey booklets, each of which contains five different contingent choice questions. 96survey. When the survey was handed to the person, they were told that there are no right or wrong answers, and that all answers wouldbe confidential. Interviewers were instructed to give brief and neutral answers to any questions. VI.B.2. Questionnaire Design VI.B.2. a. Selection of Survey Format 

### As mentioned above, we considered two methods for asking the public about their values for protecting 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 taken to protect and restore natural resources ofthe Estuary, and their results. Therefore,the survey needed to ask the public about their preferences for important natural resources that would most likely be affected by restoration and preservation programs. The contingent choice format allows for comparisons and valuation of many different combinations of improvements in resources that might be obtained by management actions. This will allow for assessment ofprograms that would affectany combination ofthe natural resourcesevaluated inthe survey,eitherby ranking alternative programs or valuing benefits of specific programs. Contingent choice may also minimize some 

### of the problems associated with contingent valuation. For example, people often have trouble putting dollar values on specific natural resources. Contingent choice does not require that values be expressed in monetary terms, but elicits choices among alternative outcomes of actions. Therefore, responses are focused on tradeoffs among resources, andrespondents are not asked to directly expressmonetary values, although these canbe inferred from the analysis. Focus group participants found this type of question to be easier to answer. An additional issue, which may be a factor in both contingent valuation and contingent choice surveys is the expression of “symbolic” values. This might be related to expressions of approval or disapproval 

### ofthe actions to be taken to protect natural resources, or expressions ofthe importance of improving or protecting the environment in general, versus values for specific levels of natural resources. For example, a survey might ask respondents to state how much they would be willing to pay to protect a particular species ofbirds from oil spills.A person answeringthe survey may not care about those specific birds, but may state a positive willingness to pay because they think that oil spills should be prevented. Thus, their response may not reveal the value 

### of the birds,

### per

### se.

97

### Table 

### V1.3 

## - SurveyLocations and Number Collected Number Location Collected Beaches: Orient State Beach, Southold 3 Southold Town Beach 10 Cedar Point County Beach, Southold 3 New Suffolk Beach, Southold 4 East Creek Marina Beach, Southold 2 Alberts Landing Town Beach, East Hampton 16 Fresh Pond Town Beach, East Hampton 9 Maidstone Beach, East Hampton 7 Indian Wells Town Beach, East Hampton 16 Hither Hills Beach, East Hampton 4 Montauk Point State Park, East Hampton 4 Wades and Cascade Beaches, Shelter Island 4 Shopping Greenport Corner 70 Areas: Greenport IGA 44 Genovese Shopping Center, Southold 71 11 King Kullen Shopping Center, Mattituck, Southold K-Mart Shopping Center, Riverhead 72 Downtown Southampton 33 IGA Southampton 16 King Kullen and Caldor Shopping Centers, Bridgehampton 107 IGA Shopping Center, Shelter Island 27 Libraries and Southold Library 4

Post Offices: Cutchogue Library, Southold 10 Riverhead Free Library 16 East Hampton Post Office 87 Sag Harbor Post Office 37 Shelter Island Heights Post Office 34 Shelter Island Center Post Office 60 Miscellaneous: New London-Orient Point Feny 19 Goose Creek, Southold 3 County Center Cafeteria, Riverhead 23 Okeanos Aquarium, Riverhead 71 Department of Motor Vehicles, Riverhead 39 Pindar Winery, Southold 19 John Drew Theatre, East Hampton 8 Location unidentified 5

Because the survey used in this study presents only outcomes of actions, respondents are not given the opportunity to focus on the means of achieving results. Thus, responses are less likely to be 98expressions of approval or disapproval for actions, and more likely to focus on the specificnatural resources. However, people may still express symbolic values for protecting the environment, beyond their values for the natural resource improvements. This is more easily detected and corrected for in contingent choice than contingent valuation, as discussed below. Finally, it has been pointed out that “survey methods are better at estimatingrelative demand than absolute demand,” even for market goods, and “absolute willingness to pay is hard to pin down” (National Oceanic and Atmospheric Administration 1993, p. 4609). Thus, the relative values or priorities for natural resources elicited by a contingent choice survey may be valid, even if the absolute dollar values of specific resource changes are not. For a policy analysis that focuses on prioritizing actions,information onrelative values andprioritiesforresourcesismore importantthan the estimationoftotal dollarvalues. Hence, contingentchoice,rather than contingentvaluation,may be more effective in obtaining the information needed to compare alternative policies. Based on the results of focus groups and past research (Mazzotta and Opaluch 1995), each comparisonwas designed to include only three attributes: Two ofthe fivenatural resources included in the survey, and the cost. This simplifies the choice, 

### so that it is more likely that choices will be based on considering and balancing all of the attributes. In the final survey,respondents were askedto select 

### from a setofthreehypotheticalchoices:no new action or one oftwo restoratiodprotection programs. Each option was described by different levels ofresulting natural resources, and the annual cost to eachhousehold. In early focus groups, only the two programs were presented, but focus groupsparticipants indicatedthat a “no new action” option should be added, for two reasons. First, it allowed people to express a preference for no action if they did not support either program. Second,itprovided a baseline from which tojudge the benefits of each 

### of the programs. VI.B.2.b. Selection of Natural Resources and Levels VI.B.2.i. Natural Resources Included in the Survey Based on concerns expressed by participants in focus groups and natural resources identified as important by the Technical Advisory Committee,we selected five natural resources to be included in the survey: farmland undeveloped land wetlands safe shellfishing areas eelgrass We included farmland and undeveloped land because most people in preliminary interviews and focus groups expressed concern about the rate of development in the area and resulting loss of farmland and open space. Both farmland and open space are important components of the quality
99of life, or “sense of place,” (Sagoff 1992; Kellert 1995) for many residents of the East End, who enjoy the rural quality of the area and shopping at numerous local farm stands. The amount of developmentalso affectsenvironmental quality ofthe Estuary. Thus, it isimportant to consideruses of the surrounding land as well as resources more directly associated with the Estuary. Many people were aware of the importance of wetlands to water quality and as wildlife habitat, and expressed concern for declines in the quantity 

### of wetlands in the area. People also expressed concern for reopening closed shellfishing areas. This is due to the historical significance of shellfishing to the local economy,and its importance as a recreational activity, combined with the declines in shellfish caused by brown tide2’ and the recent large increases in areas closed to shellfishing. Finally, we included eelgrass’fortwo reasons. First, much of the Estuary’s eelgrass was destroyed by the brown tide, and one proposed action is restoring eelgrass areas. Second, eelgrass serves as aproxy for fish and shellfishpopulations.Manyparticipantsinthe preliminary interviews 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,we hoped thatby includingeelgrass,which serves as fish and shellfish habitat, the survey would capture some of these concerns.



### VI.B.2. ii. Levels of Natural Resources in the Survey The objective 

### of the survey was to determine respondents’ values for improvements in natural resources above a baseline level. We defined the baseline as the level that would exist in the year 2020, if no action is taken to preserve or restore the resource. We determined the baseline in consultationwith the TechnicalAdvisory Committee, based 

### onhistorical declinesand thejudgment 

### of experts, for each resource. First, we determined the level of each resource in 

### 198 1 and the current level.21Next, we projected the levels in 

### 2020 based on information and judgments provided by members of the Technical Advisory Committee regarding the anticipated change in the resource if no new actionswere to be taken. In cases where no technical projections were available, we based the projection on extrapolation of past trends.

2o Brown Tide is an algal bloom of the speciesAureococctis anophagefferens, which first occurred in 1985-1988, and has subsequently recurred for shorter periods. Themajor brown tide episodes severely affected scallops, eelgrass, and other shellfish in the estuary (see Suffolk County Department of Health Services 1992). 

## ’’We compiled this information from the recent brown tide study (Suffolk County Department of Health Services 1992); a 198 1 land use report (Long Island Regional Planning Board 198 1); and information provided by the New York State Department of Environmental Conservation and other members of the Technical Advisory Committee. The acres of farmland and undeveloped land in the Long Island Regional Planning Board’s land use report were presented for the entire East End. Therefore, to calculate the number of acres within the Peconic Estuary Program study area, we assumed that the fraction of farmland and undeveloped land within the study area is proportional to the amount of total land on the East End that is within the study area. 100In the survey, respondents were presented with two pages of background information, which described the level 

### of each resource in 

### 1981,the current level, and projections of levels in 2020 

### if no new actions are taken. These levels are shown in Table 

### 4, and the survey pages are shown in Appendix A. Respondents weretold that “trends indicate approximateconditionsin 2020,” in order to make it clear that these are not precise, scientifically-basedprojections. Whenthe survey was created,the TechnicalAdvisory Committee had not completed their scientific studids 

### of how potential actions might affect specific natural resources. To get around this lack of information, we chose the levels of each natural resource to be included in the survey in order to bracket a reasonable range of changes under various policy actions, including the “no new action” option. The Technical Advisory Committee assisted us in determining the largest realistic and feasible change for each resource. Although, in some cases, anticipated results of restoration and preservation programs are likely to be smaller than those presented in the survey, this approach is conservative because it brackets the full range of feasible resource changes. This allows for interpolation when valuing resource changes, rather than extrapolating results beyond the range included in the survey. In the contingent choice questions, each resource was included at three different levels: The projected level for 2020 (the “no new action,”orbaseline, scenario), andtwo levels associated with hypothetical programs that wouldpreserve orrestore theresource. In orderto make thehypothetical context clear, survey respondents were told: “The followingprograms are hypothetical. We are tryingto learnwhich resources aremost important to you and how much you would pay to protect them.” The levels for each resource and for cost are shown in Table 

### IV.5. If no action is taken, farmland is projected to decrease by twenty-five percent. The results of differenthypothetical preservation programs wereprojectedto reduce the loss offarmland to fifteen percent or maintain it at the current level. Undeveloped land was also projected to decrease by twenty-five percent if no new action is taken. Because it did not seem realistic to preserve undeveloped land at the current level, hypothetical preservationprograms wereprojected to reduce the loss of undeveloped land to ten percent or five percent.

101

### Table 

### VI.4 

# - Past, Present and Projected Natural Resource Levels Projected Natural Resource 1981 Level 1995 Level 2020 Level
Farmland" 13,500 acres 12,000 acres 

### 9,000 acres Undeveloped Land" 

### 74,000 acres 66,000 acres 

### 50,000 acres Wetlandsb 18,000 acres 16,000 acres 12,000 acres Safe Shellfishing Areasb 28,000 acres 26,000 acres 25,000 acres Eelgrassb 10,000 acres 

### 9,000 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 Department of Health Services 1992 and information provided by NY State DEC.

Wetlands were projected to degrade by twenty-five percent ifno new action is taken; orthey might be preserved at the current level or restored to ten percent above the current level. Thebaseline for safe shellfishing areas is a twenty-five percent decrease, and hypothetical programs might maintain the current level or increase safe areas by ten percent. The baseline for eelgrass is a ten percent decrease, and it might be preserved at the current level or increased by twenty-five percent. The costs ofthe hypothetical programs were designed to learn how much people would pay for the resource changes presented above, and to bracket a range of costs that seemed feasible to members of the Management Committee. However, they are not intended to reflect actual costs of some particular set ofprograms. The no new action option was always presented at no additional cost to each household. The other levels of annual household cost included were $50, $100, $200, 

### $300, and 

### $500.

102

### Table V1.5 

# - Levels of 

### Natural Resourcesand Cost in the Survey

Farmland Undeveloped Land Wetlands Safe Shellfishing Areas Eelgrass Cost Levels Level with Moderate Level with High No 

### Action, Preservation or 

### Preservation or Baseline Level Restoration Restoration



### (YO Change) ("!

### Change) 

### (YO Change) 9,000 acres 10,000 acres 12,000 acres (-25%) (-15%) (current) 50,000 acres 59,000 acres 63,000 acres (-25%) (-10%) 

### (-5%) 12,000 acres 16,000 acres 17,500 acres (-25%) (current) (+lo%) 25,000 acres 26,000 acres 29,000 acres (-5%) (current) (+lo%) 

### 8,000 acres 9,000 acres 11,000 acres

(-

### 10%) (current) (+25%) $0 $50 $100 $200 $300 $500

In order to statistically estimate values for each resource, we chose a statistical design that required sixty different comparisons.2' However, we wanted to make sure that the survey could be answered in a reasonable amount of time 

### (10-15 minutes). Thus, we included five comparisons in each of twelve different survey booklets.

VI.B.2. iii. Description of The Survey Questionnaire The first page of the full survey booklet described the goal of the survey and showed a map of the relevant area. The firstthree questions asked aboutparticipation inrecreational activities inthe area; support for different possible actions; and opinions about brown tide. Next the 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 representative member of the public.



### VI. C. DESCRIPTIVE RESULTS



### '* The combinations of attributes and levels were selected using a method based on Addelman's fractional factorial design, which produces orthogonal arrays of attributes (Addelman 1962a, 1962b; Addelman and Kempthorne, 1961). This allows for statistical independence among the attributes so that the model estimation will be statistically efficient. 103VI. 

### C.l. 

### 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 major characteristics of our sample can be summed up as 

### follows: Most are year-round residents (73%). Most are homeowners 

### (85%). A majority live closer to the Peconic Estuary than to Long Island Soundor the Atlantic Ocean 

### (58%). 15% live on the waterfront and a majority live % mile or less from the water (51%). A majority have lived in the area for more than 10years (63%). The average household size isjust under 

### 3 members, and 46% have no children under age 18 in their household. Most are employed (67'3'0)~ and 21% are retired. Compared to 1990 population figures, the sample contains a slightly higher percent of women and people in the middle age groups, and the sample population is better educated and more wealthy than the general pop~lation.~~



### '' Note that census data are only available for year-round residents, so these results are for year-round residents in the sample. 104

### Table 

### VI.6 

## - Respondent Demographics, Full Sample Percent Question Category Number of 

### Sample 9: Primary Home Type of Second Home residence Did Not Answer 10: Own Own or Rent rent Did Not Answer 12: Peconic Bays Closest Long Island Sound water body Atlantic Ocean 12: Waterfront Distance

- < 1/2Mile From Water from any >1/2 Mile From Water water body Did Not Answer 12: Waterfront Dist. from I 1/2Mile From Water Peconic Bavs >1/2 Mile From Water 13: 

### 0- 10 Years Length 

### of 1 1-20Years residence 21-30 Years >30 Years Did Not Answer 14: Female Gender Male Did Not Answer 15: 1-2 HouseholdMembers Size of 3-6 HouseholdMembers household >6 HouseholdMembers Did Not Answer Average hh size 16: No Children Number 1-2 Children of children >2 Children Did Not Answer



### Table VI.6 

### (Continued)

105 673 73.1% 248 26.9% 47 4.86% 773 84.7% 140 15.3% 55 5.7% 564 58.3% 216 22.3% 219 22.6% 143 14.8% 347 35.8% 382 39.5% 96 9.9% 116 12.0% 234 24.2% 618 63.8% 297 30.7% 245 25.3% 176 18.2% 185 19.1% 65 6.7% 540 57.8% 394 42.2% 34 3.5% 48 1 49.7% 430 44.4% 24 2.5% 33 3.4% 2.94 

### 440 45.5% 254 26.2% 66 6.8% 208 21.5%

### Percent Question Category 17: up to 20 Age 2 1-24 25-34 35-44 45-54 55-64 65-74 75-84 85 and older Did Not Answer 18: Less Than High School Education Some High School High School Graduate Some College Associate’s Degree Bachelor’s Degree Advanced Degree Did Not Answer 19: Employed Full Time Employment Employed Part Time Full Time Homemaker Full Time Student Retired Unemployed Did Not Answer 

### 20: <$15,000 Income $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 Did Not Answer 

### Number of Sample 23 2.5% 36 3.9% 121 13.1% 207 22.5% 194 21% 162 17.6% 144 15.6%

33 3.6%

2 .2% 46 4.8%

5 -5% 16 1.7% 126 13.6% 167 18.1% 90 9.7% 236 25.5% 284 30.7%

44 4.6% 497 53.9% 123 13.3%

66 7.2% 28 3% 197 2 

### 1.4%

11 1.2% 46 4.8% 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%

### Ta ics

25-44 45-54 55-64 65-74 75-84

Some H.S. H.S. Graduate Ed. categories 1-3 Some College (Ed. cat. 4) Assoc. Degree Bachelor's Deg. Advanced Dcg.

$15,000-$24,999 Inc. categories 1&2 $25,000-$34,999 $35,000-$49,999 Inc. categories 3&4 S50,000-S71,999 $75,000-$99,999 S100,000-SI 49,999 Inc. categories 5-7 $150,000 and up (Inc. cat. 8) d Seasonal

29.07% 40.39% 26.24% 11.01% 19.05% 24.89% 1 I.O4Yo 14.99% 22.62% 11.17% 14.1 1% 19.00% 7.25% 3.170/0 3.17%

1 1.55% 1.53% 31.77% 15.33% 50.76% 16.86% 18.35% 21.26?/0 6.66% 1 1.30% 13.51 YO 23.18% 10.72% 27.39?/0 0.94% 2.83% 3.77% 8.49%
3 1

7.08Yo

.60% 49.06%

14.600/0 11.60% 1.81% 33.77% 17.05Y0 3.62% 14.40% 14.94% 4.98% 17.52% 15.99% 5.88% 3 1.93% 30.93% 10.86% 18.90% 27.94% 14.48% 7.04% 1 1.07% 18.55% 4.93% 7.38%0 18.10% 30.86% 46.40% 51.13%

3.41% 5.62% 34.39% a - The 199Upopulation figures are for )car-rounif rriidcnts only: sources 0t'da.n are Lon? Island Lighting Cornpmy I995 and Suftolk County Depannient oi Planning 1991, based on I990 census data and p:qcc:ion>. KOdemognphic &:a are xiil::blc for scasonal rcsidenti. b

- Sanplepercents refer to perccnrnges of those who answcred the question.

107

### able 

### VI.8 

## - Numbers 

### of Respondents by 

### Tom Town Number of Percent Respondents 

### of

~ 1 Brookhaven

Riverhead Southold Shelter Island East Hampton Sag Harbor 

### Southampton Other Did not Answer 

### Sample 19 1.9% 110 1 1.4% 247 25.5% 136 14.1% 182 18.8% 66 6.8% 139 14.4% 25 2.6% 44 4.5%

V1.C. 2. Participation in Recreational Activities The responses to Question 1,which asked about participation in recreational activities, are shown in Table VI.9. Almost all ofthe respondents (97%)participate in at least one ofthe listed activities, and 8 1%participate in at least one activity in the Peconic Estuary. Swimming 

### is the most popular activity, with 86percent participating. Swimmingisfollowed bywalking andhiking (71%),boating 

### (54%), fishing (53%), otherbeach use (34%), shellfishing (32%), artwork (26%) and other activities 

### (1 1%). More people participate in activities on or around the Peconic Estuary than any other water body. This result is consistent with results of a telephone survey ofresidents conducted in the fall of 1993 by the Center forCommunity Research of SuffolkCommunityCollege (the SCC survey), which also foundthat the PeconicEstuary Systemwasused more frequently than any otherwaterbody (Suffolk Community College 1994). VI. C.3. Support for Management Actions Table VI.10 summarizes the responses to Question 

### 2, which asked about support for specific management actions. We included this question in order to allow respondents to express their opinions about proposed actions, and to givethem an idea ofthe types 

### of actions that might betaken to obtain the natural resource changes described in the contingent choice questions.

108

### Table 

### VI.9 

## - Participation in Recreational Activities Atlantic Activity Peconic Bays Ocean L.I. Sound Other Fishing 391 (40.4%) 167 (17.3%) 216 (22.3%) 74 (7.6%) Shellfishing 261 (27.0%) 

### 18 (1.9%) 55 (5.7%)



### . 39 (4.0%)

Walking/

Hiking 518 (53.5%) 339 (35.0%) 306 (31.6%) 69 (7.1%) Swimming ,610 (63.0%) 468 (48.3%) 362 (37.4%) 79 (8.2%)
Other Beach Use 201 (20.8%) 169 (17.5%) 131 (13.5%) 44 (4.5%)

Boating 432 (44.6%) 144 (14.9%) 224 (23.1%) 67 (6.9%)
Artwork 204 (21.1%) 120 (12.4%) 129 (13.3%) 

### 40 (4.1%)

Other 68 (7.0%) 46 (4.8%) 

### 51 (5.3%) 24 (2.5%)



### All Activities 779 (80.5%) 586 (60.5%) 534 (55.2%) 205 (21.2%) 

### Locations 

# I

516 (53.3%) 31 1 (32.1%)
684 (70.7%) 833 (86.1%)

33.5%
53.7% 

# a 26.0%)

103 

# I 10.6%)

(96.7%) As expected, all of the actions are supported by a majority 

### of respondents. The actions with the greatest level of support are building more pumpout stations so that boat discharge can be prohibited; improving sewagetreatmentplants; public education; andmore research onwater quality issues. The actions with the least support are required pumpouts of septic systems, and restrictions on the use of lawn chemicals. The Suffolk Community College survey also asked about support for actions. Although their questions were more general and not perfectly comparable to those presented here, their results are similar. For example, 83 percent of their sample support stronger building regulations, which is comparable to the 76.3percent ofour sample who support zoning regulations, the 78.7 percent who support waterfront restrictions, and the 76.7 percent who support restrictions on vegetation. The



### SCC survey found that 71 percent of respondents were willing to change fertilizers, which is consistent with the 68.2 percent of this sample who support restrictions on fertilizers and lawn

109

### Table VI.10 

## - Supportj r 

### Actio 

# - Strongly Strongly No iverage 

# I Opinion Rank Action support support Neutral Oppose Oppose Prohibit sewage discharge from boats. (requires building 

# I

more pumpout stations) 79.0% 10.5% 

# - - - 6.7% 

# - 1 1.O% 1.2% 1.4%

~~ Improving sewage treatment plants. 78.0% 11.8% 

### 1.5% 0.3% 0.6% 

# - 7.7% 

# - 2 Public education to teach people how to reduce their impacts on the environment. 70.0% 17.4% 4.6% 

### 0.7% 1

### .O% 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% 

# - 0.4% 0.9% 12.1% 5 3

### 2% 

# - Restrictions on waterfront property, including requiring buffer zones (areas left untouched) between development and the water, 61.9% 16.8% 8.5% 2.5% 2.5% 7.9% 6 drywells for roof runoff, etc. Zoning to limit future development. 62.3% 14.0% 

# - 11.1% 2.4% 2.2% 

### 8.1% 

# - 7 Limiting removal of vegetation on newly 58.5% 

### 182 % developed land. 9.9% 2.8% 2.6% 8.1% 

### 8 Requiring repair or upgrade of septic systems when 5 1.4% 21.8% property is sold or improved. 12.4% 2.5% 2.6% 9.3% 9 Controlling stormwater runoff with diversion, catch 47.6% 26.1 

### Yo basins, etc. 12.9% 1.7% 0.9% 10.7% 10 Restricting use of fertilizer and lawn chemicals for 43.4% 24.8% residential property. 16.7% 5.4% 2.2% 7.5% 11 Requiring pumpouts of existing septic systems every 32.7% 21.3% 4 years. 20.9% 6.7% 4.2% 14.2% 12

chemicals. While 75 percent ofthe 

### 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.

110It was hypothesized that certain groups of people would be more likely to support or oppose specific actions. For example, boaters, who would be most inconvenienced by discharge prohibitions, were expected to show less support for this action. However, 90.4 percent ofboaters, versus 

### 88.2 percent of non-boaters support no-discharge regulations ( although,based on a test of equality of the means, the difference in the mean strength of support for discharge prohibitions betweenboaters and non-boaters is not statistically significant).Thus,boaters appear tobe willing to undergo some inconvenience in exchange for cleaner water. This is consistent with the 

### SCC survey findingthat 63percent ofboaters arewillingto changetheirboatinghabitsto improvewater quality. Based on similar reasoning, waterfront residents were expected to be less likely to support restrictions on waterfrontproperty. There is some support for this hypothesis, with 76.2 percent of waterfront residents, 79.8 percent of residents who live up to % mile fi-om the water, and 

### 82.5 percent ofpeople who live more than ?4mile fromthe water supportingrestrictions on waterfront property. However, there was no statistically significantdifference in the mean strength of support across these groups. Finally, the strength of support for all actionswas compared for members of environmental groups versus those who are not members.24 Although the average level of supportwas slightly greater for members 

### of environmental groups, the means were not statistically significantly different. From these results, it can be concluded that the majority of respondents are in favor of environmental actions, with slightly lower support for some actions that are personally inconvenient. However, as expected,the results appear to be primarily symbolic of a general desire for action.That is,people seem to supportmost actions. Thus, these results donot provide adequate information with which to make policy decisions. 

### VI.C.4. Opinions About Brown Tide Question 3 asked respondents whether they have heard ofbrown tide, and ifthey have, about their level of concern, whether their activities have been affected, and whether they support funding for further study. The results are reported in Table 

### V.1. Most people (90%) had heard of brown tide, and 75 percent said that at least one activity was affected by the brown tide. The most commonly affected activity was swimming, with 59 percent of those who had heard ofbrown tide affected, followed by shellfishing 

### (40%) and fishing (37%). A small number ofpeople said that boating or other activities were affected. The most commonly mentioned “other” activity affectedwas eating seafood.The majority (97%) ofthose whohad heard

24 A total score for all actions was calculated by summing the level of support for all actions, where 1indicates strong support and 5 indicates strong opposition. The mean score for members of environmental groups was 19.51, and that of non-members was 25.1, with a lower score indicating greater strength of support. 111

### ofbrown tide are concerned (21%) or very concerned (76%) about it. 

### Most ofthose who had heard 

### of brown tide (86%) also support more funding to study brown tide 

### and possible remedies for it.



### Heard of Heard of and Affected Brown Tide by Brown Tide

872 (90.1%)



### 2.3C

## - Activities Affected by Brown Swimming Boating Fishing Shellfishing

VI.C.5. Membership in Organizations 725 (74.9%)



### 'ide 512 

### (58.7%) 121 (13.9%) 319 (36.6%) 349 (40.0%)

89 (10.2%)

Question 21 asked respondents whether they are members of different types of organizations. The choices listed are sports, community service, environmental, political, business-related, farm or agricultural, religious, and civic organizations. Thepurpose ofthis question was to supplement the standard demographic questions by providing information about respondents' interests and level 

### of commitment to different causes. Table VI.12 shows the statistics for membership in organizations. Sixty-five percent 

### of respondents belong to at least one organization, and 3 1percent belong to an environmental organization. This is comparable to the results ofthe SCC survey, which found that 57 percent of respondents had donated to environmental organizations, and 21 percent had been active in an environmental group. 112

### TableVI.12 

## - Membership in Organizations Organization Number 

### (YO) sports Community Service Environmental Political Business Agricultural Religious Civic Other Any Organization



### V1.D. ESTIMATES OF 

### ECONOMIC VALUE 171 (17.7%) 182 (1

### 8.8%) 300 (31

### .O%) 146 (15.1%)

94 (9.7%) 24 (2.5%) 275 (28.4%) 152 (15.7%)

91 (9.4%) 628 (64.9%)

VI.D.l. Responses to Contingent Choice Questions As discussed above, there were 60 different choice questions, with five included in each booklet, to create twelve different booklets. There were between 73 and 

### 85responses to each 

### of the twelve booklets. The demographic groups-year-round vs. seasonal residence, gender, age, education, employment, and income-were fairly evenly distributed across booklets. Table 

### VI. 13 shows how many 

### of the contingent choice questions were answered by respondents. Of the 968 people who completed the survey, 897 (92.7%) answered at least one of the five contingent choice questions in their survey booklet, and 790 (8 1.6%) 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.

113

,

### TableVI.13 

## - Number 

### of Choice Questions Answered Number Number of Percent of Answered Respondents Total Sample 71 7.3%



### 8 .8% 8 

### 3% 31 32% 60 6.2% 790 81.6%

Older respondents, and those with lower education and income levels, were slightly less likely to answer all of the choice questions. Of those who answered at least one choice question, almost all (91.6%) chose an action, rather than no action, for more than half oftheir answers. Only 

### 27 people (3.0%) chose no action for all of the choice questions answered. These results demonstrate the strong environmental concern expressed by survey respondents, but also may suggest some symbolicbias, ifrespondents voted to “take action,” irrespective ofthe associated levels 

### ofresource protection and cost. Below we discuss some methods that we employed to identify and control for this possible bias. Of those who chose either Program 

### A or Program 

### B, more people chose Program 

### A than Program B.” This may indicate a tendency to select the option placed in the center, or may simply indicate that the options presented as Program 

### A were prefened more often. This is discussed further in the Technical Appendix.



### VI.D.2. Results and Discussion The statistical modelsused to estimatetheresults 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 ofcost to each household. These weights are measured by the estimated coefficients for each resource and cost. From these coefficients, relative values for the different resources, and dollar values for protecting an additional acre of each resource, can be calculated.26

‘j Of respondents who answered more than one question, 434 (48%) chose Program A for more than half of the questions they answered, and 76 (8.5%) chose Program A for all of the questions they answered. This compares to 287 (32%), who chose Program B for more than half of the questions they answered, and 15 (2%) who chose Program B for all of the questions they answered. 

## ’‘ These calculations are described in detail the Technical Appendix. 114The results for two different models are reported in Table 

### VI. 14.27 The modelresults indicatethat the order of priorities for protection or restoration of resources is as follows: farmland, eelgrass, wetlands, shellfish, and undeveloped land.


### Table VI.14 

# - Estimation 

### Results

_______~~ Value IAcreIhhl 95% Confidence Avg. Value Coefficient year Interval” lAcreNearb Model 1:‘ Farmland 0.0005 11 $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 $0.1 1 1 $0.098 $0.125 $ 8,186 Cost -0.003765 Model 2:d Program B -.I586

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

, with a 95% a - The 95% confidence interval indicates the range within which the “true” value is likely to : probability. b
- Calculated based on 73,423 households. c - Conditional Logit model. d -Nested Logit model.



## ’’Model 1 is the standard conditional logit model, and Model 2 is the nested logit model. 115

### As discussed above, symbolicvalues are often

### anissue forboth contingentvaluation and contingent choicemethods. The estimationresults showthat this is an issue 

### in this survey, and that respondents to the survey are more likely to choose to take action, independent of the specific results of the action. Thus, these respondents may be expressing a symbolic willingness to pay to take action, 

### as opposed to revealing values for the specificnatural resources of concern. The higher probability of choosing to take action is not surprising, given the level of concern 

### among residents of the area for the environment of the Estuary. However, Model 

### 2 provides a correction for such values, by separating the probability of taking action vs. no action, from the probability of selecting either Program 

### A or Program 

### B. Thus, the estimated dollar values for Model 2 are approximately halfto two-thirds as large asthose estimated from Model 1. The estimated dollar values range from around $2.1 thousand per acre per year for undeveloped land, to around $10 thousand for farmland for Model 1 ; and around 

### $1.2 thousand to 

### $6.4 thousand for Model 

### 2. The values from Model 

### 2 might be interpreted as the portion of respondents’ willingness to pay to take action which can be attributed to the described changes in natural resource levels. This is smaller than the estimated value in Model 1, which includes a “symbolic” effect. Although the estimated dollarvalues differ,both models result in the same orderingofpriorities and relative values for the natural resources. These results indicate that priorities and relative values are robust with respect to different model specifications, and are independent of symbolic effects,but that the estimated dollar values vary somewhat, although they are close in magnitude. Therefore, it may be concluded that the model is relatively robust to different specifications, and that the proportion of value that is “symbolic” is not great. 3. Discounted Present Values Discounting is a means of aggregating dollar values over time, and is based on the idea that natural resources are assets that provide a flow of services over time. Thus, “[tlhe economic value of a resource-environment system as an asset canbe defined as the sum ofthe discountedpresent values ofthe flows of all ofthe services.The benefit ofany public policy that increasesthe flow of onetype of service is the increase in the present value of that service” (Freeman 1993,p. 5). Thediscounted present values fortwo discountrates arepresented in Table 

### IV. 15.The 

### 7percent rate is used to approximatea “typical” interestrate for public planning. For example, 7.625 percent was the official water resources planning discount rate for fiscal year 1996 

### (US. Department of the Interior 1995).18 The 3 percent rate is included for comparison,because it is often argued that the social discountrate should be lower. The 25 year time horizon was chosen to match the time period



## ‘*

This is the rate required by the government for Federal water and related land resources planning, and is based on the average yield during the preceding fiscal year on interest-bearing marketable securities of the United States with 15years or more remaining to maturity (US.Department of the Interior 1995). 116presented in the survey. The 100year time horizon was used to identify values associated with a longer time horizon.



### Table IV.15 

## - Comparison 

### ofDiscounted Present Values

~


### Model 1: Farmland Undeveloped Land Wetlands Shellfish Areas Eelgrass 

### Model 2: Farmland Undeveloped Land Wetlands Shellfish Areas Eelgrass PVIAcre PVIAcre PVIAcre PVIAcre r=.03, t=lOO* r=.03, t=25* r=.07, t=lOO* r=.07, t=25* $315,314 $173,759 $142,387 $1 16,286 $65,728 $36,221 $29,68 

### 1 $24,240 $207,286 $1 14,228 $93,605 $76,446 $143,945 $79,323 $65,001 $53,086 $258,675 $142,547 $1 16,811 $95,398 $202,175 $1 11,412 $91,297 $74,562 $38,028 $20,956 $17,172 $14,024 $153,659 $84,676 $69,388 $56,669 $86,070 $47,430 $38,867 $3 1,742 $1 89.703 $104.539 $85,665 $69.962 

### * r is the discount rate; t is the time horizon in years.



### VI1.E. CONCLUSIONS AND SUMMARY This section described the development, implementation, and results of a survey 

### ofpublic values for important natural resources 

### of the Peconic Estuary System. We developed the survey through anextensive six-monthprocess 

### ofinterviews, focus groups, andpretests. We selected the contingent choice method to elicit values for five important resources: farmland, undeveloped land, wetlands, safe shellfishing areas, and eelgrass. The survey was implemented in August, 1995,to 968 yearround and seasonal residents 

### ofthe East End of Long Island. In summary, residents 

### of the East End are very concerned about protecting 

### of the area’s natural resources, and are willing to pay a significant amount to do so. 

### Our statistical results indicate that respondents are so concerned about protecting the East End’s environment that they would be willing to pay to take action, independent of specific results 

### of the action. We corrected for this “symbolic” value in our estimation models, and present the results of both the standard and “corrected” model.

117Overall priorities for the five natural resources included in this survey are for farmland, followed bywetlands, eelgrass, shellfish areas,andundeveloped land.Discountedpresentvalues forthemost conservative model range from around $14 thousand to preserve an acre of undeveloped land, to around $75 thousand to preserve an acre 

### of farmland. The results of this survey will be combined with those of several other studiesto evaluatebenefits 

### of a set ofproposed management actionsfor the Estuary.



### REFERENCES Addelman, Sidney. 1962a. “Orthogonal Main-Effect Plans for Asymmetrical Factorial Experiments,” 

### Technometrics 4 

### No. 1 (February): 21-46. Addelman, Sidney. 1962b. “Symmetrical and Asymmetrical Fractional Factorial Plans,” 

### Technometrics 4 No. 1 (February):47-57. Addelman, Sidney, and 

### 0. Kempthorne. 1961. 

### Orthogonal Main Effect Plans. United States Airforce, Office of Aerospace Research, Aeronautical Research Laboratory, ARL Technical Report 79. Freeman, A. Myrick 111. 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,”

### AmericanJournal 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, CatherineL., and Joseph A. Herriges. 1995. “An Empirical Investigation ofthe Consistency of Nested Logit Models with Utility Maximization,” 

### American Journal of 

### Agricultural Economics 77 No. 4 (November): 875-884. Kling, CatherineL., and Cynthia 

### J. Thomson. 1996. “The Implications of Model Specificationfor WelfareEstimationinNested LogitModels,”

### AmericanJournal ofAgricultura1Economics 78 No. 1 (February): 103-114. Long Island Lighting Company. 1995.

### Population Survey 1995: 

### CurrentPopulationEstimates 

### for Nassau and Suffolk 

### Counties.Hicksville,NY. Long Island Regional Planning Board. 1981.

### Land Use: QuantiJcation andAnalysis of 

### Land Use 

### forNassau and Sztffolk 

### County. Maddala, 

### G.S. 1983.

### Limited-Dependent and Qualitative Variablesin Econometrics. Cambridge: Cambridge University Press. Mazzotta, Marisa J. and James 

### J. Opaluch. 1995. “Decision Making When Choices are Complex: A Test of Heiner’s Hypothesis,”Land 

### Economics 71, No. 4 (November): 500-5 15.

118McFadden,Daniel. 198 1. “Econometric Models of Probabilistic Choice,” in C. 

### F. Manski and D. McFadden, eds., 

### Structural Analysis of 

### Discrete Data with Econometric Applications. Cambridge: 

### MIT Press. National Oceanic and Atmospheric Administration. 1993.

### Natural ResourceDamageAssessments Under the Oil Pollution Act 

### ofI990. 58 Federal Register 4601 (January 15). Neave, H. R., and P. 

### L. Worthington. 1988.

### Distribution-Free Tests. London: Unwin Hyman. Sagoff, Mark. 1992.“Settling America or The Concept 

### ofPlace in Environmental Ethics,”JournaZ 

### ofEnergy, Natural Resources and EnvironmentalLaw 12

### No. 2: 349-418. SuffolkCommunity College. 1994.

### PeconicEstuaryPublic OpinionSurvey. Centerfor Community Research, Riverhead,

### NY. SuffolkCounty Department 

### ofHealth Services. 1992.

### Brown Tide Comprehensive Assessmentand Management Program. Suffolk County Department of Health Services. 1994. 

### Peconic Estuary Program Action Plan. Suffolk County Department of Planning. 1991. 

### Estimated Peak Seasonal Population

- 

### 1990. Unpublished spreadsheet. 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 2 1)

119

### Appendix A 

# - Example Survey 

## * 

# * Readers who 

### would like to obtain a copy of

### the survey should contact the authors.

Appendix B 

# - Technical Appendix Based on the random utility model, relative values and priorities for the natural resources were estimated using the standard conditional logit method (Greene 1993; Maddala 1983), where 

### exp(P’zjt 

### a ’zjwi) 

## p.= 

### zexp(p‘zj t 

### a’zjwi>‘ k 

### P, istheprobability that individual iwill select optionj;zjis a vector ofattributes ofthe choice (e.g., the levels of natural resources and the cost), which may also vary across individuals; 

### wjis a vector of characteristics of the individual; and 

### p 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 form and, based on these tests, the linear form was chosen for all subsequent estimation. The second alternative specification isthenested logit model,which allows forcorrelations between the two action alternatives. The nested logit model was structured 

### so that respondents are assumed to first choose whether to take action or not, and then to choose a specific program conditional on taking action. In the nested logit model, the probability that an individual chooses alternative k is

The choice probability for each ofthe three lowest level alternatives is conditional on the choice to take action or not. In this case, if the person chooses not to take action, then the probability of selecting the 

### “No New Action” alternative is 1 

### , since it is the sole alternative on that branch 

### of the nested model. If they choose to take action, the probability of selecting “Program A” or “Program B” is

120where k is one of the two alternatives, Program 

### A or Program 

### B;j is the choice to take action; n is the number of alternatives 

### in choicej, which would be 

### 2; and 

### Jj isthe inclusive value for choicej, which represents the expected maximum utility from the choice of an alternative that sub-branch.

This 

### is defined as

The probability of choosing to take action or not is 

### exp(

## a‘? t 

### zj 

### J j) 

### P(j> = 

### Cexp(a‘Y, t 

### z,J,)’

m where m is the number of branches, which in this case is two. The parameter 

### J is the inclusive value coefficient, which is related to the correlation between alternatives within a branch. A value 

### of T between 

### 0 and 1 indicates that there is greater substitutability within, rather than across, groups 

### of alternatives. In terms ofthis study, this would indicate that Program A and Program 

### B are closer substitutes than eitherprogram and the “NoNew Action” alternative. If z is equal to 

### 1,then all alternatives are equally substitutable, and the model becomes identical to the standard conditional logit model (McFadden 198 1; Kling and Herriges 1995; Kling and Thomson 1996). The results of the conditional logit model were compared to two alternative specifications. These results are presented in Table B-1. The 

### first is the conditional logit model with two alternativespecific constants, one for the choice of an action versus no action, and one for Program B. Thus, the coefficient onthe first constant term reflects factors other than the levels of attributes that affect the choice of an action versus the choice of no action.14 For example, respondents may be expressing a symbolic willingness to pay to take action, as opposed to revealing values for the specific natural resources of concern. The coefficient on the second constant term reflects any difference in preference for Program 

### B versus Program 

### A that isunrelated to the levels ofattributes of A and 

### B, such as an order effect. The results of both of these models indicate that there may be effects on choices unrelated to the described attributes. In the alternative-specific constant model, both constant terms are statistically significant, indicating that there is an effect on choices unrelated to the quantities ofthe individual attributes, but is instead related to the choices themselves. The positive and significant coefficient for “Action” indicates that people are more likely to choose an action rather than “No New Action,” independent of the action’s specific effects on natural resources. Similarly, the negative and

l4 Note that, alternatively, this could also suggest 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. 121significantcoefficient for "Program 

### B" indicatesthat, even ifProgram 

### A and Program

### B produced the sameresults in terms 

### ofpreservation 

### ofnaturalresources,respondents aremore likelyto choose Program A. The coefficient on Action indicates that there 

### is an 

### 87 percent probability that the averagerespondentwould selectactionoverno action,

### ifthat action cost nothing andprovided zero resourceprotection. Similarly, conditional on taking action, there is a 

### 55 percent probability that the representativerespondent would choose Program A over Program 

### B iftheir costs and levels 

### of resourceprotection were identical.


### Table B-1 

## - Comparison ofModel Results

Conditional Logit Model: Farmland Undeveloped Land Wetlands Shellfish Areas Eelgrass Cost P2'1-0. (0)) Model with AlternativeSpecific Constants: Action Program B

Farmland Undeveloped Land Wetlands ShellfishAreas Eelgrass Cost p2 Nested Logit Model: Program B

Farmland Undeveloped Land Wetlands ShellfishAreas Eelgrass Cost
t PZ Coefficient Value /Acre 95% Confidence Interval Avg. Value IAcreNear" 0.000511 $0.136 $0.122 $0.150 $9,979 0.000107 $0.028 $0.025 $0.032 $2,080 0.000336 $0.089 $0.079 $0.100 $6,560 0.000233 $0.062 $0.053 $0.071 $4,555 0.000419 $0.1 11 $0.098 $0.125 $ 8,186 -0.003765 .138

1.2866 -0.1799 .000300 $0.094 $0.078 $0.109 

### S 6,872 .000057 $0.018 $0.014 $0.022 $ 1,304 .000179 $0.056 $0.045 $0.066 

### S 4,090 .000108 $0.034 $0.023 $0.044 $ 2,467 .000214 $0.067 $0.052 $0.081 $4,909 -.003207 .I71

-.1586 .000300 $0.087 $0.073 $0.101 $6,398 '.000056 $0.016 $0.013 $0.019 $ 1,203 ,000228 $0.066 $0.056 $0.077 $4,863 .000128 $0.037 $0.03 1 $0.044 $2,724 .000281 $0.082 $0.069 $0.094 $6.003 -.003441

,3397 .309 *

- Calculated based on 73,423 households.

122These constant terms for taking action and for differences in probability of selecting

### A and 

### B may be interpreted asrepresenting a qualitativeor symbolic dimensionofrespondents’ preferences, while the coefficients on the natural resources represent the quantitative dimensionthat can be attributed to the stated levels of resource protection. Thus, if respondents exhibit a tendency to a resource protection action,rather than “NoNew Action,” beyond thatwhichcanbe associated with the stated levels ofresource protection and the cost, they may be expressing a symbolic willingnessto pay 

### to take action to protect the environment of the East End. Similarly, the coefficient on the constant term for Program 

### B indicates that there is some qualitative reason that people choose Program 

### A, and measures the degree 

### of preference for A over 

### B which cannot be explained by the described attributes of the 

### two programs. Note that this constant term 

### is statistically significant, but quantitatively small. Thehigher probability ofchoosingto take actionisnot surprising,giventhe level ofconcern among residents of the area for the environment of the Estuary. However,the preference of one program over anotherbeyond the described effects isnot expected, and could occur for a variety 

### ofreasons. For example,the effect couldbe related to the ordering ofthe two programs, their placement on the page, or could possibly indicate that respondents infer somepreference from the labels (e.g., an 

### A is better than a 

### B). Theconstant forthe choiceofaction versus no action accountsfor qualitativeaspectsofthe decision to choose an action rather than no new action. However, it is estimated as a constant term and thus assumes a fixed effect,wherethe constantrepresents amean “bias” towards action versus no action, beyond that which can be explained by the described levels of resource protection and cost. An alternative approach to modeling is to use a random effects model, where the random components ofpreferences for the two actionprograms are correlated. This’impliesthat an actiodno actionbias might exist, but that the bias is randomly distributed across choices. For example, some individuals might exhibit a bias towards taking action, while others might exhibit a bias against taking action. Therandom effects model canbe implemented using the nested logit approach,which capturesthe correlation of the random componentsof utility associated with the two action alternatives. Tests of the inclusive value parameter in the nested logit model indicate that there is greater substitutabilitybetween ProgramA and Program 

### B than between taking action or not, and that there is significant correlationbetweenthe Program options. The constant term for Program 

### B is similar in magnitude 

### to that estimated in the previous model.

Economic values for the conditional logit model were estimated based on Hanemann 

### (1984), and are measured by the cost, C, that would make a person indifferent between the choice selected and the baseline, no action, which has zero cost. Thus, for the conditional logit model,

123where

### j represents the “No New Action” alternative, or the baseline levels 

### ofthe resources, 

### so that 

### Cj=O;

### k isthe option selected; and 

### C, is the maximumwillingness to pay for option 

### k. For the linear approximation of the utility function presented above, this can be solved for 

### C, as follows:

Thus, for the conditional logit model, the dollar value to the average respondent for a unit change in each ofthe natural resources is calculated as the ratio ofthe coefficient on the resource, 

### p, to the coefficient on cost, y. The calculation ofdollar values forthe nested logit model must account for the nested structureand the inclusive value parameter, 7. The formula for the compensating variation associated with a change in one of the attributes of the choice is (Kling and Thomson 

### 1996;Hanemann 

### 1982):

where V is the utility function, the superscripts on 

### V indicate whether the attributes are set at the new level or the old level, and 

### y is the coefficient on cost. The estimated dollarvalues andrelative values, calculated as ratiosbetween the coefficientson each pair of resources, were compared for each model using Friedman’s test for more than two related samples (Neave and Worthington, 1988). Based on this test, the hypothesis of equality of the estimated dollar values for the three models is rejected. However, a comparison of the estimated dollar values for the nested logit model and the alternative-specific constants model using the Wilcoxon signed rank test does not reject the hypothesis of equality ofvalues for these two models. The Friedman test does not reject the hypothesis that the relative values for natural resources are equal for allthree models. Additionally, the ordinalpriorities for all three models arethe same, with farmland most important, followed by eelgrass,wetlands, shellfishing areas and undeveloped land. These results indicate that priorities and relative values are robust with respect to different model specifications, and are independent of symbolic effects, but that the estimated dollar values vary somewhat between the base model and the two alternative specifications. However, the estimated dollar values for the three models are close in magnitude. Therefore, it may be concluded that the model is relatively robust to different specifications, and that the proportion of value that is “symbolic” is not great.

124These results indicate,however, that there may be a statisticallysignificant symboliccomponentto choices, which is comprised of 

### an effect that is unrelated to the described levels 

### of resource protection providedby the hypotheticalprograms and the associated cost. The similarityofresults fromthenested logit and conditional logitwith constantsmodels, and thefactthat theresultsarenot statisticallydifferent,indicatethatthesebiases arelikelyoverwhelmingly

### inone direction-towards taking action rather than no action. Thus,both of these models appearto account for a “symbolic” aspect of values, and to separate that from estimated values for specific natural resource improvements. Note, however, the Nested Logit model provides considerableimprovement in 

### fit, as measured by the p2statistic.